\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(x=train['sport_count_5000'], y=target)\\n\",\n \"plt.ylabel('Sale Price')\\n\",\n \"plt.xlabel('sport_count_5000')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"59b574db-6ebf-8f61-452f-896a4d8e2a7d\"\n },\n \"source\": [\n \"# Handling Null Values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"_cell_guid\": \"e399c03c-a571-ad48-c26f-ff08a66f3331\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ecology_pivot = train.pivot_table(index='ecology',\\n\",\n \" values='price_doc', aggfunc=np.median)\\n\",\n \"ecology_pivot.plot(kind='bar', color='blue')\\n\",\n \"plt.xlabel('Ecology')\\n\",\n \"plt.ylabel('Median Sale Price')\\n\",\n \"plt.xticks(rotation=0)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"_cell_guid\": \"10a541f6-d7ac-dd5a-8d3d-83624b0ae976\"\n },\n \"outputs\": [],\n \"source\": [\n \"def encode(x): return 0 if x == 'no data' else 1\\n\",\n \"train['enc_ecology'] = train['ecology'].apply(encode)\\n\",\n \"test['enc_ecology'] = test['ecology'].apply(encode)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"_cell_guid\": \"4f80c2d3-45bc-f07d-0c08-fa66fe0a20f0\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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snJkSQtW7ZMbW1tkqTOzk5lZGTIMAw5nU51d3fL5/PJ5/Opu7tbTqdT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\\ngg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"ecology_pivot = train.pivot_table(index='enc_ecology', values='price_doc', aggfunc=np.median)\\n\",\n \"ecology_pivot.plot(kind='bar', color='blue')\\n\",\n \"plt.xlabel('Encoded Ecology')\\n\",\n \"plt.ylabel('Median Sale Price')\\n\",\n \"plt.xticks(rotation=0)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"_cell_guid\": \"c6716fdb-8873-6240-6b71-39856841f66f\"\n },\n \"outputs\": [],\n \"source\": [\n \"data = train.select_dtypes(include=[np.number]).interpolate().dropna() \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"_cell_guid\": \"57f711ec-fb80-7272-665c-0bbc91e97694\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sum(data.isnull().sum() != 0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"57f3b668-f356-45e7-f4da-beb71fbf8d84\"\n },\n \"source\": [\n \"# Step 3: Build a linear model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"_cell_guid\": \"6757f9ca-acbc-2418-ef11-e7b9050a4912\"\n },\n \"outputs\": [],\n \"source\": [\n \"y = np.log(data['price_doc'])\\n\",\n \"X = data.drop(['price_doc', 'id'], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"_cell_guid\": \"1ca0e433-3f51-4b80-fe6e-c2431ec6e05d\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(\\n\",\n \" X, y, random_state=42, test_size=.33)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"11c4cee7-2b02-d63d-0d25-355611498b4f\"\n },\n \"source\": [\n \"# Begin modelling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"_cell_guid\": \"494d466e-3850-20fe-4b1b-d7657a45fa0f\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn import linear_model\\n\",\n \"lr = linear_model.LinearRegression()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"metadata\": {\n \"_cell_guid\": \"30b5dd96-a1a8-3c0b-0910-988d4ecae324\"\n },\n \"outputs\": [],\n \"source\": [\n \"model = lr.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"metadata\": {\n \"_cell_guid\": \"4ef4f481-af6e-fe15-c648-53477dd99f5e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"R^2 is: \\n\",\n \" 0.417872201429\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (\\\"R^2 is: \\\\n\\\", model.score(X_test, y_test))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"metadata\": {\n \"_cell_guid\": \"32e949e4-6f5d-c373-aeb7-88775c34f6a3\"\n },\n \"outputs\": [],\n \"source\": [\n \"predictions = model.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"metadata\": {\n \"_cell_guid\": \"f3ba3d22-b879-ec66-5e04-b1bc75a9b6b5\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"RMSE is: \\n\",\n \" 0.20634755511\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"print ('RMSE is: \\\\n', mean_squared_error(y_test, predictions))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"metadata\": {\n \"_cell_guid\": \"f025b43c-a8eb-2dc3-216c-e462614dd6a8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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FGdFeeAx2Ni48bAiOeXlJjYsqUHdXV58PvlEYItl5hOayUEWRNkf/zj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\\nTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"actual_values = y_test\\n\",\n \"plt.scatter(predictions, actual_values, alpha=.75,\\n\",\n \" color='b') #alpha helps to show overlapping data\\n\",\n \"plt.xlabel('Predicted Price')\\n\",\n \"plt.ylabel('Actual Price')\\n\",\n \"plt.title('Linear Regression Model')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"metadata\": {\n \"_cell_guid\": \"869974c1-26a6-6ba9-6f75-111e30747587\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 239,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/162/1162770.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":172,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"c4932b18-9940-a8b9-26d9-de8d27d87ec1\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Iris.csv\\n\",\n \"database.sqlite\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This Python 3 environment comes with many helpful analytics libraries installed\\n\",\n \"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\\n\",\n \"# For example, here's several helpful packages to load in \\n\",\n \"\\n\",\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"\\n\",\n \"# Input data files are available in the \\\"../input/\\\" directory.\\n\",\n \"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\\n\",\n \"\\n\",\n \"from subprocess import check_output\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"0355282e-71d4-a4bb-a830-c44a29f3bbbd\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\\n\"\n ]\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"iris_dataset = pd.read_csv('../input/Iris.csv')\\n\",\n \"iris_dataset.head(5)\\n\",\n \"species_color=iris_dataset['Species'].unique()\\n\",\n \"print(species_color)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"92ef2510-2f8a-0127-d69b-fd4f23ddbc6e\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"RangeIndex: 100 entries, 0 to 99\\n\",\n \"Data columns (total 6 columns):\\n\",\n \"id 100 non-null int64\\n\",\n \"qid1 100 non-null int64\\n\",\n \"qid2 100 non-null int64\\n\",\n \"question1 100 non-null object\\n\",\n \"question2 100 non-null object\\n\",\n \"is_duplicate 100 non-null int64\\n\",\n \"dtypes: int64(4), object(2)\\n\",\n \"memory usage: 4.8+ KB\\n\",\n \"\\n\",\n \"RangeIndex: 100 entries, 0 to 99\\n\",\n \"Data columns (total 3 columns):\\n\",\n \"test_id 100 non-null int64\\n\",\n \"question1 100 non-null object\\n\",\n \"question2 100 non-null object\\n\",\n \"dtypes: int64(1), object(2)\\n\",\n \"memory usage: 2.4+ KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"train.info()\\n\",\n \"test.info()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"9d025e51-404f-3912-7535-4e20520d216c\"\n },\n \"source\": [\n \"Empty vs Null \\n\",\n \"\\n\",\n \"if yourObject is None: *# if yourObject == None:*\\n\",\n \" ...\\n\",\n \"\\n\",\n \"if yourString == \\\"\\\": *# if yourString.len() == 0:*\\n\",\n \" ...\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"b51ed280-0bf4-d55c-277d-6e537b1afa66\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"id 0\\n\",\n \"qid1 0\\n\",\n \"qid2 0\\n\",\n \"question1 0\\n\",\n \"question2 0\\n\",\n \"is_duplicate 0\\n\",\n \"dtype: int64\\n\",\n \"test_id 0\\n\",\n \"question1 0\\n\",\n \"question2 0\\n\",\n \"dtype: int64\\n\"\n ]\n }\n ],\n \"source\": [\n \"#check for any null values\\n\",\n \"print(train.isnull().sum())\\n\",\n \"print(test.isnull().sum())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"eee5f961-0bd3-1079-8251-4669362e5491\"\n },\n \"outputs\": [],\n \"source\": [\n \"# add the string 'empty' to empty string\\n\",\n \"train = train.fillna('empty')\\n\",\n \"test = test.fillna('empty')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"277454af-45e2-a018-f77a-138c331cf34e\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Preview some of the \"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 64,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/162/1162825.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":175,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"49753661-46ec-9519-ae01-d5f284467006\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"database.sqlite\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This Python 3 environment comes with many helpful analytics libraries installed\\n\",\n \"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\\n\",\n \"# For example, here's several helpful packages to load in \\n\",\n \"\\n\",\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"import seaborn as sns\\n\",\n \"import sqlite3\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"\\n\",\n \"# Input data files are available in the \\\"../input/\\\" directory.\\n\",\n \"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\\n\",\n \"\\n\",\n \"from subprocess import check_output\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"cf6c3112-727f-50cf-6505-ed285f6bb54a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"df.life_sq,'kitch_sq']=np.nan\\n\",\n \" df.ix[df.kitch_sq<2,'kitch_sq']=np.nan\\n\",\n \" df.ix[df.floor==0,'floor']=np.nan\\n\",\n \" df.ix[df.max_floor==0,'max_floor']=np.nan\\n\",\n \" df.ix[df.max_floor>70,'max_floor']=np.nan\\n\",\n \" df.ix[df.num_room==0,'num_room']=np.nan \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"359eb732-f583-474b-ff72-127359585797\"\n },\n \"outputs\": [],\n \"source\": [\n \"#New features\\n\",\n \"for df in combine:\\n\",\n \" df['life_pct']=df['life_sq']/df['full_sq'].astype(float)\\n\",\n \" df['rel_kitch']=df['kitch_sq']/df['full_sq'].astype(float)\\n\",\n \" df['rel_floor']=df['floor']/df['max_floor'].astype(float)\\n\",\n \" \\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"c9be57ef-bcb3-c3ff-338f-d6f433c645cc\"\n },\n \"outputs\": [],\n \"source\": [\n \"for c in train_df.columns:\\n\",\n \" if train_df[c].dtype == 'object':\\n\",\n \" lbl = preprocessing.LabelEncoder()\\n\",\n \" lbl.fit(list(train_df[c].values)) \\n\",\n \" train_df[c] = lbl.transform(list(train_df[c].values))\\n\",\n \" #x_train.drop(c,axis=1,inplace=True)\\n\",\n \" \\n\",\n \"for c in result_df.columns:\\n\",\n \" if result_df[c].dtype == 'object':\\n\",\n \" lbl = preprocessing.LabelEncoder()\\n\",\n \" lbl.fit(list(result_df[c].values)) \\n\",\n \" result_df[c] = lbl.transform(list(result_df[c].values))\\n\",\n \" #x_test.drop(c,axis=1,inplace=True) \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"e4063db2-adcf-e873-9247-f752f53c5254\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(30471, 292)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_train = train_df.drop(['price_doc','id','timestamp'],axis=1)\\n\",\n \"Y_train = train_df['price_doc'].values.reshape(-1,1)\\n\",\n \"X_train.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"8b7b7718-d741-ab52-06bc-f83f946bcfbe\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(7662, 292)\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_result = result_df.drop(['id','timestamp'],axis=1)\\n\",\n \"id_test = result_df['id']\\n\",\n \"X_result.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"46e17dae-f980-cc0f-f8ac-1012251e4d21\"\n },\n \"outputs\": [],\n \"source\": [\n \"dtrain = xgb.DMatrix(X_train, Y_train)\\n\",\n \"dresult=xgb.DMatrix(X_result)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"9f919b86-aa0f-a7af-5ba3-7e43308a216d\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0]\\ttrain-rmse:8.20591e+06\\ttest-rmse:8.20837e+06\\n\",\n \"[25]\\ttrain-rmse:3.48987e+06\\ttest-rmse:3.67577e+06\\n\",\n \"[50]\\ttrain-rmse:2.52311e+06\\ttest-rmse:2.88828e+06\\n\",\n \"[75]\\ttrain-rmse:2.29588e+06\\ttest-rmse:2.75627e+06\\n\",\n \"[100]\\ttrain-rmse:2.19103e+06\\ttest-rmse:2.71604e+06\\n\",\n \"[125]\\ttrain-rmse:2.12724e+06\\ttest-rmse:2.69512e+06\\n\",\n \"[150]\\ttrain-rmse:2.07246e+06\\ttest-rmse:2.67635e+06\\n\",\n \"[175]\\ttrain-rmse:2.02523e+06\\ttest-rmse:2.66618e+06\\n\",\n \"[200]\\ttrain-rmse:1.98523e+06\\ttest-rmse:2.65696e+06\\n\",\n \"[225]\\ttrain-rmse:1.94809e+06\\ttest-rmse:2.65115e+06\\n\",\n \"[250]\\ttrain-rmse:1.91784e+06\\ttest-rmse:2.6445e+06\\n\",\n \"[275]\\ttrain-rmse:1.88668e+06\\ttest-rmse:2.64064e+06\\n\",\n \"[300]\\ttrain-rmse:1.8534e+06\\ttest-rmse:2.63574e+06\\n\",\n \"[325]\\ttrain-rmse:1.82289e+06\\ttest-rmse:2.63327e+06\\n\",\n \"[350]\\ttrain-rmse:1.79478e+06\\ttest-rmse:2.63057e+06\\n\",\n \"[375]\\ttrain-rmse:1.76883e+06\\ttest-rmse:2.62791e+06\\n\",\n \"[400]\\ttrain-rmse:1.74498e+06\\ttest-rmse:2.62676e+06\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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73Fnj17AO/60Mcff9yaPAYOHEh1dTWrV6/u8WeIlrgaoSQkJNCofqgu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\\nwhKKMcaYqPj/AVEzf9Xf1ldYAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"xgb_params = {\\n\",\n \" 'eta': 0.05,\\n\",\n \" 'max_depth': 5,\\n\",\n \" 'subsample': 0.7,\\n\",\n \" 'colsample_bytree': 0.7,\\n\",\n \" 'objective': 'reg:linear',\\n\",\n \" 'eval_metric': 'rmse',\\n\",\n \" 'silent': 1\\n\",\n \"}\\n\",\n \"\\n\",\n \"cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=1000, early_stopping_rounds=20,\\n\",\n \" verbose_eval=25, show_stdv=False)\\n\",\n \"cv_output[['train-rmse-mean', 'test-rmse-mean']].plot()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"e0f5d33b-f020-ba77-12d0-587e57ebdca4\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'model' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0mfig\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0max\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mplt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0msubplots\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m1\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mfigsize\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;36m8\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;36m16\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mxgb\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mplot_importance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mmodel\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmax_num_features\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m50\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mheight\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0.5\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0max\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0max\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'model' is not defined\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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BoAyok1AJQTawAoJ9YAUE6sAaCcWANAObEGgHJiDQDlxBoAyok1AJQT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\\noJxYA0C5/wMsUL1/FNTWuQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig, ax = plt.subplots(1, 1, figsize=(8, 16))\\n\",\n \"xgb.plot_importance(model, max_num_features=50, height=0.5, ax=ax)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_cell_guid\": \"59b9c470-d047-916c-7af0-608ea01d4c58\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0]\\ttrain-rmse:8.20943e+06\\n\",\n \"[25]\\ttrain-rmse:3.51412e+06\\n\",\n \"[50]\\ttrain-rmse:2.57926e+06\\n\",\n \"[75]\\ttrain-rmse:2.3542e+06\\n\",\n \"[100]\\ttrain-rmse:2.26321e+06\\n\",\n \"[125]\\ttrain-rmse:2.20656e+06\\n\",\n \"[150]\\ttrain-rmse:2.14973e+06\\n\",\n \"[175]\\ttrain-rmse:2.10675e+06\\n\",\n \"[200]\\ttrain-rmse:2.07328e+06\\n\",\n \"[225]\\ttrain-rmse:2.03957e+06\\n\",\n \"[250]\\ttrain-rmse:2.01038e+06\\n\",\n \"[275]\\ttrain-rmse:1.9826e+06\\n\",\n \"[300]\\ttrain-rmse:1.95667e+06\\n\",\n \"[325]\\ttrain-rmse:1.93225e+06\\n\",\n \"[350]\\ttrain-rmse:1.91123e+06\\n\",\n \"[375]\\ttrain-rmse:1.88841e+06\\n\",\n \"[399]\\ttrain-rmse:1.86702e+06\\n\"\n ]\n }\n ],\n \"source\": [\n \"xgb_params = {\\n\",\n \" 'eta': 0.05,\\n\",\n \" 'max_depth': 5,\\n\",\n \" 'subsample': 0.7,\\n\",\n \" 'colsample_bytree': 0.7,\\n\",\n \" 'objective': 'reg:linear',\\n\",\n \" 'eval_metric': 'rmse',\\n\",\n \" 'silent': 1\\n\",\n \"}\\n\",\n \"# Uncomment to tune XGB `num_boost_rounds`\\n\",\n \"#model = xgb.cv(xgb_params, dtrain, num_boost_round=200,\\n\",\n \" #early_stopping_rounds=30, verbose_eval=10)\\n\",\n \"model = xgb.train(xgb_params, dtrain, num_boost_round=400,verbose_eval=25, evals=[(dtrain,'train')])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"e5520862-a82a-495c-9f44-cb5db3d4f5d1\"\n },\n \"outputs\": [],\n \"source\": [\n \"y_pred=model.predict(dresult)\\n\",\n \"output=pd.DataFrame(data={'price_doc':y_pred},index=id_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"fadcc476-1cd2-ea5a-5e90-77012a2259d1\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: overflow encountered in exp\\n\",\n \" from ipykernel import kernelapp as app\\n\"\n ]\n }\n ],\n \"source\": [\n \"logy_pred=model.predict(dresult)\\n\",\n \"y_pred = np.exp(logy_pred)-1\\n\",\n \"output=pd.DataFrame(data={'price_doc':y_pred},index=id_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"cb827739-897b-f670-cdb3-1daaa7265453\"\n },\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"range parameter must be finite.\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mplt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mhist\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0my_pred\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0mbins\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m100\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 2\\u001b[0m \\u001b[0mplt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshow\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\\u001b[0m in \\u001b[0;36mhist\\u001b[0;34m(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, data, **kwargs)\\u001b[0m\\n\\u001b[1;32m 3080\\u001b[0m \\u001b[0mhisttype\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mhisttype\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0malign\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0malign\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0morientation\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0morientation\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3081\\u001b[0m \\u001b[0mrwidth\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mrwidth\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mlog\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mlog\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcolor\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mcolor\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mlabel\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mlabel\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 3082\\u001b[0;31m stacked=stacked, data=data, **kwargs)\\n\\u001b[0m\\u001b[1;32m 3083\\u001b[0m \\u001b[0;32mfinally\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 3084\\u001b[0m \\u001b[0max\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_hold\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mwashold\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\\u001b[0m in \\u001b[0;36minner\\u001b[0;34m(ax, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 1890\\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\\n\\u001b[1;32m 1891\\u001b[0m RuntimeWarning, stacklevel=2)\\n\\u001b[0;32m-> 1892\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mfunc\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0max\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m*\\u001b[0m\\u001b[0margs\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mkwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 1893\\u001b[0m \\u001b[0mpre_doc\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0minner\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__doc__\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 1894\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mpre_doc\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\\u001b[0m in \\u001b[0;36mhist\\u001b[0;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\\u001b[0m\\n\\u001b[1;32m 6190\\u001b[0m \\u001b[0;31m# this will automatically overwrite bins,\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 6191\\u001b[0m \\u001b[0;31m# so that each histogram uses the same bins\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 6192\\u001b[0;31m \\u001b[0mm\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mbins\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mhistogram\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mx\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mi\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mbins\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mweights\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mw\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mi\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m**\\u001b[0m\\u001b[0mhist_kwargs\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 6193\\u001b[0m \\u001b[0mm\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mm\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mastype\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfloat\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;31m# causes problems later if it's an int\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 6194\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mmlast\\u001b[0m \\u001b[0;32mis\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/function_base.py\\u001b[0m in \\u001b[0;36mhistogram\\u001b[0;34m(a, bins, range, normed, weights, density)\\u001b[0m\\n\\u001b[1;32m 667\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mall\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0misfinite\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mmn\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mmx\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 668\\u001b[0m raise ValueError(\\n\\u001b[0;32m--> 669\\u001b[0;31m 'range parameter must be finite.')\\n\\u001b[0m\\u001b[1;32m 670\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mmn\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0mmx\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 671\\u001b[0m \\u001b[0mmn\\u001b[0m \\u001b[0;34m-=\\u001b[0m \\u001b[0;36m0.5\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mValueError\\u001b[0m: range parameter must be finite.\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(y_pred,bins=100)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_cell_guid\": \"5beb30f0-e48f-6415-44e7-6766b0c343c5\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12, 5))\\n\",\n \"plt.hist(qids.value_counts(), bins=50,log = 'True')\\n\",\n \"plt.title('Normalised histogram of word count in questions', fontsize=15)\\n\",\n \"plt.legend()\\n\",\n \"plt.xlabel('Number of words', fontsize=15)\\n\",\n \"plt.ylabel('Probability', fontsize=15)\\n\",\n \"# plt.yscale('log', nonposy='clip')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"_cell_guid\": \"617013eb-3d1e-0817-942b-87d6c92fa4c8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"2559 157\\n30782 120\\n4044 111\\n2561 88\\n14376 79\\n17978 77\\n2675 68\\n28764 66\\n1772 63\\n24555 61\\n18753 58\\n4018 57\\n28133 56\\n3595 55\\n19621 55\\n18531 55\\n4951 55\\n13748 54\\n10024 54\\n10330 54\\n38 54\\n18296 53\\n11264 52\\n6551 52\\n20190 52\\n691 51\\n8461 51\\n6749 51\\n2322 51\\n33412 51\\n ... \\n95599 1\\n87403 1\\n259391 1\\n89450 1\\n1345 1\\n7490 1\\n13639 1\\n19784 1\\n17737 1\\n23882 1\\n32078 1\\n30031 1\\n34129 1\\n40274 1\\n38227 1\\n42325 1\\n48470 1\\n56666 1\\n54619 1\\n60764 1\\n64862 1\\n62815 1\\n66913 1\\n73058 1\\n77156 1\\n75109 1\\n81254 1\\n85352 1\\n83305 1\\n168274 1\\ndtype: int64\"\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"qids.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"_cell_guid\": \"0b2a4947-e9aa-064b-3d13-adb35e36d17e\"\n },\n \"outputs\": [],\n \"source\": [\n \"df_test = pd.read_csv(input+\\\"test.csv\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"_cell_guid\": \"79f1ce4c-31a3-2e9d-5eec-01b064c7f90d\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_qs = pd.Series(df_train[\\\"question1\\\"].tolist()+df_train['question2'].tolist()).astype(str)\\n\",\n \"test_qs = pd.Series(df_test[\\\"question1\\\"].tolist()+df_test['question2'].tolist()).astype(str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"_cell_guid\": \"4b4956b7-c75b-b7d8-fa6e-ac4a8a814f55\"\n },\n \"outputs\": [],\n \"source\": [\n \"pal = sns.color_palette()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"_cell_guid\": \"dc950cf2-4335-2e93-6505-6a4d3b804595\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"(array([ 0.00000000e+00, 1.24070806e-05, 5.17674742e-05,\\n 2.03861169e-04, 6.93085192e-04, 6.96507835e-04,\\n 2.43863308e-04, 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CBhDgAAoEGumQNm2uFjRwaOd2+3uiUA0AadOQAAgAbpzDHTju6c67oE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\\nEQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"dist_train = train_qs.apply(len)\\n\",\n \"dist_test = test_qs.apply(len)\\n\",\n \"plt.figure(figsize=(15, 10))\\n\",\n \"plt.hist(dist_train, bins=200, range=[0, 200], color=pal[2], normed=True, label='train')\\n\",\n \"plt.hist(dist_test, bins=200, range=[0, 200], color=pal[1], normed=True, alpha = 0.5 ,label='test')\\n\",\n \"plt.title('Normalised histogram of word count in questions', fontsize=15)\\n\",\n \"plt.legend()\\n\",\n \"plt.xlabel('Number of words', fontsize=15)\\n\",\n \"plt.ylabel('Probability', fontsize=15)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e803745e-df06-3538-dd45-2607fbd3899b\"\n },\n \"source\": [\n \"if 150 characters' steep down are the limit, how could there be some question got over 150 words?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"_cell_guid\": \"38bbf0d8-dafe-4f71-e545-8c80b3b27713\"\n },\n \"outputs\": [],\n \"source\": [\n \"import re\\n\",\n \"r = re.compile(\\\"[ ,.?|']\\\")\\n\",\n \"dist_train = train_qs.apply(lambda x: len(r.split(x)))\\n\",\n \"dist_test = test_qs.apply(lambda x: len(r.split(x)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"_cell_guid\": \"09da1a9f-637b-bb7d-e546-0872ad674e81\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"(array([ 0.00000000e+00, 2.62174432e-05, 1.79919868e-03,\\n 5.23879934e-03, 1.40926218e-02, 2.83617317e-02,\\n 4.73476367e-02, 7.04776029e-02, 9.20699055e-02,\\n 1.04061082e-01, 1.03567427e-01, 9.51518406e-02,\\n 7.72114360e-02, 6.06921021e-02, 4.85738883e-02,\\n 3.93046367e-02, 3.25217791e-02, 2.74046884e-02,\\n 2.20439673e-02, 1.80968566e-02, 1.49420243e-02,\\n 1.26871110e-02, 1.06419373e-02, 8.98277812e-03,\\n 8.04001592e-03, 7.46962667e-03, 6.71656791e-03,\\n 6.03853794e-03, 5.71774727e-03, 5.17421491e-03,\\n 4.55160392e-03, 3.77659072e-03, 3.05102831e-03,\\n 2.38088488e-03, 1.83095803e-03, 1.47180037e-03,\\n 1.16017515e-03, 9.48517254e-04, 8.37252983e-04,\\n 7.22365163e-04, 5.72520752e-04, 4.93655272e-04,\\n 4.62109080e-04, 3.80898951e-04, 3.28890365e-04,\\n 2.75389729e-04, 2.39793688e-04, 1.84587852e-04,\\n 1.76701304e-04, 1.56452059e-04, 1.28529417e-04,\\n 1.13182621e-04, 1.05935523e-04, 1.07427572e-04,\\n 9.16544763e-05, 7.95049294e-05, 8.67520275e-05,\\n 8.93098269e-05, 8.69651775e-05, 7.33235810e-05,\\n 7.28972811e-05, 5.88293848e-05, 5.64847354e-05,\\n 6.47975832e-05, 4.98770871e-05, 4.15642392e-05,\\n 4.11379393e-05, 3.77275402e-05, 2.34464939e-05,\\n 1.81177453e-05, 1.68388456e-05, 1.42810463e-05,\\n 1.00180474e-05, 1.17232470e-05, 7.46024807e-06,\\n 6.60764829e-06, 7.46024807e-06, 8.73914774e-06,\\n 4.04984895e-06, 1.91834950e-06, 5.11559868e-06,\\n 2.98409923e-06, 6.39449834e-07, 1.49204961e-06,\\n 1.91834950e-06, 1.27889967e-06, 1.49204961e-06,\\n 1.91834950e-06, 1.70519956e-06, 0.00000000e+00,\\n 6.39449834e-07, 1.06574972e-06, 8.52599779e-07,\\n 1.27889967e-06, 1.06574972e-06, 4.26299890e-07,\\n 8.52599779e-07, 8.52599779e-07, 4.26299890e-07,\\n 1.27889967e-06]),\\n array([ 0., 1., 2., 3., 4., 5., 6., 7., 8.,\\n 9., 10., 11., 12., 13., 14., 15., 16., 17.,\\n 18., 19., 20., 21., 22., 23., 24., 25., 26.,\\n 27., 28., 29., 30., 31., 32., 33., 34., 35.,\\n 36., 37., 38., 39., 40., 41., 42., 43., 44.,\\n 45., 46., 47., 48., 49., 50., 51., 52., 53.,\\n 54., 55., 56., 57., 58., 59., 60., 61., 62.,\\n 63., 64., 65., 66., 67., 68., 69., 70., 71.,\\n 72., 73., 74., 75., 76., 77., 78., 79., 80.,\\n 81., 82., 83., 84., 85., 86., 87., 88., 89.,\\n 90., 91., 92., 93., 94., 95., 96., 97., 98.,\\n 99., 100.]),\\n )\"\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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z3GOJUkVfWfkzw8Of4vxhgvb/nsAQAA9oCpnnEbY3xw1aHHVow9lFd/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\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(15, 10))\\n\",\n \"plt.hist(dist_train, bins=100, range=[0, 100], color=pal[2], normed=True, label='train')\\n\",\n \"plt.hist(dist_test, bins=100, range=[0, 100], color=pal[1], normed=True, alpha=0.5, label='test')\\n\",\n \"plt.title('Normalised histogram of word count in questions', fontsize=15)\\n\",\n \"plt.legend()\\n\",\n \"plt.xlabel('Number of words', fontsize=15)\\n\",\n \"plt.ylabel('Probability', fontsize=15)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"847aa66d-8041-021e-6b43-9fc237130b6c\"\n },\n \"source\": [\n \"max word limit may be 70? The two distribution are very similar.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"4e475988-37aa-28a2-9f39-9bf6dd56d0b4\"\n },\n \"source\": [\n \"By using the feature like number of shared words, we have the following idea.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"_cell_guid\": \"bb307674-3392-e5fb-c6dd-633a43b064e0\"\n },\n \"outputs\": [],\n \"source\": [\n \"from nltk.corpus import stopwords\\n\",\n \"\\n\",\n \"stops = set(stopwords.words('english'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"_cell_guid\": \"5acc4814-baf1-9101-e2ad-2fba1845864f\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"\"\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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/PZz36So476PBtuuBHXXHMlzz33HDvuuC3nn38Ro0evxJNPPsEOO+zM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\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"def word_match(row):\\n\",\n \" q1_word = {}\\n\",\n \" q2_word = {}\\n\",\n \" for word in r.split(str(row[\\\"question1\\\"]).lower()):\\n\",\n \" if word not in stops:\\n\",\n \" q1_word[word] = 1\\n\",\n \" for word in r.split(str(row[\\\"question2\\\"]).lower()):\\n\",\n \" if word not in stops:\\n\",\n \" q2_word[word] = 1\\n\",\n \" if len(q1_word) == 0 or len(q2_word)==0:\\n\",\n \" return 0\\n\",\n \" shared_word_in_q1 = [w for w in q1_word.keys() if w in q2_word]\\n\",\n \" shared_word_in_q2 = [w for w in q2_word.keys() if w in q1_word]\\n\",\n \" R = (len(shared_word_in_q1)+len(shared_word_in_q2))/(len(q1_word)+len(q2_word))\\n\",\n \" return R\\n\",\n \"\\n\",\n \"plt.figure(figsize=(15,5))\\n\",\n \"train_word_related = df_train.apply(word_match,axis = 1,raw = 1)\\n\",\n \"plt.hist(train_word_related[df_train['is_duplicate'] == 0].fillna(0), bins=20, normed=True, label='Not Duplicate')\\n\",\n \"plt.hist(train_word_related[df_train['is_duplicate'] == 1].fillna(0), bins=20, normed=True, alpha=0.7, label='Duplicate')\\n\",\n \"plt.legend()\\n\",\n \"plt.title('Label distribution over word_match_share', fontsize=15)\\n\",\n \"plt.xlabel('word_match_share', fontsize=15)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"c1cf9ce3-e45d-ad1f-7180-81c822c8675a\"\n },\n \"source\": [\n \"Used stops to filter all words, and compute the relateness in question 1 and question 2.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"metadata\": {\n \"_cell_guid\": \"d19059c6-d945-a8d2-454f-1d5153511a53\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": \"\"\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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JiwZ82PffLJJxg5ciQbbvjWXp2zVjUHtMzcOSK2Ba6NiG2qQtjpwM+B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\\ngMxuKqklAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(15, 5))\\n\",\n \"tfidf_train_word_share = df_train.apply(tfidf_word_match_share,axis = 1, raw = True)\\n\",\n \"plt.hist(tfidf_train_word_share[df_train['is_duplicate']==1].fillna(0), bins=20, normed = True, label = 'Duplicate')\\n\",\n \"plt.hist(tfidf_train_word_share[df_train['is_duplicate']==0].fillna(0), bins=20, normed = True, label = 'Not Duplicate',alpha = 0.7)\\n\",\n \"plt.legend()\\n\",\n \"plt.title('Label distribution over tfidf train data')\\n\",\n \"plt.xlabel('tfidf train word share')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"metadata\": {\n \"_cell_guid\": \"f07426a3-3796-a11e-6365-25735c41798b\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"Original AUC: 0.749579155461\\n TFIDF AUC: 0.738956967713\\n\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import roc_auc_score\\n\",\n \"print('Original AUC:', roc_auc_score(df_train['is_duplicate'], train_word_related))\\n\",\n \"print(' TFIDF AUC:', roc_auc_score(df_train['is_duplicate'], tfidf_train_word_share.fillna(0)))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"17fdf979-7419-80cc-21d2-51af2350d1ee\"\n },\n \"source\": [\n \"Rebalancing data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"metadata\": {\n \"_cell_guid\": \"1cf46e20-9801-cc29-0ca2-6026f39aaff6\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": \"/opt/conda/bin/ipython:17: RuntimeWarning: invalid value encountered in long_scalars\\n\"\n }\n ],\n \"source\": [\n \"# First we create our training and testing data\\n\",\n \"x_train = pd.DataFrame()\\n\",\n \"x_test = pd.DataFrame()\\n\",\n \"x_train['word_match'] = train_word_related\\n\",\n \"x_train['tfidf_word_match'] = tfidf_train_word_share\\n\",\n \"x_test['word_match'] = df_test.apply(word_match, axis=1, raw=True)\\n\",\n \"x_test['tfidf_word_match'] = df_test.apply(tfidf_word_match_share, axis=1, raw=True)\\n\",\n \"\\n\",\n \"y_train = df_train['is_duplicate'].values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_cell_guid\": \"9c05da7a-0d85-01dd-2184-e17ae3d2cd47\"\n },\n \"outputs\": [],\n \"source\": [\n \"pos_train = x_train[y_train == 1]\\n\",\n \"neg_train = x_train[y_train == 0]\\n\",\n \"\\n\",\n \"# Now we oversample the negative class\\n\",\n \"# There is likely a much more elegant way to do this...\\n\",\n \"p = 0.165\\n\",\n \"scale = ((len(pos_train) / (len(pos_train) + len(neg_train))) / p) - 1\\n\",\n \"while scale > 1:\\n\",\n \" neg_train = pd.concat([neg_train, neg_train])\\n\",\n \" scale -=1\\n\",\n \"neg_train = pd.concat([neg_train, neg_train[:int(scale * len(neg_train))]])\\n\",\n \"print(len(pos_train) / (len(pos_train) + len(neg_train)))\\n\",\n \"\\n\",\n \"x_train = pd.concat([pos_train, neg_train])\\n\",\n \"y_train = (np.zeros(len(pos_train)) + 1).tolist() + np.zeros(len(neg_train)).tolist()\\n\",\n \"del pos_train, neg_train\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_cell_guid\": \"90b5c348-71f1-0de7-718e-41987027ea6d\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.cross_validation import train_test_split\\n\",\n \"\\n\",\n \"x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.2, random_state=4242)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_cell_guid\": \"83ac7e0b-830d-1335-e8a5-720cd6d4908d\"\n },\n \"outputs\": [],\n \"source\": [\n \"x_train\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_cell_guid\": \"bdeb626c-9ef1-bce1-fc60-53d7855c8a36\"\n },\n \"outputs\": [],\n \"source\": [\n \"import xgboost as xgb\\n\",\n \"\\n\",\n \"# Set our parameters for xgboost\\n\",\n \"params = {}\\n\",\n \"params['objective'] = 'binary:logistic'\\n\",\n \"params['eval_metric'] = 'logloss'\\n\",\n \"params['eta'] = 0.02\\n\",\n \"params['max_depth'] = 4\\n\",\n \"\\n\",\n \"d_train = xgb.DMatrix(x_train, label=y_train)\\n\",\n \"d_valid = xgb.DMatrix(x_valid, label=y_valid)\\n\",\n \"\\n\",\n \"watchlist = [(d_train, 'train'), (d_valid, 'valid')]\\n\",\n \"\\n\",\n \"bst = xgb.train(params, d_train, 400, watchlist, early_stopping_rounds=50, verbose_eval=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_cell_guid\": \"0800558a-6f99-c8c7-aee8-e397f7dc37a0\"\n },\n \"outputs\": [],\n \"source\": [\n \"d_test = xgb.DMatrix(x_test)\\n\",\n \"p_test = bst.predict(d_test)\\n\",\n \"\\n\",\n \"sub = pd.DataFrame()\\n\",\n \"sub['test_id'] = df_test['test_id']\\n\",\n \"sub['is_duplicate'] = p_test\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 173,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"},"id":{"kind":"string","value":"0001/162/1162969.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":178,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"57467428-cda7-c05b-83b8-8bb01b2f9402\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sample_submission.csv\\n\",\n \"test.csv\\n\",\n \"train.csv\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This Python 3 environment comes with many helpful analytics libraries installed\\n\",\n \"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\\n\",\n \"# For example, here's several helpful packages to load in \\n\",\n \"\\n\",\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"\\n\",\n \"# Input data files are available in the \\\"../input/\\\" directory.\\n\",\n \"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\\n\",\n \"\\n\",\n \"from subprocess import check_output\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"70c742ae-fe84-945f-06e7-26cb2942083a\"\n },\n \"outputs\": [],\n \"source\": [\n \"train = pd.read_csv('../input/train.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"3b0fe5bb-4e4e-4a7e-979c-2c1496c6b72e\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"RangeIndex: 14999 entries, 0 to 14998\\n\",\n \"Data columns (total 10 columns):\\n\",\n \"satisfaction_level 14999 non-null float64\\n\",\n \"last_evaluation 14999 non-null float64\\n\",\n \"number_project 14999 non-null int64\\n\",\n \"average_montly_hours 14999 non-null int64\\n\",\n \"time_spend_company 14999 non-null int64\\n\",\n \"Work_accident 14999 non-null int64\\n\",\n \"left 14999 non-null int64\\n\",\n \"promotion_last_5years 14999 non-null int64\\n\",\n \"sales 14999 non-null object\\n\",\n \"salary 14999 non-null object\\n\",\n \"dtypes: float64(2), int64(6), object(2)\\n\",\n \"memory usage: 1.1+ MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"hr_data.info()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"1e79a209-460a-25a3-7595-c5783c9feb3a\"\n },\n \"source\": [\n \"Conveniently, there is no missing data. Given that the \\\"sales' and \\\"salary\\\" columns are non-numeric, we can check the number of unique levels and dummy code the variables.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"a8718b8b-20aa-1f75-af3a-37cfb178a458\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Departments: sales, accounting, hr, technical, support, management, IT, product_mng, marketing, RandD\\n\",\n \"Salary levels: low, medium, high\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('Departments: ', ', '.join(hr_data['sales'].unique()))\\n\",\n \"print('Salary levels: ', ', '.join(hr_data['salary'].unique()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"2f1575c8-87e1-6dbd-06eb-4dc2cf0985a8\"\n },\n \"outputs\": [],\n \"source\": [\n \"dummy_depts = pd.get_dummies(hr_data['sales'], drop_first = True)\\n\",\n \"dummy_salaries = pd.get_dummies(hr_data['salary'], drop_first = True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"4b163373-3852-a916-be79-9e5c2c3ba931\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"RangeIndex: 14999 entries, 0 to 14998\\n\",\n \"Data columns (total 10 columns):\\n\",\n \"satisfaction_level 14999 non-null float64\\n\",\n \"last_evaluation 14999 non-null float64\\n\",\n \"number_project 14999 non-null int64\\n\",\n \"average_montly_hours 14999 non-null int64\\n\",\n \"time_spend_company 14999 non-null int64\\n\",\n \"Work_accident 14999 non-null int64\\n\",\n \"left 14999 non-null int64\\n\",\n \"promotion_last_5years 14999 non-null int64\\n\",\n \"sales 14999 non-null object\\n\",\n \"salary 14999 non-null object\\n\",\n \"dtypes: float64(2), int64(6), object(2)\\n\",\n \"memory usage: 1.1+ MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"hr_data.info()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"1e79a209-460a-25a3-7595-c5783c9feb3a\"\n },\n \"source\": [\n \"Conveniently, there is no missing data. Given that the \\\"sales' and \\\"salary\\\" columns are non-numeric, we can check the number of unique levels and dummy code the variables.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"a8718b8b-20aa-1f75-af3a-37cfb178a458\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Departments: sales, accounting, hr, technical, support, management, IT, product_mng, marketing, RandD\\n\",\n \"Salary levels: low, medium, high\\n\"\n ]\n }\n ],\n \"source\": [\n \"print('Departments: ', ', '.join(hr_data['sales'].unique()))\\n\",\n \"print('Salary levels: ', ', '.join(hr_data['salary'].unique()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"2f1575c8-87e1-6dbd-06eb-4dc2cf0985a8\"\n },\n \"outputs\": [],\n \"source\": [\n \"dummy_depts = pd.get_dummies(hr_data['sales'], drop_first = True)\\n\",\n \"dummy_salaries = pd.get_dummies(hr_data['salary'], drop_first = True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"4b163373-3852-a916-be79-9e5c2c3ba931\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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vuEV/7xHB4N6jFv2Sqir12nmlU1du0/wKIvpuLoYF+ywrvw3scT8Zs2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\\nqBiS0UskEolExfD/pF1lXFLGWTUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Correlation matrix\\n\",\n \"sns.heatmap(hr_data.corr(), annot=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"503f6ac1-7553-71a8-442a-e8fa3f9fafee\"\n },\n \"source\": [\n \"The matrix above shows that, generally speaking, the data is not correlated. This is good because it means we likely won't have issues with multicollinearity later.\\n\",\n \"\\n\",\n \"It is notable, though perhaps unsurprising, that our employees' satisfaction level is the variable that is most highly correlated with them leaving.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"d0100ccd-948b-7cb6-6314-32b64cba89dd\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['satisfaction_level', 'last_evaluation', 'number_project',\\n\",\n \" 'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\\n\",\n \" 'promotion_last_5years', 'RandD', 'accounting', 'hr', 'management',\\n\",\n \" 'marketing', 'product_mng', 'sales', 'support', 'technical', 'low',\\n\",\n \" 'medium'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"hr_data_new.columns\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"16c182c4-4e2b-8bdc-8c03-2865a2dad24b\"\n },\n \"source\": [\n \"Let's first check if there are any particular departments that our people tend to be leaving from.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"aea2ebe7-53de-932e-ec35-7fc4c22fe45a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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ojYEzgHOK9WsZqZ2bpq2cS0HPg48EJZ2WhgRv77RlJS2BWYExGLI2IZ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\\n5ARhZmaFnCDMzKyQE4SZmRX6P0nUUFsP8RAwAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.countplot(x='sales', hue='left', data=hr_data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"22b6169d-f901-c811-86b0-3aa2f28cb178\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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9LRs4rZD10Uu//OteHigNzY8VVPp3XCFrH6jVMAVrt7r5mNA3D3X5vZ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\\ngIhIRqkAiIhk1P8HQT0OBbYqB+YAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.boxplot(x='sales', y='satisfaction_level', data=hr_data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"6b12e7ea-2905-92d5-2a2d-9f2d4be54b6f\"\n },\n \"source\": [\n \"While there doesn't appear to be too much of a difference in the satisfaction, we notice that both HR and accounting, the departments that have the highest rates of leaving, have slightly lower median satisfaction levels than the rest of the departments.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"5eef6474-f00b-89a8-da18-368a8feec5ec\"\n },\n \"source\": [\n \"Salary is likely to have a high impact on leaving. In fact, it is highly likely that both R&D and management, the two departments with the lowers leaving rates, have high salaries. Let's first check the relationship between leaving and salary.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"c922cfc4-245b-86ab-7d1b-1e809a0ee502\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.countplot(x='salary', hue='left', data=hr_data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"7041b29e-3462-ef60-1eef-a49d81848db3\"\n },\n \"source\": [\n \"Confirming our hypothesis, those with low salaries tend to have the highest number of people that leave. Eyeballing the plot shows us that around 40% of those with low salaries leave and 25% of those with median salaries leave. It looks like only 10% of those with high salaries leave.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e39c63c4-a878-5275-50cb-7e92252a1a69\"\n },\n \"source\": [\n \"Let's also check the spread of satisfaction level between the different salary ranges.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_cell_guid\": \"2154a824-e508-0d5c-9fae-c1c42077a5c2\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.boxplot(x='salary', y='satisfaction_level', data=hr_data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"6b5cf004-54b9-1e52-29fe-1facdc953b1b\"\n },\n \"source\": [\n \"Again, in line with some of our prior observations, low salary has the lowest median satisfaction and the highest spread.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"71a11274-d9c3-b3ab-8cc2-4ce27ed4dcd5\"\n },\n \"source\": [\n \"Something that may impact employee perception in the company is the number of projects they are assigned.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"_cell_guid\": \"c48be365-a050-aa7b-91aa-c82c07d00787\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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r6D3TzZHh3Yr4wuPjbPxYriWYRez9x23lk23qyp5f6XX8fV3umco+CQ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\\nS9rGGJNGLGkbY0wa+f/TnZOTv7MnpQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.factorplot(x='number_project', y='last_evaluation', hue='sales', data=hr_data)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"719221e7-ba42-cb89-d49e-5856f8a75b4b\"\n },\n \"source\": [\n \"It is very clear that evaluation scores are affected by the number of projects assigned to the employee. What's more, we again notice a peculiar trend in accounting -- they have a lower last_evaluation score than the other departments at 7 projects.\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 310,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163111.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":182,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"d4589c40-d404-7a9c-b966-147e2d5b1a02\"\n },\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"db7a71e2-7ab2-853d-9701-7be932d52661\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"# File sizes\\n\",\n \"movie_metadata.csv 1.49MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"import os\\n\",\n \"import gc\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"pal = sns.color_palette()\\n\",\n \"\\n\",\n \"print('# File sizes')\\n\",\n \"for f in os.listdir('../input'):\\n\",\n \" if 'zip' not in f:\\n\",\n \" print(f.ljust(30) + str(round(os.path.getsize('../input/' + f) / 1000000, 2)) + 'MB')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"3a6bc07c-f0ca-a9f1-61a9-f7b45b11c33a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mdf\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mselect\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m: select() missing 1 required positional argument: 'crit'\"\n ]\n }\n ],\n \"source\": [\n \"df.select()\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 20,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163135.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":183,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"d4589c40-d404-7a9c-b966-147e2d5b1a02\"\n },\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"db7a71e2-7ab2-853d-9701-7be932d52661\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"# File sizes\\n\",\n \"movie_metadata.csv 1.49MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"import os\\n\",\n \"import gc\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"pal = sns.color_palette()\\n\",\n \"\\n\",\n \"print('# File sizes')\\n\",\n \"for f in os.listdir('../input'):\\n\",\n \" if 'zip' not in f:\\n\",\n \" print(f.ljust(30) + str(round(os.path.getsize('../input/' + f) / 1000000, 2)) + 'MB')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"3a6bc07c-f0ca-a9f1-61a9-f7b45b11c33a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \" in ()\\n 1 # Loading the data sets\\n----> 2 aisles = pd.read_csv('../input/aisles.csv')\\n 3 departments = pd.read_csv('../input/departments.csv')\\n 4 order_products_prior = pd.read_csv('../input/order_products__prior.csv')\\n 5 order_products_train = pd.read_csv('../input/order_products__train.csv')\\n\",\n \"NameError: name 'pd' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# Loading the data sets\\n\",\n \"aisles = pd.read_csv('../input/aisles.csv')\\n\",\n \"departments = pd.read_csv('../input/departments.csv')\\n\",\n \"order_products_prior = pd.read_csv('../input/order_products__prior.csv')\\n\",\n \"order_products_train = pd.read_csv('../input/order_products__train.csv')\\n\",\n \"orders = pd.read_csv('../input/orders.csv')\\n\",\n \"products = pd.read_csv('../input/products.csv')\\n\",\n \"sample_submission = pd.read_csv('../input/sample_submission.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"_cell_guid\": \"ca0bf207-4c88-3feb-d689-308af9e513d7\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": \", line 1)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \" File \\\"\\\", line 1\\n print 'aisles, shape = ', aisles.shape\\n ^\\nSyntaxError: Missing parentheses in call to 'print'\\n\"\n ]\n },\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"Missing parentheses in call to 'print' (, line 1)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \" File \\\"\\\", line 1\\n print 'aisles, shape = ', aisles.shape\\n ^\\nSyntaxError: Missing parentheses in call to 'print'\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles, shape = (134, 2)\\ndepartments, shape = (21, 2)\\norder_procducts_prior, shape = (32434489, 4)\\n\"\n },\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'order_procducts_train' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"---------------------------------------------------------------------------\",\n \"NameError Traceback (most recent call last)\",\n \" in ()\\n 2 print ('departments, shape = ', departments.shape)\\n 3 print ('order_procducts_prior, shape = ', order_products_prior.shape)\\n----> 4 print ('order_procducts_train, shape = ', order_procducts_train.shape)\\n 5 print ('orders, shape = ', orders.shape)\\n 6 print ('products, shape = ', products.shape)\\n\",\n \"NameError: name 'order_procducts_train' is not defined\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles, shape = (134, 2)\\ndepartments, shape = (21, 2)\\norder_procducts_prior, shape = (32434489, 4)\\norder_products_train, shape = (1384617, 4)\\norders, shape = (3421083, 7)\\nproducts, shape = (49688, 4)\\n\"\n },\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'list' object has no attribute 'shape'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"---------------------------------------------------------------------------\",\n \"AttributeError Traceback (most recent call last)\",\n \" in ()\\n 9 '''\\n 10 for s in sets:\\n---> 11 print (str(s), sets.shape, '\\\\n')\\n\",\n \"AttributeError: 'list' object has no attribute 'shape'\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \" aisle_id aisle\\n0 1 prepared soups salads\\n1 2 specialty cheeses\\n2 3 energy granola bars\\n3 4 instant foods\\n4 5 marinades meat preparation\\n5 6 other\\n6 7 packaged meat\\n7 8 bakery desserts\\n8 9 pasta sauce\\n9 10 kitchen supplies\\n10 11 cold flu allergy\\n11 12 fresh pasta\\n12 13 prepared meals\\n13 14 tofu meat alternatives\\n14 15 packaged seafood\\n15 16 fresh herbs\\n16 17 baking ingredients\\n17 18 bulk dried fruits vegetables\\n18 19 oils vinegars\\n19 20 oral hygiene\\n20 21 packaged cheese\\n21 22 hair care\\n22 23 popcorn jerky\\n23 24 fresh fruits\\n24 25 soap\\n25 26 coffee\\n26 27 beers coolers\\n27 28 red wines\\n28 29 honeys syrups nectars\\n29 30 latino foods\\n.. ... ...\\n104 105 doughs gelatins bake mixes\\n105 106 hot dogs bacon sausage\\n106 107 chips pretzels\\n107 108 other creams cheeses\\n108 109 skin care\\n109 110 pickled goods olives\\n110 111 plates bowls cups flatware\\n111 112 bread\\n112 113 frozen juice\\n113 114 cleaning products\\n114 115 water seltzer sparkling water\\n115 116 frozen produce\\n116 117 nuts seeds dried fruit\\n117 118 first aid\\n118 119 frozen dessert\\n119 120 yogurt\\n120 121 cereal\\n121 122 meat counter\\n122 123 packaged vegetables fruits\\n123 124 spirits\\n124 125 trail mix snack mix\\n125 126 feminine care\\n126 127 body lotions soap\\n127 128 tortillas flat bread\\n128 129 frozen appetizers sides\\n129 130 hot cereal pancake mixes\\n130 131 dry pasta\\n131 132 beauty\\n132 133 muscles joints pain relief\\n133 134 specialty wines champagnes\\n\\n[134 rows x 2 columns] (134, 2) \\n\\n department_id department\\n0 1 frozen\\n1 2 other\\n2 3 bakery\\n3 4 produce\\n4 5 alcohol\\n5 6 international\\n6 7 beverages\\n7 8 pets\\n8 9 dry goods pasta\\n9 10 bulk\\n10 11 personal care\\n11 12 meat seafood\\n12 13 pantry\\n13 14 breakfast\\n14 15 canned goods\\n15 16 dairy eggs\\n16 17 household\\n17 18 babies\\n18 19 snacks\\n19 20 deli\\n20 21 missing (21, 2) \\n\\n order_id product_id add_to_cart_order reordered\\n0 2 33120 1 1\\n1 2 28985 2 1\\n2 2 9327 3 0\\n3 2 45918 4 1\\n4 2 30035 5 0\\n5 2 17794 6 1\\n6 2 40141 7 1\\n7 2 1819 8 1\\n8 2 43668 9 0\\n9 3 33754 1 1\\n10 3 24838 2 1\\n11 3 17704 3 1\\n12 3 21903 4 1\\n13 3 17668 5 1\\n14 3 46667 6 1\\n15 3 17461 7 1\\n16 3 32665 8 1\\n17 4 46842 1 0\\n18 4 26434 2 1\\n19 4 39758 3 1\\n20 4 27761 4 1\\n21 4 10054 5 1\\n22 4 21351 6 1\\n23 4 22598 7 1\\n24 4 34862 8 1\\n25 4 40285 9 1\\n26 4 17616 10 1\\n27 4 25146 11 1\\n28 4 32645 12 1\\n29 4 41276 13 1\\n... ... ... ... ...\\n32434459 3421080 41950 4 0\\n32434460 3421080 31717 5 0\\n32434461 3421080 12935 6 1\\n32434462 3421080 25122 7 0\\n32434463 3421080 10667 8 0\\n32434464 3421080 38061 9 0\\n32434465 3421081 38185 1 0\\n32434466 3421081 12218 2 0\\n32434467 3421081 32299 3 0\\n32434468 3421081 3060 4 0\\n32434469 3421081 20539 5 0\\n32434470 3421081 35221 6 0\\n32434471 3421081 12861 7 0\\n32434472 3421082 17279 1 1\\n32434473 3421082 12738 2 1\\n32434474 3421082 16797 3 0\\n32434475 3421082 43352 4 1\\n32434476 3421082 32700 5 1\\n32434477 3421082 12023 6 0\\n32434478 3421082 47941 7 0\\n32434479 3421083 7854 1 0\\n32434480 3421083 45309 2 0\\n32434481 3421083 21162 3 0\\n32434482 3421083 18176 4 1\\n32434483 3421083 35211 5 0\\n32434484 3421083 39678 6 1\\n32434485 3421083 11352 7 0\\n32434486 3421083 4600 8 0\\n32434487 3421083 24852 9 1\\n32434488 3421083 5020 10 1\\n\\n[32434489 rows x 4 columns] (32434489, 4) \\n\\n order_id product_id add_to_cart_order reordered\\n0 1 49302 1 1\\n1 1 11109 2 1\\n2 1 10246 3 0\\n3 1 49683 4 0\\n4 1 43633 5 1\\n5 1 13176 6 0\\n6 1 47209 7 0\\n7 1 22035 8 1\\n8 36 39612 1 0\\n9 36 19660 2 1\\n10 36 49235 3 0\\n11 36 43086 4 1\\n12 36 46620 5 1\\n13 36 34497 6 1\\n14 36 48679 7 1\\n15 36 46979 8 1\\n16 38 11913 1 0\\n17 38 18159 2 0\\n18 38 4461 3 0\\n19 38 21616 4 1\\n20 38 23622 5 0\\n21 38 32433 6 0\\n22 38 28842 7 0\\n23 38 42625 8 0\\n24 38 39693 9 0\\n25 96 20574 1 1\\n26 96 30391 2 0\\n27 96 40706 3 1\\n28 96 25610 4 0\\n29 96 27966 5 1\\n... ... ... ... ...\\n1384587 3421026 32237 3 0\\n1384588 3421026 10 4 0\\n1384589 3421026 4493 5 0\\n1384590 3421026 7781 6 0\\n1384591 3421049 40800 1 0\\n1384592 3421049 17706 2 0\\n1384593 3421049 33424 3 1\\n1384594 3421049 17299 4 0\\n1384595 3421049 26800 5 0\\n1384596 3421049 34243 6 0\\n1384597 3421056 5750 1 1\\n1384598 3421056 9340 2 1\\n1384599 3421056 21709 3 1\\n1384600 3421056 16475 4 0\\n1384601 3421056 12432 5 0\\n1384602 3421058 15629 1 1\\n1384603 3421058 4347 2 1\\n1384604 3421058 34466 3 1\\n1384605 3421058 6244 4 1\\n1384606 3421058 6858 5 1\\n1384607 3421058 30316 6 1\\n1384608 3421058 35578 7 0\\n1384609 3421058 32650 8 1\\n1384610 3421063 49235 1 1\\n1384611 3421063 13565 2 1\\n1384612 3421063 14233 3 1\\n1384613 3421063 35548 4 1\\n1384614 3421070 35951 1 1\\n1384615 3421070 16953 2 1\\n1384616 3421070 4724 3 1\\n\\n[1384617 rows x 4 columns] (1384617, 4) \\n\\n order_id user_id eval_set order_number order_dow \\\\\\n0 2539329 1 prior 1 2 \\n1 2398795 1 prior 2 3 \\n2 473747 1 prior 3 3 \\n3 2254736 1 prior 4 4 \\n4 431534 1 prior 5 4 \\n5 3367565 1 prior 6 2 \\n6 550135 1 prior 7 1 \\n7 3108588 1 prior 8 1 \\n8 2295261 1 prior 9 1 \\n9 2550362 1 prior 10 4 \\n10 1187899 1 train 11 4 \\n11 2168274 2 prior 1 2 \\n12 1501582 2 prior 2 5 \\n13 1901567 2 prior 3 1 \\n14 738281 2 prior 4 2 \\n15 1673511 2 prior 5 3 \\n16 1199898 2 prior 6 2 \\n17 3194192 2 prior 7 2 \\n18 788338 2 prior 8 1 \\n19 1718559 2 prior 9 2 \\n20 1447487 2 prior 10 1 \\n21 1402090 2 prior 11 1 \\n22 3186735 2 prior 12 1 \\n23 3268552 2 prior 13 4 \\n24 839880 2 prior 14 3 \\n25 1492625 2 train 15 1 \\n26 1374495 3 prior 1 1 \\n27 444309 3 prior 2 3 \\n28 3002854 3 prior 3 3 \\n29 2037211 3 prior 4 2 \\n... ... ... ... ... ... \\n3421053 2789700 206208 prior 35 3 \\n3421054 844592 206208 prior 36 6 \\n3421055 1541132 206208 prior 37 2 \\n3421056 2808240 206208 prior 38 0 \\n3421057 3027766 206208 prior 39 2 \\n3421058 3356245 206208 prior 40 5 \\n3421059 442304 206208 prior 41 2 \\n3421060 2675140 206208 prior 42 1 \\n3421061 167903 206208 prior 43 4 \\n3421062 2393201 206208 prior 44 6 \\n3421063 3292671 206208 prior 45 2 \\n3421064 3059777 206208 prior 46 1 \\n3421065 2239861 206208 prior 47 3 \\n3421066 1285346 206208 prior 48 1 \\n3421067 1882108 206208 prior 49 1 \\n3421068 803273 206208 test 50 5 \\n3421069 3154581 206209 prior 1 3 \\n3421070 1889163 206209 prior 2 3 \\n3421071 1542354 206209 prior 3 5 \\n3421072 688306 206209 prior 4 1 \\n3421073 2307371 206209 prior 5 4 \\n3421074 3186442 206209 prior 6 0 \\n3421075 550836 206209 prior 7 2 \\n3421076 2129269 206209 prior 8 3 \\n3421077 2558525 206209 prior 9 4 \\n3421078 2266710 206209 prior 10 5 \\n3421079 1854736 206209 prior 11 4 \\n3421080 626363 206209 prior 12 1 \\n3421081 2977660 206209 prior 13 1 \\n3421082 272231 206209 train 14 6 \\n\\n order_hour_of_day days_since_prior_order \\n0 8 NaN \\n1 7 15.0 \\n2 12 21.0 \\n3 7 29.0 \\n4 15 28.0 \\n5 7 19.0 \\n6 9 20.0 \\n7 14 14.0 \\n8 16 0.0 \\n9 8 30.0 \\n10 8 14.0 \\n11 11 NaN \\n12 10 10.0 \\n13 10 3.0 \\n14 10 8.0 \\n15 11 8.0 \\n16 9 13.0 \\n17 12 14.0 \\n18 15 27.0 \\n19 9 8.0 \\n20 11 6.0 \\n21 10 30.0 \\n22 9 28.0 \\n23 11 30.0 \\n24 10 13.0 \\n25 11 30.0 \\n26 14 NaN \\n27 19 9.0 \\n28 16 21.0 \\n29 18 20.0 \\n... ... ... \\n3421053 22 4.0 \\n3421054 15 10.0 \\n3421055 12 3.0 \\n3421056 15 19.0 \\n3421057 14 9.0 \\n3421058 9 10.0 \\n3421059 14 11.0 \\n3421060 19 6.0 \\n3421061 14 3.0 \\n3421062 16 2.0 \\n3421063 11 3.0 \\n3421064 10 13.0 \\n3421065 4 9.0 \\n3421066 11 5.0 \\n3421067 22 7.0 \\n3421068 11 4.0 \\n3421069 11 NaN \\n3421070 17 7.0 \\n3421071 11 30.0 \\n3421072 10 30.0 \\n3421073 15 3.0 \\n3421074 16 3.0 \\n3421075 13 9.0 \\n3421076 17 22.0 \\n3421077 15 22.0 \\n3421078 18 29.0 \\n3421079 10 30.0 \\n3421080 12 18.0 \\n3421081 12 7.0 \\n3421082 14 30.0 \\n\\n[3421083 rows x 7 columns] (3421083, 7) \\n\\n product_id product_name \\\\\\n0 1 Chocolate Sandwich Cookies \\n1 2 All-Seasons Salt \\n2 3 Robust Golden Unsweetened Oolong Tea \\n3 4 Smart Ones Classic Favorites Mini Rigatoni Wit... \\n4 5 Green Chile Anytime Sauce \\n5 6 Dry Nose Oil \\n6 7 Pure Coconut Water With Orange \\n7 8 Cut Russet Potatoes Steam N' Mash \\n8 9 Light Strawberry Blueberry Yogurt \\n9 10 Sparkling Orange Juice & Prickly Pear Beverage \\n10 11 Peach Mango Juice \\n11 12 Chocolate Fudge Layer Cake \\n12 13 Saline Nasal Mist \\n13 14 Fresh Scent Dishwasher Cleaner \\n14 15 Overnight Diapers Size 6 \\n15 16 Mint Chocolate Flavored Syrup \\n16 17 Rendered Duck Fat \\n17 18 Pizza for One Suprema Frozen Pizza \\n18 19 Gluten Free Quinoa Three Cheese & Mushroom Blend \\n19 20 Pomegranate Cranberry & Aloe Vera Enrich Drink \\n20 21 Small & Medium Dental Dog Treats \\n21 22 Fresh Breath Oral Rinse Mild Mint \\n22 23 Organic Turkey Burgers \\n23 24 Tri-Vi-Sol\\u00ae Vitamins A-C-and D Supplement Drop... \\n24 25 Salted Caramel Lean Protein & Fiber Bar \\n25 26 Fancy Feast Trout Feast Flaked Wet Cat Food \\n26 27 Complete Spring Water Foaming Antibacterial Ha... \\n27 28 Wheat Chex Cereal \\n28 29 Fresh Cut Golden Sweet No Salt Added Whole Ker... \\n29 30 Three Cheese Ziti, Marinara with Meatballs \\n... ... ... \\n49658 49659 Organic Creamed Coconut \\n49659 49660 Professionals Sleek Shampoo \\n49660 49661 Porto \\n49661 49662 Bacon Cheddar Pretzel Pieces \\n49662 49663 Ultra Protein Power Crunch Peanut Butter N' Ho... \\n49663 49664 Lemon Cayenne Drinking Vinegar \\n49664 49665 Super Dark Coconut Ash & Banana Chocolate Bar \\n49665 49666 Ginger Snaps Snacking Cookies \\n49666 49667 Enchilada with Spanish Rice & Beans Meal \\n49667 49668 Apple Cinnamon Scented Candles \\n49668 49669 K Cup Dark Blend \\n49669 49670 Beef Summer Sausage \\n49670 49671 Milk Chocolate Drops \\n49671 49672 Cafe Mocha K-Cup Packs \\n49672 49673 Stone Baked Multi Grain Artisan Rolls \\n49673 49674 Frozen Greek Yogurt Bars Chocolate Chip \\n49674 49675 Cinnamon Dolce Keurig Brewed K Cups \\n49675 49676 Ultra Red Energy Drink \\n49676 49677 Thick & Chunky Sloppy Joe Sauce \\n49677 49678 Large Chicken & Cheese Taquitos \\n49678 49679 Famous Chocolate Wafers \\n49679 49680 All Natural Creamy Caesar Dressing \\n49680 49681 Spaghetti with Meatballs and Sauce Meal \\n49681 49682 California Limeade \\n49682 49683 Cucumber Kirby \\n49683 49684 Vodka, Triple Distilled, Twist of Vanilla \\n49684 49685 En Croute Roast Hazelnut Cranberry \\n49685 49686 Artisan Baguette \\n49686 49687 Smartblend Healthy Metabolism Dry Cat Food \\n49687 49688 Fresh Foaming Cleanser \\n\\n aisle_id department_id \\n0 61 19 \\n1 104 13 \\n2 94 7 \\n3 38 1 \\n4 5 13 \\n5 11 11 \\n6 98 7 \\n7 116 1 \\n8 120 16 \\n9 115 7 \\n10 31 7 \\n11 119 1 \\n12 11 11 \\n13 74 17 \\n14 56 18 \\n15 103 19 \\n16 35 12 \\n17 79 1 \\n18 63 9 \\n19 98 7 \\n20 40 8 \\n21 20 11 \\n22 49 12 \\n23 47 11 \\n24 3 19 \\n25 41 8 \\n26 127 11 \\n27 121 14 \\n28 81 15 \\n29 38 1 \\n... ... ... \\n49658 17 13 \\n49659 22 11 \\n49660 134 5 \\n49661 107 19 \\n49662 57 14 \\n49663 100 21 \\n49664 45 19 \\n49665 61 19 \\n49666 38 1 \\n49667 101 17 \\n49668 100 21 \\n49669 106 12 \\n49670 45 19 \\n49671 26 7 \\n49672 129 1 \\n49673 37 1 \\n49674 26 7 \\n49675 64 7 \\n49676 59 15 \\n49677 129 1 \\n49678 61 19 \\n49679 89 13 \\n49680 38 1 \\n49681 98 7 \\n49682 83 4 \\n49683 124 5 \\n49684 42 1 \\n49685 112 3 \\n49686 41 8 \\n49687 73 11 \\n\\n[49688 rows x 4 columns] (49688, 4) \\n\\n\"\n },\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'DataFrame' object has no attribute 'name'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"---------------------------------------------------------------------------\",\n \"AttributeError Traceback (most recent call last)\",\n \" in ()\\n 9 '''\\n 10 for s in sets:\\n---> 11 print (s.name, s.shape, '\\\\n')\\n\",\n \"/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py in __getattr__(self, name)\\n 2742 if name in self._info_axis:\\n 2743 return self[name]\\n-> 2744 return object.__getattribute__(self, name)\\n 2745 \\n 2746 def __setattr__(self, name, value):\\n\",\n \"AttributeError: 'DataFrame' object has no attribute 'name'\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles, shape = (134, 2)\\ndepartments, shape = (21, 2)\\norder_products_prior, shape = (32434489, 4)\\norder_products_train, shape = (1384617, 4)\\norders, shape = (3421083, 7)\\nproducts, shape = (49688, 4)\\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles (134, 2)\\ndepartments (21, 2)\\norder_products_prior (32434489, 4)\\norder_products_train (1384617, 4)\\norders (3421083, 7)\\nproducts (49688, 4)\\n\"\n }\n ],\n \"source\": [\n \"sets = [('aisles', aisles), ('departments', departments), ('order_products_prior', order_products_prior), \\n\",\n \" ('order_products_train', order_products_train), ('orders', orders), ('products', products)]\\n\",\n \"# Observing the dimensions of data sets\\n\",\n \"for s in sets:\\n\",\n \" print (s[0], s[1].shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_cell_guid\": \"ab7feae3-9b6c-5af3-d463-d7a466550e3f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \" aisle_id aisle\\n0 1 prepared soups salads\\n1 2 specialty cheeses\\n department_id department\\n0 1 frozen\\n1 2 other\\n order_id product_id add_to_cart_order reordered\\n0 2 33120 1 1\\n1 2 28985 2 1\\n order_id product_id add_to_cart_order reordered\\n0 1 49302 1 1\\n1 1 11109 2 1\\n order_id user_id eval_set order_number order_dow order_hour_of_day \\\\\\n0 2539329 1 prior 1 2 8 \\n1 2398795 1 prior 2 3 7 \\n\\n days_since_prior_order \\n0 NaN \\n1 15.0 \\n product_id product_name aisle_id department_id\\n0 1 Chocolate Sandwich Cookies 61 19\\n1 2 All-Seasons Salt 104 13\\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \" aisle_id aisle\\n0 1 prepared soups salads\\n1 2 specialty cheeses \\n\\n department_id department\\n0 1 frozen\\n1 2 other \\n\\n order_id product_id add_to_cart_order reordered\\n0 2 33120 1 1\\n1 2 28985 2 1 \\n\\n order_id product_id add_to_cart_order reordered\\n0 1 49302 1 1\\n1 1 11109 2 1 \\n\\n order_id user_id eval_set order_number order_dow order_hour_of_day \\\\\\n0 2539329 1 prior 1 2 8 \\n1 2398795 1 prior 2 3 7 \\n\\n days_since_prior_order \\n0 NaN \\n1 15.0 \\n\\n product_id product_name aisle_id department_id\\n0 1 Chocolate Sandwich Cookies 61 19\\n1 2 All-Seasons Salt 104 13 \\n\\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles \\n aisle_id aisle\\n0 1 prepared soups salads\\n1 2 specialty cheeses \\n\\ndepartments \\n department_id department\\n0 1 frozen\\n1 2 other \\n\\norder_products_prior \\n order_id product_id add_to_cart_order reordered\\n0 2 33120 1 1\\n1 2 28985 2 1 \\n\\norder_products_train \\n order_id product_id add_to_cart_order reordered\\n0 1 49302 1 1\\n1 1 11109 2 1 \\n\\norders \\n order_id user_id eval_set order_number order_dow order_hour_of_day \\\\\\n0 2539329 1 prior 1 2 8 \\n1 2398795 1 prior 2 3 7 \\n\\n days_since_prior_order \\n0 NaN \\n1 15.0 \\n\\nproducts \\n product_id product_name aisle_id department_id\\n0 1 Chocolate Sandwich Cookies 61 19\\n1 2 All-Seasons Salt 104 13 \\n\\n\"\n }\n ],\n \"source\": [\n \"# Observing initial entries of all data sets\\n\",\n \"for s in sets:\\n\",\n \" print (s[0], '\\\\n', s[1].head(2), '\\\\n')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"_cell_guid\": \"64e1ce1a-de1a-dd11-5371-02ed506acc1f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles aisle_id \\naisles aisle \\ndepartments department_id \\ndepartments department \\norder_products_prior order_id \\norder_products_prior product_id \\norder_products_prior add_to_cart_order \\norder_products_prior reordered \\norder_products_train order_id \\norder_products_train product_id \\norder_products_train add_to_cart_order \\norder_products_train reordered \\norders order_id \\norders user_id \\norders eval_set \\norders order_number \\norders order_dow \\norders order_hour_of_day \\norders days_since_prior_order \\nproducts product_id \\nproducts product_name \\nproducts aisle_id \\nproducts department_id \\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles aisle_id \\naisles aisle \\ndepartments department_id \\ndepartments department \\norder_products_prior order_id \\norder_products_prior product_id \\norder_products_prior add_to_cart_order \\norder_products_prior reordered \\norder_products_train order_id \\norder_products_train product_id \\norder_products_train add_to_cart_order \\norder_products_train reordered \\norders order_id \\norders user_id \\norders eval_set \\norders order_number \\norders order_dow \\norders order_hour_of_day \\norders days_since_prior_order \\nproducts product_id \\nproducts product_name \\nproducts aisle_id \\nproducts department_id \\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles aisle_id 134\\naisles aisle 134\\ndepartments department_id 21\\ndepartments department 21\\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"order_products_prior order_id 3214874\\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"order_products_prior product_id 49677\\norder_products_prior add_to_cart_order 145\\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"order_products_prior reordered 2\\norder_products_train order_id 131209\\norder_products_train product_id 39123\\norder_products_train add_to_cart_order 80\\norder_products_train reordered 2\\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"orders order_id 3421083\\norders user_id 206209\\n\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"orders eval_set 3\\norders order_number 100\\norders order_dow 7\\norders order_hour_of_day 24\\norders days_since_prior_order 31\\nproducts product_id 49688\\nproducts product_name 49688\\nproducts aisle_id 134\\nproducts department_id 21\\n\"\n }\n ],\n \"source\": [\n \"# Number of unique values in each column\\n\",\n \"for s in sets:\\n\",\n \" for col in list(s[1].columns):\\n\",\n \" print (s[0], col, s[1][col].nunique())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"21824710-06d2-8cb5-7ba8-573f895821e6\"\n },\n \"source\": [\n \"# Joining the data frames\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"a053ba65-ab3d-67f9-854f-3210e1d25fd1\"\n },\n \"source\": [\n \"## Joining products, their aisles and departments\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"_cell_guid\": \"07773bbd-c6d7-d884-7184-f3f0074d8d4b\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": \", '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_tf_config': gpu_options {\\n\",\n \" per_process_gpu_memory_fraction: 1.0\\n\",\n \"}\\n\",\n \", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': None}\\n\",\n \"WARNING:tensorflow:From :6: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\\n\",\n \"Instructions for updating:\\n\",\n \"Estimator is decoupled from Scikit Learn interface by moving into\\n\",\n \"separate class SKCompat. Arguments x, y and batch_size are only\\n\",\n \"available in the SKCompat class, Estimator will only accept input_fn.\\n\",\n \"Example conversion:\\n\",\n \" est = Estimator(...) -> est = SKCompat(Estimator(...))\\n\",\n \"WARNING:tensorflow:From :6: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\\n\",\n \"Instructions for updating:\\n\",\n \"Estimator is decoupled from Scikit Learn interface by moving into\\n\",\n \"separate class SKCompat. Arguments x, y and batch_size are only\\n\",\n \"available in the SKCompat class, Estimator will only accept input_fn.\\n\",\n \"Example conversion:\\n\",\n \" est = Estimator(...) -> est = SKCompat(Estimator(...))\\n\",\n \"DEBUG:tensorflow:Setting feature info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(4)]), is_sparse=False).\\n\",\n \"DEBUG:tensorflow:Setting labels info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None)]), is_sparse=False)\\n\",\n \"INFO:tensorflow:Constructing forest with params = \\n\",\n \"INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 4, 'max_fertile_nodes': 500, 'split_after_samples': 50, 'min_split_samples': 5, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'num_classes': 3, 'num_features': 4, 'bagged_num_features': 4, 'bagged_features': None, 'regression': False, 'num_outputs': 1, 'num_output_columns': 4, 'split_initializations_per_input': 1, 'base_random_seed': 0}\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:248: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\\n\",\n \" equality = a == b\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:tensorflow:training graph for tree: 0\\n\",\n \"INFO:tensorflow:training graph for tree: 1\\n\",\n \"INFO:tensorflow:training graph for tree: 2\\n\",\n \"INFO:tensorflow:training graph for tree: 3\\n\",\n \"INFO:tensorflow:training graph for tree: 4\\n\",\n \"INFO:tensorflow:training graph for tree: 5\\n\",\n \"INFO:tensorflow:training graph for tree: 6\\n\",\n \"INFO:tensorflow:training graph for tree: 7\\n\",\n \"INFO:tensorflow:training graph for tree: 8\\n\",\n \"INFO:tensorflow:training graph for tree: 9\\n\",\n \"INFO:tensorflow:training graph for tree: 10\\n\",\n \"INFO:tensorflow:training graph for tree: 11\\n\",\n \"INFO:tensorflow:training graph for tree: 12\\n\",\n \"INFO:tensorflow:training graph for tree: 13\\n\",\n \"INFO:tensorflow:training graph for tree: 14\\n\",\n \"INFO:tensorflow:training graph for tree: 15\\n\",\n \"INFO:tensorflow:training graph for tree: 16\\n\",\n \"INFO:tensorflow:training graph for tree: 17\\n\",\n \"INFO:tensorflow:training graph for tree: 18\\n\",\n \"INFO:tensorflow:training graph for tree: 19\\n\",\n \"INFO:tensorflow:training graph for tree: 20\\n\",\n \"INFO:tensorflow:training graph for tree: 21\\n\",\n \"INFO:tensorflow:training graph for tree: 22\\n\",\n \"INFO:tensorflow:training graph for tree: 23\\n\",\n \"INFO:tensorflow:training graph for tree: 24\\n\",\n \"INFO:tensorflow:training graph for tree: 25\\n\",\n \"INFO:tensorflow:training graph for tree: 26\\n\",\n \"INFO:tensorflow:training graph for tree: 27\\n\",\n \"INFO:tensorflow:training graph for tree: 28\\n\",\n \"INFO:tensorflow:training graph for tree: 29\\n\",\n \"INFO:tensorflow:training graph for tree: 30\\n\",\n \"INFO:tensorflow:training graph for tree: 31\\n\",\n \"INFO:tensorflow:training graph for tree: 32\\n\",\n \"INFO:tensorflow:training graph for tree: 33\\n\",\n \"INFO:tensorflow:training graph for tree: 34\\n\",\n \"INFO:tensorflow:training graph for tree: 35\\n\",\n \"INFO:tensorflow:training graph for tree: 36\\n\",\n \"INFO:tensorflow:training graph for tree: 37\\n\",\n \"INFO:tensorflow:training graph for tree: 38\\n\",\n \"INFO:tensorflow:training graph for tree: 39\\n\",\n \"INFO:tensorflow:training graph for tree: 40\\n\",\n \"INFO:tensorflow:training graph for tree: 41\\n\",\n \"INFO:tensorflow:training graph for tree: 42\\n\",\n \"INFO:tensorflow:training graph for tree: 43\\n\",\n \"INFO:tensorflow:training graph for tree: 44\\n\",\n \"INFO:tensorflow:training graph for tree: 45\\n\",\n \"INFO:tensorflow:training graph for tree: 46\\n\",\n \"INFO:tensorflow:training graph for tree: 47\\n\",\n \"INFO:tensorflow:training graph for tree: 48\\n\",\n \"INFO:tensorflow:training graph for tree: 49\\n\",\n \"INFO:tensorflow:Create CheckpointSaverHook.\\n\",\n \"INFO:tensorflow:Saving checkpoints for 1 into ./model.ckpt.\\n\",\n \"INFO:tensorflow:loss = -0.0, step = 1\\n\",\n \"INFO:tensorflow:TensorForestLossHook resetting last_step.\\n\",\n \"INFO:tensorflow:global_step/sec: 41.8541\\n\",\n \"INFO:tensorflow:loss = -210.12, step = 101 (2.397 sec)\\n\",\n \"INFO:tensorflow:global_step/sec: 43.7869\\n\",\n \"INFO:tensorflow:loss = -468.8, step = 201 (2.278 sec)\\n\",\n \"INFO:tensorflow:global_step/sec: 39.9615\\n\",\n \"INFO:tensorflow:loss = -735.44, step = 301 (2.503 sec)\\n\",\n \"INFO:tensorflow:global_step/sec: 41.4266\\n\",\n \"INFO:tensorflow:loss = -997.04, step = 401 (2.414 sec)\\n\",\n \"INFO:tensorflow:global_step/sec: 38.7636\\n\",\n \"INFO:tensorflow:loss = -998.0, step = 501 (2.579 sec)\\n\",\n \"INFO:tensorflow:TensorForestLossHook requesting stop.\\n\",\n \"INFO:tensorflow:Saving checkpoints for 506 into ./model.ckpt.\\n\",\n \"INFO:tensorflow:Loss for final step: -998.0.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"TensorForestEstimator(params=None)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# Remove previous checkpoints so that we can re-run this step if necessary.\\n\",\n \"for f in glob(\\\"./*\\\"):\\n\",\n \" os.remove(f)\\n\",\n \"classifier = tf.contrib.tensor_forest.client.random_forest.TensorForestEstimator(\\n\",\n \" params, model_dir=\\\"./\\\")\\n\",\n \"classifier.fit(x=x_train, y=y_train)\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"afb9add8-4590-6ea4-3ce1-aed71247c986\"\n },\n \"source\": [\n \"While the forest is training, the **negative** of the current average tree size is reported as the loss. (This is sort of a hack to get around the fact that random forest training isn't loss-based in the way that TensorFlow expects.)\\n\",\n \"\\n\",\n \"Now let's see how well this forest does on the test data:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"5167b64b-676e-b8f0-a46f-5174702a50bb\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"x_test = \\n\",\n \"[[ 4.9000001 3. 1.39999998 0.2 ]\\n\",\n \" [ 4.5999999 3.0999999 1.5 0.2 ]\\n\",\n \" [ 5.4000001 3.9000001 1.70000005 0.40000001]\\n\",\n \" [ 5. 3.4000001 1.5 0.2 ]\\n\",\n \" [ 4.9000001 3.0999999 1.5 0.1 ]]\\n\",\n \"test labels =\\n\",\n \"[ 0. 0. 0. 0. 0.]\\n\",\n \"WARNING:tensorflow:From :10: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\\n\",\n \"Instructions for updating:\\n\",\n \"Estimator is decoupled from Scikit Learn interface by moving into\\n\",\n \"separate class SKCompat. Arguments x, y and batch_size are only\\n\",\n \"available in the SKCompat class, Estimator will only accept input_fn.\\n\",\n \"Example conversion:\\n\",\n \" est = Estimator(...) -> est = SKCompat(Estimator(...))\\n\",\n \"INFO:tensorflow:Constructing forest with params = \\n\",\n \"INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 4, 'max_fertile_nodes': 500, 'split_after_samples': 50, 'min_split_samples': 5, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'num_classes': 3, 'num_features': 4, 'bagged_num_features': 4, 'bagged_features': None, 'regression': False, 'num_outputs': 1, 'num_output_columns': 4, 'split_initializations_per_input': 1, 'base_random_seed': 0}\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:248: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\\n\",\n \" equality = a == b\\n\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:tensorflow:Restoring parameters from ./model.ckpt-506\\n\",\n \"[{'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}, {'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}, {'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}, {'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}, {'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x_test = test.drop(['Species', 'Id'], axis=1).astype(np.float32).values\\n\",\n \"y_test = test['Species'].map(label_map).astype(np.float32).values\\n\",\n \"\\n\",\n \"print(\\\"x_test = \\\")\\n\",\n \"print(x_test[:5])\\n\",\n \"\\n\",\n \"print(\\\"test labels =\\\")\\n\",\n \"print(y_test[:5])\\n\",\n \"\\n\",\n \"y_out = list(classifier.predict(x=x_test))\\n\",\n \"\\n\",\n \"print(y_out[:5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"5ac1b3e8-69e8-aa60-ade0-6f0f913a658e\"\n },\n \"source\": [\n \"The output comes both as soft (probabilities) and hard (single class) predictions, so there are a bunch of ways you can slice and dice them. Here are a few:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"4223af0e-8652-025c-cf57-a197233458f0\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Soft predictions:\\n\",\n \"[array([ 1., 0., 0.], dtype=float32), array([ 1., 0., 0.], dtype=float32), array([ 1., 0., 0.], dtype=float32), array([ 1., 0., 0.], dtype=float32), array([ 1., 0., 0.], dtype=float32)]\\n\",\n \"Hard predictions:\\n\",\n \"[0, 0, 0, 0, 0]\\n\",\n \"Accuracy = 0.96\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = len(y_test)\\n\",\n \"out_soft = list(y['classes'] for y in y_out)\\n\",\n \"out_hard = list(y['probabilities'] for y in y_out)\\n\",\n \"\\n\",\n \"print(\\\"Soft predictions:\\\")\\n\",\n \"print(out_soft[:5])\\n\",\n \"print(\\\"Hard predictions:\\\")\\n\",\n \"print(out_hard[:5])\\n\",\n \"\\n\",\n \"soft_zipped = zip(y_test, out_soft)\\n\",\n \"hard_zipped = list(zip(y_test, out_hard))\\n\",\n \"\\n\",\n \"num_correct = sum(1 for p in hard_zipped if p[0] == p[1])\\n\",\n \"print(\\\"Accuracy = %s\\\" % (num_correct / n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"3fa914c5-b107-0073-85a9-791bd61619d4\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Probs of real label:\\n\",\n \"[1.0, 1.0, 1.0, 1.0, 1.0]\\n\",\n \"Average log loss = -0.10168163037957292\\n\"\n ]\n }\n ],\n \"source\": [\n \"test_ps = list(p[1][int(p[0])] for p in soft_zipped)\\n\",\n \"print(\\\"Probs of real label:\\\")\\n\",\n \"print(test_ps[:5])\\n\",\n \"total_log_loss = sum(math.log(p) for p in test_ps)\\n\",\n \"print(\\\"Average log loss = %s\\\" % (total_log_loss / n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"4ce24ee0-4461-9fef-2c6a-e9aac8f040ca\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Confusion matrix:\\n\",\n \"{(0.0, 0): 25, (1.0, 1): 25, (2.0, 1): 3, (2.0, 2): 22}\\n\"\n ]\n }\n ],\n \"source\": [\n \"confusion = {x: hard_zipped.count(x) for x in set(hard_zipped)}\\n\",\n \"print (\\\"Confusion matrix:\\\")\\n\",\n \"print (confusion)\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 1,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163742.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":187,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"a09b70bc-05e2-b7aa-770f-9a495a02d6b3\"\n },\n \"outputs\": [],\n \"source\": [\n \"import nltk\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"6389bb66-4f2a-3de4-405a-28a5d11824aa\"\n },\n \"outputs\": [],\n \"source\": [\n \"from nltk.tokenize import word_tokenize, sent_tokenize\\n\",\n \"from nltk.corpus import stopwords\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"5345ab1e-a945-603e-0a68-1505c5d585b5\"\n },\n \"outputs\": [],\n \"source\": [\n \"ex=\\\"Hello there, how you doing? Im awesome\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"b26242dd-38bb-6f00-32e4-62c55bf16322\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Hello there, how you doing?', 'Im awesome']\\n\"\n ]\n }\n ],\n \"source\": [\n \"a=(sent_tokenize(ex))\\n\",\n \"stop=set(stopwords.words(\\\"english\\\"))\\n\",\n \"b=[i for i in a if not i in stop]\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"f308201b-bc2d-0874-69a0-b1e68e1dd378\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['Hello', 'there', ',', 'how', 'you', 'doing', '?', 'Im', 'awesome']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(word_tokenize(ex))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"646353ef-43d5-5c84-3df4-69b0f2592331\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 2,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163762.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":188,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"3944a488-e562-538f-3fc8-05472eb5e714\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sample_submission.csv\\n\",\n \"test.csv\\n\",\n \"train.csv\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This Python 3 environment comes with many helpful analytics libraries installed\\n\",\n \"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\\n\",\n \"# For example, here's several helpful packages to load in \\n\",\n \"\\n\",\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"\\n\",\n \"# Input data files are available in the \\\"../input/\\\" directory.\\n\",\n \"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\\n\",\n \"\\n\",\n \"from subprocess import check_output\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"2b62968c-608b-bb21-d44f-78db2ec12ae4\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"count 1460.000000\\n\",\n \"mean 180921.195890\\n\",\n \"std 79442.502883\\n\",\n \"min 34900.000000\\n\",\n \"25% 129975.000000\\n\",\n \"50% 163000.000000\\n\",\n \"75% 214000.000000\\n\",\n \"max 755000.000000\\n\",\n \"Name: SalePrice, dtype: float64\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"train = pd.read_csv('../input/train.csv')\\n\",\n \"test = pd.read_csv('../input/test.csv')\\n\",\n \"\\n\",\n \"train.head()\\n\",\n \"train.SalePrice.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"77a9bcc6-0856-657b-add6-b4182b9e8320\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Skew is: 1.88287575977\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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ABgCEULAADAEIoWAACAIRQtAAAAQyhaAAAAhlC0AAAADClZbIXZ2Vnt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\\nQtECAAAwhKIFAABgCEULAADAkP8DeqCFiw4AFTUAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"plt.style.use(style='ggplot')\\n\",\n \"plt.rcParams['figure.figsize'] = (10, 6)\\n\",\n \"print (\\\"Skew is:\\\", train.SalePrice.skew())\\n\",\n \"plt.hist(train.SalePrice, color='blue')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"8792bf70-6add-573b-cb8b-5d589f038029\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Skew is: 0.121335062205\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAloAAAFpCAYAAABEXYZ0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nQgsAoJD/B1NadH+QoqHlAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"target = np.log(train.SalePrice)\\n\",\n \"print (\\\"Skew is:\\\", target.skew())\\n\",\n \"plt.hist(target, color='blue')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"c74651bc-2408-37e2-a14c-73b63968872b\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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+5fy7ZcOZRBRKlh4EaUqhRPf2LilZB8fmFGUDdIaT8LJdAK2efXf06k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\\nIYQQQggL8X+KOcsPnV5z8QAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(x=train['GrLivArea'], y=target)\\n\",\n \"plt.ylabel('Sale Price')\\n\",\n \"plt.xlabel('Above grade (ground) living area square feet')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"f87728bf-30be-fe64-f96a-26a9ea1a4cd8\"\n },\n \"outputs\": [],\n \"source\": [\n \"data = train.select_dtypes(include=[np.number]).interpolate().dropna() \\n\",\n \"y = np.log(train.SalePrice)\\n\",\n \"X = data.drop(['SalePrice', 'Id'], axis=1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"88f21901-19cd-812a-23c8-af2d214f4e66\"\n },\n \"source\": [\n \"Définition du test et train set\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"d12fcbd5-d265-5528-ee15-af0d925b8f38\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=.33)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"b2d32e58-1518-1054-c6e6-06977e320508\"\n },\n \"source\": [\n \"modèle\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"daa9233c-ab0d-7db2-33b5-4009153fd5ed\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn import linear_model\\n\",\n \"lr = linear_model.LinearRegression()\\n\",\n \"lr_model = lr.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"0d56521a-55e0-a4f0-0f57-a120fe8d0591\"\n },\n \"outputs\": [],\n \"source\": [\n \"predictions_lm = lr_model.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"eeeff045-4b41-982d-73e0-98c94673292a\"\n },\n \"outputs\": [],\n \"source\": [\n \"rm = linear_model.Ridge(alpha=0.1)\\n\",\n \"rm_model = rm.fit(X_train, y_train)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_cell_guid\": \"08d20dfd-b4ac-ad27-5cc0-07ae66a9bfc1\"\n },\n \"outputs\": [],\n \"source\": [\n \"predictions_rm = rm_model.predict(X_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"d80620a6-0d83-5d69-0721-8aad012d9a86\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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RX0798fgGHDhlFcXKwCJiIi7YIdO4qtzsPyl0BtDW7oVbiRt+J6JPkd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\\nlmA8AWOMMcYYSzCegDHGGGOMJdg/a5eYH80gwRMAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"submission = pd.DataFrame()\\n\",\n \"submission['Id'] = test.Id\\n\",\n \"feats = test.select_dtypes(\\n\",\n \" include=[np.number]).drop(['Id'], axis=1).interpolate()\\n\",\n \"\\n\",\n \"submission.plot()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"818f90cb-a0e9-5dee-81c6-9eeaf1b3b820\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \",\\n ]\"\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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Fo7fm3+7atOfnWrMuvt8PX5t391yqtalXn6y78x//Ylp+7aqsxztz++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\\nYQYAgAXfqU3l+PqjeMkAAAAASUVORK5CYII=\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(figsize=(12, 8))\\n\",\n \"plt.xticks(rotation='90')\\n\",\n \"sns.barplot(x=train_na.index, y=train_na)\\n\",\n \"ax.set(title='Percent missing data by feature', ylabel='% missing')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"199c95d5-9b9c-76f0-a82f-b1c7475d481a\"\n },\n \"source\": [\n \"## Data Quality Issues\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"_cell_guid\": \"3d07162f-eb17-6c65-9a3e-fb6045f74a85\"\n },\n \"outputs\": [],\n \"source\": [\n \"# state should be discrete valued between 1 and 4. There is a 33 in it that is cleary a data entry error\\n\",\n \"# Lets just replace it with the mode.\\n\",\n \"train_df.loc[train_df['state'] == 33, 'state'] = train_df['state'].mode()\\n\",\n \"\\n\",\n \"# build_year has an erronus value 20052009. Since its unclear which it should be, let's replace with 2007\\n\",\n \"train_df.loc[train_df['build_year'] == 20052009, 'build_year'] = 2007\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"377e7a72-d216-9e1a-cb03-b192747dfb7c\"\n },\n \"source\": [\n \"## Housing Internal Characteristics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"_cell_guid\": \"212b1565-f914-66cf-172f-b8ed8b5813c0\"\n },\n \"outputs\": [],\n \"source\": [\n \"internal_chars = ['full_sq', 'life_sq', 'floor', 'max_floor', 'build_year', 'num_room', 'kitch_sq', 'state', 'price_doc']\\n\",\n \"corrmat = train_df[internal_chars].corr()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"_cell_guid\": \"c8d155df-f1a4-d957-8ace-f6458bb88f8b\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"\"\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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5rVycnJITc3h9d6D6RC+fLMnjjGrhkBnmjcgIb1HuHNASPQ6fSMH9qP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\\nAABJRU5ErkJggg==\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(figsize=(10, 7))\\n\",\n \"plt.xticks(rotation='90')\\n\",\n \"sns.heatmap(corrmat, square=True, linewidths=.5, annot=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"f9369355-52ef-6194-39f7-9f01b153217c\"\n },\n \"source\": [\n \"## Area of Home and Number of Rooms\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"_cell_guid\": \"29f327cc-fa0d-49a8-34bd-1484ed080203\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"\"\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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kiSpFwzRM00tYv5391LMvQkaXd3ZUuDG3eSPP1UZQrPEShJkoQh6gSn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\\nA0OUJElSBoYoSZKkDP5/S57IW41peMgAAAAASUVORK5CYII=\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(figsize=(10, 7))\\n\",\n \"plt.scatter(x=train_df['full_sq'], y=train_df['price_doc'], c='r')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"_cell_guid\": \"f6ac8c69-bc9d-cf60-6649-c66018b006b5\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"[,\\n ,\\n ]\"\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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g2k4EtERESkgRR8iYiIiDSQgi8RERGRBlLwJSIiItJACr5EREREGkjB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\\n4ksQBEEQBGEZEfElCIIgCIKwjPz/eEoewcdB32cAAAAASUVORK5CYII=\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(figsize=(10, 7))\\n\",\n \"ind = train_df[train_df['full_sq'] > 2000].index\\n\",\n \"plt.scatter(x=train_df.drop(ind)['full_sq'], y=train_df.drop(ind)['price_doc'], c='r', alpha=0.5)\\n\",\n \"ax.set(title='Price by area in sq meters', xlabel='Area', ylabel='Price')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"_cell_guid\": \"419bc94e-6e7f-bb79-51aa-916af0927665\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"37\"\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"(train_df['life_sq'] > train_df['full_sq']).sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"_cell_guid\": \"ab02189c-3d5c-5fc3-27d1-ae731e558115\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"[,\\n ]\"\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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kEsZ5IkSQWxnEmSJBXEciZJklQQy5kkSVJBLGeSJEkFsZxJkiQVxHIm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\\nkiQVxHImSZJUkP8PhRLCmjTVw0AAAAAASUVORK5CYII=\\n\",\n \"text/plain\": \"\"\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(figsize=(10, 7))\\n\",\n \"sns.countplot(x=train_df['num_room'])\\n\",\n \"ax.set(title='Distribution of room count', xlabel='num_room')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"b253640d-6ee4-e011-a0c2-e2a05aa7ce2d\"\n },\n \"source\": [\n \"## Sale Type\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_cell_guid\": \"a6fbac09-d1ba-175f-ee3b-f121ed6abb69\"\n },\n \"outputs\": [],\n \"source\": \"\"\n }\n ],\n \"metadata\": {\n \"_change_revision\": 199,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"},"id":{"kind":"string","value":"0001/163/1163810.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":190,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"38541fd0-854b-b5ae-dd1d-d5a181160c1c\"\n },\n \"source\": [\n \"# Import Basic Libraries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"f5fb6410-9e53-24bf-8f8e-0fd19e110c62\"\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e628c172-6edc-c818-1532-e787d782d612\"\n },\n \"source\": [\n \"# 1. Data Input, Preparation, & Exploration\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"478defd6-4f2f-31bf-8001-ad4e8fd11798\"\n },\n \"source\": [\n \"## 1.1 Read in Transaction Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"256f3663-58ec-121d-a1ae-f345bbfb9c6d\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No. of Rows: \\t\\t 284807\\n\",\n \"No. of Columns: \\t 31\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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KZ7tdqatjy9/oQx7R7SzsB9s+xrpn7OqaolVbVkwYIFW3o4kqQRjXoZ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\\ncQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"norm_count = data[data['Class'] == 0].shape[0] # Normal transactions\\n\",\n \"fraud_count = data[data['Class'] == 1].shape[0] # Fraudulent transcations\\n\",\n \"total_count = data.shape[0]\\n\",\n \"print(\\\"No. of normal transactions: \\\\t\\\\t\\\", norm_count)\\n\",\n \"print(\\\"No. of fraudulent transactions: \\\\t\\\", fraud_count)\\n\",\n \"print(\\\"% normal transactions: \\\\t\\\\t\\\", norm_count/total_count * 100)\\n\",\n \"print(\\\"% fraudulent transcations: \\\\t\\\", fraud_count/total_count * 100)\\n\",\n \"pd.value_counts(data['Class'], sort = True).sort_index().plot(kind='bar')\\n\",\n \"plt.title(\\\"Class Histogram\\\")\\n\",\n \"plt.xlabel(\\\"Class\\\")\\n\",\n \"plt.ylabel(\\\"Frequency\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"842e8fc6-56e2-1457-4edc-ab92e4973f5f\"\n },\n \"source\": [\n \"## 1.3 Standardize Input Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"f6f35ec1-b928-ac06-da74-4227c584a6f8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# Get the indices of the fraudulent and normal classes:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mfraud_idx\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0marray\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0my_train\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0my_train\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0;36m1\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mindex\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0mnum_fraud\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mlen\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mfraud_idx\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0mnormal_idx\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0my_train\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0my_train\\u001b[0m \\u001b[0;34m==\\u001b[0m \\u001b[0;36m0\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mindex\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'y_train' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# Get the indices of the fraudulent and normal classes:\\n\",\n \"fraud_idx = np.array(y_train[y_train == 1].index)\\n\",\n \"num_fraud = len(fraud_idx)\\n\",\n \"normal_idx = y_train[y_train == 0].index\\n\",\n \"\\n\",\n \"# From the normal indices, sample a random subset (subset size = # of frauds):\\n\",\n \"normal_idx_sample = np.random.choice(normal_idx, num_fraud, replace=False)\\n\",\n \"normal_idx_sample = np.array(normal_idx_sample)\\n\",\n \"\\n\",\n \"# Group together our normal and fraud indices:\\n\",\n \"# (we'll have a balanced class distribution, 50% normal, 50% fraud)\\n\",\n \"undersample_idx = np.concatenate([fraud_idx,normal_idx_sample])\\n\",\n \"\\n\",\n \"# Grab the records at the indices:\\n\",\n \"undersample_data = data.iloc[undersample_idx,:]\\n\",\n \"\\n\",\n \"# Split into features and labels:\\n\",\n \"X_undersample = undersample_data.ix[:, undersample_data.columns != 'Class']\\n\",\n \"y_undersample = undersample_data['Class']\\n\",\n \"\\n\",\n \"norm_count = undersample_data[undersample_data['Class'] == 0].shape[0]\\n\",\n \"fraud_count = undersample_data[undersample_data['Class'] == 1].shape[0]\\n\",\n \"\\n\",\n \"print(\\\"---Undersampled Data Set---\\\")\\n\",\n \"print(\\\"No. of normal transactions: \\\\t\\\", norm_count)\\n\",\n \"print(\\\"No. of fraudulent transactions: \\\\t\\\\t\\\", fraud_count)\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"54570223-ec61-0bbc-22f7-41ae876cbc22\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'y_train' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0my_train\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mhead\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'y_train' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"y_train.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"d0fe1f55-5163-5229-eafd-d96215f417df\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"78ffa6bc-ab94-7c24-9d76-bd0e03d8bfc6\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"# 70% training data, 30% testing data\\n\",\n \"X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"b75eb1fd-6c0b-cffa-5be6-0f21c76b3e5d\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mbest_c_lr\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpenalty_lr\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mget_best_hypers_lr\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mX_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0my_undersample\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'X_undersample' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"best_c_lr, penalty_lr = get_best_hypers_lr(X_undersample,y_undersample)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"5a482730-429a-4cfc-fe2f-2787cf9b26a8\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'best_c_lr' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# Use best hyperparameters:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mlr\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mLogisticRegression\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mC\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mbest_c_lr\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpenalty\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpenalty_lr\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0;31m# Train on full undersample data set:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0mlr\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfit\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mX_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_undersample\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0;31m# Test on unseen test data set:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'best_c_lr' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# Use best hyperparameters:\\n\",\n \"lr = LogisticRegression(C=best_c_lr, penalty=penalty_lr)\\n\",\n \"# Train on full undersample data set:\\n\",\n \"lr.fit(X_undersample, y_undersample)\\n\",\n \"# Test on unseen test data set:\\n\",\n \"y_pred_score = lr.decision_function(X_test.values)\\n\",\n \"# Compute ROC metrics:\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test.values, y_pred_score)\\n\",\n \"# Get AUC:\\n\",\n \"roc_auc = auc(fpr,tpr)\\n\",\n \"\\n\",\n \"# Plot ROC:\\n\",\n \"plt.title('ROC Curve - Linear Regression')\\n\",\n \"plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.plot([0,1],[0,1],'r--')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"ac1cbe69-9309-54ab-9f50-40c175d4e4f6\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.svm import LinearSVC\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_cell_guid\": \"41d4f774-cd1c-dd2a-7478-5c8d86599de2\"\n },\n \"outputs\": [],\n \"source\": [\n \"def get_best_hypers_svc(X, y):\\n\",\n \" \\\"\\\"\\\" Search parameter space for the optimal values.\\n\",\n \" \\n\",\n \" -Perform Support Vector Classifier using a range of C parameter values and two different\\n\",\n \" penalty terms (L1 & L2)\\n\",\n \" -Compute mean recall scores using kfold cross validation for each run\\n\",\n \" -Output the C parameter and penalty term with the best recall score\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" c_range = [0.01, 0.1, 1.0, 10.0, 100.0]\\n\",\n \" recall_max = 0\\n\",\n \" best_c = 0\\n\",\n \" penalty = ''\\n\",\n \" \\n\",\n \" for c_param in c_range:\\n\",\n \" print('='*25)\\n\",\n \" print('C parameter: ', c_param)\\n\",\n \" print('='*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" print('-'*25)\\n\",\n \" print('L1-penalty')\\n\",\n \" print('-'*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" svc_l1 = LinearSVC(C=c_param, penalty='l1', dual=False)\\n\",\n \" acc_score = cross_val_score(svc_l1, X, y, cv=5)\\n\",\n \" recall_score = cross_val_score(svc_l1, X, y, cv=5, scoring='recall')\\n\",\n \" l1_recall = np.mean(recall_score)\\n\",\n \" \\n\",\n \" print(\\\"Mean Accuracy: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \" print(\\\"Mean Recall: %0.3f (+/- %0.3f)\\\" % (l1_recall, np.std(recall_score)) )\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" print('-'*25)\\n\",\n \" print('L2-penalty')\\n\",\n \" print('-'*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" svc_l2 = LinearSVC(C=c_param, penalty='l2')\\n\",\n \" score = cross_val_score(svc_l2, X, y, cv=5)\\n\",\n \" recall_score = cross_val_score(svc_l2, X, y, cv=5, scoring='recall')\\n\",\n \" l2_recall = np.mean(recall_score)\\n\",\n \" \\n\",\n \" print(\\\"Mean Accuracy: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \" print(\\\"Mean Recall: %0.3f (+/- %0.3f)\\\" % (l2_recall, np.std(recall_score)) )\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" # compare l1_recall & l2_recall:\\n\",\n \" if l2_recall > l1_recall:\\n\",\n \" # compare to max:\\n\",\n \" if l2_recall > recall_max:\\n\",\n \" recall_max = l2_recall\\n\",\n \" best_c = c_param\\n\",\n \" penalty='l2'\\n\",\n \" else:\\n\",\n \" # compare to max:\\n\",\n \" if l1_recall > recall_max:\\n\",\n \" recall_max = l1_recall\\n\",\n \" best_c = c_param\\n\",\n \" penalty='l1'\\n\",\n \" \\n\",\n \"\\n\",\n \" print('*'*25)\\n\",\n \" print('Optimal C parameter = ', best_c)\\n\",\n \" print('Optimal penalty = ', penalty)\\n\",\n \" print('Optimal recall = ', recall_max)\\n\",\n \" print('*'*25)\\n\",\n \" \\n\",\n \" return best_c, penalty\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"_cell_guid\": \"9e29cbd1-f516-6059-e75c-04a863b372f3\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'X_undersample' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mbest_c_svc\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpenalty_svc\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mget_best_hypers_svc\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mX_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_undersample\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'X_undersample' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"best_c_svc, penalty_svc = get_best_hypers_svc(X_undersample, y_undersample)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"_cell_guid\": \"f98f24bc-f70c-14fc-d13d-b8a8facf5e10\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'best_c_svc' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# Use best hyperparameters:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0msvc\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mLinearSVC\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mC\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mbest_c_svc\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mpenalty\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mpenalty_svc\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0;31m# Train on full undersample data set:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0msvc\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mfit\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mX_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_undersample\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0;31m# Test on unseen test data set:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'best_c_svc' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# Use best hyperparameters:\\n\",\n \"svc = LinearSVC(C=best_c_svc, penalty=penalty_svc)\\n\",\n \"# Train on full undersample data set:\\n\",\n \"svc.fit(X_undersample, y_undersample)\\n\",\n \"# Test on unseen test data set:\\n\",\n \"y_pred_score = svc.decision_function(X_test.values)\\n\",\n \"# Compute ROC metrics:\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test.values, y_pred_score)\\n\",\n \"# Get AUC:\\n\",\n \"roc_auc = auc(fpr,tpr)\\n\",\n \"\\n\",\n \"# Plot ROC:\\n\",\n \"plt.title('ROC Curve - SVC')\\n\",\n \"plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.plot([0,1],[0,1],'r--')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"_cell_guid\": \"bef3d4f4-4138-b410-e41e-1babe77eaf12\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.tree import DecisionTreeClassifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"_cell_guid\": \"8342274d-68ea-5479-a2fa-4b4bca3c08ee\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'X_undersample' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0mdt\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mDecisionTreeClassifier\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrandom_state\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0macc_score\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcross_val_score\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mX_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcv\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m5\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0mrecall_score\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcross_val_score\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdt\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mX_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcv\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m5\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mscoring\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'recall'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Accuracy Score: %0.3f (+/- %0.3f)\\\"\\u001b[0m \\u001b[0;34m%\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmean\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0macc_score\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstd\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0macc_score\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Recall Score: %0.3f (+/- %0.3f)\\\"\\u001b[0m \\u001b[0;34m%\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmean\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrecall_score\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstd\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrecall_score\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'X_undersample' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"dt = DecisionTreeClassifier(random_state=0)\\n\",\n \"acc_score = cross_val_score(dt, X_undersample, y_undersample, cv=5)\\n\",\n \"recall_score = cross_val_score(dt, X_undersample, y_undersample, cv=5, scoring='recall')\\n\",\n \"print(\\\"Accuracy Score: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \"print(\\\"Recall Score: %0.3f (+/- %0.3f)\\\" % (np.mean(recall_score), np.std(recall_score)) )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"_cell_guid\": \"cf67a52d-685c-c331-3048-4cb8e280a851\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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/ZvHt5SjCa6LFniRiJ/+ikcc4ybwK59+6Cj8p0lBWOKycnN5/d1u6p6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\\njCliScEYY0wRSwrGGGOKWFIwxhhT5P8BeON0V8ZeSDYAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Train on full undersample data set:\\n\",\n \"dt.fit(X_train, y_train)\\n\",\n \"# Test on unseen test data set:\\n\",\n \"y_pred_score = dt.predict_proba(X_test.values)[:,1]\\n\",\n \"# Compute ROC metrics:\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test.values,y_pred_score)\\n\",\n \"# Get AUC:\\n\",\n \"roc_auc = auc(fpr, tpr)\\n\",\n \" \\n\",\n \" \\n\",\n \"# Plot ROC:\\n\",\n \"plt.title('ROC Curve - DecisionTree')\\n\",\n \"plt.plot(fpr, tpr, label = 'AUC = %0.2f' % roc_auc)\\n\",\n \"plt.plot([0,1],[0,1],'r--')\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"_cell_guid\": \"1bf91508-e637-7923-aa9c-6290aaac1e09\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.ensemble import RandomForestClassifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"_cell_guid\": \"b6f3348d-b58f-8f95-67be-6529176db193\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'X_undersample' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0mrfc\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mRandomForestClassifier\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrandom_state\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m0\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0macc_score\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcross_val_score\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrfc\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mX_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcv\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m5\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0mrecall_score\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mcross_val_score\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrfc\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mX_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0my_undersample\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mcv\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;36m5\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mscoring\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'recall'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Accuracy Score: %0.3f (+/- %0.3f)\\\"\\u001b[0m \\u001b[0;34m%\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmean\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0macc_score\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstd\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0macc_score\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"Recall Score: %0.3f (+/- %0.3f)\\\"\\u001b[0m \\u001b[0;34m%\\u001b[0m \\u001b[0;34m(\\u001b[0m\\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mmean\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrecall_score\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mstd\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mrecall_score\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m \\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'X_undersample' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"rfc = RandomForestClassifier(random_state=0)\\n\",\n \"acc_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5)\\n\",\n \"recall_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5, scoring='recall')\\n\",\n \"print(\\\"Accuracy Score: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \"print(\\\"Recall Score: %0.3f (+/- %0.3f)\\\" % (np.mean(recall_score), np.std(recall_score)) )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"_cell_guid\": \"3523c4b3-4554-3568-fd0f-558cf5ed4e3b\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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OXVy/kttL2+SLG0DHYUM7FJXr2c5kmJ+fVyCte5b1o/Ibbq5ZjgrVsH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\\nyff/2wzjUtB7qLAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Train on full undersample data set:\\n\",\n \"rfc.fit(X_train, y_train)\\n\",\n \"# Test on unseen test data set:\\n\",\n \"y_pred_score = rfc.predict_proba(X_test.values)[:,1]\\n\",\n \"# Compute ROC metrics:\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test.values,y_pred_score)\\n\",\n \"# Get AUC:\\n\",\n \"roc_auc = auc(fpr, tpr)\\n\",\n \" \\n\",\n \" \\n\",\n \"# Plot ROC:\\n\",\n \"plt.title('ROC Curve - RandomForest')\\n\",\n \"plt.plot(fpr, tpr, label = 'AUC = %0.2f' % roc_auc)\\n\",\n \"plt.plot([0,1],[0,1],'r--')\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 232,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163931.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":191,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"f06aaa16-1098-180b-86e8-b677eee19c7e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Python: 3.6.0 |Anaconda custom (64-bit)| (default, Dec 23 2016, 12:22:00) \\n\",\n \"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\\n\",\n \"scipy: 0.19.0\\n\",\n \"numpy: 1.12.1\\n\",\n \"matplotlib: 2.0.0\\n\",\n \"pandas: 0.19.2\\n\",\n \"sklearn: 0.19.dev0\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Check the versions of libraries\\n\",\n \"\\n\",\n \"# Python version\\n\",\n \"import sys\\n\",\n \"print('Python: {}'.format(sys.version))\\n\",\n \"# scipy\\n\",\n \"import scipy\\n\",\n \"print('scipy: {}'.format(scipy.__version__))\\n\",\n \"# numpy\\n\",\n \"import numpy\\n\",\n \"print('numpy: {}'.format(numpy.__version__))\\n\",\n \"# matplotlib\\n\",\n \"import matplotlib\\n\",\n \"print('matplotlib: {}'.format(matplotlib.__version__))\\n\",\n \"# pandas\\n\",\n \"import pandas\\n\",\n \"print('pandas: {}'.format(pandas.__version__))\\n\",\n \"# scikit-learn\\n\",\n \"import sklearn\\n\",\n \"print('sklearn: {}'.format(sklearn.__version__))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"eceb8730-f611-6120-fb55-6e1fff05646f\"\n },\n \"source\": [\n \"**Load Libraries**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"138e11e6-3d15-418b-45f5-60f12821ed8e\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Load libraries\\n\",\n \"from pandas.tools.plotting import scatter_matrix\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn import model_selection\\n\",\n \"from sklearn.metrics import classification_report\\n\",\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"from sklearn.metrics import accuracy_score\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n \"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\\n\",\n \"from sklearn.naive_bayes import GaussianNB\\n\",\n \"from sklearn.svm import SVC\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"c358cbfd-9cac-8a84-95f8-01dc485a00d1\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'pd' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# Load dataset\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m \\u001b[0mnames\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m[\\u001b[0m\\u001b[0;34m'sepal-length'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'sepal-width'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'petal-length'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'petal-width'\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;34m'class'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 3\\u001b[0;31m \\u001b[0mdataset\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mpd\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread_csv\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"../input/Iris.csv\\\"\\u001b[0m\\u001b[0;34m,\\u001b[0m\\u001b[0mnames\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0mnames\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 4\\u001b[0m \\u001b[0mdataset\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mhead\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'pd' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# Load dataset\\n\",\n \"names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\\n\",\n \"dataset = pd.read_csv(\\\"../input/Iris.csv\\\",names=names)\\n\",\n \"dataset.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"e7cfbcb8-56cf-614a-3bbb-ecc073a243d0\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'dataset' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# shape\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdataset\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'dataset' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# shape\\n\",\n \"print(dataset.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"ecba21bc-6392-ecf4-9951-5f0a8eaec61d\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 10,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163932.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":192,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"9b53570f-6f00-f0cd-eaca-d7dbb7e1a78c\"\n },\n \"source\": [\n \"16-May- 2017\\n\",\n \"To get the insights in House Prices Data by Exploratory data analysis and to predict house pricing using various machine learning algorithms.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"d9f57425-11af-ea3e-42f2-ae50df03c49e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sample_submission.csv\\n\",\n \"test.csv\\n\",\n \"train.csv\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This Python 3 environment comes with many helpful analytics libraries installed\\n\",\n \"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\\n\",\n \"# For example, here's several helpful packages to load in \\n\",\n \"\\n\",\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"\\n\",\n \"# Input data files are available in the \\\"../input/\\\" directory.\\n\",\n \"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\\n\",\n \"\\n\",\n \"from subprocess import check_output\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"3c1265b6-3c01-4f8f-1e84-1aa528dee508\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1460, 81)\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"#Read the Data\\n\",\n \"df_train = pd.read_csv('../input/train.csv')\\n\",\n \"df_test = pd.read_csv('../input/test.csv')\\n\",\n \"df_train.shape\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"e33fe08e-9077-724c-f05a-14dfad6109be\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1459, 80)\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_test.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"1f2c2e50-42f6-6072-9c11-36c6dd4f5d79\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\\n\",\n \" 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\\n\",\n \" 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\\n\",\n \" 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\\n\",\n \" 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\\n\",\n \" 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\\n\",\n \" 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\\n\",\n \" 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\\n\",\n \" 'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\\n\",\n \" 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\\n\",\n \" 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\\n\",\n \" 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\\n\",\n \" 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\\n\",\n \" 'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\\n\",\n \" 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\\n\",\n \" 'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\\n\",\n \" 'SaleCondition', 'SalePrice'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_train.columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"b1f6b09f-b592-7ff1-bf3b-61c3545653a6\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"RangeIndex: 1460 entries, 0 to 1459\\n\",\n \"Data columns (total 81 columns):\\n\",\n \"Id 1460 non-null int64\\n\",\n \"MSSubClass 1460 non-null int64\\n\",\n \"MSZoning 1460 non-null object\\n\",\n \"LotFrontage 1201 non-null float64\\n\",\n \"LotArea 1460 non-null int64\\n\",\n \"Street 1460 non-null object\\n\",\n \"Alley 91 non-null object\\n\",\n \"LotShape 1460 non-null object\\n\",\n \"LandContour 1460 non-null object\\n\",\n \"Utilities 1460 non-null object\\n\",\n \"LotConfig 1460 non-null object\\n\",\n \"LandSlope 1460 non-null object\\n\",\n \"Neighborhood 1460 non-null object\\n\",\n \"Condition1 1460 non-null object\\n\",\n \"Condition2 1460 non-null object\\n\",\n \"BldgType 1460 non-null object\\n\",\n \"HouseStyle 1460 non-null object\\n\",\n \"OverallQual 1460 non-null int64\\n\",\n \"OverallCond 1460 non-null int64\\n\",\n \"YearBuilt 1460 non-null int64\\n\",\n \"YearRemodAdd 1460 non-null int64\\n\",\n \"RoofStyle 1460 non-null object\\n\",\n \"RoofMatl 1460 non-null object\\n\",\n \"Exterior1st 1460 non-null object\\n\",\n \"Exterior2nd 1460 non-null object\\n\",\n \"MasVnrType 1452 non-null object\\n\",\n \"MasVnrArea 1452 non-null float64\\n\",\n \"ExterQual 1460 non-null object\\n\",\n \"ExterCond 1460 non-null object\\n\",\n \"Foundation 1460 non-null object\\n\",\n \"BsmtQual 1423 non-null object\\n\",\n \"BsmtCond 1423 non-null object\\n\",\n \"BsmtExposure 1422 non-null object\\n\",\n \"BsmtFinType1 1423 non-null object\\n\",\n \"BsmtFinSF1 1460 non-null int64\\n\",\n \"BsmtFinType2 1422 non-null object\\n\",\n \"BsmtFinSF2 1460 non-null int64\\n\",\n \"BsmtUnfSF 1460 non-null int64\\n\",\n \"TotalBsmtSF 1460 non-null int64\\n\",\n \"Heating 1460 non-null object\\n\",\n \"HeatingQC 1460 non-null object\\n\",\n \"CentralAir 1460 non-null object\\n\",\n \"Electrical 1459 non-null object\\n\",\n \"1stFlrSF 1460 non-null int64\\n\",\n \"2ndFlrSF 1460 non-null int64\\n\",\n \"LowQualFinSF 1460 non-null int64\\n\",\n \"GrLivArea 1460 non-null int64\\n\",\n \"BsmtFullBath 1460 non-null int64\\n\",\n \"BsmtHalfBath 1460 non-null int64\\n\",\n \"FullBath 1460 non-null int64\\n\",\n \"HalfBath 1460 non-null int64\\n\",\n \"BedroomAbvGr 1460 non-null int64\\n\",\n \"KitchenAbvGr 1460 non-null int64\\n\",\n \"KitchenQual 1460 non-null object\\n\",\n \"TotRmsAbvGrd 1460 non-null int64\\n\",\n \"Functional 1460 non-null object\\n\",\n \"Fireplaces 1460 non-null int64\\n\",\n \"FireplaceQu 770 non-null object\\n\",\n \"GarageType 1379 non-null object\\n\",\n \"GarageYrBlt 1379 non-null float64\\n\",\n \"GarageFinish 1379 non-null object\\n\",\n \"GarageCars 1460 non-null int64\\n\",\n \"GarageArea 1460 non-null int64\\n\",\n \"GarageQual 1379 non-null object\\n\",\n \"GarageCond 1379 non-null object\\n\",\n \"PavedDrive 1460 non-null object\\n\",\n \"WoodDeckSF 1460 non-null int64\\n\",\n \"OpenPorchSF 1460 non-null int64\\n\",\n \"EnclosedPorch 1460 non-null int64\\n\",\n \"3SsnPorch 1460 non-null int64\\n\",\n \"ScreenPorch 1460 non-null int64\\n\",\n \"PoolArea 1460 non-null int64\\n\",\n \"PoolQC 7 non-null object\\n\",\n \"Fence 281 non-null object\\n\",\n \"MiscFeature 54 non-null object\\n\",\n \"MiscVal 1460 non-null int64\\n\",\n \"MoSold 1460 non-null int64\\n\",\n \"YrSold 1460 non-null int64\\n\",\n \"SaleType 1460 non-null object\\n\",\n \"SaleCondition 1460 non-null object\\n\",\n \"SalePrice 1460 non-null int64\\n\",\n \"dtypes: float64(3), int64(35), object(43)\\n\",\n \"memory usage: 924.0+ KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df_train.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"6b0614e2-9683-eb0a-b64d-e6895b9e0ad9\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Id 0\\n\",\n \"MSSubClass 0\\n\",\n \"MSZoning 0\\n\",\n \"LotFrontage 259\\n\",\n \"LotArea 0\\n\",\n \"Street 0\\n\",\n \"Alley 1369\\n\",\n \"LotShape 0\\n\",\n \"LandContour 0\\n\",\n \"Utilities 0\\n\",\n \"LotConfig 0\\n\",\n \"LandSlope 0\\n\",\n \"Neighborhood 0\\n\",\n \"Condition1 0\\n\",\n \"Condition2 0\\n\",\n \"BldgType 0\\n\",\n \"HouseStyle 0\\n\",\n \"OverallQual 0\\n\",\n \"OverallCond 0\\n\",\n \"YearBuilt 0\\n\",\n \"YearRemodAdd 0\\n\",\n \"RoofStyle 0\\n\",\n \"RoofMatl 0\\n\",\n \"Exterior1st 0\\n\",\n \"Exterior2nd 0\\n\",\n \"MasVnrType 8\\n\",\n \"MasVnrArea 8\\n\",\n \"ExterQual 0\\n\",\n \"ExterCond 0\\n\",\n \"Foundation 0\\n\",\n \" ... \\n\",\n \"BedroomAbvGr 0\\n\",\n \"KitchenAbvGr 0\\n\",\n \"KitchenQual 0\\n\",\n \"TotRmsAbvGrd 0\\n\",\n \"Functional 0\\n\",\n \"Fireplaces 0\\n\",\n \"FireplaceQu 690\\n\",\n \"GarageType 81\\n\",\n \"GarageYrBlt 81\\n\",\n \"GarageFinish 81\\n\",\n \"GarageCars 0\\n\",\n \"GarageArea 0\\n\",\n \"GarageQual 81\\n\",\n \"GarageCond 81\\n\",\n \"PavedDrive 0\\n\",\n \"WoodDeckSF 0\\n\",\n \"OpenPorchSF 0\\n\",\n \"EnclosedPorch 0\\n\",\n \"3SsnPorch 0\\n\",\n \"ScreenPorch 0\\n\",\n \"PoolArea 0\\n\",\n \"PoolQC 1453\\n\",\n \"Fence 1179\\n\",\n \"MiscFeature 1406\\n\",\n \"MiscVal 0\\n\",\n \"MoSold 0\\n\",\n \"YrSold 0\\n\",\n \"SaleType 0\\n\",\n \"SaleCondition 0\\n\",\n \"SalePrice 0\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_train.isnull().sum()\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 48,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163937.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":193,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"29287bb0-e866-8783-add3-36ff9aca79f9\"\n },\n \"source\": [\n \"some first tests\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"f758a3dd-5d46-bf61-fe95-920c0b148b3a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"genderclassmodel.csv\\n\",\n \"gendermodel.csv\\n\",\n \"gendermodel.py\\n\",\n \"myfirstforest.py\\n\",\n \"test.csv\\n\",\n \"train.csv\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This Python 3 environment comes with many helpful analytics libraries installed\\n\",\n \"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\\n\",\n \"# For example, here's several helpful packages to load in \\n\",\n \"\\n\",\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"\\n\",\n \"# Input data files are available in the \\\"../input/\\\" directory.\\n\",\n \"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\\n\",\n \"\\n\",\n \"from subprocess import check_output\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\\n\",\n \"\\n\",\n \"# Visualisation\\n\",\n \"import matplotlib as mpl\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.pylab as pylab\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"# Configure visualisations\\n\",\n \"%matplotlib inline\\n\",\n \"mpl.style.use( 'ggplot' )\\n\",\n \"sns.set_style( 'white' )\\n\",\n \"pylab.rcParams[ 'figure.figsize' ] = 8 , 6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"f0ab400f-e7c6-3772-dc5e-3395184d632c\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"train: (891, 12)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plot_correlation_map( train )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"040b5b57-74d6-262f-df86-ee86b01c1906\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 135,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163939.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":194,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"f06aaa16-1098-180b-86e8-b677eee19c7e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Python: 3.6.0 |Anaconda custom (64-bit)| (default, Dec 23 2016, 12:22:00) \\n\",\n \"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\\n\",\n \"scipy: 0.19.0\\n\"\n ]\n },\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'numpy' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 9\\u001b[0m \\u001b[0;31m# numpy\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 10\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mnumpy\\u001b[0m \\u001b[0;32mas\\u001b[0m \\u001b[0mnp\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m---> 11\\u001b[0;31m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'numpy: {}'\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mformat\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mnumpy\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m__version__\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 12\\u001b[0m \\u001b[0;31m# matplotlib\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 13\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mmatplotlib\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'numpy' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# Check the versions of libraries\\n\",\n \"\\n\",\n \"# Python version\\n\",\n \"import sys\\n\",\n \"print('Python: {}'.format(sys.version))\\n\",\n \"# scipy\\n\",\n \"import scipy\\n\",\n \"print('scipy: {}'.format(scipy.__version__))\\n\",\n \"# numpy\\n\",\n \"import numpy as np\\n\",\n \"print('numpy: {}'.format(numpy.__version__))\\n\",\n \"# matplotlib\\n\",\n \"import matplotlib\\n\",\n \"print('matplotlib: {}'.format(matplotlib.__version__))\\n\",\n \"# pandas\\n\",\n \"import pandas as pd\\n\",\n \"print('pandas: {}'.format(pandas.__version__))\\n\",\n \"# scikit-learn\\n\",\n \"import sklearn\\n\",\n \"print('sklearn: {}'.format(sklearn.__version__))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"eceb8730-f611-6120-fb55-6e1fff05646f\"\n },\n \"source\": [\n \"**Load Libraries**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"138e11e6-3d15-418b-45f5-60f12821ed8e\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Load libraries\\n\",\n \"from pandas.tools.plotting import scatter_matrix\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn import model_selection\\n\",\n \"from sklearn.metrics import classification_report\\n\",\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"from sklearn.metrics import accuracy_score\\n\",\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.tree import DecisionTreeClassifier\\n\",\n \"from sklearn.neighbors import KNeighborsClassifier\\n\",\n \"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\\n\",\n \"from sklearn.naive_bayes import GaussianNB\\n\",\n \"from sklearn.svm import SVC\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"c358cbfd-9cac-8a84-95f8-01dc485a00d1\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'pd' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# Load dataset\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mdataset\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mpd\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mread_csv\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m\\\"../input/Iris.csv\\\"\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0;32mdel\\u001b[0m \\u001b[0mdataset\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0;34m'Id'\\u001b[0m\\u001b[0;34m]\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0mdataset\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mhead\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'pd' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# Load dataset\\n\",\n \"dataset = pd.read_csv(\\\"../input/Iris.csv\\\")\\n\",\n \"del dataset['Id']\\n\",\n \"dataset.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"e7cfbcb8-56cf-614a-3bbb-ecc073a243d0\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'dataset' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# shape\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdataset\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshape\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'dataset' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# shape\\n\",\n \"print(dataset.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"ecba21bc-6392-ecf4-9951-5f0a8eaec61d\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'dataset' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# descriptions\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdataset\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mdescribe\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'dataset' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# descriptions\\n\",\n \"print(dataset.describe())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"cd430156-7db6-7cc1-159b-f81e79244dc4\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'dataset' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# class distribution\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mprint\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdataset\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mgroupby\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m'Species'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0msize\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'dataset' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# class distribution\\n\",\n \"print(dataset.groupby('Species').size())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"ed5d8b56-9e78-8c12-ba95-fc00922c1f6e\"\n },\n \"source\": [\n \"- Data Visualization\\n\",\n \"- Univariate plots to better understand each attribute\\n\",\n \"- Multivariate plots to better understand the relationships between attributes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"308d9e14-d399-f2a2-09ec-eca01e630f3f\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'dataset' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# box and whisker plots\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mdataset\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mplot\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mkind\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;34m'box'\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0mplt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshow\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'dataset' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# box and whisker plots\\n\",\n \"dataset.plot(kind='box')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"4029fb49-cd30-bdf7-f6ea-7d5e4194748c\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'dataset' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;31m# histograms\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 2\\u001b[0;31m \\u001b[0mdataset\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mhist\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 3\\u001b[0m \\u001b[0;31m# scatter plot matrix\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4\\u001b[0m \\u001b[0mscatter_matrix\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdataset\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5\\u001b[0m \\u001b[0mplt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mshow\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'dataset' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"# histograms\\n\",\n \"dataset.hist()\\n\",\n \"# scatter plot matrix\\n\",\n \"scatter_matrix(dataset)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"7d776d7b-3ec2-8c06-87f1-f3b3676a7f59\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 204,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163955.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":195,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"53e65f34-8897-d0b3-a11f-9c71be4dab0a\"\n },\n \"source\": [\n \"This is a python jupyter notebook for Digit Recognition Competition in Kaggle were the objective will not be only to develop a model, will also be analyze the features and compare a variety of models that can help to solve this challenge.\\n\",\n \"\\n\",\n \"This notebook will be divided on the next sections:\\n\",\n \"\\n\",\n \" 1. Intro \\n\",\n \" 2. Data cleaning and Transformation\\n\",\n \" 3. Data Exploration \\n\",\n \" 4. Data Modeling with\\n\",\n \" 4.1 Dimensionality Reduction\\n\",\n \" 4.1.1 PCA\\n\",\n \" \\n\",\n \" 4.2 Classification\\n\",\n \" 4.2.1 Decision Tree\\n\",\n \" 4.2.2 Logistic Regression\\n\",\n \" 4.2.3 SVM\\n\",\n \" 4.2.4 Random Forest \\n\",\n \" 4.2.5 Classifier Perceptron\\n\",\n \" 5. Comparation\\n\",\n \" 6. Conclusion\\n\",\n \"\\n\",\n \"let's take on count that the predictive models might need different preprocessing steps, so I will use different dataframes if needed\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"429053cf-aff1-7435-ff9a-c37ff654af5e\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import matplotlib.cm as cm\\n\",\n \"import matplotlib.gridspec as gridspec #to manipulate subplots grids\\n\",\n \"%matplotlib inline\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn import preprocessing, manifold, svm, tree, linear_model, metrics, ensemble\\n\",\n \"from sklearn.decomposition import PCA\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"a9f74047-e9b9-d4e0-3db3-22cafce970e1\"\n },\n \"source\": [\n \"### Intro\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"630cf2d4-6ee0-2e38-545c-d8893a6515ff\"\n },\n \"source\": [\n \"Data load and first look on the Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"11cef696-a426-4f51-7a28-39a3f2d44ed6\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 42000 entries, 0 to 41999\\n\",\n \"Columns: 785 entries, label to pixel783\\n\",\n \"dtypes: int64(785)\\n\",\n \"memory usage: 251.5 MB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df_digits = pd.read_csv('../input/train.csv')\\n\",\n \"df_digits.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"5ddca021-3c9e-e1b7-e499-2f0b5c8e00f2\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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yxpT7/ne1XiAhiAS3rJzN40s4VlNzOAMf2GRftc0pgymxlAzWHbW+mU\\nYeXbZtk1Mtx93jjg90Od7j5J0vWSFmebt23JK/ts7XSudpmky1UZw7FH0tIym8mGlV8n6Vfu3te/\\nVuayG6CvUpZbGeHfJ2lcv+c/zqa1BXffl/3ulfSsKrsp7WT/iRGSs9+9Jffzf9x9v7t/7+7HJa1U\\nicsuG1Z+naTV7v5MNrn0ZTdQX2UttzLC/4ak8WZ2mZkNlzRX0voS+vgBMxuRHYiRmY2QNE3tN/T4\\nekkLsscLJD1XYi8naZdh26sNK6+Sl13bDXfv7i3/kXSDKkf8/0fSP5fRQ5W+Lpf0bvbzQdm9SXpC\\nlc3A71Q5NvL3ks6XtFXSx5JektTRRr09pspQ7u+pErSxJfXWqcom/XuS3sl+bih72SX6KmW58fVe\\nICgO+AFBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUP8LY5bcMlzFj/sAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"img = df_digits.iloc[1][1:].as_matrix()\\n\",\n \"img = img.reshape((28,28))\\n\",\n \"plt.imshow(img,cmap='gray')\\n\",\n \"plt.title(df_digits.iloc[1,0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"0c313200-3960-38dc-3655-1929d4f41a3c\"\n },\n \"source\": [\n \"So now let's plot only 9 images in a matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"80daee29-53fc-7d6b-11f1-1a08f197e57c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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ChTj6M6deoAcN999wHQp08fJk6cCMDYsWMB6N27NwCvv/46AEcffTQA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\\nIdShi4gkhDp0EZGEUIcuIpIQ/w8aBbIB0ujkFgAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, axarr = plt.subplots(nrows = 3, ncols=3, sharey = True, sharex= True)\\n\",\n \"i = 0\\n\",\n \"for ir in range(3):\\n\",\n \" for ic in range(3):\\n\",\n \" img = df_digits.iloc[i][1:].as_matrix()\\n\",\n \" img = img.reshape((28,28))\\n\",\n \" axarr[ir,ic].imshow(img,cmap = 'gray')\\n\",\n \" axarr[ir,ic].set_title(df_digits.iloc[i,0])\\n\",\n \" i+=1\\n\",\n \"f.subplots_adjust(hspace = 0.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"6138ecef-a475-2b6a-102f-d9af67d96878\"\n },\n \"source\": [\n \"We can see digits are centered, so we are guessing that the more dense pixel features will b the ones for the center of the picture and the less dense will be the ones that corresponds to the borders\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"c60e1034-63da-eb44-a9f1-4ac17363f603\"\n },\n \"source\": [\n \"### Data Cleaning and transformation\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"62b62bd3-b678-cb8d-baf8-30df700edf92\"\n },\n \"source\": [\n \"There is no need to transform the existent data due:\\n\",\n \" \\n\",\n \" 1.- All the features are numeric, we don't have to parse features\\n\",\n \" \\n\",\n \" 2.- We are guessing there are no missing data: the data are images converted to array\\n\",\n \" \\n\",\n \" 3.- All the features have the same range of values, we can standarize but i will not do it yet.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"934e0ec9-6a5d-0e49-b502-c4504ce0d321\"\n },\n \"source\": [\n \"### The exploration\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"401baf60-1e89-dbc7-fcf4-316e3b1aa1f3\",\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"df_features = df_digits.iloc[:,1:]\\n\",\n \"digits_labels = df_digits['label']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e335ec60-2ccc-c155-564d-88c6bc259cb0\"\n },\n \"source\": [\n \"First, I want to know the distribution between the labels\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"0f024013-3307-a8a2-c095-f50056ec2e17\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([ 4132., 4684., 4177., 4351., 4072., 3795., 4137., 4401.,\\n\",\n \" 4063., 4188.]),\\n\",\n \" array([ 0. , 0.9, 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, 9. ]),\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(digits_labels)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"7b167c41-c02d-7f66-9f7e-acc15399d88a\"\n },\n \"source\": [\n \"Well, is a mostly equal distribution between the labels, so there shouldn't be a bias towards a certain digit\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"da0514f9-9178-d201-4bf9-ee2075d5995f\"\n },\n \"source\": [\n \"Now, I want to know how many pixels are not used in general\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"2437cb33-7fc9-1753-91e2-ba1c615aaf37\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([ 81., 11., 9., 3., 10., 9., 7., 8., 8., 638.]),\\n\",\n \" array([ 0. , 25.5, 51. , 76.5, 102. , 127.5, 153. , 178.5,\\n\",\n \" 204. , 229.5, 255. ]),\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(df_features.max())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"55ee450e-e090-7ce5-6493-79b5821383cb\"\n },\n \"source\": [\n \"We can see that are 81 are pixel not used in all the images (the first and second arrays are the qty and bin boundary values), but the great majority is used and have values greater than 229. This is not helpful.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"07e94c84-8c48-6b35-b7b1-7d9ac687685c\",\n \"collapsed\": true\n },\n \"source\": [\n \"Next, I want to check the average pixels usage for each digit in an image\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"956661fc-1519-67f3-2a9a-ab4fdf8d9bfb\"\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:7: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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8LRDoVbcGulaMd1axTX1ElD+lieQbTMtSm49U8OQEYZHcZ31sdINjEz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\\nRK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, axarr = plt.subplots(2,5, figsize = (9,9))\\n\",\n \"gs1 = gridspec.GridSpec(2,5)\\n\",\n \"i=0\\n\",\n \"for ir in range(2):\\n\",\n \" for ic in range(5):\\n\",\n \" temp = df_features[digits_labels==i] \\n\",\n \" axarr[ir,ic].imshow(temp.mean().reshape(28,28))\\n\",\n \" i+=1\\n\",\n \"f.subplots_adjust(hspace = -0.5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"b23e845f-172c-4a30-3d2c-5fc78d542515\"\n },\n \"source\": [\n \"it seems that 0,2,3,5,6,8,7 and 9 have very common pixel arrange, but for 1 and 4 there is not so defined.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"ea087731-6ef8-15f8-461a-17db3e380c47\"\n },\n \"source\": [\n \"Remember, we have 784 features representing pixel values, but not every pixel is used nor representative of a digit. So lets use dimensionality reduction to choose the most digit's representative features. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"a32ff163-2de7-3ed8-9c70-8b1a6fc35aa4\"\n },\n \"source\": [\n \"But how many features do we need to have? We can try several options, so one of them is to count the average digit's pixels which have more than a certain value, like 200.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"5a615a4b-5e5d-47c0-f8fc-de0615c6a078\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"139.82614285714286\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"count, bins= np.histogram(df_features.mean(),bins = 14)\\n\",\n \"max(bins)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_cell_guid\": \"afc7f824-73ad-fdf9-b003-b41f665613e4\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[(413, 9.9875816326530611),\\n\",\n \" (49, 19.975163265306122),\\n\",\n \" (35, 29.962744897959183),\\n\",\n \" (27, 39.950326530612244),\\n\",\n \" (25, 49.937908163265305),\\n\",\n \" (22, 59.925489795918367),\\n\",\n \" (20, 69.913071428571428),\\n\",\n \" (31, 79.900653061224489),\\n\",\n \" (28, 89.88823469387755),\\n\",\n \" (38, 99.875816326530611),\\n\",\n \" (30, 109.86339795918367),\\n\",\n \" (29, 119.85097959183673),\\n\",\n \" (25, 129.83856122448981),\\n\",\n \" (12, 139.82614285714286)]\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"[x for x in zip(count, bins[1:])]\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"f2bfdfec-b179-b45b-952f-39d9aefad1f9\"\n },\n \"source\": [\n \"take in count that te bins array is bigger than the count array by 1, so I take from the second smallest bin boundary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"15bbc621-cbaa-c8be-a444-882a3dea0b6c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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2oz+a2ZWb1wsuhPoQA7d0J3d60jMTOrKSeL/uSnKjcza2LVvFLeTEk/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\\nynKyMDOzspwszMysLCcLMzMr6/8D62F3SqEA0FMAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(bins[1:]-5, count, 'ro-')\\n\",\n \"plt.title('Mean Pixel\\\\'s Value Count')\\n\",\n \"plt.xlabel('Mean Pixel Value')\\n\",\n \"plt.ylabel('Count')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"941cc935-518b-a745-c346-fa80eb5adbca\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"37\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sum(count[-2:])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"0c721dee-b2c9-cd65-6725-65ccd25248c2\"\n },\n \"source\": [\n \"So, from the 784 features using 14 bins, at least 400 features have values near to 0. Only 37 features have values greather than 119, so there is an option for the dimensional reduction. so lets use PCA for Exploratory Analysis using 35 features (for sake of the square like plots)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"e1c8c37a-d0dd-5cf8-ab5e-8493efab3e08\",\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"n_components = 35\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"dcb37986-2a82-c908-0c4d-98eaba1a21bc\"\n },\n \"source\": [\n \"### Training - test Split\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_cell_guid\": \"bcf7ac30-56c9-d342-75fc-4624d186af33\"\n },\n \"outputs\": [],\n \"source\": [\n \"sc = preprocessing.StandardScaler()\\n\",\n \"df_features_std = sc.fit_transform(df_features)\\n\",\n \"x_train, x_test, y_train, y_test = train_test_split(df_features_std, digits_labels, test_size = 0.25)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"090b5dfd-21fc-80db-bb47-90cc5fc8e57f\"\n },\n \"source\": [\n \"### Modeling: Dimensionality Reduction\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"8c0bd270-13dd-c4d5-bab6-dacef0e6feae\"\n },\n \"source\": [\n \"#### Modeling PCA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"_cell_guid\": \"8318b98b-a7c9-f5b9-2c47-48cdf28f78b7\"\n },\n \"outputs\": [],\n \"source\": [\n \"pca_ = PCA(n_components=n_components)\\n\",\n \"pca_.fit(df_features, digits_labels)\\n\",\n \"eigen_val = pca_.components_.reshape(n_components, 28,28)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"_cell_guid\": \"31d3b4c9-fefe-9cbc-5ba7-5f19780a244c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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NdHAYgYanSN2WGoe8uJ6XsyT1QpbPJKtQXdrQeizY8mvKMBfFQquTwy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\\ncDgcGcIfpg6Hw+FwZIj/DyGMjv55UpGuAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"n_row = 5\\n\",\n \"n_col = 7\\n\",\n \"fig = plt.figure(figsize=(8,9))\\n\",\n \"for i in range(n_row * n_col):\\n\",\n \" offset =0\\n\",\n \" plt.subplot(n_row, n_col, i + 1)\\n\",\n \" plt.imshow(eigen_val[i].reshape(28,28), cmap='jet')\\n\",\n \" title_text = 'Eigenvalue ' + str(i + 1)\\n\",\n \" plt.title(title_text, size=6.5)\\n\",\n \" plt.xticks(())\\n\",\n \" plt.yticks(())\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"dacb565b-5a9b-8ae3-f8f8-5c28a051525e\"\n },\n \"source\": [\n \"we can see that the greater is the dimension the greater is the complexity.\\n\",\n \"I Will like to use 7, 21, 35 and 42 features to check their performance on PCA.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"_cell_guid\": \"0fdc1910-9797-186e-7a2e-532fb86f6ae1\",\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"n_components_ = 7\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"_cell_guid\": \"86d7f37c-65eb-a7f8-73e4-a05a13092616\",\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": [\n \"from random import randint\\n\",\n \"colors = []\\n\",\n \"for i in range(10):\\n\",\n \" colors.append('%06X' % randint(0, 0xFFFFFF))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"_cell_guid\": \"ec1b22e1-d1eb-1dac-1c6e-bef816e140f3\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pca_ = PCA(n_components=n_components_)\\n\",\n \"pca_features = pca_.fit_transform(x_train)\\n\",\n \"fig = plt.figure(figsize= (12,12))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"_cell_guid\": \"ce2d42de-d66a-b8a6-4ad5-d5c3196a8250\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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aci8ETbqqwC85LWOqgOBZqmQ1UVLGmts/BsiYgSY1imgmfnRryuri5o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\\nAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(12,8))\\n\",\n \"for i in range(10):\\n\",\n \" to_plot = pca_features[:2000,:2][y_train[:2000].values==i]\\n\",\n \" plt.scatter(to_plot[:,0],to_plot[:,1], marker='.', cmap=colors[i])\\n\",\n \" plt.grid(b='on')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"bea04376-e4be-aea0-72eb-deb86287a1b8\"\n },\n \"source\": [\n \"we can see some clusters, but they aren't defined, so let's see how it work with the classifiers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"_cell_guid\": \"b4376991-5031-cf05-c55c-01c1a0e2732a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ready to learn\\n\"\n ]\n }\n ],\n \"source\": [\n \"clfs = [tree.DecisionTreeClassifier(), linear_model.LogisticRegression(),\\n\",\n \" svm.SVC(C=1000.0), ensemble.RandomForestClassifier(),\\n\",\n \" linear_model.Perceptron(n_iter=40, eta0=0.1, random_state = 0)]\\n\",\n \"n_components_ = [7,21,35,37]\\n\",\n \"clf_accu = np.zeros((len(clfs), len(n_components_)))\\n\",\n \"print('ready to learn')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"_cell_guid\": \"c3120a00-2b8e-edde-3757-b082dd201104\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"----- # of components: 7 -----\\n\",\n \"----- # of components: 21 -----\\n\",\n \"----- # of components: 35 -----\\n\",\n \"----- # of components: 37 -----\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i,n_component_ in enumerate(n_components_):\\n\",\n \" print('----- # of components: %d -----' % (n_component_))\\n\",\n \" pca_ = PCA(n_components=n_component_)\\n\",\n \" pca_features = pca_.fit_transform(x_train)\\n\",\n \" pca_test = pca_.transform(x_test)\\n\",\n \" for j,clf_ in enumerate(clfs): \\n\",\n \" clf_.fit(pca_features,y_train)\\n\",\n \" y_pred = clf_.predict(pca_test)\\n\",\n \" #print('Accuracy with %d components: %0.3f' % (n_components_,metrics.accuracy_score(y_test,y_pred)))\\n\",\n \" clf_accu[j,i] = metrics.accuracy_score(y_test,y_pred)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"_cell_guid\": \"e40d2b68-48ca-a1c6-da68-cc20c64ed8d2\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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eouoVpbYfPmvvD9xhvw7LPwv//bt09amgvgoeF78WKYMWPi/46qR1tE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\\nDU6Jtog0JaXAJO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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig = plt.figure(figsize=(12,8))\\n\",\n \"colors = ['ro-','yo-','go-','bo-','ko-']\\n\",\n \"ax = fig.add_subplot(111)\\n\",\n \"legends = []\\n\",\n \"for i in range(len(clfs)):\\n\",\n \" ax.plot(n_components_,clf_accu[i], colors[i])\\n\",\n \" legends.append(str(type(clfs[i])).strip('>').split('.')[-1])\\n\",\n \"ax.set_title('Classifiers Performance over # of components')\\n\",\n \"ax.set_xlabel('Number of PCA components')\\n\",\n \"ax.set_ylabel('Accuracy')\\n\",\n \"ax.legend(legends)\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"d8c1bbb1-8556-d7f2-06f6-c955226a8585\"\n },\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"_cell_guid\": \"89d6bf0f-c76f-8358-7646-d86e9295756d\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": 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\"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"_cell_guid\": \"b38b8d9d-5d75-76d5-bfc2-e0176184b034\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"_cell_guid\": \"27583891-2355-fda6-fcb4-fd52221c0482\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"_cell_guid\": \"476e6356-7f80-b78b-d452-561f71b2dabf\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 230,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/164/1164098.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":196,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"4f6efbe0-1afa-f1bb-0490-461ad661d162\"\n },\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"99bd3245-4345-09ab-06c4-db4dc5e08dde\"\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"from sklearn import linear_model\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"from sklearn.utils import shuffle\\n\",\n \"from sklearn.model_selection import GridSearchCV\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn import neural_network\\n\",\n \"from sklearn import preprocessing\\n\",\n \"from sklearn import svm\\n\",\n \"from sklearn import metrics\\n\",\n \"from sklearn import tree\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\\n\",\n \"plt.rcParams['image.interpolation'] = 'nearest'\\n\",\n \"plt.rcParams['image.cmap'] = 'gray'\\n\",\n \"\\n\",\n \"%load_ext autoreload\\n\",\n \"%autoreload 2\\n\",\n \"\\n\",\n \"# list all the input file\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"a553b76a-8c00-801f-b3b8-272de5e2cad7\"\n },\n \"outputs\": [],\n \"source\": [\n \"# Load the training data\\n\",\n \"\\n\",\n \"data_dir = '../input/'\\n\",\n \"Realty = pd.read_csv(data_dir + 'train.csv')\\n\",\n \"Realty['timestamp'] = pd.to_datetime(Realty['timestamp'])\\n\",\n \"Realty.set_index('id', inplace = True)\\n\",\n \"Macro = pd.read_csv(data_dir + 'macro.csv')\\n\",\n \"Macro['timestamp'] = pd.to_datetime(Macro['timestamp'])\\n\",\n \"Macro.set_index('timestamp', inplace = True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"187d051b-af36-1c9f-b3c9-3e26c2aff9a6\"\n },\n \"outputs\": [],\n \"source\": [\n \"# correlate relaty price with macro economics\\n\",\n \"RealtyMacro = pd.concat([Realty.reset_index(), Macro.loc[Realty['timestamp']].reset_index().drop('timestamp', axis = 1)], axis = 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"4a6958f0-0259-dc7d-d697-e6406f9bcf2c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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73+HAkjapN8zVq7RQ/91mD9Y2ru6XVp/Yjuw/A1+PsmNrkn9Gvst7DG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\\n+P9zbXfc7TvDiwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"FontSize = 20\\n\",\n \"FigSize = (20, 10)\\n\",\n \"fig, RealtyScatter= plt.subplots(figsize=(10, 10))\\n\",\n \"\\n\",\n \"RealtyScatter.scatter(Realty['full_sq'], Realty['price_doc'])\\n\",\n \" \\n\",\n \" \\n\",\n \"RealtyScatter.legend(fontsize = FontSize)\\n\",\n \"RealtyScatter.set_xlabel('Apartment area', color = 'k', fontsize = FontSize)\\n\",\n \"RealtyScatter.set_ylabel('Price', color = 'k', fontsize = FontSize)\\n\",\n \"RealtyScatter.set_title('House Price vs. Apartment Area Scatter Plot', fontsize = FontSize)\\n\",\n \"\\n\",\n \"RealtyScatter.spines['bottom'].set_color('k')\\n\",\n \"RealtyScatter.spines['left'].set_color('k')\\n\",\n \"\\n\",\n \"RealtyScatter.tick_params('x', colors = 'k', labelsize = FontSize)\\n\",\n \"RealtyScatter.tick_params('y', colors = 'k', labelsize = FontSize)\\n\",\n \"\\n\",\n \"# RealtyScatter.set_xlim([0, 1000])\\n\",\n \"# RealtyScatter.set_ylim([-1000, 1000])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"e3482450-80ca-7472-7b6f-137686780545\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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nxL0yj8l0DittdlC6R5GIzbyCTxpZpt7GJC4rH0bmY9EsFuJj7YnYxv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\\ndQgDO0mSpA5hYCdJktQhDOwkSZI6xP8FQfEX5cpe46QAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig2, RealtyScatter2= plt.subplots(figsize=(10, 10))\\n\",\n \"\\n\",\n \"RealtyScatter2.scatter(Realty['life_sq'], Realty['price_doc'])\\n\",\n \" \\n\",\n \" \\n\",\n \"RealtyScatter2.legend(fontsize = FontSize)\\n\",\n \"RealtyScatter2.set_xlabel('Living room area', color = 'k', fontsize = FontSize)\\n\",\n \"RealtyScatter2.set_ylabel('Price', color = 'k', fontsize = FontSize)\\n\",\n \"RealtyScatter2.set_title('House Price vs. Leaving Room Area Scatter Plot', fontsize = FontSize)\\n\",\n \"\\n\",\n \"RealtyScatter2.spines['bottom'].set_color('k')\\n\",\n \"RealtyScatter2.spines['left'].set_color('k')\\n\",\n \"\\n\",\n \"RealtyScatter2.tick_params('x', colors = 'k', labelsize = FontSize)\\n\",\n \"RealtyScatter2.tick_params('y', colors = 'k', labelsize = FontSize)\\n\",\n \"\\n\",\n \"# RealtyScatter2.set_xlim([0, 200])\\n\",\n \"# RealtyScatter.set_ylim([-1000, 1000])\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"b2305247-de52-4cb6-0256-7d9dd381c476\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Fk7cfrpZ7LNNtty6aUXc9NNN9DZ2cG2227HSSd9mjlz9tpwjFtu+QPX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\\nRESkACkJFBERESlA/x99w6/nZ6OfOwAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"fig2, RealtyScatter3= plt.subplots(figsize=(10, 10))\\n\",\n \"\\n\",\n \"RealtyScatter3.scatter(Realty['floor'], Realty['max_floor'])\\n\",\n \"RealtyScatter3.legend(fontsize = FontSize)\\n\",\n \"RealtyScatter3.set_xlabel('Floorr #', color = 'k', fontsize = FontSize)\\n\",\n \"RealtyScatter3.set_ylabel('Building floor #', color = 'k', fontsize = FontSize)\\n\",\n \"RealtyScatter3.set_title('Floor # vs. Building Floor #', fontsize = FontSize)\\n\",\n \"\\n\",\n \"RealtyScatter3.spines['bottom'].set_color('k')\\n\",\n \"RealtyScatter3.spines['left'].set_color('k')\\n\",\n \"\\n\",\n \"RealtyScatter3.tick_params('x', colors = 'k', labelsize = FontSize)\\n\",\n \"RealtyScatter3.tick_params('y', colors = 'k', labelsize = FontSize)\\n\",\n \"\\n\",\n \"# RealtyScatter2.set_xlim([0, 200])\\n\",\n \"# RealtyScatter.set_ylim([-1000, 1000]) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"9f4881ee-bb6b-ad32-2bd3-d0b8c32d4e62\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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uZSOkpEgG2Ay+rHjwK2AH4EfAW4f90AtiuNEpKI+H63B5AkSfOgnSqb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\\niSRJGrj/D30VNENrWyPlAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f, ax = plt.subplots(figsize=(10,10))\\n\",\n \"\\n\",\n \"# Generate a custom diverging colormap\\n\",\n \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n \"\\n\",\n \"corr = Realty.loc[:,['full_sq', 'life_sq', 'floor', 'max_floor', 'build_year', 'num_room', \\n\",\n \" 'kitch_sq', 'state', 'price_doc']].corr()\\n\",\n \"# Draw the heatmap with the mask and correct aspect ratio\\n\",\n \"sns.heatmap(corr, cmap=cmap,\\n\",\n \" square=True, linewidths=.5, cbar_kws={\\\"shrink\\\": .5}, ax=ax)\\n\",\n \"\\n\",\n \"ax.tick_params('x', colors='k', labelsize = 13)\\n\",\n \"ax.tick_params('y', colors='k', labelsize = 13)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"a89fcb82-3a4e-b491-3c5e-2e541a2bda59\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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OCYuyb5epJLk1ye5DWzqGM34GPApsAlSZatz88pSZIm30SFtEaAR1TV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\\nkkVakiRJkiRJhWSRliRJkiRJUiH/HyygJHa3LILsAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f1, ax1 = plt.subplots(figsize=(10,10))\\n\",\n \"\\n\",\n \"# Generate a custom diverging colormap\\n\",\n \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n \"\\n\",\n \"corr1 = Realty.loc[:,['area_m', 'raion_popul', 'full_all', 'male_f', 'female_f',\\n\",\n \" 'young_all', 'young_female', 'work_all', 'work_male', 'work_female', 'price_doc']].corr()\\n\",\n \"# Draw the heatmap with the mask and correct aspect ratio\\n\",\n \"sns.heatmap(corr1, cmap=cmap,\\n\",\n \" square=True, linewidths=.5, cbar_kws={\\\"shrink\\\": .5}, ax=ax1)\\n\",\n \"\\n\",\n \"ax1.tick_params('x', colors='k', labelsize = 13)\\n\",\n \"ax1.tick_params('y', colors='k', labelsize = 13)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"5502f1c8-4a64-7c73-fd58-7af403134273\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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T3fqZp+Yb9hTgTMzMzMzBqkNicCZmZmZmatx1ODzMzMzMxakKcGmZmZ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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f2, ax2 = plt.subplots(figsize=(10,10))\\n\",\n \"\\n\",\n \"# Generate a custom diverging colormap\\n\",\n \"cmap = sns.diverging_palette(220, 10, as_cmap=True)\\n\",\n \"\\n\",\n \"corr2 = Realty.loc[:,['children_preschool', 'preschool_quota', 'preschool_education_centers_raion',\\n\",\n \" 'children_school', 'school_quota', 'school_education_centers_raion', \\n\",\n \" 'school_education_centers_top_20_raion', 'university_top_20_raion',\\n\",\n \" 'additional_education_raion','price_doc']].corr()\\n\",\n \"# Draw the heatmap with the mask and correct aspect ratio\\n\",\n \"sns.heatmap(corr2, cmap=cmap,\\n\",\n \" square=True, linewidths=.5, cbar_kws={\\\"shrink\\\": .5}, ax=ax2)\\n\",\n \"ax2.tick_params('both', colors='k',labelsize = 13)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"6a9919e1-c9af-3883-eb0d-c682655f0cde\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"09581265-4f60-0891-6d0e-827480f5f4e9\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"261b5e76-61fe-6b01-4ae6-903aa46e22c3\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"97a205c7-39eb-b001-b6f0-666de459167e\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"c03b5d7f-e1b7-14f4-5937-6d9e45efd73c\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"1d2f50a8-81f9-9ec4-37a0-76fb0cd5fcaf\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"9d7f21f1-0b09-f618-d090-d80a4652154b\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"966c7b6d-6efb-9c19-4ab7-d7ab2fa350c1\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"743b22d8-941a-2d53-80df-228d7baaa6d2\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"544a34eb-0b17-58e4-8038-d95297f45b4f\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"c19e10d2-8f39-6e68-be17-4d7119356f77\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": 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\"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"2d34b72c-b379-fdc6-8d71-1920cc96beda\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"79ebadbf-9b71-bb7d-95e9-d3ba59f4f1e4\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"1f595595-0b88-2a4b-4b00-da6d23f63f54\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"de79f0fe-894c-874f-eb1c-c30acc2dfd6a\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"14bcddf6-2f9a-4e7a-6461-0eef8f9ab033\",\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"669988d3-a222-1c49-065a-86c840a0cc81\",\n \"collapsed\": true\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 154,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/164/1164140.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":197,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"38541fd0-854b-b5ae-dd1d-d5a181160c1c\"\n },\n \"source\": [\n \"# Import Basic Libraries:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"f5fb6410-9e53-24bf-8f8e-0fd19e110c62\"\n },\n \"outputs\": [],\n \"source\": [\n \"%matplotlib inline\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e628c172-6edc-c818-1532-e787d782d612\"\n },\n \"source\": [\n \"# 1. Data Input, Preparation, & Exploration\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"478defd6-4f2f-31bf-8001-ad4e8fd11798\"\n },\n \"source\": [\n \"## 1.1 Read in Transaction Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"256f3663-58ec-121d-a1ae-f345bbfb9c6d\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No. of Rows: \\t\\t 284807\\n\",\n \"No. of Columns: \\t 31\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"norm_count = data[data['Class'] == 0].shape[0] # Normal transactions\\n\",\n \"fraud_count = data[data['Class'] == 1].shape[0] # Fraudulent transcations\\n\",\n \"total_count = data.shape[0]\\n\",\n \"print(\\\"No. of normal transactions: \\\\t\\\\t\\\", norm_count)\\n\",\n \"print(\\\"No. of fraudulent transactions: \\\\t\\\", fraud_count)\\n\",\n \"print(\\\"% normal transactions: \\\\t\\\\t\\\", norm_count/total_count * 100)\\n\",\n \"print(\\\"% fraudulent transcations: \\\\t\\\", fraud_count/total_count * 100)\\n\",\n \"pd.value_counts(data['Class'], sort = True).sort_index().plot(kind='bar')\\n\",\n \"plt.title(\\\"Class Histogram\\\")\\n\",\n \"plt.xlabel(\\\"Class\\\")\\n\",\n \"plt.ylabel(\\\"Frequency\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"842e8fc6-56e2-1457-4edc-ab92e4973f5f\"\n },\n \"source\": [\n \"## 1.3 Standardize Input Data\\n\",\n \"\\n\",\n \"Before we tackle the class imbalance, let's bring the transaction dollar amount onto a standard scale (i.e. mean = 0, std = 1.0). This is a common preprocessing step. The other feature variables have been outputted from Principal Component Analysis so we can leave them alone. Additionally, we'll drop the unscaled dollar amount column and the time column. A time series analysis would be interesting, but we don't have information for which account is making a particular transaction so the time column won't be very helpful.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"f6f35ec1-b928-ac06-da74-4227c584a6f8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Use best hyperparameters:\\n\",\n \"lr = LogisticRegression(C=best_c_lr, penalty=penalty_lr)\\n\",\n \"# Train on full undersample data set:\\n\",\n \"lr.fit(X_undersample, y_undersample)\\n\",\n \"# Test on unseen test data set:\\n\",\n \"y_pred_score = lr.decision_function(X_test.values)\\n\",\n \"# Compute ROC metrics:\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test.values, y_pred_score)\\n\",\n \"# Get AUC:\\n\",\n \"roc_auc = auc(fpr,tpr)\\n\",\n \"\\n\",\n \"# Plot ROC:\\n\",\n \"plt.title('ROC Curve - Linear Regression')\\n\",\n \"plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)\\n\",\n \"plt.plot([0,1],[0,1],'r--')\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"39f3ba8c-df8f-4f26-0d11-54abdb5c060d\"\n },\n \"source\": [\n \"# 3. Support Vector Machine Classifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"ac1cbe69-9309-54ab-9f50-40c175d4e4f6\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.svm import LinearSVC\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"9f1ab31d-3884-05ea-a62c-134b668cb185\"\n },\n \"source\": [\n \"## 3.1 Tune Hyperparameters with Cross Validation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"41d4f774-cd1c-dd2a-7478-5c8d86599de2\"\n },\n \"outputs\": [],\n \"source\": [\n \"def get_best_hypers_svc(X, y):\\n\",\n \" \\\"\\\"\\\" Search parameter space for the optimal values.\\n\",\n \" \\n\",\n \" -Perform Support Vector Classifier using a range of C parameter values and two different\\n\",\n \" penalty terms (L1 & L2)\\n\",\n \" -Compute mean recall, accuracy, and f1-scores using kfold cross validation\\n\",\n \" for each run\\n\",\n \" -Output the C parameter and penalty term with the best f1-score\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" c_range = [0.01, 0.1, 1.0, 10.0, 100.0]\\n\",\n \" f1_max = 0\\n\",\n \" best_c = 0\\n\",\n \" penalty = ''\\n\",\n \" \\n\",\n \" for c_param in c_range:\\n\",\n \" print('='*25)\\n\",\n \" print('C parameter: ', c_param)\\n\",\n \" print('='*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" print('-'*25)\\n\",\n \" print('L1-penalty')\\n\",\n \" print('-'*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" svc_l1 = LinearSVC(C=c_param, penalty='l1', dual=False)\\n\",\n \" acc_score = cross_val_score(svc_l1, X, y, cv=5)\\n\",\n \" recall_score = cross_val_score(svc_l1, X, y, cv=5, scoring='recall')\\n\",\n \" f1_score = cross_val_score(svc_l1, X, y, cv=5, scoring='f1')\\n\",\n \" l1_f1 = np.mean(f1_score)\\n\",\n \" \\n\",\n \" print(\\\"Mean Accuracy: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \" print(\\\"Mean Recall: %0.3f (+/- %0.3f)\\\" % (np.mean(recall_score), np.std(recall_score)) )\\n\",\n \" print(\\\"Mean F1: %0.3f (+/- %0.3f)\\\" % (np.mean(f1_score), np.std(f1_score)) )\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" print('-'*25)\\n\",\n \" print('L2-penalty')\\n\",\n \" print('-'*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" svc_l2 = LinearSVC(C=c_param, penalty='l2')\\n\",\n \" score = cross_val_score(svc_l2, X, y, cv=5)\\n\",\n \" recall_score = cross_val_score(svc_l2, X, y, cv=5, scoring='recall')\\n\",\n \" f1_score = cross_val_score(svc_l2, X, y, cv=5, scoring='f1')\\n\",\n \" l2_f1 = np.mean(f1_score)\\n\",\n \" \\n\",\n \" print(\\\"Mean Accuracy: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \" print(\\\"Mean Recall: %0.3f (+/- %0.3f)\\\" % (np.mean(recall_score), np.std(recall_score)) )\\n\",\n \" print(\\\"Mean F1: %0.3f (+/- %0.3f)\\\" % (np.mean(f1_score), np.std(f1_score)) )\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" # compare l1_recall & l2_recall:\\n\",\n \" if l2_f1 > l1_f1:\\n\",\n \" # compare to max:\\n\",\n \" if l2_f1 > f1_max:\\n\",\n \" f1_max = l2_f1\\n\",\n \" best_c = c_param\\n\",\n \" penalty='l2'\\n\",\n \" else:\\n\",\n \" # compare to max:\\n\",\n \" if l1_f1 > f1_max:\\n\",\n \" f1_max = l1_f1\\n\",\n \" best_c = c_param\\n\",\n \" penalty='l1'\\n\",\n \" \\n\",\n \"\\n\",\n \" print('*'*25)\\n\",\n \" print('Optimal C parameter = ', best_c)\\n\",\n \" print('Optimal penalty = ', penalty)\\n\",\n \" print('Optimal F1 = ', f1_max)\\n\",\n \" print('*'*25)\\n\",\n \" \\n\",\n \" return best_c, penalty\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"9e29cbd1-f516-6059-e75c-04a863b372f3\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"=========================\\n\",\n \"C parameter: 0.01\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.943 (+/- 0.015)\\n\",\n \"Mean Recall: 0.902 (+/- 0.029)\\n\",\n \"Mean F1: 0.941 (+/- 0.016)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.943 (+/- 0.015)\\n\",\n \"Mean Recall: 0.926 (+/- 0.016)\\n\",\n \"Mean F1: 0.950 (+/- 0.012)\\n\",\n \"\\n\",\n \"=========================\\n\",\n \"C parameter: 0.1\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.948 (+/- 0.012)\\n\",\n \"Mean Recall: 0.917 (+/- 0.020)\\n\",\n \"Mean F1: 0.946 (+/- 0.013)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.948 (+/- 0.012)\\n\",\n \"Mean Recall: 0.923 (+/- 0.014)\\n\",\n \"Mean F1: 0.948 (+/- 0.014)\\n\",\n \"\\n\",\n \"=========================\\n\",\n \"C parameter: 1.0\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.946 (+/- 0.015)\\n\",\n \"Mean Recall: 0.917 (+/- 0.015)\\n\",\n \"Mean F1: 0.943 (+/- 0.016)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.946 (+/- 0.015)\\n\",\n \"Mean Recall: 0.917 (+/- 0.015)\\n\",\n \"Mean F1: 0.943 (+/- 0.016)\\n\",\n \"\\n\",\n \"=========================\\n\",\n \"C parameter: 10.0\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.945 (+/- 0.020)\\n\",\n \"Mean Recall: 0.920 (+/- 0.020)\\n\",\n \"Mean F1: 0.944 (+/- 0.020)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.945 (+/- 0.020)\\n\",\n \"Mean Recall: 0.920 (+/- 0.015)\\n\",\n \"Mean F1: 0.945 (+/- 0.019)\\n\",\n \"\\n\",\n \"=========================\\n\",\n \"C parameter: 100.0\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.946 (+/- 0.018)\\n\",\n \"Mean Recall: 0.920 (+/- 0.020)\\n\",\n \"Mean F1: 0.945 (+/- 0.019)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.946 (+/- 0.018)\\n\",\n \"Mean Recall: 0.926 (+/- 0.009)\\n\",\n \"Mean F1: 0.942 (+/- 0.014)\\n\",\n \"\\n\",\n \"*************************\\n\",\n \"Optimal C parameter = 0.01\\n\",\n \"Optimal penalty = l2\\n\",\n \"Optimal F1 = 0.949671472332\\n\",\n \"*************************\\n\"\n ]\n }\n ],\n \"source\": [\n \"best_c_svc, penalty_svc = get_best_hypers_svc(X_undersample, y_undersample)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"87823185-ea69-23aa-1f1d-46dd166f9d46\"\n },\n \"source\": [\n \"## 3.2 Train Model on Undersampled Training Data & Evaluate on Full Test Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_cell_guid\": \"f98f24bc-f70c-14fc-d13d-b8a8facf5e10\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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RN49104++ygI4o7QQ809wEWA42B44CnReSw/h1VHa+qqaqa2qBB6a4x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\\nYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Use best hyperparameters:\\n\",\n \"dual_svc = (penalty_svc == 'l2') # 'dual' option must be set to false if penalty is 'l1'\\n\",\n \"svc = LinearSVC(C=best_c_svc, penalty=penalty_svc, dual=dual_svc)\\n\",\n \"# Train on full undersample data set:\\n\",\n \"svc.fit(X_undersample, y_undersample)\\n\",\n \"# Test on unseen test data set:\\n\",\n \"y_pred_score = svc.decision_function(X_test.values)\\n\",\n \"# Compute ROC metrics:\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test.values, y_pred_score)\\n\",\n \"# Get AUC:\\n\",\n \"roc_auc = auc(fpr,tpr)\\n\",\n \"\\n\",\n \"# Plot ROC:\\n\",\n \"plt.title('ROC Curve - SVC')\\n\",\n \"plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)\\n\",\n \"plt.plot([0,1],[0,1],'r--')\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e269cd92-dc59-a611-6ef5-0277884a4e7f\"\n },\n \"source\": [\n \"# 4. Decision Tree Classifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"_cell_guid\": \"bef3d4f4-4138-b410-e41e-1babe77eaf12\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.tree import DecisionTreeClassifier\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"2a8e9ff8-7a68-ded6-3178-baba32a98ca7\"\n },\n \"source\": [\n \"## 4.1 Omit Hyperparameter Tuning, Use Cross Validation to Get Mean F1-Score\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"_cell_guid\": \"8342274d-68ea-5479-a2fa-4b4bca3c08ee\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy Score: 0.894 (+/- 0.024)\\n\",\n \"Recall Score: 0.914 (+/- 0.017)\\n\",\n \"Mean F1: 0.897 (+/- 0.021)\\n\"\n ]\n }\n ],\n \"source\": [\n \"dt = DecisionTreeClassifier(random_state=0)\\n\",\n \"acc_score = cross_val_score(dt, X_undersample, y_undersample, cv=5)\\n\",\n \"recall_score = cross_val_score(dt, X_undersample, y_undersample, cv=5, scoring='recall')\\n\",\n \"f1_score = cross_val_score(dt, X_undersample, y_undersample, cv=5, scoring='f1')\\n\",\n \"print(\\\"Accuracy Score: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \"print(\\\"Recall Score: %0.3f (+/- %0.3f)\\\" % (np.mean(recall_score), np.std(recall_score)) )\\n\",\n \"print(\\\"Mean F1: %0.3f (+/- %0.3f)\\\" % (np.mean(f1_score), np.std(f1_score)) )\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"1cb4dc32-5f6f-686e-b3de-bbc9d02b3f60\"\n },\n \"source\": [\n \"## 4.2 Train Model on Undersampled Data & Evaluate on Full Test Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"_cell_guid\": \"cf67a52d-685c-c331-3048-4cb8e280a851\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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306XHklzJ7tqooeecQlhARlScGYYhQs+cxPAOEln0lVXclnZpsGXNgs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\\nrkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Train on full undersample data set:\\n\",\n \"dt.fit(X_train, y_train)\\n\",\n \"# Test on unseen test data set:\\n\",\n \"y_pred_score = dt.predict_proba(X_test.values)[:,1]\\n\",\n \"# Compute ROC metrics:\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test.values,y_pred_score)\\n\",\n \"# Get AUC:\\n\",\n \"roc_auc = auc(fpr, tpr)\\n\",\n \" \\n\",\n \" \\n\",\n \"# Plot ROC:\\n\",\n \"plt.title('ROC Curve - DecisionTree')\\n\",\n \"plt.plot(fpr, tpr, label = 'AUC = %0.2f' % roc_auc)\\n\",\n \"plt.plot([0,1],[0,1],'r--')\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"0ad95cd4-8c1e-2f06-2fba-149f4be541bd\"\n },\n \"source\": [\n \"# 5. Random Forest Classifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"metadata\": {\n \"_cell_guid\": \"1bf91508-e637-7923-aa9c-6290aaac1e09\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.ensemble import RandomForestClassifier\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"d871ac79-4584-90b3-6ebe-b7e40a7bf760\"\n },\n \"source\": [\n \"## 5.1 Omit Hyperparameter Tuning, Use Cross Validation to Get Mean F1-Score\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"metadata\": {\n \"_cell_guid\": \"b6f3348d-b58f-8f95-67be-6529176db193\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accuracy Score: 0.940 (+/- 0.021)\\n\",\n \"Recall Score: 0.911 (+/- 0.027)\\n\",\n \"Mean F1: 0.939 (+/- 0.021)\\n\"\n ]\n }\n ],\n \"source\": [\n \"rfc = RandomForestClassifier(random_state=0)\\n\",\n \"acc_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5)\\n\",\n \"recall_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5, scoring='recall')\\n\",\n \"f1_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5, scoring='f1')\\n\",\n \"print(\\\"Accuracy Score: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \"print(\\\"Recall Score: %0.3f (+/- %0.3f)\\\" % (np.mean(recall_score), np.std(recall_score)) )\\n\",\n \"print(\\\"Mean F1: %0.3f (+/- %0.3f)\\\" % (np.mean(f1_score), np.std(f1_score)) )\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"fdc4efd8-40b2-1415-e240-09ddc56c2818\"\n },\n \"source\": [\n \"## 5.2 Train Model on Undersampled Data & Evaluate on Full Test Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"_cell_guid\": \"3523c4b3-4554-3568-fd0f-558cf5ed4e3b\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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aa1nCUFUyPkl1so1pwTctLftKv4vLctGjcgOSm+xHILyUkJNG8UZ+UW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\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# Train on full undersample data set:\\n\",\n \"rfc.fit(X_train, y_train)\\n\",\n \"# Test on unseen test data set:\\n\",\n \"y_pred_score = rfc.predict_proba(X_test.values)[:,1]\\n\",\n \"# Compute ROC metrics:\\n\",\n \"fpr, tpr, thresholds = roc_curve(y_test.values,y_pred_score)\\n\",\n \"# Get AUC:\\n\",\n \"roc_auc = auc(fpr, tpr)\\n\",\n \" \\n\",\n \" \\n\",\n \"# Plot ROC:\\n\",\n \"plt.title('ROC Curve - RandomForest')\\n\",\n \"plt.plot(fpr, tpr, label = 'AUC = %0.2f' % roc_auc)\\n\",\n \"plt.plot([0,1],[0,1],'r--')\\n\",\n \"plt.legend(loc='lower right')\\n\",\n \"plt.ylabel('True Positive Rate')\\n\",\n \"plt.xlabel('False Positive Rate')\\n\",\n \"plt.show()\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 6,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/164/1164152.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":198,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"eb4de64d-63cc-b64a-e9c0-5a7830f6202a\"\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"\\n\",\n \"train = pd.read_csv('../input/train.csv')\\n\",\n \"test = pd.read_csv('../input/test.csv')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"b47c08d7-20fe-0c24-fda9-fc6bc8182fdf\"\n },\n \"outputs\": [],\n \"source\": [\n \"million1 = np.where (train.price_doc==1e6, 1, 0)\\n\",\n \"million2 = np.where (train.price_doc==2e6, 1, 0)\\n\",\n \"million3 = np.where (train.price_doc==3e6, 1, 0)\\n\",\n \"basedate = pd.to_datetime(\\\"2010-08-19\\\").toordinal()\\n\",\n \"time = pd.to_datetime(train.timestamp).apply(lambda x: x.toordinal()) - basedate\\n\",\n \"times = pd.DataFrame({\\\"time\\\":time })\\n\",\n \"testtime = pd.to_datetime(test.timestamp).apply(lambda x: x.toordinal()) - basedate\\n\",\n \"testtimes = pd.DataFrame({\\\"time\\\":testtime })\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"19374122-01be-39af-0b59-567e4178abca\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"logit = LogisticRegression(max_iter=1000)\\n\",\n \"\\n\",\n \"logit.fit(times, million1)\\n\",\n \"p1train = logit.predict_proba(times)\\n\",\n \"p1test = logit.predict_proba(testtimes)\\n\",\n \"\\n\",\n \"logit.fit(times, million2)\\n\",\n \"p2train = logit.predict_proba(times)\\n\",\n \"p2test = logit.predict_proba(testtimes)\\n\",\n \"\\n\",\n \"logit.fit(times, million3)\\n\",\n \"p3train = logit.predict_proba(times)\\n\",\n \"p3test = logit.predict_proba(testtimes)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"993d6bfe-9ca2-8754-de09-60e21cb504b9\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.0246604369554\\n\",\n \"0.0131234830445\\n\",\n \"0.0252902034333\\n\",\n \"0.0202878764523\\n\",\n \"0.0110977245936\\n\",\n \"0.012630247156\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(p1train[:,1].mean())\\n\",\n \"print(p1test[:,1].mean())\\n\",\n \"\\n\",\n \"print(p2train[:,1].mean())\\n\",\n \"print(p2test[:,1].mean())\\n\",\n \"\\n\",\n \"print(p3train[:,1].mean())\\n\",\n \"print(p3test[:,1].mean())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"568e9f1b-d83e-3474-6cc2-dcebfc4be418\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 550,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/164/1164155.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":199,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"b934473c-1fe1-7a54-3128-535411714f31\"\n },\n \"source\": [\n \"This notebook explores the .tif files based on the notebook: https://www.kaggle.com/fppkaggle/making-tifs-look-normal-using-spectral-fork/notebook\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"71f263cf-564c-1e80-a7e9-320625329853\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"sample_submission_v2.csv\\n\",\n \"test-jpg-v2\\n\",\n \"test-tif-v2\\n\",\n \"train-jpg\\n\",\n \"train-tif-v2\\n\",\n \"train_v2.csv\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"import numpy as np \\n\",\n \"import pandas as pd\\n\",\n \"from spectral import *\\n\",\n \"from skimage import io\\n\",\n \"import os\\n\",\n \"from sklearn.preprocessing import MinMaxScaler\\n\",\n \"from subprocess import check_output\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import cv2\\n\",\n \"%matplotlib inline\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"6b10a2fd-a24f-c368-20ac-3c4deae50131\"\n },\n \"outputs\": [],\n \"source\": [\n \"basepath = '../input/train-tif-v2'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"605b2132-8cd7-728b-1cf1-b25431613208\"\n },\n \"outputs\": [],\n \"source\": [\n \"tif_image = io.imread(os.path.join(basepath, 'train_213.tif'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"6370014d-661c-1331-544f-dc9669a9ba53\"\n },\n \"outputs\": [],\n \"source\": [\n \"def scale(img):\\n\",\n \" rescaleIMG = np.reshape(img, (-1, 1))\\n\",\n \" scaler = MinMaxScaler(feature_range=(0, 255))\\n\",\n \" rescaleIMG = scaler.fit_transform(rescaleIMG) # .astype(np.float32)\\n\",\n \" img_scaled = (np.reshape(rescaleIMG, img.shape))\\n\",\n \" return img_scaled\\n\",\n \"\\n\",\n \"def ndwi(image):\\n\",\n \" return (image[:, :, 2] - image[:, :, 0]) / (image[:, :, 2] + image[:, :, 0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"b1b9104d-826a-8997-5d39-57e200013255\"\n },\n \"outputs\": [],\n \"source\": [\n \"def calculate_ndvi(image):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Calculate normalized difference vegetation index as follows: (NIR-R)/(NIR+R)\\n\",\n \" Input image is in the format (NIR, R, G)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return (image[:, :, 0] - image[:, :, 1]) / (image[:, :, 0] + image[:, :, 1])\\n\",\n \"\\n\",\n \"def calculate_nwvi(image):\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" Calculate normalized difference water index as follows: (G-NIR)/(G+NIR)\\n\",\n \" Input image is in the format (NIR, R, G)\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" return (image[:, :, 2] - image[:, :, 0])/(image[:, :, 2] + image[:, :, 0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"7cefa15d-75b6-31f4-9634-f56b72a6f907\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[-0.64620708 -0.61404804 -0.64696179 ..., -0.08567782 -0.09723438\\n\",\n \" -0.12993439]\\n\",\n \" [-0.59258377 -0.60934832 -0.64268934 ..., -0.08312351 -0.08922028\\n\",\n \" -0.10483405]\\n\",\n \" [-0.55659178 -0.60568732 -0.63023793 ..., -0.09236684 -0.09652823\\n\",\n \" -0.09617846]\\n\",\n \" ..., \\n\",\n \" [-0.40093921 -0.43867698 -0.44076132 ..., -0.36296032 -0.39482716\\n\",\n \" -0.41896656]\\n\",\n \" [-0.39008329 -0.42150767 -0.43093556 ..., -0.37194927 -0.37042462\\n\",\n \" -0.38110279]\\n\",\n \" [-0.41886856 -0.41449818 -0.42542998 ..., -0.38399311 -0.35288765\\n\",\n \" -0.35929043]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"img2 = scale(get_rgb(tif_image, [2, 1, 0])) # RGB\\n\",\n \"img3 = scale(get_rgb(tif_image, [3, 2, 1])) # RG NIR\\n\",\n \"img4 = scale(get_rgb(tif_image, [3])) # \\n\",\n \"img5 = ndvi(tif_image, 2, 3) \\n\",\n \"img6 = ndwi(get_rgb(tif_image, [3, 2, 1]))\\n\",\n \"print(img6)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"f75fd72b-b52a-f59a-781b-8cf6a11dde31\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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rs8h8f4fEYaQcBRjxtDc1hkgkR1EpKG7IRzNskxhOgokNCE1KDI0cKG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\\nYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(img6*255)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"84fe4fe9-a14e-3139-2c6e-32394166e328\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'water' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m\\u001b[0m in \\u001b[0;36m\\u001b[0;34m()\\u001b[0m\\n\\u001b[0;32m----> 1\\u001b[0;31m \\u001b[0mplt\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mimshow\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mwater\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m: name 'water' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"plt.imshow(water)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"e0945e00-8fa1-4a2c-65f4-1134b7b03a24\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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7QIXJ0uBtoxWwFH+ULrCaVsrHnhrA/SGAx0Iv+t8iQsJxLvbe60ksky\\n2RD1hqUJNoZPUFJcOMUR7SzFSMkmfpAMcehDGFEptmNlI6vwdjbqE1fAZ7LBBfeODOZ76DDG+aQc\\nhdEHZx/kJFhOLHln0YXzcaCaSTpA05PFaazLlSULrYPZSmt3xDt1DD5sL7xeVoY4Qyofriv7/j2X\\ny5X18kL3d0SU1/23bGXnfnzm9j5IupJLoaQFU4gR1HrnOCutQckLuQioTzg7FzgOSs7wDzExeARm\\nhazw/n5iUcE3UsoUU/aS6eedJp0UA4mOuPLp9k6Wyu3x4JWE7ZnwQbiTSBA86Srhp/PGhzVzzZkW\\nBsfBWW9sa6Hayarwcd95rxesHhATnFyWnXN0tpTRlIheJ888IKJz98GaARHqmDvvSbCEUVsQWYkY\\n9NEZUsAWulc6SjiIPBd8KOqKOERviAjZJlfvI7D0BCQt09tBLtsEFb1isjAQlpSIaOS04fUkRkUx\\nGgNDGe6IV1p3VDJFlDbGs9oITDKLJYYfjHo8S23IZcEB6YFM9IEQcIzmJ/fjpHtHtU5EnamCGO6s\\nOdNxztEBEEmTIuxgkjn7SfeD4YE89SmWC9U7qOPunB1inORcENpMZjGvZUkLwxvBYPIfTouKpoIM\\np3uj+6ww+/2Bq6NirBL0py6ljYFKmhhGqxyPN9QKmGEiuCg+JjCalwuPekdHx9L3VA6yKckWcOfW\\nHmS7IO48joOEkNSJcJZtZ8kbauCiWEkQwW9++095ef2O77/7gX2/8FY/seYF6xnLC/ouqDbU79Rw\\ndIFeT3qciA5Ge3Aclb4U9suOpI1jTJzIVckpE2P8rDX5i0gMBPT2YIjzcXul9caIgT0z9dv9IJM5\\njwO6c/bGeQ7q8c6tB2txjvrOkq/4Y1DLOetCccQ2XrYLtd+4XneKJagndZzse0FTBhd6dK6XnX+2\\n/gFHu/O53fkD2WhtEK1zb2PuiiKIg9eDkRNlNWRRtutOfiREMqU5Xge9O35/pxdj31eOs1Ky0PHn\\nnVd4irYgGKPjY7CU9ckgCNkWkEGLzpINDyOnld6NPcG9f0KSsZkQ4iyxEj7pR6fjIohkxAqLCU0a\\nGh2PztE6GgnB2JMRY1CPG546QsWTsm+v1FoZddKAKRkiyhid8JOmQYizZqMzCA1anJgGeUu0cWIE\\noomB4r0zfOIdHUeTTZ2EQ/dOzpnjuGMKl8sLt5/eSGkhUnm2apCKgE72p45K9MqyXcAUxFCU5g4S\\nWE6YOpWBWWK0g9ZOks1NJEkhSebeGr2f4POamjtZFtZlJRTe3/4Ci0IPnzoJU/7y9jtert/z9jho\\nJdP6g+6wbcGhFY2KeiUwxIw9J16uK5cPO+u28vpy5Te/+Sf8Z//5f0FSI22FrWx0b9TW8ceDgVJK\\nYkmNt7dzVsv1IMRIaixp58yd2+PAZPD5vGM6uKyTlt5fvyNG43zcftaS/GUkBhEkoMhE180SNiCi\\n8Kif8daR00jqmK24w+fbQT2mnkEi2E0ZvXO2ByNWIhwzoWhjsY0//P4/5vB33BNJwXUCNYuteG94\\nOCYK2Uhh/Kp8R3fo/pnGRq9/Dinz/vYJwsl5xS0QDUpeeLl+oG9X/Dg4pXKMoPeKkJCA2+2OpYSR\\nIC+Yd3pMDICnii8nI0bQR5B1Iv+OEzEolvEhU/ii+aloTOSyo080WxHMNupMPaA61XnqwOD0iZ5H\\nCISRTRl9iqOyKiNmabouOyeAOI/zPnECMdQUy3Mh99EnKEpjBIxnie/EXJya6OEkyxiKx3h+36ki\\nGMis7gJQaNFJKVHWjJ9t4kFkxBIhkyZ9jMp13ydYqFP8hAg5b3jIVP+JzVYIEDUGQW8DsWDIIOdM\\nFkCMNZiMg4M9laFNguGBJSO0c+uVkpUQ5ezBpkIfgddKzgsSNvUG5fJkqpThDRWlmAIrIR0zYdsy\\naStcL1d+9atf8f33v+Xjr37Dx+9/i6Y0K7hoJF+QfMdFiKqcduNoldYaxZRoFc0JvE/MSqCZkzxo\\nIahX9vxrXOB+vKEB6/rys5bkLyIxCMFwobuDVsrygojyOB9Yzohkfnr/kS0trDk4auN+diKMojpf\\nTlc6gatRcZJOBVrJGacSfb5rIQ/WJRFkVNMT6Ap6u2PZgMajHSzLC601jjHwdoeyUNLO8Sd/QraN\\n7icuBRMYOE3mC9SHso1EHvA2OsOd6HD0xr4kahLU38nL3AGnVmfyCs0H6kGWDKFoyFPVONui2gdZ\\np/TVY2AihDc8jO4CMcjmDJ9qOXefcm2ZJfoIJ6dM7x0fU0poOsHf5kH3CmaEJFAwcaZAu5JyRuj0\\nGCQr8zz9RHQmmBiPidLb+qTk7Ck9D3y0WblYxr0SIXSv8/77AIRlmYllRCOXDCMBCeQgfKACyYwl\\nKedomEzmaoxBWJBTZjApVgQMe8qc0xS/B3QmRpPEv9K/vd9wNzSE5IoiHDglFXTZ6PVOr05oIalz\\ntgOLjJIRBu/vP8739zwnD5QWkMTR6jz3ONnXF/Y98+GHH/j48gMfPn7kh+9/yw8//Jpf/fbXjNSe\\nPFKhnidLmSrOt/tfAkZvB/W8c7aD0ZWSgpKWqSMJYS8rv4vg6A/seAd2bvaOuuKWMQVNy89ak7+I\\nxBAEEidmC+4nrd0YIYRByRu1zx45WSbIeKuMs4FMJL9Ymoo+M1wTalNYk9NKyZmSBs0F4eCoB+HC\\nuhSGC3UY23qhlJUlLdyON/qAfg7qeXC2jiK4TZZgWy9I3pBuYDsjTghjDOHeDzbNpLXQOWgBbdwJ\\nU1JnKvqOTt43NsvA9G8QDcJY9bl4omExmQUVJaVCG3dUA5eCtzZpMISUMqEgzN24+ZhMRQ/0i5Y/\\nFDX/KhEXEiqOIrP8j7lQQgZWCoGQzJ6gm9N7R20Cl+I+d35RUroSNNo4Wa0g0Wcprone/SkNn6o7\\n90ZEI5g7vKliapztpLuz5ERgc4F7gAlt+GQz+iCJkU2o3en1QJcJa3oM0EwIX70aiuAjuJ83VL9U\\nFFNQlS1NACQ6polkg1IKMSZI/fLyQovvaaPx6Hf6ODEpEE6NQbZnFTcalYBxYrpwtnckLZN1cYik\\naN4RAS0vk3XYjJePr/z6N3/I9z/8mo9/8FuuLxdcleECWsjrVHyO3klp5X5743HcaGOAZ0pOJBNM\\nBiKDlBLbeuXD9QP3440xBn6eHBJcL9+hcUDaGFF/1pr8RSQGd6ePk2x56uwZX40vRz3wIeT8gdo7\\n5o3jHNTRSWnuTJoU0UQqO0suU6O+GMUMZRCm7EuiELxsO4/eEIRiBXfFvcGAGpX7Mcv/5pXRBoaB\\nKuGdYjuiv8PHiWlC3DlrI6fG+Zh+giVDEiMW46PsHCe81QO1p6QWpbfG7QHFABHWvE6xkxhmEyQc\\nNEYoEsE5boRCySvtrBznJ2y9ksOJ6ASJgeNjEKNBgBHTh8DclUUuMBreZ6/dnwabJEILSEkZkRh9\\nPgfRudO6wmV/nZVEf+AhmBhJ5+K7lJ23s6EycQSVTDictZPomC4TRFUhXGnesKcWgehffQ8kEHcI\\nxwAs0cWIdFAwwisiGcQo+UIAHoNkeVYz4rTeMCvE6Piz1TErREwQFU14yFRzjuDsJ7V1km2EGbYn\\nRhJ2TVQX+tE5e5vmOxRL6cnIGLgzRmVosFgQYYjfsVhIsoDA43HjJRe2JCQdvLy+cv1w5frxwssP\\nLywLjNTJUkBsKmRtp5+faceJe9DCGWFk3bjL3NRCBLUEJiyyoRyYGEvZGC60UadOg4opFHFM889a\\nk7+IxGCWuFz/MeKDx/ET6o37+SBhdM8Ml1keh9PagfeBBGgxNCuahbI9deISrLZjQI1BIrOknWw7\\n+/bOweBFLrgrb293ujdav7PlMpH9khij87jdae1BWS6zbwvjPCpLuXI87lNIpEE2GMcDHcqyXzj6\\ngepOa4N2r1zWwqsJpMR9nPhQzluFqtz9pJSEXhdkBIslaquEDcBhwCBINvURtTtZF9JyQVOZIJrA\\n2RuiCUOeuLwzdDz1Ho0sgyW94F3RNFuGJI0egCk5T2docmN0Z7KyMdV6GdpoM0M8acPm7anHDw4/\\nueQFVeWoB4kxlY95egcknOM4wKbHwHLCcmLNhcd5ZyuZyBkVQboTLtO3YTPxystGq53RFEmzQoSp\\nSVCf9+Zsd/KSofcpe3ZBxLiuO+cYYLOKGMwqxYdB9AkkXl7oEox+ow+fFC862zx1Ssm0Jw199pjS\\nfcmc3VlkOhiPPqYvR5TjvLFFMEZCR+LRHizlgmwXvrv+iu8+vrJdF3QRlu1CVuP+uLPkbVLQo1HH\\nOSu8bUGbsS2FsV+x1DlxLtePXJfrU/NQ+JR/In9K3N7/km39jpQLixb6ELJ1EsZe/gHSlQC93+lD\\naN0Zo031mUyYqg+fsmKzycFLmj4CAktlJoZlp7Ybg4Rs34FOoO9e7wwPljLYUmHLmU6ZO1Y2ujuq\\nQcorOcZc8AL7emFZC5ZX3u4/gRQyBzKU41HJtk6p6hH4OOm9I+cs328q1NY5awUDkY6ljI1GO8ek\\nENsd1albONuDJIkgYWnKjNvoNMuIOyYrSqMpWNkoKhzHY7I0oU8GpqMExfI0L/XJcqArli/UelLP\\ngyVnsk1xUqbSYuoaTAvhBykvU8zjU+DlDjEaOW/U3nGd9KtbIaKylmV2Qh7TsOVKj3PmAQu8DZKt\\ns//HEXO6wKMf1NbRZaFInscE+qioCdGD0Z0YjlkgUghJuM+WcxEFz4gksiolG612ihVS0WdP/TQl\\nRUKkYWKo5amq7DBUIRpb3udGQ8VU6WMgA8wu7CbcuOGtkaI97dGQ9AlWtoqZ0UclwkAanYWkyuv1\\nSskrti0kCV5ffsWH5RWLTOlTO9HPPltgHLEM407KK45z3N/wMfAetB5sywdeioMUXDOG0cMxW9j2\\nBa8vlLJMnOdp+TZdMVGO8x8gXSkyzV/nWTmrT1urF6oHeGeMiuRp85UQZHS6nyS7TIArEsf9MypT\\nPXm7f+b9vVOyIVKxZHy4fk/NC8dSaO2GpTTL+KSEL0QMxAT1oNG5rlfuI/GoN3o7MHVMg+v1iqly\\n9HgqB5W0vHDWG2Lla7/efZq5zugToOyVPhyTYJhznJ29KKYZl502HrRxojjVGxHBZk6gtHpjaCKX\\nlWyOhnHzgYfwftwoupByUGzaqicFOoHAJDbxmiGkXLBSpv9hnM/dN+gDzCujd9SUIU8QMkAY5Lyw\\nLStjvKMqiK4I0EmMMciS6SFYUmo/yOuClaCocgwnxpiUqQ/2nLjXB+tyoWyJ2jv3x/35EkOPwULQ\\ne2MaOscE9XJBEdoo2LNtUhGSBf05+yBpmqpKgojKo88kPMKpvbInozZHxjRoWUDSjS+zJcwy56ik\\nEHoEi8KaF+5nZ0Tnsl54O26EtznzYTSSGZaMPuZ5hGnx96604eyXuePbtqA6NRP1cfCyXzkfBzkL\\nOWUEJdNxXUk03sM4j5OzVlp/cFkWNCuWg5w2OpM2loBLyXPjzGNSwMzKdmiitTujK238vEltv4jE\\noKKYFEz709nWGUyTkQ9Is9PHmIKl7kxAaQxCM4zZg7olknT++Mc/ocjUuW85kUqfCeT111hZOXpH\\nHZKMOWAlTUpPbKXQWRK8H0rSShbY8z7BLYWX6/eTKru/89PtnZIX1v2Vo92mUUlXwpk8vdm0TgVQ\\nH6RQStp53H/HFPlmajsQfyAqlJ4oAvnptYiQr7bjrLMsH63iCGqGZMXqgYugIeRUnrbnk/Agq01b\\ntzTKWlBZUC0g0x2pT6mxe+AelLIT0WnD8dGnohOdct7aSGl5OjON1g/MJvovkVAJBmXOI1g2ut+n\\nwCsUNNH7fPXbYzDOG1WMlCar8FXOa0/jW6u0Nih5zhaYvgLwMVs3Y7YKE7dRDEelcQwnkwkcDWVN\\nK63OanRfdqI7k9ERRObQnKxQ1NCikxUbjZCChFLHxHoswbJOTKOUWRWNNqAHoU5rE5wNpu0fnMu6\\nsCwLuSxs14/88JvvsE05emfLShsPShdyeeHD5QUthc2MDtT7T/i68/npFvWYm9VuhZyWqQwdB2Ib\\nSyq0x8mH/SMPWUCUWiuHGEnq9LY8hx79nPhFJAZg7rCSWdLK4znAYmhCvU/DEhttdHqrZNvn5J0h\\n5Dyl1APDW5tA3HBCB7kZKGwj01BuvZNqpbZgkUFaM9kCUsG0oKKcLjxqZ7OMlAtzCs6BxwRFS85c\\nLzualAZ4qQb1AAAgAElEQVSAsuTEy/qBZNtMXvWkmUNv02k3+nyZQ/BxkMqFS9mooyOq5MS0TGsg\\nHpx9GsqWvDAi4d0paSaIsx+zc5CE6aCsr6yieKvI8Ol+zAWPSvZKlwTilKVw1ApjsKZMyIpHZeBo\\nLozWQGa1ppJo7UHSBGpIDI7zIC8rHoHiSEnkvNIfd3RqN8kadBLuDR+Cd0c1Y7bj/ZxVgDdyulBH\\n59Yqiy0kLUTMSVNmT2+Hn9hTf2G5IKYcxx3xiktiScYTTQE6xxiYlekVYVLcRTJHe0PSSvLp80CD\\n6n2+bzrp3oFgT3TmjOmsNIy3x++QlChZSdq5nXcWm8rZjlAsTdn2k9HRL4NztHPddyBQBsk6a9HJ\\nRCnzWXqHGNMjVDIpJYYOihp1y8T7yeO4c7/fOfoNyy/YlylnMZ2pbd5tTmks6wtHHVMyp52tGJgx\\nHr+ju6D+DzAxOM8dOwlLghhCPdvcSUJJusLoJM1oMlSfOvevIpn+HMM2v05Khrfg5gcXnUM09Axe\\nXq84fbov6w2LAqOxbVcGzPNJYssb0QLVwmJ1tjaWERKdk9ODJWd+/f0PjOH0UNaaURKjNsBYbHtq\\nLc7pz+gDsUSXxHXfuGwLR32HWOntYLSJq0hJpFDC1jnrQXxy1nMyAdmM5kL0BhIseU62kjRZgzbG\\ntEjritiVJJ3qldv9/hT/nPTurMnIkrmPB+eohPNsg+Dl8vp1jNiIBpawmCxH8/4cFCo87ifeZaL1\\nzPF7YiDhJBVImTGc87xxHOes+8TpqoQPtrwikRjeJ7skhgSTsh4PRDYkLcQQYpxzzJ5kVIX2xcsR\\n8lRerowuxHN+xxwLGFz2jbPPRLWUjbO/MUYDLdNTIEqWNGXTbbZ9NZzTZ3uymOIhJFvY7GS4oZIR\\n7nQcE/siEp/TrVQBxdKGoUQYtQ/O1ng/Hqgo276zl5V9/45lvRJpoYvjraOXwkt+5dP7n+FD5vAZ\\n21i3V1qvuASXy2WOFVChthtaMraubKPSWmXTBVGltZPR5ti5+JlL/ReRGAinFCXJIF9W8lboLfP+\\nPvuj6NMX74CtxofXj9zrHYkgFaN7Y7ixrpllzfTaWVJiiJCToMko68Ky70QXUlG27R+h0slq2ChY\\nzty5UceJ6BS33B+/w/LKhzLlp7PnVj6shW37QKs/0WzlOA4s4NPbn4EtUDvSGzzeIZTD7wjCQxt7\\n2Xh52fluTawf/xmff/fH/On7ncf9RjE46hyEsi5Bfs4CRCBGf85q8KnUUyWpQsxZCGpg5cLST8aY\\noOHZbmgycl7QsGdfDqsOFtto/WBZdtrjjQgoaUUY9OP4OltRY9JjJQkvHz+QJFG9Iq3ydjt5Ox88\\nnqVqtjQ1EzIBvBgxWx6DD+tHeq/gxoiBSCJJwYoSFEaMqeefSvbn/Q689QkIJ2HNOzUeSLQJ9mqg\\nBIutpAbb9WWyAKkgERBOH44M4YiT0T8RTxzCNChJEQWPzuO8c7bx9XsvJVPyC6nMBH4/b7g7jPNZ\\nsU0cB5/6CY9OMP0ffQxuj8989+GHOVjoDM5Pd/7o3/1fXD/+wcSLJdHGZ14eTtYpVd/WjzyOKcZ7\\nf/tEqwfn4+C6vXApLziVl63gPkjLwp4LZ3/h9vYT7m/UgPf3z5gmWn/nbB2JzP3xzuPx489akr+M\\nxACTJlSDbCiCtDsXET5/fkfGfVqKn4vk0e8EE63uEajOmXrbsuAabOsCDhYgqfCy7gROPTq7Gbhh\\nUXn0g1ESqg7niYaz2MLn88bF5iwD4ZziG590lItSlsLob9x6JcfUAqRwtrzz09sbzR8MLaSc6Wdl\\nyxuWE496gEDvg7fHg8KPuDa2bc6MuLeBACuJs3Xaw9mWwpL6HJLiz0lMYtP0A7gL5TnTso9B9Zh4\\nzFPebXnKir3VSWbKdHHGmNOCzlZxmVWYM4VTSYxWD1TSHIImgcjEHM5653HcOc9Gvd/JuuMBI0Bx\\njvOk5EwgeK8kyZASzkHzk9XSpD4JoM3JRYDL1C8MB4mg5P0Jmg4sLYSlaUbrByHBdbmgorR6J3xg\\n68bR77h3HueNIkIwQdE2HqRUyPkDox8EjR7nnOcx4GjyBLbtaRJzJA6yQu8z0UR9ajgsUc+K90HO\\nOy06InN0rKNE69R+8rjfEWBPCS0r9/eDP/+zP+U8O/fjA7V1rpeTP/zBcZmJ+FHeZ9XpjfunTzid\\nNa2oCH0cND+wNKsgNeURdbbTKI/zTu2DxVaO48H7caP3AyNRz0Y9289aj7+IxCAyFXaE8hjv6FLY\\nU57/Jkbtgsk0zqS0sOWN009U/GmGmZN8xxCKCEhFU6L1B4saQ2HNSvQbyAYUfrrfSBYkLZwjiNFo\\n7aAsC5satU9BkzHAC3hDl2VOHaJw9BN35b09aLWRHTa7cto7oyuughqs60brjQgl6/we9Wx8qtP5\\nWHSw7RuPfsM6nOfsl9ehFFZUB+vilJKQmBiEhyMx5xdAUMecDNV9sJZ9Oj9HnwNpik2jkQAqmAqW\\nCr03uvdJIUanM0fsLfkFIU+B1Ii5S0qw7lfOdvA4Tu73c+74+YJq5hz+dDjujD4TSkpCXhZGdOQ5\\nn3P4yegQoZPlESAW1J2UneED05n85wTpMVkoAveDBqRiJDNaZbpwY1LNZgW8Msb0plSfqk7JsG6X\\nJ+3XkDHdlcmEo/WnFXzHNRhjUBTCJ/MFBfpJWhJr3lFvc35mTKt29cpEXOaA2vBBFyUlo52NJjfe\\nbwcRP5G3hdrf+fx458PjleDkOO8g8EMr5LSDd87zMSvAVOhdCM2E2JwKHUFHcLGnPiVxO9847gcS\\ng/vjR97eOuN45/3xRuuN3hwfMM5/gCYqQZGYfHQtyn698HjcuJSVvjb+9P13JFvmCK2YmgaTudC6\\nB0kNS3n2+ppw7eRL5jgaqTiXS8bE6f3kfTiLNI5jgA7u4+S763cwJsLeW6dj9HGiDi6JEYOSV/4f\\n6t7dV7Jty9P6xpiPtVZE7J2Z90kVJXWrETYYLSwMEBIuXrsYSP0vgI3Fv0B7OEjgtMBColvCx8DF\\nQl2q573nkZl7R8Ra8zkwxjqnyyh130OJ0r0hpU5m7rN37kesGWuO+ft9Xz+J6t0Ovj4PEnCvB7Ud\\nvOQPMDqXywcsZvbWGGlgY2L4ii+6nAMkT899fbyzbvDpcsFeroTD2MtnZgtIiMhQ2lBqeXpTLl1I\\nqpTu7AUzQ4E+zibhuU8/rHNZMyLGsEYAunWi+ts9JFY9DZgCIaxY2R022wcqgWBCKZ3eHqzrlZCV\\njY2DQu/mLVU1unXqcfispe840EDJcWVZsoeCZmO2naSZcrxzXV9pKLV31HwxK3YA4tHp2RBdAPUO\\nixoShGmNHIRyPFnlwpzdK8WYh7Dw4lZCfXiaL5gGgnRAGbWSQnS0nSmBwDwHcyqJYd4vCShRk3+N\\nZ0p0CKyXV9oQxoDS75TmeMB5npKIGCoe7HrWQgyRL+/fgxqRRjYh9YZZRVSwVskpoeNCnF9odXI/\\n3oghsF0WSDdet4UyKke5E3XSD6HG6Vg3VWrrmFUez3esdtr+hfZ45/E8KL2z75WED1l/yuP3YmHA\\nzLHl1ljzQj3euWiipOT7SzvrtCERwkIg0GZjTkHmQFen7QgHM9zoCjIb1+snBCPmDWV38Gfd+bIf\\nPI7OmpJzIxcvsfTpEehpxhgHxsqYHmdVyQQKOQZKLcy+82Aw5u5HaQHWa2S1BXkK2IMjNMxfFinH\\njsYFq4UpgceENexI2HgmeMk3og6+fvkNhZVZhbf2ZCVwzR/RmRkEWt0R8SGp4hRtFfdHpKSOcQs7\\n5A9OXx6c4bB4UqWdo2kGww6wGzYOclSsBlopjJForVCHwag0iZgO8nJlDRfKAqP717WEjUd5oxyF\\n+/Hkur0y0+SoxkyVJSRvUM7uSdHtxTMANn2LMYVqJ4vBOqIJUWVbNvbjIOeLF8DmQE3o3ehHwbaV\\nGBeWJTHmZE4jpsASItF8UNxsgFWmJHJecLB3Yo6deeZM2nB8oOLHl1H8VEZOV4XIZIlKXBIxZr77\\n+h0DQLycVEc/7378VEuZ5xD1oNggjkIbBRnQS8Kkkg7Bjp22N55fPjMe3xHs4tQxE3QRpt1Yboky\\nL0xz3uMcwqQS5uqzpdo49gf1Wbl/fvDYnzzf3tifhffHF/ZycOwFEdhOMPDv+vi9WBhUlZiU0pWA\\n14lFDJudyaRLZwkZzJFfU07hh02WZaPMRpyCaGMvb8RVSeHmEdV6sNevbDpYQuSYwjDvHcwQmVNp\\ntZCTU4IBggbey+CShTQEJZNwMElSo+lE1HmES1h4PA+Mg9vykTkah+CM/6igif34ypiTTYP3NKRx\\nyRc0XHjfC0f7jvn6Cta5Li/kGnhvOyFtTFv48vWr8wcBGzuXGEAiUb1iHGOiz+IIMYVtSQiFpCt9\\ndGp9YMFZAjZOLsE4qNNYzVCc09hNqMdglEH5QXijsJkitvH5u+8JS/Kakriroo2DPj1lkmSjm9Ga\\n3/prfiFZx4azHKZ1v3s4uwEqypjFISvTKdKK0UdnL4fnFs4ch+KBtKnCuoYfG7ESr9h53DgATSuj\\nHox+R+NK0IX3feeW8SG2DlKIfgowgFlcB5DymRkYiE0mwl7fvaZtShKhtCfWh5MuREiSqKOjkjzE\\nZjjWvj6YOmnl4ADe9sCn+InRGnlODhE+f/+VfpKhQwqs4Z3L8oHBJNYAsWPH/TyVct6kWCeEyPP+\\nRjB49ko5Bo/3Nx6PnW+/fuHtyxeez6/UNqm9c+x3gkaS/CEmHxVijqyXC2EKR90pVgk58vrpA1OU\\n989fyS8/43p9wayio2FMRjCWtDDNkWrX+ILoZH8U1sWASL3v1Fpoy+qEaHEQbDhRaLWdJBw11iQ0\\nm4QUGQpinRwdN5ajcr9/h6aFD9dXhhnv9wcXXUlxEkNFs/In+SN/9v03fP8+qe1BToFffHylz8nW\\nJmJCH405jNIHnUafhV99jPx7f/wnfPd4wveTPpT9/uBg59EK15cXssLIiSUbM2UkAiESibQJr8tH\\nr6vr5NmL06MlgCRfTMNwEvPoECMiypp8JvLp9Wd8J9/yp3/6l36cmRamRPreKe2dQwb2rrRRyRrR\\nWcnrjQ+fPrK/Pwg0vpY7OQzCqkRfDYicFqsJMW2+BTqDTMdxJ6WVmCJzDjQkclox9Xj3/nicforu\\nA+eU0ODtzaHwHN68FPWBqcxG3Z9sROIU7l/v5EvifryzoCTLVFNCFCxMlmWB2pijYwaRRK27D7rP\\nu705J696JSboq/HW352M3SHLWYOng0zH8JkQ5oWkiVIn+l75bv+W7RL57uv3vN5utJef0Sd8+/aZ\\nP/7Fv8PMAyyS08IxjfE0rA3a/k4OiW29YHLhtl345jd/QS2FvRX2Unj/8j3fffctj+fO/v7Ofuxn\\nHWCn18ElZb7dj590Tf5eLAwAeY3EsPB8vKExskyvY1/WK/UyKI9CzO47UBMwPYUzE3BvweiNR2t8\\n3CIhZVIwrBz0PoldGeJ5iW4nXVAESYljVGZphKDwg2ouOJEnpBuyrITg8ejW4BIDMV3I42DkwS6d\\ny5qowNU2yphYmZ4NmNN9Gb2TsJP/V/1WHucJhhl57AfjeqNeVn5+uzLGyucv31FKwzRi9aDchZF8\\nSp1zRMV9C9MZccToSLRBY0z8+E+i8xUAzJnSKWTUMkuM7KWzRuWWHQc/bEBYXPgyfY5RRyNuV/po\\npGXF6pMpk219YcmRX3/6Od9KYMiDKxX0ZFNZJ8hEdWX0p7sgJHgDNKzecI0XYkjknKi90s2IQVHz\\nuYGE4DmJ0R3BRiDEhUfZgXHeySksERsH4zjIoqgs1FFRgVEHEWEwOdqddVkYlogaf2xOMptTsC2w\\nxI0+7sgp0eljUMY45yqDEJNvpdRTiSqn7o5JiIo1XFgzB2aNTiAT6QUUoxyNL/JOtca6XTj2A6bS\\n2lfStrEuN2p9g/XC0pUjNEbvSPKP+dff/qWTsIZwf7yzP+4wGjL916ILe630dmAG97rzQxzvd338\\nXiwMdkZaQ8wM9VvPFFbUhMsWmFP58s03SHlS5iTg3f603Hzgg1C659YXhZB8EWizE0PAGrRZmc25\\niBoCS4qOCTND1W1NAYFptNnQEGk4ibc2KBWetXCUw/0SY7LlwLZsxNT4sr8zZ4CkHM9Onz4Ymmf6\\nzUyctDPbeQznF/MwqP0gs3HUyeNRGFm8lIMDX4MoNiKMSMqJbpFWd4zhQaKQiHEjh8Sj+zHVHH6C\\nEePK1ICebITaG9IPaj+R70smrxdygn4IMbuo5/H4Qh+RLW/EsHnBrcIahWkLg8mzfiFcf46sgWVd\\nufWOsGJn0EgJNJuYFV/I5dzjTxxjJx6CQoLDes8BXtBI79VLYOLFKmH+jeeLcVkWji60NhhW2NJK\\niplaT4DLLNThs5V2HCzrSs7KaJPr5crRG7V5tV7xz3dKJObEUZ80hMTALCO60Z4PRlfWcKXrnarm\\ntXUDEz+ViaqU1rDpi4iInvV1v0O0mbikK6UPxtOzLXT4lt/wsq5IWoiPlZw/exjs9RN9ScxRkeQz\\nttYqX96+IS1XgmS+3Ascg1Yr0wSCMuvO6AOmnLjAA5M/wIVBxI9lRvcVt80dC5ktZpaQ6a3yur5S\\n2huBwBiNGNJ5RDaJpiQGQb0MtSzKo97Z0g2YNPXQ0KQyJPCybmzJLxJrvnh0m84LaO4aCDGTU0Bj\\novROb8PdmgF2E+R4J8ePLOuVOAvFOm/3N95HZEzh3g+qTaRPJCkpCSEIk0JvjTrP/LpGPwLTSGkH\\nz3anDphRiWvgNldq64j6tPxZGmHceVl+TiQ62eiUqrazpNXM2442HQ4jAWLIrMsrRj+5ER71zVFZ\\n0iSGyXrNfJwX/kLefK/cCp1A00iYndftRg6dmZRiUAfUmXhdb8yPjn0RGRxlZ00rOWYmxuN4J+iV\\nHPJJXv5B4Bt8Wh4WVB1hN+ak9QLTWRtjuINBVX8UusqcJ0Yelrwh0z/Xl9vFZw190M67qDmdOIX5\\nTCQQKbW5D7QHz6eEC0pGdFLMQTQxLyiRoAFRO7V/B1PDybusSFiZoyFTCDadyeDdcmp/ouJ3JTME\\nJAghDKoJwsD6oO3CbAeBTt39pGJa9WPmsNHKzseXG4aiMqijMWv166Mby/pKmOWc61Sv0wfxWHf3\\nz0vFMD25Ij/h8XuxMJi5kxDtZ6AlMqwQaPQ5WGIgroPjZB3OZ/fQimbikokpY1PIwQM9z/pg0UyY\\nE+Y8a61ynrtDUOMYhXjWOt107fbiOgox5xMCEpm9YfigDBss643RKvf9naDKxSpLEJaQCfHGsxTq\\n8eCoO3Eapexkjd60HIWYI6M4RGTM4bTfaeQYGGKMWXjMyZJWSPB6yXx+bz9GWhcNVFspRyGIkv1+\\nhHjyJ48+aGP4K6E1pkSCBKiDIuZfs5qTioOwborNB0+ENSaqVoJUoDJHwLQRQ8Y003Uw6IgOlwEB\\nccLX8saaFsbL6v//mggijHm4VCV9ICG0ObD5gxrP69x9tDNBaKQY3Rlhbqg2c6ZEFMfjh+guz9Yb\\noxuERNDIkO4eylHPVCK06T4LgNkPsMicmWteEZQyimdCWOi9Uw+XEw/xsphKoPWOnnOYXr/QYyTP\\n7uzO6UYxU6XbZAkrYwzW9UoFxvHuEiIZLLJ6pFsTw4oH+ELi2QqxO5VqjO9JGpHo2Z01+xD17W0h\\nxeh/luz9G4w0DvowAomghfdSmDJovdPHD3cvB2nJjFKJ4Q+wXenEQw+3mCgpZnJwY3GWQNwSLz/7\\nBeEofP3yW2YUVrmgCiln5ok1M4Rug5+lK30odt4mOhLNgy++Z65OLo4rMSUmHWsFI3kCOSSiqt8K\\n9wLiVqIcnRuACpd8ZcqkzZ0l3lgDXHOm1+r4ODpLMMJyobUdjUqOvnVYlxutVnoXhEYIkWIVmQkr\\nzjDc63dc9IbplRAaJo7zUjEuGhgjMMbCjA7/BMXMlXe9u+glkTHlRMRFqJUZXaGGKOtyJa2JOZUs\\ngXvdmTPSZyCGiyc6w8kKnDtpWfnFywvPGulvX6j3wtdnw45Buxkvt587CalN2vGOtUlSEAKlHph0\\nPGeVTjuTkZNzM2spRHEOZx+HuzMAVcMk/NiTCNrYYmSY8yf77JhO0rIwZycEZyIgncmKzeJ1515I\\ncXBUyHkjTGGvT0JcQSOqShuNdU3M4HOZNh4YgdEPxDIy4X48GXaaqVIkLpHe3skpY2M4/GYc3G6f\\nqPWgtwdDOkF8AfI5ViQvN0b53tOqxaj7k6jCy/aBlBrP/WA1RcZBjYm5FNJypVt3xoMF5GiYmucw\\nzO9kVs300JnBSHnjKPcTs6c/6Zr8vVgY5pyMbth8oMsv6f3OHOb9gOxtve22sF1WPry+8nb/whjV\\nK8Vm0L0+LQJrChzvnV69XJVigODSER1K+BtsQVXfy6tEUrgyhtdmVeC5d5ZsjkEbk1J2UkxElJA2\\nLLtzIIgHZF62C5frjRC/5fPjtwgrv/zlB768fSb0FWWS8gVToYl7BYiFNa70Pnge77ztyiXfSIvT\\nft/mEx07S1qIIfHhZePjzZOaf/7Xv+F9P7hdEtfLhZDgKvD22FELLOtGb7ubtkNCdRKD4/g78RzW\\nVj5/91tSuhKG8vn7nW9+8w21nPLZdaOdwNdPH6/86h/9mo/bhT//q79i3ANMo+4PPn/7lUvr6KWT\\nQibHxp5W9vJGExizEKLSG1gHQgVOa5Z62CuEK70NxngiIYM1FwSR6d23DqUVcjLicuMf/OofQoq8\\n37/h27e/xobRemFGBfNSFqP731NcQ58U64X7vTC6sm0Le6+keCEtBnT68LtDsUbWlU0CRxfu94dH\\nxwU3qsdM0IBpQNdPJJk89ie9VOiTGcx5GFERqdyPd27hA0zX31nthJRp9Z1HNTAjLRce9YlWYY6G\\nje94lI3byxUwphiBjs3IbneiKKUe1Hqwt0qfDgmuzbeLs3ekBUrttP4HyHzE4DjeIQU+hcmzC0Ma\\ngtGHtxVj9H3aTb15WMpkPwazNGrduaxCnUoYgTQ9LbjljSVsXrQyeD4/I9PLRC0IanD0wet2PTHg\\nRhuF2jtrWBjTeI6dhOcGWjdq3VkWY0mZWnb6KHC9sceAzEbUwR//4h/Qjspe/U5mCcKj7tCV6+XG\\naA7oMHUUe1BhSRcUZW8PRliIccWo9JPPkKPCrIhmXvQKdGafmKxEiU7JFiGnK6U3H/4FB7wn9buq\\nR29kIre4MXDhb9REP+D7t698+80b1jPRBiberNTY2bYLYXXle59vbDGzWufR36Eb339JHP3g/ZEc\\n0hL1HOQOR6Cj1Nao1QlXCQEGURcHuKAs0RFtFWUNyhIuDIRRHZ0vQehtEAN0gcvLwvE8XForiTYG\\nXQIhZGozT0Vq9u+FeEK0TS9pGUoMTnKeFkk4W7FN//jDhDkGvRd0dmqpYO7B/FFgOwdTvFU5B+yj\\nUvYdGz5Y7QmyGtYbZXY0RI7R2Hsj0Ej5E0FWsg4etqMKpbyT5EzbzgmjEueKSqDaIMzGPhqvNN4e\\nDz9pmfPHExw7o/x9GmHi/Zn25BoXwvYHiI83jNYH15x5e//Ktq5enTZfvSfO7s/Jc/+Xy6vvndo3\\n3GdhzRmLSggntShELptP0Nfl4jKbMRhTERJzTrKumChCcblI9AKNAcEmb/ud4BxYynRoio3irwbD\\nsEVorRFzJMTIUSeqnSzKy/LKs78zTbldL+zdWK2Q4kIKgZgiNhQ1z79DQLQywnlagR+hdwy30Qy6\\nZCRENgHJCxcB4mAcbzyiErMR4yeWTTgeb9TpF2Y+Pz8BXsNCKZ1HKVw0kYKQ9IWvb2/cvz6p+44G\\nh5SEoMS8+LHntrG+Ji4vG2mvlOyGqCw3hiTK3plzR8pO2uOZNekk0pnOnMQQ0cXThCkkxig4vqAz\\nSRzTh6RLXJgYdQ76aIzqQZ11WQlhMkdglMqfffOXmFXq8YWoL+zlna7Opcgho8sKKKsu9NkwCbT+\\nIMmChkjtxnLKc9s5zI4p8igTtd1/LjZRgSVlSnXSVR+dvCzEmLzqro7QU0sEIsP8uSVqQAZZSNIh\\nLFQ7PIkbnL8Qo/J4Vq9rWyDK+PGINATFTKgdjjrduWKFHAJ72+l9p5vHr+tsDJtOBx/G7IM5AyGu\\njOh3D3H8QQ4fvXG4l8maBAnDRZ5Ml4aMifrdlmO1VRgmLFHRywrA3iqqC8eo9NGQWVEScTbKdNCI\\n6UKcLlctyQtYOSzU+iDYgol65Fj9k5oWsSG0YSQxajdGL3h5qRBTZgi8Hwcf8gWVhcfxxmiVOo3O\\nSetphW17ISZ3Lkxrp7JekBiZoxO3jVwGzTzqHAS6OqFIFaBx1Ad/dT/45Q0+vb6yDeM5K2E0oq1I\\nP8gqxBhJFpxUpf4EbJa9VSmToI6ae9ZG7sb9yzvl0WlDybMTNTpURRudwocw+Hj7OXF23k5V27au\\nfJGvqC1EWTmasQKFlS3CMQ1LgzU6iVsVNPj2cJifCnBi/4NMulWW4H7Qo+4eNZaAqqPs+/AZxBRj\\nGcbn92+4rJlqk9UeGJBCpgyvTqtVTAKinngUJkKgj0HAk44aIkaj1sOTlVNZY6ZJZNan07Uwclj8\\n/SzwbO1MTio2CkOBKeicflGOSZ8HYVn9tKHuqAbEIZJMc9OVl1+dWLXEzHMUSjeiOfMxpYyEBUVp\\npZ+nOAvG5F7fz4JZxBjepajOo/zhhaVZpxWPWPdptPL+k67J34uFQYAxlFYHOQfq6KQUkaCEmEEb\\nQR2PbeK0J5lPbtcPPOULj2fhqB4o0rDwbI1IhyFMHog5TASbGMYSPSPRRiGYv7IOnT/KXUQnJu58\\nmCjMzlQhqktX66h+FBYCOhpJI0cphG7UUmlDOIZbjhiBOIS0vLgvQAbTAsMUo59OhEFKK2McqC2M\\nfgp05sBmIeUrNoYr7a0hfOGPfvEnPJ5fSfs73YQ4BmF0LsuNOo1hGQn/umSlOXiBKC3UWmljYt34\\n8pIACLQAACAASURBVPkLVgM6YQuXE3aaYIBJI2mgdqO0wfePd+az814eFJqn/yRgERYGKp2X5Uag\\ncMkrIQ00esK09nkWrPwUSuOKhsm0TjX8BMhcYS0SWGM4TzAgrgu1PZ3JqeK+zdG5t4MUlX1A65O0\\n2NmHyIyB5xOYBI142l2Z4heOidFnZ06vuhuKzEFcgrshoiEW/fNrgxCV0RspKH0chDDp7eBy+8Ax\\nGkfZEZQYTvdmP6jq7VONIOpHhyFmRDzdO8+j2DKFWt8JcWUSGSZIN4I19ukwGANEjGaDJS206VLm\\nPhpj+nGlqp4EpxWsc9TDIb8xIVZ+0jX5e7EwgNKHIGnh/e2Nl+2Czh1NzhlwAOmkFn+1DhMWudDS\\nkw8ff8a0O8/9jvTBPN4JpoSwYd0vwCWmM6pakfkEvfLcXUv/st7ofXLMA8N8aixGSjeEQu8HW1zI\\nMTGJPNUQicwxCOBbklbYmZhVeneDM7Xz+fODVnfi8pGL7Hy6fSCkla+PnRwTtcLz+Mw824Gv2wdG\\nytQTCiLDBbXH80BVWJcrNpTH91/4uP6cqyY+/OIf0Y437uXBX3/3Ha+WIWTX12vw4tZsxOgC1Zwy\\naoPaAq01SqmMQ5gh+oQ/Knnt6KgsWwTbmK3yzZ/9ls+/7WzLRj0qSY2f/+rXlHKwt69c15X1duHy\\niwsxvDoxavrQVyR6AxFw6aznLUw4G40DseyTfjsYcxJJyFBCSLR2d0iPeoR9zsmSrnBuz7oZecnU\\n6kGqoZVei1fMQ0SmEQRi2DjGjqkgOjiOHZUEsnCvhRACqT5YcmRJKzML7+/fYJKYyQNis04OM4I0\\n0rZxTGPvu8+CEuR8cXjwPrH5xnJ9ZVkyQwb78XbeHS4EcxuY0tDSuPLqd6Mh+iDeOroGxhy+iHAl\\nhE5S41G6q/808La/YRKYZII6Mk5mJ4pyWy+8rK9clwuuFP6/fucr8vdiYTCM3jpz3IlReNQ7jcBr\\nSGg4o7Ux0oYPc4QIJqRwo7Una1j5sE7aePI4DrblxphwlUCrxhydUne2M7bMcCloHXC04jXjE4kn\\n6qo87c2HSMPDP3U0WqvMYCfTsLPYAqZ0hNb6Wb/1bP3X451yDI4+uUijBaGOg4v6+XgUYy9vIEqI\\nHsXuvdJrBVFyUkof5LxQzANMfU5X0U/hz7//Lb98+chq75S28/58Utsg5gppsmRhiYvDVSWAqV9U\\nDGYIMAdxWclr5b3dyXGljINr2EhrJC0Ll22jtojUyefHV6x1jvsbwuDl9SPby0eO+iC938krvHy6\\n8nK78f3bN24Ft4zE5aRVd+69Ao0UcEfIGCRVTFwnaG33LaQodbjVatuSMxtsMGx6SzNkxmzI7Dz2\\ngZpBXImSOFphqkfkp4jzEf0oAZNJTNF9GwZbvjo3YVSO0Xndko90Jt5Sxec5mDCO7swLG2jw7Yaa\\nEayzhEQODug1HSQRau0/djxak5NaldhSRENmzorMwjEaQTce+531coO0MNuO9cqcy+nChFoe9D7p\\nY+ElRd6fnyEE7uVO0MS2+BDaMcqCYagaagdDEn32n3RN/l4sDIKDUvtRmWuk9Hde5Gf0rGTVc1jk\\nMelKpY/uzb8pSOvY1HOSHImq5O1KKQf3AksIcOJKEWVbPlHbw29tqRw9cEkLwypBIjaNSKBU3zaY\\nzbNVmYiLMPvuRigrtIEDSyXwaA9nUSLUWs8UYyNM/7dtnvvr3LnkRDOvSWs8k49iHO0gBh/Y9e4+\\nz9IcstJ9wELUhTEi3779FgsLL4ugNv3ri8LXvbCOyhI20rIBBYvuvkQU00nUld4fhLSwXhfSkol6\\nJb77E//Xn165fPo5H7bEd/fG/rZzHJ+R4QGsGK/06UnSX//i13xeLoRU2S4b9+d3tL2h04ddEcen\\n7e35ryWs5/1DsHAKZrzTgS7k4GlNCwELxnMepOWC9idKcpeEBnqHaBFoWPCjw9GnOzNEfQsoyhgP\\ncsqeVqTRzevLZl7vVk2M+kDmQRsBNWNI9kTksrBtV577joi6ASpl9mnM9s6abiwIUxaPbavrAEp3\\n6zcKdTRKO2nUWz4VhB11FA5rWtlHJq8DjZE+9nPrezkNWkKrTw6rXJcFGYkuDr4luGuzj05rBTvr\\nBDGuntgUIa8vaExn9/R3f/ydFgYR+VfAO94H6mb2j0XkZ8D/BPxD4F8B/8TMPv+bPs6c4+QWRt9/\\nhhdGmTx4Em4bF820YQwtzN48vBMcbT4tIHOwhcx7KURN7MeToBno9JN/MKwSELJmRjNGPwgKb8/P\\npNdPMEFCp5qHrUQjU+Q0TiuSIoLSHt8xpmva1AZ1b/RzK3C9XKhlZ5RK3X3avI/KYlfK6Bw9sPTm\\n0WHE24tML/CEBY2Oie+eiebDtnAfDet3/1FJQsx4HA+W7crsk6KTlyVjKdDLndk7hwixNqZ8JiwJ\\n1YVWD5YUTrHMhB9Iy5eMEml1klIlWOL1F5/IW+ZhBzF21kvgcltY4yeO42D0xgLIXjiWr3x6XWmt\\n8djfkJlYSF7msoksjlYrvbr3AqEOjymvceVoxym1KezHOzlvGHIO27zcRD9YgrMO62jYhDAUwxDx\\n2207wTPGPFFzRpDJ7JMRB8jEmByzkWQlE3g+H9j0qb2qeoHvsvnJQIiYJESG/xt2IGEhBWFOqCzn\\niwYegAoJCMzeaaX4LMEcKRDM+SEXzTheKyPSmWS3kdNBodSnQ38nmB2s6UbAHeI5L+TLR2JIfvfa\\nKx2cXt4HQzoS7VwQIApYcPZj0I2cf5qi7qfFof72x39qZv+hmf3j88//DfAvzezfB/7l+ed/y8PA\\nW7kEgTBg35/U4+luCUvIVNSCT2bHoNbO6BW1QYgBDYEUV6Ypo+zU/U5vhYnHmlvpBMm04u9rrVCe\\nnf3xcPlIdBWZuzPh0Qd7eTDFv0vdlNZ3croSLJwXr6Dx6qDaAb0Ll7CQz45GXBe2LSM5nkUbP3qa\\nwwiibHnlsiykuJLiitrkOJ6U+kCj+REe+wlZUYIMz8OrcTkTmyKVEISX25XrEl04Oxp9TvZeMPFc\\n6bZmYnQpTdRw/ruJl8uNmCN52cjLDXLg5cMnRjC2/IGQNz58/DW/+tW/y/W28nJ7cdDuaCQysQK2\\nMGVhI6BjOH7dlG1bIcCwxpZXgqyInUU2NUenq5JDJmhiyVdCSOcUPzqCDSXZ9OCUZmLIhBBpoxJC\\n9lOHc2FIaicRGcS8jn1ZPjmxKSwMCSxpRc74dK2N597ozZCRfUCp6t8zhUE7jd3qKLuwIpqJMZPV\\nCdKoIjGR8gI0xjn/yEF53RIpqGsLzzDevt8dFDN2xqzMMzMCp/h3dD+qnQNmASaXbePjy8ayKHUO\\n2qy01qjlYNTuDFP11/iUlh8htWt+pc9Ba4OfuJP4/2Ur8V8A/8n5+/8B+D+A//rf/C5eH5Y5mF0w\\nGVyWjRD9qHCvOxIjgkdXNU5Gb2d4xpj9oI1O6U8HelpgijMAjnEAShmDNow2D+7Hg2AuVb2kF2pv\\nLrhVNzqPCbPu1PFAZXLLC4nOMPW7my7cnw/sogQJDAk+JCyDsEREHGCahicnjcbAm33VGkveiCGA\\nCnUemFU4t0Z1FM9sLFCPRpZIrS6B7aP6mbT6q0wz9TReMl6XSGuJsO/06ce6g0luzk54ub7y9vxM\\npKF5IZ6vbvTJmjaOUUh5sqQb9/ZGLQOJMEpF14MluVBGrTFaRIbAKPQqUPz7726IQAxGs0qKxnM2\\nRDOLJvZjR3DXhAbhqJUoipjQesUY9OmovxD8vqqNioSEjobrAubZv0igwy9azrzJ8KPWiNIk0pmU\\n4jX+OQyVFZXpJ0PaEYmU3nhZV4+cx6v3L+KKqZ9ePGthtOp3D3PQ5yREx7rX2Vk4nxMzEDVhcXqy\\n0eSUJyvr6pGuGIScM0uKPFr1uVbygJc2d0iJnBIgEbDmjAidEC7kvNFt5+39gNFBFkJYiOY9n3SS\\ntUW89360BypuTT/K32/y0YB/ISID+O/N7J8Bvzazvzrf/tfAr/+2dxSRfwr8U4CYIuuykWLEE3Gc\\nqbLG3gxCQHvhfUw0KkvKGN05f8Plq3uplPaZGTOX9Ursg4JR5/nUERjhrELbZKKkYEgSnvUgamaL\\nTjyavXCRBZuZW7hhXbz8YgOb/opyu24MBq3dKfXgaM/T9vOBXhsiyuWykRCqDcxwOap1Psbbjwvf\\n19IRLkQyUTqv20aplcdRWCR4s7N2lrSxpCu6GL12vt7v3DbhyziIeMX6db3y+kcv7MN4HDtH/crz\\nUVnTpO1K1kxFKPudLErSDAKqgdv1FVHI24UYV6R7468dd95bQ3WwbJ+I2ROVx/GgHwePx0GWwWCy\\nLpmcV45WQC7sh5usp5nXvAPQI4st1GOwkt3nMZ2N6eTlwAzGsz5+9GgKQi+VEKLX6AGL5pizEElB\\n6LNz0ytHOZjSWaKwY+Tlxv7+DjNgo5zZAYhJyTmQ2p23+pXX640QXVRTmrDGyLMcHiePQoyD2isx\\nKkcp7u64vHCvX31xa4bM7lV9ES4xYxqwqIw6GbXx3H2yMMN0xL0k6jDGHOylOVLuDLR5WM95D3vr\\nbNMJ2n0Msi6EsP4oANpLIYsj82stJBnUmEj5Qn3eMQ0sq/ykC/vvujD8x2b2FyLyK+B/F5H/+2++\\n0cxMRP7WWte5iPwzgO2y2pId3SWGn+e24u2zZIhVUsg4LHQiktEIc2+IRUorlA7L8jOWy4JZZoaO\\n7XdGK5gaoon7/pkg51GmeVOwzM6q/g1/9Cc5Kpt6jz+nT/4DVJizYnZWhUdFNJDEEFMO8x7Ctryw\\n5cy9Dfbe+LC9OG+xuQLOUXJPYv4lSPJhqJ6EYZv8fL3SJfGlf4slkJDp8wSYjsG2XskR7vYgloSo\\ny1InSumFj9srv3y98v1+MPrkeEZMYXZhIJQ22OsdZiBqpvMg5QQ6yXklLonGYFPlYcpoPnNgDh+w\\nHl8RLk5GiAlJGzIOYgjndie4jLfugLMNAuaDVxP3OcRAb5WoTkpCvf3pWM+zSm94tT65lTpIIucL\\nrT18/hAXUgxOsNbIYCfHBSUQ44L7LhX6Qe1uDtfwA9Qn/OiUeFpDTGEejNGJp9fTrGDTXZlHraSk\\nmOn5tokipPQBTIghuRQpZpSFVu6M3gjbKzI7dTYwt6zVeqcZ5PyJNSnFAmNUYk7kljmqg3t/sH3N\\nlEGENoXcjcfje8p+J7GwXj6y5pVujZxXvrx9JqnzOZpFp573h+vpbGLl75HgZGZ/cf73tyLyz4H/\\nCPiNiPyRmf2ViPwR8Nvf4SMBXj9W8R/oMKEO6HPQ+04Mg2XxIaSNr0QR9vvO6OJuhZTpo9K6wztF\\nPOU2raOykhYfuvk33UgxUcek1IPr+pGOEGW64j0lcgCZnRmyP9lbIfvLKxZ+oBn7DzGFRFKHmgyB\\nEJQtLr76z4DqoJV3lrg5AQkhxeinKyRUqrsg11cEYd0uhDkotZMERg6oLsCgmZLSylOe2DC6TJ5t\\nkJarP8FW4WfcOPbGN2NwGRvlqOToF6GGG+kkZk/1hTiHwLJGUtwwMYp1mk2W5cZu95O7aMhUbPoT\\nbJggeWMJmSauo28Yox0MM4SKSqL2Rq+NHBfqHEwrmClZBQsBmRObHjbq7Qka/egtZdKaeZZ3TAUo\\nTPUOhmK0Wt0iFRRjZc7G3p/0Xj3vMV1wPKeci2NBevVjTJsUU4KC6eRle8VUETuTrTZ4SD0j66fU\\np3WCJlK+EYLnJHoXQtwAD8ZlMXYCcm4BPJ8VERpTKnHdCAhjVJ4t0nFFYe+VNQZXGJ7DtiCKTY9e\\n7aOhu5Ki0nvjsl5BusOKEDQYW/JtROmN+/GVnG6u+0vZkXfy97SVEJEroGb2fv7+Pwf+W+B/Bf5L\\n4L87//u//Ns+lhm+txQhxpU+AAmuGmfQa4f0Q1qt0KyRwuYo7XYgGgjnhF8RajNaO4gSMF0RVSbe\\n+xfRs3ptLsXFIK3McdAJHldNyXsVGuhjstc3gmTmUNIS2NaF2g5abT98LwhRCCkgfXAYmEZyemHM\\nh7f3cJjHtrwybafMeFqPIjbAbYgR08maV+axswUlry+U552QV3or0Bw+n9PC0RvLnGgKlPJG67/g\\nY9x4XFcu73cWUd5rpd93lm314JcNTHwPH1N0U9X5a7veiCEw6AT5is1EjMCsqEVq7Tzrg8nCNIXg\\nx3ejOzfDVXqnFr4OjqOQgofGmnVU3aLVptHLTgrrKZFpznzskaAKOLRnlodP7y1RzMhxYfROqw/6\\nHMS8Uls9a9niQ8ugSMzQdx8mtoLOk2YhGZsd1cQqCXJgzoqmBTGnOzugTWFOSj1PHYYTokb3+USK\\nK8eo1NH4uGb6hDm8JJX0gqbVeRwSiSFRjqenT0Mnpc0pVnI2KAnE0RDZmLMwmQQDhjHVMxij7exm\\nWAysYSWFzTs9Z9N3lDv5ZGZMPQi9ILowqjHHk2E+t/opj7/LHcOvgX8ubtGNwP9oZv+biPyfwP8s\\nIv8V8KfAP/ldP5U5O61PhkUCypziLLzeCUGo9cBssNfKkpVhgSXfKHMw20Ev7+zTRZ/3snt4xiZR\\n8VBMOU6dmGvIhjmG/TgeBBUkiJdtWifYoLVKSCvYivVJ6Q1J3srLIdG1u8QkRNxeZByz8Sx3eoX9\\n/XvvU2ghRSGEFZVGSjfGOJApgFB6cZJvDJgsHgWexpJWejBsF0bdaaZkMUo7iDkhR6P3nVCUtq7s\\nbef7kgmzYWHQpTGe7tK4ls62eKM0BJ+6C3amIwVNQgiwLJEhgRuvWOs0WaldHegqgoyNNmBJV56j\\nMXCpa6sNG3Zq0RxoZ9NOL+VgTCPG4Dg9CZQuRMx/PimD4q/aMTHMuOaF59iJyduwZYq3FWejmzMV\\nJCn3shNV0LA4xWhU+pgn7QnEplu8+qD1nRgyfTiXY4kBy5ljFi75ChaobWBzUvvgKO5LLa2wLi9k\\nFfZS0dbZW2WL2YW4krFaub99dbGOGpIiKXs9+6F+93qJqw+EgRSzDzSteyvUJmvK7KPDaMzpR+9H\\nLWcNHO+yiKsXpwkyO6He6d21eYzJs1VSfkU6PI4ny8ePRBOn1/yEx//nhcHM/h/gP/hb/v474D/7\\nSR9r2lmpdSNV608sXTyFOPp59uunFn1ArccJg71ATCwYYQQyH1E6b6UQ1fsRgeS39NbIeaG15q/U\\n6tVJsUDdK3ldCDppHYIN0IicrEQzToKxMGZFm/mJgMUf8/u9vdNSPQW2Rp/Kl8+fuV031ixIvpLT\\nypBJL44uk3POkGLidnuhzYZoZ82Z8PoBEaOMwfoMPKpLXacm1jUxfuBJWjxPShr7mBxtsm0bP3sx\\nXpaNz++/Jdw77VPhkjIxJoJOtzjHwIyRmAJpWdEkpLTxmhUQmviCHFqjt4ZOwboi9WAPQtCJiuPa\\n7JwPOJuxA8ndDQr2/1L3bjuSXMm23TBbF/eIyKwqdvc5W4IgQU+C/v9f9Ava2ru7SVZlRrivm5ke\\nzMnzeggdCOx4JFBIZqT7ukybc0xP4NEDEicCoRa5sgV7HPUxbveA74w56V5QL+E2XBZdFBJlsVV7\\nOAYRSrpFtLzc6O0ZsBs3tG4UNbrp1aGZSVLQXBl98DkmOWe27Z3cPxEJzUKu6ZAtYQ2DlBBXjEWu\\n79xT4WxPihorKe/1FgnIuRBTnscLF+H+/oU1ZhT0+LxMecJeKnI1gbkNkiS2BGiUFPnqV1NWtFMl\\nolAZmYw5+BhO4acIGDrM8Ymticzw2bASY52owP1ekfSKNGv/F3Q+OtCHsSyzVWUrN9Zs2JpB4lWP\\nbL5XLDm3L/8Lcx64xh9MBIYQyPHyhfP4mZQAj+CQimMmTGskgXZ25nqBKhkn7zcWxiIUcP3NRNIb\\nx3JYL76+/w01ZY3BthXa6+Dz9UHddxD429e/0aZwfj5ZJ7zai2mg/YxThy36CLjMebxoc5AEvr79\\nlffbX7kXZaUZVXb5jZsM1nrCFL49vqB6AoapgCzEYKXAwE8T/PlJW39nT85/9cRf98L/8b//n/z0\\n+MbH5ycfvfP39YPHvnF73KkiSC489nf2bYfVkXWjjUHaQtSKkJMETHRGdPrb441xq/T+opZ3bIJt\\nhe/PH/SWr+B6YV3wHE2F5ZNS76FrOCBgV35WNU5bCw09yBUt4WA9XmAz6FuIMX0iHifFXPx33Dvu\\ntPNkkZGcQWOykcqO2gGTKyuTGOcLMy60+wei27VzR4uUO8w+GWeHLlChjcX5uZBbI9VAAKgqY7x4\\nncLrOOnHjLSjCK/2Sb7toXVAtLWX0DuWCe148jp+oKqUWlGuMWshfDi+0JrpCSS9MccTsw44IvDj\\n8x/kutE0cADijvpijoFTqEVQfeIyuOW3aKLaHn/onfxTLAxwwSsl0dZgSxGwwQ3RjGvFF1ESi4DN\\nOKZ6p+Rb3AmvTsG1GnsSPP3GD7SY7Xrm+TwD5z0nW34w0Fg0fCKyX1pHLDQ+FiyjmOBpo/WDRBhI\\nXq1ho5Gu1X/0xj++f3AeAzF4jU5OhZIVV6WbIGtx+EG6PBserGv67IxhtLpfDdaF961yjkkXIZfK\\nYwvoyOd5hM6CMjkRNbhs2Msr62j88/uvlCSgd36qb9jXN7JNzhV5i61sJC1hqdXC1/dvpLKxbCIu\\nEV2ece+fszPbk94HyWGtFUGsVKDsqEDKzj8+vyMpsRVhNLumEKGFuM2YHniIhWvNy9sR5UHIugxJ\\nyrTEpjnyJ2sgPimyhYtPM8sc1kBVo0ouZbCF+MIkRVeEzYh4a+IYJ/iIBUoKLEMlEpOgrFwDXWfx\\njCxfDDPa6OAw5olr1AWOYdHl0Ywk0btpmkIP03eKvni2n+kSkezRT15Z+HL7wp7B1GhmtNkDKuyJ\\nMUdQwJNxLwlHQQYpFWqJmvs5T5b1y8nprDnRtDAJToWQuKe47oAHLzVFYdAuCebg9EnWf8HCGSBC\\nPgZqTvdFTRXJiomzGFdCz1jrAFNqCg9+SpW1nldByEYRCcuxxZ1V1amqnEeLvH+ClmqATLVTti+I\\nGFjDpKIaHZDLE4U7yiTljbpd0M9yQwzaipzD2U96n+xZ6GuyzKlbIeftqqt3RHMQdpYj5S1Ey5TA\\nOufs7J5J44zjbtmZNqnbDZuLj/4jdIo1qSl+niaLJGAGt43wajWeU3iezj8+Flv+N+r7G4/tG+Xf\\n/sav//5/MdgQz4iXCI6lEtcpcbZcyfWGpCscpDsk4eP4B7aEUm9gkcbMOaOawyFoFoixORAvJN2Y\\n66SoUHJFiQLcOWc0TelvzkQnacEl7L/TwC36MNI1Lcm6xfHYV4xIPdySduH3zSclpQDVjhAR0RAh\\nbXX0sn67KC4bOZ9IyiTdGASIZq6rQdwGiYRqIaUA4Jo565pUeM2M0WI3RyhV2OqNoo74ZJSC5jtu\\nC5kDvJA9ovdrDDTtXNnQsDPbRByWjauDAjQpmXoRryY+23WKUYoGJ1PE4nTbX6hm1pyc67fyJOW0\\nibUPtm2L1PCIiU8u/4owWA9X336Nl8YapCrMcf7elrTGE5dEScpSw7wxB9RUWJ7iK189eIGEhpCT\\n4Ahn69gabLf3mGjIREWvLscAXKhEsm6uyefqbKvCmuz1xvBxNahtQAhGo3fEBdc7KgfPYZxzYO48\\n9kJbZwSvUo7GZilMid39aCf7/hYVakkwiSKWafF7DYfN4jh9r1/4j1/+b9pQ2jzxy8sR26KRkrBc\\n42WYg+9Hx0ul/PjO6s63txtJhL+8feG5lNc82LgBCfcV9mu5se3vCDHedXf668XRXrzOzp5iHKhJ\\n2eoW92MHU4lYuVRGqnH9m42SKjknjvF5MTUSIPQ5wY1pi5Iqj+3GovM5DJEVUN7LubosiuXXClKz\\nW5yz0IxaBMxMYZixaVjMf9v5Va7ouxLgkmUknay8cbQP1nhdjEnBRKkKZjk4GFeqFjOWnTgZx/B1\\n8HkEoflWNsazM5ryeL+h6c6eB999UUmQ7ozWaSXjQgiH/YRa6fNgzBbhMYkIP5bianUh9dXiNLCS\\nwBr4WtxrwV0jwemOr0DjzzUv709BxKkJzlVhOE7m4zi4lRtD/9ir/j8iK/H/+SMCWSvmUEslS9w9\\n6/ZgywnxGQUao6FktrSBCbdtYzJDTd/fIk2YMpISmmIHEvMweLhwjjOYACn/DvPk2nXKhSLHAYO2\\nHJeMaaJumZKVrSoqk2WdUvYrI3GyJFDq88KRv84Yy+VUWAtsLuQ6vn+eH2jaLvur4iSWh8iHaDRX\\nuzA9sbqzWuC9xhWonWLx/IhSSuzyJSXmHHy0hqvyen7n++ev/Ho8+fdff+Yfr0/+8tP/xrf3b6CT\\nX87Pix4c5iFNN1K6I3lHtFBTpeYdtURhx2SnLY8FFgUptGGcR/RPCIKPFsQFgWET88ViUEolDEZC\\nzRn1TJYULVBB1uFR38JrohtbDmpRrMTp+lvmS1WX6L4kyFTi0VDex2LMgP8KgzEP1MeVtAxR49fP\\nf9LbK8qFkLjfo7DCt5AgQD4+rpcieBZbSey5IkvJvrDRovuiNVo7+emx8+XLznYTvn29UcuiuqPk\\ny6J8hclUwUfg+z0hksI0BYw2WJOrkCcWyJwUtTgl1rKF5qOCajSQJRGyXGxNEZBCTsqWhVsSNs08\\ntgc1JWY8ZX/onfxTnBiQMA11a3yRR3jWbTBwUg5/u5foLez9RdI3it7IVREx5hy03qO4RoR8+4rK\\nQGfHB7QZSK2UlVw21oxgTMnOT28/geWo9Oona8SuOdqJlYpxUshIP9hvP10vsOP8JohONAnzPEkk\\n5pxIFrJeXQhJyPuduTrZK4nMXC9edpCn8P7+FaQyp/Pt8RbTElv0edBG1LLf7nd2rRznZ/g8+smP\\n40lOd0QHxqD7ILnBFOR2p5cH/378k7v+z9yPk29fhY/j4GiTwYH6RIHXftCX8To/ISVK2tlwVjN8\\nTHzC8/Pv1LqzxkTzjL4EF4TMsBAU1UBcYlHrP0DeeK+3YBXWHbPOKY7NhEhlq0opMBF8ZWb7SP17\\nmgAAIABJREFUmX27gUUjVd7vjHWyPI7YkUdwotIlauV8QZ8NWxGksnWNXLNiE8YcoM4qnfe/fLui\\n+hv1cSdfi42N4HSKayy9SjR/607KiW17Z9knZgtc0c+PoJbfb2xfvnCWHZFOfVP+9v5f+Pj+yS+/\\nfPBsJ8fKFPKlt5zIClPbXItavyDeyKqk6szRObtT6w1wSBIBK62oG0icEkWFpAWITA/ecevYhNv9\\ngXDick0oWHz59hdAGOP4Q6/kn2JhECSAmzMqw9vxK1Y2JG8ksyuYE9QkkUJVx3DWXJQiQQ6+RprL\\nlC3vjOW0eZDWQg3O0SnyxpqTOQaP/Q2T8B1sBB9RltNGJ0kip1C2h0h0QHqC1rjfgh49+guXgial\\nzzD1uDWGhwuw2+KWoyzEPSLHMcprOCdYisZlX6S0kTRm8rdSL6iGBnJuvthTIZXEHJki4Qf41AKE\\nJdv8oiJfO0imsvog6QM/TuZW6MeL0RfmO1UCejNJvPoTXR+85uS+3chy422/M1uLnS45KRc0JV6f\\nn5RZsZR+94gkiY4H0RR5gPGKk8/84Gt9J3xQzrCAo9haPLbgFiAzUrU+qbVwvz+w0VmjRQz+6sqY\\ncwS/gQB/uiTEL0QbiqTAA8qCPjpaC85iqzunDRaLZU8ShZJqwGo0YLvTjV1yTF7Cykp2I4mw3R7s\\nm5K4McR4nR0sk8sdzUK9JfQiYWstVL3R9s5tV1J5j9TkCqRASsKYL6ZrBMHWYHkY+JYsai1krSTd\\ng1xmUU9oZswVI+xJgGfrtTxmUYYq3Qa3TdFSEIvMRk2JhKM2Wark8sdi13+KhcHc6COKO6cOJN0i\\nobcOnmtSc47k2johpbBI1521Gnl7Q5Oy504bC+ZiLGOtcan1wnBnyzfE4gHOObPUyVm41xt9LEQG\\npEItJe6EFgBRlRxXENGwoZ5PWIZRmCtMTULk4PtS3AZjBAk6acBC8m9INQSrlWSLSYh+ORX6PFme\\neGjAUGPmn8LnTuHr+7+xb5mkQm+LZuBX96MTVwvVBCkanjBD+2Da5J/9F759/cov9y/88v1npkHL\\nO/v1qr3OybKGMRlHp5TJljLdevR9mF223RY4uvFJ1XeqFkQLy14g0GfEvDUnpL7RPv+DlB/hAxFn\\nqxVksqZyjhe3spM9avpSSRgZZ16i6hfWOMAzcy3EY7oR1OYSEw07KKnEZuBQstLaYMuFmnKMWS+k\\nG5IDJecpqupVSZq5lYrNH4wVfZl7hjWFJDc+z++kktD8hf39J0b/T3Iu5McXtGzk6jzKzq+vJ7la\\nTLmsAxnSxp4KgvOan7QWmLllk31/x0WxFah6LrCuSg3WiM1ok7JJ72d0mZQgM5lHF6kS06FpRs2F\\nVB6QnMkrhMqaQxdawWvAVsTx/8DnT7EwQPwBS94JN2hHvVBKwR22kljzxXDnXo2pUCqMwxgzdISg\\niF40KFmXmBeCVBINLcID8+YyGOYo9yhAnS1MUCtSbZiRiGIR84sUJYK5cp4jxqgO6hK5HIRpg3M0\\ntnwLl58nfqu986T4HEy5kOQq0bEoDrYYfbEUvt2+UfKN6cLoUdeHLc7zO6I772XnWAezx05XJfoN\\n5ujkXNgt41x9ArZw6yjKqz359fhgidBWQ5fTHD6fldt+5xyTpIKpsK+Tv9t/hMUYYdoiaQqE/w2m\\n7khSci3M2ejA1EytysOCh9Fbw23HJ3gNgVDUkaRoCQS6SPgv4r8Vdr2RNMcpBJimpGXXQhdR4ukr\\nintSpDKzgBHazCLwcItJHxN8omVjCXhSxpokWSQ15jLmb85XLazRkeUUrWx7Za1FseArHOOFHie1\\nFDoHvp40r+z1zvd2kEwoutNnI/sNW43ePihyAw0wzq0K3Yx2HIz5pNvg2+OdUnJ4DpLGsyvlWogD\\nBjNWBwk3ajNDVozRZ5qoGZoqJW2IGZNO2d9p6wAKn+2J94mUN2pRxP4FTwwCrBkt0/ei5FTDs28T\\nFWMupffoMTyTU0umz0ktN4ToMsx5C+/5ZYP1NYOwY053Z99CnGl9xL0/cQVLdrJCxjCJUhtDSSKg\\nQpIS4aC1cL9al20h1NjRLyR4TnC/hYj3GyAm5w0JyiSOgBlZC7JX+rq6JX3FIjJnlMHe7my5co6T\\nDhTJHLPzsC1qzfpJLZU9KXvdec0PVv9E5AHpt115IKyoZlcBhx/nzwjxffV1cpuV1SaHn0FLzhJd\\nHpLxSdhuRYJkxZVTAertEck9QMqGmCK+cFuk5MgyVvvgLT8ih+AZJLgUgqH7zpoRZDNRUkrc6oPn\\neTDWeQm6CzfwaSiKrQ6SKCoXASoapUl3mCdc+YXWT4pEGZvKDVLYhkWCOC4kIJN0kq9wnNjFh8BI\\nmpjrvGhIiT6E1xx8nidvX7/RO7RDMI16+5ISr37wl23npvBasGZiDMeZ1OtZFo9mtOyV6UbJTt2U\\n27ZFwQzQViOVN6yfVxv6JKUaFv3l3OvGc0yO0fjpvRDfDKhDHwe5OOfZmKNfG45hE4QfuNwinPgH\\nPn+KhSGElBVut9VDhMJp52cAWnIce++5sC4fPqJkjL4WczoJQ7wg1sMz4JepRQePUihbQqxRkmI6\\nQBzRxDGeyDK2VEAj1HSsFyZ+FX4MWmuUXDCPVOTyGLFG4/YCgyEe8BAPw04FVh/UWmKs5JOkymJR\\nrrHr9MT02JGz7Cx3ensyqpJ1o68D0UTvi4/zxVcSE2N5iG+9HyH+pUrWwoON14r+yiwpRmWE/qDy\\nLYJCiQtm45xnJyM0a+xSKXVjrUlHKSUu7kkDeAPCHJOcr6vffLKlQhK9LiXBu6g5c3v/QiLiy30a\\nJHCJ5uh8ZUzUYuozXZn9REbHMebq2EWa762Ry46WegF0rzyNWYSCFCRlClFqk2qMRW1O1nhhM5FK\\nxT2zVkPEUI1Fpc3FBFK+UdQYY8I8WN7I+UaaxnF2fBkvd8jO6p3j1Ul5p49Bks5WDTPh1z75fH4y\\nj8FaF441Q1Yojweeb4zzP9m3O7IBeYG9eA0LLUIEW5+sywInONv2FoLygnMYrzn5WoJ3UVIm5xLA\\n11qis8IUrMaC7rDoFJSzv7iV7Q+9kX+ShcERNZbArbyFuGcx6loSI7CtBhlazPAVs/F1dpJmzrk4\\n18meStwoRMipohiveTLsZM0d75HiC0Fwob8V2HiAObt1thTItf/8fKI+QMLOOsUDZuqZUgtZMn0a\\nYzlC5pZ2lsKwxvujhoMSY7bObdtJJRaDe45EIAvUQnwcFvTmzROvPpEJYpl1GsYg7YnlwlJFa+We\\n4H8tX/n5xwvSG7dNOI4Ba/Goj4g3r8H0GUfTtRg/Ptg1du9bKSgbNOef7Ve2vaCurPFiT0KqKdq5\\nSmJdc/xlDnMw3ONlW5O+QiM42zM8H/sbqcR1MEtmtiN0EEn4Us7eedtv3MrG6i88OwXndZyMPpke\\n7MO4DgZMpRSDnJBcmKFVRuwao7czyoclyEdaiTYosyAxXaJtXw0T0Fyjy2GGplAVbB40N77cH4gL\\nbWV6X3z2xhgnZom9bByfn/T2ordxcRcnKsLjkcjaKA8lpUrzA7fGFJgzkTLRir4b9aedc1h0Tphw\\nTFCL0WWumVKc+5ZoMzai5gH6fWTlxPny2C4UfsFTBgn+RckZpGJSSHnittCrWCiXG3MMjvYvqDEI\\noCmovdMJM4tF2amosZVoGrZ1wT/Hitp0reEBkABmuMS1Agl6cDNjuz1AnOWTZRkk7LTig+Ev1OQS\\npBal3IP3AOyl0s7FYOF06n5HEqScKGkDMowX5vHzfhwvUoJSCm06wqLuN5I4pSRyjutJzpUxDJEo\\nasUWq3eW1As86+gaHOfBeUzu9YaYkfQ6+lLi39oMroQYadvi+tFDO0kWVKQ5Iww25wI5eErlsVdK\\nDsbg6/wBBJzVtsVeK90Tew5fidiMVmqXIBZ7aCVguF2HWbd40a+SF2sHNhtD4hpnMlCtCJmSKiqF\\nnPYo0hX4eD0jIxcHY9zGtYMmVJVcbpSsfM5+WbLjVCcyA+gC2BpkuVElXIXGirG0JJYfoYeYM+eL\\nJHc0RQtYUiNLNH+t5SDBk2zj7xw9+Iuoco6TkgqZ0K7EoUjiHE9eLfFYN95M8JqR253X60k7e6SE\\nq7DNgdYb1DvWP5F1mZtUMSYfR+MhlVLupLzzlhJPmcjZgynhiszzOv0s0hat6yYa0wwZ0Y2q7fLL\\ngKLk9ODoL9pUiv4LXiUC7R6Ak9E7AmGXJbwLte6s9kKTkixccWMZtj4wzYhmSi4B1JgvRISyFcyN\\nfSssj/vdq/0KEt2VQiTY1uqABa7bnNf5geSMkun9E9HKSs5anZRTZDdE4h6fFF0aHgUq7oU9b/xo\\nT5I1ZhZKDZp1zTVgJ+6M0dAUJSzRKJbIHpHeXFJcFTxxq/kSUkMErNN4v995roqNxV/uXwL4irJp\\n5rMnfn4Gh8JEIlS0oK/Fl/t7HLdT4rUW97LQWthGps2GqSN7xXFMU1xBNKLTMRpUfD5xzZEKpUV/\\nooFI9H7iKxKJCMsGyweGUInrhxOTA/JlVsJ4HhORyuK8ronXRkHoEn02pkdZa9VCm5Ms0a5Vcwm8\\nn2TU43ogCKkUkpa4Js0wDanFhaf89qNZ9DXZ8k5CGX1SE/Rl0AVdHvmMNZnL8bVos8dzI5VlAaO9\\nb3eQRHchm3D0J2d/sizzkLBwH0BeJyltvO8PPpqE+apUznFQ10LqRnfQdiCXbVxFyS6XtbtcLsfQ\\nqVK6B73LJ6/VEcKiX3IiBWg/4vue2ASib+u///OnWBiAMHDYYI2OSoW00JQRnKM9SRdsw1w520Eq\\nkMoW4FdbrCWX2BWGp9MjpHNqR+mxa4mSrtGam0UzFSGAudvVEg013XAb7PuDYYOqgfUyDBcNjUFW\\neNtzQjHWipCN6CRJo9bt4vqFJXpZrOLmUOuOIciKOnifg4OFnsbOja0oA2diZIEsi/tWeex3RBK3\\nDIkbLSXq3MKanTYknXw/JmN84r641YqvhbIjpqw1OM+DXCstDfbHzscvL7I7oz85XouUM7ct032x\\nqTJmY/QRhiBP2JqM/nmd6oycNo6jX3HqK+eQFPcRk4O8BexkNJCEeab3iL5DBJdIG8MvbJoGoUkF\\ncr4z1+sKl0XFe9KwbPsc4R51Czu4DWp9/O6KdFvMFaE0s0WSHJ4MPJgPq8XUZTWivGjD54v+cbJG\\nqP+OU1RjYZ6DJGE/n75wrrbrrBepazDtJIuz5TtjDV7zIPnGY3tEhb0KbXXuWuN52TLb9savrx/o\\nbIju0YatAfFRJzaxcsfGk+fxMylt/O3bjinxPWrnVnfGFGopvN+/sWxg/Yxo/Vy8WiOWmv/+z59o\\nYfC4t5HoNtjLlSv3DaWg3uk2meMJEmUmHnBA1lokmRgJtIAJ5ilaj68WonspHK/vNKsx9yYzupM0\\nqsf6WowZvvQyZqT7ZJKyIhrjMJWErUFKiazhxov7t4Xt2iafI2IMOWf86smoWkns4eGfA+y3BWhD\\nbWBZ6WuBJtoKvqIQOSv3yRLl2V58KRWtTkk7KU9Gb+QkoIWjdfps+IoGpFKEDJzAHIvX6wcpFTwV\\nshRUC1tN6KPy/PgZkw35zRIOzPZiLMi5hNPTrzKTVDETWos+iBUec2YKvSAMPXpBb8K27FcfxH9L\\nOIb/YrlFLN46RYQpkXbNqVzGnsFyePW4t7snYpos3Oo9MH1SyUU4+uKhmUHG6SgBpFlLGCNIy46H\\nqGyD6UbU4TgmhqZFW0YHXuNEJVCBi0vI81jUowvTWLoouVBrxtShROOW5k4unawpRrQ5MdwoWnjc\\n75zzxUOUMZQq8GM0vpSCloIJrGXX3z6McebOGE+w+F0fdafNTimQkqFkWFysjZ2alKWF1zqjnV3t\\nygz9C14lRCSIRanyHAvNQqo7Yk5JlTlPNG1kWTGSuiK/tiLSW1IJWKsIuWxMa9jqHLZz03DajemI\\n7Oxl47meLPersLWB75gM3m53+jkZa8ZcWa/otDhzDGbrSFps+y1IPbMF1i3tTJ+UnPjLbafNUNw1\\nRVFqmycpZ8yughADmyMeajMk7az1PQpu3XGP3gVNCmys/mSlRFtQx4FJFJomFNN84dJi0jJWjEdT\\nyoBQRRlzBYsR4W5x5PfVSatG6c32HiO19hlhqJXC1UlneCdJubIKiTlbAFPccQ8IbS5xZVmE/39Y\\nQ0qmy2BppWic1MaY8SBfUfFEQFSWPVG9BahXooR2WXRjJC1kzQyLRcOBogs8X4zGwZrOlpSjv0il\\noG6IpKibI76HuKIsXJXlExPDTCmMcMCuxrJJZ+J0XN8pklFiCmIXEk8UyJlkk7xl0lbIG2xbxTHu\\ne2GO/TIVPdGtsCVlmvE8GzVljj4ZfaI1cVOl6aLm+Pef/aAvp5QwMY01YowuhXu9sW87NgedFgRr\\nT/hs5HIjq0YSNGdyKRwrFsC2BvlfcVwpwK2+497jiO7C8fzlcnyFiry2iaggujOmYRKOw+VGSk4u\\nO60PzgW5Ct4WZi2I0mWn9diR12q/G2nIiZofqKzYTW0RupTRuyHL8XWQthtFFEpUgoUz7cm1QbJW\\nTBXmEs4erdn3nOKB8BUjwHGCRN5hKzvIDVco+xf6eFJudyTHOFOyBPOAMCqpFIzCj+OF6cRfv/Jt\\n/xtaNuwSA3+zCuet0p4nfRlb2cI/oUrJG4vJskGfSh8H/fXk3x5/4b98/Tf2IjznwfP1ycc/f2XM\\nF9stc3//Qs3OlAlEX0bvR2g1TFwzKUHvkzE6mRxdnEu45Z1kgzWUNgJY47IwmyTdccmgk7Lfg4Eg\\ngqQtduY1SeUOy+n9JOXE/vjCq/2C6h2fNRaRmq/T3UafL3x8BmNhtBileoYlUa1XCnOe8f3mzDp7\\ndC6cn6S8s2Znnic1PZjLOcYr+j/TzmwvcimoO+P1C1/ev3K7VfZ7wqXjWcAn6X7jUYxpnU3faecH\\nrpUqXMnPnaQDyoYLaN742399RBBsCNvUq9tyMkXI24N+PHELsnUbJ3M4JTtszlYq+/1B6wsfB5oq\\nc0STmuoWVC2NRf2PfP4U6UoHpjW6Dcr1f5RzBUkcfV0V6CmOo0Qc1zD0OtKrG70Hl7+ocvQTpFOy\\n4UyeF1E4Z6PJIueAviARIw68d5h4UgbNmUx4DCRHqWnWyAdgIxT/5bETz1Du7WIGtBHE5+mLYVze\\neBg92pVfLf549/2GiyAav+utbIFdyxVbjSLEdcU9ZtxmHO0VQSF9UDVRk1BKjV0SJy0nE0UtJW+M\\nZYQWuKHM35OQNhZm8Gwn5+z0/sHtXvnp9oWUE210Po4PfjxPfnx+crTOHJCkkhyyFKIRNJNdaWMx\\nXYM7YY3pAdoRUboHfKeWOykpLouk0T6exOAqDWqrIapsOa56Jor5uCrWoofRRiN5RUzYS0YkjEFo\\njLTFgrtgM1rChBzEpKs3JKVCToWqO+M8mfNkeqDl5xLWVGxlxghBGk0MX5fAqeGyTfC4v6ElNpd2\\n/mC2g358XCVG7SrR2ZBurK6kBfl61tyNjJAlQlKSwNMg5YWmSU4dvMPqQImU6TW2bOPgbD36QFIl\\n542tVrIK7zW4HHMa53GweieRQ5iV+keRj3+OE0OIUI2cMs0XSSPBOOck6UYqGq64MXGPuvYY9wWj\\nb1m9xCDFyWQhGnryBqKICecMV92X2xdaP4hXyEJAEkUlvAwui5o2fozvlCzse2XOGdFpqahEiMqs\\nI2R8Xc3CKYWFORMjubJFGW//wVyTe6mM2cGcMWMqse2Z+3ZjLgWPduJ5fgQ2XAtmDdxIab8CN5Ak\\nUUkBjGGQPB6y1Y3P4z9Q7WQ58cur7664NTQ/qHYwp3POF6VURIXm8FYze66sWikf3/l+/MpqkyEd\\n+4ysSrkyJL6Mohll0F3pK3atNjqS9jjua7AxbYLmgusMFydCLhqjZYsQlGDgmb3cEBVOm6BC3b9c\\nfSLpv010LEbKioc70CFriu9GYsqga6Gq1HInizBkkTyDysWRuFyxffG4b7TlTDJ2HswZpGgzQ0XD\\nzLYmww72vGN+UJJSb4m6bagmNnWQzGyNKZmcAxSzmPTZ2SSzZifnnaO9WH2iK3H0Rq1OlUrNSse4\\nyY5PBYsr2mKinklaaeuJeIwvb7cHOTXeHw/2mqnqHCfsuVJyxszwOVge8XC0oOlf0BLtOGj08iV1\\nRIw2BlB43zZevWErRER8BZRDhJw2xjgZ8yDlG0vCtDI8UzVfY6mrds4d+omniS8ly8Iw+jq4pZ2E\\nBOLcE32c3GuBbHSbgCEpxUx/BGsQjavHmh1NW8A7WIFOt8nZT0RvUa6rxKI353WtOaPkRRJjxuQh\\nlRRCZQnUPLY4WkBPcDjHCAHtDCus+wxQyexYTpAab293fvnnB6U47gMTvQxfBehMSZw2EBJjCZtk\\nenvxKov/58fir+krzMk4XixrlJY4SLS52HH6RTlas8c8ncx92zmtIShZDFIIqSXvjHaySeJW3+iz\\nMebgXu+Yj4hAOyxfmAcyb/rgnCd7vnEvlWMYZk9MrolSCs+BQ6QIER7bg+dsLJ/UHME2E2X6IOVw\\ncpYc049ljkoOOld947ROLW+odyztoAMXY9oZNm9f4R3RsIvf979Adsotcd/eyZo41pP9wgi6OdFz\\nGY9qLoVOcCw3UZ4D2jnR+Qy/gWiMdQmgz2drfBy/kv1OO1/49sZcndZ+gCtbiTb2L7Wyb3cet8St\\nFF6rQY4AVh+DPk/cfxOSgwsx7Y+9k3+OhcGdORc1Z15roRJHe03KRC/hLqAi08NHLyKsucKIk2rA\\nPqXRLUZPYbFOQJhiskPNb2T3qA4Twy1mxWF+mYgvBrB6j+kGwpZL8BARjldjNTBvFyrLSVJBMybO\\nVjJvj3d6P5gE9kxcmSPGp+LRkVmKIjRsVTxF5iDh1MsMu2zxGge9HehW6RI259Emsjszh7++lg1L\\nET6LHfvG+8cPZrlj5jwP55gG1mOqkis+DjxlxCbTEkc/qCf8re4ctxuv13+CKEX2GB0aAWj5sbjv\\nBdVJTUJSglKMUVXC+GMTW4qPjkmE22aLWvegA2jsygk0p4jNp51aN5opMNlyHNE/20ESR/OOpA18\\nxknDoY2DlJz7dg8oaxssSxxXfiHXeKzX1eEohDZgHiPGZSdTClWEtY4LwZfw1MllYxNHxFlTA3Hn\\nk1KdcivcHpXbFovfXJFVaCM6S3IOwTPVQpE41TAbcy5erfLzP/4elQcIKZ/RiCYvSvkJTxu1ZDRF\\nCGq/feFcHU9XXCBnSt7ZauZtf/DTt2/cpNNTplhi6ifLg5saTI9E752a4zvQP9hq+6dYGOIjzKsF\\n2oiXNqlh9oqgVA9F2fxalVGEO1kXczxxcVRTdABI3K/dOzltmCymL2x26jVB+I2CFAHHQWsnOdeo\\nlPOCXCYavebXbTbmNOa0qCWTYAAqxp5uDGsUz2SPmX+zic1OMmEvNyZGqTeyCl4iPWjeyZ7IFzNZ\\nSCBOVsF8Y8vKMuc1J+KTcza2dKdlY/ZBSY6K08crkqPu/PT2VxrC5+sHnC9KSrQJ9FeEnlKMEEnp\\nqpRbHJb5dSrl+y/05eiKhmqbF0uhTZYn7rcd0c623bDZ0bUQBqUU1OE8J/jg/fGFQaL1f9KHMtZ3\\ncr1BqnifrCzc379ycLDWuBaF/PsLOkN1wn2R2XAz5loR0cYotdDsRbIfPEcwHsyFx8Uc6KtTU2ba\\nGbzH62oQvEkn5yi0cReKFIZMJh1JMeJty2hrIRRueeNYB/teSGWR8+SYi2QGVCQVxuhsOUp13GG/\\nwLspGffbg8NPWh/0WcgunNIpmrDx5H17YFPobTCX4VKD0qQeJiVxJG9seY9Qn2aQgazOKZ3j+ckE\\nbHYkb9ho+AoT3XG+uG13FoOa/pj4+KdYGNwdm/Fi+zLsEmeYM3SGVFC9xUx3draSwR3Gkx75Z4Yt\\nCju3utGtRS25G2M0yAXNoLWw1C6Q6LW8uEQuI0fEGl9YykiCWwnWoU1jHIPZF54KniJVmT2BG201\\nvr7dqVVAGj/d3zin8OvriOht3QPXTmC+XYScC7dSqXljKxmTwZpGvYJhqik6Hc0pthhr8X7bEDF2\\nXXy935nSSZ4ZbXK8Gpo2Hl/uPFKh+yffqHw8Z7gjs7DnhM/gI2QRXJzb/o4k4T+P79z05GO+4iRy\\neRRUFSeY1tgga3QX7HpnSQddzPFCKHzZH7TaaHbga0U+RCsLj5f8t3o6q4xXtGz3suMaXpS1jjhW\\n10otyuhBZN5yVBCaGsMym2ZGn7w8fCSiFfpJ1sykIn0EKckd1Y2kGy6LkpQxOrYWhRxX0WWxqeQY\\nI29lx0SRI+ruPCXum/L+7a/RlO2Ft6y8zg+WzujEnANXYbpF5TwZTJhzXdeu0K7u90DEncuotx2S\\n46nQutPXQkR57HdIiprzdYcfNtn3L4hXsmbuOaCwP84PFoPn60WqN8hEb2XMoS6KuiMragv6HyO7\\n/TkWhvhF7LoHRtqwaLAFIvFmTE5UC7mU6KmcC3VHdNGmM8zweYJuaFamDXKu1JxIIiHCKeSccDkR\\nKuP8wKSy/cZmXCPgH0kZa7AUatoZ3vBlJPIVCpI4tqmAX3VjojiLPReWaFiFNUZp5VrIxAal3AGj\\nMEhs+JhYiiai1k9IGiAZM8iKTiNdRNFC+AFqecM1k33SpzPGlYugQVt0d3SF7dv8YKuJlcBFeatv\\nJCbniKavNhfZJonOkS6Mmk+y7pgEklzTfvEUuaZC0OaibIW97JzPf5DSHhMQTdzLFh0aIogoNidi\\nK4636ySlwp5ztGhtYX2XVDDb8VRxb6HPlEyVinmn1IpcOo2Tgigu6UqkHmQL0bOND+Za7HkjE3AX\\nRH6P4LtLCLo5UowuM66u5hzzRUlvFwnMqeVG2nZSdSTb5cp1nmcY34rA2YPRidjvFYhrBULfzKKo\\niMxWd7b1wViZhzt13yKanSp9EVcXU0QmWy50e7L0ziYvXhagl6SCSJT4hG5VICunTwqnPE9XAAAg\\nAElEQVTOsivHIsLEUHFah+N8cvb/n7or/8d+JP5gIsi1Q7U1w968QJPB9eItV57zRC0apCFCUInf\\nUnbBS3CbmAr1dme5YQOSB1k3647aoq/FsoNc36MmDg1ct8BetqtUVICNJJXuDdeMasGDNovaIkv5\\n3ViyXGCMKMsxAgm3FkawHdocTGs8LqNKySEMDVNy2bntD5ZPxroqxurGOk9cCrns1FrD2qsR+V62\\nIu3nC++DqQuVjVu6Qerh7EwW049JGI1WCpxaecOWBcRWFJkTW3C73xjHxxXdzjA6aCj2fnU21BLo\\n+TFPmgsbwaQYfvU/eIpjsHJ1ZQYFGw0UnNkLzxLIM4tekTUGVQrd4eP1wWO74XSmL3aHNoN32Psz\\nXnR3FrAkgmW2Frl8wdfz0o40JlyR0roIBrA0syROYWhE0O9bCvFyGkUIkCuBs5dlZHlnKBSdHDYp\\nIny2QdWdj/HEpHCvYdFfnvB5suwq7LGF9V/Z9zupxyJp1tFUmB79q1kr8/xkzMbcG4tE6/9gu91Q\\n3akpymvJzslEx8AtMYOHDRou2yupElfceuf7zz8zXXD5FyycUb1i1PPK/a+gLPuaEYAyR0uKxZ9F\\nIu51t7xH+cjVcJQ9/qABay2MtejjYNvfGd5gBgMPgYTwqF9o4wmp4Bx0g2SJnKBc3MbWDsxmlNvU\\nGq1ImnFWHG+XM+akrAVW2dM7axm9fQCVvbxdHglYElVtKScoBQcGgve4C38plZI3pL9YFNKasdDI\\nhgcriaWV7oNhBwln2xJzZR63G5/9yb3EAjDGICmojAhB+SVtKrxtd3Lu5HTjx48PcspozrxeL/pS\\n/qevf+VMic/Xd1Z7MVHe72+stdjqDdfLWrwaaw7utxtmQl896McWFGbJsYiEqSmsz6vPCCMNUNnQ\\neUT/RMp44newLOK01iELe90wvcN4hpdgEsEmMVwzeMc0cRxhlx/jRNhQVmRpfIFP3GNqkXKhlEqn\\nY2uSJVHLTrHGL88XawpHH6S02Goi6c5a0fbdPYA7RwOWs9SouXLborCo94nrxOcCJriy/l/q3p7X\\nti1bz3pa6x9jjDnX2vucqvItrjEWBCaAnBiJgJAMmcgBkhN+gE1EZMk/gMgBAhKQM0jBEiIBEYMl\\nhCXL0kX3ukyd/bHWnGOM/tEaQRv73BLIVJ0LQlVL2jprr7322vPMOUcfvbf3fZ+XzPRyvbdSZDv8\\nINV4vT99/oSOg+M8WfKCzx20Bno/hfwuGknjIcZUvXwgkQ6YNln0Rp97mP4MZh+IFFwzJS1o/wPc\\nMbgHKlwlqt8hkUw4TX80+YhNXCDpEszF6XiywMqTcR9gAd4UMUoSlISYRrNyrrTxmaGZkgQjkouS\\nokcwpUpVAeaf++RHBH9cgSQ4nWHOkjPTL1yYK0tK2Bi0c/LUgz46g0xJBZUZ8NKwFkVxq0aZq1wS\\nFySSJlQS2MlBHHleX75jPybvLSS8bV3xb+7F80GpNZQa6dQCdaZLHp2kZcFskEuErM62Uyg4iqUB\\nYqRsLAXO82A/I/Ox1Y2pDdF+yXUdceVt/4FtT7ysC0veQGIRmFJJVakOj2OPtKrlmEsswbPMWi7g\\nyxpFKZK4uuCATJ9nELTGyVJvzLTihF+lSES25wSn4ibR73i9Z8RDGTpnQ3LFbYaZKhewkFX9KoF1\\n6xGwIuAzOS10KksSRjtpPi9gbQv5WVvMKebJHC+IC82iC2LvgyKF9+MriUnpGZUcOxWXK5mrsfPo\\ng711dCi3mjAJXmfGmNZRgz5OsmaMk2MWtppQF8bsLBrvHU1xHB6zIeNkyoqmzOgH3jtIYR9HsDpd\\nmb1hUxhtp+Y/QFXCgTk7Y85YMR2OsbOUAIVvWSnLHXGn9c7oJ5lBGNyUWj/wUhPnaBz94F6XwIOp\\n4wNkeHQmWqGkhTGcrVYGC+ot2IJiaFFsXtXjTtiuIx18ZSOM7gfncbKpxuAphUMSE/rjjeNo3G83\\nVBN9dsZ4BmUoxV00pUQiXUk9yAh9RjpxPxuid1a9MXAsnWyLY4+Dcxo3yyxlvabrmZSXaAT3jnuQ\\ntnOqnH7iYuRy46/84o/5/Oj8ya/+LMpvmIg4L9tKStB1BoD0GMx0+TjmJSfiV72c4N14f7xH01NK\\nfPdhY0mVr+cXxsy89/My3RqShZJWhjk+GlevO0gMEaN0Jy6cl/WVl9sr3Qcf8sJ7O0gmDBfmHHw+\\nD2rdmBysOe62c3TUnX1/D0u8arRqDef5eOPl/l3sXFAkKXvvpJQZEtkRm53nubPmDU3G12Nn0xU7\\nhX4QZTZuAfEpK6aFt/dPGI4sAkmxLMwOapk5DEseXSaXwxQpJKkcLSzga1KME9eoBtzygk2nJuVe\\nb5wWnZzuGXXl8fga3on6Ega4GZi9pIqhOAt059kOFpRnO8h1Y5U7X+fJnIPjaHz69I6Y0NPzJ12T\\nvxcLg3BVlotwtB31sIvOmSKFJxkZ44JmR3RWZb3epIL7zpfTWEq5qENyXQSROByWWMUvlmTBvGEM\\nEMeIlp8lbSQ6zy5XDdq4bL35skwnPAnSd+6lwkzB1MuFpEobV/GKx+yjt3eW9RWoJC0Rxy5RFpJT\\nJaX0Y02bqFwE5pPn82s8pvudTYXdlEVviCbME90EGw4ai9zp30pgYFlW5oCiYU/OZSG1Tl8yWS3y\\nGHZtKUWR2UhYDDP3T/hwiqxIM445w2fv6ZKIC2cblD758v4p0G+3hfvywmMK4/iE5heW8kpS4Xl0\\n+pjYOElcfpKklFLo01CBWitONEGnnDlHZ5owzNC0hBQ7nzgLCTjbiMp7iRuCpsuG3fagdU9jK/ew\\nG/doH/smy3JFo+WbijAb8+r5WE0YQxhnC5syIRMWzTHsk0GtSxwjND5/7k+aC6s4KVVsTCYS9v28\\n8Nx3JA/UB5jgVthulduaiVesgHUYnfOMdnCxSHyOMQOELE6zyEdIWiAN1rTg5YLpq4KfTIR+Hty0\\nUNKkalj7U105lyePMdDxh1g4A/iM0dDwBu7UFMahoIIqvU86A9WoY2v9YKl3aimUXIEHNYNr8A9t\\n7JxzkjWjKKqV3t4xN/p40H1hXbaAwurE5nlBX08GF+8wFXx0em+IVh79QCQzPTPGyVZujOloVlTD\\n8VNKsCNnLpdc1fmQCyKFNhovy0rvO5MFzQUYYa+eQpYr+Ygw3HkOw9uglFswFyz6MQMbV7A5UVPU\\nIomHhzFHk8SRwhtdnaUauQjiQR0eHrMWE0HqQrLBy/0Dz26oV7rHkWdZJ4c9YMSZdsyEtcFDjCV/\\nBbsFA1ITSW+4G9NOzilx8ecthsAexiEn8Hi4MYmSFZfYlWzrK+6ZKsrpgywa+YU5OdrBbblTVK4+\\nSWPM2DJnKZcL9bIzSxQPiRMeFsI4N2xSJMMctNF/bDJzrszDfkSF3gU/UYnouOAUcZINXtcXDq+0\\nM8qFcpo8joPUD0Z+oXpmmFDJjHYiZtxvL7Tz5Nnf+VC+oxMmt/P5Tm8n70NIXugWSsi0Ri4RmqOf\\ntPYAM+7LgnqOxconoPTe6a6gmVISxzw5L+u81oXkg5fXF2rrnN3gh8fvfD3+XiwMboYw6T0quWwQ\\nnlLsAnqky/c/8Au0kUXRFKSlOeePBGGziac1pvji0VY0J5IaSFygOd8pJTzlIjmGinNAPyPdN52a\\nw4R0tkHOK5YK9Af37WesdePt/QeaQ/aGUhFNuA2aR44i50TvjVoqfXbUFXCe1skl7sLZB2t9uXIQ\\nTp9OSZlhnfn8xIftFcqdsX/FqLHwJUF8pywV79E70HAWLVGIO50+By7BJUy6cPpOrS8BZrVJt6BO\\nrcsWNfAZNl3Q1Hm2I56jNFjWG6NHUtB8BqZ8Nlbu+IDeL9d5HiwpquhMnKyFmSZjvmNMSiqsy8rZ\\nHjgSjEPJ5MurkSQzZywqsMT8YNqFbKsUiV5JJwxwSRX14CC4CFkdTYV5Ra3vdbvIXOEtEHdWUWaq\\nV9/IYDAZczK94z24D202TJVQupzkHkpWB08LUBEadNCUyWKMlBktYyOs3TklzuOk6kZJHr2lKVPW\\nxHL/yCKdx3EyzkbWG3DwpR2XKheQ48kAS6RZr9lZtHgngt+RXBk4vR/4nHjeON0pMtmWQNBjUaRc\\nl9gx3dY78Kvf+Zr8vVgYjChkSZIZLYZ+mhe01MgFGBGLNYDBOXpUo5vT2kl0CMSxI/Sxa4BJUKWX\\nlEIqbF8RD6R65AcSSQaPcw9jk1lcwGbsYwA5JMH5jSt5w8ak+/5jQSspch41p0s7tkDN42QFzXGh\\nJosOxP04Wa8G7iIrMkP6+qaMug0KQU9+PHfUTuYw7lU57cCHUGolw6WlewSRrNEbuCRGD7n2vZ3U\\nnPBRUM309h6EaingyrG3oBi5k9OkFJBh+GyIlijRFafWhWGOaqfPSyKewmNvTDKjPVhKopQNKDHP\\nEYEUBcBmO+fptNFRcmRNCB+FiIM4z/2JOFSNwhXJGSxs0CUtDLeQ+PSyGrtfCLR57S46YsbRT7a6\\nMFpwIrhMYs0mZ/8SisRyAz8wrnKi0bF5Xqtc6P9CvCDRiTuY9mCYU5f7JZ8bPRXaYUwUJXFa4AL3\\ndoB3it5oY5BzIi0vHO1JlyAvpXqnpBtOweUrRkBr8JPTYM137DiibdsbCyvdG+dMfNhesBmZoaKg\\no129J4a5UUTpBCQ2L3D6Cf2nzRh+a+xaRP4TEfmViPzPv/G1n4nIfyMi/9v13+9/48/+QxH5RyLy\\nv4rIv/27PAhBOFuj9Y6KUMoWQz8fiBok4WhhXJnzcnYRttbWBuaC6IJx0X+us20fDYgWo37skVuY\\nUS1uHnVxqpnRncf+vPwEIVWVnGnjyT5nHCsE7uuGuPE49h+nxBBpQU0BxwjnadTnBQG6843DeYxA\\nbLXzxEZHZkSlt/WFWy2h7zv4dHRGJ4L54JYzLpMlKx+3hZdcae09JunTSG6IbMzu0bcgEtASEbI4\\nuWycx4PzfDLO+Nk6Mz4H54yLLSVnWVdua+ZsB8fzHbVBqZFuVU6URBXnbJ2v+ztnD4oT5qEaXHOi\\nyECD2ZOUFCWjAkut6OXM/abCiGTabBGTNhjjQU2gPhCL58IsFr+kCSVHkMsCppoI09Lsgc56va3c\\nt4WXlw9xA/Yew20MLjxcKFeh9Y150s0Y168Id32beSWEKBp+Hjtfvvyax/sXjueOG7zmjAJVE1oS\\nORlkodbEst5Iy4aoYQrLWjnnQZthEdO8cM4n53TqslGXyrK8gmwUCufzLfouUo2kbq2YhJHv7PG+\\nuNfKS71TiJ1KScKWK8vywqIFQZlYvP9S/V0uxR8/fpcdw38K/MfAf/4bX/vbwD9w978rIn/7+v3f\\nEpF/DfjrwL8O/GXgvxWRf9Xd/x8nHyJ6na8sGHrJ44wsjiQ4969UWUEmUWroiExspugDmIV2fqGs\\nG70/oh1KwuFmeNR0CbiGFJUtQQudd4z3K98ePYvH2JFSOb68IV7g3PFaEaJU1Wc44ESdnDUGRuPE\\nU6Lbk7ze8HMy9pM1veAOJum628XjWspGrjfaeCJk8lBUJrf7wn6emNx4vv0KXHjZFvLqfGsxz0nZ\\nlopIcAbAIb/w6fOf8rZ3vv/Z96GS1DvGSX39JcfbD2y3LRqih/w5BFcSuVRSmpQIYvLz8pEvv/5M\\nLSt1u+HZ+Vi+p3jhPL8y0uDt8WvMFjTBmB/IupK10M6GVmK4qs6WatjITfBScAnJWTIkI87KAtmC\\nToUMsgQJ++yNlOI1H/09jmoIHrw7nnOQWmfOiU1DpDL2B6/f/QK3namQasaHM7rh04P7SGRgilb2\\ntzes+1XQk8ImfsXB3TwG33qd/T2UoP25o6mgEvbwn3/3XfAPLCzguRSagTXnebxTcqImxcdgHM6Y\\ne9T2+VvY8oeRBmheGcfB/v5kupOTMPKTiZKK08fOMSbuEkAcTSxZ0VxQNWpO4Df2Zkz/AqJYimLf\\nmTLW3n+3FeH6+K0Lg7v/9yLyL/9fvvzvAP/m9fl/Bvx3wN+6vv5fuvsJ/GMR+UfAvwH8D7/l3yBp\\nhF0mgSybdkZikgwEXJSAEdFnZ8kRv03fau9lw0kMJGy7zuUaD0dlLBTzwrCFC85n2IzdJs7APVHq\\nK/TGZKX3g6IXAOO6Q0VhTJCVa4m7oGPYaBHjbgelLDCC8zckcbYd7AhcnQiqQsZJqYRpZZ6kq+tC\\nHAqDZfmAzDgH1yUi5GGLhZorYw6mONKNt/0TX9/faaZ8enuj9RPBGD45no0+jKXemf1zdCh6Y84H\\npyfuSVGFNg5kdjytfHj9QBsPJsZS7ohOvkuZnit/uj/ZtpXWYpF97u+sSyxWUzPmMaj02ai6INIY\\nFl4IM72S8x2VwTmNJS+BSMch1ShrMY9dY66IN85xUJihTokyLBqqITFtx3rsCm650NsTWxTNNdia\\n0+geNYV7i3LhNua1CCRaP6NizoxFAuduHgDePg96nyRVSroBQckqmjFzhgnJOsPPgLAoIB2VKF3e\\nyhIzCxf6szGun4XG7vDsRrawl7tPrEfGI2mmu2PnM94jSXjuYfHHhd0FlZOxVNI80JppZ6decfLu\\nGfNOzlDGgvbJ+P8JH/9Ld//T6/M/A355ff4vAv/jb3zfn1xf+y0fxhyN6RKr67V7UDHuy8Zu7fKp\\nx50/a2zhzeGck0Ik0Gx2qi4XWfhbE5MFl99Desy1gDRqScxhjDBCkvMKOukespPkTLlafSDaj0kg\\napzurDlYfoGJGxwjBo5qHgaZ9caczuwnPgWRNTol5s6cJ2eDj68fqMmYFpCR21pJJEbv3HLmOUfA\\nYFkokskpkSW2us+2s2R4fw5++PyJOYWRhOf7l6tCb6BeGMcPpLwhyRA/SPkjuDD9RlXl2RprgSUp\\nklfWcmN8f+P9658xRDifv+a2/CXYPiLz5LV1vp4PSkrYFPb3A7eFpBPJTtIwiKlWVOKOpZqjRj7F\\nmzOnAp5ZNHZ1juAysDlY6h2u3Ip7vwaQRNZFNGoLccboaMokE7pVzI2RnRHQ/JAmfVLqwvH8guQb\\nm6Zroh9HFARqKagUygi0nNm8lBtDULZSceTqmzhx7+ymrFKR1thqZd02csnR8mXRRn0yeewNcWfR\\nOyCRaVGHPEgaNXXeFetGO79EUCpVmjfU+wVXcbCFZMGziMq6RElEJsiNNRfMnROhtxNNUFK5hrUD\\nw1jy60+6wP9fDx/d3UXkp7GpARH5m8DfBMKwY0bRkGDiWnSGDd6f78wRgxaRREqV6YH8BmdJOfzu\\ndrnWPN4wSJxNywVFnRB3MRJo4tkPVokWINcULUhOxLndGDYZdiJaMbGrgn2Sl4XNO+IRDrZx/vmg\\nUQEEm3HXtBHFOVULe3+jWKaWO1knk4GPB91TIN1SApw1C10ygpLzjechnM1ICzQzJCmLxP/T+/Fk\\nP3ZUciT15jeMnZFmJBO35UauhZoN5IUlJc4RMeelrjyOtzh7y8DHTk+Zhckp387tC9Ybzy//jPc5\\ncITzjHr66YJ6oijsabLdElBiIDxPyvpCyYVujfOqta+lBtDFIgY9zOgzHIp2+UNdPc7VGm1cLVRW\\n1lyxVBjjEXODERff2/7GWl+p1fBr/lAFugqqRk5hi9YUxTI5Z7rFxekqJCbNJiKRtIwi4wihCUEQ\\nhxacUInjqWtD6w2WwpLi59ScONqgykR0Ycxgh/ZpnG1gU66dCnhWluXOc3yN44suMCfOyZozuS7M\\nS806xwSCIC5Xw1TXGJankpl9Z6lLFNvmySoLRjyvItE10fffXaqEv/jC8E9F5I/d/U9F5I/5cx3k\\nfwf+pd/4vr9yfe3/9uHufw/4ewClFA9EVywAcw5yWljKDRWjJ4kdAFEpN2ccDYIZHHg38x7W2PPk\\n9cMHWj/BCLMSIFl/ZA2KxRHCPHHOFp57n2QJv3vvI7wAecE1kbzhSgR1zGg22bJSs0amw4Lv4D4R\\ncky15frzqzlpS6+81BXPmbF/ZamJ53GS0+R2+54lF9QGuS6UHM6/dYlKtc/7Z55pUtwo5ZU5O/fb\\nd6zJaXtjyoF7VMDFsSbi3ktZcXVSFpblhVo+0vpBpZBzJ6eFNhuaTmregpuJ4jZ4uf2MvH/maPB+\\n7PQ0ecw3lmXFPYZxIolcXnjvndeh1OnQjJo88gVGWMApSM5MO0kS8/64g3acGG4sS72+PwNGKivw\\nIGdhtOvnzYEzcXNUhT4ybR6MPmklGrW5LmdJC8yTx/kgpRuz71erdyVdLVRSwoZv46CWiEmnFAYy\\nwckpM72jKXwZ0zx8NLkEyLXe2NaVPg5EEsc44zgrwrDgOyylkIjjozi03lhSJQF9HKgYqk62wAzm\\nUq+LP1CDwyYQUmiSGJCevVNUUDGqG7NkZq4BUr4CiWPYtdDOGPLq9pMu8L8oDPa/Bv7G9fnfAP6r\\n3/j6XxeRRUT+FeCvAf/Tb/1p38YAKCWvqK6UqzA1hj5hXFMJmbFqja0lfklLcWHmoiy10s4zSLkk\\nzDwgHZJxO+PFu/IJiETFV5bLJAVzDtTjzZsUqsJSKsokOSTNbDWBGs1DD08p/wb4JYxGc44AiXpo\\n5d0ODhuYK+PsMGDvjXOG4Wja4Nlb/L3RGO2IUhRxtChjNNaUEXOU2D11SUwsFi2NQhOMqDpLmWUp\\nUasnis6MzogniHWSDY7jBzJRFnuOE7fBOU+aDVIWtMbA0l35ejxoJjzaGSAdgnx1XCGzPidnH4wW\\nkqaJ0C2kXNEwryXNFAn7OzRUJWZKuTDGuEDvBjhZBlmEkgjGYfZQOCR8BqfD3k+QzClh0yYFNNaJ\\nqHcoGoU29utxVDIJ98n0GYyOPOjSqcVIOXgGQ4VUFySDlETZ1pjuS0JcKUS0+tgfjH7Q9ifWGmNo\\nvDZkkHLZ4I0uE7JSayLnK+SnKXa9OaFZL2OaQgkr+jkHWhY0KWXZKLmSc9jJxZwxGnOAeUGp8RqX\\nO66ZczTaGJznoHXn7DtjHj/pAv+tOwYR+S+IQeMvRORPgP8I+LvA3xeRfx/4J8C/C+Du/4uI/H3g\\nHwID+A9+myLx44eCpESuiaQWAzSPmO6wIAl9S8m1abgYRa4n1zvDJzk6rxEKtaxc82UcvyyysCwL\\nPkPXXtZ0bRntuiMpR2/INNYsUY3nTknK2/6I4NaldS810XxSNApufcTRggs754QjsuaEe+wgxtk4\\n9oPXWhkIWRfutzslVR7twKfQ+oP7srBcZ8QP9498/WJsOcOQkM/S4Hl8Jefo2Hy0g/f9iXvASG/r\\nSu8N9xxFLufgKQ/cA0xzPp+MftJtsm2v9BkA0+ELszdGd76Mk/OMDsZKOAiXvFyzk4SPNx7PB9M6\\nz2SQLaLWGWxWlExZgn+o38CtksksSC4/JtIXgoCdZyFTyVLZRxCP1lrJ5ZVtW/j1519z9BNJV5Ap\\nCaUKKSt/qbxSSo4G6FRjRjBDSaia6eogRrnEzjbibPK6bTA/spWTx/OJJqNmJ/lgrYWlZs72vND/\\nsWBNM7wbt7Lxun6gj5NC2LWXdcVdEBfu95VjnxQdPM+Ja6dZCxHJEzIFiwl5DDe3NW6COpkUvv/4\\nwmgn8IJQIpw1YN/febyfDGt8ePlIXRduW8Z18GErHINgh6hSpl1DUsV/Ggv2d1Il/r1/zh/9W/+c\\n7/87wN/5aQ8DkBztRciPaoJqxlVJzCvXrpHDl/A+KIXWD0quFD2Zw8MFWRPDvp3BCjkFzCRJjui2\\npktnD9iHahwJcMCNNgfVCn3G9z7P88ceQBuXuQVFkzOsoaosufA8G3M6NZcI01i0MC9JGWRme5BQ\\nTmIRK7kilmjdsGmU5QXxCPwYGfrkzIKNzlBAlNF6AFS/MR1kQV3IOfOcob33fhKoNEM0zvVttpi+\\nt8FxPGMuYc7RA1tOamheaf6kO5e0Z7g1TonuzZxzoOncKAprLjx69HSMttOpFJY4u9cohUnuDAKk\\nO6axk0J21rhLul+uPrnawfsedz8RWt/J2wu5vpKWg2N/Mo+GLh94WQTTQm+B65Or2g5ituToVd4T\\n2YqsnSaJ86qov9eVimN55dgdt1Bhtm0DgvaUa2atcJwD60If3zoslRcpuDnj6BGUUke9UUpGa8G9\\n87LcaRjrbEx1LE/G6JgZ5/EklYWtLqDKmDPqC7SQNGFkctWIubvjlrD2RIegS+KuN24f7ixr4VYL\\nb493zjNMUpYMfDCdyx9kJPlpU4PfC+cjThCCpEQc+Rt4xUcgwVMifEZx7jIPS6pq5ra9RqmLh1Eh\\n53A7AuS6YAKNmCHUFLRhd2HNFVImyllbOB+dH7sNwrQzQWOLLCQ0Z0Y/KZI4z5O61JDFgHbRi5ZU\\nImXnEfnq7cC/PSZVzDpbeeFWolhlP09a3yFlvo/3JMdoyGgx42iDsz0ZnnmpKzVlJvA8Hpyj0c9G\\nl6iJt/5gjM5Sb4Gl06Bqy2yIVEyIgFJeGGa0KbA/WFZnqvC6JA5LFIQ04PS4M9tVUKvT2LujSBxR\\n0mSjROuVDXqbTBlorczpvD8/o6UEDNZDSRozqu6ThkytXP0ZOHPuDI8G7awbaOF4fKb1jtlkLRu7\\nFe5S6eMIlHsmTFdw9V6OSB9ezeJOYjJ5jFCv3GMHiSiPOcB2lqR8tcmwgNzXWllqupYZpXpB05Oi\\nhZQqd0uoNXqHac5+drb1zrDELWWGNSrRqm1+Eg7KgeR0NXhFjeA5RjyXRL3Bst7QuvGyJjqJ0T+h\\nYmx5YaixFmVWMM9Mb2hxak10jb7Lx/PBbV3JyTnHSZGCSA0sHz9NH/j9WBgQ8Ip6wvq4eHl2Idm+\\nyUgwpoY5RC28B7nEnXheRaYOJgXRKJghL+BPtrKwj1itc8moKm02cjJUJuICFIoKjfBJMI1aIgSE\\nxbkozUEqsbtY0koJtB/dBrXUeONo3B1VoshVSHSbICGdpRkGIBUPLiXRpmwkIDMsnJnNJrZPnvsP\\n9O5sW6a7cZw7iyXWuuAunCZMqZztc1i3bUS2YF7NzCldZ2sN2VB2TiZznkx1enGsKSMAACAASURB\\nVHOK3VgkcYwZ0JYSOzGbURGHgHnMR6qEb6MWjUHt+eRWb7EAzsa0cJFWn3Rt3NPKGI0k0TTdRpjW\\nzEf0ezJxjz7OIUpBmMMRoo3pbJN5PjnnSVblvq4IwlYkMihE/mWlkbRcu7/GtIGnikjMn17qK32M\\niEzPhotSivL++MpsC0tZwDoqdh1RYHin6MpUY9lWzJTjcfDs0Xxlz3eeo1NKxSWGrpqFVGrsHHyl\\nVAXv3G7O1/eveCqoT8qy4u2I/E6e3HRFloKWxnm1jqoGpwMtSB/R3l4XJFemH6zrHcmCzZO3OfB2\\n0jQhGRa5jm92hmnQ/wBhsBD+8VU0mIoWMWezEwOSOUhmy5l2tQGjQRByYmsmKaym5RsYNinMg2mT\\nnSPOtONElCsJGerBhIjunnsg41WptZAu63W3WGmzJLqNcDuqgEIDxAciiRDKwvNgHtHndj0eR8ip\\nkETY1g+0/gStLEZc7NPYgM+ffsXob9euZWHMzNkja/H1eXBbLkz+SLh1+uz0Pqk1MWcMxmq5I3SG\\nK0WVnJeLp+m4Eu1XPpliIYmdJ8d5UJYPERbSzrG/058PnBzj6em4KO/HE9Ua85kMkibrUlF32jRW\\nrSDCMTreE7t1zN/RlOL7NUxnVSTu6nOEacxCORmSab3FgE+Uo8Nx7ExTvrR3aq28LBsvZWP6t+xK\\n4/34TK0r9xQAHCekY/fwQsCF43MlaTgh937wnAE0SdoQmeSRg96VwaT/2O+xaKWmxHvbUTGSwDEe\\n4cVYbmhVchacxj6N1+UFptLsZIwzgmVZKanivXOcD845qGUNiZTBUhXPjmRhH28sKeFcMzQL+PA8\\nG713StHIEumEaUyMPBvHPKBXukksGDJYS0G1co4/QIITOLWGSSUnp1v8Ur5NoUE95JelVMqSI56K\\nBvdRor69zxGVY1eUN8pGlDUr+xx4SqhyTcmFMUasxppBZ4A4CMBLG5PqgAvisVUvAqgzTUIFSTky\\nFxJOOEkBSlU1pkyMFLbX69+rLuS80u0g1QWSUlGOc7KPwYt2xApqQYqa3hljcrYoPnGbbOuKaGHM\\nM9yWUzjOk0QAHetyQ2yQNYfTs1SY3zorIl4+VXlZb3x8eeUf/h//hG0KR7vF3KAk3s6O+RI7q/MI\\nGdEmqpnTLZ4rXQDjZd2Yo7HkzjSFpnQ6hkHOHGen1pD+8IgbDRdWLYj1qzwXHudXkDB4oZd06fFz\\nvhzv9P6krr9gYKCVD3mwT+XBjFav7R7zJokauumGqGAONuFoHdFEzvWiOQ2e55MighaJ+HY2lrWQ\\nc2LLC+fp9N6hRK6jiDGk4NrxOSDD4comCdKG5jCKjRnxcjOjjegkaTbpLeC9IhW93LJNjNuikGr4\\nOa6AHR5KmplhmQsdkC4jnlESMb3NsHnMrV7XV7IGH8Ixkk+++/ALFDjmH+LCIIIlQzUkIpvPqFd3\\noncvCo5YstCncj4bLp2ZBdDQpRPRHWFxNLDRmbNR08YYPXYYGmqGSsLHYJri/oSrAVlTJPYSSnfD\\nelTN13oHDFIKKlTKOB1SYRwPBKNmAfPokkwr4pPRToyEpC1yFGVj7F+jK/GU8DTMI7a45Cs9WkC2\\n4AKOPQAxDFwWjuYc/aSUne/ur4EyG0/UjY/bC7WWAJboC21ezUT7k5IzaV0Qi4HfWivbWkkYH7eN\\nIplVY+Ano5HKynP/guo9KgJxUs30K9QmAo/jE1vZ8Fz5xYeVbf2rHM83Pn39NY9RGXayPx8MU17q\\nRiuD2+3Gzo6jvKyTl22DMTCgVrAeJOkkDdV2WcCNWxW63mB0pjlv8yvf/fIX4XcpN8a5orMHeyNF\\nklQ8hnnTJqSKatjVhwWsN6Nsul5T/RdKLSHpaVjyNd0peZBS1O29bLd4/PuDX5S/jCrMfvDD2w/0\\n/clb2/nw4SO1FkyF9v6k9cZ57OS8YNOwc+ATSgrs37JGdcCzvzNtZwCzdXKqFAlmZqqFbseFFzDa\\ngHuKOLqPI6TfVPju4x8x+lvc+ICUXhGPtrWBksof4PDR8asafufsjmqmpBmEpMtT4AZnP+hjUiWz\\n22QpCxlHvGMm6Dev+5Q4sacY9vXhrMuNOR8EiVqYFgh41dBxxmwkTVRd8NZD71alrhslR1R7apTB\\nJLOIbY9wS1aRCFNdaUslsdSVL88IzFh7AsZucRGnktCU6ONb/iPOt4/WqHmh1lD0hzvORJJErbYm\\naskxQJSrjWq5KEQXVt+ykDxh3bEZz+6YJ94j3o4IWy7gwtvRWZcVZVCWgqvQrNH7yXTDbcRAT5WU\\nlN5G9FaWBbPObaksGZzJwkm5L2T5Bfn54Ms+SF5gekB4VZktOhW2rOx0BKgpoqvTMo6S1Wj9SZP4\\nfcKDwCR27SKMAvTZIhVHLPpzXkNjTQxrzNGwlHBJnDYiCu8ZnYbNGFTWUkmpXjsLIyUNLockbM5w\\nuqbwcQjXa10S6+qseWM/jVf7yNdnsB3VBU2F2/o9zx/+Mc/3g9F2LHlAh1TJFrxJ8aBMHWnQfF4R\\n/oRbRUxBnZIkrO1cQ2SuPIfHnEQJVOA8OzkL5nGsrrkgEm3fAcHZ4t/4CR+/FwsDF5bLLVqGJcWb\\nYZiCeQzRctylhw+GZu7rHc1OooANFIulUgtHP9G8Yt7o5xFWh2mhowshDYVxImYT3lnqyrqEnXf0\\nM5qdL3SYi3H+iJarMDqpLGHaKQtVL5ssQU4quZI1UVPi+Wz03rit97B1pwhAfTqeJCEkwFyx2Rlu\\npOQ0h0tLIZdK8U5rcbadNiklGAMiMSvZ6iulRpXe63bneTb6GWi2nCp4CVUmV17WqJ8/z4PzPCBX\\nclrJWYOkbVERWPIWGDkLS7GLsa53XCaj72RJ125OSCTadD5uH1i+z4z5J7y9/TqkmnlZgs93tnni\\nKhwOiyueje4ZcUeTUZIiUljKK5qEt/0LKW1UMawd4R78RoTiilDbpTKkQnfB+nFFqgtJM1OM5/4r\\nVG5UWRjjDATlNxT+tdMUi04RJYx0c4bHwC0KdWpdLmt1zCu6NY7xoK6ZFxbahKUUshnH8xO9xw4o\\nzZXRdzwtkWcxCXgwkOqCy8EUDXm0FDgP5nSKZJJklgReMoyJzMTpsNY11J1p4XGYAxJkzdHWrsqa\\nI3atsuAiYH+IRwnCIeYaDc02ehwlUvQ82JxkSQELEYmuBHXcM2N0en9ASmx1vezU9QKsCllrbJG1\\nEr62FF0BeMA5kscvF3BhXN6FlBMqEsEYTYFG19iqh08hmA6TxkwB8MSFooksGoaYmegeINnopBSY\\njdNbcP2yon4Vm3rw/tDE8Ij0ppRQBqJwq5U2n0wSL8s9Sl1VME9UgYKze2fvj3D4i3H6vDoMBtUS\\nPhtbCmlwiLJoZbowbGdSQzZMmVQWsg18JLp1clKywmk7Y0a6s9aYr+z9RA1MD9aUuZVQa+5l5cv4\\nSpKIeA8MHS1yLN55zInmF4paDDBJ5LQEpcuNYwgilffjK1kX+oiwECmxlszRg9AkLoHym3HXPceg\\naJQerxI9oPflI+LpcnhGdaC5MmyykoCA16rEnT2ngo/GfrwzpjHHg59//y9gwCqCJ4/Brzf6eLAm\\noaYFzYkxB+002lSyrhy2YwQVLGmlqqKlxIDTO8fobFpAIv6dU0FFcJ+0tqM5CFbNO+6DIon97CBC\\nMsHtRDT6JcAxj92L4VSMpCNi+PwBUqJVY1bgHrbQXCqalImjnhE1+ohOyFQL5oM2Bt47fQySFiqJ\\n2YXRGuQVrJPSjYQwfEQgW2PS6xa4tlgsCrVm3ANfVqvSWzgB0avAw/3yuls0SfnJ8BlacRGWdYM5\\n8BGwgZwyVePF0Hkg64KZMayAOef5oKxBkha5jlIeNWXaR1CNXEkSd6fKyZiQdUFSCm5Fqfh8ggvv\\nj06S7xER7nlh70KU+RIBMzrCZNHCtn7P7Dvuzo7x7Jk1O/c1MyyON7UsjCF0T4gdqC6UZeXt/Y1p\\nOezh6cKxTQtNffnA8X4wbpmNyrp8h49/RrsYnaphN0ckKtpyKDUOHG2Qq1IKYFGIp6708+Tr21eW\\n7Tu2UliXG0tOvKxRONymXTOVKIfRXFhTvnwxzjkaLo1bvtEbtPMZprdU4gjpRh9QxMKxaYa40caT\\n2cK4lQW6gZlSJKNl42hvtGnkvJLMsRYtVh9qHNOyFr6Mz3Sp5LKhZWPfP+PWGZpQjMOc6sK9ZHo7\\nkVTJGJILEC7SssRi3doOnpCLX+rmHK1hWqNkxmBbBKioZITMkhZSrozzEQG18Qe4MISzy0klY9YR\\nkygjlQiiTItzX7uitkJAU7OCLoUBuC6hMuSFxGRbv4d5YEhsuXLGh7GfX7ltd3Se0T05Jv39SVlW\\nNBtSY/J77h0fMM1Yl42aF8wnu7zHDmA0skZScBzv3JYXKMpWElUhSeev/tEv+fr8js9vn3k/O++9\\nI/Kk1vUqWQ0nJ0Zo8BYhrTHDOBPt2AuQEZ20bkwLEvEzfeK2LNzrB4TM0Z9s941jNh7d8bxFM9XZ\\nWOuG0AP00R7gASEpBW4pAUrrO2MqRmLNCkXJTCzdqVnp50Hyes1tRpRTSYUJX46d59H5p/uG/+pP\\nL9+JkW/f0edkzJOlbGEVt2BNGvDluTPnV162j5Q9jE5VYa0/59jfQqGYyrEf1BTPV11v1LXwbCO8\\nGnlljAcpJY55XDAcv0hMkUT0szMPI/6oRfHMaCx1RS2BK+f5Hkc9M+aMXVK7tu+iC8+zk4rjdCbO\\nPieaX6FPSnXqurKuG1nDxn9LG21O8hJGp5I+4NYpubCu0UE6cIY7Zb1zHu9ML2Q6t/uddp7s7YFy\\nQ3yjn0+GexQyawnl4zoeKtCOHVBkqViqvHew9oWKXrkj+UnX5O/FwoCDjQOTennCO4skzuns4w1l\\n8vLyRyEdEVyCPjqZg8ecpLowrOHWyClW0X71OWTNV9kJ4M623KjrnTGeJBqqK5QVmyNQWip4ikFi\\nt0lNS2QdNPBudbkzRgugisaTPS7fRbwrO+c8IxgDYaLSxOSI5iDNmA/QGED6CPUgp0LRgN5Oi0CY\\noaS60vc9GpjbiUhGa0LLC8OisUtVONoDqc6ZI1syp9BMWDRxtJNlLeBOH988/9GvvajRhmESktyi\\nGo5HddaaONuIafgcl8U4KExjGiqBSJNUSakwp2NUxCO4I1g8nxJpRLMeXRYXck+QYEs+3/n57Zfc\\ntu/JaZLdGVnJHvHoRTfadJ79IJ1GSXeqRIoRG0wDFSdfpujpHS4So0jCRiyOzaH1hHrIpwlntgdG\\nhaIIDSacozFZwDyOdAjNDo6hVF2ZozHGoEoLk1VSSq4YguUFHxFLL7ky6UxmJD8Vlq1G4jLB0Tuz\\nHxxthMljTvo4+DI7t1xIujGmX7JuCuqWJsyiC6RqxAjAOdvEZ1jdUwqQsqN4SleP5k/LS/5F05X/\\nn3+ILGHkEEElR/TZoKCUdKfNM4CgDNqcMYH1QvKEX/kETRUuEGYn+ihOMyZy+Q2iqnzMB5mB6Mo5\\nOqWWQMW74qbUtOEquA9azKAoGrDT0U+8j4jFemzPVBNjxvbaNaGlsG0fgMrUxJRQEVLOZFkCepol\\nsGU2yBefTy9CMSg2Ix362N+ZHtxEscwcAxvf2ISTNjr7NHK5cys3Xup37OfkyxFBm6nhPzYz2nTO\\n3pgeGZGlLogoS9liNqAZXKgpsa6FbVsj5FXqxTUI+dB90MZBt7h7huVXOc+GzUbJkRGZgKTMsm6s\\ntcbMRwpLXhHPMBN5JpLPa1Am3MuKyaTNAxjclhuNSe+dNpXH3rDTgoAlkbzNGsrVGC1guhcXY0yL\\n0FJeL/u7UXJYnfsc2DxIqcbi0h74bFGSQ2YpL0hKiDqWhG7X7OP8Qp87S85X12lGUsZTgZxRrezt\\nDCivQMrCfVtYF+X1ZeVn3/+c++uNuqyUWoPvMIwxBMjgCzqd85wBrBkjnuMUUqygJDW2miE1XGbc\\nwHJmyS9RNLN/oZ9fUWtxfBk7qj/NEv17sTCYO+c4ECe6GGa/pLVMrS8B7ZBMkkiiubcYVvpgq3eQ\\nhT5jyCY/Nj7FoND8mjIbiDi1JGpd6ddRZds2zt7j7yIcbdKOFg3NqbLkWH2HhcvMv6HtxegXoDRp\\nZcplyLGQOo/zDFOMLjErKSUGqRb/b90tXuiUw0kp4YMQE/5P6t7mxbZ0W/P6jfF+zDnXitg78+Q5\\n91TdW0IpKGKJ2PKvEMSeLRuK1dOOLW0J1fWjIxSUIGJDbIsIgi07iti0KdjQqsu952Tm3jtirTnn\\n+zGGjTF3elHw3ryI5FlJQmZkZOyIWHO+c3w8z++prizplWyC96ANiSvDQaQwHcr6gVRuPz3FBzAl\\n8/5l5/E8sH4yBPo8Yl1JIqMME2wGEHWYxbymPzn7IMC2URqVkkEHEAnKgeqfDA++YMoZxwKsitJH\\nZEHMMXnfH5ztCC2oXiCey/dq0xjnxL1SNdN6Z+8xyG2t0ZkkETQnSoH7y8ptWzFJ7O8H7/uguzK4\\nAKgaoregcGUM55whs0ZDDbuPnWGRbzottldVFckJo8esYc6wwbfG4wxa0jEmp1tAfBDUPH5GG9R8\\n8TmGkbXEIT8nn57vfHk+mD6pRSh1oWQnc5LVKdp5KSuik247A4/WwCL7ZHgCX3HPkeylOarMyyus\\nRMjS/VZINbNuldv9jmrg+8W5KoqJ0CK7NCsv99vPuid/EQcDOCklDEMRUop0JwTatEirnoN+CTp6\\nH5hBWtZryztiG5AXhBy5BA59hAqylsRaC6A4Su9H7MZLoV9uQU0RrJpUmMNYclx0fCVH4YwxsR4X\\n+PCAi57NeJ4eHoHjYDSj9co8BvvROcbA52T2EfJqUe7bndd1YdpgGOGd7zvCiHyM8SDTSbNH4rQb\\nfURcu5Qcg9l5UPIKojTrSIJSNh57I5kGfGaeyJW/IZchrSShpEQtd2reOK1zjsHZT8YMsVG5iMI5\\nKZojZTyJkdPCvbyCZ2pewOeFkVN6f9J8stSV+3JjKeEIG2axIlThtt15KRXxThGnmQf1Wzf2Pumz\\noZ5wMlmXsLwvG+tSmOc7X57v2Jih73BlGhd3IQ6CcNJKZEC602bkaQ48TG44JUcaWCNUjiTnmCfq\\nk5oTzSOnotMoRVhvr0hJZC0h4Z7CTQT3wuwW73ELIO3RDkaH0Q5ySZSaWdfEWpS1vLLVF3ROJC8R\\npNOPYFuIkAtoqZSscEXeu2n8HmxcDBHBJHFfbtTlA1vZ4riVhXk5Sof5TzS0uNxiS9bmH+DwUYis\\nhGkB1wjGWqDOYmMhF+I8U+sN5eQ432PvfQXEjjlwD/GRTaglx4NdggO41cx5doS4oFChpECwISHX\\nPUccTSnHhZWyoB5rpC4pBCNz4qaBPBsda5PCpElIozUZj+MTpWRSTmHnJoxIScJQk1LlMMfI1BQR\\nZNOcLSlrFaavrDnzuC4en/7VUIzaYCJEmFKPdRXBtDzOR8BP50k3pa4LVYRFczAadZJLYc7ObAfZ\\nVx7nk2lBr0IGKYe7cfQzbMAphW4rR35lNuV5GqmAXwa2pOFCLCIsuVzI8gimsxHVmOXEy+3Od7dv\\n+Hw+eBuNt+cDG4Ot3JlmHH3w6fGGTAKT1sGtXQd35kVWDh/8+PYJ7BYCL5WQJ2tstYpE6pRREIe3\\nNrnf7qhF2E7E+Tk1K+fcST7hqjSnCS+lMJg8zzc+1o2qyqwfGPMLllbOtuOiiDXchC1vPI+T96NT\\nysYVk8S6LSy1kFV4O3cKkw8vfwOzg94etBkHXIQezZiFXAa6QJgk9scXNj5gquHJcafqFRl46Xtk\\nJoRxVcd2Vd0dF+euGSOGqH+gOoYQklxABERDnQiOy/W3zRCbzPOnEJSUIpU6+byEHOknG6ubhbrM\\nQaeHVlwToomaa5SPzRDiALDpmBh53SJT0pdQSM4emz+HvYVEGQcfCjPhBn0apoOlCk1iov/WT9Ll\\ncZhXNsKWEh3ldXvlOZ6M0Tk6ZN34uN0pMng8Dz7eN852lfAlMfzBmBal6FLZ1pX7eguK0BRyEvbz\\nYG8HzRKeVz6UGlHrnGwpKFXHMFp7kjSz3F7o7aS1gZSVbbnjrmgKZd5WlNNGzBzKQpdK8x959Dc+\\n3go/tjCoiSt1y9TlI7N33vc3TBK5LrTzgbuxlZUlhVvjt7/5lvxwnv/ojWUY3TuP9oYfJ8bBffkj\\nXm4rb+2dUpziN27FuWkFrTztZPrBY3fa7NS6hrMxZ24p4y5UyYgOpsKvyj0I31gE/+SVqaDWOfYD\\nsZixcDE63Bz1yrelhuvWjJQGUxquikuwEt6PnayKy6CkSqUilkGE1/WVWgt1WVnTB769Z354/zHa\\nyNkiu+Q8OI8HSSuS5pXaXTAl4vGmsW4vMUg/dmpO1FwCNzcHrcdhoBJaDrMgqrc5eRzGUpQ+YJ2D\\nosZO/1n34y/iYHCPvIacYV4QlsBuhCmJa8AFfg388sVmCDUgRM5CxEkopSTMjG5QUwiFQpAURqk5\\nGmY9SnGNoeDEYB74eF4TYAumYcqQCRlMvo4ut1BV9pg7mDm/qhmSkjSGXm7O0Z+oCOuyUHKCOWM4\\nte/0/mRYQknX7nynLomXlxvbmuie+PK+wwgoaclB/q05E1PokIEbhLFo7BgD6m+w9oanOy+18nFN\\n8fPP+Bl6ezAMZAZK/8P2SvdOH09KXpnmZHklpcI9h8XYUIYdnN3DsJUqCQn7st4opTLN6TaYGM0k\\nMO7WgEEfHpBe3XjzzofbKx9eO4/9B5gZKSV8DloxU04nenmBWlbGubMsN4bBLXWqJs4zrpFmxpgP\\nUv018lUSb4CGhdscTAeaCyZgmhn9E4suFBWmSMx7XPFpPylk+zRKyYhbsBS0kqahWpgm6PXAKrng\\n8+IrijP6IJcYIq+pkIvQugeWzY1x7gwKSsyfJEVl2ceIxG5m+HvOA/OwtpeU4s/DaNZjboBenNH4\\neX0OJhW3SZbM3k/yPCk1kZNeQUB/9dcv4mCIeC2PIZvEIEpIF5U3BnNihkpGpcQbYCFscr7q2iVO\\nfIIWLQIly8USiEFVvdxq4U8oaIppr/tF3y0l+neboZuYna9fYAqkHKq089IwmJxMCWrOECFrDNuq\\nJYoqnxlA7NJzDt5BccWGcfbQYb6uC33uDBuQQkvR0sotF56tIjYu9D1oujIppDCPAyShSUkKJSvH\\nhBvGmQLOMSacs3KXzBzz+jrXBdqNY7yHxblU9v5ErTM9s5QbX95+JJW4+XBDzenupLzSe5TktbwA\\n8OwPisfB4Kqk4dj0eF/NmX4gLHQffH5/40t2unWST96PL1RNtFMoqfM4nww67icVGHJwWCfTQ9tC\\npzERk4AHewLZoDW6hD3Zry1HSoVsl1x6RLK3kvD8inrHJV8BsoqYIGQkS5TlNmmjoVfI0NFO8MTZ\\nBy45XLweyMDRzrC769fVdLAwiwofiLX3mMGkeL82N36xIcVg9h4kdD9w9wtsHC1FSoG3t3Hi88Q0\\n0dUQH+ARkjPNEC300ZGUWFMi90hCaxe+P/3MaeIvY/go8hMnIaUU7jjJEREmsdvnknmatSibNNKk\\nVbhOWidmhYmUg/tnIhHwArhJqCj9WrtlxTWgKWMapMtbrzFs1Au8CSk8CT7j0LHOHAdzHPRx4maY\\nKrUsaAo2Y00Zn53X9UbRq/JJmY+3j0gqV3KSknWgGUq+cgKSQFpYy41OCnu4xGGZ8qWoTAW9nnCj\\nHeh0lrSQ08J394/M3rjXGyU5w0/6mDzboA9jP06mhXfhaDvH0egj/FlbueGaSWI8W/g03IwxheQL\\noyda63QITcMc1+pVsG6c3eMiH502Gj4nNkKAky5j2DTn7Xwinjhn590c1TvjGhbPmdhP5/PbF2Yz\\n+tj4/tMX9mbsI5gPoomPtzvHOMgSJqxbjuGwAmYnyAAJm3jKK9gkuZG0kkWpqpEsLinYGVfgzdkv\\n0pFHrgQilLxENNzsTFG4CFqiwbeY13aJrIHJ98GwwbhyVY9pjN54Hjuf3t5ofdDNaOMkp0rWTFFh\\nLZUksfVwi0rBJeYQc47rOt/oM/Bxkb0a6lMHyAlJ0Yaow5KVl+2OEJxM/Zknwy+iYuBym4UAJ56Q\\nSrgIi8RmwH2QJCEq9BHeibN1rAjlWvkZVzq2hcrNfSJaoszzoBNLVlJO7OcTkRwiJ5FLagpVF0wm\\nvccmxBmoenj+y0IV5TOOSKRs+xjX9kIoeWXMhppFoI3DKcRFEMVLEJT7zlJWWhLa3KkKt+2FlGLQ\\n2qdzthNH2NY7R9vBB4vGwYYJk6BGoYlConhmSZln6ri/8d6MmittDPrsrFox4uban+/IjNzP9X7H\\nu7IIGIWzv+P+Rp8nfjo+GtULb4+dIZlkUQlIDh5D8B5HRMppJFmXFDOVOQbkjIn+pNR/OzoqB8kK\\nN1XeJXBqZsrsk6GdIUHyyv7gfTj31bEaKPQxDDuMZVsQjRtb3BCNAGIuQ1tNJeROFlVf7LvsqoDi\\n95xIdHPGnIH7DwFEtKgEi+Hzlx8QcfoUxOLQiydwkLpsRqvhQzFJJPHLUCoc09HHJx6908xQz6gu\\nzIvQpVcg7/Qw5ppBufw45xjU7Yaa0HvkbNSSsHLHr2gCNY0QaA8snajiI0xu67IgHKFCnR3k59Fg\\nfxkHA4Rayxsp3a7yc8YvyycpzNXxCzQPkwwSZp4SG16/yu3ouTwi6mwyZ2C7p1mkStlknoMxDmp5\\nofV+eSUEhtN9kMVILmTKxYSQ641r0ceNwfARw6dUEA3ikIxOFiXVgrvz9jhZc8XT5Bydx+G080H1\\nEpiunFjX9JPLM5JIlDknfe64XyWdR/S5kJDREd1waZCdrSaKOPftBZGDAbRmoEI3o64Zo2FJOY83\\nvjx39v0ke6XNzvryiuM8zwk6rgSwHMpRc3wMfjy+ILIQ7pVyzWVWet8RFsZ4J13tYEmh6mtjkFmv\\nlm2wLGtEC1ZhlcRRCuf4fViaJcp3TDn3QJF5UaYMsnemJ2LpEyljSlCQKt6J0QAAIABJREFUf4oU\\nIB4KX5Oel6IYmT6MnAtu0YbgMD1gPNFyS2hjRBjXOtDxCw/XwZR5HmjayFnDpuQTpFymOXD1mI34\\nDJZHXrF5lfAsfGmdNk6GTUrZro1YR3JijhO0UvMS8m13bBqtdWopxPSostSVnHO4QLWQrkT2EMqt\\nzHHiNjED0UJelVIKhlG5ckn9D5D56CJMcWq5MVsMEF0cY5I1KE4jkmoDzy0SenjxGCiJEvHr4UAb\\nlxKyLnf62JmiGI0OZLkEPp5+ojzd1so0jQ2CddJ249x3prVw5aWVee7xZsJFrD64rS+oFSRXCiPE\\nT+rXpBK++fghZKlz8GU82J+fItSkCOvtxu1WSSkuxKIJn51aZ+zadWO0zhid0R9ofWFv78gVROM6\\nMYWpJ7p95PAnZ+t0d47Wwy1ok9k/U5bMMY4gLGkhLyuznWi60eYTtxxrUcAsc8sDRuI8d1IS1tvG\\neRpLirTvfhrvb7/HLNHmO7/+ow9MF/ajoUlZlwXOE51OXhY0O7VURA6Kbvz550/sj3cO7+SyXAyF\\nr8FBXJP7wstN+PjxhSVVSrnxOD/juZBKjqAWIom61jAP+ez0GU9Q58Qtw7nH+zf2+LPccL7Oma4W\\n8aowc620dqJJ8JTp8yTpC8exY65kVZZ8w+YR76unAP/KjBvVnH4eQVZyeDsekTheMtMGSwi2L52O\\nM/MSqVL9iABfEpKc7X7DvUWKtgopBZKuz0kyox2htnWJPE9LSsklciZGSPR733EPVmWu4e78Oa9f\\nxMEgwJwXKZjY6aqmS54aT/mvEevhW9AAifiJWWQJVFX6vMJfc8BGR59kAcYZ1YSENNrMqFJQXVAJ\\nrf20jmhIbM9+hmGnLhS/Ami1YkSm4poKI6Rv4I1VK2MMrB1QX5gCWS6dgWdsxIVTdMG0c7+/Uhbl\\nvm44I3bxZuiSedlWnm2wpERZF1ovyPsPnO0IDqAlzvGOlqA5t/Pki30ipxUMnmesQZXLk5EqasJs\\nM7IvdcGlMwj69sGg2IxtRLqx5sz7/iMyepCuXNiPk9GMqTfEnaMduEXA7MsaiVMlL0gBkwhLERWy\\nKPlKqVZTUnqheCZ7jAEBRHokhHGRlKXgmig5wopL1uiP/Rk5GmswJ4umkEuPTi0rx3ngTmQzuERe\\nqQWWX0og9cKoFhb3boOqCWaobbt1UAkCksfqXCTFg0IVT0uwkOYRsyuULE63GEpPa6HHSZVjWAB5\\nRCPExsYVX3i1YeJ0D/VpkjgQVHOAhiwi7q8V20W+DsWpJuc5WhChfDLFQ3uSEmc/QieTK7NFMK5I\\nbO9GvzJPfsbrF3EwgFMv1aHmq+wnIT6uC34iBG3JLezTevELakpM7zzbJCdhUWWqMNoZcmWpASxZ\\nMlIixSeRaV+zKzVHbmSYGZht4B74eq7IvJQrPo/gKGpiurAk575keg+U/LoWRi24OL0/OR1e1sp0\\np/dgMH64Z87eqUsmSZiI1hRp3nM8WUtULhpmhus3M3hZv8F9cDZ49saC8fF2o6aC6AfGONjbiblw\\nX195f7whupFkkHNBGNS8RkaiGebBHTDJbApnj2HhmCdTQG1iV8p4hPJ6PJHVyFo59KBPZ1FiyDcn\\nw2MomVMKMZUkChmRSzXJwT1vFHnl+/PPsTbCdq6R4lVLZszA2xc1JBkpReWkqvR5uUKnsCGRC5om\\nOhvTlXlGS5gutkHvHaFS68acDSMHPNgaKa1XaR0DScHQKyGKrwNn71fymTCTYn5eD4eoftp50Hrc\\npD6dLPFk1wTZ4xoKIdIlpJvx0JkRS8K63jl8j9gDQkQ3SVcGRsxAcg5WqKiSGHQ3ciocZ4+BfA4a\\nuMsS945UcvoQswab16zNmOMk1+Vn3ZG/kIMhenhXuVyOGtLnMSkadJ0QKk16b0i4rQJ0MeUCn8Z6\\nzkxIFk869UwfO6XWmOwnBYWco19daqV3i9LzApokAjAbHgQgRVsTLrVJzpUiCUkGtVBzopTM2Xam\\nD4YL3cF7Y8+XvDvFxFq0kLKxLeVSqVnEql9IMMgoFdWJzx6W3FSQCgPlORsvujL1JBO06si1zjz6\\nEUPcNjiPkyWFgzM5uGfaCAXl6I1uwa3UOdj3SRDghLOfLDmegtY7Wcq1bXDMA8m/jz3mPpqwLFCi\\n91agXkPgGORVwFnrQiqTD0vm17ff8P3nBzkVOgZSGTLJmgKp5sqSCqYRu5fySjsPyraSdAPbaWfk\\ndNRUrsFSSNrWsgaqT7jmJOliXBwRvJsElyBTZDGWUnkcX8g0luUV9xmIelGmNQbCVMXnjI1H3vB2\\nch47YkEPF03c6p3jIjAXnH3s5BwbmHR9b3ppcGq9M8cZgN/95Gt40oUNCkALxLzLO5rWyKGYI7w4\\nFFSMMY8YXCYBVZIY0+NQTBLzl+4T0YlmxSwRPPS/+usXsa6MX9tF4rEYwAiRVC1okJO4sOylxPDI\\nIaUX1nTHHdroV0DM5egrt5jkLhuaEklDI6+qmEog44eBrCx5wz3hw7iGCCBGzoqrM7whSdEU4Sq5\\nLLy+fMd3r7+mrAtpuV949oxLpp0757DAyNmT0ztlvXG7fcO6fbwCVCQ2MKacI8AzrQ324y3i0meo\\n38wmdfsQ+YWlMXKg5TUvnKPxvn/irY34vFoZfVC1cl1i9NZjZoMzzJhzcradcT6j7dJ0GXMiGvAc\\ne6xONfBjOaVI/goee1ibHQ6fuHTyWgNJT2RNLFnZMhTv3Ovk4zd3/uRXd/65v/1P8Hf+6X8evNEw\\nbHZmilSqWoJk5TZpPklZeNleKFfrkNRRiff/ArmF2nS2n7QqgxBy5Rz5IEkj19OsRVyhXx6ClBij\\nMw1uy4bmhSkJY0b5fcmIp0/O4xHvFQm3HfPBUla6NXJySo2cSPGJuiKyUcm4yU9XdkqxqShFGeMt\\ntlYIPiKpLH1NXLdx3QXR8qWUcZkxU9MF8VD/dhNSrnEtqlJkUlWpWrExOfaA92x1JecN13LBjH/e\\n6xdRMTjQ20kilIBzdIaAlpWsGsMfjFqire8G6aLcJCnXhdvpfuIWZdfoJ1Zu4YMXjzeOjMlAKbid\\njCEsOnjapM9OJibrisTwsi4ksWs/HkyGUjduNcrf4YNpEhp31XDZzRm99GwcwynrjTH360aNg+Do\\nk7XAw40xOvv5Tkb45v4RYQEbYSEWwSWz+sFg8qvX74LJMCdznLzt7xBZygiZOUK5B5NzJFIuNHP2\\nfQ+3qcUaLqMgEd7Th9Fnp6TEkpW9hc+i26DkO1mV3uNJPPp5xQQ6r7WybBvKvBSHM0Q5KVSLuij/\\n+B//Cb/+cOePXr/h44eN16VyjC8km3gSFgVPS1RGBmaXaCdtgUEHzJ8cI128TYGLWrTkgPo8z/je\\nq8qFx9PLg9BRUTyXyIxIeh0mQQNTjTXhnP0nARluDJ/4aJgoLoUqCbFOXm7RTtjEXbktG33ExiFd\\nDzWfRkoLppksIw4/Oxnu5DSRixfpApnK8zwuw98AMZxOyglhMudBSi+IOPPc6X75Uz20Dmc/mdap\\nWRizsaUPvI0jRH4eCdcqIaASEZKuP+ue/EUcDCrCUm/hIEzCkjeST4zOsx+UAkXXyFo8o1RDQj23\\nH+MKoRHMC6suMajLlSVXmsWb5wRezMXIycJ56Z3vnw+2250xG+TKbVnwadzWwlKVpMoqlZwqp3VE\\ndjzlyKrogRbrc3K2g2E7WTMy9zBplY+0LqzlA92N9/dPrEXwdHAQWK8ve2eMYPX9/vGFpd7JBAh1\\nivO6vtD2iEdftwUmPJ8HZ06oLPGkmI54ivkIGvg3oHdjqzciOvZq1QgojPtAmBfOTDjPiTpkFo5x\\nUuqN2Sdvjz0aY1WSD1JZ+O71W5aiSFbm2Hm5LZQlcasL33145cO28c0q/PG3H0l146V84JyTf/hn\\nf8pvvvmOswtlc7qHTfq2LjweJ1UK9+3GlMHj+Zl8W2MdjNAttlVtTJac6UxKXrgl5b2dJGawTjwA\\nNw7X93x9+xJVUE6FpNBskHXjMRvFB/WqhGKuETLiBMF2sBmyc4EpSnanP95JaQ3TmEXoy7STGXn1\\nkQnh4CSK5hhC94O6KGToY6cuHtb8vIUBzyejvzNlBpHqPMCE8wisYdT38xK8bSwAo5FTYkrnZYtB\\nfJvtomg7R1fWUhD5A0yi8stl5mZISgwbF3EJ1Az/CkW54CIq4eYjTcYM+aiKciu360QVRBNnjxXV\\nlhZIBZOJitLOxtlGiGByIZXt2kMHGcrM+aArJRdeakF1o/ceZbfced02xhwsEgRqFWOfT4oqmjPF\\nlIlGL20Hezdw4+gn5spMnY/1FVipvDNHZ5+dLa+871/wy5SRtfLcgxZU3NntCzbi4p++s6wfsQ59\\nnOSScbnCc/yIVdycMd1G6b4jlLBelxsqNVSesyOmjDaxuVPLAsMDp0fs+JsPdECqW2xqsiBpktPg\\nZbnz3etH1qrcysY3Ly98u26sa7nCbga/bz8wZqUzuC3f8HqLJ7MiuAxeX+6orrTzPTY6Cq/rnVoq\\nSyqM3kLqrIKOkF2TEp4MS8J2i0zSs5/MeTBNYmisYXJz90tElECUlFeqd46Lyr3WV6w1jvMLa6lo\\n2hj9QHO+TE4ZsxatojeWXHBfURWGgGtI6FVmtKo+aeNEJVqAOc+QrZcF6DFA9xMIEZyQMXuS9Y5+\\n1eZ4YkSqMqISmDhCtSuXTSBLIfuKqF/iuBwVkMCSM0O42uAV9T9A27VffwUeK7ztEG9ozguqwhxH\\nhJ1cttMxnrx++I7352dKIlSM4kx3comyqdtELQJUkCBGz3GQLpVgLTc2nLO14BeaAcZWltASAFOE\\n7B1Rp6avGY2CDCV5Rn1QLvNUKV+n1446EU82gjyV08KiBZsN8u0nYMpWXmhTcNsRS3QzoLOWGzKM\\n577jSZh7w5ZKcmhjZ7t/xM3po1NKYM+Hjfj6TFprFM10e3Cce7QO9mCMEy7rcfIgNvkUFjKfmzHm\\nzmu+MwbXBZfQESs2Eqw1Rx6jOh9vd7ZU+O5l4zcvvyInwB2l48+D0xa6TPaj82lvDIe3tx9CoJQz\\nGWOqcNK4lYy1gMas9c5SbuFktdj7/+Rb0RTaABWWktntYBEJG7t4DHG/2pn9/7rCviZhc4F2s2bG\\n+UatGzUpTw+eqInQxztTExCqxqwauao+ccnsvXO+/5777Tskf2WQXpsbn5co7vo+MHLJjHlSdKHb\\nIIlFO5FgTmPMk1xuLGIc7cnTJa6L84CUY64lcW2OaaSspHKLlrLtpLRFtOM8Q8B1uYrlaonFxjUA\\n/au/fhEHQ+x2g9JXY4zOnBa6bzJzhNAoZOyBUBN9oZ1n4LilIB4p1nNOJGemhUa8T2Mw0SRkBtZ2\\nVLawS/cDCBJUrjV65Bm4+ii8lCQLbV5cwTRYlyXMUkvl8TxJKXNPlZflhl3IepcK17ova8bmyXk+\\n6F1Yk1LUSZesVsi8LJVTBiaEgjLKAlqb5Hyjzz3aIQdJCzkvMGPvP70FJcrBZ4/NwwwmpZR7xN2T\\nmWdj+mBZ7hGaOyJwN7thJpxjsKZMSiEr35aF83xHrlwJJVyrU2BLyut2p+RKlZUXeSXnjWQnWsO7\\nAYnDDPXJ44Df/f73fN539pHYx0RzZS0JS4OPt294+/EzS44pfJHgHQqhOXCNLMaUC2qTJJneG6qB\\n6+8WUN2cEzZmCJi4qFhwKR6jarPLqOfm3FJg5vtoaM4sJcVG6krsmtcMKalcZCQHn6zbLWjWpcYK\\n2C1MUklifSsScQcOpQrbGglW5zgYZlSBWkowLGqwMpNMhik5beGu7X6tXiMsxyzWmEnDU+N+xlCy\\n3BjuVImUNPGJ2wUTlkQmgzulbj/rnvxFHAxf37ikk24j3HwIjuDTOK+1Zex0E+M6nc/zBDWW4pek\\nNGAobiN2zj6wK7E45hChQcgCL6Wyj5Phg9u6BszU2hVmksCMMQatXaW8CiKDIcYhK6uEi26OwRBh\\nLRuNr87QCMzpP63NBKSQfTDHyZK+Q0TIqqwlkZdX9pb5fBxkTUFr7tFWHb1doTthnFEVNC+8vf8I\\nhGb+Ye+01pEZMnA0TGX7sVPKyrTEMYylhDP1bAG16T4ZGquxnARTiam3OEkNyQkfHoh0BrlmSuai\\nMwnj7PzQdrJ3+nhnvWW+LR95b0/SMH5ojW6d3gTTO5/Ok5QTqQbS3y0O/953jvZO8cGtfETSShGP\\n75EIDY5Wv1PzErwCy8zp5Cs3NKXIlsgoJs4grgmIrZcQp/4wo+TIxPgqYGp2cTlIiAidKOGNAK8y\\nJ9Mknvh9MsZACEJW0hJV6hVc5DhZS8wpVECN83hDbeJS2XJgWltrzDlZFqGmCiitPbGxo/oCVDQN\\nBrENkrAQIilW70VreFG+UqNtRoXr4eNRDSBRkmBs9PEHyGOIl/8U/z7dKQQkVEvmdbth40EfTpZE\\nLhuuiTwd84rPkBM3i8GMNv0pmeeUgUhD7YlyZ1tWjMR+PrD2YNlemdJC1DOEMZxTjfcj5NhJHZGV\\nz4/PtOONstwoqfNsT8x7lKai1JKpyxZXoYWOvreTWip7m/TzILOQamVR5VZqTNOz8qqJ21bZx+DL\\n4x3RzNvzLcpBsRBbSSWVRBuN9ug4G1uqnI8TknDOSWZGnoZL5FqKXH6LgK2IOF8eO6UU6rLQxglT\\nEdEwInm0ZC7pQt0LZamkJbNk5XVbkRyZD3/+Z9/z9uUzkl/54b3z8SXzq9eFXz9hjCe/++EHvh+N\\nNWW2Wqnbjdst8dw/079mHvRGO3dw+M3HP6IdZ5TzdB7HZ9aSKHVlfMXSaboSvHMwPkdjWqWPE53h\\ngj1mKGPtGrYFNyGxaOEcLVLQjesmisqipsxzBAVbk+B6aSRmTPdbH6T6yq0sEXCsjhWjj4nm4CSM\\n0cjYFWIcWwB152xPIFayGQctmDW6Z+q6Yh4tIDjrsuCX61fUOd53ar3T/at9P9SRNOgX33FK+Hmk\\nQ/MYwG7LNWyeho2TY07OP8RcCQgPQzzpE8N2XBOJFSdET+6ZpA6egiBdAoYyesSwmTlJIzHp+dyp\\nyw3DKSWzlHqV1DMOHnuS80JN34bVGaMPwa2gCocb0hpVY9KsunG2k7MNijWMgz5DU5/EEHVKKZw2\\nESJNK19rJSe2LDPBlEQq8zJtxWpt0Y1Goe8785zQnamDrAGJUV3Zz5PpynlGboJKDoOYGWaDOfpP\\nFU0uYTn/CufIVwSbcs1npqA5qqlaVlpvYd+WSk03YOJqUZICRaDWhVoy6/JtsBePz+yPByorQ41P\\nx4nZk5I/gD5Qa7yfg7f3T9jLB5xMt5OpQhsl8jH4EoYfCYCNF0dLmOVUlKT5InddyDIGKRdQZcxY\\nRUperqFr3OlTMu4ng0taLInkjqozJIRtKpdVygMs3K9qqOgE4ut7fydpVJFLroFKmzsdY6sbc5x0\\nzWjRYHCaU3zBrYP3qDwNkpSw9xu082S7/4atbpw28fM9BudpYTDI0qnrK/hCazutPViXF6Y7WKxb\\nS15Rmez9SZLMsIHmTM4rYYXwaJdskLi0OzXFAF/ef979+Jd9goj8p8C/CPy5u/+z18f+PeDfAH53\\nfdq/6+7/zfXf/h3gXyekQv+Wu/+3f/m3Ebvc6VDqDSkr43jS2ht1eWFSolW4vA45ZUQrNg5KXlCN\\nPsrFaW7RTylITpSceI6T0XaWersUkCtjhGHqVl44x2SczstSeIxBPztbXnicDx6nkXhQysZhmfP9\\nPSTX4txrIdWKSOdsJzkt188xOQXWtES+I7GnftluvNxqPMXckeEcdvB4fKbNwecvb+z9geeVZd0Q\\nDfryMZyXGhVASqGQO/aT2Tt7P0ManfzSMwiuobGI4lMZ6myXjKznhVsqdFlJdKYLmm+xXycyD/f+\\nTs7CppVcK3WJbIIvX/6cYcKnZwxrm+/YLNzSyt4Hj33wHG+M/qCfnc7C85jM/sA0sSwrj/1kjsto\\npge+VYqFiCwefTnmLxImaet75HeuH8JD4Gdg2FDmODENf0YE9QRdSg3IFbtYBYLhvpOV4EcSxrs2\\nJ2M+8VRYykqoiAdVFxDnbO8oNy56Jacd5Bw+DsZXfH8EEWsxznNgCBlo7Y2cV7IImJDzDSXDJWYq\\nV6aoQvBMc6HZTvWgUI8Ja5JAyF+ZHxACvqVG/mkWxcSYswUlWvXCFwjmM9S2KVMu9PzPef1VKob/\\nDPiPgf/8//bx/8jd//2/+AER+WeAfwX4O8AfA/+diPxT7n9Z1K7grhFDPmKlVEO3TPdOkgZZrl4y\\noxpYscBbGfigjTNK5FroXsD26P+mIZ4jN1JSTKdFkSzMedLsJEtBirPP57Xfjwl3yilkwZcQZY49\\nptNE3zjszrep8rII+x5koe5OzrDdXhgu+DjYe0NV2ZYlAlHnk3u+gS+8PT7x49s7b8fgsX9mqS/X\\nRaCkXHB10jjZbXBLsOaMA2ktvD3eKWrkspIV6pLZW0e9U5ca0/e6ktsXvqsfGfbgPhOuxs2NNg5M\\nTkr6SFpW9uMNKNwKocCrxof7Sq2FH7584nGcmGZcGqPkC30HkmBJC8c8GONLDPgsoRRsOl/2gzYb\\ndQnNxXM/eF1fGPpA9E5ab9S8hMPy8hgc7SBr9N/JHJmhEoz1tCO6kvItQmlJZIfz+X0IrFKNJ/HY\\n43AxR/3EciGLo7oiHi2EIyy1MEwYMzJDl+0OPmMIrRWfPeLqstPmifpGyTfmfF5y/Yl7VGTp2n7k\\ntF5K3kjJLilT6sr0jHEgCtkBl9A+WAi6mkVLEw/BYEhkidmHM1lShZyYXohEg6g84yHgTLerohJS\\nToGeSJn6/7VXwt3/exH523/Fr/cvAf+lu5/A/yYi/yvwLwD/w1/2P4p4MAcJ+6rkCvQL6XatojRj\\n3kMDJ5EJIJrD2SfhkJxzojZCjdc7x7kHNYkSO20JE0tOimrmcT54Sa8gdmUmZFa908ZJ1cSwydvz\\njdTHZfgx3KCkG9IHcwifxcmy8PZ8xwzqmsm5U0qc/ldON30c+Nyh3mlj0p6/59P7g33fOU+BtJLX\\nV9alcthkqtDHQa4FHZM5G21G8OttvbP5Fr4EMq09UZSXkkOT4bEOe1kK6eW3/EY7U7+l1vWnJOcv\\n+5NPb0+aOd/e73wpmYJR8wupCpnJSy08CTBO6yeeQMtC7wcigYJz7cyxc5K5rXf6OAB4Px9kLxxj\\n8FKiqlhy5eO24Gqsy0eW9ZWqlT5j4OfENsBsIlo5ZueWF2Zr1PR1sNjZlpAmF72R0oKORtItSm+X\\nGF2LklxoM1LUE5MhSraJSCWRmN15H2c4GQleZ+oH5zzp54F63CKqgz5jyxQAmPOKnlvDyYshEolk\\nc8wLtxYVm6oyuCoatYtH2sJ5anEtzxkOYvxCEeaK9x7/DWPNC64eztIsLMuN89yxDnM6TSYpR77G\\n8BCxaYpNlVCo+v8fqOXfFJF/FfifgX/b3X8E/gT4H//C5/wf18f+Hy8R+bvA34XwxRtXyjP5cjxG\\nUAtfwSsFcl4YI35ZeFBtkgpGhxRT4CST53lQ8oaqB8gieFCYW5zGKZKTHKUmZR8na93Yslwg0UK5\\n1j6qmW19YboxrTMMfE6wzPvxmZQ/subEY8a0eiIsVkkGmXShv4xFVpa6Medk9IG5cPbwQvRz8DgH\\ntw8VUmfqjZyM2+01YKLPB8PDifl6f0FoRMDUxjw7WQvb8i1Jgk+geqPmIEyvtXC7rfz2w9+iMqCu\\ncETw73E++fHbJ+fxZF2/49djB4m0I6RTyNTbnfXxI1/kZHYunmIIxUyWqzwvNBsUh+fjwdFCidra\\nGS1VFtJauEng+E931uLctsq6REJUHyGtRhO9PUMLoFuwHNY7szfa6KzrRsoTTwslJXKpV+5miSCh\\nvsfhMM9IqpIVtxB8dTPWumKmiOeQMMuG2ZNuHU2Vfu5hYU4RIW8WQ3EkbNY2ZuRZpES3xkxh1lry\\nGmwL5JpjhFZhzs7pTiayNMcIJ2lOxjRlTmFdbkw/wglpIDYZ1kMno8661kitwqipMP3kHOcVGCQM\\n5wLK7jiZQnBKpk2WcqemmNX9nNdf92D4+8DfI2wOfw/4D4B/7ed8AXf/B8A/ACilOKpkagBLzOnn\\nA9Uw8USq0KVRMHA/UK1ozdS80vpxpVcZXgpyU0afzN4RjxSqdbsz5nGl3RtzHJG65Im1rkybmEVc\\nmfEephaDYT22ICRqDTXhcZ6M+eD24VexETgdlc5tfWUy8QT77PTjwWyNnAo1L7THgz6VgWB+8L4/\\nEVfIC4XEVgtFlOQPXBQ7v5Bs8N0WmvlaVj6sH3l92ShrRk7nd59+z+cvn3nfn7SZOb2j0vj1r37L\\nmhNJnXM/+L38wFYquU9uuVAY/PZ+52//6pWnC9JBy29pzwf7cH547vzp7/53Hv3k/fmFt6fTZ4iX\\nxIKq7aLUcufz247ORq2w1JWXDLlsV0JTIpdMLeFhmL0jKRyde98hObluVK7QH+uUesNUmOwseue5\\n/0itC8vLhiiR1jQnqkJ/fsaH0s531Cfl6qXl0nY8zy+kfAPrjDZ4tiPSzRLs40mzybq80vYnn98e\\nvN5emf1k35+4OZIq27KQVy4YreMqtPlgekOo2HDOo+MuwdoEssZWLcR2xnToszPmjBmTgXtEBw6J\\n7dawE5sgsiBj0OVgqZVUV1QNpFzqS7lo6dHC+JystVLSgnj4NdQN0RUfk8/vv7vyLP/qr7/WweDu\\nf/b1n0XkPwH+6+tf/yHwj/2FT/1b18f+318iuCSyekyAx8F0oS6ZORWYLPqR2c8gQquiGrv9Phqz\\ntSihbGAjBkyCXaBN5ZgTJGLeAjgbCPZaKqOf9LPhFBaFYV+/JSVlxYaQpFBSYiqM9sRmx9yIOPgb\\nIsawhnJE6pPA9EDLByUo3rxH7xyt08P3FzvvpFChSsImDE40LZS6UhNYM9yfNFO2Nf4sf548z53P\\njx84H+98ej/4sg/WfGN6Z+RIRepTOCQi/470N9nOzn4++fXLx3A1v4CsAAAgAElEQVRC/upXIdY6\\nJ8/zpJxv/Ng63z8O3r584h/98D3vszOH8P58hrgKvVD/hW4CzahkBj22DDWCa3IS1rxgHiDV4QXR\\nFKtjCVVrzH4uoGlS2jyvkJ5JTZVcX/B5stZKrRnzRtXKMMEFkuYrCOhrmA+hMcgJxrjiCePzC5mv\\n6pj92Mm54Rotas6KeWKTQjKlTSXJykyhNTFr5PyR6WHYGm0wvDF8kq/eXhHe9zfutxeGG8k6bRIq\\nRRHamGzrB7J0hHiaKyE5x0OP0HonUSkCR2+ke7RYqZ94zjiTXLf4E805xgwGqMpPYGSTyFuTXBjt\\ncUXcxe/r57z+WgeDiPxNd//T61//ZeB/uf75vwL+CxH5D4nh4z8J/E9/6dcDSoko+DEHkyV6RC+s\\nNUV2QItDIRKEI5tvtieXUhTUSKlQUqH7wTlO3BJ5uV9bgzdqeWHaifcIxe2AT8d6J+XMGJcugfkX\\n1HLRL3ZzpDh9RrdaSzgAVcLOrKqIBsm5jxAlnT2wYn10LL8wZlCibA7KEiKhiOZ7ApmclZQIN6B0\\n7nXD8sIPP37POQe9/UhbhHH/yOf33/P29j1jwLM/mVPYNbMulVzvlLxwtC+cfqKSeTu+56w3shpv\\nTNb+5G28UiTxuy8Pfv/973BiHvDDs/P+eOd3j3fmOHn5+Deoc14O1woetmZvk36+M01DHXkxOsvF\\nVIh80XB/Gor4xFzjYOdiXsyG5QUpC0kgl8j0mG4Um6T/k7p3yZEkW9L0PpHzUFUz98jMqmr2jGvg\\nEnobXAEHXARHvQyOCXA/5KyBRoFNVte9+YhwNzPV8xDhQE4GJwRZCXQ18hqQk8zIiHB31XPk8f/f\\nn5RSNIhYvwfciHNNGPNEPBK/p3loMVJIl2f/3Vtz53F+Y2iE6rZxMa5PdO78m7//B4p3jq0wvfHh\\nzm0XUj44r09sGN0q7oPpI0xOJrQ5YtoPaKq4XWgu7LIjtTDOr7iFEG+Oc63EE9d1hvU75++Fvbsx\\nR6glN9XQkUy4He80PYEgWE9roBEd+Hh8Q38fWnpQoRiTLddYSYuQyp3Wnd5eMT/5L412E5H/Bfh3\\nwD+IyP8B/E/AvxOR/45oJf4j8D/EF+n/m4j8r8D/Dgzgf/z/30iwuAAB4DQJsc693jH32CZ4ghnT\\n2aNsfPYXIhpT7Glc/YWmslBssfvOKYdGxRqawiOfNMAoDigpeP0MJG3x50d9F9ZrUQxIObIDU8oR\\nhtIaRSulJqRGDqTP301HEnOI3mONmjdyLjQPzoGmGuIXgS1vnLMxbFBKpYiFgKcqqUbvPH2we+Lj\\n1RjNuNJgvH7h6+dvfPSGNOHZH0H42TZ0xbSZDV7t4hx9yZuV4/YPKI1S4447ffKXx6+8ZOf/+svP\\n/Przb3z2hrJh7Dxn5dAvfOrJbD1AH0lgEaABnI6oUlOGFOCSpJnbvnPZSc3Kqy/K9vp+jjFIkrEk\\nmMA1O2Umdu+oGs2NYzvQKWSPNR4WGDpy5RxXDNYsaE/uMyhfKcjMmlOQpAm0WgyLDZXwGMwRa+/R\\nO90uhMbX6+SH+47LwRQhNUNtWejH5GUnP8kPa77ikS8pcC6cXx8Xmgeqxnl+ICTE4VYPujfOeXFL\\nAQWqJRGpKfr92Q/QscczQ1rsjA9gp+ZMny8+24vbrlh7cbVOdtjyzpAY3AsJn0ZJhTYTj+cn42pM\\nd5KGD+mPfP4lW4n//v/lX//P/x+//t8D//6P/CXCCm/MNigp0dRoRH5h0hjg4E7rIUF1MyaOyUSS\\nUnQDrWG7tcmeoM/Mwy7MjL18QWgM66E8m+cSCTlmoCmv4Nx1o1uPwyJHZXBdF7ZCSMSFZk4V5Vb3\\nmDp7HCqqEpPzIREhpoHskhRpyNHCwPv9QJMtjNzy+9ftuy+h1krKcNMd553r+R94dqXMyau+OLZC\\nZ4YVmYCYphVA22eQl4KitHO/bag4R4G0hmklFWYfnP3i6+ODb48Hf7l+Y5wDzxtJ4ibSrHyp76Ed\\nKAeeJrVqqPuoiK9qSSwEWbVSamQsFFFK2pnrZwAZ9QjquUZnK4WUE3OOYDDO8AIUVcZobHoLU918\\ngQwkvZFzODBT3ni9HnHQ28n0RB+OrK9fJaTBfU58nBzbndEbo704csb0x2AhaGUa5Dn4PAfoG7VW\\nmjzYEGY/+eXx4L6B2RlVjqzVoHVUI8JgK0eQsfuE2UiibPUg5cLonXsuuMWBUrcIhpkzuBWRJJ5w\\nazHg9iBbkW6YTV49TIWZTL8E0UHJe+D/x/geBh2t78awJ9OVfln4hyTSw0X/Cx8M/zU+TvAIsiRe\\n1wtNiupg+u9gLCeXAsgquwpdIMtkCrzf/w79Ptx58eo9sh1UmBTGaIzxwlXYi0bfOSdDAvSJxYwA\\nEYo603VxgoQ+G2M2DAnPRKnhjSf6QPEAiMicoIlLJjAoqzW5+knSiZa1buqdKsI1R4SxWBCwswpF\\nlErcSm/1B67riVy/4lRSj/zNe95w3dAsbGb0cVG0UFPCxMP3oRG/3kdIepP0iFqbk9Y7Wg5woU1h\\nph124RjCZ3oxRwilxrioOeOcK3wFUhKateBlAFIyVSDVQpFwAZ520m2SsqIWKeDmyqbQLXT7VQpJ\\nhWs2dAUSJ0KYM1rg8xLOnB1PAqIRKzgd90QfLWYzYrSpbCmxVeWa5zqoQgYuEr33XgvHVvh8Nc4Z\\nDMqimb/+8hdutaCauSYkHox2sm87lo3bbaOoInqxaaVbMA4CQ2eYCqlkshKGqbSRS4kYu3Eypq/q\\nMRSVWcO8VlJdUOLFL9Voq67eUEnkUsIjMaPlhRRByL1TjoM0HR8vXnNw10rSwn7s9OuDj37xvr1z\\nzYG3F1ILL5ts+W9QEu0GZoZJpDHvNfj7g0m32KE/mrGlEhsFGxwlZNBZM6N3GJ1hHce41XdGHzyv\\nb6E7d2XOxJZzDHgks+eD1xx41B6IFjDj68cnW42X2HBEjeYrYen1wft+C9DnUIpkmg2GNW7lFqYb\\nHwtnr6RaudVCzoWUt7Dgeufz+cG2xctt3oPiY4qL8uig0/jP//wXuDq/vV74cLa6UfJGTk4fL97f\\nf2LOTh1GSRtbOQJemgwpSqnwlhL3ray2KkxDSZVEwYjBrMnFj8fOVgr7s/CtXXTLyHXyHC9sNmra\\n2cuOl0Syi4yxFTjuG79dL95uiVtKPMegALmEkvDz8xv3befYAXPeXPj6EMY1+PXzyf2442VpE2a0\\nYnvRSPROwrYXcq7M6VwtwDrdLrTkyL6Yk1w2Xr2BwfQzoKgkcq6gmTY7ucS857ZtFL3xeXVyEWRs\\nfD5PjqqoJOZotI+LsUf+5F6E9KYc6Z1nGzyuRtJETRuSDvx64dbpGluznAunj3ieU0EFtlKRpORc\\nsD6QQUTJSbQUIeSLle9WaiDoJEx4Kkqbhs3J18eL5/nkeHYeX5+8rm8cxxvXOxzbZM8VuPGWD/7y\\ny0XuwsfZKDXsBt++/vqH3sk/xcEA4Ba7eUcX8TiTs7DlDUFi1aihgZ/WyRZ24qSVMc5AcS8Qh7kt\\nFVhHzaHuZMnktIXbUQRyian/jJh2ZcNcOLYbKWmg693Cyj0aWSqid4RAoLXpPM9Pcs6ITz6uMzIr\\na8U82INotBOaKzYH1ifWo/RmOT/j5vQVPWYxfOqT5/OTz9cg++B+/wnJG0aYjPIiWG+asLSHj2Hf\\nMDcaJ8k7WYS3+xeOEgq4cCEXknSG/c5QNPKYqB7sPJn7jYHznFGyozMCfrKy1Y3n+ErJlVu9oz4Y\\nUyl6BMeiVr7k4BlkmVFGb5la3xBJyAw1Y3oO+nhQUOYIMlHSBFnJyRbLMEB4zSS8IzOGuWjh7Xin\\n1o1v1wXeFxS4YR6aChtxyZRaqCX2/80/2TQO66lKygND6TLRHMKgLJk9J/754y9c7UkrFTs27m87\\nr+l8PD5xLeRUQIXWT67eqCrgEQt3thfDw50qEth7kSN+1jN0MVGHBnsUImvzahHuo2lHNeY3gjFc\\nwDce1yfX5ZyfJ5Yn5zU4n41n/5UfvZF/+gkxeH/7bxBrfHv+E3/5+MY8X5zXCD7Iv4Ik+l//IwAR\\nlBrtgi3oh2BjIj5p/ZOq9wXXjEyDOeZKoI51TPjUlcsmReC+3wNgRMBPzCsl71xuuEzER8hZ10ox\\n5RxJQGOgHlTh3y3PasDyZAyLvEkR59huDBu0ESlS1qHPScmRarWnivUlpZ6TZ/vGD2/3GIhqwUZn\\nzAvxymmdvWTOFpmFWROqk1qcVAXRW7gfUgnfhMAYD0q5cRwHKQvPriTtlJqpOdob1gHnRP8LAxGn\\njcGmG8/XrwudHVzLHeGhCZlKLpVb3UjSOPY3jrxR6kY7P7leL3KqPLxTi3HcN8aq4tQa27ZRszKn\\nkpLx+t6yKZ5BNDQqguDWwuPiwVvQXMn5DdqTOcN2LBJraJsz5OyzMa9OsriZny2YmofukWzF70kl\\nsEkmizP8pDG42hXW7JwY4qFzqAf3tx94PU+KF57PC9yYmkHuHCljBHjYZ8xVfM2+ciqrjQlFra80\\ndlkZFGPOhRMIuz5YiKpGrJYrxNedcrTSqXA9H1zt4rffPuhX53k+6fmg5p2hT5Jnrj44z8bzdpGf\\nvyJb4bbt7NuTizhMPBdy+i+8lfiv8REJ8m1sEdYKzw1GePJVEzUf7DXTLWDlLit+jJi6Jg0dw9lH\\noN5SiYjx6ahuoQ9OQiNgGrHVCP98LccK5Yho8ZIFnxGwW1JCvTOua4FVlCIB9IhJhCF+fcfVo8Kx\\nV3JVfG7MYdAvUnrHWUO6fJCzQI4k7HHtPJpx5AjuTRJBq0Mb4oqUSC6SFDv1lAo5w1Y29q1Qt4rk\\nvjINN0pW7kdYh5NGxiTmjH6SdKe1J0mFnBO53NkkYDYynux7xbzy9/JGaxnmIJdETcKRb9HyWYuD\\ne9t49c5e93A9knCN3AYRYdPI5XAZtMvINXGNztOFyoRU2Wphy0qaAx+O+8DSRkK4rg9uWiiphv08\\nRSpZwFNi5VcX7AYT1MGtM6Ry5HtwGJKT5qRqZStvvK5vJFhp5QG6SfVO8sKUje022fJGu+J7Nf0E\\nnxTdmcuankUYqzItGnbvpGFyikPOcYkMVl/VYawnE5MImM3lwGcMrN+2d2YbjB5yf/OBjUEfg8/n\\ni9fjyRhKmhXVQM7v204thawJFsnrkRu3VhkzUXTjMR6M6Vyzkc3+0Dv5pzgYIAQqfTEKNWXGHEF8\\nlkTrDVKnTUVSWRbtifuJeIk5gYTt+rZtCBoZFO1kjMGWHRMj7YpqZ2Cc88JHDNsSNZKYQtkQgpUk\\nYMY5eoTbJEi61lwzAlY1L8vvtlEU5gq52bLwHI1bfadPYzLpM4xUpMycF7f7F2S78/n5M+YPklXO\\naVGJpBhM7wiv56+4QoB4roCl1EQpGhbsDMeeA2U+O1+2g+f5ybfHk3vdAym/JLNGRUSo7DBbULJy\\nxxm4T2pKqAjdB1/SjV4y7YqYtb0WvOTVi3emGFsKB+ux3TARxgiEXJ9Pentwu+80u6hp47IXyWGM\\nzpvulD0SuWPWAqlUJMUhMN2QOePwLVBzxeX3cGBb+R2O685sT4r1IIVP4djeo/UcYa33JXJyd6x9\\nBUkc2xeqDr72B7VW+A6AmXy0T+5AuW0MHLONkhYmXlamthOH7gqk9d8BpR5EKDz+7l1CCdlnX47o\\nFBeWZFhb/Ge7+CH/wGX9O3qg9YmmDRnC+XhyXp1slWGTlLZFHItoxD4udrnRhqGX0c+v/Przr5yt\\ncfW5wpsL/Y+dC3+Sg8HBLBrEaSNezSXcMEmL+nNbslpi9SId8Vh1qfqSTmtkUljEjtk8Sbox7YqI\\nMpF1IwtbvvFoE/wMFFeKjUfSlT1hhdEa53VRkqIpitKU63eLsqtgCrmGMzBlX3LVxK6JNkbcgBKH\\nSsobhzj7HkNEmx/8/f0nfn4qX8/BPXm8pJrJxSj7nW0rkcpUd8waUiouDdXEvt/ISzau6mzLm5FS\\n6OWFlQkpMfCK9ebJUfYA3/YXbbwQFQoFzQdZhWePmYfUjSxRxu/7Gy8zcj5CrqQlRDl7EIhjLQZI\\neEpyqczh2Gy83BnnhaYvpJl49JOtCqRJ3Q5uW8TVqxip3HG7wjdhF2OWSI8qN9QUm2UpWx3rT5iD\\nlN9xO2NV6W/RPWmMJTALaKpWXKGYcw2j9UGVePyLN1JKjDHIqeJ5X/CVR/w+weqKfA7v4Xr0aIWC\\ntyer2hQgNi9OJ+mB+7nkyIprZgxHZgj0RJw9Cc0Nzb7W9jGvwASbQj+f+HnRFO77O5pnsEryxv3t\\nR1r/xPOGzYvGC/pgzMm3xwe9O+d14WlwT3+T60roY+AEtpu1J06lkOpGH58M74wRXvhaK+0KFRzz\\nRCS4eYZRNE7HLoN8f2f2hqU7ngblPVP1oLdOX1F0eb+jecNaY85BTpmc4HGNNSyKqiCFPD/kp94D\\nLls22nwxn2t4pJWE4M2YfnF8udNm8CPUnN4uvry98f5T4r/96d+y5x+Yj5P8TLw+/pn2aNStUDJY\\nTuQUuHbxCK9xUe65UHLlx7e34DzkSCKCzJiduiWa3sJMJZGQrQlmn9gYhM+vU3P4PySF23SY06wx\\nJMJq7mWn80LKEStdyYzr5C0X0C9c7RsmIcrp/VpdM6QEZT+CVjwFZYsckPrGx9cnW71Tb5m3e+V+\\nP8gVtiRkF/AC1wcpFUZ/QFIuPdnrRh8vRjeqK8MHqYQ6sOfB4/qGpcLb3/1benuS00ag8ee6zcF5\\nkSQtpWEM5Lpv2HhQjhtXfwCJmTKf44GkFC0cFnLjlIL+JILaBM9c54mkwsBQj3nUnJ2aM1t94/X4\\nimYCJ58TkkIQN3ongCFBZfLRKLlg/cKlImXnmoN/+vpP/NY9ZM01c+qFkNlUcDd6e6G5cvYXbkaW\\nhozBOaIFZQyqbJSSOI4bX3/7+i9+J/8UBwM4wwZJCppSqBXXBiLPk5IzPkNclMkRv25xCARtyBke\\n8I0kgsiE37MT3NkENJfIb5wXZQWLIMYYRpFwPG5acZzmjhK7+5ICtXVahMHV7Qihkw0YPW46HyG2\\nKjlsvu0RcWjzCrEQwn07mAVEPsnpR7aa+Dut/FaeKBdVRoh0SgKfiPfowXOhtwu3GFLtuTIQ3ldr\\n4tNJJVNSXduKgOPWRGj2160msvC0WpgeZbmbgxFrzHXw+OwhMyf+35RWvJkN9nKQxeLrSmv1650s\\nymBh+0WYcwRrMEdYcZJC7w3HeP/h77jak94H1xWydd0UzQmfkZHgDCRrxAbMxucZAbQTx3xwzbDP\\n11JJamzbkp1bCw+CtKW0tIjTyyG1xxpjkb5sQsZolmmvi2mTmvPSFQQSD6DNGT2/efyZSWgM9hS5\\nGiMIvbHC9siDmOb0GbqZUmLulOvGaE/mHIiP79LlywLd5xb8BbUT04lqIaWdQyYtdzRFi52TxBDT\\nG607u+6RQD6JKs+dq3emTab3WJvmGhucP/D5kxwMEmWDCqIFlZCXOkIfF1UjkmuMETezGJMUEXOe\\nwzHpUban8saYT5IYt1qhvNHnDKmpTyqCpQObn4hrgE9FAs02Iu5rK4lfP76hBK4tiZFrAYzuF1va\\nQsbaG3vJbPXGxEKHL4VUNnLZ2UuwANQEI3POiffOjxN2r5T3n9iev5GLMsU5W6OUFHHteWPLle6T\\nmmus3rKiW6aWDClQ5JkILREx6laZwzAfNGeV1xt1y8hcVZgstP5sAS1FiBSpQtFEMmdKDHKdAIG4\\nRT987PdQZ26FRHgS8ND+4x7QkOmhHhWYODlVpsBWMq0mntc3HtcVQS4ymTMHgfu+k3LGNSLbhB5V\\nYkuAMzWQ7OeMAywpYZ03JavgGi5W2RQfF76Yjy7BtEgo0zUOXKmIDl6vF1vdwE4UpY8nKR/gK1jZ\\nggA1ZtithwWJXGXybM+gX0khLJGZXBJ7OQIQ7AEOmtMZ7Vz5EbK+lwFdCWBs5XFOsAssxwB7Cq5O\\n0Uqzb5GXWnPIvFH2tDMJQ98lASBOqWIWP0cnBtW17nRJeN4x/RsUOIEzLRRiSRajAOPt2KMEthY0\\nIl0/MJEY+nSjpoRbwFPQgo8Ri6KU6Ti7TEiJ2DkUrnmi4yInpVnBZ4ioSlIkLlxevZNLQcw5ZA0Y\\nHVLOwVMgBpIlF8RhzEa3wcRRjVDeBLxaY46BWpTyrYcRqZ3OX59fcYN/fv4VvFPEybDUkqGl1xFu\\nz98NSpoiVn3b9pBCayYttuOYE7eBDYNpJJdlagoNgIujKceEXCoui28YdrXY9nhi9Ei0nihFxopl\\njz5a1Wk+SEQ1kkuKOYJEf5wkLO4xh9O1hYnkcJNBycZBhWl0MaY5z/MD0o1zdu5JOUfoMJoKt3rn\\nt+cviByoClnD+6FZuaaz58peCpqUZw/ug1lieIQi6/LFYEsFaZOaCgnnctCSMBmxqsUjS8MmQnA1\\nrxmbpzEiDnH/fWbjgWlPKeT5SGzFwvPQ6fMJ+UA9ksRTCZK1iIbhazywVKg5M22gKCI70wfXdTJm\\nXIpjDt6OA/OTvgJ7JWfQFSxTtph1/a65sQhPxjzoU+JsxxeKCP43OXwkOHYpRexcygmVQZ9XjOc1\\nfA3hYiQqC9GFV++UFDt9mxbGmGyoO3vO2EycfSIyeT2+RZ6hEQEmHu69LIlpI0g/Gpr9Y7vRrtD5\\nJ8nBhPC0ZhD1u5szcqgjkkzU2bed1jrXdLYsnH1wXZPsqxTNBfGN6+z8p/Yz52cImZ7tonsE0qoG\\nRDQlwX2y72/se2Gqr/VYAGTimQwuAZLWwyq4Z6w1JG2IzOVVEEq5B1nZgwmASFQpOYEIvXdsKodm\\nhECtJxFKDpXeNVeJGl8xuH6/+dzCgZgkpLuaPFayy/VYU4HbD8jrxYMWB18pjJEwE9o5Gf3EF30p\\nJeXn14OvX19ojoRqVWerB1s9EHFevdOvTt0ze6qcvUWeiBbmHGBOlpAqi1gkdmsJ6pQH4qqPDySF\\njiJo2YDE9zYyJSFLXArdDBknpYYepuZKa5H7IBKp6KIJyTuIcc1A7m/bFkNAZyWev5OzRPKaJAqZ\\nc3Su8+Lz8xuvNigaQ/ZOAi/kBJqF/Th4XYPQoiiv1wfCRvaIYcwoYomcHNHMtMZrxsX3Rz5/ioNB\\nVClbZBSKh1Cp5DemBZzFpyOpIGkyrJEs43Pgl4EFXkxyIiVIZQl1WG5H7xHPNY3sdWG4QD3yHiNp\\nuMeaVJXBRa5vaH9j9it6Uzek1NDY522VurL8cbrCaOKHYN1QD7xau8IVOVrcjmU/MFV+/fjg2Qqi\\nnWKOkQNGmnbMovdWPXiORq4ZqvLZryBUrVAUdsg5TDiTyEIsUtj0YCQob1/4+vmfSb5x5B0ZHdGK\\nW0T6uSf6K+LUt+0LczzRcoNacYmXFHd8Eg7HDL13JhkbJ1kzW9oo6lzXi5QLtZSIz7uCt5BXMEwc\\n4ooWYdeNL7xx9UGfRp+T+bq4XqHOTBID3zFeEQhkynh9UHIiFed5Ta72C/W2oXLx5e1H3tnYExiP\\n0HgUwfJtCbug07iVCMix0TivT+aEJDMORW+YZkCwFowMFbB5IuJsWjn2HaYhcqPZFfJpD59uLSE7\\nb9O5zlckldVM2bdwup6fuOtiIggsrKBZVJo2E1+//sLH5wePbw+6Ke/HQb4lBnC1B8zEjY3dIwrg\\nPJ+M55PRnGs8+PGHnyipEnXaRNiX7qZhdP5gwcAfO0b+lT4hVKnIjNCYgFxG1Lk4+LTAdYlQ0x7D\\nmjmXRz3T1u2QVJnzAh/MOcJdB4DjNlCZoJnhkTsw1mqzzU7rnyQHpPJ4fSK98Xy+sB4vta5pVJTt\\njTFOhs+4AcjY6PH3npM+OlmiN3XzCL61CwhZb++D0SdjVD7PBhZJSJmAlgRqQ0lSGSNoP1kyEIDX\\n3jvDjbN90tpnKPH0IKWKeObz+aSPhkmh2+AaUbX4GMxp8QBPyPlGrbdotTQx6bgMhnU0JWwOev9c\\nTssdm51kwd6Mw7fRxxWDOcAIefCeS8BBXCjp4PdA1awxld9qQHtTSvF7GcxufLwa13DGSjEf5tQ9\\nfj3imFTK/s5xe4/gmHpDLFD3r9EpVLJuuEewkLmujUIQupIv12iKNmotmWL1ii5OYzxDEboTmQ15\\nsRwFR31QtZB0I2n4IXDjGp1pkXotCKPFZZO0RHK2d1wMI55Ds1g5g9LHSZ+DqxsD5yg3VkArWxa2\\n4x4zkjHobTDOF2PEABU7mRZEcGEyZHC/LQGdxJrVZl/ZFf/yz5+iYsDBRoSybPtOSXkZf1pw/eXg\\n2b9x23YgY/2FeGQo1Hpw9lfo/0dkBIjWoEnjSykpYcQZE5IiKVSTc05e3Xi/VfpiAiaMmoTz+eSo\\nOy4BcWGVYjZizVlSIakyZmP2yb4JCGHZjhk9CSMJzJQoVFBBpVPSBtaY1jhfT169cvXIzti3d3IJ\\n5u8wQ5PSWmev4fRLmvAEvT9w72xpj136vBC9MUmUWlbmRWjxzn5SdMNtrgc17MqUH8jFMa4AnIwZ\\nWYnA6IOUwvzlIsx5UtLOGE5JylYyvcVcIvb7AR1xYkOTANUYQIpGBGHsM6MsXwA2bCqdyaO9UJzp\\nE7EJWvny/iWUlh4bBNdEa59MVxIWyVvSgYPbQqpXOYLj4Y2cAlwyeySJWarMEZcJCbIs5DoDlVA0\\nxvMR6WFbkpULui13Z0fzjoqvw8FpZrhk2vXE/CTrHorc2WBFJiYtwQr10Mm4e1j8U0UdxCIXJOdM\\nksxMlURfmglHie1YSYWqwsf5JJb6QkpvbDrXtgWSO23AmJEcnzXjJZNy5Ssf/+JX8s9xMBBlQi0h\\nrWX56dFCs8Ccz7QzRRjXJ2LRa5tLODJzRVb4irnjvZO2wjUGPidmwkQYNmKoKImzv0LolIRzhP03\\n5UwuO26NrgNLJ7kcbKXSRuDjhHVzSGQ+KAHKePYWQBKUvWoMI1dIblINJ4g4aDzMrpmsB3OefHt8\\nctQfOG43TD1SvD1MYQ2H8wzp7+rlYVDqHqtJgvisksK+bA82QtAAACAASURBVJFReZ0Xc5y4Zs4J\\nZk9mzsw5qGWj5B33E3OJLExrCAPxHuu21kKKHJcabhH9t6VQBI4RUXdb2ZEU2PNuI6hWShxWTER1\\niZUm0y7q/h79v8fUfysFdaWVDiUjzkqbiof68XgsyE5a4TOCzc79diOXQsnOnJPpSpsdHU9SORAi\\nVzLm/5lcN7w3unmkdbssAdgk6ZoteGRUxKZqrp+1UICZKioRrRfApMGwgM+ICDlXRsggwTvu8T10\\nIKV1cErAhoXo/53AGaLRHuw6aAL77+Isj2eolKgAzvbksztaS4j5etC5NR+Ih3XbJNHH+T1YB4FS\\nt4Dp/IHPn+Jg8JjKhOAjhftRJQ4IV1AXbjWzpYPZzygf48uG+fvqLaFJwQd9dopVihQuS8tANPAU\\nu3GzE/WOeeZsg30TXCIP2CxAJ3krK0I8Md2xYYiUuBEXJdp98RlyAVL0pFpIcrDVyc/nI9RxKhSL\\nfXJSyPUgudPnRbg1JidOLQVNA80JG4NUN9ROsq80JvfgFqzh4LbdmONCPLYYIs5zvNCUuGvlRLms\\n0dtvbPsPpC0SpUVyBLXaCStn4sgbgx3M6ApWDgwJT0oqoWasO+qZTTwMaDXKZPMzVmREEnnOgUUH\\nD60F9p2G1ccJHhzNey3UdNBzp4uF8KrcyRqtoqlwXo0xhS3F3zmXG2RFljGo1EpOcXDklJnmKwci\\nrYrOEenkcme6M16CTFvJ6pnZvkXOhMHAKCWFWnX9miqALxZDTis/IkMfuMTB4uNJ1o0iDrNjcyki\\nPSTM8X4q7nMxQYMxYnbRp3GvGzaUrJ2j3EFYQ/JgTpYSX5uLQ0p0D9/LFEHTnVJuZI2Vcqzxn+z1\\nFhJrXQFL/fxD7+Sf4mCASM4B4gGTFCsjN0hK2UPH0NsTRqcPC9mrJ9rVuWZjqxVdcM9tu4HuoW0Y\\nJ1spNNlDsmudRBh+csocRfB1sgLYcDodMaGuKDc8LwnxRdYjfh9SKAAWaDZQcXeSG6/ng7yv+DVp\\nQTzKmZpie6G6kZjMLtBj06J+4jMUd3iYq7J08n5g7YXbZIwz2hARbDjNX0EBcmPMB8oWq1UJEEhr\\nT16jh/dDJuf5Sck7JccqLGk4VW1Rn3t7oVICYZ4KFact7YW5U0t4RNwHlwcqDZ88ryc179SUGRbi\\nsVBuSMSl5Yr4YPSGe0Bl2oycjqqJLGmlJt3Dy6HC17PBVM7XiVJpVdjSjrlx2yKqTWziHrfHZUYt\\n+6r+JhnBJ6jHgXS1Fzfd+fu3v1vhwx0ZE1+HAi5UDb9OqZXpv2MAIhc15YrTA+rSL2R0XJehyuW7\\nUWriiFjkoyDByJBA7qkE4Vkk8i1FhNe4mDPmaO6Ds3+AbnGZ1J37tqO50Ocn4hc2C+aQtDK1Mxm8\\n5cQ1G0kSKs40pZ+vUOweP8bPcfwxGuyf4mBwQrmYUsI0hEjqEa6azbEZffK8LkZfwhYyH4+vYfHN\\nE5ccngqJDaeIY21ytm88X0LOE7YbOiL4s+QUlJ8cfXh3iwFUn2Qp2FolCQGQmTIxbYGXSzulaNyO\\nSen9inRqFaYUNEdaNUO42iSVgi+yU04DUjgEW7+oWrBc+BxPjrExE5Qk/HjsDJtkyZxTuW07KTvn\\nuLjXSuaAaaH21JW2ZLr8IzOCXI8fqDbovVFyJY0eJG55Uba43ZudZFdezbB+kQrktGHWuCyoyJHo\\nHOvMYY6mxFFvjNHpM0hPsS4e8dBLprvBuFBNzNmWRT1jftHcObbKsEQfwr2+R+KXxZbpvCZ2Kn/9\\n9a+872+YKh3IJWOMgPcMJ2+V3ll2Zkh5kLcVb9c7SWq4UDel9cb0yH1IHqIwcqYQJrwxTl7tCmZk\\nD+1Fn84gmJU+L6aBa6VajgT2FHLSrFHlRriMcPWL23bQ3fEUYTKpFGQKWdIyX8Xwuary7Zcnv/z6\\nG2YR4vucHUXIDC7v4ebFg6bNJHmij8GRK5+p8evrZ3443mjWwGyJuEq0Wlvidb7i8vsDnz/JwRD/\\nmAglKWNErzTnE9FKrTf8/Mrz/IQZRqoiyrG/oarBNKyV4ZPROylPLvsAixi1aRfFK+CYQ9VC1hJm\\nrRTMxmTRL1/WUCpjPDAfSAqNQC0HzdeATnI4/VIErCJQty3oxhbrVZW4bVOqiK5CO2U0Qc1Q043H\\n55Ofr05BOPYvdAkUaNUEHuEsSeDY7qBC742M0K+IqU+/6zkQVAtZKqaNyy58VUwqhVp2woEa7VnN\\nwjRBPIegbP4u4DoA4xwXSYWjBhMxDD8hzjEcfOByIAjM0GaYR0ukssVqzBoplzigPTI6rhGHfxHH\\nSGTNMfT8XSzQegiyxAKfNhrl9iN5K1weSL8kEpDZ62SME02FY0u85ZgpaBJu243TGlHMKecVDZtm\\nYy8bc0J3i7h7A2HDPCTmZWVeDp8kDZevqkb4C+P7fKBGdjAqhqsyPeIA0IzIi5J3ev+k9W+YZ6on\\nsieSKzXf6eNB80Ym8fH8WHDaiEW8l4yFppVrOF2+saeKS2YOQ8yoyxxY1NFUSWUjzZMYCBXQA8+D\\nbg88bQh/g5Lo34ucosripsVtLTXsvB496rTozUrawTNaM+I9QBmzRlydBugFlH5F5HvJlTGvFX4b\\nbEE3w9fvzQKHqgzGNI4cw8WgPi8pq8eaz/vg0MiAMAm+IIRm3oXFiJDoqUuoIiExrPNDDgLw7oXX\\n+UkW5ZBA0mdKVDuaGdPZiBZA3LnaNx6ns2nmXm9c46TebqgEoMaAOY2ixnBAQ5tRJaovmEiOnIpr\\nPHk12MsbeyqBuvdY04mybj1WmR6bnWHGVndaf6Fr8HO9HqjHLMjXsCcGjb+nJVWEBqz/7rJManv4\\nGDwzzTj7SU0rjlAGLhbIuWkMOnDhGlFwYoN2vTjbxFr021/2nd6d62q4JvYSQqcxW1Qhs+N9ciuF\\nidLmybxeTDKnd3Y9IiNjZZCwblbVRK473i/EE7O/kOWS1CwwA2RrvgxUCBpTWva6AU9KLpR0w0YP\\nonObEWY7ezwjnrhm5/l8rsi/N/qIA9op+DhBFCmRti5zBl1aAE5MhLfbj6ivHBNZSeCauGVdUQxO\\nRoOy/Qc+f4qDQUXYt4pK5V4yL3LIcNf66nl9jQl8CkQ2ulR1autG1qV2i6BbnRHEMVuj1htaK0wP\\nZZ/HS362MERlLQyf1JLiwSjC9IuUS5SZEmrMYVGGpz28BlmEPRfGtFDKoajGcEklKoSpTk6TvN1w\\ne1KOQpLB+/4D/ttf+PpqXL1FkOv0gHB0Q/WgjxBnfdrJdYa5yBJcw9EU0tdJpmhlrze6vOgOx3bw\\n6heKkjyF50FDeitEvHxRiCBg1hFmYQYyCyBuCriLeLheR2uRXU70sGaT3h9s9R0I8pBoCm+AGzlt\\npCRgIeVufYDrCleJCHqEOEx9Ln1KtIdbUp5Lf7Llg6mRNT3shVjkLuyp8uxzaUsCrde7cdxuKIWq\\nCc9xsLtdAbFlshES+pduzOtkLwVcFoRV0LTR+gmEEtbaKzY+7rGyltB5mEcrEHLkjot8vzxsBIey\\n+wWSwrausOdEs/jC42cRmL2kW0jle1Ctaknf8QI2tlhZXo0tCZOKW1uMSGfLiopgYyxFUsBsfDrd\\nna0cDGuRx/K3iHYzj741Ez6FPlqs5gg5bdIKybntN57n47u4RvQI3YCEvmD0oDN7XtSfKmRZ7EfN\\nvF4flLSRNNKOkhZKKZyvr8s7ceF28hrGe/kBcSVnZfRwFGaxCDE1cJtcDdxim5BLZfjAGWuYNvHZ\\nSMnZcsiSe/9g2++8k/j0zByDPRVO60wbFElkvcEIjT04TyKn4l4zfXb6NFQH5RZORzVjLc0Y3hFP\\nOKGAGzMkveZwrfagpkReQzC8rzzPsGhP0aX9X8Mw0SA854PLJ6IRODNnZ6SC5kT8AeBzYCm8CUmM\\nnCvXmLFhWq2RL3GWaqxto32JTMa2YCsQpqm97HzWkzEaqSQkBZdhjM4gk7wwmCHcmR0zJUvBLSqR\\nuZgNjrHv9xAUifP1Opk9Xkj1GkPXebLXgOlmLZhP1BIuCVnUblWLmYFFVoObRGwAkCWt72P4V3yO\\nIHWVSN8qOUWVqc5ojbbCakeD1/VAZ+a0TiUyJ5tPkiS6XeS0cY22ZPmNnOtyf4Y+x2yELgUJHP5s\\n8QzmYIwMyeEa/YPaxz/FwRBJ1UY3uO2ZseSpPs9AhJUtIts1U0rMB3TFggPr5B7/zws/Y5frBlru\\nmD0wDCXTh1FH9O61RCBMWilSYoqpBTVY94DCjgcik5o2RmsBBFXFPVHUMYlE62kJ9xYBJ3kDN9oc\\n/LDtvO0HTqVmpxRF8hve/5mMkLdKaRGckpAoS6eBCR/XVxzj9vZTlPkLY58lHoCtvuPeebVvMUxT\\nYtptnUlh32/fk6erRP/ss393VsYDLzhBkZ4zbrmUKsgOdmE2GcTLrjmSl50VUecdlUrWTJbMthyA\\nWYVSjgU0ygxpwecUxZzgN4xA0ZVyhMcihxmo6s6sHeqLY26rjxfcV+aHhjr02U+2ohQVxCvTGnNe\\n5JKXkCtW35jixABVVdlUeU1D08717IGMU18qQlmU5jXzGuf3/E0DtnLQRrS5JkprE/G4wVe+NmhB\\nLKGpkjUx7KTkd0o+OObJc3QSwhg9zFz9EYcuSvPJXZSjpqB26U4qB3bNAN8QuSlZNeYNswUZLAUI\\nWJio5tBelBCm+WyU451kf4PrShHFgW6N4VHyJKDWG4PJ1X6jcGPaM9qJ0Jou4U2o6iQvpxs5HJZp\\nY4rx7fwVk9C+p7zj0wIPVytDgqs33NFxcUs3cv0Hnq/feNoTa58UFYre4yUiKDilHLT+GWdwilSr\\naWA+cDrSAgHm02DGPlrzyn6Yhf/4f/4HfntcnH0wZZJrItWNtJVwza3Nxl02NBfu719IJfPt+Rdy\\nuSE0fArtbKQ8qbmAG0cqvOeDRwl6VFuScRsv3vZ3fJwMUVCne9xCkZjcKOmgpjvYwKk8Xj/zfvuR\\nXSBnweTGNU9sXLGTX/Hs+AwxTs6o5gj+9bWhcaFdJyULedO47W3gssFYNx5R8c3R4kCikavy048/\\ncrtXZuu8nh/0LrxGQFxTPijVERk0EkfdqNs7ZSuoho1e0RVcnDnHi61+Ae9Uybzt/4Z2vUDzmgtM\\nfDh9PtAiK0LP8LRxzVcQvPOBS8JMmXOgEg7abXvn6mdE1XmEzL764K//9I/U+sZb3clvhS8/vrPf\\n7si4wMLp2a0z8o2yv0G+yNuNnGOAnUsBHwxf+adJ6HMypFBqYbQnmm8kbxHNNyeqFRfHUmLYxZyT\\nI9/h9Qt5u/+hd/JPcTBArCzTcvipOJLi5vM5KLIBPfp3laXJX0gtWRqENUTUlTZtduFrcBhIt8g7\\nyDkeqGmCTmXYQCxmA8MH7fUzbooQu+s+GmVVJqITpdDHM0pK0fDPu1EEGhHJJnRKOjjyLYaXUshp\\nD/bjOWjdg/ZDWKldLTwKHmo/0ZDM1luQn/v4Rq0/cN/uiDguhWs8yTlzmXLzHHOSrHy+Pnk9v/Lt\\nmuSs3PY3kBh+mhu9PUjbwdQcsJuUSfkLrV/RIyPQXlx98MUGc3Rq2UDS0jhcqHdca3ggDESFlMGZ\\nRI62M+bFBHLScHj6oCbn1Awz1H5ICqWrB0cgJ+GyTpLEW60U3RgKNnfGvChS8awkDaNUzZBK4X4U\\nPMsKxh3cty0CjFcf/mkBrrn6YMTYEURWLKKwpUKf4cDVtSOzlBCZobJNNaoGm4jDljLNLAKEh2LD\\nSMxY39rg8Xrxupzkwtf24qDwqYkfbzv7vvGkY+1itk4/O8mN4YMsHlyHnBFP6Gz0/smYL1x2RBLV\\nZ3htzos9KXmrDLsWHl6W09aYEoDwKSeaM/wtbiUAnMF0sNapJZFzuBZpT0p557TXkpFGItIYy3Wp\\nskRRFi/31Ti2ymu8sKkULaQMfZV6qmGWScp3JFkWJYuSkvIxToQdkYkPIXtZiduCqIaRaFGScmKt\\n6ZSzNUSNXTNjTPCB6noxVil7L+/88vkXzuZcPQJ6dVO6R4G+3Bh0jFyEvNdw9+XCj28/Yi709kmX\\nztlezGQkmQw52LfKl9sb//iP/4nX4+JCKDnajX2/0ewzuJfmEWU2voEE9cmG42MyNaEpc7XBj7c7\\nP7y9ofkLWeFyY347MTQ8FKo0i+BY1UQfgX2nJhJz9eNPTMLjsaUdJCPPX5hzhk1dYninIrhFm3Er\\nldZnuFFTmJjuu2PDeVlHvDI8pNRbDXYmuhSCuSI11neMC6aQZadIkJ0ED8z7HKGGlbCKP4es8nws\\nifSOaqHPB0XBqfTRQcPCHXMkQdKO2KTke8wAgNYuHv83dW/va1u25mc97/icc62196lTt9uX/jA4\\ngQAiEhISJAJCMkSCQEJyQgASgQ25JUf+Ayw5IAAhSyBBahAESHxIICTLbSEMFo3b3bfurapz9l5r\\nzTnHx/sSvLOOGgnat6RuqbySKu17zq1z9l5rzDHe8fs9z+NB0Eofho0n36NclsTv/Px3qdb43P+Q\\nFCrjeOf5eJKiUNKrr5PFW7NjDsam9LFT1kqJgefRaEP4+pI44oXn88kt+C4pFd/N6pnRKLmytztq\\nG7flz3mF/Ee8fhILgyvRxSWiElELdN+bE8uNFLO/gXUwZvfNAl57nnoSnUOgxApD2Y/Drco62ec7\\ngZWUL/Sp1Cy0dpCmR7A5JbN9NB7KeV0ptKHEvJJCcgxYSmf1+Ak4LFVD8sGTjjN+67sDtYmJa8kR\\no7Ud00hdArNF3tpOCgHCoNupc0Mch+aHKkfYnyWp2/rRd1C9U2slk5n9jTac2jTGgxkubMdG3xq/\\n/O5blvKBUSGld/87xMIcB6M93SAdbzC84be34wS6JnQoJQRinHR2XuPKCHgXRZScF3rff7jU868z\\nUGBNMMeAaBzHkzUvfjty3kag7g5J4aQNBWGcNydrKrR+fDE/21QnUp9kr1QKxQLP7Xl+OB3WGuOK\\nnAv7Ur3rot2vpkMO/meN0McD1G981II/iBhIGF6bDwk9I9TBhs8b1DsROQRSTIgoKTjfIOLlscc4\\n0McTkZWte+9l66D7xoiK0kg5s89Bu3/DFgOX+BUP/RX3x/foiJSlMmbDUkCCsvWNoJlPj29Z6+3k\\nZhwEDVxi5K0PCkJdLnTmedNkTok6SWAiRmsHt2Whb+8e6f4Rr5/MwqDdOYcqnosPYgxxj+Ix+9my\\n81uHnITDBtoMNR9q1ZjZ9gdzGMURvn7VJRefpBuUmlhLxrQzx/mEPq/LoiwsQZk5kmI5O/ADi/FM\\n7/nQDfP+RkqZEovPKPpwVXtylFcS3AMhkSDJn2bRYByseeFon5jrR2paqNm7HkRv3wk+O8kpU3Ig\\nxcK1eoz5afMLckznwpIDcxhIw9JCmu7vtG586k+uI1JSYF8atSYiQl0+EGQy9QztxkQq+qVjYOrZ\\nhJQKwYzDhHYMDj3I5YJZOstA5tzLEIjRr/SOcRATLMV9EUu6OKbeAn10z3lEVwCM4bcuwaaTpOSE\\nv2hDQvHjimRC8l3JEsIJPj1oA7LgQZGYiDFSy8qao7MWp9OVCEJJV0bfXfAjejIpD+Y08imQSaIM\\n/eGDVfwKWjuI+XDz/DmWFFAbrDHSprIfGzVduEvg8dg49jt73xAyOV9Qj+Tw7J1ffPdLrumFJRi2\\n/IxskVqvjKCktTLbQCXR+kYKfjUfUmDGhOokB4FU2I6NXC/s46DZoErjtryc2YaGxIV8zjACE5mR\\nfX/+0Cz6tV8/iYUBQFJkqYtz9AKYJPrxoKSL3yWHyVIvLjuxAJrooxNCpenOnB69JVemeNy528As\\nMKS4LixNT6INY987JQXSUpgSMTFyfsXmYEyhrFe/cz/jSgFfHEQire/ksp4T7IREoRTX2kE8m26D\\nGC9+PtWDEhI2Xc7ym7/5O/ShzOGcvxzdVp1OKlQOAmKeqtTBy+WfxOaTIwl6HPSj8+13v2R5/U3W\\nbHx8/UBdvmKfjT/6vNMaaFT2bYJALRs5XzGJlLJiendQi0XeDgeyzN7JIWFz59v373i53GjrQtj7\\nmeqbvNQrx/4OJ8EpiCt3Qs7+9CVyjMnbp+/c9HUt3kx97hyH06VSKX4l+OyYuc1cbaBRHMcWL1gw\\nQl04jif7OAiaKPlK378nhkRJ/vPq5zAxhsyaoIyDYMERdjGjOhyZr8H7E6mgekAqWIm07kfQGAOo\\nUCwCkWle4OpzokSPGTN9uDgnMfl8pCbhH/7iOx6Ppzcr9RQim/nOLFUu60dqhL3B3/79/5OUKzl+\\nwz4a+2iQKmE8sD452sBGZy6FnnZ3b/jGl7dtEEOjLpW5PSkkUlDKcqWWld7v/hDdPhNCReeDFBee\\njzeG+o7vx7x+EguDiBDjOYAxwYYTdwP+pkkxIeoDrZIrszXMzq2gxTPC2k6js28jU76gya9BowQk\\n+H3685gwD0pcfMw0OfsAwnN/8zqu+TY/iENO1SY2Jr1trB9+xpoL0yYN+1K17qpfotAmniuo0ReN\\nXCofrl+j7cl+fzDmQesDO1HjIRZ0Nqb8ECsO9Pb0aXTJ6NzowYGoM4CGCSlQCD7AM8N649E3bunC\\nH43vkSn0JbDGwr3tfMy/wRIcRnpM5zMmYE4DORcjUyxkarqwTxj7IMibU5GWiowDM2PaIEhEVRk6\\nCNk5hAP1XUm6ej5gHIzRUTVySrTZkBTZjyejd2qKPOZkWKM2pYQMooypbuW2gajPGWZrvO+Nx/Eg\\nhQvBa4NIMKCxD2MfF/o4cOnN4/R/xBNlB0anmQ8JLRTEAm02+nTTeAyBqcd5pItoXGEeHgETXFGv\\nOzr9e2RMZPrAc0xIMlB1rQCzs9RKzJWjP+nHhuWF2CfTIkNxqYwp922DEUg98L4fvOQFqVfQN/eE\\nKCyS2NpGjBMLSsRdKYIDh0IslFAxDS5JCgspFOdycJzj81//9dNYGM4cwRIzOjxmrDoIUoHJnE8u\\n5SvUTr5izMwYsZpQE6qIn19DQPRg39+/1H0hfaksa++subIdeEhG+plWc9iG9O4kYRK3C7TpVWvV\\niQjkcmHocdavPeG3H+8YhatTT7+EhWKE59i4RVivH1gXl55oC7z9we+j+UZNHlPVORmzk5aMhEmq\\nK0gnJD8738fhQFHr7nSsk6+uH5GYuZR4FqcGeQ62MSjJizg1BKQmcgEJyn46QQN+FJjTSVdCB3O2\\nY0R404ao393P3klV+apGrrffxh7v3LfPTAFwCKnqQNWvg4c1oLDklb19xkyIIbskJUS6DsQ8Hfh2\\nPDEapS7QOrGujOGuSv+5JcSUbWts+2fe98PDQdcEychpUuvNjzIhMYlYdHXc3p/+vjJ3Qpj53ELE\\nFQHRTjuTRVKKZwfiZC+YC3pCCs4XVUHjIMXqW3SF1tVNUb0TSKzLyrE/3M3ZDy7LB4iwt88e7pLA\\nsd9JOfvNgXkadM5BNRgWabNxW254efogl0gh08fO0MGSq2d4ku+EQ4jn4BbH1MdA08ne3in5laKD\\nD7cbxzYY4x9D5iNmjDE84KM4JedkCZQQvVNvgw/LCyoFHU+adkQmj+3pM4ofqq8xU6vz/Psc6LET\\ncKhJ70qf3jas0V0AQwdBCmPuxLQiJiwh8NY+eWgnFCcDh3ii4QrM4RZt7cgIbs0+sfYhiKf6ApQg\\nlJK4rheuZaVZR+zB1gfFBhpgnuf0pa7k5HSkKArR/YzX5Yp2TzAGzidkrNS8OyJtKagdjGnsXZE5\\nSDVy00JeE7VEluK27RRPKrQ58EUChOmV93S6IJXAy8tH7rvXgR9952OuRFnQPilnVXufnRS8U3A/\\nNiLGRRxrHwPukFT8GpnulqmY/AYk+LHJmvsTHAAb0HF4zZuFGjJvz3eUTNs6335+cyGvJBg7Yxqr\\nVNrxpJbCIPBsx9nLMIxKwuvdKSaYnTbHWXf2J20WQ0NxaAycOPwJ6jdGMRfaMII2Yl44jo1xPJiW\\nGf0giBOYEB+ONw3UsCApAodT5Q3E9Lyedfq0RKeL0zvH6IgUOkqp1W1mScjiKgXG4Oh3xLIDdKU4\\nV2S6K0Snw4JGcPygiWKSkTFQButXH1CthB+Jj/9HLiMi8udF5L8Rkd8Tkb8jIv/u+fWvReRvicj/\\nfv7z4x/7Pf+BiPw9EfnfRORf+XX+IGupXEr2c7UdWISlRl5uKx9fXskRet+I7JRgXC8rqLHvD9p+\\nYGfsOZ+I+T6e1LyQs//gmZ2g3uFPEpmc9+/n4CkEKPVKipMRhK+uH0mhMkn+5Ilewpljx+Ykh0pU\\nY40VsUEw799zxlX1hHQsaWE9668f6oVUb1T1u4eAIKIQJhoDNQWudeWWK5d65VoqQQqq3s3IMVFi\\n8MblcjvPusPjrmGw1EJnJwJSjKUK17VwOSPB4AvYVEA8NRjw2wKH5Tgv58PHP8flekXKSjTFBCLK\\n1h+8P+6gkRpd0zctcMkXcly9A2BuWVIRVH+QtXTGdCDptMExOvd9c8BMyERx6OmwL75z9nGgKI/t\\njW/vn5jtztDGmldKqlzLBRsCHXRm50aaHzfBn645uStjtI02mi8YsxHUvw8/xMSDOC9RJJCCB+7m\\nVFrbiARiXv3qUyMmK7CwH4PnNmljBztOZ8fJ+BJDz11sNB9kupi5etFpPEniN1hmAcmBWIyZOBdN\\np1UxJykmlvXKsq6n+WyyLiu5FGJOxPBDjgf63D3jYX5FnssLW3PE3WV9+RHLwq+3YxjAv29m/4uI\\nvAD/s4j8LeDfAv5rM/urIvKXgb8M/CUR+WeBfx3454DfBv4rEflnzOxPPOaYCPsc1JxZa0WCQIxY\\nnNSUaHNHUeZsDjtBIQkv14+8He8sdfXJq06y4Wfs7emuBc+s4lWXRMkuDpk22MfgpXo7Ufd3kkxm\\n2Okj049GCt4fSjSOtpPresJRhkdORcmhEJPTeW0OYgyoGi8he6rPJlfJHHkQZWI54MHVSVcoZaEk\\n8XSmDS+G4R3/ocYcO0SvFE8xVAcijRAKOSaGBFJIG+QipQAAIABJREFUHHR+8+PPOPrGerkhNVIy\\nRO0whSnd3zDRexdHa9g0hjlQdahRl4XRH4TRib0TQ8BP6ucVLZPWdpb1hRkqok+yFHbbaGNwSYkf\\n0HcmgqhDYCfuDjHDAzpjsNaLv8F0+JznVMiZdrbu7Au//Tz/7nP48JDkrlMiIUYe+8ZCIYs4xFZc\\nCoPxBSdfQ/YauHZk+Pvtqc09kz8Qw/AYuxOmCscYp0xYaXs7obqDfR9s20GW6UnVIDy2z8jolHQj\\nyMIwx9cVgUMN2o6IV7cV2GmMbn605WC93phng1JMsOl19V26X5WbeEvSjD78ejyct8CcqH8h0drB\\n0RspR7axwQEv6+pzoD/NhcHM/hD4w/Pf30Xk7wK/A/yrwL90/rL/CPhvgb90fv0/NbMD+Psi8veA\\nfwH47/+k/07JC+jkw7ISaFgcrEtkRC+bpPqC6eSxP8jJbU23lyt67SxHoKZM0MGmkyHKbBAsnL5J\\nw4gEEkn0/GYaqguv5eY+ienT91Izks7tdXTm3rVW9tlP0EhDQ2Sf3mAkKMNceJOC30ykfGGJ7oZ4\\nvd74arnC0Xh7vPHN23dINPo4kDEhVpI5Cm1JKxKNfT5YYmJaJotv7/fjQMR1bDWvDIITqY8DnYbK\\nnYDwW7/9AePiZmpzuEeVzPa4n3TkzOx+BLtcFsotUVLm2bxbMjrMR6M1ZfZGluhY/QlPfTolIE26\\nbl4QCsrb8StKLFzjSm9GCI1tPKnXK9adfzinW59NPYCTSuIYHT25Fh6LF3Q6FVuHh8DGcJahmTdD\\ndQqzK2rGdckMmdTofM+cCrUslOqD0d4OrkumD6OUQCmJOSYpJW9jhsTAKNkTnF7F9/LetEEbne/f\\n79zvT3QWZm/sRyeSeLaDpYBk1yemlJ1JGoxLcjGNTmhtUtPKY3YP7c1BloHkwu165XG/k0Mh5IJZ\\np8TA3udJeor4Y0lOloaQQ6KkytTuntC48nw+accAGbRxILrzedtZivD61QdSiazlz5D5KCJ/Afjn\\ngf8R+Pm5aAD8EfDz899/B/gf/thv+wfn1/6E/19n+EOkm3IpF28ypsqHNfMed6wnWrtzWRbnQEoj\\nxECQxYGacxLyAjZ53weiB2MKORSUTp8+ae46wCYhBnIWiOL0Y1EOBJ0HdXnlVleeFqFPevc3jWSn\\nA5VcaOz+JlMjJd/+jako5nYrES6Xr6k5cc0rM924/+IP+Pz2xthBSvLbgFgRdaDrsIlIZMk3bKqj\\n8oOTjdt0CxPqGvg5nAsolmAenq3Hz8JdjZqWLxXiboNcL37Gn+5vfLm8UKtnLyQGVlW2A+YQHvvG\\n6E+GNXJMhBg5hqPk47I6U4FTAS+Fr1ahDXN5bRAHmeQLgcAIxUUygHhayWc7vTEVllzd6D0no88T\\nYQfj2JnNkfxNPReaY3as/JjnDsYIOGbORsPyQpBING95llQQDeTkx7zZO4yGxUhOmUu4MMMkxsTe\\nntgJ1W3zQC3y3Haez4396R6JMRpCxopQaqTTyJIJuRBiom/vqAS2tlHXi2sPnzttDGrJPMcdYaGE\\nlWTG3h40ncRQ2ffPRISmmT53l/hYACnOsUiuYnTbUHbLOQ015eidffcjQyIQ5ErOzuqMIYAOcvwz\\n6kqIyA34z4B/z8zeXPntLzMzEflRCQoR+YvAX4SToju9YbeNJ+CK8sfxoNkLKV2IsqGlkGPl6Ifj\\nw3G/YsyRhoI2dCq3UrG0MlpnjM4+ExP5Aowdw28jgg2eCku++rXfcF9DGoMjuCoskNj7zlIWiJn9\\n8Y0DQWMkxXmqxxJIQQOsASRn1pJ4qZlkwq+2d1J78M37nWebfk60gEhmTQszOMi1q7KGKzVWmgWS\\nwBw+P0kx+9HKPDg1bSKSQQLLWpja6RM47i67UUXVSCExVPycqw68TUnIOTAxigg6lKMN9kMRzd4x\\nCUIkErLfnPiTdqX1RsowNCI426FNo6sQmK7Kw0gB1nSDMLgrjNHY+0aK1fXzMbAkr7XbKY09xiSa\\nnLCRzGT3YlZMTNdGg3Y3QE1FbGNZCpwyYsSLayPgi+4JmI0x0IYPcE2Ss0TpIN7L6KN5ToVJmEbQ\\nwtF37tvOsXWefWBWsBkZ88kSoF4vMFzua2bu8TAhTq+ap7Qw2t1TqSRKLIS5EKf6oiiJmCrMxlIC\\nIX7FGA8fgsdEySvbfiDqcOMomRkDWeQM0LmKr0rF9B1I9Gms9cJzNIoIbU4krcQ0GHP7MR/PX29h\\nEJGMLwr/sZn95+eXfyEiv2VmfygivwV8c379D4A//8d++++eX/t/vczsrwN/HSCXZDqgxECfnc/H\\nZwiVS4S0b9gavJ8QvDS1pBWvZfuTR/0t7LMAMV6WTJ+RXafXiGVg/YHKK8hEorifcUBOntG3M8xU\\n0np2sgYSE1MSKQ8sKpHGkq/uBmCSaoQZQQylUePEorGkyu3yc0pcGAj7Y+OX33/Drz69s2+Dmnwo\\nWnLwZmNwQUtJxSnZ6rXoPuxEm/ufEZ0eBT/TmCZe9OlSQBq1VGw4ui7YYIp/mFI4Kdji4SvF3O6c\\nE5hj6vSM0z51py43ohRiPE4tYDwLV4tnF7rhUBBj2MS+fPh8At/FMXYlJfrRWUuFDE5GPpGp9gOs\\nawA+vo85IBNKcd9DnYV9mxzDkDkp5exXoNR8ZRufnZagjngL0QE/szdC9vmMWWAcvvDlUlnqytAd\\n7TtDJ9P0hAJ5UMpm57K8Iurlpn0e/ue2gWTfXe7zSbJEKj+kNQ0ZkxLTl13H2/2XMI0glRQcvkv3\\nolyQQp6DoY3ZB5KVZSk03TnMr0VTyuRwcLTmtzEoIobESmuDnCAHeByNt+2JBD+KxhqpAULOrNF/\\nhrleTuv6n+LCIL41+BvA3zWzv/bH/qf/Evg3gb96/vO/+GNf/09E5K/hw8d/Gvif/qT/hivv/Twm\\nMWEhEWfnsAPdhUvK2PON9fo1Od2IMhi9M+Z0C/Y0Yswcc3MsGH6VuSwJ2uSY5lSc+fDtrwGj0/aN\\nXFckDexMxnkD03cJGbdha0mkBClmnsOlIhYcLlNLJEnm9XrxZKUe3MqFKoFfff9H7G3SmvLLz3fo\\nyntrhFIoKbir01yGEkJhSYWgg53JmhIpO3Jc1aPhIr6drqnQFJgumJ1j8xCWngueGHaWuVw025CY\\n0PkDFj0TQqGZse0b49iREKg5M2zwclt5Pj85gzNlgnkLNMaIsKI6STEw1THMFo2XZeWxHwTzvZyZ\\nMuaBRp/uT8zBu8dB0+mdCIuoQE2RGZzmrKNh55BTxTsk1ZTn7KyIfzhE6cfOMTrIxofrq2c2zNOk\\nUhMiQmfQ2452AYuMY/i1ndmXK2s9LV9D5QTEFkQHmymKYirUpdKGR69jUJJGrrWw6XB2g3qBL6dA\\nPw5G23me2sMSA5flBUWYZpSz6h6kgGSEwTEaZXQm+IwjBCcv4RJk1R2ZQkkX5r4RYmIfyhEdd/jx\\n5Wd8en4PsmGSKSWwlMyyZJaU0Xl4WO5Pc2EA/kXg3wD+toj8r+fX/sNzQfibIvJvA/8X8K8BmNnf\\nEZG/CfwefqPx7/yjbiREAjEmpk3vSZgybRKlIgjH/uRavkYk0cfGMK/qhoQ/mVQRMsUOyknMedsP\\njJXJ5KqVROLTeCfmC7MdYNG/0fMgpOL3dOdVZ1AlG+hQhm0sRbjUTJBBWAJpWchiLMuVePoHfuOr\\nV0qFNUceLbF/3vn06R/wvj3Zp9GOjsVKzolYnOZTU6ZmjxaX6AzBGNw2pLjqLoicrEpHHvmp2hjz\\nwdg7SyxIVGKIHvwiIlEppWByMLv5LYcoXbsbw22S44UQjU/3N1LvhDAhreRglJLpM524fR8Gxujw\\nGyE6ti2AiN//1yC01pCpHnNOntXfhs+BppuIaa3RzXz7PdXt08kBuSqBMXdyWqC4JXulcL93+uws\\nKZGiI9WnvvPcn5ScWUMiqD9lRZzHKBLoc0fwBfRoB607aHd0yFEpeG8jVbeWPZvRuzkFPJ8sT8n+\\n4ZxCXS+eopzvxFgcx26JVM/C3zjFNzpJeSE+uzMi5QdnpVKzm8tSdOmse0X8JqvMcnZUVkIUtrb5\\nsTFGolx98DqGD4QvlRgzitF1Z8bM9XKjyuDDywfmbKSYWddKAsIw2o/UXf86txL/HWdF4//j9S//\\n//yevwL8lV/3DyECy5LQmLlePzCPO6ZXagqUsjBMibgOfk6fK4QoaJhoUGJ2jgFhQRE0NIoWVAOB\\nlSVUjuRG6dEHmYTKRKqyLlfuz40YfEHKYSHGRC4rBaEUIYeDZU2sNfL15S9wLRdirA5ukUEOkTh3\\n0nqjjQffvv2Kf/j2zrfbBA2oGHkt5LxgBnEJlFpYlsplvaDq0ejrUn2rPg3hB5bgD1tuH5K1/s4x\\njJxW6qWgXTHNgKf98nLB1JiHE3skurlpKZ64UzuPBgyiwhqUuCZyXdwVKheEnXJKX7AzmyGO1xcB\\nxQ3bo7nn8lJuzIGfg8/ZxrDJ+/NOwDBNJyujkGMkMbisrxz9yQxGl8mcmSyVNSxYLex89nP6ywOW\\nn3HsdzQt7PZOLIUSC0sUXvILr+sLMTz47r4hOiglYqkQTPl0f/K4f+a579RYSOl7Skp8uLxS4sLU\\nyJwHx5ykuEKI3PfNBb7TsYKmkz4Ocsis6ZXn85ds5leet8vXhBKRoLSjk+JK7xslVy7X2/m9ikwa\\ny6UQwkrQySUvHqOOb+zzcfpLIjY6WCFKQaM6nXw/eL/7bVyIghrkkP2hxuBSEi+ycls/UNOgy+Jq\\nPvqXVKpTgn/9108i+RhEqFFdz9afpwFaEXCOoQTQ7kEWc2w6eIDE6KRYvagUnSy07Q9n8Ys4pScb\\nptNRZ1s7uX2KRJzlGCIxZ68+2yRqJCRFwuR1eeXDuvL6uvLx9hV57FgUXnOClAkWubfNYa3Hk/f9\\nwdE9UMU0JxvppJTs7UJRyrp63LVGJE6WFCnRB5BBOJHwAdNJSk6H8uvVJ4cqt/WCWURRbx7GyjF2\\nBA8tOS78LGGhZ6fEWRRDIYpheL5jqUIMcKDk5LzEMQdzDCJeTIvJt71q3ROKOj02rIOSV/d8mJ3o\\nNoedBEleiANa61ySL/BDXKwiuHHLBZeDRDxx1+Y3QKGQCdzWytSdXSe6P9nmznKpvNbKkhdKioz+\\nzpGV47nz6f0XXK5fkZcbgvF+fzhEloAG49EP9r555m0OjjnIyeEy1zo55s7eO2MMeu/QHUFnrGff\\nxkjLijC47zutN4YJNjt7O4gzsh+dZYns7cFaMt0mIShrvnmE3MCC0fYnJUd2Fbde94N+HKxFHCuo\\nxlAflOdcadaI6qqANprPWkqlxEjNkRw3xwpOyCL0o5OuK20+XQD9I14/mYWhpBtjTMb9QQwLzRpq\\nhSU71rymek6QO9N8u6gzIGpoUJJNUn4FhGN+RwgLEEjiFiiRiHx+Y56UpKlPQEgo2zCidi7LR1Je\\nECa1BJIdfPXygd/92Ue+ruYTXl2oIRHLjdkG76Nhu9AJ9BCwrtQovL4sfPseGf3wroYWQorEGClF\\nqEsipUBJxRmJIs6cNK9299ZQnQRbv8BNY4gsMTOHYbp5CMoCj7aTgpKia89yin5TQkRwzXtMCzFG\\nDy/FUzmPkEqlm3lVd3qBaYkRKZeTwuwSVpvtvFd3UEtI3qzM5cJsTgcSmYSIH/NEQCY5VWzKSRkq\\nzPEg5nhGpxNYd2HOMUAq/WhYdBZlbw2dxiVnRl54e36iBEFkspbIpVyBhKZOCp1vH3fmASEfNIUl\\nFWwoOjtmgzFdP9/2nQeNZxuMsXN5+Zqc8HnUONiPgyT5nJc8qeWVYYMxI9MO1ITRO8F8qEkwakr0\\nGDn6oGSnKWnvHOLXjDMaOhXTCSeFzKKS00oZnb09CaFS8wWzcPo65ctO0SJIexDzlQFM3cnJb2xy\\nDOSSkZSppSDDPR41VY72QELw3sePeP0kFgYzSJKJkk51/SCY0KdRTvPRDxJUOSUsk+SYspjQbDQg\\n6ySnTAoLYuaLggQvyYhTlF6y0bSzTyMHdxMsKTj9cwwsDpbFz/mvyysf18Rriajs2PYt6/IBMMZx\\n8P3znW++/47j6AzxcpdFI5eVxSa/9fEjb/vOaMOJkSkxw8FlXSAaa3aobYrpjG3bOeWeTA1k8ayD\\nnrcUJr6zaKOdqbf5gwMYt1wlf4qkeBaBFBWQlDzKfTYVhw4PY+GhI7+tcEBOCnCMcVqahTY8Zisi\\nnu9nMsdBV9+d6ZgE/NdNMUpODj5R/95u/YnOHdNEEndKOvwkntj5haE7FmDfP1PKipr4gq+G6GTJ\\nhXEpWHiB4EbuaRBQ1hzoaryPgyzCfWy0ZyRkmAUO7SRRYqpMqeiYtK7EFMGgNSNtB7I63La1wWPb\\nyWEiNj2Vuiz0+xsakqP4TGlDySEzxsACzDmxIOQcyTE6i2EGbJ4GrdOONa2ftq0nUYwZItf1lTkD\\n2hslZkaf7PtGvn04N1Q+l4jLB4+o44PyHAM1RXIMSDRUd44xCTiEto3NTWWRk+j9679+EguDmDHH\\ncDVYyOhodOnUIIAbq2NdsO5QzmM2zDo2jUd/MufBV68/w0LkaE9SmB6ZVaOkijBIOXBdCp/e3hwb\\nljJmnSzCmAEhs7UNkU7MlZ8vH/n68upS0xiILLCskK483j9DiTyOjV99/o5fPN4ZptzqwnK5Ukvh\\nNRVYYc5GC05eHqqU5H7GNa0E8+2zopQgHM2JSjDJySPAkeDDPhFC8NRmlOALhQ707CVg5ji66QTr\\ngANF+2jYnMyQGW340yPAGN3J2FLOodkpxjEc3S/T6UhxYMhZLvIdRJBCFJ8sm4pj6ULx2yVT3y0A\\nmGPUR6yck0zWcsH0wCx6SCu5u9NKJllHRU9yc2KpheW8ItVQiTWeeveFEvzX3vcnKWcWMsfolHwj\\n5IWpB10dNmuheFR6DiBR8+t5NDooaWXbn0iohKz+wfqSE4kMBrM9selcBsNRgjlFSr4wWjuZoZ6q\\naToQqect0umSnH5DMSUhJqgmgg0sFDT40z4jPPc7I16Z+LzG9Iy9Z/XBdygc+50lrZgpJZ2eD5uU\\ntKKqBPO2ck4CsviMCF+0fszrJ7EwIOIEITVKyLSxUXLm5fbKoCOoDx6pHvo53ZKIUSX7GXcoW39j\\nWmNqw8ZBlStzPgilkEPkshYeR6Y9N3KsNFXe94MiL+z9IMVMSMIU5bZUXoqQaySZ8X48CNpp4qGW\\nIJkugfucHLsxAoQizONAJZAyWBgQGrWuLPXiPgJTUnL5rQ0l58Iw3BeRCmO6Ybvm6rFpAJvk6FTm\\nY/azsBWcEimBFKGNwXPAa/kKVUMT1LwQu9B234mkEDjmgRFO0Yux5IJPItRRdyaIOb9x9B2f5tj5\\nplMvGKkr4gnREWKzIwo1LXQCYo05d8jp9GjiMx8zDxGJF7CW4thzC4PWNpDGOMU0apxzlicWIiUI\\nFhJLvZBDoY2NnBe28cC2xtabD0+DHxX7PHgeGzk5wk8tIMFvZ/b+JOqCyWDNzmUQE3p3PFcqxbHs\\nqliIpHihzc+oRK9LRyd47c93cnCpDgjr5YUgD6eDk0g4LaqNQUkRnQWd/Wz0CjoHlenA1pio5cJo\\nB2aJSSPbKyUFEKdR5RTc8CXOdkwi5JQINv1ncCIJ9AzgERJze2DB0QM/5vWTWBgM/KmsnHbjwEUq\\nY9sh+03dbP70adOba007L9dEm0YMriJ77neuywULcJ9PNDZ/4o5Jb3dUEpfrhb3v7Ptwm0++crSz\\norrcKDXwGx+urGvm9fYz9tH5vd//JY/3B9+9/5Kf/+4/RaQR5I/Y6ay3D/xGuvGcjZwzwkQ1cTRh\\nSmR9+YDE6JVs8VBWOmvOdV3pOjnzzUg034biLcSUOH0TAxVI8cIaMm+PNyzOs83X2ecgxswlLvQR\\n2W0S+0Bb53a98FI/st8/0TDiOeRrR6ekha6NnH1XEiXSTygNgKpiYg5IpWBT2ebGkrM7PHC+Zk6F\\nbg5RkeQdDQw3TuOch6Hm3YpzMBpDYK2ZIMY2Jn0XDgI5uYvTA2cRCwOLgZqvvJYLQ4XWnoiuPHcw\\njfzq+++4bxu1LEx1WniN2Z/YKTmxWhWsocPIYTh3I/v7SFTp3Ts2agGCMzgez8aSFp6jYWN6YU1B\\nR2c2I5gQF+OafXiZ1FuyMzj7chIYFljDymPbGOPuR7J0pixJXqnPwu2ycsTJ8xEJWn2gTTir9Vdc\\n92fcluKt4eQy5SXA7EYa4SRouy/FQuTZOpar90/+cUS7BXEnJSfAIkSfaEcN1BDZ50ZaCr09sFDo\\nBOY8GCRCdHpzSSuGouIcw5IjlhKqjRwWjxzPDubn6TnPHoD5vf0triyXlWVRPlyvLCnznJPn1vnV\\n23f84tM77f6JLXbKUigSWJZCCDeaHR58SYUkyc/xcxBTxHogkBCBmjM9DKIFD9kMN1Njg0jy4RR6\\nbrvdHC1ESIGzn8iw4gJd9a5BEKOkjKkRQ6IN0NOzmPNKloRk4XL5QGqNqNP5lRoIkhj9Tg0XR5Hn\\nK9kevO1OQdJTR+dxBj25h3ZawiZjKLMfnqEIGaIwdfqAVxI6OikmohQI87wBOlF3MbGkRCyZRSNv\\njwdJx+nCPN/E5ruolHxY12Zg6JNC5j6MPt4JoWLBHZ5yznGCKAMP9ug0VCMpJsZo5BCYVjl656vb\\nSu9Kc5YwJa98fjQSGymlkxVqnpzNjuD3oy5eppPISyyYZKJ2Zu/MvjMlkkv1vyu7R5Uvld4bblWd\\n3nuxSVflllYQZakLY7rGvqiSg1ByJEYlIJSUqDGCJRDhkqM/VMTnKClkEk4sCwF6gH02pmVq+DMs\\nUf3ZvbwObda4LKsjtEx5bk9UVjQI+/HOPholGTFWUrzCPPwJKwnTTvR8HWbCmiutHwSMfWxoM47N\\nr5RMlZoXVKbr1UWJaRCT8/yWklhjZu6N98eD7x8Pno+NXQ8iH7BmHEFP1mOn1ODXosG3wOG0GTWM\\npsoyjdabHwt8bH8OATsxRaa55jyKD7aMM/Bzautiyn721YYEF7ronKhOSoxOL5JIm/08O0cygo1G\\n18hLfGGGjXC5IFN47k8sKGKBY55D2+VKSYVpAws7ox+uhTsRbjF4Vb2mymSyz46aUZYrRD9/68QX\\niDPPZk64o4/jjPgu/oQ2I1qDsFBEGCnzVa08tJ0x5tMcfcpij+67O0+vJo7tzn7f2ebBlCfHnOcu\\nY2AqzBgY5rBbO5/cBswZ0Tlhdi650uYgpswSEqiytYNk0PeGVZ+1RJ3+c4nGUycSsken1av8bQKz\\nEUwZdqAxsFsnS2FaIwa/YVmyF6VSuZHTwhy7v3eDurBG/eGRkpEFan4hYuQUyKUi5mZrZbKkhUFi\\njcK+PyFWcq2EIJ6KHRuHeuekpIJp9LDQj3j9RBYGHFleV4IsbPs7bc4TXbUgNLa+MS0i08tTOQTW\\nemHaAbphoZJSwbQjlrAfyiWSqTJ5mnFsn9mPByKXExa6EwJsrbEsL9R65VqNf+LlZ1xU+Uxi3z7x\\nfP/MW5uU4PbnmDMlZ5wd6vJQQmTrG2r5XKBcuBtjgJQoFk7Vmp/3/UZBSNmhHDonQ4USCxY8n1FT\\nBnX+wpLX0+JtnuUw3/oH8cannCRrFdfJGW6COrYnppPr9StnWbI7rl4m+5gupMGtzRMIYSHkJ9EM\\nk8Cc7XTduvh1G3aKV10baMn1dF0HvR8EfKZQkpAlORuSTsATkY/+iVI/ui4uV8py4YLyiYMaV8Se\\ndDPPsOj0I6aoW63ag6mB+/GZvTWSVPq809sOUpxQJb5Nn+oLj879rO/6zZYQkFSZRC5RGECIK30e\\n2Bj0MU/lHgw7MJ2EtDqF2zaO/kC7sMSKmv+8HUijLMtCWCvW38iloM37KnN67Dmkq9feZ3dD13ol\\nhkYbg6SJnCu1VK5LQU0c5BOFII0YPImag3eG4tyZVMqyIOHGUiOBTgl+FKkSUPt85mwO0o+kPv4k\\nFgY7QQCa3f9o6m9UNWHsG8ZBvV48pmow+sGSC2LKGIcXj2YjxcAIgZIujhjPxmNzD0TrzbFuKgTp\\nlJgY+BS95kJIgSzqtV4G70fjl79649tPn9m7OUE4JoyCpMxyfTkXKP/Bi3lhJguY+dM7BX+SiZ3I\\nN8MjskynFWnHhqPi+3QZro4DxdH3YSpJjDbxoet0pb3ZadU2GLPRgtedzYwgLjntcwCOszuOJ8ty\\nJQRhlcAUpVb3WN7KlW1stDnheBJSps2Gjp0Q3OgUxbMmUYY3Ts0hsi5RPUhRMJxQNMZwCG6K9P2N\\nMbLPKCRg84CcWeSHGYISpxJz4FoLe3s40+AUxE7r5OAWKC8iKYkfzNONY7yxDUNSJUuimSI2TjpV\\nRedkqqAxOlh4OiY/iXnFuSkxXRyY05UkiRTwUpZUlnpl294dLGuFXBY0XRk8GeMdyZWYMkH9itIl\\nW5O13ohmdIHG9A/zFA+NxULIVy6XiIWOpkw5/RklFwidSWcthRwCHSipOq9SlSTRCeRBmTQikRq8\\nqp0Z3LsSyaRyY5iTpF+W6ru6H/H6aSwMajz2N46Hsa6vnkEIgabwOA7ElBQCKu56tDmRpMi6+BMz\\nZqYOnm0jFf8mTXXDc5sTVNh25yLGkJk0Pu1PaoqIJdfIj42umWW50tvO3/+//5D/45tfcOyBbW6U\\nVLnkRC4REdiOg0uMPMeD61J9tNM3JK2uGEuVMQ+uJVDyeub3/TihFG5LZm+NiHkWIARGNBI+2Kux\\nEkxJCCVC740lBfp0wemx71jISMhfhnXYxC8wAzVf0AD7cSe0yTfvnZWBjJ1QC5clI0XYbefjyxUh\\ncL+/8f64k0gcwZVokUCUwD6GJzMRenc4bq2FqcZjfPYbEEtnW1X9Wo7oWLiauM+Dmgq9Nbawc10y\\nb/tn5v6OkriPDUvDm47JTV9iC6hSQmRqJ4kRUuC1VuKHgMjXqBr3484xd0aDfQx3ffcdlcDlsvDY\\nOr13jueTEBI9JUpQKqdurnsSs03jkhOcpukPcLCOAAAgAElEQVRrWFluH3l7bEwdX/iR6+0Doy9M\\nG3TtyNyxuJDFITjWA30aS8nEdYHtnf3pkNs5+zl4rpTlhesaTgt78natuKIwhwiSWRCObfcYfomk\\neBbJzibn0Q+2vnEpFeLizhAyKRZuyxUbuyeK/7HExwcBIoh/4/UM4cBkudzox0YICdOBjektN8mM\\n1gnJ0OTddlEP0HD6/Vrz8I5zDrPDYM3vzZfkKvggRpvNpS55ceuRCY9jpx2Nlm4IgVAX78/TyOlC\\njI6OXy5XgrgNiGAed50Nop/Hc1g8q2B+P+7wKE8yhmCo2XkUCBBdOJKJXxBonFViHRMs0dtOysWL\\nVeIx5yDGVK9S17rShxeYUjQutTBT4fH+DbMsLDGQEdqYDHO+w/3oRMODM2FyzAQMBK8Q93EQgsNH\\nkQjqVuVARiTw8VZpw68sBa8um6k3PHX6EShE9n64UjBk/5DJyudmTG0eWycydWfqJCafVaSY4LzF\\nCCY+nA3C68uN7Zi04UPg1SLfzx26Iuexq+ugPx+oXxOQY2UfB1E6lBfKSeqacwCO/t/bk6UWSjRg\\nECRTUuL98fD3IIMpoGfkvC6vjAk6I0NdlBtRLstClkkqlTmFdFvRx0bI8qUMtq6B2/9D3fu06rZt\\n612/1vqfMcY751z77HOjksQLImhBKwrBryBYEWt+AmuiYMmaH0BjUVC0pljRglhTsChKFBE1CAEL\\nMSS595x79l5rvu8Yo/9pzULr++QWTG62JHLOC5u99txrrjnXfMfoo/f2PM/vOUpwMs2RFZGvOeC4\\nmoS9PCgSQbuyeJ+msVN0T2Qt1MziYBjDHdGxGBxRaVBTodvvYXelAClX3vUtNOzRcLOI3c6Y3LbR\\n2OvG9MSjBDXnaj9SPHP1QZqTNjpIdEm0qzFnQjR6LJ/tCt3fHJcEa86fVIm0bQ3noDz4Vb/5enc+\\n503RN+rxC7YkoWTkXwBOVkGzUqvQJlhv9H6R07YAKSXgKkGCjNmIBloNu8OrkXJYdq2FWiIRu+2j\\nExdl1NNr2rHkMJyaH+vs/xXNBReNJKYP0qqID+ZiPCECoOKI7mEis2A8NouUo0zhR+tkBpsMzt4Q\\njyDZmBP3T0yDOeEEw1GkICitN2qp1FwoWukWpjMn03oPibWUOAKNGcPUnNDeEHnn8/Pi2+cn3z2+\\n0K0FdEcVIwjcWYImPc3wn1qvRCkF7nnho1HTQS0Pen9Rc+KbK/26sNFW30hkTwxAgm0QANVB90QZ\\nLRYdTdiIykBU1kMAnv3JGFf0iTqM9mKmQhKjJOV+fnJfz2BYbBsyB65OrYJLYq/CZYXv9xouRq3k\\nonzshZKF4Tcf+45YIpEDasxKdxJc0FJ0zZNiToXH/Ao32giVKxPwloSRcqHbja1djmqKgf7PeP1O\\nLAwIPPadYZ0jG/XxHZ+vXyGE469kpezKsdXw0M/BoYnH4w0TxfzGRUkaxZ6YM0w574aL8HndvO4n\\ns0dAaNOCpihf1VTRHASeH8+TvH2y142L6JfwlMEH1+jsJajSWRyRikj4KLJWTEYMISGeCApXu4HA\\nrGnaQi4VBzzO6X1EUCpValVmEmZbgzYzsnq0H/krUh2+Cl4kvm8TQDqSK8JE0x6MiZTZShyvSq6M\\nOTlgAWEKxmC0J5IffJ5P3C4e247ldyrw9e5oLuRccItI/Bg/fQ8r/7BgL92cdwqWBbHEaWfg/92x\\n+yZrYfaOTaekjYwwzk8SmfP8xhgXT9t5pBzbcg2ZWnwlU1nhLE2oJMbsDI+ttqWokxMfqBlv+4Pn\\neXOfBiNathMBtiEpZ/vG8MSeDo5ayQl8yk/x1bVLXbtK36KzVCpDB8pAUiU5EQ33xp4SX18/wFR6\\na+EryUYulWkxM2ijx84rK1/ev+eeRrcwmdWt8JYURkGUJTMm1G5YlKuUwxchWhfN2yKNW8IWXjUg\\nRm5hSrNpqCeydy7v5FpIuYY79me8ficWBhGQ3XlPD1yEfjeKP5AeLdBJAQaXPdcKWjCZlFRiy+p5\\nDSyduTBlrY/QhH1ic66b92YvBZFw4CHKmDcqgtRCwfEu/KZ/Mq5YVLJPVAaTyb5Fn6SJh8vMYTYL\\nBgCx8wga0GRYOA2nsXo3r6BJBXwg6u18tU6Nk7K/M2agxgCu0Rg+2ZNCPsAjtx/t3oL5iUgOh+Lo\\n7Dki22gKhWFC652sBUTIObBuV7+iv3E4yTsZgfrBMOdsnVLeqHWuYhhFiKHXppWSA/7iEtlXF0EU\\nPs9vQZn2KAVGDSPTxghOgw32+obmEoixlOmz0cekzYt5fmXbvkTs2Du+NPneo4lKFMyjRav3wLRP\\nDe9LEgLyZ8bn6ysuiSzwHGfg4PO2dl6RBD0CbbOGs8K4G6I5CFIrmWrUqCC4G7UWklRKhmX+jgYv\\nTwFo1cKUttKhccSqpS5oThCySk1UzbzuJ25w3d84xgP8Oz774OGKzkHZH6RSEHfGfZLTzrBGFsE8\\n6OMulVwzRy587S2UudHplsml0oi5EhK9JFqiJMd/H9uuNSnf/XKn5g+mZ65f/3Wy7uRitNWtCJOj\\nPhg643zcX6BvnO21VkNHNOPjZg7n7halKePJGI6axvZaNYo9inL1C7PQsGdrDDdmfWPTMFgV6goE\\nEXh6jWHbUaImXUjUnNcC0HE03jyiLyKnTIo9LH3MmBV0I3lm9kEpiVRrGLnKG/P1I92N1gftbigV\\nLQqz4xa8SlUwRtiR3cGcZBORA5lBwxIJ2pRKpCgVp5TK3Tto+OrFVr4iD1yDgtXsoo9Is2aJC9un\\nRBdHfeDeV4lw1KvZjKT06zzjiJEAKfR+BrBUdHWGblEqaze5BsKu3TfDhZLeli8gjFMpFVKabPVB\\nah0sFjrvF7isaryGys4kjGQiQk4Vm098QifO5JqCeZlSSKqWjqjM858WPUjrJhZZHhgR+viKcqD6\\nU2mwLLn4RhQiy5JJSZjZkcmSejNpScQfX94QD/7lT0cZLZWSJgfvtDH44YdfcTzeOEpm2x5oTuRq\\nvJcv3OXAgc2NMZW+vg9NAmTaVHwozYPOpKnQZpjLosPDqPURpUtzpWx/xut3YmFIKnz/5QsF+M0P\\nv+H7ty/k+sbn6zcc853r+cRv+PHXf8x2vIfRhYLRAyJihq0A1RjO1x+fiCcwZfRO60aWiaUwLyEa\\nE2rZ6cNpIpRdeLx98Dpv/tYPv+K+A5WmsyGpohJ1eDajZGWOHrizRYfejg/u9i1q9CTcnKOdiNSA\\nx8QJGptw+ytcdWbcnz+w13fG85NE4XU3zvPidU5MjKzfgnXJTsnOfmQmg5yc5CHBmQXduuiByItp\\nkZwUEtNie9rnjEp4h2tEI7RqzCdaf4FHuY2PuNDSCnMNG5gU7vuiFuHt2DAvvO4npGgOT5oje+Ex\\nkM2pYhi6J0r6YN4/kjWGf7fdtOtF68IYAehNCL0b0y628mCv71g/GT55nj+Q9GDLGy5Ou09ElXE2\\nVJWSawwQhyPyjo2viE/27Y1pL6Y3sh+cfVKKs78fXO3CpoF3zDKfn1/J6Ygd4nD27YOcjM/7pPUE\\nwygfJahVGN0adfWT1ly5+yBnXYPgzPCLu92ohGltpkSuArtxpB1/Xlg3sgfjQr+HqRMbJ+6Z14zp\\nQmxuwiXrWDh8y0a74/vPHrua205yekdMEMm8vf0S4+LsN9bDifszTxJ/dhPV/z8vYU4J7FcOeKuN\\nM7b4OknZETX27YNmgSmPv23wCOeYjLvRm9GuO85ccwbJyGXp/Z17TuaqU0/lgCj9wh1EN77eF6Nb\\n5OHnqixTDdBJErZtW+g5p2ihJuHImSqCjzBLdR/xRCXq1H4LG5WCmKPT2Mo7iFNLYdveI58viTYn\\nfThmleaJLVW6h8nI7aLNi2/Xi9d9YQ59NUgJGki4BK/eULFA0ksma6V1uPtcoSRZen5sp5s5M34K\\nIY9qIafMnI2zBwi1qKLlAZoYCG6Nuh3k/YHnzPCBqIVt2i3Yi2Zs+aCdP3CfP/DZPlGMOZxniy35\\nW93Dquy2ZNo3CoLdJz6dTKbmIwp35xVPbCmMGYuQjYHNjrozxk2ecPcVUirKtr8hmrDZSGbMMWij\\nRUdnFpqFu3HbPhhOgGo10+6b1zVgZmQYc3aO7ZdkLRz7B7lUxrx53nGEiKbrAeOKRirdOO9JH4ST\\nEycnqDXmOzkljrLFzU7CkyBqPO+T13Uyu3PdFwY0HzgnY16Ix9+3z5s2P+l+B9rNFmzWnaqRoThH\\nDK+FcNjrzzQ4/c4sDCogWjgelX1/RzwgoR9vD/bHg1x3rGyIRv4dUfp4xgUhGV03nRrrRqkxwbUZ\\nEo4Utn1nqw+M6FEUEa4esqiNSZkxeDrHzWXBSiy1kIuQc8I95hrZnSLKoSkGhwKjNWafVC2BNrNO\\nyiFDTusLcjrI6cD6TbLoGEypLgkuFrHp4BrwjbZuVrfE2RujR81aSgUkhnE2PfbzwNlfPHIlySPk\\nLgBN5KTgLVq43SJyLSlapnIF7wyP8zEIiK5q+Q1NkTTET3IWHttO2d8hH4FmIz7HJSMoeAWUmgvj\\nevH59Sv9NpI7P54X7TZqKrgZ1wym5Z4DVJusY+MKt6FBssQjv3HkGlV9fazh2oa5xlN/hj28eON1\\nXzAmrhmXyf544/H2zhyDz+sEF6oIc0ZiQRWGDzxnXDyANfUAKWQryFRmbxGZbk/mfYVELgKSqVpx\\nL+wlkHUiO7MH/UpFkHWsbGNgFErKfDneeOwPPBdyEiaNrBv3ktbdjLMH9Xqak2TD/CBJYs7YAdtS\\naLZ9J+WKaF7+h8w5On1OquxR4WeRiHX7PQS1hDQT9WSf/UbNObaDPiVWf1XMniTZoRwIUTyac8Ey\\nYLKm7wmTjvvFmIM2RmDTfdDGxSP9QaysK56bNHFsFYiW66YVG4GV2zQckgbUUuIw4BKDUo22onMO\\ndHwLN+ayEBcSQyItOQTIgkiNElLNJIXsG8OixORRKubO2Rv3cK7RETI5p5CtdOduZ0hSGlivVI6Y\\ngM87JL1cAkXug8zGMKeWA08becV2fZSYx9hAcsbwlYPwmHjPqIaLsFE8hTVlpg2ywr5taGIhyAxc\\ng1FA8CuyFCadPjqphEuQ1ydv5eDz/srzNcl5LghvRZIgPqKgZl0DfV50H1QRMu9M8YjBG/TZaAbD\\niIHhjJ3JlivdJs1lDS6dXULeu9uThNJ9BMJelXsMrvtkK5VO1NhpytETIhqEsBFtWWdrkbZMg217\\no6hEB6YImrbowPBBLhvXfTL9ZDu+hPKTo5quJuHYHxHfJjEtIMAVR+RgiqDmzKaB/c9g4rFoeABi\\nG8KxVe520mc8GJ2gTbH4HNYH6KBNAxGOHO1hqoXZGs/79fPuyX/A9/j/p5djQSl241Hf0ZzRUtm3\\nR9hrRVANuS/c7XMN3xJHfSclRbFFb1Lu2WmjcffYerlOtu1Bco9/UtxkKExR9n0LOGt5p81OG517\\nDkQyNcWUu+TMsVe0CAaM5QloNuma0JJRCeZmKYX98QFpx4BSH2HWEKHZXEcUx2l4grpFa1MwBMOx\\nl1PmKJnH9vFbulVND/YcWQzmjVin5C12Jd4ptTKQGFJmoW6h2asYNmZYs5OS6oZrBL/MHVmcBSQG\\nm/HszwvsGguOirBtlb2Ed+K8TtrrG+f5DP+EO4OMSY15xhiUvFPrzpEfJIlQV9WIoG9ZKcnCJSoS\\nHIWceNSNlA5cMqkkXAUjrz6GRLtunueJYOzbgxS+MLacMSCRGTNCXzIHfUTvZVdHZAQOrVTchT0/\\nyPWglAORRLco1Y3ezZATpWZyLvT+I+aTrSQeJdyo5pPWR3RDSMJ1D3aoBkcz54IrDNXokTQhzYiF\\nTxvU7eC7tzcU4W3bIk3qshreDbObNhuSRjxgVCKxqoU5iUyQRzny3V6YQUk5eijMedSDLcX7Wn4v\\n0W44uSh4QqeiJeQl7402J0bCZFCKkrPw2QJgMrvxtkfH3+Unbd7RTL3sn4lI2mFGKjHAcRSzKKQF\\n4VEzmkOEutpJuy6Ykz1lxBv4TtKAoeQUuwzxSesNJTMcso1wMlqPrDyF+3yFg3MYNk5sxmDOhRW5\\nlSAs0aPzURKtD7o79DsankhY/wQtvJedQYv04kKmx1DxiXgg8ZIu+pAaFKEm57Xq4WKw5TEzWeYf\\nFUcXwzERTdGv3tll427fqLVG+Ys4025Ud8ZsARtpN/f9DaOy1Y0tBZi1i5ByYXoMRKfZkmgTvtiF\\nqeS1kzlQhGmKWVvRasfHRdGCkrA+Mev4tOVDgSOH56XPJ6YZQ+kIkhKmcdwT4HlP1I2tviEWtud7\\nybXhiE1M0UCgzU4W4fX6xMeg7l/I24FqNDiphSlq+uRlIC6c7QbLoAVR5zVeZKtUEWr9iMYyH7T2\\nIpUMc2IipORsW1xfozl7VVLNQOR/8Ni5aI4AWkrKs93YuBFNvM47nKMacfiaE9t2kDSUidFPej85\\nkrBtH7H7LP/g267/ob9UEskWkEUme8k4mXNoNBVNx8tOaxfPzyfNjKmdum38+n4GELMG77B7p3uj\\nm69z9E9MxEmtCcQitDImLh1PhhFmmt6c2a+AsKYorNUEWw5PQtKCuMaQTiL+HOzFhSdDcHOus0dY\\nKgWtdzhozsu70ElaKRLhIPHJH31+0q6T3uGcjqSJa6e+P9jfH8j5wgX2lNFkbGlGqCq0SJqNyD5o\\nRLBT3VEVfvj2G8C5zbHRUSlr6yzxe0S4V3R9DEd14z09MHWOKowRw0efTrfGfE/Be6DyvH/D57eb\\nPW+8pPEn8yuPLdgBQbh0ZFSKV7a8IyWxb4X8KJwjCoHFM3cbkcWwJ1UzqOKqTAuzl4jS78G8GiVt\\n/OI4kKwkeQSqTWC6RvfFvLheJ12d82y87Tuk6Jm00bCrY0OjLzJXPGXanPiaCxyp0GfjON7ZauGz\\nn1zjGXBcP8KclgolK9er4d0CD1i2MDV1i/lXSrQbqiY+26SqcevFlz94kGTyPE/e9weTyWOLkp3r\\nerGVDdEaA2VXsj5i0Nic6TBMoqz3NuY0ZjYej0cMJhHabWRP/PrrC7PGvlX+wv49ORk/s4jqd2Nh\\nACXlL5zXN0jObXGYvPvNeX5D9D2e0HnDx+AenwjKs99xRvXGIQeP+s6z/cgAzG5S+SC7Mte03Zlr\\ngu2MflPKHp0WKUxHbmAWiPWyshPH/raCKxF3tTFWknPDF9refLLnA9MUKUJVxgx028AhLwy7brjF\\nANAlmp2GdaacSE14cjYcT0LOBzVttPtF1S3KXmxQHSaVkgvWb9w1NHozas64SAzw5MF2bKgoXC/O\\nn8pLNOES5ipbPMA+Gr4MWU54IO5+LVu1M0dnq+98+/aV5+sVQz+EJJlrPAMGw43KQbIaEV/VNSCO\\nmzCnoG/1PgK91p3eviLsvNqLUqH1GYwaD+xZIsxciqJaGeOmlEoiLPRTBJ/3cgt2xCZv6UEngC+S\\nUlQE+M1eNu5R6OePocpM53jfuc5P1IU9Bw3r2FKQszBKgrQ/4j1fFnOfQW0WH7g3Ui44DZF9uSkn\\nKIz2DMajDVyVLVUkxxHxoRr+Ci040L2Tcw2/RrT/YVNoM4qFtcT7G/V+MW+ZbXD2K7I1Cl8e30NW\\nrtaoWTinMaxxzpNHSkD5WXfk78jC4FyvH2l9YNrB4W4tQk+ijHEyp4brTzIlVdCNWTJqF9uacuPB\\nY3yvB1aPhfiekSfwjrIxLeYOKVdSDnSPoTCNMSaz32RSsPrrQdFEVqWWTCkHNs71uYp7CYlyNRC5\\nWwBSPYJhOoSrnRQ28rIJi8RFYeoMb1w2SOXBGBfHvuFkdIL4xNrATGjpDpKSToyIeee6MVOhGSRX\\nrvYEFlHbJuqQa1kdCtHwLT+RtjVFd4WEKUfIiIdGjztjTvrskYHYH6Q0mO1mciBTGUOoUjizBheR\\nSdkfQEjHH9t7VLR546jRTm4zWAcpvYNGLLy5cFRlk527f0Y5ikS5kEjGNTo1RALq2myifkdRCwbW\\ncY0SlplzkJ/zQDxHEnfeYUwi89ku+ojfP8cgTeF6fnLene+2nZLhdZ9rSPhTLqNQkvOcd3Aj52SM\\ngbrHw6ZGMlPnBeyUJJhCyoXRnrFVVY2FLUVFXZs9QLo2YmdKJiVIMrjHXN4b526dlHNU2G+LK+GB\\nnxd3pocB7vZodGe1sxmdY694V+p+YKSA6tzPn3VH/k4sDObO53kzp3HscNvk7id3Nxq+Yqm/XDJN\\nB23kJXHtqVLThvs3XCGXxJYFqFy9MSxYAUkd9xbuOU3ICglhynvJdPlJ793pbvR2U7JyM6j1F+iC\\nf+QShaPRKbhSexrQFNO8SEE3KhuizrHtiC5wCwpaUa2gxmWv6GqwSc2F/V1pdxStqmfUo9no6/Vr\\nUj3YVHkcB25RiGIuCIbmjYdAzlDyxhzhGJ0+V9JROLYvuEeBT+wrgxWRNCQwN6WmEqpLLlQyqkqb\\nF3v9QGTjen7S+ysSsNr58uWBI8v9mdg1YVOYZpFe1QCSIj0Q5yRGOxk5vAuy8PKYMCdLuYmdDTaD\\nV8HGNGhD8IUbySqL1xAOx3u8IgQloTCUFHZq8/BuCLKq4n8kpTdqrZh1Su78sr6TdUOYpDSoZQ9V\\nJacYc69I/E8lOVnLkiNh2x+IDNyj0CaXTB+Nu30jUePIljJJo/16+oiHhijDFqcBCQBOC8fuHJGU\\nHTNgx4zJ+frKT92l4oPHtvwfoky/qfsR0XGNBvE2Eu43o5307Oz7wfTHz7onfzcWBjPOdnGUB2e7\\n0ZyY/IQ7E8r2RpoNSqUSgJWjDGzu7HmLi1/zMssMjkew/PKotH6xLXCpkjDvmM31WA/6sP1kaBqN\\nPhubHlGKkp1ZlOYvxDa0Sxh4kqK6kenc/QZ1HmUP+Ig7NcVk3mSS1BnEDiDmxxNmRMdLegdvYa1N\\nLaS+5Gx7CcJx3SOKO974qBWnheNSldEuNO+YFBRniMcRyKLcNecNUpCA7uuJ9sgrYPJbKcpXuU2A\\nTZQssRAzW2xtrQfXUeKo8ewXpWyRKMV5r1FHz3TEJ1N07WokoC19oBry2WCS1HBSzH+0BITGB33e\\nzGbc7UmuGxuBU1dXnr1hw7ja4Pv3L3Rx2niiPunjIvuOSQYblARTY8gbcqhjswUjQzOlvC3rtYW7\\n8+3Bd/svY8Zwd7pOJIUP5Z4R6Gp243RK3rn7xPzE2YIUngsf6Z2rd2otlKSczdFUwDs5vXGsWD7L\\nzJSEuE6J9Ok0o1+D0Qd4JFZdM5LDmm7TmcBRc4CDYSUlo9RH88GYLa45C2ByVuNtO5CkiCrP+yL/\\nPvoYIFZ6NHGdg2ROLTsiE3Gl6uC6Jmihj5vHAp8UibCPEhARw8l5oxzO8G+UfWObg6t1ssfqq+KB\\n1V7lNbd3/rH9g6bQ7yvOwmYk3TDp7I8vaDIoBWRAKsS5+6KNGfV42jGi1DT++DeStMjtSxS3zJUr\\nwCfmN7MDogGlqTs1OUet3HXgvpE2ZSs793XyC6m8HwcuD0b7yrC+sgtRijI9o5ooJdqbRUPVmf2F\\npi2sw/sWSLrhC72/Mv8a4SQ84SjinatdoTSUwjRZPg3DaaT9ezQbD4ndUZJEKqH8qISU+9N7QUqh\\nwEg8t9u0BTKduBozijCpmvij8xUwkez4/SSVA5HK6D+AbGxbYSbA+2JBesBcACEKeLQqb1QkHTw/\\nB214LPyzoQhHfWP0J+6OifL9xy/pY/J6fkOt0rti2Ze6oyhRX+d9IqI8tsrTJJqx3UKCRVaa0rlf\\nL1pr1Dx5P34R4REf4emgwRwEtVF+W3/gLlTNeIoBqqphkmKekjZUnFI3UkpMD4lcNVgibhMJXy71\\nyMiS2LMIPvo6wgUCQH/m9PF3YmEwi2HQ4MIQZgu/fs2VIoLWD341f6A72HA8xawgwjFrG+pGazdm\\njfv8Ebudq3Xq8UA3w/3GB7hGHNlQVMYq4giZMWsAZbs73pW8bejo7Pt35ORIVo68BfhUEp5gMsKl\\n5hGSCWPMJ+aZfa8MuwJHPidIbPG897XFdnKuMJ5s2xYDVkm4GHVLHDWT9KBdJ7e94vt2oeY3ugVS\\nLc3ogXA3+tnIKa8wz0AlobOj6T1Uk9HCISctZFtSPHVE6DbDXpwTb9uDqz2xAZrDuPS6To7yHuBY\\noqyHJNScuK8wxEjOMG2pNwkfq9bOhN92X67B27CBj4ESRy9hBrehT/bjg7ct08fFaDeiTkrCff6K\\nVHY8MrbcCw6Ts+JiHG8Zyzv9NrJEs9Tz+Q1PmbQ9gJsvb/8Icxifn1/5o1/9CrOb2Rt2KvcofLce\\nUNmh7gdCJ+/vlP1AZUD/hrUIfGmuXNeFuTMQ+v2VrT747tj4/ssXZo9jm/hY5jbn2N/D4i4xbBXi\\nhgfANZKcWqgU6qbLUQmjn9HpKYTRTjPFo6OjtZNm3yKSrY1aNmqt4ZnpVzg5dftZ9+TvxMLg7pz3\\nhdSCMphijDlJ4twuVLGYCs9FFULxtGrTcHwubLzmdaFXjiLk/IaXg+KNOTc+f/j1YgsYKSulbJiH\\nO/KhiS+PD3qDfSZ6CmvwdAEbSCrUuiNzYh7buZwy12jMMShli+Hj7GSFMW5G64gGQ8AnbEXoUpnz\\nCpoQq/zWjbS2emMYVePg00b0VyaJ/875wJcvYJC4542M6D90JMwtSZk+lh03ciXihuQvqF5xE/iC\\nfiAxZwFKSrjHk2y0izZi+IiFg1Ql/dZl5xaFwEmVq51UjiAuJ43GrRElQUkzYxXDBvgkrMrT4li3\\n7QevpayUpIt7WIDJ7QkYTHGKeEzsJTwqdwuFp7uwSWZYdFc4Ri1vzP6k9xc6ElM8hns2cMlc/cZv\\nY9ODe14olT6CuHWkwqs1inb2+hF+AiJaPs24PXaM+xb8zdf5imo6CRv+vu1RUKwTtxPVv2OW6i6U\\nGRCi6Z1ukymJt7xzXWcg+Nf+J+Nc80b0EeYyj/dpjhtSpewbc8xgVGj8zJlBcTq2UL5c4v0p+1tw\\nHvz3cMcAIN5xE7as3DP+Eq074ilYCRju1oMAABg7SURBVCUAFSUVBgmRgYpjMyK3jkW4yoWEk9KD\\ns534fCJaiMKNzNUbsEwT62eVHba3d7aUEM385tuTswl9NMos9DFIQ7i9U8vOPeIMrjlRKHEjSmLO\\nifX1dPCGtYbWt9WeFfbjLMZ2/ILhg2SNWnbycgN2G6hHxHarO1hfUNlK8hFORDeGx79LUtxjIVAm\\neCFrilxlDnyaoCDO8BcYAaW1QLOvTS2gGAvZLkF2quzkrMzZUZtA1NbdY7JpiZAaUCS+ftZC0byA\\ncNDHC9FKQiBt9P5C0rbgNcpj23BRtk049g8mQr6dbd+pubCVd17Xj4jANTolhyw7ukXNpwRle4rg\\nDHwIJRuSnbmyFbcbR6nkVCMaLyEbfvOTuzcKjZILkiamlSECMxyG0wc17yRLDIxX+4yjU0rkGv2S\\n/b4RCZBPTpmt7OwlUWtaqkEh5T1yMONi3z4wfy6UPrQpuCakAzYJKLb+ljjtzNhNe/SLiNQInElm\\n0nEk4DaL7HRsD/ZaMBEccB8c24EyIgrwM16/IwtDXBwqtuLJca6d0xETLuvoprz6TZpGye+xOFjU\\ncoHHhN4VEVnnrxd7KVAqcwZt+Md5cw8n58S2GIK48OyNcn2llsRjU378HLyXnbbYkaOfzBoR4X3b\\nMOL40+cdmQUPCQwXRMuiLBVY/nyzEKeFteW3J2hBc/Q9Tm/0EbbeTKa3ICxNwqSl7hgaEND1E3NJ\\ndBuxALkGQm3cvFqnC8sZWaI41W5y+kDEVr4hauQkFSYxGBWRUH1SXFiT6L6YBk4OK3nO3KNxEE/p\\nn4hD3RtiN32+4VR2LVwSdKUkCZdG08hGXGOSrJNqLExb3phzUJOjR0UYVDbUBvcVrAVnhiJh0NuI\\nEYOGMjDMYj61vt8jbXyOE2NEQ3oW2jwRPWgzVIoJ7LWiAknB/EAsbPVuitGYUuMGlsSr38GlXOEw\\nV6Guzk/xhK9CoWmTOWH0wVZ3xIwxn0EPL5WyQTNFkzEiV4n7RbToBb7f7KaLUEzoTSipYFNiAbaV\\nwelnyKaqkYXwEe5WGzCD1LXVClIoakyfcS/8jNfvxMIgeExT07p9bOIizPvinBbWz9tJVBBo01Cf\\na1GILausgZpSUDSkK83R0DQnrgWVikjHV1zZLGzSw4XnuNm3L6SqPOrFrz6XNGoFm4ouX/rVe3RD\\nuqE2MSmx2o+J6E+JOmhER8SYPbTqRV5yjKkTTXHRiQpzxOBujgv3jTEaeAynRA9IGz5uTILgFKqK\\nRdORJ5iBTtMlh47xAhLJOqQKhDkovB7hudD0WNTncOvFKLIsiTGRc0BB3EbwFZoxenyNPmRFhh03\\no+Qdm3Ptmm5ug+SJaTcXk5orv3j7ntd9Mj//Nt2UaZkHEzMnWcxuuufA7pG571cYrFwQJH4mxC6v\\nz8FRM9MTvV/ovJG8U1aHpEzHZuK2aA5PNZG3TCYk080G6E4SQWmktBOo/cHwRNI97OrWI3MhhHya\\njFw+UIdnu7j7Tc1bHO0sfA6qIfu6O9MdBbJq0JXmheSYIT3qwXU35j3wPsCjpNgloQmaRcZDc8Js\\nLHZlwq1h/UZX41nv0fuRpCAWu7skwpxB7HYP34Xq76EqEWCQkJCKO4/jC/0+ScdBvi8M47wGNnxZ\\nYC9ky1Ci9Tn0+A2Z0chccuJuxnlfuAxy3RnjJG2JwkSMkLpKPLVfzbDx4j0p748/z7ndtK9/i3Gf\\n3C3h442zXdQj+gtqzYg7rvGmBYForqGbI7LB7OgID0HWGbLTbNS6sW07eWHZsipyfEfOO1+//THj\\nXjKkRZ1Z5BqiwgYJhC0o6gEvFfIiBAXFedyDnA8CKR8gF58zMPAYfSqv3rj6D+RcOWoKpuAKeeGB\\nbic5W64c5UEhcZfGn3z7I/b6C3JWej9j2OUeISJV6I0+Br2F07AchS1Viu74uKmifKm/5B6DYZNc\\n3nDJMXybxmM72FOhdaMzuMaPiL6xyRYQGAwrGebNj+1CuSnpQc2VrMZ1v9B+c14nNkO6k/UEzTIh\\nF5Ir4pXr89eUtxKYPv0JzfcgbYnz/hrhpR59IMNmoNJyYAHHFKZbwHbaSd0fIJHIVAVJhb4o3DnN\\nZZ3vHLph7SLXjTQiVm5jkMpBt5u0bYu3EGdcQejtSU6PMKC5spUPpocl2j36MbI6NSXwyhwxDO3u\\nKGk5RRXmz5sx/JnLiIj8oYj8dyLyf4jI/y4i/8b6+L8jIn9DRP6X9c+/9Kc+598Wkb8mIv+niPyL\\nf9bXcHdsXFztZgqkNPAq5B20wpgnRgBIxrzJGs3JsX2azDGDiYhFM0tOmN1kEZAUDjOF9/c3tqok\\n7hi+5QCpzN7pc3DO0Jupwn1fDJ/xRMgZ0Q1jD4S5rS2yWVTI+0RTopSNlAoqylH3sAXTaXNg3hbh\\nd1DyEdLeiICTGBR3kmUShSMVjvpB1kJanZz4T/OF9eTw2CHFRH8VsSLhZAzeLLgiTmDn1gU3emPP\\nG7m+LRCIxdEoaZSnCIjNxbPQWE4Uxqqdm3aBt1ikUiQaXeNrtDkxUWreQI2tHuxbYSuT53xxtSff\\nzj9hWuc5jGcPdPuUGYNLzUvb7/FsTzuugbi/7hf3HNz9k+k9eA16RHMXYVm3tPiGmhk6cGthW08l\\nADNzco+Oy0T2ulq7lLt1WhvIdK72jIh3PzGLGHks+jnmVy4Mi3o+TWkdB/PC4luExARENt7rg6N+\\nINPw7rTrQiWOBn04eFrkcBCFsz8pJUejFoSMrIXjeJAyK8A2F1c0UsKlKJogJycnDZVLE0IExZQS\\nzEv7Bz9jGMC/5e7/s4h8AP+TiPw36//9++7+7/7p3ywi/wzwrwL/LPAXgP9WRP5pd/+7ImQEQA7M\\nekBESaDhcc/bg+pxTjaBgmJKUIrW0EU1x3YqZVKuHHsk8c7Wo+RzOR37nDz2B53EOcMdWOp73Dgu\\n9H7yOT9XKOYNn1+RHGfuhMM8SfpLdm1Y2pijMayjOWAl08YCzjhmPQZeWiMvP1vEcVMKzV0TliGn\\nBCmashJKLjEsotRlzlGaDdoIdSIap3q46UTpTsig1kETSgBSIShKY65iXClh5Z0dkwGLXiTBoaOk\\njdFWxmAdepI4kgutda72oujGnlMg7tzw2dbimWCuhuc5GSSO9WdgTvOIKd+zc7cb35U3wuQkZSPZ\\nHV0SKQA1lzZclL288dnOKC/OBVPofcYC7GN1YQgiPd6jkrnmIG/vlNc3TJWSN0qtJBk8X43nbZQ0\\nON4/yAJMJzEQqcFfwHFjKUxREOsQkqs7XSxm1z7wtAcAGKfNSUoT0YN7jqCE342tVIrG0WrbdkpR\\nujnjp7pBjfnEOKOAtw+nzejqTB6drn3epKQcb18iXCVCHzHEhb5AtRFbl2mMEbMcTU6xFEpeqn/f\\niwL8fSwM7v43gb+5fv1NRP4q8Bf/Hp/yLwP/ubvfwP8lIn8N+BeA//7v/inCIGyjIIt1MJjDEBpb\\n3bhaQ2M6Exl0kd++aaqAyKJE34zZwK+wsgoxaQfO3mjtQn2gptxTyBIt0rdNTt/ZunPfL6o5jcx5\\nfgKFLT1iUt6feCkkD015zEnqwUKwOZagHzYWNwnfQcohIYV7AjcLBNwc3AqMm+d1s3klMZHjQJNQ\\n9h2mhyNS4kZzj5g0s3GPDtMpJTMsUp8mUSFnhHlGAHdljOjAvO4nw6Mo9X0LTFrWDXOjjc6j7IgE\\ngKST2HxE74VmXO94gpuz1x1JynV/RpAprhZSSuwpc5QFH1WhXyEv1n1Dyw/xnpSDgWHXkzYuimRG\\nKjFkJVFyYfhJWrODgXGORpsDyBE/B9KiO4+5bpQBm0ApO6N3ht+Q/oBvzxfj7jzk4DKimTolmEqt\\nHwzJCC+kd3LawSPH4KvIJyVdHM8e15zDdb/4sr+zaQkuoyTaeYIa6eNL9EiMm31/xJHQlHsYQl5u\\nNyErfLsvpgt7fosKBG9xjftKHU+hq8UMbgx8hqqUxMMP4lCLkdfPL0khlRLD4jkYEhLoz3n9rImE\\niPwTwD8P/A/rQ/+6iPyvIvKfiMj362N/Efjrf+rT/m/+XxYSEfnXROSviMhfuVrn6i9e46L3C3Mj\\nSTi2euv0HjHoWgu1BLaM6bhFZZoRTz8AN6GmjZwf1BrVazVtYQS5B61NpoV89aiZnJzB3zn/T6KY\\n9RoX6oUx1jYTBRc6DU+Z4ScutqbboYzEUzrw6vtWSTkca05kv8WNPpzrukkIkg9sOt+eX7mui7N3\\nLBVyydx+0z1asvpsjNkRVgfj6CuAE/IZGOqDOcLh58vfoRKgFfcZn/9TexSGjYu7nzAlgK4pse+Z\\nXAt122PwJYnX+cnzdSI2OR4P6vZYuv4MlqLDJhmhrMBZEKa3/I7KZN93Hh8Paq1sVfny8T3HsYPf\\ntGHrmMVvJbYikUUJDP7EPerhUglITCIthmYYs8KNUdhI7FLZyg5u7DlRNiGVxHn9gLqw5Qeuwtvy\\nG/TlKfgcF82emLCODj1gKqPjPtm3Gs5BiZwDE3RuMIP+PUWxGbbqdt2MobR2U+obdT9IZUe0cI9o\\nrb/a5PMM/kTKSl0O0bN37p9i/+5s5T1mRMtqf96f9DlWvgIgjrRVF4VcFpYvJ0rdcF09oBqS6895\\n/X0vDCLyDvwXwL/p7l+B/wD4J4F/jthR/Hs/5wu7+3/o7n/J3f/SVgueApMe3a/rLJ0SOReu9gr/\\nuHhgxlKg233OFVN1bPZ1tg4H5OgdnYZ5ikLQSUypvaybJMUQzS7G9QwDyXQg9Oy3xxtaEzk9MC2Y\\nC3PclLzRrieMxnk/qUmoKTFW47H7xG3SbK4nvOGMmCybMyzw92eLbfV5X7RmWBd6u3ldX/nx+sqr\\n35i3wK2VjZzz8iqExSsc3hbW59ni964335GYN6S0/AkjlJQ+8KVmmAptXBwpUzx4BW+lsOkkMymS\\noXVmG2xA1czH9oHSw5E4G+IsFyNcvSE5jnKIY/2FmHKPMzBlGsnIPYf/wCXTxkW7T0bvkRg0Ah5j\\nAS+dHq3motE/0W3SbMTNycK1q+IerehzxkMiF6Hk6D+tmyJSOLZ3Sq08tkJdNKOaE6UoWxFqfcQx\\njNhxiRljtVRt28acdwTniAm/jUGxHAvk6My5VC5TWp+cr8b5+ZXRGs/zB+7ryfN18vXunD3Sm9f9\\njV99+00QvRfBSjWRUg3KFKtMSEtYp9Me1xT8tmFbVCBV3h8fPLY3SqnRku4WTVykaFX/h6FKiEgh\\nFoX/1N3/y3Vj/+0/9f//I+C/Xv/5N4A//FOf/o+vj/3d/3wE67BJgnV5D5v0OXGZHFsm7Qfeb5Iq\\nyKTdxrgmXhyRn8JAUQRjGMfxAMnIyIgbPhdIxYMjMEmMMVHJfOzfYeq4bZS084/+uXc2Uf74N7+h\\n3eHW8zYxnVg3igbG6z0p19UjlVdLNE2ZgSXyVOYASoSfbF3QoiExTZSkG6/XFWoLmeOjUGs0MiHG\\ns3W8T1oX9lVRxlpcYhZQMUnUnIL5OFvcXCnKR6Yo2UFdmHYhSdiPN2rdabOBD+bq8/zYd65+8n4c\\nbEWZVweEY4tFT+iM64/pJmzbjg/juoOH0d3oGNJubF542lAVvv/4Jc/7R66rI5b4+oLPr4O6H0xx\\nPueJzBmAFv/kI8Hdbqo7W3a+PL7jOqPNq3vYxR+PjawSU3ptdIJk1N2RDHVxH76rH3xPYbjy4+tF\\n0knagkNR609/XjQ5fV4DvDHmZLROTo6um1NVua6LlDL39WSOgUjBp2JrB9CkR7cHA0+Cys7Xry/E\\nC80TV/uRdj5BH2y18hqDj+0NSfCx1d8ujjVVpkIS4bo7YyvUImDRNjV90saNlugtdRbA14zeXszR\\nF8PC2VLGM8z54tvzExk/j/n4Zy4MIiLAfwz8VXf/y3/q439+zR8A/hXgf1u//q+A/0xE/jIxfPyn\\ngP/x7/U1HKgi8e2Y06wFG2E0zGGvB7O/SA5ad6o4V7/pneU0jO1xaMQSkeyV40/Jw9rqk+w3z9mo\\nC/rqMsglgxR83ryuk70I5fhz/OH3f8D9OvlhGNadboPHHngsWcUvAyGXjKbY3aQIY5BIiA0+L+OQ\\nQi4T00xO6f9p73x+IznKMPx8Vd09M7aTJT/Qak9okbjkFEWIS6IcEcmFHOGAckAKh1xzWJELV/gT\\nQEKKOIC4IPYauHAloCSbIJEfsJcoZIOWJLvrnu6uqo/DV3aMx+O1WdvdI9UjWTNqj+1Xn2bK1VX1\\nvh8hWUAnsWPZDXz+xWf2JvORRx67TJ8Ghm5AvKd2NdT1/pqILVXH/d2WkCzAVfcewZKFPQyxY9CB\\npnoIT6LWzvID+p6EZ1ZvUXlH292l9h6aGRLM/UgC8Q0p5EBdJ2YOcnsOT2sn77MD0GuD71tiHGhc\\nw2I2o/Jbts0Xgm1hBltld25O10UG7D9e292lqrfYigONFy49/BXm823q2JHalk7MZl07S1y24+4L\\nhiEStLWTi2pb3RGI2jNvLJ+gcXNUPSHu0kdrWBMRxHl2mjxNR2lcxxDyrpZ4y/psFoiPNHVtnb36\\ngbhsqaotKi90ybIXmiqngSVr6uMroet7nK9pk9Ivv2DWLMDZIiGS8Cmx2/d4l6i80BBJ0bEcepqZ\\nUNc2o9sLPQ4x0Q/3SJqo/DwH5URULbS39jVNPbM4gRzm4kSJCC44hBmn25M42YzhaeAHwA0ReTNf\\n+zHwfRF5Mn+ubwI/AlDVd0Xkt8DfsB2Nl4/bkQDLrYv5NuDucgDv8zqDs0MjktCk+Y2h4GwKFTRQ\\ny5w+BprKRk7xjqhKjDbtF2Ju0RWZNZ7Qewi9nTWoZrYNF82oUzlHF3qWsaVxDqcRFwb61DOfXSJg\\nicWW4mtbhlEDooIOS2qxfSeXBpY4Lu3ssDvsMgRbwIvJ1iycswEiUeH9gt3YU4We25/ftg5HkJvY\\nzO1sgU8IjqG7h/Pe/nZSFlVFmyJD7Gm87Yw0UhNSSxsHM9OInST1MtB2A/NqYX0agzKrFrkxr7lW\\nvXiiOu6EYD0iw0DXt/mEZENvuWVoygYtD332gSDmVRg00IbIwg2EUBPUmvQO/cAQE3e7O8TguNcv\\nqaqaPibqYZf5TmM1SYk6DqScAO18yEG2AcSm2taO3oPO8fa2ARJD36LeDE61r2wRUAMP78z5z27A\\nS8UWVW6115lhzVVIFaznR1VTV7Zz47OZyo6b24etqrdREl2yA3SzWWPT+piY1bXdLvaJtuvY3t5B\\nQyBoReNtbWJrsc2yt7g8Ubv90lZxKeJSwLuFdSILPaTAwm+jovjUWqant2bC9nmJeHGEZD0rQrAF\\ncPEQ+i7XR0lBqLylU58G0VOaK84DEfkUuAf8e2wtJ+BxNkMnbI7WTdEJm6P1KJ1fU9WvnuSHJzEw\\nAIjIG6r6zbF13I9N0Qmbo3VTdMLmaH1QnZPoK1EoFKZFGRgKhcIKUxoYfj62gBOyKTphc7Ruik7Y\\nHK0PpHMyawyFQmE6TGnGUCgUJsLoA4OIfCfbsz8QkWtj6zmMiNwUkRvZWv5GvvaoiLwuIu/nx0fu\\n93vOQdcvReSWiLxz4NpaXae1wl+A1jOz7Z+hznURA5Oq60VEIewbNMb4AjzwIea5aIC3gCfG1HSE\\nxpvA44eu/Qy4lp9fA346gq5ngaeAd+6nC3gi13YGXM019yNr/QnwyhGvHU0rcAV4Kj9/CHgv65lU\\nXY/ReWY1HXvG8C3gA1X9h6r2wG8w2/bU+S7wWn7+GvDCRQtQ1T8Btw9dXqdr3wqvqv8E9qzwF8Ia\\nresYTauqfqyqf83P7wB7EQOTqusxOtdxap1jDwwnsmiPjGJhM38RkZfytcv6pU/kX8DlcaStsE7X\\nVOv8f9v2z5tDEQOTretZRiEcZOyBYRN4RlWfBJ4DXhaRZw9+U22uNrmtnanqOsAD2fbPkyMiBvaZ\\nUl3POgrhIGMPDKe2aF80qvpRfrwF/A6bgn0iIlfAXKbArfEU/g/rdE2uzqr6iapGVU3AL/hyajuq\\n1qMiBphgXddFIZxVTcceGP4MfENEropIg2VFXh9Z0z4isp1zLhGRbeDbmL38OvBiftmLwO/HUbjC\\nOl3Xge+JyExErnICK/x5s/dByxy27Y+idV3EABOr63FRCAde9mA1vYjV3vussD6Prap+CLw6tp5D\\n2r6Orea+Bby7pw94DPgj8D7wB+DREbT9GpsuDtg94w+P0wW8mmv8d+C5CWj9FXADeDu/ca+MrRV4\\nBrtNeBt4M389P7W6HqPzzGpaTj4WCoUVxr6VKBQKE6QMDIVCYYUyMBQKhRXKwFAoFFYoA0OhUFih\\nDAyFQmGFMjAUCoUVysBQKBRW+C+S5OLjGVu2XAAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(img2/255.)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"8efd5e81-2487-c3c3-3a88-c90e4c285ce4\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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AYjwaAinYqjwRw11UDxFXNSmEWho5QIxcgH6ghmEZwgD+046hl/QCGA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M2CXna2Lgeu8dVzu58wa90JKWDrR886xVzLRKF3odxeXbEPL2kmj9PFu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\\nrU+tu8Bwt+7W3frU+ldyp8073VAfLQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(img5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_cell_guid\": \"c35ce98b-1747-bd73-8c2d-f209359b551a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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X3p27cv69at4+DBg8yYMYOQkBBWr17N/PnzKSwsZMKECbS2tmJmZsbx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\\nxGDGjJnbMCcGM2bM3IY5MZgxY+Y2/h8RkMhaONM0QQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(img4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"8c3c0986-9e90-7d67-a049-13c74108ada8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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MKbwx8d2d1FB70tVqy+Mxxzta28jvlK2b2nzEwOwqivA2vKh/DnR3fc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\\nLjHUUUcdv6EuMdRRRx2/4f8A9ybVIwbagjcAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.imshow(img3)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"e604445e-003f-cae9-9ad4-d11d9a3a1f3a\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"c033d0fd-1460-c68d-27f7-a1171aa8b6d3\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 459,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n 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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Null Count\\n\",\n \"Feature \\n\",\n \"hospital_beds_raion 14438\\n\",\n \"build_year 13592\\n\",\n \"state 13546\\n\",\n \"cafe_avg_price_500 13272\\n\",\n \"cafe_sum_500_max_price_avg 13272\\n\",\n \"cafe_sum_500_min_price_avg 13272\\n\",\n \"max_floor 9562\\n\",\n \"material 9562\\n\",\n \"num_room 9562\\n\",\n \"kitch_sq 9562\\n\",\n \"preschool_quota 6688\\n\",\n \"school_quota 6685\\n\",\n \"cafe_sum_1000_min_price_avg 6521\\n\",\n \"cafe_sum_1000_max_price_avg 6521\\n\",\n \"cafe_avg_price_1000 6521\\n\",\n \"life_sq 6379\\n\",\n \"build_count_frame 4991\\n\",\n \"build_count_1971-1995 4991\\n\",\n \"build_count_block 4991\\n\",\n \"raion_build_count_with_material_info 4991\\n\",\n \"build_count_after_1995 4991\\n\",\n \"build_count_brick 4991\\n\",\n \"build_count_wood 4991\\n\",\n \"build_count_mix 4991\\n\",\n \"build_count_1921-1945 4991\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"nulls = pd.DataFrame(train.isnull().sum().sort_values(ascending=False)[:25])\\n\",\n \"nulls.columns = ['Null Count']\\n\",\n \"nulls.index.name = 'Feature'\\n\",\n \"nulls\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"a1b71aec-73fa-e8e1-8ad2-630c98b03d65\"\n },\n \"source\": [\n \"# Wrangling the non-numeric Features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"metadata\": {\n \"_cell_guid\": \"881cad27-eda8-de58-9891-a87bab51ff60\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | Null Count | \\n\",\n \"
|---|---|
| Feature | \\n\",\n \"\\n\",\n \" |
| hospital_beds_raion | \\n\",\n \"14438 | \\n\",\n \"
| build_year | \\n\",\n \"13592 | \\n\",\n \"
| state | \\n\",\n \"13546 | \\n\",\n \"
| cafe_avg_price_500 | \\n\",\n \"13272 | \\n\",\n \"
| cafe_sum_500_max_price_avg | \\n\",\n \"13272 | \\n\",\n \"
| cafe_sum_500_min_price_avg | \\n\",\n \"13272 | \\n\",\n \"
| max_floor | \\n\",\n \"9562 | \\n\",\n \"
| material | \\n\",\n \"9562 | \\n\",\n \"
| num_room | \\n\",\n \"9562 | \\n\",\n \"
| kitch_sq | \\n\",\n \"9562 | \\n\",\n \"
| preschool_quota | \\n\",\n \"6688 | \\n\",\n \"
| school_quota | \\n\",\n \"6685 | \\n\",\n \"
| cafe_sum_1000_min_price_avg | \\n\",\n \"6521 | \\n\",\n \"
| cafe_sum_1000_max_price_avg | \\n\",\n \"6521 | \\n\",\n \"
| cafe_avg_price_1000 | \\n\",\n \"6521 | \\n\",\n \"
| life_sq | \\n\",\n \"6379 | \\n\",\n \"
| build_count_frame | \\n\",\n \"4991 | \\n\",\n \"
| build_count_1971-1995 | \\n\",\n \"4991 | \\n\",\n \"
| build_count_block | \\n\",\n \"4991 | \\n\",\n \"
| raion_build_count_with_material_info | \\n\",\n \"4991 | \\n\",\n \"
| build_count_after_1995 | \\n\",\n \"4991 | \\n\",\n \"
| build_count_brick | \\n\",\n \"4991 | \\n\",\n \"
| build_count_wood | \\n\",\n \"4991 | \\n\",\n \"
| build_count_mix | \\n\",\n \"4991 | \\n\",\n \"
| build_count_1921-1945 | \\n\",\n \"4991 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" timestamp product_type sub_area culture_objects_top_25 \\\\\\n\",\n \"count 30455 30455 30455 30455 \\n\",\n \"unique 1161 2 146 2 \\n\",\n \"top 2014-12-16 Investment Poselenie Sosenskoe no \\n\",\n \"freq 160 19439 1776 28528 \\n\",\n \"\\n\",\n \" thermal_power_plant_raion incineration_raion oil_chemistry_raion \\\\\\n\",\n \"count 30455 30455 30455 \\n\",\n \"unique 2 2 2 \\n\",\n \"top no no no \\n\",\n \"freq 28801 28141 30159 \\n\",\n \"\\n\",\n \" radiation_raion railroad_terminal_raion big_market_raion \\\\\\n\",\n \"count 30455 30455 30455 \\n\",\n \"unique 2 2 2 \\n\",\n \"top no no no \\n\",\n \"freq 19595 29320 27634 \\n\",\n \"\\n\",\n \" nuclear_reactor_raion detention_facility_raion water_1line \\\\\\n\",\n \"count 30455 30455 30455 \\n\",\n \"unique 2 2 2 \\n\",\n \"top no no no \\n\",\n \"freq 29593 27413 28119 \\n\",\n \"\\n\",\n \" big_road1_1line railroad_1line ecology \\n\",\n \"count 30455 30455 30455 \\n\",\n \"unique 2 2 5 \\n\",\n \"top no no poor \\n\",\n \"freq 29675 29563 8014 \"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"categoricals = train.select_dtypes(exclude=[np.number])\\n\",\n \"categoricals.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"52be1499-e496-bda9-80c8-49377f3a8572\"\n },\n \"source\": [\n \"# Transforming and engineering features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"_cell_guid\": \"258a6428-27fa-8f9a-bbc8-ca91c4cae6e2\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Original: \\n\",\n \"\\n\",\n \"Investment 19439\\n\",\n \"OwnerOccupier 11016\\n\",\n \"Name: product_type, dtype: int64 \\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print (\\\"Original: \\\\n\\\") \\n\",\n \"print (train['product_type'].value_counts(), \\\"\\\\n\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"metadata\": {\n \"_cell_guid\": \"f7f12de3-c99a-e943-9a4b-98b745f42edc\"\n },\n \"outputs\": [],\n \"source\": [\n \"train['enc_product_type'] = pd.get_dummies(train['product_type'], drop_first=True)\\n\",\n \"test['enc_product_type'] = pd.get_dummies(train['product_type'], drop_first=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"metadata\": {\n \"_cell_guid\": \"f5afc59d-52cd-d242-9e86-6a09596c422a\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Encoded: \\n\",\n \"\\n\",\n \"0 19439\\n\",\n \"1 11016\\n\",\n \"Name: enc_product_type, dtype: int64\\n\"\n ]\n }\n ],\n \"source\": [\n \"print ('Encoded: \\\\n') \\n\",\n \"print (train['enc_product_type'].value_counts())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"metadata\": {\n \"_cell_guid\": \"377f698c-9166-e49f-40f7-2c470cb391f3\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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mBgYI92EREREavxWSB8+OGHsdvtNDc388gjjxATE9PjfcMwMAzDV905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\\nlClTuPfeexk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\"text/plain\": [\n \"| \\n\",\n \" | timestamp | \\n\",\n \"product_type | \\n\",\n \"sub_area | \\n\",\n \"culture_objects_top_25 | \\n\",\n \"thermal_power_plant_raion | \\n\",\n \"incineration_raion | \\n\",\n \"oil_chemistry_raion | \\n\",\n \"radiation_raion | \\n\",\n \"railroad_terminal_raion | \\n\",\n \"big_market_raion | \\n\",\n \"nuclear_reactor_raion | \\n\",\n \"detention_facility_raion | \\n\",\n \"water_1line | \\n\",\n \"big_road1_1line | \\n\",\n \"railroad_1line | \\n\",\n \"ecology | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"30455 | \\n\",\n \"
| unique | \\n\",\n \"1161 | \\n\",\n \"2 | \\n\",\n \"146 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"5 | \\n\",\n \"
| top | \\n\",\n \"2014-12-16 | \\n\",\n \"Investment | \\n\",\n \"Poselenie Sosenskoe | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"no | \\n\",\n \"poor | \\n\",\n \"
| freq | \\n\",\n \"160 | \\n\",\n \"19439 | \\n\",\n \"1776 | \\n\",\n \"28528 | \\n\",\n \"28801 | \\n\",\n \"28141 | \\n\",\n \"30159 | \\n\",\n \"19595 | \\n\",\n \"29320 | \\n\",\n \"27634 | \\n\",\n \"29593 | \\n\",\n \"27413 | \\n\",\n \"28119 | \\n\",\n \"29675 | \\n\",\n \"29563 | \\n\",\n \"8014 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\\n\",\n \"count 150.000000 150.000000 150.000000 150.000000 150.000000\\n\",\n \"mean 75.500000 5.843333 3.054000 3.758667 1.198667\\n\",\n \"std 43.445368 0.828066 0.433594 1.764420 0.763161\\n\",\n \"min 1.000000 4.300000 2.000000 1.000000 0.100000\\n\",\n \"25% 38.250000 5.100000 2.800000 1.600000 0.300000\\n\",\n \"50% 75.500000 5.800000 3.000000 4.350000 1.300000\\n\",\n \"75% 112.750000 6.400000 3.300000 5.100000 1.800000\\n\",\n \"max 150.000000 7.900000 4.400000 6.900000 2.500000\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"iris_dataset.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"e6efd0e0-2839-fa15-81bb-99da382e832e\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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i6FeckSnLBhAw4G6dxfPnAAPZYtg2HxYvRavhxvHToUsL7K7cYff/kF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\\n\",\n \"text/plain\": [\n \"| \\n\",\n \" | Id | \\n\",\n \"SepalLengthCm | \\n\",\n \"SepalWidthCm | \\n\",\n \"PetalLengthCm | \\n\",\n \"PetalWidthCm | \\n\",\n \"
|---|---|---|---|---|---|
| count | \\n\",\n \"150.000000 | \\n\",\n \"150.000000 | \\n\",\n \"150.000000 | \\n\",\n \"150.000000 | \\n\",\n \"150.000000 | \\n\",\n \"
| mean | \\n\",\n \"75.500000 | \\n\",\n \"5.843333 | \\n\",\n \"3.054000 | \\n\",\n \"3.758667 | \\n\",\n \"1.198667 | \\n\",\n \"
| std | \\n\",\n \"43.445368 | \\n\",\n \"0.828066 | \\n\",\n \"0.433594 | \\n\",\n \"1.764420 | \\n\",\n \"0.763161 | \\n\",\n \"
| min | \\n\",\n \"1.000000 | \\n\",\n \"4.300000 | \\n\",\n \"2.000000 | \\n\",\n \"1.000000 | \\n\",\n \"0.100000 | \\n\",\n \"
| 25% | \\n\",\n \"38.250000 | \\n\",\n \"5.100000 | \\n\",\n \"2.800000 | \\n\",\n \"1.600000 | \\n\",\n \"0.300000 | \\n\",\n \"
| 50% | \\n\",\n \"75.500000 | \\n\",\n \"5.800000 | \\n\",\n \"3.000000 | \\n\",\n \"4.350000 | \\n\",\n \"1.300000 | \\n\",\n \"
| 75% | \\n\",\n \"112.750000 | \\n\",\n \"6.400000 | \\n\",\n \"3.300000 | \\n\",\n \"5.100000 | \\n\",\n \"1.800000 | \\n\",\n \"
| max | \\n\",\n \"150.000000 | \\n\",\n \"7.900000 | \\n\",\n \"4.400000 | \\n\",\n \"6.900000 | \\n\",\n \"2.500000 | \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" Id SepalLengthCm SepalWidthCm PetalLengthCm \\\\\\n\",\n \"Id 1887.500000 25.782886 -7.492282 67.667785 \\n\",\n \"SepalLengthCm 25.782886 0.685694 -0.039268 1.273682 \\n\",\n \"SepalWidthCm -7.492282 -0.039268 0.188004 -0.321713 \\n\",\n \"PetalLengthCm 67.667785 1.273682 -0.321713 3.113179 \\n\",\n \"PetalWidthCm 29.832215 0.516904 -0.117981 1.296387 \\n\",\n \"\\n\",\n \" PetalWidthCm \\n\",\n \"Id 29.832215 \\n\",\n \"SepalLengthCm 0.516904 \\n\",\n \"SepalWidthCm -0.117981 \\n\",\n \"PetalLengthCm 1.296387 \\n\",\n \"PetalWidthCm 0.582414 \"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"iris_dataset = pd.read_csv('../input/Iris.csv')\\n\",\n \"fig , ax1 = plt.subplots()\\n\",\n \"x1 = iris_dataset['SepalLengthCm']\\n\",\n \"y1= iris_dataset['SepalWidthCm']\\n\",\n \"x2 = iris_dataset['PetalLengthCm']\\n\",\n \"y2= iris_dataset['PetalWidthCm']\\n\",\n \"ax1.scatter(x1,y1,c=iris_dataset['Species'].apply(lambda x : 'c' if x =='Iris-setosa' else ( 'b' if x == 'Iris-versicolor' else 'r')))\\n\",\n \"#ax1.scatter(x2,y2,c='b')\\n\",\n \"plt.show()\\n\",\n \"\\n\",\n \"iris_dataset.cov()\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 152,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/162/1162780.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":173,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"4717f4ea-0b6e-e952-aff7-557636a3c973\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"mnist.pkl.gz\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# This Python 3 environment comes with many helpful analytics libraries installed\\n\",\n \"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\\n\",\n \"# For example, here's several helpful packages to load in \\n\",\n \"\\n\",\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"\\n\",\n \"# Input data files are available in the \\\"../input/\\\" directory.\\n\",\n \"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\\n\",\n \"\\n\",\n \"from subprocess import check_output\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"bc07a791-35a0-2f2b-cfb6-c3392dd5aaf8\"\n },\n \"outputs\": [],\n \"source\": [\n \"import gzip, pickle, sys\\n\",\n \"f = gzip.open('../input/mnist.pkl.gz', 'rb')\\n\",\n \"(train_X, train_y), (valid_X, valid_y), _ = pickle.load(f, encoding=\\\"bytes\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"9626dec5-60f9-9c42-10f9-4ec5b8fea0fa\"\n },\n \"outputs\": [],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"import tensorflow as tf\\n\",\n \"from sklearn.utils import shuffle\\n\",\n \"from sklearn.model_selection import train_test_split\\n\",\n \"from sklearn.metrics import f1_score\\n\",\n \"from sklearn.preprocessing import OneHotEncoder\\n\",\n \"\\n\",\n \"import gzip, pickle, sys\\n\",\n \"f = gzip.open('../input/mnist.pkl.gz', 'rb')\\n\",\n \"(train_X, train_y), (test_X, test_y), _ = pickle.load(f, encoding=\\\"bytes\\\")\\n\",\n \"\\n\",\n \"oneHot = OneHotEncoder(10, sparse=False)\\n\",\n \"train_y = oneHot.fit_transform(train_y[:, np.newaxis])\\n\",\n \"test_y = oneHot.fit_transform(test_y[:, np.newaxis])\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"3e540dd6-0b7a-2e74-237f-412509ecea38\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 1., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" ..., \\n\",\n \" [ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 0., 1., 0.]])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"test_y\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 71,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/162/1162790.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":174,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"ca6ad12b-48ae-a54d-4138-060d7ee040f6\"\n },\n \"source\": [\n \"Features Extraction\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"eab5fd01-7bd8-699d-73f5-69d5bd37d8c8\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import nltk\\n\",\n \"from nltk.corpus import stopwords\\n\",\n \"from nltk.stem import SnowballStemmer\\n\",\n \"import re\\n\",\n \"from string import punctuation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"7565f379-9887-7c91-fab8-9a188dd7a519\"\n },\n \"outputs\": [],\n \"source\": [\n \"train = pd.read_csv(\\\"../input/train.csv\\\")[:100]\\n\",\n \"test = pd.read_csv(\\\"../input/test.csv\\\")[:100]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"df2ba0b6-d476-c6c1-c375-ef56acca722f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"| \\n\",\n \" | Id | \\n\",\n \"SepalLengthCm | \\n\",\n \"SepalWidthCm | \\n\",\n \"PetalLengthCm | \\n\",\n \"PetalWidthCm | \\n\",\n \"
|---|---|---|---|---|---|
| Id | \\n\",\n \"1887.500000 | \\n\",\n \"25.782886 | \\n\",\n \"-7.492282 | \\n\",\n \"67.667785 | \\n\",\n \"29.832215 | \\n\",\n \"
| SepalLengthCm | \\n\",\n \"25.782886 | \\n\",\n \"0.685694 | \\n\",\n \"-0.039268 | \\n\",\n \"1.273682 | \\n\",\n \"0.516904 | \\n\",\n \"
| SepalWidthCm | \\n\",\n \"-7.492282 | \\n\",\n \"-0.039268 | \\n\",\n \"0.188004 | \\n\",\n \"-0.321713 | \\n\",\n \"-0.117981 | \\n\",\n \"
| PetalLengthCm | \\n\",\n \"67.667785 | \\n\",\n \"1.273682 | \\n\",\n \"-0.321713 | \\n\",\n \"3.113179 | \\n\",\n \"1.296387 | \\n\",\n \"
| PetalWidthCm | \\n\",\n \"29.832215 | \\n\",\n \"0.516904 | \\n\",\n \"-0.117981 | \\n\",\n \"1.296387 | \\n\",\n \"0.582414 | \\n\",\n \"
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| 0 | \\n\",\n \"1 | \\n\",\n \"Belgium | \\n\",\n \"
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| 3 | \\n\",\n \"7809 | \\n\",\n \"Germany | \\n\",\n \"
| 4 | \\n\",\n \"10257 | \\n\",\n \"Italy | \\n\",\n \"
| 5 | \\n\",\n \"13274 | \\n\",\n \"Netherlands | \\n\",\n \"
| 6 | \\n\",\n \"15722 | \\n\",\n \"Poland | \\n\",\n \"
| 7 | \\n\",\n \"17642 | \\n\",\n \"Portugal | \\n\",\n \"
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How can I solve... \\n4 Which one dissolve in water quikly sugar, salt... \\n5 Astrology: I am a Capricorn Sun Cap moon and c... \\n6 Should I buy tiago? \\n7 How can I be a good geologist? \\n8 When do you use \\u30b7 instead of \\u3057? \\n9 Motorola (company): Can I hack my Charter Moto... \\n10 Method to find separation of slits using fresn... \\n11 How do I read and find my YouTube comments? \\n12 What can make Physics easy to learn? \\n13 What was your first sexual experience like? \\n14 What are the laws to change your status from a... \\n15 What would a Trump presidency mean for current... \\n16 What does manipulation mean? \\n17 Why do girls want to be friends with the guy t... \\n18 Why are so many Quora users posting questions ... \\n19 Which is the best digital marketing institutio... \\n20 Why do rockets look white? \\n21 What's causing someone to be jealous? \\n22 What are the questions should not ask on Quora? \\n23 How much is 30 kV in HP? \\n24 What does it mean that every time I look at th... \\n25 What are some tips on making it through the jo... \\n26 What is web application? \\n27 Does society place too much importance on sports? \\n28 What is best way to make money online? \\n29 How should I prepare for CA final law? \\n... ... \\n404260 Which phone is best under 12000? \\n404261 Who is the overall most popular Game of Throne... \\n404262 How do you troubleshoot a Toshiba laptop? \\n404263 How does the burning of fossil fuels contribut... \\n404264 Is it safe to store an external battery power ... \\n404265 How can I gain weight on my body? \\n404266 What is the green dot next to the phone icon o... \\n404267 What are the causes of the fall of the Roman E... \\n404268 Why don't we still do great music like in the ... \\n404269 How do you diagnose antisocial personality dis... \\n404270 What is the difference between who and how? \\n404271 Does Stalin have any grandchildren that are st... \\n404272 What are the best new car products or inventio... \\n404273 What happens if you put milk in a coffee maker? \\n404274 Will the next generation of parenting change o... \\n404275 In accounting, why do we debit expenses and cr... \\n404276 What is copilotsearch.com? \\n404277 What does analytics do? \\n404278 How did you prepare for AIIMS/NEET/AIPMT? \\n404279 What is the minimum time required to build a f... \\n404280 What are some outfit ideas to wear to a frat p... \\n404281 Why is Manaphy childish in Pok\\u00e9mon Ranger and ... \\n404282 How does a long distance relationship work? \\n404283 What do you think of the removal of the MagSaf... \\n404284 What does Jainism say about homosexuality? \\n404285 How many keywords are there in the Racket prog... \\n404286 Do you believe there is life after death? \\n404287 What is one coin? \\n404288 What is the approx annual cost of living while... \\n404289 What is like to have sex with cousin? \\n\\n question2 is_duplicate \\n0 What is the step by step guide to invest in sh... 0 \\n1 What would happen if the Indian government sto... 0 \\n2 How can Internet speed be increased by hacking... 0 \\n3 Find the remainder when [math]23^{24}[/math] i... 0 \\n4 Which fish would survive in salt water? 0 \\n5 I'm a triple Capricorn (Sun, Moon and ascendan... 1 \\n6 What keeps childern active and far from phone ... 0 \\n7 What should I do to be a great geologist? 1 \\n8 When do you use \\\"&\\\" instead of \\\"and\\\"? 0 \\n9 How do I hack Motorola DCX3400 for free internet? 0 \\n10 What are some of the things technicians can te... 0 \\n11 How can I see all my Youtube comments? 1 \\n12 How can you make physics easy to learn? 1 \\n13 What was your first sexual experience? 1 \\n14 What are the laws to change your status from a... 0 \\n15 How will a Trump presidency affect the student... 1 \\n16 What does manipulation means? 1 \\n17 How do guys feel after rejecting a girl? 0 \\n18 Why do people ask Quora questions which can be... 1 \\n19 Which is the best digital marketing institute ... 0 \\n20 Why are rockets and boosters painted white? 1 \\n21 What can I do to avoid being jealous of someone? 0 \\n22 Which question should I ask on Quora? 0 \\n23 Where can I find a conversion chart for CC to ... 0 \\n24 How many times a day do a clock\\u2019s hands overlap? 0 \\n25 What are some tips on making it through the jo... 0 \\n26 What is the web application framework? 0 \\n27 How do sports contribute to the society? 0 \\n28 What is best way to ask for money online? 0 \\n29 How one should know that he/she completely pre... 1 \\n... ... ... \\n404260 What is the best phone to buy below 15k? 0 \\n404261 Who is the most popular character in the Game ... 1 \\n404262 How do I reset a Toshiba laptop? 0 \\n404263 Why does CO2 contribute more to global warming... 0 \\n404264 How do I make a safe and cheap power bank? 0 \\n404265 What should I eat to gain weight? 1 \\n404266 My boyfriend says he deleted his Facebook Mess... 0 \\n404267 What were the most important causes and effect... 1 \\n404268 Should I raise my young child on 80's music? 0 \\n404269 What Does It Feel Like to have antisocial pers... 0 \\n404270 What is the difference between \\\"&\\\" and \\\"and\\\"? 0 \\n404271 What was Joseph Stalin's 5 year plan? How did ... 0 \\n404272 What are some mind-blowing vehicles tools that... 1 \\n404273 What would happen if I put milk instead of wat... 1 \\n404274 What kind of parents will the next generation ... 1 \\n404275 What is a utilities expense in accounting? How... 0 \\n404276 What is ContenVania.com? 0 \\n404277 What are analytical people like? 0 \\n404278 How did you prepare for the AIIMS UG entrance ... 0 \\n404279 What is a cheaper and quicker way to build an ... 0 \\n404280 What are some outfit ideas wear to a frat them... 1 \\n404281 Why is Manaphy annoying in Pokemon ranger and ... 1 \\n404282 How are long distance relationships maintained? 1 \\n404283 What will the CPU upgrade to the 2016 Apple Ma... 0 \\n404284 What does Jainism say about Gays and Homosexua... 1 \\n404285 How many keywords are there in PERL Programmin... 0 \\n404286 Is it true that there is life after death? 1 \\n404287 What's this coin? 0 \\n404288 I am having little hairfall problem but I want... 0 \\n404289 What is it like to have sex with your cousin? 0 \\n\\n[404290 rows x 6 columns]\"\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_train\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"29b1ab03-e31f-d0d1-a252-835decbda380\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": \"| \\n | id | \\nqid1 | \\nqid2 | \\nquestion1 | \\nquestion2 | \\nis_duplicate | \\n
|---|---|---|---|---|---|---|
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| 5 | \\n5 | \\n11 | \\n12 | \\nAstrology: I am a Capricorn Sun Cap moon and c... | \\nI'm a triple Capricorn (Sun, Moon and ascendan... | \\n1 | \\n
| 6 | \\n6 | \\n13 | \\n14 | \\nShould I buy tiago? | \\nWhat keeps childern active and far from phone ... | \\n0 | \\n
| 7 | \\n7 | \\n15 | \\n16 | \\nHow can I be a good geologist? | \\nWhat should I do to be a great geologist? | \\n1 | \\n
| 8 | \\n8 | \\n17 | \\n18 | \\nWhen do you use \\u30b7 instead of \\u3057? | \\nWhen do you use \\\"&\\\" instead of \\\"and\\\"? | \\n0 | \\n
| 9 | \\n9 | \\n19 | \\n20 | \\nMotorola (company): Can I hack my Charter Moto... | \\nHow do I hack Motorola DCX3400 for free internet? | \\n0 | \\n
| 10 | \\n10 | \\n21 | \\n22 | \\nMethod to find separation of slits using fresn... | \\nWhat are some of the things technicians can te... | \\n0 | \\n
| 11 | \\n11 | \\n23 | \\n24 | \\nHow do I read and find my YouTube comments? | \\nHow can I see all my Youtube comments? | \\n1 | \\n
| 12 | \\n12 | \\n25 | \\n26 | \\nWhat can make Physics easy to learn? | \\nHow can you make physics easy to learn? | \\n1 | \\n
| 13 | \\n13 | \\n27 | \\n28 | \\nWhat was your first sexual experience like? | \\nWhat was your first sexual experience? | \\n1 | \\n
| 14 | \\n14 | \\n29 | \\n30 | \\nWhat are the laws to change your status from a... | \\nWhat are the laws to change your status from a... | \\n0 | \\n
| 15 | \\n15 | \\n31 | \\n32 | \\nWhat would a Trump presidency mean for current... | \\nHow will a Trump presidency affect the student... | \\n1 | \\n
| 16 | \\n16 | \\n33 | \\n34 | \\nWhat does manipulation mean? | \\nWhat does manipulation means? | \\n1 | \\n
| 17 | \\n17 | \\n35 | \\n36 | \\nWhy do girls want to be friends with the guy t... | \\nHow do guys feel after rejecting a girl? | \\n0 | \\n
| 18 | \\n18 | \\n37 | \\n38 | \\nWhy are so many Quora users posting questions ... | \\nWhy do people ask Quora questions which can be... | \\n1 | \\n
| 19 | \\n19 | \\n39 | \\n40 | \\nWhich is the best digital marketing institutio... | \\nWhich is the best digital marketing institute ... | \\n0 | \\n
| 20 | \\n20 | \\n41 | \\n42 | \\nWhy do rockets look white? | \\nWhy are rockets and boosters painted white? | \\n1 | \\n
| 21 | \\n21 | \\n43 | \\n44 | \\nWhat's causing someone to be jealous? | \\nWhat can I do to avoid being jealous of someone? | \\n0 | \\n
| 22 | \\n22 | \\n45 | \\n46 | \\nWhat are the questions should not ask on Quora? | \\nWhich question should I ask on Quora? | \\n0 | \\n
| 23 | \\n23 | \\n47 | \\n48 | \\nHow much is 30 kV in HP? | \\nWhere can I find a conversion chart for CC to ... | \\n0 | \\n
| 24 | \\n24 | \\n49 | \\n50 | \\nWhat does it mean that every time I look at th... | \\nHow many times a day do a clock\\u2019s hands overlap? | \\n0 | \\n
| 25 | \\n25 | \\n51 | \\n52 | \\nWhat are some tips on making it through the jo... | \\nWhat are some tips on making it through the jo... | \\n0 | \\n
| 26 | \\n26 | \\n53 | \\n54 | \\nWhat is web application? | \\nWhat is the web application framework? | \\n0 | \\n
| 27 | \\n27 | \\n55 | \\n56 | \\nDoes society place too much importance on sports? | \\nHow do sports contribute to the society? | \\n0 | \\n
| 28 | \\n28 | \\n57 | \\n58 | \\nWhat is best way to make money online? | \\nWhat is best way to ask for money online? | \\n0 | \\n
| 29 | \\n29 | \\n59 | \\n60 | \\nHow should I prepare for CA final law? | \\nHow one should know that he/she completely pre... | \\n1 | \\n
| ... | \\n... | \\n... | \\n... | \\n... | \\n... | \\n... | \\n
| 404260 | \\n404260 | \\n182494 | \\n691 | \\nWhich phone is best under 12000? | \\nWhat is the best phone to buy below 15k? | \\n0 | \\n
| 404261 | \\n404261 | \\n281150 | \\n124172 | \\nWho is the overall most popular Game of Throne... | \\nWho is the most popular character in the Game ... | \\n1 | \\n
| 404262 | \\n404262 | \\n537905 | \\n466328 | \\nHow do you troubleshoot a Toshiba laptop? | \\nHow do I reset a Toshiba laptop? | \\n0 | \\n
| 404263 | \\n404263 | \\n375195 | \\n537906 | \\nHow does the burning of fossil fuels contribut... | \\nWhy does CO2 contribute more to global warming... | \\n0 | \\n
| 404264 | \\n404264 | \\n537907 | \\n537908 | \\nIs it safe to store an external battery power ... | \\nHow do I make a safe and cheap power bank? | \\n0 | \\n
| 404265 | \\n404265 | \\n25994 | \\n16064 | \\nHow can I gain weight on my body? | \\nWhat should I eat to gain weight? | \\n1 | \\n
| 404266 | \\n404266 | \\n155813 | \\n146284 | \\nWhat is the green dot next to the phone icon o... | \\nMy boyfriend says he deleted his Facebook Mess... | \\n0 | \\n
| 404267 | \\n404267 | \\n20171 | \\n290649 | \\nWhat are the causes of the fall of the Roman E... | \\nWhat were the most important causes and effect... | \\n1 | \\n
| 404268 | \\n404268 | \\n537909 | \\n537910 | \\nWhy don't we still do great music like in the ... | \\nShould I raise my young child on 80's music? | \\n0 | \\n
| 404269 | \\n404269 | \\n537911 | \\n349794 | \\nHow do you diagnose antisocial personality dis... | \\nWhat Does It Feel Like to have antisocial pers... | \\n0 | \\n
| 404270 | \\n404270 | \\n537912 | \\n35364 | \\nWhat is the difference between who and how? | \\nWhat is the difference between \\\"&\\\" and \\\"and\\\"? | \\n0 | \\n
| 404271 | \\n404271 | \\n537913 | \\n537914 | \\nDoes Stalin have any grandchildren that are st... | \\nWhat was Joseph Stalin's 5 year plan? How did ... | \\n0 | \\n
| 404272 | \\n404272 | \\n128018 | \\n14005 | \\nWhat are the best new car products or inventio... | \\nWhat are some mind-blowing vehicles tools that... | \\n1 | \\n
| 404273 | \\n404273 | \\n537915 | \\n537916 | \\nWhat happens if you put milk in a coffee maker? | \\nWhat would happen if I put milk instead of wat... | \\n1 | \\n
| 404274 | \\n404274 | \\n178643 | \\n87385 | \\nWill the next generation of parenting change o... | \\nWhat kind of parents will the next generation ... | \\n1 | \\n
| 404275 | \\n404275 | \\n97922 | \\n537917 | \\nIn accounting, why do we debit expenses and cr... | \\nWhat is a utilities expense in accounting? How... | \\n0 | \\n
| 404276 | \\n404276 | \\n24305 | \\n308365 | \\nWhat is copilotsearch.com? | \\nWhat is ContenVania.com? | \\n0 | \\n
| 404277 | \\n404277 | \\n355668 | \\n537918 | \\nWhat does analytics do? | \\nWhat are analytical people like? | \\n0 | \\n
| 404278 | \\n404278 | \\n537919 | \\n169786 | \\nHow did you prepare for AIIMS/NEET/AIPMT? | \\nHow did you prepare for the AIIMS UG entrance ... | \\n0 | \\n
| 404279 | \\n404279 | \\n537920 | \\n537921 | \\nWhat is the minimum time required to build a f... | \\nWhat is a cheaper and quicker way to build an ... | \\n0 | \\n
| 404280 | \\n404280 | \\n537922 | \\n537923 | \\nWhat are some outfit ideas to wear to a frat p... | \\nWhat are some outfit ideas wear to a frat them... | \\n1 | \\n
| 404281 | \\n404281 | \\n99131 | \\n81495 | \\nWhy is Manaphy childish in Pok\\u00e9mon Ranger and ... | \\nWhy is Manaphy annoying in Pokemon ranger and ... | \\n1 | \\n
| 404282 | \\n404282 | \\n1931 | \\n16773 | \\nHow does a long distance relationship work? | \\nHow are long distance relationships maintained? | \\n1 | \\n
| 404283 | \\n404283 | \\n537924 | \\n537925 | \\nWhat do you think of the removal of the MagSaf... | \\nWhat will the CPU upgrade to the 2016 Apple Ma... | \\n0 | \\n
| 404284 | \\n404284 | \\n537926 | \\n537927 | \\nWhat does Jainism say about homosexuality? | \\nWhat does Jainism say about Gays and Homosexua... | \\n1 | \\n
| 404285 | \\n404285 | \\n433578 | \\n379845 | \\nHow many keywords are there in the Racket prog... | \\nHow many keywords are there in PERL Programmin... | \\n0 | \\n
| 404286 | \\n404286 | \\n18840 | \\n155606 | \\nDo you believe there is life after death? | \\nIs it true that there is life after death? | \\n1 | \\n
| 404287 | \\n404287 | \\n537928 | \\n537929 | \\nWhat is one coin? | \\nWhat's this coin? | \\n0 | \\n
| 404288 | \\n404288 | \\n537930 | \\n537931 | \\nWhat is the approx annual cost of living while... | \\nI am having little hairfall problem but I want... | \\n0 | \\n
| 404289 | \\n404289 | \\n537932 | \\n537933 | \\nWhat is like to have sex with cousin? | \\nWhat is it like to have sex with your cousin? | \\n0 | \\n
404290 rows \\u00d7 6 columns
\\n\\n\\n \\n
\\n
\",\n \"text/plain\": \" id qid1 qid2 question1 \\\\\\n0 0 1 2 What is the step by step guide to invest in sh... \\n1 1 3 4 What is the story of Kohinoor (Koh-i-Noor) Dia... \\n2 2 5 6 How can I increase the speed of my internet co... \\n3 3 7 8 Why am I mentally very lonely? How can I solve... \\n4 4 9 10 Which one dissolve in water quikly sugar, salt... \\n\\n question2 is_duplicate \\n0 What is the step by step guide to invest in sh... 0 \\n1 What would happen if the Indian government sto... 0 \\n2 How can Internet speed be increased by hacking... 0 \\n3 Find the remainder when [math]23^{24}[/math] i... 0 \\n4 Which fish would survive in salt water? 0 \"\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_train.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"93aa62e3-d225-8ea8-bd3f-f76c6776fb67\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"lenght of train data: 404290\\n\"\n }\n ],\n \"source\": [\n \"print(\\\"lenght of train data: \\\"+str(len(df_train)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"fe958fc8-7747-a69e-f0a3-f13de38db885\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"length of train data: 404290\\n\"\n }\n ],\n \"source\": [\n \"print(\\\"length of train data: \\\"+str(len(df_train)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"28a3be25-ffb5-0257-673c-65ad6234a5f5\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"length of duplicate pairs404290\\n\"\n }\n ],\n \"source\": [\n \"print(\\\"length of duplicate pairs\\\"+str(len(df_train['is_duplicate'])))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"3ccd3906-0aaa-2f82-3cf5-7d557368a174\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"length of duplicate pairs: 149263\\n\"\n }\n ],\n \"source\": [\n \"print(\\\"length of duplicate pairs: \\\"+str(df_train['is_duplicate'].sum()\\n\",\n \" ))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_cell_guid\": \"307c127f-85d4-238b-2ac4-b72a60f0966d\"\n },\n \"outputs\": [],\n \"source\": [\n \"qids = pd.Series(df_train['qid1'].tolist()+df_train['qid2'].tolist())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"e404ff2a-2fe9-a2d5-3e75-4d49d3d4322a\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"0 1\\n1 3\\n2 5\\n3 7\\n4 9\\n5 11\\n6 13\\n7 15\\n8 17\\n9 19\\n10 21\\n11 23\\n12 25\\n13 27\\n14 29\\n15 31\\n16 33\\n17 35\\n18 37\\n19 39\\n20 41\\n21 43\\n22 45\\n23 47\\n24 49\\n25 51\\n26 53\\n27 55\\n28 57\\n29 59\\n ... \\n808550 691\\n808551 124172\\n808552 466328\\n808553 537906\\n808554 537908\\n808555 16064\\n808556 146284\\n808557 290649\\n808558 537910\\n808559 349794\\n808560 35364\\n808561 537914\\n808562 14005\\n808563 537916\\n808564 87385\\n808565 537917\\n808566 308365\\n808567 537918\\n808568 169786\\n808569 537921\\n808570 537923\\n808571 81495\\n808572 16773\\n808573 537925\\n808574 537927\\n808575 379845\\n808576 155606\\n808577 537929\\n808578 537931\\n808579 537933\\ndtype: int64\"\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"qids\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"7bb40bbf-037e-ddec-e4c9-6e4e8fdace3c\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"How many unique qids: 537933\\n\"\n }\n ],\n \"source\": [\n \"print(\\\"How many unique qids: \\\"+ str(len(np.unique(qids))))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"94906c38-c7c4-f8bb-4cbc-386fd94dc4de\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"Number of questions that appear multiple times: 111780\\n\"\n }\n ],\n \"source\": [\n \"print('Number of questions that appear multiple times: {}'.format(np.sum(qids.value_counts() > 1)))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_cell_guid\": \"39ba0c6d-3350-2930-bdd8-181129f60435\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"2559 157\\n30782 120\\n4044 111\\n2561 88\\n14376 79\\n17978 77\\n2675 68\\n28764 66\\n1772 63\\n24555 61\\n18753 58\\n4018 57\\n28133 56\\n3595 55\\n19621 55\\n18531 55\\n4951 55\\n13748 54\\n10024 54\\n10330 54\\n38 54\\n18296 53\\n11264 52\\n6551 52\\n20190 52\\n691 51\\n8461 51\\n6749 51\\n2322 51\\n33412 51\\n ... \\n95599 1\\n87403 1\\n259391 1\\n89450 1\\n1345 1\\n7490 1\\n13639 1\\n19784 1\\n17737 1\\n23882 1\\n32078 1\\n30031 1\\n34129 1\\n40274 1\\n38227 1\\n42325 1\\n48470 1\\n56666 1\\n54619 1\\n60764 1\\n64862 1\\n62815 1\\n66913 1\\n73058 1\\n77156 1\\n75109 1\\n81254 1\\n85352 1\\n83305 1\\n168274 1\\ndtype: int64\"\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"qids.value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"_cell_guid\": \"0f55d953-74e6-2bdc-7708-6ff7273cb6c6\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"(array([ 5.21491000e+05, 9.55300000e+03, 2.93000000e+03,\\n 1.44500000e+03, 7.90000000e+02, 5.32000000e+02,\\n 3.70000000e+02, 2.68000000e+02, 2.03000000e+02,\\n 1.01000000e+02, 7.70000000e+01, 3.50000000e+01,\\n 3.50000000e+01, 3.30000000e+01, 2.10000000e+01,\\n 1.70000000e+01, 1.50000000e+01, 6.00000000e+00,\\n 1.00000000e+00, 2.00000000e+00, 1.00000000e+00,\\n 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\\n 1.00000000e+00, 1.00000000e+00, 0.00000000e+00,\\n 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\\n 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,\\n 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,\\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\\n 0.00000000e+00, 1.00000000e+00]),\\n array([ 1. , 4.12, 7.24, 10.36, 13.48, 16.6 , 19.72,\\n 22.84, 25.96, 29.08, 32.2 , 35.32, 38.44, 41.56,\\n 44.68, 47.8 , 50.92, 54.04, 57.16, 60.28, 63.4 ,\\n 66.52, 69.64, 72.76, 75.88, 79. , 82.12, 85.24,\\n 88.36, 91.48, 94.6 , 97.72, 100.84, 103.96, 107.08,\\n 110.2 , 113.32, 116.44, 119.56, 122.68, 125.8 , 128.92,\\n 132.04, 135.16, 138.28, 141.4 , 144.52, 147.64, 150.76,\\n 153.88, 157. ]),\\n )\"\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": \"| \\n | id | \\nqid1 | \\nqid2 | \\nquestion1 | \\nquestion2 | \\nis_duplicate | \\n
|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n1 | \\n2 | \\nWhat is the step by step guide to invest in sh... | \\nWhat is the step by step guide to invest in sh... | \\n0 | \\n
| 1 | \\n1 | \\n3 | \\n4 | \\nWhat is the story of Kohinoor (Koh-i-Noor) Dia... | \\nWhat would happen if the Indian government sto... | \\n0 | \\n
| 2 | \\n2 | \\n5 | \\n6 | \\nHow can I increase the speed of my internet co... | \\nHow can Internet speed be increased by hacking... | \\n0 | \\n
| 3 | \\n3 | \\n7 | \\n8 | \\nWhy am I mentally very lonely? How can I solve... | \\nFind the remainder when [math]23^{24}[/math] i... | \\n0 | \\n
| 4 | \\n4 | \\n9 | \\n10 | \\nWhich one dissolve in water quikly sugar, salt... | \\nWhich fish would survive in salt water? | \\n0 | \\n
\\n\\n \\n
\\n
\",\n \"text/plain\": \" id qid1 qid2 question1 \\\\\\n0 0 1 2 What is the step by step guide to invest in sh... \\n1 1 3 4 What is the story of Kohinoor (Koh-i-Noor) Dia... \\n2 2 5 6 How can I increase the speed of my internet co... \\n3 3 7 8 Why am I mentally very lonely? How can I solve... \\n4 4 9 10 Which one dissolve in water quikly sugar, salt... \\n\\n question2 is_duplicate \\n0 What is the step by step guide to invest in sh... 0 \\n1 What would happen if the Indian government sto... 0 \\n2 How can Internet speed be increased by hacking... 0 \\n3 Find the remainder when [math]23^{24}[/math] i... 0 \\n4 Which fish would survive in salt water? 0 \"\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_train.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"metadata\": {\n \"_cell_guid\": \"23fe0a4a-60b0-6443-d1ea-2cd5818fa72e\"\n },\n \"outputs\": [],\n \"source\": [\n \"from collections import Counter\\n\",\n \"\\n\",\n \"# If a word appears only once, we ignore it completely (likely a typo)\\n\",\n \"# Epsilon defines a smoothing constant, which makes the effect of extremely rare words smaller\\n\",\n \"def get_weight(count, eps=10000, min_count=2):\\n\",\n \" if count < min_count:\\n\",\n \" return 0\\n\",\n \" else:\\n\",\n \" return 1 / (count + eps)\\n\",\n \"\\n\",\n \"eps = 5000 \\n\",\n \"words = r.split((\\\" \\\".join(train_qs)).lower())\\n\",\n \"counts = Counter(words)\\n\",\n \"weights = {word: get_weight(count) for word, count in counts.items() if word != \\\"\\\"}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"ff0a24a3-5086-5356-7cae-847b0772503b\"\n },\n \"source\": [\n \"Because we filter the only-once appearance, so we need some specific operation on x==0.\\n\",\n \"Also, the re.split() will create a lot null str, I also remove that from weights.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"metadata\": {\n \"_cell_guid\": \"5acb4dcb-b758-2d83-2b81-85868eeb2f65\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"Most commonly used words are: \\n\\n[('the', 2.587951532843693e-06), ('what', 2.9944154152505576e-06), ('is', 3.576717002997289e-06), ('i', 4.318852221833526e-06), ('how', 4.33715291933763e-06), ('a', 4.5256445649271595e-06), ('to', 4.646818555675857e-06), ('in', 4.854910985207086e-06), ('do', 5.853772756541591e-06), ('of', 5.894314933246883e-06)]\\n\\nLeast commonly used words are: \\n\\n[('\\u30b7', 9.998000399920016e-05), ('\\u3057', 9.998000399920016e-05), ('dcx3400', 9.998000399920016e-05), ('(employment', 9.998000399920016e-05), ('1-855-425-3768', 9.998000399920016e-05), ('confederates', 9.998000399920016e-05), ('asahi', 9.998000399920016e-05), ('oitnb', 9.998000399920016e-05), ('samrudi', 9.998000399920016e-05), ('prospering', 9.998000399920016e-05)]\\n\"\n }\n ],\n \"source\": [\n \"print(\\\"Most commonly used words are: \\\\n\\\")\\n\",\n \"print(sorted(weights.items(),key=lambda x: x[1] if x[1]>0 else 9999)[:10])\\n\",\n \"print(\\\"\\\\nLeast commonly used words are: \\\\n\\\")\\n\",\n \"print(sorted(weights.items(),key=lambda x: x[1],reverse = True)[:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"metadata\": {\n \"_cell_guid\": \"2c317f05-32a4-1442-5eb9-07c4f65b67b3\"\n },\n \"outputs\": [],\n \"source\": [\n \"def tfidf_word_match_share(row):\\n\",\n \" q1words = {}\\n\",\n \" q2words = {}\\n\",\n \" for word in r.split(str(row['question1']).lower()):\\n\",\n \" if word not in stops:\\n\",\n \" q1words[word] = 1\\n\",\n \" for word in r.split(str(row['question2']).lower()):\\n\",\n \" if word not in stops:\\n\",\n \" q2words[word] = 1\\n\",\n \" if len(q1words) == 0 or len(q2words) == 0:\\n\",\n \" # The computer-generated chaff includes a few questions that are nothing but stopwords\\n\",\n \" return 0\\n\",\n \" \\n\",\n \" shared_weights = [weights.get(w, 0) for w in q1words.keys() if w in q2words] + [weights.get(w, 0) for w in q2words.keys() if w in q1words]\\n\",\n \" total_weights = [weights.get(w, 0) for w in q1words] + [weights.get(w, 0) for w in q2words]\\n\",\n \" \\n\",\n \" R = np.sum(shared_weights) / np.sum(total_weights)\\n\",\n \" return R\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"metadata\": {\n \"_cell_guid\": \"aa3e43e9-4b94-fe44-d060-89b0239a3940\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"| \\n | id | \\nqid1 | \\nqid2 | \\nquestion1 | \\nquestion2 | \\nis_duplicate | \\n
|---|---|---|---|---|---|---|
| 0 | \\n0 | \\n1 | \\n2 | \\nWhat is the step by step guide to invest in sh... | \\nWhat is the step by step guide to invest in sh... | \\n0 | \\n
| 1 | \\n1 | \\n3 | \\n4 | \\nWhat is the story of Kohinoor (Koh-i-Noor) Dia... | \\nWhat would happen if the Indian government sto... | \\n0 | \\n
| 2 | \\n2 | \\n5 | \\n6 | \\nHow can I increase the speed of my internet co... | \\nHow can Internet speed be increased by hacking... | \\n0 | \\n
| 3 | \\n3 | \\n7 | \\n8 | \\nWhy am I mentally very lonely? How can I solve... | \\nFind the remainder when [math]23^{24}[/math] i... | \\n0 | \\n
| 4 | \\n4 | \\n9 | \\n10 | \\nWhich one dissolve in water quikly sugar, salt... | \\nWhich fish would survive in salt water? | \\n0 | \\n
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Id MSSubClass LotFrontage LotArea OverallQual \\\\\\n\",\n \"count 1460.000000 1460.000000 1201.000000 1460.000000 1460.000000 \\n\",\n \"mean 730.500000 56.897260 70.049958 10516.828082 6.099315 \\n\",\n \"std 421.610009 42.300571 24.284752 9981.264932 1.382997 \\n\",\n \"min 1.000000 20.000000 21.000000 1300.000000 1.000000 \\n\",\n \"25% 365.750000 20.000000 59.000000 7553.500000 5.000000 \\n\",\n \"50% 730.500000 50.000000 69.000000 9478.500000 6.000000 \\n\",\n \"75% 1095.250000 70.000000 80.000000 11601.500000 7.000000 \\n\",\n \"max 1460.000000 190.000000 313.000000 215245.000000 10.000000 \\n\",\n \"\\n\",\n \" OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 \\\\\\n\",\n \"count 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 \\n\",\n \"mean 5.575342 1971.267808 1984.865753 103.685262 443.639726 \\n\",\n \"std 1.112799 30.202904 20.645407 181.066207 456.098091 \\n\",\n \"min 1.000000 1872.000000 1950.000000 0.000000 0.000000 \\n\",\n \"25% 5.000000 1954.000000 1967.000000 0.000000 0.000000 \\n\",\n \"50% 5.000000 1973.000000 1994.000000 0.000000 383.500000 \\n\",\n \"75% 6.000000 2000.000000 2004.000000 166.000000 712.250000 \\n\",\n \"max 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 \\n\",\n \"\\n\",\n \" ... WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\\\\n\",\n \"count ... 1460.000000 1460.000000 1460.000000 1460.000000 \\n\",\n \"mean ... 94.244521 46.660274 21.954110 3.409589 \\n\",\n \"std ... 125.338794 66.256028 61.119149 29.317331 \\n\",\n \"min ... 0.000000 0.000000 0.000000 0.000000 \\n\",\n \"25% ... 0.000000 0.000000 0.000000 0.000000 \\n\",\n \"50% ... 0.000000 25.000000 0.000000 0.000000 \\n\",\n \"75% ... 168.000000 68.000000 0.000000 0.000000 \\n\",\n \"max ... 857.000000 547.000000 552.000000 508.000000 \\n\",\n \"\\n\",\n \" ScreenPorch PoolArea MiscVal MoSold YrSold \\\\\\n\",\n \"count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \\n\",\n \"mean 15.060959 2.758904 43.489041 6.321918 2007.815753 \\n\",\n \"std 55.757415 40.177307 496.123024 2.703626 1.328095 \\n\",\n \"min 0.000000 0.000000 0.000000 1.000000 2006.000000 \\n\",\n \"25% 0.000000 0.000000 0.000000 5.000000 2007.000000 \\n\",\n \"50% 0.000000 0.000000 0.000000 6.000000 2008.000000 \\n\",\n \"75% 0.000000 0.000000 0.000000 8.000000 2009.000000 \\n\",\n \"max 480.000000 738.000000 15500.000000 12.000000 2010.000000 \\n\",\n \"\\n\",\n \" SalePrice \\n\",\n \"count 1460.000000 \\n\",\n \"mean 180921.195890 \\n\",\n \"std 79442.502883 \\n\",\n \"min 34900.000000 \\n\",\n \"25% 129975.000000 \\n\",\n \"50% 163000.000000 \\n\",\n \"75% 214000.000000 \\n\",\n \"max 755000.000000 \\n\",\n \"\\n\",\n \"[8 rows x 38 columns]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"8d47ac97-c47a-4676-e648-29303db95407\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([ 0, 1, 2, ..., 1457, 1458, 1459])\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"pd.isnull(train).any(1).nonzero()[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"8c249f5f-c2e6-5fdd-5377-3303d9f3b8c8\"\n },\n \"outputs\": [],\n \"source\": [\n \"missing = train.isnull().sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"189938d6-6fb1-9d97-2732-9c2457eaf8fc\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(81,)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"missing.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"1525a151-0eab-d468-8b9b-13995eef8b2c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"missing[0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"3b8c4d62-dc1e-0460-f2f9-81f4b2c455b2\"\n },\n \"outputs\": [],\n \"source\": [\n \"miss_fields = missing.where(missing>0).dropna()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"9300203e-ceaa-fd2d-0172-c73ff26275ce\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LotFrontage 259.0\\n\",\n \"Alley 1369.0\\n\",\n \"MasVnrType 8.0\\n\",\n \"MasVnrArea 8.0\\n\",\n \"BsmtQual 37.0\\n\",\n \"BsmtCond 37.0\\n\",\n \"BsmtExposure 38.0\\n\",\n \"BsmtFinType1 37.0\\n\",\n \"BsmtFinType2 38.0\\n\",\n \"Electrical 1.0\\n\",\n \"FireplaceQu 690.0\\n\",\n \"GarageType 81.0\\n\",\n \"GarageYrBlt 81.0\\n\",\n \"GarageFinish 81.0\\n\",\n \"GarageQual 81.0\\n\",\n \"GarageCond 81.0\\n\",\n \"PoolQC 1453.0\\n\",\n \"Fence 1179.0\\n\",\n \"MiscFeature 1406.0\\n\",\n \"dtype: float64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"miss_fields\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"bb06df28-d8aa-4237-664c-49354c108cec\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn import model_selection, preprocessing\\n\",\n \"lbl = preprocessing.LabelEncoder()\\n\",\n \"lbl.fit(list(train['PoolQC'].values)) \\n\",\n \"train['PoolQC'] = lbl.transform(list(train['PoolQC'].values))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_cell_guid\": \"9a0d83f8-450b-4c53-993c-1ef87175b121\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 0, 1, 2])\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train['PoolQC'].unique()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"9b7db345-5463-0bee-b592-0d80b2adaa0c\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 4\\n\",\n \"1 4\\n\",\n \"2 4\\n\",\n \"3 4\\n\",\n \"4 4\\n\",\n \"5 2\\n\",\n \"6 4\\n\",\n \"7 4\\n\",\n \"8 4\\n\",\n \"9 4\\n\",\n \"10 4\\n\",\n \"11 4\\n\",\n \"12 4\\n\",\n \"13 4\\n\",\n \"14 1\\n\",\n \"15 0\\n\",\n \"16 4\\n\",\n \"17 4\\n\",\n \"18 4\\n\",\n \"19 2\\n\",\n \"20 4\\n\",\n \"21 0\\n\",\n \"22 4\\n\",\n \"23 4\\n\",\n \"24 2\\n\",\n \"25 4\\n\",\n \"26 4\\n\",\n \"27 4\\n\",\n \"28 4\\n\",\n \"29 4\\n\",\n \" ..\\n\",\n \"1430 4\\n\",\n \"1431 4\\n\",\n \"1432 4\\n\",\n \"1433 4\\n\",\n \"1434 4\\n\",\n \"1435 0\\n\",\n \"1436 1\\n\",\n \"1437 4\\n\",\n \"1438 2\\n\",\n \"1439 4\\n\",\n \"1440 4\\n\",\n \"1441 4\\n\",\n \"1442 4\\n\",\n \"1443 4\\n\",\n \"1444 4\\n\",\n \"1445 4\\n\",\n \"1446 4\\n\",\n \"1447 4\\n\",\n \"1448 1\\n\",\n \"1449 4\\n\",\n \"1450 4\\n\",\n \"1451 4\\n\",\n \"1452 4\\n\",\n \"1453 4\\n\",\n \"1454 4\\n\",\n \"1455 4\\n\",\n \"1456 2\\n\",\n \"1457 0\\n\",\n \"1458 4\\n\",\n \"1459 4\\n\",\n \"Name: Fence, dtype: int64\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"lbl = preprocessing.LabelEncoder()\\n\",\n \"lbl.fit(list(train['Fence'].values)) \\n\",\n \"train['Fence'] = lbl.transform(list(train['Fence'].values))\\n\",\n \"train['Fence']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"metadata\": {\n \"_cell_guid\": \"ed76391c-a5a8-a0e5-26f9-8a1c75ca9f12\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([4, 2, 1, 0, 3])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train['Fence'].unique()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"141d8cce-faf2-d458-61a1-9579e348d0c4\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 278,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/162/1162984.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":179,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"50cfce73-0cd8-092a-fe57-9c3ac000e875\"\n },\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"\\n\",\n \"# visualization\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"\\n\",\n \"import xgboost as xgb\\n\",\n \"from sklearn import model_selection, preprocessing\\n\",\n \"\\n\",\n \"\\n\",\n \"pd.set_option('display.max_columns', 500)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"912d98a7-bb10-cab8-0fa8-d38f4b341d7c\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_df = pd.read_csv('../input/train.csv',parse_dates=['timestamp'])\\n\",\n \"result_df = pd.read_csv('../input/test.csv',parse_dates=['timestamp'])\\n\",\n \"train_df.shape\\n\",\n \"combine=[train_df,result_df]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"c9be57ef-bcb3-c3ff-338f-d6f433c645cc\"\n },\n \"outputs\": [],\n \"source\": [\n \"for c in train_df.columns:\\n\",\n \" if train_df[c].dtype == 'object':\\n\",\n \" lbl = preprocessing.LabelEncoder()\\n\",\n \" lbl.fit(list(train_df[c].values)) \\n\",\n \" train_df[c] = lbl.transform(list(train_df[c].values))\\n\",\n \" #x_train.drop(c,axis=1,inplace=True)\\n\",\n \" \\n\",\n \"for c in result_df.columns:\\n\",\n \" if result_df[c].dtype == 'object':\\n\",\n \" lbl = preprocessing.LabelEncoder()\\n\",\n \" lbl.fit(list(result_df[c].values)) \\n\",\n \" result_df[c] = lbl.transform(list(result_df[c].values))\\n\",\n \" #x_test.drop(c,axis=1,inplace=True) \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"e4063db2-adcf-e873-9247-f752f53c5254\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(30471, 289)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_train = train_df.drop(['price_doc','id','timestamp'],axis=1)\\n\",\n \"Y_train = train_df['price_doc'].values.reshape(-1,1)\\n\",\n \"X_train.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"8b7b7718-d741-ab52-06bc-f83f946bcfbe\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(7662, 289)\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_result = result_df.drop(['id','timestamp'],axis=1)\\n\",\n \"id_test = result_df['id']\\n\",\n \"X_result.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"46e17dae-f980-cc0f-f8ac-1012251e4d21\"\n },\n \"outputs\": [],\n \"source\": [\n \"dtrain = xgb.DMatrix(X_train, Y_train)\\n\",\n \"dresult=xgb.DMatrix(X_result)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"59b9c470-d047-916c-7af0-608ea01d4c58\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0]\\ttrain-rmse:8.2095e+06\\n\",\n \"[25]\\ttrain-rmse:3.49658e+06\\n\",\n \"[50]\\ttrain-rmse:2.56442e+06\\n\",\n \"[75]\\ttrain-rmse:2.35659e+06\\n\",\n \"[100]\\ttrain-rmse:2.26672e+06\\n\",\n \"[125]\\ttrain-rmse:2.20367e+06\\n\",\n \"[150]\\ttrain-rmse:2.15031e+06\\n\",\n \"[175]\\ttrain-rmse:2.11448e+06\\n\",\n \"[200]\\ttrain-rmse:2.08329e+06\\n\",\n \"[225]\\ttrain-rmse:2.05057e+06\\n\",\n \"[250]\\ttrain-rmse:2.02361e+06\\n\",\n \"[275]\\ttrain-rmse:2.00002e+06\\n\",\n \"[300]\\ttrain-rmse:1.97267e+06\\n\",\n \"[325]\\ttrain-rmse:1.95074e+06\\n\",\n \"[350]\\ttrain-rmse:1.92801e+06\\n\",\n \"[375]\\ttrain-rmse:1.9092e+06\\n\",\n \"[399]\\ttrain-rmse:1.89102e+06\\n\"\n ]\n }\n ],\n \"source\": [\n \"xgb_params = {\\n\",\n \" 'eta': 0.05,\\n\",\n \" 'max_depth': 5,\\n\",\n \" 'subsample': 0.7,\\n\",\n \" 'colsample_bytree': 0.7,\\n\",\n \" 'objective': 'reg:linear',\\n\",\n \" 'eval_metric': 'rmse',\\n\",\n \" 'silent': 1\\n\",\n \"}\\n\",\n \"# Uncomment to tune XGB `num_boost_rounds`\\n\",\n \"#model = xgb.cv(xgb_params, dtrain, num_boost_round=200,\\n\",\n \" #early_stopping_rounds=30, verbose_eval=10)\\n\",\n \"model = xgb.train(xgb_params, dtrain, num_boost_round=400,verbose_eval=25, evals=[(dtrain,'train')])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"e5520862-a82a-495c-9f44-cb5db3d4f5d1\"\n },\n \"outputs\": [],\n \"source\": [\n \"y_pred=model.predict(dresult)\\n\",\n \"output=pd.DataFrame(data={'price_doc':y_pred},index=id_test)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"5beb30f0-e48f-6415-44e7-6766b0c343c5\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | Id | \\n\",\n \"MSSubClass | \\n\",\n \"LotFrontage | \\n\",\n \"LotArea | \\n\",\n \"OverallQual | \\n\",\n \"OverallCond | \\n\",\n \"YearBuilt | \\n\",\n \"YearRemodAdd | \\n\",\n \"MasVnrArea | \\n\",\n \"BsmtFinSF1 | \\n\",\n \"... | \\n\",\n \"WoodDeckSF | \\n\",\n \"OpenPorchSF | \\n\",\n \"EnclosedPorch | \\n\",\n \"3SsnPorch | \\n\",\n \"ScreenPorch | \\n\",\n \"PoolArea | \\n\",\n \"MiscVal | \\n\",\n \"MoSold | \\n\",\n \"YrSold | \\n\",\n \"SalePrice | \\n\",\n \"
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| mean | \\n\",\n \"730.500000 | \\n\",\n \"56.897260 | \\n\",\n \"70.049958 | \\n\",\n \"10516.828082 | \\n\",\n \"6.099315 | \\n\",\n \"5.575342 | \\n\",\n \"1971.267808 | \\n\",\n \"1984.865753 | \\n\",\n \"103.685262 | \\n\",\n \"443.639726 | \\n\",\n \"... | \\n\",\n \"94.244521 | \\n\",\n \"46.660274 | \\n\",\n \"21.954110 | \\n\",\n \"3.409589 | \\n\",\n \"15.060959 | \\n\",\n \"2.758904 | \\n\",\n \"43.489041 | \\n\",\n \"6.321918 | \\n\",\n \"2007.815753 | \\n\",\n \"180921.195890 | \\n\",\n \"
| std | \\n\",\n \"421.610009 | \\n\",\n \"42.300571 | \\n\",\n \"24.284752 | \\n\",\n \"9981.264932 | \\n\",\n \"1.382997 | \\n\",\n \"1.112799 | \\n\",\n \"30.202904 | \\n\",\n \"20.645407 | \\n\",\n \"181.066207 | \\n\",\n \"456.098091 | \\n\",\n \"... | \\n\",\n \"125.338794 | \\n\",\n \"66.256028 | \\n\",\n \"61.119149 | \\n\",\n \"29.317331 | \\n\",\n \"55.757415 | \\n\",\n \"40.177307 | \\n\",\n \"496.123024 | \\n\",\n \"2.703626 | \\n\",\n \"1.328095 | \\n\",\n \"79442.502883 | \\n\",\n \"
| min | \\n\",\n \"1.000000 | \\n\",\n \"20.000000 | \\n\",\n \"21.000000 | \\n\",\n \"1300.000000 | \\n\",\n \"1.000000 | \\n\",\n \"1.000000 | \\n\",\n \"1872.000000 | \\n\",\n \"1950.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"... | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"1.000000 | \\n\",\n \"2006.000000 | \\n\",\n \"34900.000000 | \\n\",\n \"
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| count | \\n\",\n \"14999.000000 | \\n\",\n \"14999.000000 | \\n\",\n \"14999.000000 | \\n\",\n \"14999.000000 | \\n\",\n \"14999.000000 | \\n\",\n \"14999.000000 | \\n\",\n \"14999.000000 | \\n\",\n \"14999.000000 | \\n\",\n \"
| mean | \\n\",\n \"0.612834 | \\n\",\n \"0.716102 | \\n\",\n \"3.803054 | \\n\",\n \"201.050337 | \\n\",\n \"3.498233 | \\n\",\n \"0.144610 | \\n\",\n \"0.238083 | \\n\",\n \"0.021268 | \\n\",\n \"
| std | \\n\",\n \"0.248631 | \\n\",\n \"0.171169 | \\n\",\n \"1.232592 | \\n\",\n \"49.943099 | \\n\",\n \"1.460136 | \\n\",\n \"0.351719 | \\n\",\n \"0.425924 | \\n\",\n \"0.144281 | \\n\",\n \"
| min | \\n\",\n \"0.090000 | \\n\",\n \"0.360000 | \\n\",\n \"2.000000 | \\n\",\n \"96.000000 | \\n\",\n \"2.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"
| 25% | \\n\",\n \"0.440000 | \\n\",\n \"0.560000 | \\n\",\n \"3.000000 | \\n\",\n \"156.000000 | \\n\",\n \"3.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"
| 50% | \\n\",\n \"0.640000 | \\n\",\n \"0.720000 | \\n\",\n \"4.000000 | \\n\",\n \"200.000000 | \\n\",\n \"3.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"
| 75% | \\n\",\n \"0.820000 | \\n\",\n \"0.870000 | \\n\",\n \"5.000000 | \\n\",\n \"245.000000 | \\n\",\n \"4.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"
| max | \\n\",\n \"1.000000 | \\n\",\n \"1.000000 | \\n\",\n \"7.000000 | \\n\",\n \"310.000000 | \\n\",\n \"10.000000 | \\n\",\n \"1.000000 | \\n\",\n \"1.000000 | \\n\",\n \"1.000000 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" satisfaction_level last_evaluation number_project average_montly_hours \\\\\\n\",\n \"0 0.38 0.53 2 157 \\n\",\n \"1 0.80 0.86 5 262 \\n\",\n \"2 0.11 0.88 7 272 \\n\",\n \"3 0.72 0.87 5 223 \\n\",\n \"4 0.37 0.52 2 159 \\n\",\n \"\\n\",\n \" time_spend_company Work_accident left promotion_last_5years RandD \\\\\\n\",\n \"0 3 0 1 0 0 \\n\",\n \"1 6 0 1 0 0 \\n\",\n \"2 4 0 1 0 0 \\n\",\n \"3 5 0 1 0 0 \\n\",\n \"4 3 0 1 0 0 \\n\",\n \"\\n\",\n \" accounting hr management marketing product_mng sales support \\\\\\n\",\n \"0 0 0 0 0 0 1 0 \\n\",\n \"1 0 0 0 0 0 1 0 \\n\",\n \"2 0 0 0 0 0 1 0 \\n\",\n \"3 0 0 0 0 0 1 0 \\n\",\n \"4 0 0 0 0 0 1 0 \\n\",\n \"\\n\",\n \" technical low medium \\n\",\n \"0 0 1 0 \\n\",\n \"1 0 0 1 \\n\",\n \"2 0 0 1 \\n\",\n \"3 0 1 0 \\n\",\n \"4 0 1 0 \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"hr_data_new = hr_data.drop(['sales','salary'], axis = 1)\\n\",\n \"hr_data_new = hr_data_new.join(dummy_depts)\\n\",\n \"hr_data_new = hr_data_new.join(dummy_salaries)\\n\",\n \"hr_data_new.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"84f5247f-fa53-51b5-5b5d-58e65169d75f\"\n },\n \"source\": [\n \"Observe that \\\"IT\\\" and \\\"high\\\" are the baseline levels for the assigned department and salary level, respectively. Also note that we saved the data with dummified variables as another dataframe in case we need to access the string values, such as for a cross-tabulation table.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e5fb9a81-b4b9-b9e7-a0f6-0b02539d5970\"\n },\n \"source\": [\n \"## Exploring the Data\\n\",\n \"\\n\",\n \"Now that we have data in an analysis-friendly form, we can do some basic visualizations to see \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"4db9f644-8481-fa86-278f-3b2a2bfd403a\"\n },\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"_change_revision\": 164,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163071.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":181,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"4ce5338a-86f9-2f6b-4025-3cacc7b06cf9\"\n },\n \"source\": [\n \"In this notebook, we do a brief exploration of the data and try to discern which factors matter the most in determining why our personnel leave. The notebook will primarily be divided into two sections -- data analysis and machine learning. \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"abe6bb6a-a0cd-b345-15f1-e83156e1b242\"\n },\n \"source\": [\n \"# Data Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"387ee789-b190-32a4-b24d-f21ad7094ef9\"\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"%matplotlib inline\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"67309f99-937d-91e5-26dd-8f7c97e2efcc\"\n },\n \"source\": [\n \"First, let's read in and get an overview of the data we'll be working with.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"44488b81-87fe-58ed-fc2f-b920fd880df3\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | satisfaction_level | \\n\",\n \"last_evaluation | \\n\",\n \"number_project | \\n\",\n \"average_montly_hours | \\n\",\n \"time_spend_company | \\n\",\n \"Work_accident | \\n\",\n \"left | \\n\",\n \"promotion_last_5years | \\n\",\n \"RandD | \\n\",\n \"accounting | \\n\",\n \"hr | \\n\",\n \"management | \\n\",\n \"marketing | \\n\",\n \"product_mng | \\n\",\n \"sales | \\n\",\n \"support | \\n\",\n \"technical | \\n\",\n \"low | \\n\",\n \"medium | \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \"left 0 1\\n\",\n \"Department \\n\",\n \"IT 77.750611 22.249389\\n\",\n \"RandD 84.625159 15.374841\\n\",\n \"accounting 73.402868 26.597132\\n\",\n \"hr 70.906631 29.093369\\n\",\n \"management 85.555556 14.444444\\n\",\n \"marketing 76.340326 23.659674\\n\",\n \"product_mng 78.048780 21.951220\\n\",\n \"sales 75.507246 24.492754\\n\",\n \"support 75.100942 24.899058\\n\",\n \"technical 74.375000 25.625000\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dept_table_percentages = dept_table.apply(lambda row: (row/row.sum())*100, axis = 1)\\n\",\n \"dept_table_percentages\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"ce9bccee-8e80-8136-85bd-898ff76d9126\"\n },\n \"source\": [\n \"R&D and management tend to have lower rates of leaving, and HR and accounting tend to have higher rates of leaving. The other departments are fairly similar, all between around 22 to 25 percent. We can also visualize the above data with a countplot.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"da700fcb-c8b7-d9bf-84d8-bc87713a5161\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"| left | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"
|---|---|---|
| Department | \\n\",\n \"\\n\",\n \" | \\n\",\n \" |
| IT | \\n\",\n \"77.750611 | \\n\",\n \"22.249389 | \\n\",\n \"
| RandD | \\n\",\n \"84.625159 | \\n\",\n \"15.374841 | \\n\",\n \"
| accounting | \\n\",\n \"73.402868 | \\n\",\n \"26.597132 | \\n\",\n \"
| hr | \\n\",\n \"70.906631 | \\n\",\n \"29.093369 | \\n\",\n \"
| management | \\n\",\n \"85.555556 | \\n\",\n \"14.444444 | \\n\",\n \"
| marketing | \\n\",\n \"76.340326 | \\n\",\n \"23.659674 | \\n\",\n \"
| product_mng | \\n\",\n \"78.048780 | \\n\",\n \"21.951220 | \\n\",\n \"
| sales | \\n\",\n \"75.507246 | \\n\",\n \"24.492754 | \\n\",\n \"
| support | \\n\",\n \"75.100942 | \\n\",\n \"24.899058 | \\n\",\n \"
| technical | \\n\",\n \"74.375000 | \\n\",\n \"25.625000 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" color director_name num_critic_for_reviews duration \\\\\\n\",\n \"0 Color James Cameron 723.0 178.0 \\n\",\n \"1 Color Gore Verbinski 302.0 169.0 \\n\",\n \"2 Color Sam Mendes 602.0 148.0 \\n\",\n \"3 Color Christopher Nolan 813.0 164.0 \\n\",\n \"4 NaN Doug Walker NaN NaN \\n\",\n \"\\n\",\n \" director_facebook_likes actor_3_facebook_likes actor_2_name \\\\\\n\",\n \"0 0.0 855.0 Joel David Moore \\n\",\n \"1 563.0 1000.0 Orlando Bloom \\n\",\n \"2 0.0 161.0 Rory Kinnear \\n\",\n \"3 22000.0 23000.0 Christian Bale \\n\",\n \"4 131.0 NaN Rob Walker \\n\",\n \"\\n\",\n \" actor_1_facebook_likes gross genres \\\\\\n\",\n \"0 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi \\n\",\n \"1 40000.0 309404152.0 Action|Adventure|Fantasy \\n\",\n \"2 11000.0 200074175.0 Action|Adventure|Thriller \\n\",\n \"3 27000.0 448130642.0 Action|Thriller \\n\",\n \"4 131.0 NaN Documentary \\n\",\n \"\\n\",\n \" ... num_user_for_reviews language country content_rating \\\\\\n\",\n \"0 ... 3054.0 English USA PG-13 \\n\",\n \"1 ... 1238.0 English USA PG-13 \\n\",\n \"2 ... 994.0 English UK PG-13 \\n\",\n \"3 ... 2701.0 English USA PG-13 \\n\",\n \"4 ... NaN NaN NaN NaN \\n\",\n \"\\n\",\n \" budget title_year actor_2_facebook_likes imdb_score aspect_ratio \\\\\\n\",\n \"0 237000000.0 2009.0 936.0 7.9 1.78 \\n\",\n \"1 300000000.0 2007.0 5000.0 7.1 2.35 \\n\",\n \"2 245000000.0 2015.0 393.0 6.8 2.35 \\n\",\n \"3 250000000.0 2012.0 23000.0 8.5 2.35 \\n\",\n \"4 NaN NaN 12.0 7.1 NaN \\n\",\n \"\\n\",\n \" movie_facebook_likes \\n\",\n \"0 33000 \\n\",\n \"1 0 \\n\",\n \"2 85000 \\n\",\n \"3 164000 \\n\",\n \"4 0 \\n\",\n \"\\n\",\n \"[5 rows x 28 columns]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('../input/movie_metadata.csv')\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"66f9f7de-4f86-7c41-3c5e-52264ce93095\"\n },\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"select() missing 1 required positional argument: 'crit'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m| \\n\",\n \" | color | \\n\",\n \"director_name | \\n\",\n \"num_critic_for_reviews | \\n\",\n \"duration | \\n\",\n \"director_facebook_likes | \\n\",\n \"actor_3_facebook_likes | \\n\",\n \"actor_2_name | \\n\",\n \"actor_1_facebook_likes | \\n\",\n \"gross | \\n\",\n \"genres | \\n\",\n \"... | \\n\",\n \"num_user_for_reviews | \\n\",\n \"language | \\n\",\n \"country | \\n\",\n \"content_rating | \\n\",\n \"budget | \\n\",\n \"title_year | \\n\",\n \"actor_2_facebook_likes | \\n\",\n \"imdb_score | \\n\",\n \"aspect_ratio | \\n\",\n \"movie_facebook_likes | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"Color | \\n\",\n \"James Cameron | \\n\",\n \"723.0 | \\n\",\n \"178.0 | \\n\",\n \"0.0 | \\n\",\n \"855.0 | \\n\",\n \"Joel David Moore | \\n\",\n \"1000.0 | \\n\",\n \"760505847.0 | \\n\",\n \"Action|Adventure|Fantasy|Sci-Fi | \\n\",\n \"... | \\n\",\n \"3054.0 | \\n\",\n \"English | \\n\",\n \"USA | \\n\",\n \"PG-13 | \\n\",\n \"237000000.0 | \\n\",\n \"2009.0 | \\n\",\n \"936.0 | \\n\",\n \"7.9 | \\n\",\n \"1.78 | \\n\",\n \"33000 | \\n\",\n \"
| 1 | \\n\",\n \"Color | \\n\",\n \"Gore Verbinski | \\n\",\n \"302.0 | \\n\",\n \"169.0 | \\n\",\n \"563.0 | \\n\",\n \"1000.0 | \\n\",\n \"Orlando Bloom | \\n\",\n \"40000.0 | \\n\",\n \"309404152.0 | \\n\",\n \"Action|Adventure|Fantasy | \\n\",\n \"... | \\n\",\n \"1238.0 | \\n\",\n \"English | \\n\",\n \"USA | \\n\",\n \"PG-13 | \\n\",\n \"300000000.0 | \\n\",\n \"2007.0 | \\n\",\n \"5000.0 | \\n\",\n \"7.1 | \\n\",\n \"2.35 | \\n\",\n \"0 | \\n\",\n \"
| 2 | \\n\",\n \"Color | \\n\",\n \"Sam Mendes | \\n\",\n \"602.0 | \\n\",\n \"148.0 | \\n\",\n \"0.0 | \\n\",\n \"161.0 | \\n\",\n \"Rory Kinnear | \\n\",\n \"11000.0 | \\n\",\n \"200074175.0 | \\n\",\n \"Action|Adventure|Thriller | \\n\",\n \"... | \\n\",\n \"994.0 | \\n\",\n \"English | \\n\",\n \"UK | \\n\",\n \"PG-13 | \\n\",\n \"245000000.0 | \\n\",\n \"2015.0 | \\n\",\n \"393.0 | \\n\",\n \"6.8 | \\n\",\n \"2.35 | \\n\",\n \"85000 | \\n\",\n \"
| 3 | \\n\",\n \"Color | \\n\",\n \"Christopher Nolan | \\n\",\n \"813.0 | \\n\",\n \"164.0 | \\n\",\n \"22000.0 | \\n\",\n \"23000.0 | \\n\",\n \"Christian Bale | \\n\",\n \"27000.0 | \\n\",\n \"448130642.0 | \\n\",\n \"Action|Thriller | \\n\",\n \"... | \\n\",\n \"2701.0 | \\n\",\n \"English | \\n\",\n \"USA | \\n\",\n \"PG-13 | \\n\",\n \"250000000.0 | \\n\",\n \"2012.0 | \\n\",\n \"23000.0 | \\n\",\n \"8.5 | \\n\",\n \"2.35 | \\n\",\n \"164000 | \\n\",\n \"
| 4 | \\n\",\n \"NaN | \\n\",\n \"Doug Walker | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"131.0 | \\n\",\n \"NaN | \\n\",\n \"Rob Walker | \\n\",\n \"131.0 | \\n\",\n \"NaN | \\n\",\n \"Documentary | \\n\",\n \"... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"12.0 | \\n\",\n \"7.1 | \\n\",\n \"NaN | \\n\",\n \"0 | \\n\",\n \"
5 rows × 28 columns
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" color director_name num_critic_for_reviews duration \\\\\\n\",\n \"0 Color James Cameron 723.0 178.0 \\n\",\n \"1 Color Gore Verbinski 302.0 169.0 \\n\",\n \"2 Color Sam Mendes 602.0 148.0 \\n\",\n \"3 Color Christopher Nolan 813.0 164.0 \\n\",\n \"4 NaN Doug Walker NaN NaN \\n\",\n \"\\n\",\n \" director_facebook_likes actor_3_facebook_likes actor_2_name \\\\\\n\",\n \"0 0.0 855.0 Joel David Moore \\n\",\n \"1 563.0 1000.0 Orlando Bloom \\n\",\n \"2 0.0 161.0 Rory Kinnear \\n\",\n \"3 22000.0 23000.0 Christian Bale \\n\",\n \"4 131.0 NaN Rob Walker \\n\",\n \"\\n\",\n \" actor_1_facebook_likes gross genres \\\\\\n\",\n \"0 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi \\n\",\n \"1 40000.0 309404152.0 Action|Adventure|Fantasy \\n\",\n \"2 11000.0 200074175.0 Action|Adventure|Thriller \\n\",\n \"3 27000.0 448130642.0 Action|Thriller \\n\",\n \"4 131.0 NaN Documentary \\n\",\n \"\\n\",\n \" ... num_user_for_reviews language country content_rating \\\\\\n\",\n \"0 ... 3054.0 English USA PG-13 \\n\",\n \"1 ... 1238.0 English USA PG-13 \\n\",\n \"2 ... 994.0 English UK PG-13 \\n\",\n \"3 ... 2701.0 English USA PG-13 \\n\",\n \"4 ... NaN NaN NaN NaN \\n\",\n \"\\n\",\n \" budget title_year actor_2_facebook_likes imdb_score aspect_ratio \\\\\\n\",\n \"0 237000000.0 2009.0 936.0 7.9 1.78 \\n\",\n \"1 300000000.0 2007.0 5000.0 7.1 2.35 \\n\",\n \"2 245000000.0 2015.0 393.0 6.8 2.35 \\n\",\n \"3 250000000.0 2012.0 23000.0 8.5 2.35 \\n\",\n \"4 NaN NaN 12.0 7.1 NaN \\n\",\n \"\\n\",\n \" movie_facebook_likes \\n\",\n \"0 33000 \\n\",\n \"1 0 \\n\",\n \"2 85000 \\n\",\n \"3 164000 \\n\",\n \"4 0 \\n\",\n \"\\n\",\n \"[5 rows x 28 columns]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('../input/movie_metadata.csv')\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"66f9f7de-4f86-7c41-3c5e-52264ce93095\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | color | \\n\",\n \"director_name | \\n\",\n \"num_critic_for_reviews | \\n\",\n \"duration | \\n\",\n \"director_facebook_likes | \\n\",\n \"actor_3_facebook_likes | \\n\",\n \"actor_2_name | \\n\",\n \"actor_1_facebook_likes | \\n\",\n \"gross | \\n\",\n \"genres | \\n\",\n \"... | \\n\",\n \"num_user_for_reviews | \\n\",\n \"language | \\n\",\n \"country | \\n\",\n \"content_rating | \\n\",\n \"budget | \\n\",\n \"title_year | \\n\",\n \"actor_2_facebook_likes | \\n\",\n \"imdb_score | \\n\",\n \"aspect_ratio | \\n\",\n \"movie_facebook_likes | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"Color | \\n\",\n \"James Cameron | \\n\",\n \"723.0 | \\n\",\n \"178.0 | \\n\",\n \"0.0 | \\n\",\n \"855.0 | \\n\",\n \"Joel David Moore | \\n\",\n \"1000.0 | \\n\",\n \"760505847.0 | \\n\",\n \"Action|Adventure|Fantasy|Sci-Fi | \\n\",\n \"... | \\n\",\n \"3054.0 | \\n\",\n \"English | \\n\",\n \"USA | \\n\",\n \"PG-13 | \\n\",\n \"237000000.0 | \\n\",\n \"2009.0 | \\n\",\n \"936.0 | \\n\",\n \"7.9 | \\n\",\n \"1.78 | \\n\",\n \"33000 | \\n\",\n \"
| 1 | \\n\",\n \"Color | \\n\",\n \"Gore Verbinski | \\n\",\n \"302.0 | \\n\",\n \"169.0 | \\n\",\n \"563.0 | \\n\",\n \"1000.0 | \\n\",\n \"Orlando Bloom | \\n\",\n \"40000.0 | \\n\",\n \"309404152.0 | \\n\",\n \"Action|Adventure|Fantasy | \\n\",\n \"... | \\n\",\n \"1238.0 | \\n\",\n \"English | \\n\",\n \"USA | \\n\",\n \"PG-13 | \\n\",\n \"300000000.0 | \\n\",\n \"2007.0 | \\n\",\n \"5000.0 | \\n\",\n \"7.1 | \\n\",\n \"2.35 | \\n\",\n \"0 | \\n\",\n \"
| 2 | \\n\",\n \"Color | \\n\",\n \"Sam Mendes | \\n\",\n \"602.0 | \\n\",\n \"148.0 | \\n\",\n \"0.0 | \\n\",\n \"161.0 | \\n\",\n \"Rory Kinnear | \\n\",\n \"11000.0 | \\n\",\n \"200074175.0 | \\n\",\n \"Action|Adventure|Thriller | \\n\",\n \"... | \\n\",\n \"994.0 | \\n\",\n \"English | \\n\",\n \"UK | \\n\",\n \"PG-13 | \\n\",\n \"245000000.0 | \\n\",\n \"2015.0 | \\n\",\n \"393.0 | \\n\",\n \"6.8 | \\n\",\n \"2.35 | \\n\",\n \"85000 | \\n\",\n \"
| 3 | \\n\",\n \"Color | \\n\",\n \"Christopher Nolan | \\n\",\n \"813.0 | \\n\",\n \"164.0 | \\n\",\n \"22000.0 | \\n\",\n \"23000.0 | \\n\",\n \"Christian Bale | \\n\",\n \"27000.0 | \\n\",\n \"448130642.0 | \\n\",\n \"Action|Thriller | \\n\",\n \"... | \\n\",\n \"2701.0 | \\n\",\n \"English | \\n\",\n \"USA | \\n\",\n \"PG-13 | \\n\",\n \"250000000.0 | \\n\",\n \"2012.0 | \\n\",\n \"23000.0 | \\n\",\n \"8.5 | \\n\",\n \"2.35 | \\n\",\n \"164000 | \\n\",\n \"
| 4 | \\n\",\n \"NaN | \\n\",\n \"Doug Walker | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"131.0 | \\n\",\n \"NaN | \\n\",\n \"Rob Walker | \\n\",\n \"131.0 | \\n\",\n \"NaN | \\n\",\n \"Documentary | \\n\",\n \"... | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"12.0 | \\n\",\n \"7.1 | \\n\",\n \"NaN | \\n\",\n \"0 | \\n\",\n \"
5 rows × 28 columns
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" director_name gross \\\\\\n\",\n \"0 James Cameron 760505847.0 \\n\",\n \"1 Gore Verbinski 309404152.0 \\n\",\n \"2 Sam Mendes 200074175.0 \\n\",\n \"3 Christopher Nolan 448130642.0 \\n\",\n \"4 Doug Walker NaN \\n\",\n \"5 Andrew Stanton 73058679.0 \\n\",\n \"6 Sam Raimi 336530303.0 \\n\",\n \"7 Nathan Greno 200807262.0 \\n\",\n \"8 Joss Whedon 458991599.0 \\n\",\n \"9 David Yates 301956980.0 \\n\",\n \"10 Zack Snyder 330249062.0 \\n\",\n \"11 Bryan Singer 200069408.0 \\n\",\n \"12 Marc Forster 168368427.0 \\n\",\n \"13 Gore Verbinski 423032628.0 \\n\",\n \"14 Gore Verbinski 89289910.0 \\n\",\n \"15 Zack Snyder 291021565.0 \\n\",\n \"16 Andrew Adamson 141614023.0 \\n\",\n \"17 Joss Whedon 623279547.0 \\n\",\n \"18 Rob Marshall 241063875.0 \\n\",\n \"19 Barry Sonnenfeld 179020854.0 \\n\",\n \"20 Peter Jackson 255108370.0 \\n\",\n \"21 Marc Webb 262030663.0 \\n\",\n \"22 Ridley Scott 105219735.0 \\n\",\n \"23 Peter Jackson 258355354.0 \\n\",\n \"24 Chris Weitz 70083519.0 \\n\",\n \"25 Peter Jackson 218051260.0 \\n\",\n \"26 James Cameron 658672302.0 \\n\",\n \"27 Anthony Russo 407197282.0 \\n\",\n \"28 Peter Berg 65173160.0 \\n\",\n \"29 Colin Trevorrow 652177271.0 \\n\",\n \"... ... ... \\n\",\n \"5013 Eric Eason NaN \\n\",\n \"5014 Uwe Boll NaN \\n\",\n \"5015 Richard Linklater 1227508.0 \\n\",\n \"5016 Joseph Mazzella NaN \\n\",\n \"5017 Travis Legge NaN \\n\",\n \"5018 Alex Kendrick NaN \\n\",\n \"5019 Marcus Nispel NaN \\n\",\n \"5020 Brandon Landers NaN \\n\",\n \"5021 Jay Duplass 192467.0 \\n\",\n \"5022 Jim Chuchu NaN \\n\",\n \"5023 Daryl Wein 76382.0 \\n\",\n \"5024 Jason Trost NaN \\n\",\n \"5025 John Waters 180483.0 \\n\",\n \"5026 Olivier Assayas 136007.0 \\n\",\n \"5027 Jafar Panahi 673780.0 \\n\",\n \"5028 Ivan Kavanagh NaN \\n\",\n \"5029 Kiyoshi Kurosawa 94596.0 \\n\",\n \"5030 Tadeo Garcia NaN \\n\",\n \"5031 Thomas L. Phillips NaN \\n\",\n \"5032 Ash Baron-Cohen NaN \\n\",\n \"5033 Shane Carruth 424760.0 \\n\",\n \"5034 Neill Dela Llana 70071.0 \\n\",\n \"5035 Robert Rodriguez 2040920.0 \\n\",\n \"5036 Anthony Vallone NaN \\n\",\n \"5037 Edward Burns 4584.0 \\n\",\n \"5038 Scott Smith NaN \\n\",\n \"5039 NaN NaN \\n\",\n \"5040 Benjamin Roberds NaN \\n\",\n \"5041 Daniel Hsia 10443.0 \\n\",\n \"5042 Jon Gunn 85222.0 \\n\",\n \"\\n\",\n \" movie_title \\n\",\n \"0 Avatar \\n\",\n \"1 Pirates of the Caribbean: At World's End \\n\",\n \"2 Spectre \\n\",\n \"3 The Dark Knight Rises \\n\",\n \"4 Star Wars: Episode VII - The Force Awakens ... \\n\",\n \"5 John Carter \\n\",\n \"6 Spider-Man 3 \\n\",\n \"7 Tangled \\n\",\n \"8 Avengers: Age of Ultron \\n\",\n \"9 Harry Potter and the Half-Blood Prince \\n\",\n \"10 Batman v Superman: Dawn of Justice \\n\",\n \"11 Superman Returns \\n\",\n \"12 Quantum of Solace \\n\",\n \"13 Pirates of the Caribbean: Dead Man's Chest \\n\",\n \"14 The Lone Ranger \\n\",\n \"15 Man of Steel \\n\",\n \"16 The Chronicles of Narnia: Prince Caspian \\n\",\n \"17 The Avengers \\n\",\n \"18 Pirates of the Caribbean: On Stranger Tides \\n\",\n \"19 Men in Black 3 \\n\",\n \"20 The Hobbit: The Battle of the Five Armies \\n\",\n \"21 The Amazing Spider-Man \\n\",\n \"22 Robin Hood \\n\",\n \"23 The Hobbit: The Desolation of Smaug \\n\",\n \"24 The Golden Compass \\n\",\n \"25 King Kong \\n\",\n \"26 Titanic \\n\",\n \"27 Captain America: Civil War \\n\",\n \"28 Battleship \\n\",\n \"29 Jurassic World \\n\",\n \"... ... \\n\",\n \"5013 Manito \\n\",\n \"5014 Rampage \\n\",\n \"5015 Slacker \\n\",\n \"5016 Dutch Kills \\n\",\n \"5017 Dry Spell \\n\",\n \"5018 Flywheel \\n\",\n \"5019 Exeter \\n\",\n \"5020 The Ridges \\n\",\n \"5021 The Puffy Chair \\n\",\n \"5022 Stories of Our Lives \\n\",\n \"5023 Breaking Upwards \\n\",\n \"5024 All Superheroes Must Die \\n\",\n \"5025 Pink Flamingos \\n\",\n \"5026 Clean \\n\",\n \"5027 The Circle \\n\",\n \"5028 Tin Can Man \\n\",\n \"5029 The Cure \\n\",\n \"5030 On the Downlow \\n\",\n \"5031 Sanctuary; Quite a Conundrum \\n\",\n \"5032 Bang \\n\",\n \"5033 Primer \\n\",\n \"5034 Cavite \\n\",\n \"5035 El Mariachi \\n\",\n \"5036 The Mongol King \\n\",\n \"5037 Newlyweds \\n\",\n \"5038 Signed Sealed Delivered \\n\",\n \"5039 The Following \\n\",\n \"5040 A Plague So Pleasant \\n\",\n \"5041 Shanghai Calling \\n\",\n \"5042 My Date with Drew \\n\",\n \"\\n\",\n \"[5043 rows x 3 columns]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# checking the director/movie with the biggest grossing\\n\",\n \"dfD=df.loc[:,['director_name','gross','movie_title']]\\n\",\n \"dfD\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"c851b52c-23da-85fb-1734-7afca576d820\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | director_name | \\n\",\n \"gross | \\n\",\n \"movie_title | \\n\",\n \"
|---|---|---|---|
| 0 | \\n\",\n \"James Cameron | \\n\",\n \"760505847.0 | \\n\",\n \"Avatar | \\n\",\n \"
| 1 | \\n\",\n \"Gore Verbinski | \\n\",\n \"309404152.0 | \\n\",\n \"Pirates of the Caribbean: At World's End | \\n\",\n \"
| 2 | \\n\",\n \"Sam Mendes | \\n\",\n \"200074175.0 | \\n\",\n \"Spectre | \\n\",\n \"
| 3 | \\n\",\n \"Christopher Nolan | \\n\",\n \"448130642.0 | \\n\",\n \"The Dark Knight Rises | \\n\",\n \"
| 4 | \\n\",\n \"Doug Walker | \\n\",\n \"NaN | \\n\",\n \"Star Wars: Episode VII - The Force Awakens ... | \\n\",\n \"
| 5 | \\n\",\n \"Andrew Stanton | \\n\",\n \"73058679.0 | \\n\",\n \"John Carter | \\n\",\n \"
| 6 | \\n\",\n \"Sam Raimi | \\n\",\n \"336530303.0 | \\n\",\n \"Spider-Man 3 | \\n\",\n \"
| 7 | \\n\",\n \"Nathan Greno | \\n\",\n \"200807262.0 | \\n\",\n \"Tangled | \\n\",\n \"
| 8 | \\n\",\n \"Joss Whedon | \\n\",\n \"458991599.0 | \\n\",\n \"Avengers: Age of Ultron | \\n\",\n \"
| 9 | \\n\",\n \"David Yates | \\n\",\n \"301956980.0 | \\n\",\n \"Harry Potter and the Half-Blood Prince | \\n\",\n \"
| 10 | \\n\",\n \"Zack Snyder | \\n\",\n \"330249062.0 | \\n\",\n \"Batman v Superman: Dawn of Justice | \\n\",\n \"
| 11 | \\n\",\n \"Bryan Singer | \\n\",\n \"200069408.0 | \\n\",\n \"Superman Returns | \\n\",\n \"
| 12 | \\n\",\n \"Marc Forster | \\n\",\n \"168368427.0 | \\n\",\n \"Quantum of Solace | \\n\",\n \"
| 13 | \\n\",\n \"Gore Verbinski | \\n\",\n \"423032628.0 | \\n\",\n \"Pirates of the Caribbean: Dead Man's Chest | \\n\",\n \"
| 14 | \\n\",\n \"Gore Verbinski | \\n\",\n \"89289910.0 | \\n\",\n \"The Lone Ranger | \\n\",\n \"
| 15 | \\n\",\n \"Zack Snyder | \\n\",\n \"291021565.0 | \\n\",\n \"Man of Steel | \\n\",\n \"
| 16 | \\n\",\n \"Andrew Adamson | \\n\",\n \"141614023.0 | \\n\",\n \"The Chronicles of Narnia: Prince Caspian | \\n\",\n \"
| 17 | \\n\",\n \"Joss Whedon | \\n\",\n \"623279547.0 | \\n\",\n \"The Avengers | \\n\",\n \"
| 18 | \\n\",\n \"Rob Marshall | \\n\",\n \"241063875.0 | \\n\",\n \"Pirates of the Caribbean: On Stranger Tides | \\n\",\n \"
| 19 | \\n\",\n \"Barry Sonnenfeld | \\n\",\n \"179020854.0 | \\n\",\n \"Men in Black 3 | \\n\",\n \"
| 20 | \\n\",\n \"Peter Jackson | \\n\",\n \"255108370.0 | \\n\",\n \"The Hobbit: The Battle of the Five Armies | \\n\",\n \"
| 21 | \\n\",\n \"Marc Webb | \\n\",\n \"262030663.0 | \\n\",\n \"The Amazing Spider-Man | \\n\",\n \"
| 22 | \\n\",\n \"Ridley Scott | \\n\",\n \"105219735.0 | \\n\",\n \"Robin Hood | \\n\",\n \"
| 23 | \\n\",\n \"Peter Jackson | \\n\",\n \"258355354.0 | \\n\",\n \"The Hobbit: The Desolation of Smaug | \\n\",\n \"
| 24 | \\n\",\n \"Chris Weitz | \\n\",\n \"70083519.0 | \\n\",\n \"The Golden Compass | \\n\",\n \"
| 25 | \\n\",\n \"Peter Jackson | \\n\",\n \"218051260.0 | \\n\",\n \"King Kong | \\n\",\n \"
| 26 | \\n\",\n \"James Cameron | \\n\",\n \"658672302.0 | \\n\",\n \"Titanic | \\n\",\n \"
| 27 | \\n\",\n \"Anthony Russo | \\n\",\n \"407197282.0 | \\n\",\n \"Captain America: Civil War | \\n\",\n \"
| 28 | \\n\",\n \"Peter Berg | \\n\",\n \"65173160.0 | \\n\",\n \"Battleship | \\n\",\n \"
| 29 | \\n\",\n \"Colin Trevorrow | \\n\",\n \"652177271.0 | \\n\",\n \"Jurassic World | \\n\",\n \"
| ... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"
| 5013 | \\n\",\n \"Eric Eason | \\n\",\n \"NaN | \\n\",\n \"Manito | \\n\",\n \"
| 5014 | \\n\",\n \"Uwe Boll | \\n\",\n \"NaN | \\n\",\n \"Rampage | \\n\",\n \"
| 5015 | \\n\",\n \"Richard Linklater | \\n\",\n \"1227508.0 | \\n\",\n \"Slacker | \\n\",\n \"
| 5016 | \\n\",\n \"Joseph Mazzella | \\n\",\n \"NaN | \\n\",\n \"Dutch Kills | \\n\",\n \"
| 5017 | \\n\",\n \"Travis Legge | \\n\",\n \"NaN | \\n\",\n \"Dry Spell | \\n\",\n \"
| 5018 | \\n\",\n \"Alex Kendrick | \\n\",\n \"NaN | \\n\",\n \"Flywheel | \\n\",\n \"
| 5019 | \\n\",\n \"Marcus Nispel | \\n\",\n \"NaN | \\n\",\n \"Exeter | \\n\",\n \"
| 5020 | \\n\",\n \"Brandon Landers | \\n\",\n \"NaN | \\n\",\n \"The Ridges | \\n\",\n \"
| 5021 | \\n\",\n \"Jay Duplass | \\n\",\n \"192467.0 | \\n\",\n \"The Puffy Chair | \\n\",\n \"
| 5022 | \\n\",\n \"Jim Chuchu | \\n\",\n \"NaN | \\n\",\n \"Stories of Our Lives | \\n\",\n \"
| 5023 | \\n\",\n \"Daryl Wein | \\n\",\n \"76382.0 | \\n\",\n \"Breaking Upwards | \\n\",\n \"
| 5024 | \\n\",\n \"Jason Trost | \\n\",\n \"NaN | \\n\",\n \"All Superheroes Must Die | \\n\",\n \"
| 5025 | \\n\",\n \"John Waters | \\n\",\n \"180483.0 | \\n\",\n \"Pink Flamingos | \\n\",\n \"
| 5026 | \\n\",\n \"Olivier Assayas | \\n\",\n \"136007.0 | \\n\",\n \"Clean | \\n\",\n \"
| 5027 | \\n\",\n \"Jafar Panahi | \\n\",\n \"673780.0 | \\n\",\n \"The Circle | \\n\",\n \"
| 5028 | \\n\",\n \"Ivan Kavanagh | \\n\",\n \"NaN | \\n\",\n \"Tin Can Man | \\n\",\n \"
| 5029 | \\n\",\n \"Kiyoshi Kurosawa | \\n\",\n \"94596.0 | \\n\",\n \"The Cure | \\n\",\n \"
| 5030 | \\n\",\n \"Tadeo Garcia | \\n\",\n \"NaN | \\n\",\n \"On the Downlow | \\n\",\n \"
| 5031 | \\n\",\n \"Thomas L. Phillips | \\n\",\n \"NaN | \\n\",\n \"Sanctuary; Quite a Conundrum | \\n\",\n \"
| 5032 | \\n\",\n \"Ash Baron-Cohen | \\n\",\n \"NaN | \\n\",\n \"Bang | \\n\",\n \"
| 5033 | \\n\",\n \"Shane Carruth | \\n\",\n \"424760.0 | \\n\",\n \"Primer | \\n\",\n \"
| 5034 | \\n\",\n \"Neill Dela Llana | \\n\",\n \"70071.0 | \\n\",\n \"Cavite | \\n\",\n \"
| 5035 | \\n\",\n \"Robert Rodriguez | \\n\",\n \"2040920.0 | \\n\",\n \"El Mariachi | \\n\",\n \"
| 5036 | \\n\",\n \"Anthony Vallone | \\n\",\n \"NaN | \\n\",\n \"The Mongol King | \\n\",\n \"
| 5037 | \\n\",\n \"Edward Burns | \\n\",\n \"4584.0 | \\n\",\n \"Newlyweds | \\n\",\n \"
| 5038 | \\n\",\n \"Scott Smith | \\n\",\n \"NaN | \\n\",\n \"Signed Sealed Delivered | \\n\",\n \"
| 5039 | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"The Following | \\n\",\n \"
| 5040 | \\n\",\n \"Benjamin Roberds | \\n\",\n \"NaN | \\n\",\n \"A Plague So Pleasant | \\n\",\n \"
| 5041 | \\n\",\n \"Daniel Hsia | \\n\",\n \"10443.0 | \\n\",\n \"Shanghai Calling | \\n\",\n \"
| 5042 | \\n\",\n \"Jon Gunn | \\n\",\n \"85222.0 | \\n\",\n \"My Date with Drew | \\n\",\n \"
5043 rows × 3 columns
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" director_name gross \\\\\\n\",\n \"0 James Cameron 760505847.0 \\n\",\n \"26 James Cameron 658672302.0 \\n\",\n \"29 Colin Trevorrow 652177271.0 \\n\",\n \"794 Joss Whedon 623279547.0 \\n\",\n \"17 Joss Whedon 623279547.0 \\n\",\n \"66 Christopher Nolan 533316061.0 \\n\",\n \"240 George Lucas 474544677.0 \\n\",\n \"3024 George Lucas 460935665.0 \\n\",\n \"8 Joss Whedon 458991599.0 \\n\",\n \"3 Christopher Nolan 448130642.0 \\n\",\n \"582 Andrew Adamson 436471036.0 \\n\",\n \"3080 Steven Spielberg 434949459.0 \\n\",\n \"186 Francis Lawrence 424645577.0 \\n\",\n \"13 Gore Verbinski 423032628.0 \\n\",\n \"509 Roger Allers 422783777.0 \\n\",\n \"43 Lee Unkrich 414984497.0 \\n\",\n \"32 Shane Black 408992272.0 \\n\",\n \"439 Gary Ross 407999255.0 \\n\",\n \"27 Anthony Russo 407197282.0 \\n\",\n \"161 Sam Raimi 403706375.0 \\n\",\n \"36 Michael Bay 402076689.0 \\n\",\n \"125 Chris Buck 400736600.0 \\n\",\n \"338 Andrew Stanton 380838870.0 \\n\",\n \"236 George Lucas 380262555.0 \\n\",\n \"339 Peter Jackson 377019252.0 \\n\",\n \"31 Sam Raimi 373377893.0 \\n\",\n \"521 Pierre Coffin 368049635.0 \\n\",\n \"812 Tim Miller 363024263.0 \\n\",\n \"1805 Jon Favreau 362645141.0 \\n\",\n \"79 Jon Favreau 362645141.0 \\n\",\n \"... ... ... \\n\",\n \"2794 Martin Scorsese 0.0 \\n\",\n \"1802 NaN 0.0 \\n\",\n \"3755 Frank Borzage 0.0 \\n\",\n \"4335 Harald Reinl 0.0 \\n\",\n \"4336 James Cox 0.0 \\n\",\n \"4337 Richard Lester 0.0 \\n\",\n \"4338 Michael Moore 0.0 \\n\",\n \"4339 Tanner Beard 0.0 \\n\",\n \"4340 Scott Dow 0.0 \\n\",\n \"4341 Derick Martini 0.0 \\n\",\n \"4342 Jerry Dugan 0.0 \\n\",\n \"1842 NaN 0.0 \\n\",\n \"2765 John Blanchard 0.0 \\n\",\n \"4345 William Kaufman 0.0 \\n\",\n \"2767 Vidhu Vinod Chopra 0.0 \\n\",\n \"4348 Jehane Noujaim 0.0 \\n\",\n \"4362 J. Lee Thompson 0.0 \\n\",\n \"1831 Kenneth Branagh 0.0 \\n\",\n \"2772 Ted Kotcheff 0.0 \\n\",\n \"2775 NaN 0.0 \\n\",\n \"3752 King Vidor 0.0 \\n\",\n \"4354 John Laing 0.0 \\n\",\n \"3751 Don Coscarelli 0.0 \\n\",\n \"1819 Walter Murch 0.0 \\n\",\n \"2786 John Boorman 0.0 \\n\",\n \"4358 Elia Kazan 0.0 \\n\",\n \"4359 Stanley Kubrick 0.0 \\n\",\n \"1818 NaN 0.0 \\n\",\n \"1815 Wolfgang Petersen 0.0 \\n\",\n \"3354 Serdar Akar 0.0 \\n\",\n \"\\n\",\n \" movie_title grossMil \\n\",\n \"0 Avatar 760.505847 \\n\",\n \"26 Titanic 658.672302 \\n\",\n \"29 Jurassic World 652.177271 \\n\",\n \"794 The Avengers 623.279547 \\n\",\n \"17 The Avengers 623.279547 \\n\",\n \"66 The Dark Knight 533.316061 \\n\",\n \"240 Star Wars: Episode I - The Phantom Menace 474.544677 \\n\",\n \"3024 Star Wars: Episode IV - A New Hope 460.935665 \\n\",\n \"8 Avengers: Age of Ultron 458.991599 \\n\",\n \"3 The Dark Knight Rises 448.130642 \\n\",\n \"582 Shrek 2 436.471036 \\n\",\n \"3080 E.T. the Extra-Terrestrial 434.949459 \\n\",\n \"186 The Hunger Games: Catching Fire 424.645577 \\n\",\n \"13 Pirates of the Caribbean: Dead Man's Chest 423.032628 \\n\",\n \"509 The Lion King 422.783777 \\n\",\n \"43 Toy Story 3 414.984497 \\n\",\n \"32 Iron Man 3 408.992272 \\n\",\n \"439 The Hunger Games 407.999255 \\n\",\n \"27 Captain America: Civil War 407.197282 \\n\",\n \"161 Spider-Man 403.706375 \\n\",\n \"36 Transformers: Revenge of the Fallen 402.076689 \\n\",\n \"125 Frozen 400.736600 \\n\",\n \"338 Finding Nemo 380.838870 \\n\",\n \"236 Star Wars: Episode III - Revenge of the Sith 380.262555 \\n\",\n \"339 The Lord of the Rings: The Return of the King 377.019252 \\n\",\n \"31 Spider-Man 2 373.377893 \\n\",\n \"521 Despicable Me 2 368.049635 \\n\",\n \"812 Deadpool 363.024263 \\n\",\n \"1805 The Jungle Book 362.645141 \\n\",\n \"79 The Jungle Book 362.645141 \\n\",\n \"... ... ... \\n\",\n \"2794 New York, New York 0.000000 \\n\",\n \"1802 Limitless 0.000000 \\n\",\n \"3755 A Farewell to Arms 0.000000 \\n\",\n \"4335 The Torture Chamber of Dr. Sadism 0.000000 \\n\",\n \"4336 Straight A's 0.000000 \\n\",\n \"4337 A Funny Thing Happened on the Way to the Forum 0.000000 \\n\",\n \"4338 Slacker Uprising 0.000000 \\n\",\n \"4339 The Legend of Hell's Gate: An American Conspir... 0.000000 \\n\",\n \"4340 The Walking Deceased 0.000000 \\n\",\n \"4341 The Curse of Downers Grove 0.000000 \\n\",\n \"4342 Shark Lake 0.000000 \\n\",\n \"1842 It's Always Sunny in Philadelphia 0.000000 \\n\",\n \"2765 Towering Inferno 0.000000 \\n\",\n \"4345 The Marine 4: Moving Target 0.000000 \\n\",\n \"2767 Broken Horses 0.000000 \\n\",\n \"4348 The Square 0.000000 \\n\",\n \"4362 Battle for the Planet of the Apes 0.000000 \\n\",\n \"1831 The Magic Flute 0.000000 \\n\",\n \"2772 First Blood 0.000000 \\n\",\n \"2775 Ghost Hunters 0.000000 \\n\",\n \"3752 Solomon and Sheba 0.000000 \\n\",\n \"4354 Abandoned 0.000000 \\n\",\n \"3751 The Beastmaster 0.000000 \\n\",\n \"1819 Return to Oz 0.000000 \\n\",\n \"2786 Exorcist II: The Heretic 0.000000 \\n\",\n \"4358 A Streetcar Named Desire 0.000000 \\n\",\n \"4359 Dr. Strangelove or: How I Learned to Stop Worr... 0.000000 \\n\",\n \"1818 The Honeymooners 0.000000 \\n\",\n \"1815 The NeverEnding Story 0.000000 \\n\",\n \"3354 Valley of the Wolves: Iraq 0.000000 \\n\",\n \"\\n\",\n \"[5043 rows x 4 columns]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# it's kinda hard to read so we will check only millions, and replace the nan\\n\",\n \"dfD['gross']=dfD['gross'].fillna(0)\\n\",\n \"dfD['grossMil']=dfD['gross']/1000000\\n\",\n \"dfD.sort_values(by=\\\"grossMil\\\", ascending=False)\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 82,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163180.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":184,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"5cd0738f-89de-f11d-1adb-e1ff5a4a27d9\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"Ms.\\n\",\n \"Me\\n\",\n \",\\n\",\n \"how\\n\",\n \"are\\n\",\n \"you\\n\",\n \"doing\\n\",\n \"today\\n\",\n \"?\\n\",\n \"Its\\n\",\n \"a\\n\",\n \"funny\\n\",\n \"little\\n\",\n \"world\\n\",\n \".\\n\",\n \"And\\n\",\n \"i\\n\",\n \"am\\n\",\n \"learning\\n\",\n \";\\n\",\n \"nltk\\n\"\n ]\n }\n ],\n \"source\": [\n \"from nltk import sent_tokenize, word_tokenize\\n\",\n \"example_test = \\\"Hello Ms. Me, how are you doing today? Its a funny little world. And i am learning ; nltk\\\"\\n\",\n \"#print(sent_tokenize(example_test))\\n\",\n \"#print(word_tokenize(example_test))\\n\",\n \"\\n\",\n \"for i in word_tokenize(example_test):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"d40ea282-3704-4426-c83f-f569535931e4\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['This', 'example', 'showing', 'stop', 'word', 'filtration', '.']\\n\"\n ]\n }\n ],\n \"source\": [\n \"from nltk.corpus import stopwords #words like a, and, more, so which dont give any meaning to analysis\\n\",\n \"from nltk.tokenize import word_tokenize\\n\",\n \"\\n\",\n \"example_sentence = \\\"This is an example showing off stop word filtration.\\\"\\n\",\n \"stop_words = set(stopwords.words(\\\"english\\\"))#set of predefined english stopwords\\n\",\n \"#print(stop_words)\\n\",\n \"\\n\",\n \"words = word_tokenize(example_sentence)\\n\",\n \"filtered_sentence = []\\n\",\n \"for w in words:\\n\",\n \" if w not in stop_words:\\n\",\n \" filtered_sentence.append(w)\\n\",\n \"#one line replacement \\n\",\n \"#filtered_sentence = [w for w in words if not w in stop_words]\\n\",\n \"print(filtered_sentence)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"fd664990-2dfe-0fc8-c0cf-61d731e74d79\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"python\\n\",\n \"python\\n\",\n \"python\\n\",\n \"python\\n\",\n \"pythonli\\n\",\n \"It\\n\",\n \"is\\n\",\n \"import\\n\",\n \"to\\n\",\n \"be\\n\",\n \"pythonli\\n\",\n \"while\\n\",\n \"you\\n\",\n \"are\\n\",\n \"python\\n\",\n \"with\\n\",\n \"python\\n\",\n \".\\n\",\n \"all\\n\",\n \"python\\n\",\n \"ahv\\n\",\n \"python\\n\",\n \"poorli\\n\",\n \"at\\n\",\n \"least\\n\",\n \"onc\\n\",\n \".\\n\"\n ]\n }\n ],\n \"source\": [\n \"#Stemming - data preprocessing method (finding stem of word 'riding-ride')\\n\",\n \"#Porerstemmer\\n\",\n \"from nltk.stem import PorterStemmer\\n\",\n \"from nltk.tokenize import word_tokenize\\n\",\n \"\\n\",\n \"ps=PorterStemmer()\\n\",\n \"example_words = [\\\"python\\\",\\\"pythoner\\\", \\\"pythoned\\\", \\\"pythoning\\\", \\\"pythonly\\\"]\\n\",\n \"for w in example_words:\\n\",\n \" print(ps.stem(w))\\n\",\n \" \\n\",\n \"sent = \\\"It is important to be pythonly while you are pythoning with python. All pythoners ahve pythoned poorly at least once.\\\"\\n\",\n \"words = word_tokenize(sent)\\n\",\n \"\\n\",\n \"for w in words:\\n\",\n \" print(ps.stem(w))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"38abc1e4-12ea-5c28-e8d4-5f955bc60bdc\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.04020979020979021 0.07357859531772576 0.03836930455635491 5720 299 230 22\\n\",\n \"0.002972027972027972 0.0033444816053511705 0.002951484965873455 5720 299 17 1\\n\",\n \"0.0008741258741258741 0.006688963210702341 0.0005534034311012728 5720 299 5 2\\n\",\n \"0.0024475524475524478 0.016722408026755852 0.0016602102933038186 5720 299 14 5\\n\",\n \"0.0013986013986013986 0.0033444816053511705 0.0012912746725696365 5720 299 8 1\\n\",\n \"0.0034965034965034965 0.0033444816053511705 0.003504888396974728 5720 299 20 1\\n\",\n \"0.017132867132867134 0.04013377926421405 0.01586423169156982 5720 299 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0.0033444816053511705 0.0014757424829367274 5720 299 9 1\\n\",\n \"0.00034965034965034965 0.0033444816053511705 0.00018446781036709093 5720 299 2 1\\n\",\n \"0.001048951048951049 0.0033444816053511705 0.0009223390518354548 5720 299 6 1\\n\"\n ]\n }\n ],\n \"source\": [\n \"#Partofspeech\\n\",\n \"import nltk\\n\",\n \"from nltk.corpus import state_union #presidential address text\\n\",\n \"from nltk.tokenize import PunktSentenceTokenizer #trainable ml\\n\",\n \"\\n\",\n \"train_text = state_union.raw(\\\"2005-GWBush.txt\\\")\\n\",\n \"sample_text = state_union.raw(\\\"2006-GWBush.txt\\\")\\n\",\n \"\\n\",\n \"custom_sent_tokenizer = PunktSentenceTokenizer(train_text)\\n\",\n \"\\n\",\n \"tokenized = custom_sent_tokenizer.tokenize(sample_text)\\n\",\n \"\\n\",\n \"def process_content():\\n\",\n \" try:\\n\",\n \" for i in tokenized:\\n\",\n \" words = nltk.word_tokenize(i)\\n\",\n \" tagged= nltk.pos_tag(words)\\n\",\n \" \\n\",\n \" print(tagged)\\n\",\n \" except Exception as e:\\n\",\n \" print(str(e))\\n\",\n \" \\n\",\n \" \\n\",\n \" \"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 15,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163258.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":185,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"2480b61d-3b74-80ff-a1ad-858bd14a2958\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": \"aisles.csv\\ndepartments.csv\\norder_products__prior.csv\\norder_products__train.csv\\norders.csv\\nproducts.csv\\nsample_submission.csv\\n\\n\"\n }\n ],\n \"source\": [\n \"# This Python 3 environment comes with many helpful analytics libraries installed\\n\",\n \"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\\n\",\n \"# For example, here's several helpful packages to load in \\n\",\n \"\\n\",\n \"import numpy as np # linear algebra\\n\",\n \"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\\n\",\n \"\\n\",\n \"# Input data files are available in the \\\"../input/\\\" directory.\\n\",\n \"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\\n\",\n \"\\n\",\n \"from subprocess import check_output\\n\",\n \"print(check_output([\\\"ls\\\", \\\"../input\\\"]).decode(\\\"utf8\\\"))\\n\",\n \"\\n\",\n \"# Any results you write to the current directory are saved as output.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"e3959be5-327a-d40a-98c2-e02df98e7b8e\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'pd' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"---------------------------------------------------------------------------\",\n \"NameError Traceback (most recent call last)\",\n \"| \\n\",\n \" | director_name | \\n\",\n \"gross | \\n\",\n \"movie_title | \\n\",\n \"grossMil | \\n\",\n \"
|---|---|---|---|---|
| 0 | \\n\",\n \"James Cameron | \\n\",\n \"760505847.0 | \\n\",\n \"Avatar | \\n\",\n \"760.505847 | \\n\",\n \"
| 26 | \\n\",\n \"James Cameron | \\n\",\n \"658672302.0 | \\n\",\n \"Titanic | \\n\",\n \"658.672302 | \\n\",\n \"
| 29 | \\n\",\n \"Colin Trevorrow | \\n\",\n \"652177271.0 | \\n\",\n \"Jurassic World | \\n\",\n \"652.177271 | \\n\",\n \"
| 794 | \\n\",\n \"Joss Whedon | \\n\",\n \"623279547.0 | \\n\",\n \"The Avengers | \\n\",\n \"623.279547 | \\n\",\n \"
| 17 | \\n\",\n \"Joss Whedon | \\n\",\n \"623279547.0 | \\n\",\n \"The Avengers | \\n\",\n \"623.279547 | \\n\",\n \"
| 66 | \\n\",\n \"Christopher Nolan | \\n\",\n \"533316061.0 | \\n\",\n \"The Dark Knight | \\n\",\n \"533.316061 | \\n\",\n \"
| 240 | \\n\",\n \"George Lucas | \\n\",\n \"474544677.0 | \\n\",\n \"Star Wars: Episode I - The Phantom Menace | \\n\",\n \"474.544677 | \\n\",\n \"
| 3024 | \\n\",\n \"George Lucas | \\n\",\n \"460935665.0 | \\n\",\n \"Star Wars: Episode IV - A New Hope | \\n\",\n \"460.935665 | \\n\",\n \"
| 8 | \\n\",\n \"Joss Whedon | \\n\",\n \"458991599.0 | \\n\",\n \"Avengers: Age of Ultron | \\n\",\n \"458.991599 | \\n\",\n \"
| 3 | \\n\",\n \"Christopher Nolan | \\n\",\n \"448130642.0 | \\n\",\n \"The Dark Knight Rises | \\n\",\n \"448.130642 | \\n\",\n \"
| 582 | \\n\",\n \"Andrew Adamson | \\n\",\n \"436471036.0 | \\n\",\n \"Shrek 2 | \\n\",\n \"436.471036 | \\n\",\n \"
| 3080 | \\n\",\n \"Steven Spielberg | \\n\",\n \"434949459.0 | \\n\",\n \"E.T. the Extra-Terrestrial | \\n\",\n \"434.949459 | \\n\",\n \"
| 186 | \\n\",\n \"Francis Lawrence | \\n\",\n \"424645577.0 | \\n\",\n \"The Hunger Games: Catching Fire | \\n\",\n \"424.645577 | \\n\",\n \"
| 13 | \\n\",\n \"Gore Verbinski | \\n\",\n \"423032628.0 | \\n\",\n \"Pirates of the Caribbean: Dead Man's Chest | \\n\",\n \"423.032628 | \\n\",\n \"
| 509 | \\n\",\n \"Roger Allers | \\n\",\n \"422783777.0 | \\n\",\n \"The Lion King | \\n\",\n \"422.783777 | \\n\",\n \"
| 43 | \\n\",\n \"Lee Unkrich | \\n\",\n \"414984497.0 | \\n\",\n \"Toy Story 3 | \\n\",\n \"414.984497 | \\n\",\n \"
| 32 | \\n\",\n \"Shane Black | \\n\",\n \"408992272.0 | \\n\",\n \"Iron Man 3 | \\n\",\n \"408.992272 | \\n\",\n \"
| 439 | \\n\",\n \"Gary Ross | \\n\",\n \"407999255.0 | \\n\",\n \"The Hunger Games | \\n\",\n \"407.999255 | \\n\",\n \"
| 27 | \\n\",\n \"Anthony Russo | \\n\",\n \"407197282.0 | \\n\",\n \"Captain America: Civil War | \\n\",\n \"407.197282 | \\n\",\n \"
| 161 | \\n\",\n \"Sam Raimi | \\n\",\n \"403706375.0 | \\n\",\n \"Spider-Man | \\n\",\n \"403.706375 | \\n\",\n \"
| 36 | \\n\",\n \"Michael Bay | \\n\",\n \"402076689.0 | \\n\",\n \"Transformers: Revenge of the Fallen | \\n\",\n \"402.076689 | \\n\",\n \"
| 125 | \\n\",\n \"Chris Buck | \\n\",\n \"400736600.0 | \\n\",\n \"Frozen | \\n\",\n \"400.736600 | \\n\",\n \"
| 338 | \\n\",\n \"Andrew Stanton | \\n\",\n \"380838870.0 | \\n\",\n \"Finding Nemo | \\n\",\n \"380.838870 | \\n\",\n \"
| 236 | \\n\",\n \"George Lucas | \\n\",\n \"380262555.0 | \\n\",\n \"Star Wars: Episode III - Revenge of the Sith | \\n\",\n \"380.262555 | \\n\",\n \"
| 339 | \\n\",\n \"Peter Jackson | \\n\",\n \"377019252.0 | \\n\",\n \"The Lord of the Rings: The Return of the King | \\n\",\n \"377.019252 | \\n\",\n \"
| 31 | \\n\",\n \"Sam Raimi | \\n\",\n \"373377893.0 | \\n\",\n \"Spider-Man 2 | \\n\",\n \"373.377893 | \\n\",\n \"
| 521 | \\n\",\n \"Pierre Coffin | \\n\",\n \"368049635.0 | \\n\",\n \"Despicable Me 2 | \\n\",\n \"368.049635 | \\n\",\n \"
| 812 | \\n\",\n \"Tim Miller | \\n\",\n \"363024263.0 | \\n\",\n \"Deadpool | \\n\",\n \"363.024263 | \\n\",\n \"
| 1805 | \\n\",\n \"Jon Favreau | \\n\",\n \"362645141.0 | \\n\",\n \"The Jungle Book | \\n\",\n \"362.645141 | \\n\",\n \"
| 79 | \\n\",\n \"Jon Favreau | \\n\",\n \"362645141.0 | \\n\",\n \"The Jungle Book | \\n\",\n \"362.645141 | \\n\",\n \"
| ... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"
| 2794 | \\n\",\n \"Martin Scorsese | \\n\",\n \"0.0 | \\n\",\n \"New York, New York | \\n\",\n \"0.000000 | \\n\",\n \"
| 1802 | \\n\",\n \"NaN | \\n\",\n \"0.0 | \\n\",\n \"Limitless | \\n\",\n \"0.000000 | \\n\",\n \"
| 3755 | \\n\",\n \"Frank Borzage | \\n\",\n \"0.0 | \\n\",\n \"A Farewell to Arms | \\n\",\n \"0.000000 | \\n\",\n \"
| 4335 | \\n\",\n \"Harald Reinl | \\n\",\n \"0.0 | \\n\",\n \"The Torture Chamber of Dr. Sadism | \\n\",\n \"0.000000 | \\n\",\n \"
| 4336 | \\n\",\n \"James Cox | \\n\",\n \"0.0 | \\n\",\n \"Straight A's | \\n\",\n \"0.000000 | \\n\",\n \"
| 4337 | \\n\",\n \"Richard Lester | \\n\",\n \"0.0 | \\n\",\n \"A Funny Thing Happened on the Way to the Forum | \\n\",\n \"0.000000 | \\n\",\n \"
| 4338 | \\n\",\n \"Michael Moore | \\n\",\n \"0.0 | \\n\",\n \"Slacker Uprising | \\n\",\n \"0.000000 | \\n\",\n \"
| 4339 | \\n\",\n \"Tanner Beard | \\n\",\n \"0.0 | \\n\",\n \"The Legend of Hell's Gate: An American Conspir... | \\n\",\n \"0.000000 | \\n\",\n \"
| 4340 | \\n\",\n \"Scott Dow | \\n\",\n \"0.0 | \\n\",\n \"The Walking Deceased | \\n\",\n \"0.000000 | \\n\",\n \"
| 4341 | \\n\",\n \"Derick Martini | \\n\",\n \"0.0 | \\n\",\n \"The Curse of Downers Grove | \\n\",\n \"0.000000 | \\n\",\n \"
| 4342 | \\n\",\n \"Jerry Dugan | \\n\",\n \"0.0 | \\n\",\n \"Shark Lake | \\n\",\n \"0.000000 | \\n\",\n \"
| 1842 | \\n\",\n \"NaN | \\n\",\n \"0.0 | \\n\",\n \"It's Always Sunny in Philadelphia | \\n\",\n \"0.000000 | \\n\",\n \"
| 2765 | \\n\",\n \"John Blanchard | \\n\",\n \"0.0 | \\n\",\n \"Towering Inferno | \\n\",\n \"0.000000 | \\n\",\n \"
| 4345 | \\n\",\n \"William Kaufman | \\n\",\n \"0.0 | \\n\",\n \"The Marine 4: Moving Target | \\n\",\n \"0.000000 | \\n\",\n \"
| 2767 | \\n\",\n \"Vidhu Vinod Chopra | \\n\",\n \"0.0 | \\n\",\n \"Broken Horses | \\n\",\n \"0.000000 | \\n\",\n \"
| 4348 | \\n\",\n \"Jehane Noujaim | \\n\",\n \"0.0 | \\n\",\n \"The Square | \\n\",\n \"0.000000 | \\n\",\n \"
| 4362 | \\n\",\n \"J. Lee Thompson | \\n\",\n \"0.0 | \\n\",\n \"Battle for the Planet of the Apes | \\n\",\n \"0.000000 | \\n\",\n \"
| 1831 | \\n\",\n \"Kenneth Branagh | \\n\",\n \"0.0 | \\n\",\n \"The Magic Flute | \\n\",\n \"0.000000 | \\n\",\n \"
| 2772 | \\n\",\n \"Ted Kotcheff | \\n\",\n \"0.0 | \\n\",\n \"First Blood | \\n\",\n \"0.000000 | \\n\",\n \"
| 2775 | \\n\",\n \"NaN | \\n\",\n \"0.0 | \\n\",\n \"Ghost Hunters | \\n\",\n \"0.000000 | \\n\",\n \"
| 3752 | \\n\",\n \"King Vidor | \\n\",\n \"0.0 | \\n\",\n \"Solomon and Sheba | \\n\",\n \"0.000000 | \\n\",\n \"
| 4354 | \\n\",\n \"John Laing | \\n\",\n \"0.0 | \\n\",\n \"Abandoned | \\n\",\n \"0.000000 | \\n\",\n \"
| 3751 | \\n\",\n \"Don Coscarelli | \\n\",\n \"0.0 | \\n\",\n \"The Beastmaster | \\n\",\n \"0.000000 | \\n\",\n \"
| 1819 | \\n\",\n \"Walter Murch | \\n\",\n \"0.0 | \\n\",\n \"Return to Oz | \\n\",\n \"0.000000 | \\n\",\n \"
| 2786 | \\n\",\n \"John Boorman | \\n\",\n \"0.0 | \\n\",\n \"Exorcist II: The Heretic | \\n\",\n \"0.000000 | \\n\",\n \"
| 4358 | \\n\",\n \"Elia Kazan | \\n\",\n \"0.0 | \\n\",\n \"A Streetcar Named Desire | \\n\",\n \"0.000000 | \\n\",\n \"
| 4359 | \\n\",\n \"Stanley Kubrick | \\n\",\n \"0.0 | \\n\",\n \"Dr. Strangelove or: How I Learned to Stop Worr... | \\n\",\n \"0.000000 | \\n\",\n \"
| 1818 | \\n\",\n \"NaN | \\n\",\n \"0.0 | \\n\",\n \"The Honeymooners | \\n\",\n \"0.000000 | \\n\",\n \"
| 1815 | \\n\",\n \"Wolfgang Petersen | \\n\",\n \"0.0 | \\n\",\n \"The NeverEnding Story | \\n\",\n \"0.000000 | \\n\",\n \"
| 3354 | \\n\",\n \"Serdar Akar | \\n\",\n \"0.0 | \\n\",\n \"Valley of the Wolves: Iraq | \\n\",\n \"0.000000 | \\n\",\n \"
5043 rows × 4 columns
\\n\",\n \"\\n\\n \\n
\\n
\",\n \"text/plain\": \" order_id products\\n0 17 39276\\n1 34 39276\"\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_submission.head(2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"dce08e34-4858-963e-f291-e5ed26c9d892\"\n },\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"Missing parentheses in call to 'print' (| \\n | order_id | \\nproducts | \\n
|---|---|---|
| 0 | \\n17 | \\n39276 | \\n
| 1 | \\n34 | \\n39276 | \\n
\\n\\n \\n
\\n
\",\n \"text/plain\": \" product_id product_name aisle_id \\\\\\n0 1 Chocolate Sandwich Cookies 61 \\n1 2 All-Seasons Salt 104 \\n2 3 Robust Golden Unsweetened Oolong Tea 94 \\n3 4 Smart Ones Classic Favorites Mini Rigatoni Wit... 38 \\n4 5 Green Chile Anytime Sauce 5 \\n\\n department_id aisle department \\n0 19 cookies cakes snacks \\n1 13 spices seasonings pantry \\n2 7 tea beverages \\n3 1 frozen meals frozen \\n4 13 marinades meat preparation pantry \"\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"products = pd.merge(left = products, right = aisles, on = 'aisle_id', how = 'left')\\n\",\n \"products = pd.merge(left = products, right = departments, on = 'department_id', how = 'left')\\n\",\n \"products.head(5)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"1334708c-abf7-b25c-a1b4-26338b008b8b\"\n },\n \"source\": [\n \"## Joining the above data set with orders\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"metadata\": {\n \"_cell_guid\": \"cb5b7bd0-242e-ab46-0772-c649c9a15613\"\n },\n \"outputs\": [],\n \"source\": [\n \"order_products_prior = pd.merge(left = order_products_prior, right = products, on = 'product_id', how = 'left')\\n\",\n \"order_products_train = pd.merge(left = order_products_train, right = products, on = 'product_id', how = 'left')\\n\",\n \"orders = pd.merge(left = orders, right = order_products_prior, on = 'order_id', how = 'left')\\n\",\n \"orders = pd.merge(left = orders, right = order_products_train, on = 'order_id', how = 'left')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {\n \"_cell_guid\": \"584e60bb-1617-6dc0-1e34-b041540a670d\"\n },\n \"outputs\": [],\n \"source\": [\n \"orders.head(5)\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 179,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}"},"id":{"kind":"string","value":"0001/163/1163436.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":186,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"8ad618db-cd05-e3dd-aa92-823a3927a752\"\n },\n \"source\": [\n \"Hi, everyone. I'm Thomas Colthurst, and I work on a random forest implementation called TensorForest. As the name suggests, TensorForest is built on top of TensorFlow, which makes it easy to use all the goodies that TensorFlow provides (feature preprocessing, distributed training, etc.). You can find out more about TensorForest by reading our [NIPS 2017 paper][1].\\n\",\n \"\\n\",\n \"This is a simple example to show you how to use TensorForest on the Iris classification task. (TensorForest also works for regression problems, but this won't cover that.)\\n\",\n \"\\n\",\n \"First, let's load the data:\\n\",\n \"\\n\",\n \" [1]: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxtbHN5c25pcHMyMDE2fGd4OjFlNTRiOWU2OGM2YzA4MjE\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"_cell_guid\": \"a42958c9-7e07-f0ad-73a0-1c00ee992093\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training = \\n\",\n \" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\\n\",\n \"0 1 5.1 3.5 1.4 0.2 Iris-setosa\\n\",\n \"2 3 4.7 3.2 1.3 0.2 Iris-setosa\\n\",\n \"4 5 5.0 3.6 1.4 0.2 Iris-setosa\\n\",\n \"6 7 4.6 3.4 1.4 0.3 Iris-setosa\\n\",\n \"8 9 4.4 2.9 1.4 0.2 Iris-setosa\\n\",\n \"Test = \\n\",\n \" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\\n\",\n \"1 2 4.9 3.0 1.4 0.2 Iris-setosa\\n\",\n \"3 4 4.6 3.1 1.5 0.2 Iris-setosa\\n\",\n \"5 6 5.4 3.9 1.7 0.4 Iris-setosa\\n\",\n \"7 8 5.0 3.4 1.5 0.2 Iris-setosa\\n\",\n \"9 10 4.9 3.1 1.5 0.1 Iris-setosa\\n\"\n ]\n }\n ],\n \"source\": [\n \"import tensorflow as tf\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import math\\n\",\n \"import os\\n\",\n \"from glob import glob\\n\",\n \"\\n\",\n \"tf.logging.set_verbosity(tf.logging.DEBUG)\\n\",\n \"\\n\",\n \"all_data = pd.read_csv(\\\"../input/Iris.csv\\\")\\n\",\n \"\\n\",\n \"train = all_data[::2]\\n\",\n \"test = all_data[1::2]\\n\",\n \"\\n\",\n \"print(\\\"Training = \\\")\\n\",\n \"print(train[:5])\\n\",\n \"\\n\",\n \"print(\\\"Test = \\\")\\n\",\n \"print(test[:5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"19d8211b-3e26-eaf9-a816-aa860d201885\"\n },\n \"source\": [\n \"I split the data into halves for training and test. Next, let's split the training data into features and labels:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"_cell_guid\": \"04b55bda-37f3-7bd3-08e2-16f75cc99aca\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Training features =\\n\",\n \"[[ 5.0999999 3.5 1.39999998 0.2 ]\\n\",\n \" [ 4.69999981 3.20000005 1.29999995 0.2 ]\\n\",\n \" [ 5. 3.5999999 1.39999998 0.2 ]\\n\",\n \" [ 4.5999999 3.4000001 1.39999998 0.30000001]\\n\",\n \" [ 4.4000001 2.9000001 1.39999998 0.2 ]]\\n\",\n \"Training labels =\\n\",\n \"[ 0. 0. 0. 0. 0.]\\n\"\n ]\n }\n ],\n \"source\": [\n \"x_train = train.drop(['Species', 'Id'], axis=1).astype(np.float32).values\\n\",\n \"label_map = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}\\n\",\n \"y_train = train['Species'].map(label_map).astype(np.float32).values\\n\",\n \"\\n\",\n \"print(\\\"Training features =\\\")\\n\",\n \"print(x_train[:5])\\n\",\n \"print(\\\"Training labels =\\\")\\n\",\n \"print(y_train[:5])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"51d571e6-6feb-4fe6-98e7-e8dd23bd67da\"\n },\n \"source\": [\n \"Great, now let's configure the forest. We need to specify how many classes there are, how many features, how many trees we want, and the maximum size of those trees. TensorForest will intelligently set a bunch of other training parameters based on those values. In this example, we choose to override one of those -- we say we want to split any node once it has seen 50 examples.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"e4b8772a-e8f2-490e-1018-ee2510cb13e4\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Params =\\n\",\n \"{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 4, 'max_fertile_nodes': 500, 'split_after_samples': 50, 'min_split_samples': 5, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'num_classes': 3, 'num_features': 4, 'bagged_num_features': 4, 'bagged_features': None, 'regression': False, 'num_outputs': 1, 'num_output_columns': 4, 'split_initializations_per_input': 1, 'base_random_seed': 0}\\n\"\n ]\n }\n ],\n \"source\": [\n \"params = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(\\n\",\n \" num_classes=3, num_features=4, num_trees=50, max_nodes=1000, split_after_samples=50).fill()\\n\",\n \"\\n\",\n \"print(\\\"Params =\\\")\\n\",\n \"print(vars(params))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"41bcdffc-c9e4-2414-c9fa-82bdbd0ef11a\"\n },\n \"source\": [\n \"Everything is set up, so it's time to train the forest:\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"cb3cc9b7-b008-ed61-914e-1e6e8092b48c\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INFO:tensorflow:Using default config.\\n\",\n \"INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': | \\n | product_id | \\nproduct_name | \\naisle_id | \\ndepartment_id | \\naisle | \\ndepartment | \\n
|---|---|---|---|---|---|---|
| 0 | \\n1 | \\nChocolate Sandwich Cookies | \\n61 | \\n19 | \\ncookies cakes | \\nsnacks | \\n
| 1 | \\n2 | \\nAll-Seasons Salt | \\n104 | \\n13 | \\nspices seasonings | \\npantry | \\n
| 2 | \\n3 | \\nRobust Golden Unsweetened Oolong Tea | \\n94 | \\n7 | \\ntea | \\nbeverages | \\n
| 3 | \\n4 | \\nSmart Ones Classic Favorites Mini Rigatoni Wit... | \\n38 | \\n1 | \\nfrozen meals | \\nfrozen | \\n
| 4 | \\n5 | \\nGreen Chile Anytime Sauce | \\n5 | \\n13 | \\nmarinades meat preparation | \\npantry | \\n
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Id SalePrice\\n\",\n \"0 1461 119176.319130\\n\",\n \"1 1462 120836.395320\\n\",\n \"2 1463 168225.543024\\n\",\n \"3 1464 195642.589726\\n\",\n \"4 1465 181910.799054\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predictions = rm_model.predict(feats)\\n\",\n \"final_predictions = np.exp(predictions)\\n\",\n \"submission['SalePrice'] = final_predictions\\n\",\n \"submission.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"metadata\": {\n \"_cell_guid\": \"01ef80fc-0363-842f-84b8-bab6b5b47fd7\"\n },\n \"outputs\": [],\n \"source\": [\n \"submission.to_csv('submission1.csv', index=False)\"\n ]\n }\n ],\n \"metadata\": {\n \"_change_revision\": 79,\n \"_is_fork\": false,\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.6.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"},"id":{"kind":"string","value":"0001/163/1163805.ipynb"},"file_path":{"kind":"string","value":"s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz"}}},{"rowIdx":189,"cells":{"text":{"kind":"string","value":"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"7da4abe3-92df-0613-28af-3d1c60875510\"\n },\n \"source\": [\n \"This notebook is the reproduction of [A Very Extensive Sberbank Exploratory Analysis](https://www.kaggle.com/captcalculator/a-very-extensive-sberbank-exploratory-analysis) notebook in python (work in progress).\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"4adbd1a5-36f4-5485-035a-a35c443c71b5\"\n },\n \"source\": [\n \"This notebook is the reproduction of [A Very Extensive Sberbank Exploratory Analysis](https://www.kaggle.com/captcalculator/a-very-extensive-sberbank-exploratory-analysis) notebook in python. Not all code is implemented yet. Here i copied only titles for more detailed description please see original notebook.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"metadata\": {\n \"_cell_guid\": \"2ee37461-707a-4225-d8b0-6feda0fe7f53\"\n },\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"color = sns.color_palette()\\n\",\n \"\\n\",\n \"%matplotlib inline\\n\",\n \"\\n\",\n \"pd.options.mode.chained_assignment = None # default='warn'\\n\",\n \"pd.set_option('display.max_columns', 500)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"570eceb9-13e7-c896-d8dd-8ceb7abe4a9e\"\n },\n \"source\": [\n \"## Training Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"metadata\": {\n \"_cell_guid\": \"dcfd9c4f-d434-7792-6279-85842972a5ca\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_df = pd.read_csv(\\\"../input/train.csv\\\", parse_dates=['timestamp'])\\n\",\n \"train_df['price_doc_log'] = np.log1p(train_df['price_doc'])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"e34fd6a9-277a-847e-6ea6-f146e71638fe\"\n },\n \"source\": [\n \"## Missing Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"metadata\": {\n \"_cell_guid\": \"90c5dc41-b939-63fc-3ee2-0e59745b89ff\"\n },\n \"outputs\": [],\n \"source\": [\n \"train_na = (train_df.isnull().sum() / len(train_df)) * 100\\n\",\n \"train_na = train_na.drop(train_na[train_na == 0].index).sort_values(ascending=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"metadata\": {\n \"_cell_guid\": \"ae466f09-2bd6-1e9b-6ef3-29038249418b\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": \"[| \\n\",\n \" | Id | \\n\",\n \"SalePrice | \\n\",\n \"
|---|---|---|
| 0 | \\n\",\n \"1461 | \\n\",\n \"119176.319130 | \\n\",\n \"
| 1 | \\n\",\n \"1462 | \\n\",\n \"120836.395320 | \\n\",\n \"
| 2 | \\n\",\n \"1463 | \\n\",\n \"168225.543024 | \\n\",\n \"
| 3 | \\n\",\n \"1464 | \\n\",\n \"195642.589726 | \\n\",\n \"
| 4 | \\n\",\n \"1465 | \\n\",\n \"181910.799054 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Time V1 V2 V3 V4 V5 V6 V7 \\\\\\n\",\n \"0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \\n\",\n \"1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \\n\",\n \"2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \\n\",\n \"3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \\n\",\n \"4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \\n\",\n \"\\n\",\n \" V8 V9 ... V21 V22 V23 V24 \\\\\\n\",\n \"0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 \\n\",\n \"1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 \\n\",\n \"2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 \\n\",\n \"3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 \\n\",\n \"4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 \\n\",\n \"\\n\",\n \" V25 V26 V27 V28 Amount Class \\n\",\n \"0 0.128539 -0.189115 0.133558 -0.021053 149.62 0 \\n\",\n \"1 0.167170 0.125895 -0.008983 0.014724 2.69 0 \\n\",\n \"2 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0 \\n\",\n \"3 0.647376 -0.221929 0.062723 0.061458 123.50 0 \\n\",\n \"4 -0.206010 0.502292 0.219422 0.215153 69.99 0 \\n\",\n \"\\n\",\n \"[5 rows x 31 columns]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.read_csv(\\\"../input/creditcard.csv\\\")\\n\",\n \"print(\\\"No. of Rows: \\\\t\\\\t\\\", data.shape[0])\\n\",\n \"print(\\\"No. of Columns: \\\\t\\\", data.shape[1])\\n\",\n \"data.head()\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"302cfc01-d0c3-ddf7-a897-73d3cca6dc73\"\n },\n \"source\": [\n \"## 1.2 Class Distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"d7d5ce17-5636-39fd-e257-787010f848f1\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No. of normal transactions: \\t\\t 284315\\n\",\n \"No. of fraudulent transactions: \\t 492\\n\",\n \"% normal transactions: \\t\\t 99.82725143693798\\n\",\n \"% fraudulent transcations: \\t 0.1727485630620034\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"| \\n\",\n \" | Time | \\n\",\n \"V1 | \\n\",\n \"V2 | \\n\",\n \"V3 | \\n\",\n \"V4 | \\n\",\n \"V5 | \\n\",\n \"V6 | \\n\",\n \"V7 | \\n\",\n \"V8 | \\n\",\n \"V9 | \\n\",\n \"... | \\n\",\n \"V21 | \\n\",\n \"V22 | \\n\",\n \"V23 | \\n\",\n \"V24 | \\n\",\n \"V25 | \\n\",\n \"V26 | \\n\",\n \"V27 | \\n\",\n \"V28 | \\n\",\n \"Amount | \\n\",\n \"Class | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"0.0 | \\n\",\n \"-1.359807 | \\n\",\n \"-0.072781 | \\n\",\n \"2.536347 | \\n\",\n \"1.378155 | \\n\",\n \"-0.338321 | \\n\",\n \"0.462388 | \\n\",\n \"0.239599 | \\n\",\n \"0.098698 | \\n\",\n \"0.363787 | \\n\",\n \"... | \\n\",\n \"-0.018307 | \\n\",\n \"0.277838 | \\n\",\n \"-0.110474 | \\n\",\n \"0.066928 | \\n\",\n \"0.128539 | \\n\",\n \"-0.189115 | \\n\",\n \"0.133558 | \\n\",\n \"-0.021053 | \\n\",\n \"149.62 | \\n\",\n \"0 | \\n\",\n \"
| 1 | \\n\",\n \"0.0 | \\n\",\n \"1.191857 | \\n\",\n \"0.266151 | \\n\",\n \"0.166480 | \\n\",\n \"0.448154 | \\n\",\n \"0.060018 | \\n\",\n \"-0.082361 | \\n\",\n \"-0.078803 | \\n\",\n \"0.085102 | \\n\",\n \"-0.255425 | \\n\",\n \"... | \\n\",\n \"-0.225775 | \\n\",\n \"-0.638672 | \\n\",\n \"0.101288 | \\n\",\n \"-0.339846 | \\n\",\n \"0.167170 | \\n\",\n \"0.125895 | \\n\",\n \"-0.008983 | \\n\",\n \"0.014724 | \\n\",\n \"2.69 | \\n\",\n \"0 | \\n\",\n \"
| 2 | \\n\",\n \"1.0 | \\n\",\n \"-1.358354 | \\n\",\n \"-1.340163 | \\n\",\n \"1.773209 | \\n\",\n \"0.379780 | \\n\",\n \"-0.503198 | \\n\",\n \"1.800499 | \\n\",\n \"0.791461 | \\n\",\n \"0.247676 | \\n\",\n \"-1.514654 | \\n\",\n \"... | \\n\",\n \"0.247998 | \\n\",\n \"0.771679 | \\n\",\n \"0.909412 | \\n\",\n \"-0.689281 | \\n\",\n \"-0.327642 | \\n\",\n \"-0.139097 | \\n\",\n \"-0.055353 | \\n\",\n \"-0.059752 | \\n\",\n \"378.66 | \\n\",\n \"0 | \\n\",\n \"
| 3 | \\n\",\n \"1.0 | \\n\",\n \"-0.966272 | \\n\",\n \"-0.185226 | \\n\",\n \"1.792993 | \\n\",\n \"-0.863291 | \\n\",\n \"-0.010309 | \\n\",\n \"1.247203 | \\n\",\n \"0.237609 | \\n\",\n \"0.377436 | \\n\",\n \"-1.387024 | \\n\",\n \"... | \\n\",\n \"-0.108300 | \\n\",\n \"0.005274 | \\n\",\n \"-0.190321 | \\n\",\n \"-1.175575 | \\n\",\n \"0.647376 | \\n\",\n \"-0.221929 | \\n\",\n \"0.062723 | \\n\",\n \"0.061458 | \\n\",\n \"123.50 | \\n\",\n \"0 | \\n\",\n \"
| 4 | \\n\",\n \"2.0 | \\n\",\n \"-1.158233 | \\n\",\n \"0.877737 | \\n\",\n \"1.548718 | \\n\",\n \"0.403034 | \\n\",\n \"-0.407193 | \\n\",\n \"0.095921 | \\n\",\n \"0.592941 | \\n\",\n \"-0.270533 | \\n\",\n \"0.817739 | \\n\",\n \"... | \\n\",\n \"-0.009431 | \\n\",\n \"0.798278 | \\n\",\n \"-0.137458 | \\n\",\n \"0.141267 | \\n\",\n \"-0.206010 | \\n\",\n \"0.502292 | \\n\",\n \"0.219422 | \\n\",\n \"0.215153 | \\n\",\n \"69.99 | \\n\",\n \"0 | \\n\",\n \"
5 rows × 31 columns
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" V1 V2 V3 V4 V5 V6 V7 \\\\\\n\",\n \"0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \\n\",\n \"1 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \\n\",\n \"2 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \\n\",\n \"3 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \\n\",\n \"4 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \\n\",\n \"\\n\",\n \" V8 V9 V10 ... V21 V22 V23 \\\\\\n\",\n \"0 0.098698 0.363787 0.090794 ... -0.018307 0.277838 -0.110474 \\n\",\n \"1 0.085102 -0.255425 -0.166974 ... -0.225775 -0.638672 0.101288 \\n\",\n \"2 0.247676 -1.514654 0.207643 ... 0.247998 0.771679 0.909412 \\n\",\n \"3 0.377436 -1.387024 -0.054952 ... -0.108300 0.005274 -0.190321 \\n\",\n \"4 -0.270533 0.817739 0.753074 ... -0.009431 0.798278 -0.137458 \\n\",\n \"\\n\",\n \" V24 V25 V26 V27 V28 Class Amount_scl \\n\",\n \"0 0.066928 0.128539 -0.189115 0.133558 -0.021053 0 0.244964 \\n\",\n \"1 -0.339846 0.167170 0.125895 -0.008983 0.014724 0 -0.342475 \\n\",\n \"2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 0 1.160686 \\n\",\n \"3 -1.175575 0.647376 -0.221929 0.062723 0.061458 0 0.140534 \\n\",\n \"4 0.141267 -0.206010 0.502292 0.219422 0.215153 0 -0.073403 \\n\",\n \"\\n\",\n \"[5 rows x 30 columns]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"\\n\",\n \"data['Amount_scl'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))\\n\",\n \"data = data.drop(['Time','Amount'],axis=1)\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"dd464857-7a67-7b12-e682-12911cfe145e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"X.shape: (284807, 29)\\n\",\n \"y.shape: (284807,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"X = data.ix[:, data.columns != 'Class'] # features\\n\",\n \"y = data['Class'] # labels\\n\",\n \"print(\\\"X.shape: \\\", X.shape)\\n\",\n \"print(\\\"y.shape: \\\", y.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"35336b32-eea8-330d-04e4-fa356b077d24\"\n },\n \"source\": [\n \"## 1.4 Handle Class Imbalance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"2779886c-d420-e1fa-be0b-bed2aa09a2d0\"\n },\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"22fea321-d877-f4e1-8497-aa8447ac0d43\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'y_train' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m| \\n\",\n \" | V1 | \\n\",\n \"V2 | \\n\",\n \"V3 | \\n\",\n \"V4 | \\n\",\n \"V5 | \\n\",\n \"V6 | \\n\",\n \"V7 | \\n\",\n \"V8 | \\n\",\n \"V9 | \\n\",\n \"V10 | \\n\",\n \"... | \\n\",\n \"V21 | \\n\",\n \"V22 | \\n\",\n \"V23 | \\n\",\n \"V24 | \\n\",\n \"V25 | \\n\",\n \"V26 | \\n\",\n \"V27 | \\n\",\n \"V28 | \\n\",\n \"Class | \\n\",\n \"Amount_scl | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"-1.359807 | \\n\",\n \"-0.072781 | \\n\",\n \"2.536347 | \\n\",\n \"1.378155 | \\n\",\n \"-0.338321 | \\n\",\n \"0.462388 | \\n\",\n \"0.239599 | \\n\",\n \"0.098698 | \\n\",\n \"0.363787 | \\n\",\n \"0.090794 | \\n\",\n \"... | \\n\",\n \"-0.018307 | \\n\",\n \"0.277838 | \\n\",\n \"-0.110474 | \\n\",\n \"0.066928 | \\n\",\n \"0.128539 | \\n\",\n \"-0.189115 | \\n\",\n \"0.133558 | \\n\",\n \"-0.021053 | \\n\",\n \"0 | \\n\",\n \"0.244964 | \\n\",\n \"
| 1 | \\n\",\n \"1.191857 | \\n\",\n \"0.266151 | \\n\",\n \"0.166480 | \\n\",\n \"0.448154 | \\n\",\n \"0.060018 | \\n\",\n \"-0.082361 | \\n\",\n \"-0.078803 | \\n\",\n \"0.085102 | \\n\",\n \"-0.255425 | \\n\",\n \"-0.166974 | \\n\",\n \"... | \\n\",\n \"-0.225775 | \\n\",\n \"-0.638672 | \\n\",\n \"0.101288 | \\n\",\n \"-0.339846 | \\n\",\n \"0.167170 | \\n\",\n \"0.125895 | \\n\",\n \"-0.008983 | \\n\",\n \"0.014724 | \\n\",\n \"0 | \\n\",\n \"-0.342475 | \\n\",\n \"
| 2 | \\n\",\n \"-1.358354 | \\n\",\n \"-1.340163 | \\n\",\n \"1.773209 | \\n\",\n \"0.379780 | \\n\",\n \"-0.503198 | \\n\",\n \"1.800499 | \\n\",\n \"0.791461 | \\n\",\n \"0.247676 | \\n\",\n \"-1.514654 | \\n\",\n \"0.207643 | \\n\",\n \"... | \\n\",\n \"0.247998 | \\n\",\n \"0.771679 | \\n\",\n \"0.909412 | \\n\",\n \"-0.689281 | \\n\",\n \"-0.327642 | \\n\",\n \"-0.139097 | \\n\",\n \"-0.055353 | \\n\",\n \"-0.059752 | \\n\",\n \"0 | \\n\",\n \"1.160686 | \\n\",\n \"
| 3 | \\n\",\n \"-0.966272 | \\n\",\n \"-0.185226 | \\n\",\n \"1.792993 | \\n\",\n \"-0.863291 | \\n\",\n \"-0.010309 | \\n\",\n \"1.247203 | \\n\",\n \"0.237609 | \\n\",\n \"0.377436 | \\n\",\n \"-1.387024 | \\n\",\n \"-0.054952 | \\n\",\n \"... | \\n\",\n \"-0.108300 | \\n\",\n \"0.005274 | \\n\",\n \"-0.190321 | \\n\",\n \"-1.175575 | \\n\",\n \"0.647376 | \\n\",\n \"-0.221929 | \\n\",\n \"0.062723 | \\n\",\n \"0.061458 | \\n\",\n \"0 | \\n\",\n \"0.140534 | \\n\",\n \"
| 4 | \\n\",\n \"-1.158233 | \\n\",\n \"0.877737 | \\n\",\n \"1.548718 | \\n\",\n \"0.403034 | \\n\",\n \"-0.407193 | \\n\",\n \"0.095921 | \\n\",\n \"0.592941 | \\n\",\n \"-0.270533 | \\n\",\n \"0.817739 | \\n\",\n \"0.753074 | \\n\",\n \"... | \\n\",\n \"-0.009431 | \\n\",\n \"0.798278 | \\n\",\n \"-0.137458 | \\n\",\n \"0.141267 | \\n\",\n \"-0.206010 | \\n\",\n \"0.502292 | \\n\",\n \"0.219422 | \\n\",\n \"0.215153 | \\n\",\n \"0 | \\n\",\n \"-0.073403 | \\n\",\n \"
5 rows × 30 columns
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" V1 V2 V3 V4 V5 V6 V7 \\\\\\n\",\n \"95707 1.167692 0.225779 0.239349 1.087745 -0.146246 -0.503627 0.128902 \\n\",\n \"134264 0.174399 -2.141734 -0.049317 0.299497 -0.987938 0.588867 0.170460 \\n\",\n \"160199 2.024826 -0.176528 -1.434116 0.085700 0.148483 -0.575064 -0.036593 \\n\",\n \"202778 2.063972 -0.917513 -0.791070 -0.050962 -0.833235 -0.528734 -0.449784 \\n\",\n \"221937 2.095684 -0.760005 -1.177668 -0.710860 -0.729446 -0.619706 -0.984984 \\n\",\n \"\\n\",\n \" V8 V9 V10 ... V20 V21 \\\\\\n\",\n \"95707 -0.070329 0.053785 -0.022168 ... -0.173409 0.059678 \\n\",\n \"134264 -0.110208 0.879512 -0.708416 ... 1.286884 0.166210 \\n\",\n \"160199 -0.053517 0.677803 0.098181 ... -0.304399 0.311083 \\n\",\n \"202778 -0.223829 0.129043 0.603585 ... -0.527694 -0.727778 \\n\",\n \"221937 -0.001752 -0.147767 0.252731 ... 0.062998 0.274847 \\n\",\n \"\\n\",\n \" V22 V23 V24 V25 V26 V27 V28 \\\\\\n\",\n \"95707 0.190525 -0.078399 0.073477 0.591273 -0.298849 0.023668 0.018848 \\n\",\n \"134264 -0.587754 -0.652408 -0.854682 0.135290 1.008324 -0.137383 0.108613 \\n\",\n \"160199 0.952930 0.029675 0.693949 0.257401 -0.465650 -0.005209 -0.059796 \\n\",\n \"202778 -1.277798 0.313903 -0.095561 -0.281492 0.396796 -0.029100 -0.051121 \\n\",\n \"221937 0.819967 0.044441 -0.438308 -0.147740 -0.088197 0.021530 -0.023101 \\n\",\n \"\\n\",\n \" Amount_scl \\n\",\n \"95707 -0.267311 \\n\",\n \"134264 2.045622 \\n\",\n \"160199 -0.349231 \\n\",\n \"202778 -0.183511 \\n\",\n \"221937 -0.233487 \\n\",\n \"\\n\",\n \"[5 rows x 29 columns]\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"X_test.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"912bd6a7-b37c-b22a-723a-02ce696b9876\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"from sklearn.metrics import auc,roc_auc_score,roc_curve,recall_score \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_cell_guid\": \"bb202318-c46c-d889-4693-196003360b78\"\n },\n \"outputs\": [],\n \"source\": [\n \"def get_best_hypers_lr(X, y):\\n\",\n \" \\\"\\\"\\\" Search parameter space for the optimal values.\\n\",\n \" \\n\",\n \" -Perform Logistic Regression using a range of C parameter values and two different\\n\",\n \" penalty terms (L1 & L2)\\n\",\n \" -Compute mean recall scores using kfold cross validation for each run\\n\",\n \" -Output the C parameter and penalty term with the best recall score\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" c_range = [0.01, 0.1, 1.0, 10.0, 100.0]\\n\",\n \" recall_max = 0\\n\",\n \" best_c = 0\\n\",\n \" penalty = ''\\n\",\n \" \\n\",\n \" for c_param in c_range:\\n\",\n \" print('='*25)\\n\",\n \" print('C parameter: ', c_param)\\n\",\n \" print('='*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" print('-'*25)\\n\",\n \" print('L1-penalty')\\n\",\n \" print('-'*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" lr_l1 = LogisticRegression(C=c_param, penalty='l1')\\n\",\n \" acc_score = cross_val_score(lr_l1, X, y, cv=5)\\n\",\n \" recall_score = cross_val_score(lr_l1, X, y, cv=5, scoring='recall')\\n\",\n \" l1_recall = np.mean(recall_score)\\n\",\n \" \\n\",\n \" print(\\\"Mean Accuracy: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \" print(\\\"Mean Recall: %0.3f (+/- %0.3f)\\\" % (l1_recall, np.std(recall_score)) )\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" print('-'*25)\\n\",\n \" print('L2-penalty')\\n\",\n \" print('-'*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" lr_l2 = LogisticRegression(C=c_param, penalty='l2')\\n\",\n \" score = cross_val_score(lr_l2, X, y, cv=5)\\n\",\n \" recall_score = cross_val_score(lr_l2, X, y, cv=5, scoring='recall')\\n\",\n \" l2_recall = np.mean(recall_score)\\n\",\n \" \\n\",\n \" print(\\\"Mean Accuracy: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \" print(\\\"Mean Recall: %0.3f (+/- %0.3f)\\\" % (l2_recall, np.std(recall_score)) )\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" # compare l1_recall & l2_recall:\\n\",\n \" if l2_recall > l1_recall:\\n\",\n \" # compare to max:\\n\",\n \" if l2_recall > recall_max:\\n\",\n \" recall_max = l2_recall\\n\",\n \" best_c = c_param\\n\",\n \" penalty='l2'\\n\",\n \" else:\\n\",\n \" # compare to max:\\n\",\n \" if l1_recall > recall_max:\\n\",\n \" recall_max = l1_recall\\n\",\n \" best_c = c_param\\n\",\n \" penalty='l1'\\n\",\n \" \\n\",\n \"\\n\",\n \" print('*'*25)\\n\",\n \" print('Optimal C parameter = ', best_c)\\n\",\n \" print('Optimal penalty = ', penalty)\\n\",\n \" print('Optimal recall = ', recall_max)\\n\",\n \" print('*'*25)\\n\",\n \" \\n\",\n \" return best_c, penalty\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"metadata\": {\n \"_cell_guid\": \"6e6ca420-fa59-8a81-3919-10fbb984ec2f\"\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'X_undersample' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[0;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[0;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m| \\n\",\n \" | V1 | \\n\",\n \"V2 | \\n\",\n \"V3 | \\n\",\n \"V4 | \\n\",\n \"V5 | \\n\",\n \"V6 | \\n\",\n \"V7 | \\n\",\n \"V8 | \\n\",\n \"V9 | \\n\",\n \"V10 | \\n\",\n \"... | \\n\",\n \"V20 | \\n\",\n \"V21 | \\n\",\n \"V22 | \\n\",\n \"V23 | \\n\",\n \"V24 | \\n\",\n \"V25 | \\n\",\n \"V26 | \\n\",\n \"V27 | \\n\",\n \"V28 | \\n\",\n \"Amount_scl | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 95707 | \\n\",\n \"1.167692 | \\n\",\n \"0.225779 | \\n\",\n \"0.239349 | \\n\",\n \"1.087745 | \\n\",\n \"-0.146246 | \\n\",\n \"-0.503627 | \\n\",\n \"0.128902 | \\n\",\n \"-0.070329 | \\n\",\n \"0.053785 | \\n\",\n \"-0.022168 | \\n\",\n \"... | \\n\",\n \"-0.173409 | \\n\",\n \"0.059678 | \\n\",\n \"0.190525 | \\n\",\n \"-0.078399 | \\n\",\n \"0.073477 | \\n\",\n \"0.591273 | \\n\",\n \"-0.298849 | \\n\",\n \"0.023668 | \\n\",\n \"0.018848 | \\n\",\n \"-0.267311 | \\n\",\n \"
| 134264 | \\n\",\n \"0.174399 | \\n\",\n \"-2.141734 | \\n\",\n \"-0.049317 | \\n\",\n \"0.299497 | \\n\",\n \"-0.987938 | \\n\",\n \"0.588867 | \\n\",\n \"0.170460 | \\n\",\n \"-0.110208 | \\n\",\n \"0.879512 | \\n\",\n \"-0.708416 | \\n\",\n \"... | \\n\",\n \"1.286884 | \\n\",\n \"0.166210 | \\n\",\n \"-0.587754 | \\n\",\n \"-0.652408 | \\n\",\n \"-0.854682 | \\n\",\n \"0.135290 | \\n\",\n \"1.008324 | \\n\",\n \"-0.137383 | \\n\",\n \"0.108613 | \\n\",\n \"2.045622 | \\n\",\n \"
| 160199 | \\n\",\n \"2.024826 | \\n\",\n \"-0.176528 | \\n\",\n \"-1.434116 | \\n\",\n \"0.085700 | \\n\",\n \"0.148483 | \\n\",\n \"-0.575064 | \\n\",\n \"-0.036593 | \\n\",\n \"-0.053517 | \\n\",\n \"0.677803 | \\n\",\n \"0.098181 | \\n\",\n \"... | \\n\",\n \"-0.304399 | \\n\",\n \"0.311083 | \\n\",\n \"0.952930 | \\n\",\n \"0.029675 | \\n\",\n \"0.693949 | \\n\",\n \"0.257401 | \\n\",\n \"-0.465650 | \\n\",\n \"-0.005209 | \\n\",\n \"-0.059796 | \\n\",\n \"-0.349231 | \\n\",\n \"
| 202778 | \\n\",\n \"2.063972 | \\n\",\n \"-0.917513 | \\n\",\n \"-0.791070 | \\n\",\n \"-0.050962 | \\n\",\n \"-0.833235 | \\n\",\n \"-0.528734 | \\n\",\n \"-0.449784 | \\n\",\n \"-0.223829 | \\n\",\n \"0.129043 | \\n\",\n \"0.603585 | \\n\",\n \"... | \\n\",\n \"-0.527694 | \\n\",\n \"-0.727778 | \\n\",\n \"-1.277798 | \\n\",\n \"0.313903 | \\n\",\n \"-0.095561 | \\n\",\n \"-0.281492 | \\n\",\n \"0.396796 | \\n\",\n \"-0.029100 | \\n\",\n \"-0.051121 | \\n\",\n \"-0.183511 | \\n\",\n \"
| 221937 | \\n\",\n \"2.095684 | \\n\",\n \"-0.760005 | \\n\",\n \"-1.177668 | \\n\",\n \"-0.710860 | \\n\",\n \"-0.729446 | \\n\",\n \"-0.619706 | \\n\",\n \"-0.984984 | \\n\",\n \"-0.001752 | \\n\",\n \"-0.147767 | \\n\",\n \"0.252731 | \\n\",\n \"... | \\n\",\n \"0.062998 | \\n\",\n \"0.274847 | \\n\",\n \"0.819967 | \\n\",\n \"0.044441 | \\n\",\n \"-0.438308 | \\n\",\n \"-0.147740 | \\n\",\n \"-0.088197 | \\n\",\n \"0.021530 | \\n\",\n \"-0.023101 | \\n\",\n \"-0.233487 | \\n\",\n \"
5 rows × 29 columns
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| max | \\n\",\n \"1460.000000 | \\n\",\n \"190.000000 | \\n\",\n \"313.000000 | \\n\",\n \"215245.000000 | \\n\",\n \"10.000000 | \\n\",\n \"9.000000 | \\n\",\n \"2010.000000 | \\n\",\n \"2010.000000 | \\n\",\n \"1600.000000 | \\n\",\n \"5644.000000 | \\n\",\n \"... | \\n\",\n \"857.000000 | \\n\",\n \"547.000000 | \\n\",\n \"552.000000 | \\n\",\n \"508.000000 | \\n\",\n \"480.000000 | \\n\",\n \"738.000000 | \\n\",\n \"15500.000000 | \\n\",\n \"12.000000 | \\n\",\n \"2010.000000 | \\n\",\n \"755000.000000 | \\n\",\n \"
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| Alley | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
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| Utilities | \\n\",\n \"AllPub | \\n\",\n \"AllPub | \\n\",\n \"AllPub | \\n\",\n \"AllPub | \\n\",\n \"AllPub | \\n\",\n \"
| LotConfig | \\n\",\n \"Inside | \\n\",\n \"FR2 | \\n\",\n \"Inside | \\n\",\n \"Corner | \\n\",\n \"FR2 | \\n\",\n \"
| LandSlope | \\n\",\n \"Gtl | \\n\",\n \"Gtl | \\n\",\n \"Gtl | \\n\",\n \"Gtl | \\n\",\n \"Gtl | \\n\",\n \"
| Neighborhood | \\n\",\n \"CollgCr | \\n\",\n \"Veenker | \\n\",\n \"CollgCr | \\n\",\n \"Crawfor | \\n\",\n \"NoRidge | \\n\",\n \"
| Condition1 | \\n\",\n \"Norm | \\n\",\n \"Feedr | \\n\",\n \"Norm | \\n\",\n \"Norm | \\n\",\n \"Norm | \\n\",\n \"
| Condition2 | \\n\",\n \"Norm | \\n\",\n \"Norm | \\n\",\n \"Norm | \\n\",\n \"Norm | \\n\",\n \"Norm | \\n\",\n \"
| BldgType | \\n\",\n \"1Fam | \\n\",\n \"1Fam | \\n\",\n \"1Fam | \\n\",\n \"1Fam | \\n\",\n \"1Fam | \\n\",\n \"
| HouseStyle | \\n\",\n \"2Story | \\n\",\n \"1Story | \\n\",\n \"2Story | \\n\",\n \"2Story | \\n\",\n \"2Story | \\n\",\n \"
| OverallQual | \\n\",\n \"7 | \\n\",\n \"6 | \\n\",\n \"7 | \\n\",\n \"7 | \\n\",\n \"8 | \\n\",\n \"
| OverallCond | \\n\",\n \"5 | \\n\",\n \"8 | \\n\",\n \"5 | \\n\",\n \"5 | \\n\",\n \"5 | \\n\",\n \"
| YearBuilt | \\n\",\n \"2003 | \\n\",\n \"1976 | \\n\",\n \"2001 | \\n\",\n \"1915 | \\n\",\n \"2000 | \\n\",\n \"
| YearRemodAdd | \\n\",\n \"2003 | \\n\",\n \"1976 | \\n\",\n \"2002 | \\n\",\n \"1970 | \\n\",\n \"2000 | \\n\",\n \"
| RoofStyle | \\n\",\n \"Gable | \\n\",\n \"Gable | \\n\",\n \"Gable | \\n\",\n \"Gable | \\n\",\n \"Gable | \\n\",\n \"
| RoofMatl | \\n\",\n \"CompShg | \\n\",\n \"CompShg | \\n\",\n \"CompShg | \\n\",\n \"CompShg | \\n\",\n \"CompShg | \\n\",\n \"
| Exterior1st | \\n\",\n \"VinylSd | \\n\",\n \"MetalSd | \\n\",\n \"VinylSd | \\n\",\n \"Wd Sdng | \\n\",\n \"VinylSd | \\n\",\n \"
| Exterior2nd | \\n\",\n \"VinylSd | \\n\",\n \"MetalSd | \\n\",\n \"VinylSd | \\n\",\n \"Wd Shng | \\n\",\n \"VinylSd | \\n\",\n \"
| MasVnrType | \\n\",\n \"BrkFace | \\n\",\n \"None | \\n\",\n \"BrkFace | \\n\",\n \"None | \\n\",\n \"BrkFace | \\n\",\n \"
| MasVnrArea | \\n\",\n \"196 | \\n\",\n \"0 | \\n\",\n \"162 | \\n\",\n \"0 | \\n\",\n \"350 | \\n\",\n \"
| ExterQual | \\n\",\n \"Gd | \\n\",\n \"TA | \\n\",\n \"Gd | \\n\",\n \"TA | \\n\",\n \"Gd | \\n\",\n \"
| ExterCond | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"
| Foundation | \\n\",\n \"PConc | \\n\",\n \"CBlock | \\n\",\n \"PConc | \\n\",\n \"BrkTil | \\n\",\n \"PConc | \\n\",\n \"
| ... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"... | \\n\",\n \"
| BedroomAbvGr | \\n\",\n \"3 | \\n\",\n \"3 | \\n\",\n \"3 | \\n\",\n \"3 | \\n\",\n \"4 | \\n\",\n \"
| KitchenAbvGr | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"
| KitchenQual | \\n\",\n \"Gd | \\n\",\n \"TA | \\n\",\n \"Gd | \\n\",\n \"Gd | \\n\",\n \"Gd | \\n\",\n \"
| TotRmsAbvGrd | \\n\",\n \"8 | \\n\",\n \"6 | \\n\",\n \"6 | \\n\",\n \"7 | \\n\",\n \"9 | \\n\",\n \"
| Functional | \\n\",\n \"Typ | \\n\",\n \"Typ | \\n\",\n \"Typ | \\n\",\n \"Typ | \\n\",\n \"Typ | \\n\",\n \"
| Fireplaces | \\n\",\n \"0 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"
| FireplaceQu | \\n\",\n \"NaN | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"Gd | \\n\",\n \"TA | \\n\",\n \"
| GarageType | \\n\",\n \"Attchd | \\n\",\n \"Attchd | \\n\",\n \"Attchd | \\n\",\n \"Detchd | \\n\",\n \"Attchd | \\n\",\n \"
| GarageYrBlt | \\n\",\n \"2003 | \\n\",\n \"1976 | \\n\",\n \"2001 | \\n\",\n \"1998 | \\n\",\n \"2000 | \\n\",\n \"
| GarageFinish | \\n\",\n \"RFn | \\n\",\n \"RFn | \\n\",\n \"RFn | \\n\",\n \"Unf | \\n\",\n \"RFn | \\n\",\n \"
| GarageCars | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"2 | \\n\",\n \"3 | \\n\",\n \"3 | \\n\",\n \"
| GarageArea | \\n\",\n \"548 | \\n\",\n \"460 | \\n\",\n \"608 | \\n\",\n \"642 | \\n\",\n \"836 | \\n\",\n \"
| GarageQual | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"
| GarageCond | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"TA | \\n\",\n \"
| PavedDrive | \\n\",\n \"Y | \\n\",\n \"Y | \\n\",\n \"Y | \\n\",\n \"Y | \\n\",\n \"Y | \\n\",\n \"
| WoodDeckSF | \\n\",\n \"0 | \\n\",\n \"298 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"192 | \\n\",\n \"
| OpenPorchSF | \\n\",\n \"61 | \\n\",\n \"0 | \\n\",\n \"42 | \\n\",\n \"35 | \\n\",\n \"84 | \\n\",\n \"
| EnclosedPorch | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"272 | \\n\",\n \"0 | \\n\",\n \"
| 3SsnPorch | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
| ScreenPorch | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
| PoolArea | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
| PoolQC | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| Fence | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| MiscFeature | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"NaN | \\n\",\n \"
| MiscVal | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"
| MoSold | \\n\",\n \"2 | \\n\",\n \"5 | \\n\",\n \"9 | \\n\",\n \"2 | \\n\",\n \"12 | \\n\",\n \"
| YrSold | \\n\",\n \"2008 | \\n\",\n \"2007 | \\n\",\n \"2008 | \\n\",\n \"2006 | \\n\",\n \"2008 | \\n\",\n \"
| SaleType | \\n\",\n \"WD | \\n\",\n \"WD | \\n\",\n \"WD | \\n\",\n \"WD | \\n\",\n \"WD | \\n\",\n \"
| SaleCondition | \\n\",\n \"Normal | \\n\",\n \"Normal | \\n\",\n \"Normal | \\n\",\n \"Abnorml | \\n\",\n \"Normal | \\n\",\n \"
| SalePrice | \\n\",\n \"208500 | \\n\",\n \"181500 | \\n\",\n \"223500 | \\n\",\n \"140000 | \\n\",\n \"250000 | \\n\",\n \"
81 rows × 5 columns
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass \\\\\\n\",\n \"0 1 0 3 \\n\",\n \"1 2 1 1 \\n\",\n \"2 3 1 3 \\n\",\n \"3 4 1 1 \\n\",\n \"4 5 0 3 \\n\",\n \"\\n\",\n \" Name Sex Age SibSp \\\\\\n\",\n \"0 Braund, Mr. Owen Harris male 22.0 1 \\n\",\n \"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \\n\",\n \"2 Heikkinen, Miss. Laina female 26.0 0 \\n\",\n \"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \\n\",\n \"4 Allen, Mr. William Henry male 35.0 0 \\n\",\n \"\\n\",\n \" Parch Ticket Fare Cabin Embarked \\n\",\n \"0 0 A/5 21171 7.2500 NaN S \\n\",\n \"1 0 PC 17599 71.2833 C85 C \\n\",\n \"2 0 STON/O2. 3101282 7.9250 NaN S \\n\",\n \"3 0 113803 53.1000 C123 S \\n\",\n \"4 0 373450 8.0500 NaN S \"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# get titanic & test csv files as a DataFrame\\n\",\n \"train = pd.read_csv(\\\"../input/train.csv\\\")\\n\",\n \"test = pd.read_csv(\\\"../input/test.csv\\\")\\n\",\n \"\\n\",\n \"print ('train:' , train.shape)\\n\",\n \"train.head()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"886341a4-f985-d423-493f-10bac89b1b36\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"| \\n\",\n \" | PassengerId | \\n\",\n \"Survived | \\n\",\n \"Pclass | \\n\",\n \"Name | \\n\",\n \"Sex | \\n\",\n \"Age | \\n\",\n \"SibSp | \\n\",\n \"Parch | \\n\",\n \"Ticket | \\n\",\n \"Fare | \\n\",\n \"Cabin | \\n\",\n \"Embarked | \\n\",\n \"
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"3 | \\n\",\n \"Braund, Mr. Owen Harris | \\n\",\n \"male | \\n\",\n \"22.0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"A/5 21171 | \\n\",\n \"7.2500 | \\n\",\n \"NaN | \\n\",\n \"S | \\n\",\n \"
| 1 | \\n\",\n \"2 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"Cumings, Mrs. John Bradley (Florence Briggs Th... | \\n\",\n \"female | \\n\",\n \"38.0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"PC 17599 | \\n\",\n \"71.2833 | \\n\",\n \"C85 | \\n\",\n \"C | \\n\",\n \"
| 2 | \\n\",\n \"3 | \\n\",\n \"1 | \\n\",\n \"3 | \\n\",\n \"Heikkinen, Miss. Laina | \\n\",\n \"female | \\n\",\n \"26.0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"STON/O2. 3101282 | \\n\",\n \"7.9250 | \\n\",\n \"NaN | \\n\",\n \"S | \\n\",\n \"
| 3 | \\n\",\n \"4 | \\n\",\n \"1 | \\n\",\n \"1 | \\n\",\n \"Futrelle, Mrs. Jacques Heath (Lily May Peel) | \\n\",\n \"female | \\n\",\n \"35.0 | \\n\",\n \"1 | \\n\",\n \"0 | \\n\",\n \"113803 | \\n\",\n \"53.1000 | \\n\",\n \"C123 | \\n\",\n \"S | \\n\",\n \"
| 4 | \\n\",\n \"5 | \\n\",\n \"0 | \\n\",\n \"3 | \\n\",\n \"Allen, Mr. William Henry | \\n\",\n \"male | \\n\",\n \"35.0 | \\n\",\n \"0 | \\n\",\n \"0 | \\n\",\n \"373450 | \\n\",\n \"8.0500 | \\n\",\n \"NaN | \\n\",\n \"S | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass Age SibSp \\\\\\n\",\n \"count 891.000000 891.000000 891.000000 714.000000 891.000000 \\n\",\n \"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \\n\",\n \"std 257.353842 0.486592 0.836071 14.526497 1.102743 \\n\",\n \"min 1.000000 0.000000 1.000000 0.420000 0.000000 \\n\",\n \"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \\n\",\n \"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \\n\",\n \"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \\n\",\n \"max 891.000000 1.000000 3.000000 80.000000 8.000000 \\n\",\n \"\\n\",\n \" Parch Fare \\n\",\n \"count 891.000000 891.000000 \\n\",\n \"mean 0.381594 32.204208 \\n\",\n \"std 0.806057 49.693429 \\n\",\n \"min 0.000000 0.000000 \\n\",\n \"25% 0.000000 7.910400 \\n\",\n \"50% 0.000000 14.454200 \\n\",\n \"75% 0.000000 31.000000 \\n\",\n \"max 6.000000 512.329200 \"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"train.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"52e65464-ef27-88d4-4533-7d4808d7011d\"\n },\n \"outputs\": [],\n \"source\": [\n \"def plot_correlation_map( df ):\\n\",\n \" corr = df.corr()\\n\",\n \" _ , ax = plt.subplots( figsize =( 12 , 10 ) )\\n\",\n \" cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\\n\",\n \" _ = sns.heatmap(\\n\",\n \" corr, \\n\",\n \" cmap = cmap,\\n\",\n \" square=True, \\n\",\n \" cbar_kws={ 'shrink' : .9 }, \\n\",\n \" ax=ax, \\n\",\n \" annot = True, \\n\",\n \" annot_kws = { 'fontsize' : 12 }\\n\",\n \" )\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"252f6aa9-1a29-4b19-d270-04c2d88cd481\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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YMHrZqYiIiIiNifRcXip59+ysCBA80XIm3SpAlDhgyxamIiIiIiDs1g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\"text/plain\": [\n \"| \\n\",\n \" | PassengerId | \\n\",\n \"Survived | \\n\",\n \"Pclass | \\n\",\n \"Age | \\n\",\n \"SibSp | \\n\",\n \"Parch | \\n\",\n \"Fare | \\n\",\n \"
|---|---|---|---|---|---|---|---|
| count | \\n\",\n \"891.000000 | \\n\",\n \"891.000000 | \\n\",\n \"891.000000 | \\n\",\n \"714.000000 | \\n\",\n \"891.000000 | \\n\",\n \"891.000000 | \\n\",\n \"891.000000 | \\n\",\n \"
| mean | \\n\",\n \"446.000000 | \\n\",\n \"0.383838 | \\n\",\n \"2.308642 | \\n\",\n \"29.699118 | \\n\",\n \"0.523008 | \\n\",\n \"0.381594 | \\n\",\n \"32.204208 | \\n\",\n \"
| std | \\n\",\n \"257.353842 | \\n\",\n \"0.486592 | \\n\",\n \"0.836071 | \\n\",\n \"14.526497 | \\n\",\n \"1.102743 | \\n\",\n \"0.806057 | \\n\",\n \"49.693429 | \\n\",\n \"
| min | \\n\",\n \"1.000000 | \\n\",\n \"0.000000 | \\n\",\n \"1.000000 | \\n\",\n \"0.420000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"
| 25% | \\n\",\n \"223.500000 | \\n\",\n \"0.000000 | \\n\",\n \"2.000000 | \\n\",\n \"20.125000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"7.910400 | \\n\",\n \"
| 50% | \\n\",\n \"446.000000 | \\n\",\n \"0.000000 | \\n\",\n \"3.000000 | \\n\",\n \"28.000000 | \\n\",\n \"0.000000 | \\n\",\n \"0.000000 | \\n\",\n \"14.454200 | \\n\",\n \"
| 75% | \\n\",\n \"668.500000 | \\n\",\n \"1.000000 | \\n\",\n \"3.000000 | \\n\",\n \"38.000000 | \\n\",\n \"1.000000 | \\n\",\n \"0.000000 | \\n\",\n \"31.000000 | \\n\",\n \"
| max | \\n\",\n \"891.000000 | \\n\",\n \"1.000000 | \\n\",\n \"3.000000 | \\n\",\n \"80.000000 | \\n\",\n \"8.000000 | \\n\",\n \"6.000000 | \\n\",\n \"512.329200 | \\n\",\n \"
\\n\",\n \"\\n\",\n \" \\n\",\n \"
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 \\\\\\n\",\n \"0 1 0 0 0 0 0 0 0 0 \\n\",\n \"1 0 0 0 0 0 0 0 0 0 \\n\",\n \"2 1 0 0 0 0 0 0 0 0 \\n\",\n \"3 4 0 0 0 0 0 0 0 0 \\n\",\n \"4 0 0 0 0 0 0 0 0 0 \\n\",\n \"\\n\",\n \" pixel8 ... pixel774 pixel775 pixel776 pixel777 pixel778 \\\\\\n\",\n \"0 0 ... 0 0 0 0 0 \\n\",\n \"1 0 ... 0 0 0 0 0 \\n\",\n \"2 0 ... 0 0 0 0 0 \\n\",\n \"3 0 ... 0 0 0 0 0 \\n\",\n \"4 0 ... 0 0 0 0 0 \\n\",\n \"\\n\",\n \" pixel779 pixel780 pixel781 pixel782 pixel783 \\n\",\n \"0 0 0 0 0 0 \\n\",\n \"1 0 0 0 0 0 \\n\",\n \"2 0 0 0 0 0 \\n\",\n \"3 0 0 0 0 0 \\n\",\n \"4 0 0 0 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 785 columns]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df_digits.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"a4061a05-15d7-173b-62e5-b003be10191a\"\n },\n \"source\": [\n \"So, we have 42000 rows with 785 features, the first feature is the digit that the image represent and the others are pixel value of the image. Let's see how are the original images.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"_cell_guid\": \"c0b367f7-df9b-e21b-cb4d-5580305b4457\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"| \\n\",\n \" | label | \\n\",\n \"pixel0 | \\n\",\n \"pixel1 | \\n\",\n \"pixel2 | \\n\",\n \"pixel3 | \\n\",\n \"pixel4 | \\n\",\n \"pixel5 | \\n\",\n \"pixel6 | \\n\",\n \"pixel7 | \\n\",\n \"pixel8 | \\n\",\n \"... | \\n\",\n \"pixel774 | \\n\",\n \"pixel775 | \\n\",\n \"pixel776 | \\n\",\n \"pixel777 | \\n\",\n \"pixel778 | \\n\",\n \"pixel779 | \\n\",\n \"pixel780 | \\n\",\n \"pixel781 | \\n\",\n \"pixel782 | \\n\",\n \"pixel783 | \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" Time V1 V2 V3 V4 V5 V6 V7 \\\\\\n\",\n \"0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \\n\",\n \"1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \\n\",\n \"2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \\n\",\n \"3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \\n\",\n \"4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \\n\",\n \"\\n\",\n \" V8 V9 ... V21 V22 V23 V24 \\\\\\n\",\n \"0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 \\n\",\n \"1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 \\n\",\n \"2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 \\n\",\n \"3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 \\n\",\n \"4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 \\n\",\n \"\\n\",\n \" V25 V26 V27 V28 Amount Class \\n\",\n \"0 0.128539 -0.189115 0.133558 -0.021053 149.62 0 \\n\",\n \"1 0.167170 0.125895 -0.008983 0.014724 2.69 0 \\n\",\n \"2 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0 \\n\",\n \"3 0.647376 -0.221929 0.062723 0.061458 123.50 0 \\n\",\n \"4 -0.206010 0.502292 0.219422 0.215153 69.99 0 \\n\",\n \"\\n\",\n \"[5 rows x 31 columns]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.read_csv(\\\"../input/creditcard.csv\\\")\\n\",\n \"print(\\\"No. of Rows: \\\\t\\\\t\\\", data.shape[0])\\n\",\n \"print(\\\"No. of Columns: \\\\t\\\", data.shape[1])\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"302cfc01-d0c3-ddf7-a897-73d3cca6dc73\"\n },\n \"source\": [\n \"## 1.2 Class Distribution\\n\",\n \"\\n\",\n \"As described in the Credit Card Fraud summary, this is a highly unbalanced dataset. Check out the histogram and class counts below (Class 0 for normal, Class 1 for fraudulent).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"_cell_guid\": \"d7d5ce17-5636-39fd-e257-787010f848f1\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No. of normal transactions: \\t\\t 284315\\n\",\n \"No. of fraudulent transactions: \\t 492\\n\",\n \"% normal transactions: \\t\\t 99.82725143693798\\n\",\n \"% fraudulent transcations: \\t 0.1727485630620034\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"| \\n\",\n \" | Time | \\n\",\n \"V1 | \\n\",\n \"V2 | \\n\",\n \"V3 | \\n\",\n \"V4 | \\n\",\n \"V5 | \\n\",\n \"V6 | \\n\",\n \"V7 | \\n\",\n \"V8 | \\n\",\n \"V9 | \\n\",\n \"... | \\n\",\n \"V21 | \\n\",\n \"V22 | \\n\",\n \"V23 | \\n\",\n \"V24 | \\n\",\n \"V25 | \\n\",\n \"V26 | \\n\",\n \"V27 | \\n\",\n \"V28 | \\n\",\n \"Amount | \\n\",\n \"Class | \\n\",\n \"
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| 2 | \\n\",\n \"1.0 | \\n\",\n \"-1.358354 | \\n\",\n \"-1.340163 | \\n\",\n \"1.773209 | \\n\",\n \"0.379780 | \\n\",\n \"-0.503198 | \\n\",\n \"1.800499 | \\n\",\n \"0.791461 | \\n\",\n \"0.247676 | \\n\",\n \"-1.514654 | \\n\",\n \"... | \\n\",\n \"0.247998 | \\n\",\n \"0.771679 | \\n\",\n \"0.909412 | \\n\",\n \"-0.689281 | \\n\",\n \"-0.327642 | \\n\",\n \"-0.139097 | \\n\",\n \"-0.055353 | \\n\",\n \"-0.059752 | \\n\",\n \"378.66 | \\n\",\n \"0 | \\n\",\n \"
| 3 | \\n\",\n \"1.0 | \\n\",\n \"-0.966272 | \\n\",\n \"-0.185226 | \\n\",\n \"1.792993 | \\n\",\n \"-0.863291 | \\n\",\n \"-0.010309 | \\n\",\n \"1.247203 | \\n\",\n \"0.237609 | \\n\",\n \"0.377436 | \\n\",\n \"-1.387024 | \\n\",\n \"... | \\n\",\n \"-0.108300 | \\n\",\n \"0.005274 | \\n\",\n \"-0.190321 | \\n\",\n \"-1.175575 | \\n\",\n \"0.647376 | \\n\",\n \"-0.221929 | \\n\",\n \"0.062723 | \\n\",\n \"0.061458 | \\n\",\n \"123.50 | \\n\",\n \"0 | \\n\",\n \"
| 4 | \\n\",\n \"2.0 | \\n\",\n \"-1.158233 | \\n\",\n \"0.877737 | \\n\",\n \"1.548718 | \\n\",\n \"0.403034 | \\n\",\n \"-0.407193 | \\n\",\n \"0.095921 | \\n\",\n \"0.592941 | \\n\",\n \"-0.270533 | \\n\",\n \"0.817739 | \\n\",\n \"... | \\n\",\n \"-0.009431 | \\n\",\n \"0.798278 | \\n\",\n \"-0.137458 | \\n\",\n \"0.141267 | \\n\",\n \"-0.206010 | \\n\",\n \"0.502292 | \\n\",\n \"0.219422 | \\n\",\n \"0.215153 | \\n\",\n \"69.99 | \\n\",\n \"0 | \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" V1 V2 V3 V4 V5 V6 V7 \\\\\\n\",\n \"0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \\n\",\n \"1 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \\n\",\n \"2 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \\n\",\n \"3 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \\n\",\n \"4 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \\n\",\n \"\\n\",\n \" V8 V9 V10 ... V21 V22 V23 \\\\\\n\",\n \"0 0.098698 0.363787 0.090794 ... -0.018307 0.277838 -0.110474 \\n\",\n \"1 0.085102 -0.255425 -0.166974 ... -0.225775 -0.638672 0.101288 \\n\",\n \"2 0.247676 -1.514654 0.207643 ... 0.247998 0.771679 0.909412 \\n\",\n \"3 0.377436 -1.387024 -0.054952 ... -0.108300 0.005274 -0.190321 \\n\",\n \"4 -0.270533 0.817739 0.753074 ... -0.009431 0.798278 -0.137458 \\n\",\n \"\\n\",\n \" V24 V25 V26 V27 V28 Class Amount_scl \\n\",\n \"0 0.066928 0.128539 -0.189115 0.133558 -0.021053 0 0.244964 \\n\",\n \"1 -0.339846 0.167170 0.125895 -0.008983 0.014724 0 -0.342475 \\n\",\n \"2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 0 1.160686 \\n\",\n \"3 -1.175575 0.647376 -0.221929 0.062723 0.061458 0 0.140534 \\n\",\n \"4 0.141267 -0.206010 0.502292 0.219422 0.215153 0 -0.073403 \\n\",\n \"\\n\",\n \"[5 rows x 30 columns]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.preprocessing import StandardScaler\\n\",\n \"\\n\",\n \"data['Amount_scl'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))\\n\",\n \"data = data.drop(['Time','Amount'],axis=1)\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"_cell_guid\": \"dd464857-7a67-7b12-e682-12911cfe145e\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"X.shape: (284807, 29)\\n\",\n \"y.shape: (284807,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"X = data.ix[:, data.columns != 'Class'] # features\\n\",\n \"y = data['Class'] # labels\\n\",\n \"print(\\\"X.shape: \\\", X.shape)\\n\",\n \"print(\\\"y.shape: \\\", y.shape)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"2779886c-d420-e1fa-be0b-bed2aa09a2d0\"\n },\n \"source\": [\n \"## 1.4 Split Entire Dataset into Train-Test Sets\\n\",\n \"\\n\",\n \"Next, we will break up the data into training and test sets. The test set will be untouched until the final evaluation of each model so we have an unbiased estimate of model performance.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"_cell_guid\": \"78ffa6bc-ab94-7c24-9d76-bd0e03d8bfc6\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"# 70% training data, 30% testing data\\n\",\n \"X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"fb839282-f238-85e1-d33d-25f4aa9569f1\"\n },\n \"source\": [\n \"## 1.5 Undersample Training Data to Create Balanced Class Distributions\\n\",\n \"\\n\",\n \"Now we will handle the skewed class distribution using a technique known as undersampling:\\n\",\n \"\\n\",\n \" 1. Get the number of fraudulent transactions and the indices of the corresponding rows in our training data\\n\",\n \" 2. Get the indices of normal transactions in our training data\\n\",\n \" 3. Take a random sample of normal transactions with a sample size equal to the number of fraudulent transactions\\n\",\n \" 4. Combine the fraudulent transactions and the random sample of normal transactions to get balanced training data set (50% fraud, 50% normal).\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"_cell_guid\": \"22fea321-d877-f4e1-8497-aa8447ac0d43\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"---Undersampled Data Set---\\n\",\n \"No. of normal transactions: \\t 336\\n\",\n \"No. of fraudulent transactions: \\t\\t 336\\n\"\n ]\n }\n ],\n \"source\": [\n \"# Get the indices of the fraudulent and normal classes:\\n\",\n \"fraud_idx = np.array(y_train[y_train == 1].index)\\n\",\n \"num_fraud = len(fraud_idx)\\n\",\n \"normal_idx = y_train[y_train == 0].index\\n\",\n \"\\n\",\n \"# From the normal indices, sample a random subset (subset size = # of frauds):\\n\",\n \"normal_idx_sample = np.random.choice(normal_idx, num_fraud, replace=False)\\n\",\n \"normal_idx_sample = np.array(normal_idx_sample)\\n\",\n \"\\n\",\n \"# Group together our normal and fraud indices:\\n\",\n \"# (we'll have a balanced class distribution, 50% normal, 50% fraud)\\n\",\n \"undersample_idx = np.concatenate([fraud_idx,normal_idx_sample])\\n\",\n \"\\n\",\n \"# Grab the records at the indices:\\n\",\n \"undersample_data = data.iloc[undersample_idx,:]\\n\",\n \"\\n\",\n \"# Split into features and labels:\\n\",\n \"X_undersample = undersample_data.ix[:, undersample_data.columns != 'Class']\\n\",\n \"y_undersample = undersample_data['Class']\\n\",\n \"\\n\",\n \"norm_count = undersample_data[undersample_data['Class'] == 0].shape[0]\\n\",\n \"fraud_count = undersample_data[undersample_data['Class'] == 1].shape[0]\\n\",\n \"\\n\",\n \"print(\\\"---Undersampled Data Set---\\\")\\n\",\n \"print(\\\"No. of normal transactions: \\\\t\\\", norm_count)\\n\",\n \"print(\\\"No. of fraudulent transactions: \\\\t\\\\t\\\", fraud_count)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"d0fe1f55-5163-5229-eafd-d96215f417df\"\n },\n \"source\": [\n \"# 2. Logistic Regression Classifier\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"_cell_guid\": \"912bd6a7-b37c-b22a-723a-02ce696b9876\"\n },\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"from sklearn.model_selection import cross_val_score\\n\",\n \"from sklearn.metrics import auc,roc_auc_score,roc_curve,recall_score,f1_score\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"4d42b86c-ced4-6952-002b-56c041e9c5fc\"\n },\n \"source\": [\n \"## 2.1 Tune Hyperparameters with Cross Validation\\n\",\n \"\\n\",\n \"Here we'll search the hyperparameter space and use 5-fold cross validation on the undersampled training data to output accuracy, recall, and F1 metrics. The goal is to find the best combination of hyperparameters that gives the highest F1-score. The cross validation step here further subdivides the training data into two smaller components: (1) training subset and (2) validation subset. The training subset is used to train the model on with a pair of hyperparameters, and the validation subset is used to score the trained model. 5-fold CV means we repeat this process of splitting up the data 5 different times to get a more accurate score. \\n\",\n \"\\n\",\n \"Once we identify the pair of hyperparameters with the highest score, we will use them to train the model on the full training data (i.e. training subset + validation subset). Keep in mind, we still haven't touched the test dataset from Section 1.4. We will save that for final model evaluation to get an unbiased result.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"metadata\": {\n \"_cell_guid\": \"bb202318-c46c-d889-4693-196003360b78\"\n },\n \"outputs\": [],\n \"source\": [\n \"def get_best_hypers_lr(X, y):\\n\",\n \" \\\"\\\"\\\" Search parameter space for the optimal values.\\n\",\n \" \\n\",\n \" -Perform Logistic Regression using a range of C parameter values and two different\\n\",\n \" penalty terms (L1 & L2)\\n\",\n \" -Compute mean recall,accuracy and f1-scores using kfold cross validation \\n\",\n \" for each run\\n\",\n \" -Output the C parameter and penalty term with the best f1 score\\n\",\n \" \\\"\\\"\\\"\\n\",\n \" c_range = [0.01, 0.1, 1.0, 10.0, 100.0]\\n\",\n \" f1_max = 0\\n\",\n \" best_c = 0\\n\",\n \" penalty = ''\\n\",\n \" \\n\",\n \" for c_param in c_range:\\n\",\n \" print('='*25)\\n\",\n \" print('C parameter: ', c_param)\\n\",\n \" print('='*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" print('-'*25)\\n\",\n \" print('L1-penalty')\\n\",\n \" print('-'*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" lr_l1 = LogisticRegression(C=c_param, penalty='l1')\\n\",\n \" acc_score = cross_val_score(lr_l1, X, y, cv=5)\\n\",\n \" recall_score = cross_val_score(lr_l1, X, y, cv=5, scoring='recall')\\n\",\n \" f1_score = cross_val_score(lr_l1, X,y,cv=5, scoring='f1')\\n\",\n \" l1_f1=np.mean(f1_score)\\n\",\n \" \\n\",\n \" print(\\\"Mean Accuracy: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \" print(\\\"Mean Recall: %0.3f (+/- %0.3f)\\\" % (np.mean(recall_score), np.std(recall_score)) )\\n\",\n \" print(\\\"Mean F1: %0.3f (+/- %0.3f)\\\" % (np.mean(f1_score), np.std(f1_score)) )\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" print('-'*25)\\n\",\n \" print('L2-penalty')\\n\",\n \" print('-'*25)\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" lr_l2 = LogisticRegression(C=c_param, penalty='l2')\\n\",\n \" score = cross_val_score(lr_l2, X, y, cv=5)\\n\",\n \" recall_score = cross_val_score(lr_l2, X, y, cv=5, scoring='recall')\\n\",\n \" f1_score = cross_val_score(lr_l2, X, y, cv=5, scoring='f1')\\n\",\n \" l2_f1 = np.mean(f1_score)\\n\",\n \" \\n\",\n \" print(\\\"Mean Accuracy: %0.3f (+/- %0.3f)\\\" % (np.mean(acc_score), np.std(acc_score)) )\\n\",\n \" print(\\\"Mean Recall: %0.3f (+/- %0.3f)\\\" % (np.mean(recall_score), np.std(recall_score)) )\\n\",\n \" print(\\\"Mean F1: %0.3f (+/- %0.3f)\\\" % (np.mean(f1_score), np.std(f1_score)) )\\n\",\n \" print('')\\n\",\n \" \\n\",\n \" # compare l1_f1 & l2_f1:\\n\",\n \" if l2_f1 > l1_f1:\\n\",\n \" # compare to max:\\n\",\n \" if l2_f1 > f1_max:\\n\",\n \" f1_max = l2_f1\\n\",\n \" best_c = c_param\\n\",\n \" penalty='l2'\\n\",\n \" else:\\n\",\n \" # compare to max:\\n\",\n \" if l1_f1 > f1_max:\\n\",\n \" f1_max = l1_f1\\n\",\n \" best_c = c_param\\n\",\n \" penalty='l1'\\n\",\n \" \\n\",\n \"\\n\",\n \" print('*'*25)\\n\",\n \" print('Optimal C parameter = ', best_c)\\n\",\n \" print('Optimal penalty = ', penalty)\\n\",\n \" print('Optimal F1 = ', f1_max)\\n\",\n \" print('*'*25)\\n\",\n \" \\n\",\n \" return best_c, penalty\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {\n \"_cell_guid\": \"6e6ca420-fa59-8a81-3919-10fbb984ec2f\"\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"=========================\\n\",\n \"C parameter: 0.01\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.784 (+/- 0.038)\\n\",\n \"Mean Recall: 0.958 (+/- 0.011)\\n\",\n \"Mean F1: 0.817 (+/- 0.024)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.784 (+/- 0.038)\\n\",\n \"Mean Recall: 0.946 (+/- 0.012)\\n\",\n \"Mean F1: 0.952 (+/- 0.012)\\n\",\n \"\\n\",\n \"=========================\\n\",\n \"C parameter: 0.1\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.943 (+/- 0.015)\\n\",\n \"Mean Recall: 0.911 (+/- 0.025)\\n\",\n \"Mean F1: 0.941 (+/- 0.016)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.943 (+/- 0.015)\\n\",\n \"Mean Recall: 0.929 (+/- 0.020)\\n\",\n \"Mean F1: 0.951 (+/- 0.013)\\n\",\n \"\\n\",\n \"=========================\\n\",\n \"C parameter: 1.0\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.952 (+/- 0.011)\\n\",\n \"Mean Recall: 0.926 (+/- 0.013)\\n\",\n \"Mean F1: 0.951 (+/- 0.011)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.952 (+/- 0.011)\\n\",\n \"Mean Recall: 0.926 (+/- 0.013)\\n\",\n \"Mean F1: 0.950 (+/- 0.013)\\n\",\n \"\\n\",\n \"=========================\\n\",\n \"C parameter: 10.0\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.945 (+/- 0.022)\\n\",\n \"Mean Recall: 0.923 (+/- 0.019)\\n\",\n \"Mean F1: 0.944 (+/- 0.022)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.945 (+/- 0.022)\\n\",\n \"Mean Recall: 0.923 (+/- 0.019)\\n\",\n \"Mean F1: 0.944 (+/- 0.022)\\n\",\n \"\\n\",\n \"=========================\\n\",\n \"C parameter: 100.0\\n\",\n \"=========================\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L1-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.948 (+/- 0.020)\\n\",\n \"Mean Recall: 0.929 (+/- 0.019)\\n\",\n \"Mean F1: 0.947 (+/- 0.020)\\n\",\n \"\\n\",\n \"-------------------------\\n\",\n \"L2-penalty\\n\",\n \"-------------------------\\n\",\n \"\\n\",\n \"Mean Accuracy: 0.948 (+/- 0.020)\\n\",\n \"Mean Recall: 0.929 (+/- 0.019)\\n\",\n \"Mean F1: 0.947 (+/- 0.020)\\n\",\n \"\\n\",\n \"*************************\\n\",\n \"Optimal C parameter = 0.01\\n\",\n \"Optimal penalty = l2\\n\",\n \"Optimal F1 = 0.952230431393\\n\",\n \"*************************\\n\"\n ]\n }\n ],\n \"source\": [\n \"best_c_lr, penalty_lr = get_best_hypers_lr(X_undersample,y_undersample)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"_cell_guid\": \"aab23f26-2d3b-8ffb-038e-fee04ccdc38e\"\n },\n \"source\": [\n \"## 2.2 Train Model On Undersampled Training Data & Evaluate on Full Test Data\\n\",\n \"\\n\",\n \"With our optimal hyperparameters found, let's set them on a fresh Linear Regression model and train it on the \\\"full\\\" training data (this is still our 50/50 balanced training set).\\n\",\n \"\\n\",\n \"The final step is to use the unseen test data from Section 1.4 to evaluate the model performance. It's important to remember that the test set has a similar class imbalance as the original transaction data we started with in Section 1.2. For evaluation, we're using Receiver Operation Characteristic (ROC) curves and Area Under the Curve (AUC) to strike a balance between model sensitivity and specificity.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"metadata\": {\n \"_cell_guid\": \"5a482730-429a-4cfc-fe2f-2787cf9b26a8\"\n },\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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Yu7pDB79TZmrd7O69NXUbV8Ka45NYF/nG+T3RgTV1auhBNPhHr1YOxY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5 rows × 30 columns
\\n\",\n \"
Search is not available for this dataset
text
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a0736384-ca97-7518-b84b-e8382bb2ea85"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data.csv\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt \n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"from sklearn.linear_model import LogisticRegression # to apply the Logistic regression\n",
"from sklearn.model_selection import train_test_split # to split the data into two parts\n",
"from sklearn.cross_validation import KFold # use for cross validation\n",
"from sklearn.model_selection import GridSearchCV# for tuning parameter\n",
"from sklearn.ensemble import RandomForestClassifier # for random forest classifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import svm # for Support Vector Machine\n",
"from sklearn import metrics \n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler \n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "0bcbb970-634f-b24b-a93b-ee79b1d1a15d"
},
"outputs": [],
"source": [
"df = pd.read_csv('../input/data.csv',header=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "0d397e23-2c04-bd93-709a-eeb5f6cc9e75"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
"0 842302 M 17.99 10.38 122.80 1001.0 \n",
"1 842517 M 20.57 17.77 132.90 1326.0 \n",
"2 84300903 M 19.69 21.25 130.00 1203.0 \n",
"3 84348301 M 11.42 20.38 77.58 386.1 \n",
"4 84358402 M 20.29 14.34 135.10 1297.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"0 0.11840 0.27760 0.3001 0.14710 \n",
"1 0.08474 0.07864 0.0869 0.07017 \n",
"2 0.10960 0.15990 0.1974 0.12790 \n",
"3 0.14250 0.28390 0.2414 0.10520 \n",
"4 0.10030 0.13280 0.1980 0.10430 \n",
"\n",
" ... texture_worst perimeter_worst area_worst smoothness_worst \\\n",
"0 ... 17.33 184.60 2019.0 0.1622 \n",
"1 ... 23.41 158.80 1956.0 0.1238 \n",
"2 ... 25.53 152.50 1709.0 0.1444 \n",
"3 ... 26.50 98.87 567.7 0.2098 \n",
"4 ... 16.67 152.20 1575.0 0.1374 \n",
"\n",
" compactness_worst concavity_worst concave points_worst symmetry_worst \\\n",
"0 0.6656 0.7119 0.2654 0.4601 \n",
"1 0.1866 0.2416 0.1860 0.2750 \n",
"2 0.4245 0.4504 0.2430 0.3613 \n",
"3 0.8663 0.6869 0.2575 0.6638 \n",
"4 0.2050 0.4000 0.1625 0.2364 \n",
"\n",
" fractal_dimension_worst Unnamed: 32 \n",
"0 0.11890 NaN \n",
"1 0.08902 NaN \n",
"2 0.08758 NaN \n",
"3 0.17300 NaN \n",
"4 0.07678 NaN \n",
"\n",
"[5 rows x 33 columns]\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 569 entries, 0 to 568\n",
"Data columns (total 33 columns):\n",
"id 569 non-null int64\n",
"diagnosis 569 non-null object\n",
"radius_mean 569 non-null float64\n",
"texture_mean 569 non-null float64\n",
"perimeter_mean 569 non-null float64\n",
"area_mean 569 non-null float64\n",
"smoothness_mean 569 non-null float64\n",
"compactness_mean 569 non-null float64\n",
"concavity_mean 569 non-null float64\n",
"concave points_mean 569 non-null float64\n",
"symmetry_mean 569 non-null float64\n",
"fractal_dimension_mean 569 non-null float64\n",
"radius_se 569 non-null float64\n",
"texture_se 569 non-null float64\n",
"perimeter_se 569 non-null float64\n",
"area_se 569 non-null float64\n",
"smoothness_se 569 non-null float64\n",
"compactness_se 569 non-null float64\n",
"concavity_se 569 non-null float64\n",
"concave points_se 569 non-null float64\n",
"symmetry_se 569 non-null float64\n",
"fractal_dimension_se 569 non-null float64\n",
"radius_worst 569 non-null float64\n",
"texture_worst 569 non-null float64\n",
"perimeter_worst 569 non-null float64\n",
"area_worst 569 non-null float64\n",
"smoothness_worst 569 non-null float64\n",
"compactness_worst 569 non-null float64\n",
"concavity_worst 569 non-null float64\n",
"concave points_worst 569 non-null float64\n",
"symmetry_worst 569 non-null float64\n",
"fractal_dimension_worst 569 non-null float64\n",
"Unnamed: 32 0 non-null float64\n",
"dtypes: float64(31), int64(1), object(1)\n",
"memory usage: 146.8+ KB\n",
"None\n",
" id radius_mean texture_mean perimeter_mean area_mean \\\n",
"count 5.690000e+02 569.000000 569.000000 569.000000 569.000000 \n",
"mean 3.037183e+07 14.127292 19.289649 91.969033 654.889104 \n",
"std 1.250206e+08 3.524049 4.301036 24.298981 351.914129 \n",
"min 8.670000e+03 6.981000 9.710000 43.790000 143.500000 \n",
"25% 8.692180e+05 11.700000 16.170000 75.170000 420.300000 \n",
"50% 9.060240e+05 13.370000 18.840000 86.240000 551.100000 \n",
"75% 8.813129e+06 15.780000 21.800000 104.100000 782.700000 \n",
"max 9.113205e+08 28.110000 39.280000 188.500000 2501.000000 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.096360 0.104341 0.088799 0.048919 \n",
"std 0.014064 0.052813 0.079720 0.038803 \n",
"min 0.052630 0.019380 0.000000 0.000000 \n",
"25% 0.086370 0.064920 0.029560 0.020310 \n",
"50% 0.095870 0.092630 0.061540 0.033500 \n",
"75% 0.105300 0.130400 0.130700 0.074000 \n",
"max 0.163400 0.345400 0.426800 0.201200 \n",
"\n",
" symmetry_mean ... texture_worst perimeter_worst \\\n",
"count 569.000000 ... 569.000000 569.000000 \n",
"mean 0.181162 ... 25.677223 107.261213 \n",
"std 0.027414 ... 6.146258 33.602542 \n",
"min 0.106000 ... 12.020000 50.410000 \n",
"25% 0.161900 ... 21.080000 84.110000 \n",
"50% 0.179200 ... 25.410000 97.660000 \n",
"75% 0.195700 ... 29.720000 125.400000 \n",
"max 0.304000 ... 49.540000 251.200000 \n",
"\n",
" area_worst smoothness_worst compactness_worst concavity_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 880.583128 0.132369 0.254265 0.272188 \n",
"std 569.356993 0.022832 0.157336 0.208624 \n",
"min 185.200000 0.071170 0.027290 0.000000 \n",
"25% 515.300000 0.116600 0.147200 0.114500 \n",
"50% 686.500000 0.131300 0.211900 0.226700 \n",
"75% 1084.000000 0.146000 0.339100 0.382900 \n",
"max 4254.000000 0.222600 1.058000 1.252000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \\\n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.114606 0.290076 0.083946 \n",
"std 0.065732 0.061867 0.018061 \n",
"min 0.000000 0.156500 0.055040 \n",
"25% 0.064930 0.250400 0.071460 \n",
"50% 0.099930 0.282200 0.080040 \n",
"75% 0.161400 0.317900 0.092080 \n",
"max 0.291000 0.663800 0.207500 \n",
"\n",
" Unnamed: 32 \n",
"count 0.0 \n",
"mean NaN \n",
"std NaN \n",
"min NaN \n",
"25% NaN \n",
"50% NaN \n",
"75% NaN \n",
"max NaN \n",
"\n",
"[8 rows x 32 columns]\n"
]
}
],
"source": [
"print(df.head())\n",
"print(df.info())\n",
"print(df.describe())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "1bd64466-67f9-bf27-c051-18cd20b9f825"
},
"outputs": [],
"source": [
"df.drop(['id','Unnamed: 32'], axis = 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "7ed3b383-4c27-dac4-becd-fad08c398fa9"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
" 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
" 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
" 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
" 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
" 'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
" 'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
" 'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
" 'symmetry_worst', 'fractal_dimension_worst'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "d52cdcf0-e6e7-5e26-a769-920f048ae7e1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']\n",
"['radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se']\n",
"['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']\n"
]
}
],
"source": [
"features_mean = list(df.columns[1:11])\n",
"features_se = list(df.columns[11:21])\n",
"features_worst =list(df.columns[21:31])\n",
"print(features_mean)\n",
"print(features_se)\n",
"print(features_worst)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "67d9594b-3a97-198d-2aeb-24cb1174401a"
},
"outputs": [],
"source": [
"df['diagnosis'] = df['diagnosis'].map({'M':1,'B':0})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "a005bbdd-99af-1230-a4f7-9648a466b926"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>diagnosis</th>\n",
" <th>radius_mean</th>\n",
" <th>texture_mean</th>\n",
" <th>perimeter_mean</th>\n",
" <th>area_mean</th>\n",
" <th>smoothness_mean</th>\n",
" <th>compactness_mean</th>\n",
" <th>concavity_mean</th>\n",
" <th>concave points_mean</th>\n",
" <th>symmetry_mean</th>\n",
" <th>fractal_dimension_mean</th>\n",
" <th>radius_se</th>\n",
" <th>texture_se</th>\n",
" <th>perimeter_se</th>\n",
" <th>area_se</th>\n",
" <th>smoothness_se</th>\n",
" <th>compactness_se</th>\n",
" <th>concavity_se</th>\n",
" <th>concave points_se</th>\n",
" <th>symmetry_se</th>\n",
" <th>fractal_dimension_se</th>\n",
" <th>radius_worst</th>\n",
" <th>texture_worst</th>\n",
" <th>perimeter_worst</th>\n",
" <th>area_worst</th>\n",
" <th>smoothness_worst</th>\n",
" <th>compactness_worst</th>\n",
" <th>concavity_worst</th>\n",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.372583</td>\n",
" <td>14.127292</td>\n",
" <td>19.289649</td>\n",
" <td>91.969033</td>\n",
" <td>654.889104</td>\n",
" <td>0.096360</td>\n",
" <td>0.104341</td>\n",
" <td>0.088799</td>\n",
" <td>0.048919</td>\n",
" <td>0.181162</td>\n",
" <td>0.062798</td>\n",
" <td>0.405172</td>\n",
" <td>1.216853</td>\n",
" <td>2.866059</td>\n",
" <td>40.337079</td>\n",
" <td>0.007041</td>\n",
" <td>0.025478</td>\n",
" <td>0.031894</td>\n",
" <td>0.011796</td>\n",
" <td>0.020542</td>\n",
" <td>0.003795</td>\n",
" <td>16.269190</td>\n",
" <td>25.677223</td>\n",
" <td>107.261213</td>\n",
" <td>880.583128</td>\n",
" <td>0.132369</td>\n",
" <td>0.254265</td>\n",
" <td>0.272188</td>\n",
" <td>0.114606</td>\n",
" <td>0.290076</td>\n",
" <td>0.083946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.483918</td>\n",
" <td>3.524049</td>\n",
" <td>4.301036</td>\n",
" <td>24.298981</td>\n",
" <td>351.914129</td>\n",
" <td>0.014064</td>\n",
" <td>0.052813</td>\n",
" <td>0.079720</td>\n",
" <td>0.038803</td>\n",
" <td>0.027414</td>\n",
" <td>0.007060</td>\n",
" <td>0.277313</td>\n",
" <td>0.551648</td>\n",
" <td>2.021855</td>\n",
" <td>45.491006</td>\n",
" <td>0.003003</td>\n",
" <td>0.017908</td>\n",
" <td>0.030186</td>\n",
" <td>0.006170</td>\n",
" <td>0.008266</td>\n",
" <td>0.002646</td>\n",
" <td>4.833242</td>\n",
" <td>6.146258</td>\n",
" <td>33.602542</td>\n",
" <td>569.356993</td>\n",
" <td>0.022832</td>\n",
" <td>0.157336</td>\n",
" <td>0.208624</td>\n",
" <td>0.065732</td>\n",
" <td>0.061867</td>\n",
" <td>0.018061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>6.981000</td>\n",
" <td>9.710000</td>\n",
" <td>43.790000</td>\n",
" <td>143.500000</td>\n",
" <td>0.052630</td>\n",
" <td>0.019380</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.106000</td>\n",
" <td>0.049960</td>\n",
" <td>0.111500</td>\n",
" <td>0.360200</td>\n",
" <td>0.757000</td>\n",
" <td>6.802000</td>\n",
" <td>0.001713</td>\n",
" <td>0.002252</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.007882</td>\n",
" <td>0.000895</td>\n",
" <td>7.930000</td>\n",
" <td>12.020000</td>\n",
" <td>50.410000</td>\n",
" <td>185.200000</td>\n",
" <td>0.071170</td>\n",
" <td>0.027290</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.156500</td>\n",
" <td>0.055040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>11.700000</td>\n",
" <td>16.170000</td>\n",
" <td>75.170000</td>\n",
" <td>420.300000</td>\n",
" <td>0.086370</td>\n",
" <td>0.064920</td>\n",
" <td>0.029560</td>\n",
" <td>0.020310</td>\n",
" <td>0.161900</td>\n",
" <td>0.057700</td>\n",
" <td>0.232400</td>\n",
" <td>0.833900</td>\n",
" <td>1.606000</td>\n",
" <td>17.850000</td>\n",
" <td>0.005169</td>\n",
" <td>0.013080</td>\n",
" <td>0.015090</td>\n",
" <td>0.007638</td>\n",
" <td>0.015160</td>\n",
" <td>0.002248</td>\n",
" <td>13.010000</td>\n",
" <td>21.080000</td>\n",
" <td>84.110000</td>\n",
" <td>515.300000</td>\n",
" <td>0.116600</td>\n",
" <td>0.147200</td>\n",
" <td>0.114500</td>\n",
" <td>0.064930</td>\n",
" <td>0.250400</td>\n",
" <td>0.071460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>13.370000</td>\n",
" <td>18.840000</td>\n",
" <td>86.240000</td>\n",
" <td>551.100000</td>\n",
" <td>0.095870</td>\n",
" <td>0.092630</td>\n",
" <td>0.061540</td>\n",
" <td>0.033500</td>\n",
" <td>0.179200</td>\n",
" <td>0.061540</td>\n",
" <td>0.324200</td>\n",
" <td>1.108000</td>\n",
" <td>2.287000</td>\n",
" <td>24.530000</td>\n",
" <td>0.006380</td>\n",
" <td>0.020450</td>\n",
" <td>0.025890</td>\n",
" <td>0.010930</td>\n",
" <td>0.018730</td>\n",
" <td>0.003187</td>\n",
" <td>14.970000</td>\n",
" <td>25.410000</td>\n",
" <td>97.660000</td>\n",
" <td>686.500000</td>\n",
" <td>0.131300</td>\n",
" <td>0.211900</td>\n",
" <td>0.226700</td>\n",
" <td>0.099930</td>\n",
" <td>0.282200</td>\n",
" <td>0.080040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" <td>15.780000</td>\n",
" <td>21.800000</td>\n",
" <td>104.100000</td>\n",
" <td>782.700000</td>\n",
" <td>0.105300</td>\n",
" <td>0.130400</td>\n",
" <td>0.130700</td>\n",
" <td>0.074000</td>\n",
" <td>0.195700</td>\n",
" <td>0.066120</td>\n",
" <td>0.478900</td>\n",
" <td>1.474000</td>\n",
" <td>3.357000</td>\n",
" <td>45.190000</td>\n",
" <td>0.008146</td>\n",
" <td>0.032450</td>\n",
" <td>0.042050</td>\n",
" <td>0.014710</td>\n",
" <td>0.023480</td>\n",
" <td>0.004558</td>\n",
" <td>18.790000</td>\n",
" <td>29.720000</td>\n",
" <td>125.400000</td>\n",
" <td>1084.000000</td>\n",
" <td>0.146000</td>\n",
" <td>0.339100</td>\n",
" <td>0.382900</td>\n",
" <td>0.161400</td>\n",
" <td>0.317900</td>\n",
" <td>0.092080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>28.110000</td>\n",
" <td>39.280000</td>\n",
" <td>188.500000</td>\n",
" <td>2501.000000</td>\n",
" <td>0.163400</td>\n",
" <td>0.345400</td>\n",
" <td>0.426800</td>\n",
" <td>0.201200</td>\n",
" <td>0.304000</td>\n",
" <td>0.097440</td>\n",
" <td>2.873000</td>\n",
" <td>4.885000</td>\n",
" <td>21.980000</td>\n",
" <td>542.200000</td>\n",
" <td>0.031130</td>\n",
" <td>0.135400</td>\n",
" <td>0.396000</td>\n",
" <td>0.052790</td>\n",
" <td>0.078950</td>\n",
" <td>0.029840</td>\n",
" <td>36.040000</td>\n",
" <td>49.540000</td>\n",
" <td>251.200000</td>\n",
" <td>4254.000000</td>\n",
" <td>0.222600</td>\n",
" <td>1.058000</td>\n",
" <td>1.252000</td>\n",
" <td>0.291000</td>\n",
" <td>0.663800</td>\n",
" <td>0.207500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.372583 14.127292 19.289649 91.969033 654.889104 \n",
"std 0.483918 3.524049 4.301036 24.298981 351.914129 \n",
"min 0.000000 6.981000 9.710000 43.790000 143.500000 \n",
"25% 0.000000 11.700000 16.170000 75.170000 420.300000 \n",
"50% 0.000000 13.370000 18.840000 86.240000 551.100000 \n",
"75% 1.000000 15.780000 21.800000 104.100000 782.700000 \n",
"max 1.000000 28.110000 39.280000 188.500000 2501.000000 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.096360 0.104341 0.088799 0.048919 \n",
"std 0.014064 0.052813 0.079720 0.038803 \n",
"min 0.052630 0.019380 0.000000 0.000000 \n",
"25% 0.086370 0.064920 0.029560 0.020310 \n",
"50% 0.095870 0.092630 0.061540 0.033500 \n",
"75% 0.105300 0.130400 0.130700 0.074000 \n",
"max 0.163400 0.345400 0.426800 0.201200 \n",
"\n",
" symmetry_mean fractal_dimension_mean radius_se texture_se \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.181162 0.062798 0.405172 1.216853 \n",
"std 0.027414 0.007060 0.277313 0.551648 \n",
"min 0.106000 0.049960 0.111500 0.360200 \n",
"25% 0.161900 0.057700 0.232400 0.833900 \n",
"50% 0.179200 0.061540 0.324200 1.108000 \n",
"75% 0.195700 0.066120 0.478900 1.474000 \n",
"max 0.304000 0.097440 2.873000 4.885000 \n",
"\n",
" perimeter_se area_se smoothness_se compactness_se concavity_se \\\n",
"count 569.000000 569.000000 569.000000 569.000000 569.000000 \n",
"mean 2.866059 40.337079 0.007041 0.025478 0.031894 \n",
"std 2.021855 45.491006 0.003003 0.017908 0.030186 \n",
"min 0.757000 6.802000 0.001713 0.002252 0.000000 \n",
"25% 1.606000 17.850000 0.005169 0.013080 0.015090 \n",
"50% 2.287000 24.530000 0.006380 0.020450 0.025890 \n",
"75% 3.357000 45.190000 0.008146 0.032450 0.042050 \n",
"max 21.980000 542.200000 0.031130 0.135400 0.396000 \n",
"\n",
" concave points_se symmetry_se fractal_dimension_se radius_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.011796 0.020542 0.003795 16.269190 \n",
"std 0.006170 0.008266 0.002646 4.833242 \n",
"min 0.000000 0.007882 0.000895 7.930000 \n",
"25% 0.007638 0.015160 0.002248 13.010000 \n",
"50% 0.010930 0.018730 0.003187 14.970000 \n",
"75% 0.014710 0.023480 0.004558 18.790000 \n",
"max 0.052790 0.078950 0.029840 36.040000 \n",
"\n",
" texture_worst perimeter_worst area_worst smoothness_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 25.677223 107.261213 880.583128 0.132369 \n",
"std 6.146258 33.602542 569.356993 0.022832 \n",
"min 12.020000 50.410000 185.200000 0.071170 \n",
"25% 21.080000 84.110000 515.300000 0.116600 \n",
"50% 25.410000 97.660000 686.500000 0.131300 \n",
"75% 29.720000 125.400000 1084.000000 0.146000 \n",
"max 49.540000 251.200000 4254.000000 0.222600 \n",
"\n",
" compactness_worst concavity_worst concave points_worst \\\n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.254265 0.272188 0.114606 \n",
"std 0.157336 0.208624 0.065732 \n",
"min 0.027290 0.000000 0.000000 \n",
"25% 0.147200 0.114500 0.064930 \n",
"50% 0.211900 0.226700 0.099930 \n",
"75% 0.339100 0.382900 0.161400 \n",
"max 1.058000 1.252000 0.291000 \n",
"\n",
" symmetry_worst fractal_dimension_worst \n",
"count 569.000000 569.000000 \n",
"mean 0.290076 0.083946 \n",
"std 0.061867 0.018061 \n",
"min 0.156500 0.055040 \n",
"25% 0.250400 0.071460 \n",
"50% 0.282200 0.080040 \n",
"75% 0.317900 0.092080 \n",
"max 0.663800 0.207500 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.options.display.max_columns=80\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "d5b7c0f7-59bd-b4db-2c08-7b1e76d6a314"
},
"outputs": [
{
"data": {
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Bsu03rXYeGJjac6JP+LXrF5/oKaRKrL7sc3WXoC40UUYGI53wm70kqX0TJQy2U4wEGn4f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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feaa5d49a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(df['diagnosis']);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f8e7a032-31d2-3464-2c34-1e31b3bc34e1"
},
"source": [
"##相關性"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "6338d0f0-5ad9-4f5f-e6b9-f7813b4bfc13"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7feaa5cdbdd8>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Es8pkp6aRnZZByHXXkHDO+7jKrgOHyMrOpk71KnbT61Svwva9BxzKB/r70avrrf96/XOW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EIIEV4yFRVCCCGEEEKIEJDIlhBCCCGEEKJBNFnevl4S2RJCCCGEEEKIEJDJlhBCCCGEEEKEgKQR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dyhUYN7AXoy47jRH9T2VE/1N54DRnGPsR/U+le4uGdE6vD2F4aep8MnPzyc4LMGz6AsJh6Na8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\nD1VNFP2Wdv/dM1PSTgCSdmu6js6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"text/plain": [
"<matplotlib.figure.Figure at 0x7feaa5ccd470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corr = df[features_mean].corr()\n",
"plt.figure(figsize=(14,14))\n",
"sns.heatmap(corr, cbar = True, square = True, annot=True,fmt= '.2g',annot_kws={'size': 15},\n",
" xticklabels= features_mean, yticklabels= features_mean,\n",
" cmap= 'coolwarm') # for more on heatmap you can visit Link(http://seaborn.pydata.org/generated/seaborn.heatmap.html)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "dd137fe9-a0cb-4e04-89a7-e2c8106c1766"
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fea9be04b00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fea9be04cc0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Sm3cVmZnZFqLa8P85cFAa3gcIYDmwr6Sxkraj6O+/e/AlmplZrVXs9pG0DzCPop9/k6RZ\nwNHAtySdCLwAnBARGySdRfFNoBO4ICLW1q1yMzOrWn9O+D5Icelmd0f1MO9CYOHgyzIzs3ryHb5m\nZhly+JuZZcjhb2aWIYe/mVmGHP5mZhly+JuZZcjhb2aWIYe/mVmGHP5mZhly+JuZZcjhb2aWIYe/\nmVmGHP5mZhly+JuZZcjhb2aWIYe/mVmGHP5mZhly+JuZZajiaxwBJO0J3AxcHBGXlsZ/ELg9IprS\n52OAOcArwBURsaD2JZuZ2WBVPPKXNBqYDyzuNn4kcDawujTfucBMinf+niFpXI3rNTOzGuhPt89G\n4GBgVbfxXwIuA/6cPk8FHoiItRGxAbgH2L9WhZqZWe1UDP+I6Ehh/ipJuwJ7R8QNpdETgLbS5zXA\nxJpUaWZmNdWvPv8eXAx8rsI8TZVW0tIyiubmEVWWYH1pbR3T6BLMBsTb7NAacPhLmgRMAX4gCWCi\npLuA8yiO/rtMAu7ra13t7esH2rz1Q2vrGNra1jW6DLMB8TZbH73tVAcc/hHxR+CtXZ8l/SEiDpC0\nLXClpLFAB0V//5zqyjUzs3qqGP6S9gHmAZOBTZJmAUdExPPl+SJig6SzgEVAJ3BBRKytfclmZjZY\nFcM/Ih6kuHSzt+mTS8MLgYW1KMzMzOrHd/iamWXI4W9mliGHv5lZhhz+ZmYZcvibmWXI4W9mliGH\nv5lZhhz+ZmYZaurs7GxY421t6xrX+FbMz/axRvrsJct48aWOurczemQz8+dMr3s7w11r65geH7JZ\n7VM9zcx69OJLHVx11oEDWqaaA5bZc5cMaH7bnLt9zMwy5PA3M8uQw9/MLEMOfzOzDDn8zcwy5PA3\nM8uQw9/MLEMOfzOzDPXrJi9JewI3AxdHxKWSdgK+D7wO2AQcGxHPSDqG4qXtrwBXRMSCOtVtZmaD\nUPHIX9JoYD6wuDT6qxThfgBwE/CFNN+5wEyKd/6eIWlczSs2M7NB60+3z0bgYGBVadxpwI1puA0Y\nD0wFHoiItRGxAbgH2L+GtZqZWY1U7PaJiA6gQ1J53IsAkkYApwMXAhModgRd1gATa1msmZnVRtUP\ndkvBfx2wJCIWSzq62yw9PkmurKVlFM3NI6otwfrQ2jqm0SVYxqrZ/oZqGSsM5qme3wcej4gL0udV\nFEf/XSYB9/W1gvb29YNo3nrjRzpbow10+6t2m/V2XllvO8iqwj9d1fPniDivNHo5cKWksUAHRX//\nnGrWb2Zm9VUx/CXtA8wDJgObJM0CdgBekrQ0zfZIRJwm6SxgEdAJXBARa+tStZmZDUp/Tvg+SHHp\nZkURsRBYOMiazMysznyHr5lZhhz+ZmYZcvibmWXIL3A3s5o68albeOykawe0zGPVtPP6scDAXhRv\nr3H4m1lNLdj5UK46a2ChXM11/nPnLvHzYwbB3T5mZhly+JuZZcjhb2aWIYe/mVmGHP5mZhny1T7D\n2PTpU1mx4tEBLTNlym4sW7a8ThWZ2XDh8B/Gegvx2XOXDPhSOzPLi7t9zMwy5PA3M8uQw9/MLEMO\nfzOzDDn8zcwy5PA3M8tQvy71lLQncDNwcURcKmkn4DpgBLAaOC4iNqYXu88BXgGuiIgFdarbzMwG\noeKRv6TRwHxgcWn0hcBlETENWAnMTvOdC8ykeOfvGZLG1bxiMzMbtP50+2wEDgZWlcbNAG5Jw7dS\nBP5U4IGIWBsRG4B7wI/bNjPbElXs9omIDqBDUnn06IjYmIbXABOBCUBbaZ6u8WZmtoWpxeMdmgY4\n/lUtLaNobh5RgxKsu9bWMY0uwTJWzfY3VMtYodrwf0HStql7ZxJFl9AqiqP/LpOA+/paSXv7+iqb\nt0oG+ko8s1oa6PZXzWscq2knR73tIKsN/zuAI4Hr0/9vB5YDV0oaC3RQ9PfPqXL9ZjaMzZ67pO5t\njB7p51IORsXfnqR9gHnAZGCTpFnAMcDVkk4BngSuiYhNks4CFgGdwAURsbZulZvZFqmaJ8r6SbRD\nrz8nfB+kuLqnuw/0MO9CYOHgyzIzs3ryHb5mZhly+JuZZcjhb2aWIYe/mVmGHP5mZhly+JuZZcjh\nb2aWIYe/mVmGHP5mZhly+JuZZcjhb2aWIYe/mVmGHP5mZhly+JuZZchvQxgGPnvJMl58qWNAywz0\nZRqjRzYzf870AS1jZsOXw38YePGljgG96KKaV+INxZuXzGzL4W4fM7MMOfzNzDJUVbePpO2Aa4EW\n4A3ABcAjwHXACGA1cFxEbKxRnWZmVkPVHvl/EoiIeB8wC/gWcCFwWURMA1YCs2tSoZmZ1Vy14f8c\nMD4Nt6TPM4Bb0rhbgZmDqszMzOqmqvCPiH8Gdpa0ElgGfBEYXermWQNMrE2JZmZWa9X2+R8LPBUR\nB0naG1jQbZam/qynpWUUzc0jqikhO62tY+o6f7XLmNWKt7+hVe11/vsDiwAi4jeS3gy8KGnbiNgA\nTAJWVVpJe/v6KpvPz0Cu26/mOv+BtmFWa97+6qO3nWq14b8SmArcKOktwAvAUuBI4Pr0/9urXLd1\nc+JTt/DYSdf2e/7Hqmnj9WOB/t9IZmbDW7XhfzlwlaS70jpOBR4FrpV0CvAkcE1tSrQFOx9a9zt8\n585dwv4DLczMhq2qwj8iXgA+2sOkDwyuHDMzGwq+w9fMLEMOfzOzDDn8zcwy5PA3M8uQw9/MLEMO\nfzOzDPlNXmY2ZKZPn8qKFY/2OG2Hi3peZsqU3Vi2bHkdq8qTw3+YqPdrFkeP9KZg9ddbiFf7SBKr\nXlNnZ2fDGm9rW9e4xrdis+cuGdAdwWaN5vCvn9bWMT0+aNN9/mZmGXL4m5llyOFvZpYhh7+ZWYYc\n/mZmGXL4m5llyOFvZpYhh7+ZWYYc/mZmGar6nn5JxwBnAh3AucC/AdcBI4DVwHERsbEWRZqZWW1V\ndeQvaTxwHvBe4BDgMOBC4LKImAasBGbXqkgzM6utart9ZgJ3RMS6iFgdEZ8CZgC3pOm3pnnMzGwL\nVG23z2RglKRbgBbgfGB0qZtnDTBx0NWZmVldVBv+TcB44HDgLcCdaVx5ekUtLaNobh5RZQnWl9bW\nMY0uwWxAvM0OrWrD/1ng3ojoAJ6QtA7okLRtRGwAJgGrKq2kvX19lc1bJX48rg0nfqRz/fS2U622\nz/8XwIGStkknf7cD7gCOTNOPBG6vct1mZlZnVYV/RPwRWAjcB/wc+CzF1T8nSLobGAdcU6sizcys\ntqq+zj8iLgcu7zb6A4Mrx8zMhoLv8DUzy5DD38wsQw5/M7MMOfzNzDLk8Dczy5DD38wsQw5/M7MM\nOfzNzDLk8Dczy5DD38wsQw5/M7MMVf1sH2u86dOnsmLFoz1O2+GinpeZMmU3li1bXseqzGw4cPgP\nY72FuJ+NbmaVuNvHzCxDDn8zsww5/M3MMuTwNzPLkMPfzCxDDn8zswwN6lJPSdsCvwO+AiwGrgNG\nAKuB4yJi46ArNDOzmhvskf85wPNp+ELgsoiYBqwEZg9y3WZmVidVh7+kKcDuwG1p1AzgljR8KzBz\nUJWZmVndDKbbZx7wGeCE9Hl0qZtnDTCx0gpaWkbR3DxiECVYb1pbxzS6BLMB8TY7tKoKf0nHA7+K\niP+Q1NMsTf1ZT3v7+mqatwr8eAcbbrzN1k9vO9Vqj/w/BOwi6RBgR2Aj8IKkbSNiAzAJWFXlus3M\nrM6qCv+I+FjXsKTzgT8A+wFHAten/98++PLMzKweanmd/3nACZLuBsYB19Rw3WZmVkNNnZ2dja7B\nzMyGmO/wNTPLkMPfzCxDDn8zsww5/M3MMuTwNzPLkMPfzCxDDn8zsww5/LcCkpolvaXRdZjZ8OGb\nvIY5SROAi4DRwHjgG8AdEfFiQwsz64WkNwJTgV9HxHNpXFNEOIyGkI/8h7+zgIcj4jDgO8CngW+n\n9y2YbYnOBf4eOFbSdElv6Ap+Sf16IrANnsN/GEuv0RwDPAsQET+geOLq48AiSZ9oYHlmvfkZ0Am8\nCTgUmC3pnWnayPTNwOrM4T+MpcdnXwnsJ2mmpO0j4uWI+CpwLOCjf9sSPQJcEhEXAPcDk4GjJH0Y\nuBk4pIG1ZcN9/sOcpG2AWcCBwMPpv98A+wIXRsSMxlVn1jNJ20TEK2n4rRTb67HAdt5mh4bDfysh\naRJFf/8bgL2BPwMXRcSShhZm1k+Sfgt8ISJ+2ehacuDw38pIagVGASMjIhpdj1l/SNoJODQiLmt0\nLblw+JvZFqHcFWT15/A3M8uQr/YxM8uQw9/MLEMOfzOzDDU3ugCzepN0PfA7YJ+IOKqBdXwSGBER\nCxpVg1kXh7/l4plGBj9ARFzdyPbNyhz+ttVJdz0vAPYCnqR44imSno6IHdND7y4HOoA3AudExCJJ\n44EfpvkfB3YGvpbmOwt4GtgD2AQcFBHrJc0GTgXWUzxj6eQ0fCUgimfY/DoiTpd0PsW/ufN7ml7P\n34lZd+7zt63RTIrnGu0LHEdxx3PZBODLEfF+4HPAP6TxZwC/i4j9gW8C7y0t8x7gSxHxHuBl4IOS\ndgYuAN6fHknwn2kdewFTI+I9EbEf8LCkN5XWVWm6Wd05/G1rtBdwb0R0RsR6YHm36auBL0q6G7gE\n2D6NfwewFCAifgeU75B+NCLWpOEngXHAXwEPRsS6NH4pxQ7nUeA5ST+T9GngxxGxtryuCtPN6s7h\nb1ujJqB8p+iIbtMvBX4SEdOAE0vjt+m23Mul4Y4e2uh+h2QT0BkRL6V1nwO0Ag9Imtg1U6XpZkPB\nff62NXoEOCy9GGQ7irdG3Via/hfA79PwxygehgewAtgP+Kmk3an8SOwHgUsljUlH/zOB+yS9C9gj\nIq4BHpK0F7Br10J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"text/plain": [
"<matplotlib.figure.Figure at 0x7fea9be04ac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fea9c2ee160>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7feaa5c7cba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fea9bd898d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fea9c19de10>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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kOcBJwHW5+GxJKyLi88COwPURUVnVZ4CLgdkRsR5YB3yupXtjZmYt09QYhKSZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7feaa5c7c048>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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EkW5CRL/kmB1e7QSIZdSuGIAdgeXdC6W76SbgVNsLW8rOAE7qXrB9W23dAuBLvVW8YsWq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\nIiIiGv1/PX+OuwQRAvYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fea9c137f60>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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KYkmqnEEgSZUzCCSpcgaBJFXOIJCkyhkEklQ5g0CSKmcQSFLlDAJJqpxBIEmVMwgkqXIG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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fea9c0b3f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for i in features_mean:\n",
" df.boxplot(i, 'diagnosis', rot=60);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "dd92f4a9-dd68-e3bb-5c0c-7ebd7fa029aa"
},
"outputs": [],
"source": [
"pred_vars=['radius_mean',\n",
" 'texture_mean',\n",
" 'smoothness_mean',\n",
" 'compactness_mean',\n",
" 'concave points_mean',\n",
" 'symmetry_mean',\n",
" 'fractal_dimension_mean']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "52608a57-98e7-23c9-e851-4488cf52d928"
},
"outputs": [],
"source": [
"X = df[pred_vars]\n",
"y = df['diagnosis']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "5bec3626-8792-1045-e91f-0670fc18df32"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(455, 7) (114, 7) (455,) (114,)\n"
]
}
],
"source": [
"print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "23de4682-cd45-572f-e6bc-3eb3cb6e3a8e"
},
"outputs": [],
"source": [
"# Setup the pipeline steps: steps\n",
"steps = [('scaler', StandardScaler()),\n",
" ('knn', KNeighborsClassifier())]\n",
"\n",
"# Create the pipeline: pipeline\n",
"pipeline = Pipeline(steps)\n"
]
}
],
"metadata": {
"_change_revision": 726,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159269.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e51f945d-4ed6-de73-1eac-d4e6e7cdc267"
},
"source": [
"Explore **Google** stocks"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f6f8ff1a-7b6f-a825-6fa4-8b33ff2b5745"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Google.csv\n",
"\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from pandas.tools.plotting import scatter_matrix\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "b26352e8-d6f8-98c4-c9f6-72823eff01fb"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Ex-Dividend</th>\n",
" <th>Split Ratio</th>\n",
" <th>Adj. Open</th>\n",
" <th>Adj. High</th>\n",
" <th>Adj. Low</th>\n",
" <th>Adj. Close</th>\n",
" <th>Adj. Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2004-08-19</td>\n",
" <td>100.01</td>\n",
" <td>104.06</td>\n",
" <td>95.96</td>\n",
" <td>100.335</td>\n",
" <td>44659000.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>50.159839</td>\n",
" <td>52.191109</td>\n",
" <td>48.128568</td>\n",
" <td>50.322842</td>\n",
" <td>44659000.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2004-08-20</td>\n",
" <td>101.01</td>\n",
" <td>109.08</td>\n",
" <td>100.50</td>\n",
" <td>108.310</td>\n",
" <td>22834300.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>50.661387</td>\n",
" <td>54.708881</td>\n",
" <td>50.405597</td>\n",
" <td>54.322689</td>\n",
" <td>22834300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2004-08-23</td>\n",
" <td>110.76</td>\n",
" <td>113.48</td>\n",
" <td>109.05</td>\n",
" <td>109.400</td>\n",
" <td>18256100.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>55.551482</td>\n",
" <td>56.915693</td>\n",
" <td>54.693835</td>\n",
" <td>54.869377</td>\n",
" <td>18256100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2004-08-24</td>\n",
" <td>111.24</td>\n",
" <td>111.60</td>\n",
" <td>103.57</td>\n",
" <td>104.870</td>\n",
" <td>15247300.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>55.792225</td>\n",
" <td>55.972783</td>\n",
" <td>51.945350</td>\n",
" <td>52.597363</td>\n",
" <td>15247300.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2004-08-25</td>\n",
" <td>104.76</td>\n",
" <td>108.00</td>\n",
" <td>103.88</td>\n",
" <td>106.000</td>\n",
" <td>9188600.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>52.542193</td>\n",
" <td>54.167209</td>\n",
" <td>52.100830</td>\n",
" <td>53.164113</td>\n",
" <td>9188600.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Ex-Dividend \\\n",
"0 2004-08-19 100.01 104.06 95.96 100.335 44659000.0 0.0 \n",
"1 2004-08-20 101.01 109.08 100.50 108.310 22834300.0 0.0 \n",
"2 2004-08-23 110.76 113.48 109.05 109.400 18256100.0 0.0 \n",
"3 2004-08-24 111.24 111.60 103.57 104.870 15247300.0 0.0 \n",
"4 2004-08-25 104.76 108.00 103.88 106.000 9188600.0 0.0 \n",
"\n",
" Split Ratio Adj. Open Adj. High Adj. Low Adj. Close Adj. Volume \n",
"0 1.0 50.159839 52.191109 48.128568 50.322842 44659000.0 \n",
"1 1.0 50.661387 54.708881 50.405597 54.322689 22834300.0 \n",
"2 1.0 55.551482 56.915693 54.693835 54.869377 18256100.0 \n",
"3 1.0 55.792225 55.972783 51.945350 52.597363 15247300.0 \n",
"4 1.0 52.542193 54.167209 52.100830 53.164113 9188600.0 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"../input/Google.csv\",sep=\",\",parse_dates=['Date'])\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "891848ce-e4af-ea6b-8eed-199b5bbe1bcb"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Ex-Dividend</th>\n",
" <th>Split Ratio</th>\n",
" <th>Adj. Open</th>\n",
" <th>Adj. High</th>\n",
" <th>Adj. Low</th>\n",
" <th>Adj. Close</th>\n",
" <th>Adj. Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3125.000000</td>\n",
" <td>3125.000000</td>\n",
" <td>3125.000000</td>\n",
" <td>3125.000000</td>\n",
" <td>3.125000e+03</td>\n",
" <td>3125.000000</td>\n",
" <td>3125.0</td>\n",
" <td>3125.000000</td>\n",
" <td>3125.000000</td>\n",
" <td>3125.000000</td>\n",
" <td>3125.000000</td>\n",
" <td>3.125000e+03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>560.171567</td>\n",
" <td>565.480031</td>\n",
" <td>554.309518</td>\n",
" <td>559.907342</td>\n",
" <td>8.404691e+06</td>\n",
" <td>0.181751</td>\n",
" <td>1.0</td>\n",
" <td>354.861790</td>\n",
" <td>358.112859</td>\n",
" <td>351.231911</td>\n",
" <td>354.691981</td>\n",
" <td>8.404691e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>200.944961</td>\n",
" <td>201.563829</td>\n",
" <td>199.979903</td>\n",
" <td>200.813213</td>\n",
" <td>8.399679e+06</td>\n",
" <td>10.160186</td>\n",
" <td>0.0</td>\n",
" <td>195.252794</td>\n",
" <td>196.367956</td>\n",
" <td>193.820339</td>\n",
" <td>195.144724</td>\n",
" <td>8.399679e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>99.090000</td>\n",
" <td>101.740000</td>\n",
" <td>95.960000</td>\n",
" <td>100.010000</td>\n",
" <td>5.211410e+05</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>49.698414</td>\n",
" <td>51.027517</td>\n",
" <td>48.128568</td>\n",
" <td>50.159839</td>\n",
" <td>5.211410e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>445.250000</td>\n",
" <td>450.720000</td>\n",
" <td>438.560500</td>\n",
" <td>444.080000</td>\n",
" <td>3.209500e+06</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>223.314350</td>\n",
" <td>226.057819</td>\n",
" <td>219.959243</td>\n",
" <td>222.727539</td>\n",
" <td>3.209500e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>548.490000</td>\n",
" <td>553.520000</td>\n",
" <td>543.670000</td>\n",
" <td>548.650000</td>\n",
" <td>5.620700e+06</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>288.992091</td>\n",
" <td>291.399522</td>\n",
" <td>286.760201</td>\n",
" <td>289.578902</td>\n",
" <td>5.620700e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>667.490000</td>\n",
" <td>673.027500</td>\n",
" <td>659.240000</td>\n",
" <td>667.120000</td>\n",
" <td>1.107890e+07</td>\n",
" <td>0.000000</td>\n",
" <td>1.0</td>\n",
" <td>517.492450</td>\n",
" <td>520.810000</td>\n",
" <td>513.108918</td>\n",
" <td>517.372078</td>\n",
" <td>1.107890e+07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1226.800000</td>\n",
" <td>1228.880000</td>\n",
" <td>1218.600000</td>\n",
" <td>1220.170000</td>\n",
" <td>8.215110e+07</td>\n",
" <td>567.971668</td>\n",
" <td>1.0</td>\n",
" <td>838.500000</td>\n",
" <td>839.000000</td>\n",
" <td>829.520000</td>\n",
" <td>835.740000</td>\n",
" <td>8.215110e+07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Open High Low Close Volume \\\n",
"count 3125.000000 3125.000000 3125.000000 3125.000000 3.125000e+03 \n",
"mean 560.171567 565.480031 554.309518 559.907342 8.404691e+06 \n",
"std 200.944961 201.563829 199.979903 200.813213 8.399679e+06 \n",
"min 99.090000 101.740000 95.960000 100.010000 5.211410e+05 \n",
"25% 445.250000 450.720000 438.560500 444.080000 3.209500e+06 \n",
"50% 548.490000 553.520000 543.670000 548.650000 5.620700e+06 \n",
"75% 667.490000 673.027500 659.240000 667.120000 1.107890e+07 \n",
"max 1226.800000 1228.880000 1218.600000 1220.170000 8.215110e+07 \n",
"\n",
" Ex-Dividend Split Ratio Adj. Open Adj. High Adj. Low \\\n",
"count 3125.000000 3125.0 3125.000000 3125.000000 3125.000000 \n",
"mean 0.181751 1.0 354.861790 358.112859 351.231911 \n",
"std 10.160186 0.0 195.252794 196.367956 193.820339 \n",
"min 0.000000 1.0 49.698414 51.027517 48.128568 \n",
"25% 0.000000 1.0 223.314350 226.057819 219.959243 \n",
"50% 0.000000 1.0 288.992091 291.399522 286.760201 \n",
"75% 0.000000 1.0 517.492450 520.810000 513.108918 \n",
"max 567.971668 1.0 838.500000 839.000000 829.520000 \n",
"\n",
" Adj. Close Adj. Volume \n",
"count 3125.000000 3.125000e+03 \n",
"mean 354.691981 8.404691e+06 \n",
"std 195.144724 8.399679e+06 \n",
"min 50.159839 5.211410e+05 \n",
"25% 222.727539 3.209500e+06 \n",
"50% 289.578902 5.620700e+06 \n",
"75% 517.372078 1.107890e+07 \n",
"max 835.740000 8.215110e+07 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "9eb5b0bd-fac1-d755-2ab2-5f37758ee484"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Ex-Dividend</th>\n",
" <th>Split Ratio</th>\n",
" <th>Adj. Open</th>\n",
" <th>Adj. High</th>\n",
" <th>Adj. Low</th>\n",
" <th>Adj. Close</th>\n",
" <th>Adj. Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2396</th>\n",
" <td>2014-02-26</td>\n",
" <td>1224.0</td>\n",
" <td>1228.88</td>\n",
" <td>1213.76</td>\n",
" <td>1220.17</td>\n",
" <td>3960400.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>613.895035</td>\n",
" <td>616.342591</td>\n",
" <td>608.759181</td>\n",
" <td>611.974105</td>\n",
" <td>3960400.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Ex-Dividend \\\n",
"2396 2014-02-26 1224.0 1228.88 1213.76 1220.17 3960400.0 0.0 \n",
"\n",
" Split Ratio Adj. Open Adj. High Adj. Low Adj. Close Adj. Volume \n",
"2396 1.0 613.895035 616.342591 608.759181 611.974105 3960400.0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.loc[data['High']==1228.880000]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "531f7957-bf5c-4268-e58d-98aaf189b812"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Ex-Dividend</th>\n",
" <th>Split Ratio</th>\n",
" <th>Adj. Open</th>\n",
" <th>Adj. High</th>\n",
" <th>Adj. Low</th>\n",
" <th>Adj. Close</th>\n",
" <th>Adj. Volume</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>2004-09-03</td>\n",
" <td>100.95</td>\n",
" <td>101.74</td>\n",
" <td>99.32</td>\n",
" <td>100.01</td>\n",
" <td>5152400.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>50.631294</td>\n",
" <td>51.027517</td>\n",
" <td>49.81377</td>\n",
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" <td>5152400.0</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Ex-Dividend \\\n",
"11 2004-09-03 100.95 101.74 99.32 100.01 5152400.0 0.0 \n",
"\n",
" Split Ratio Adj. Open Adj. High Adj. Low Adj. Close Adj. Volume \n",
"11 1.0 50.631294 51.027517 49.81377 50.159839 5152400.0 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.loc[data['High']==101.740000]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "2a9d35fc-29ba-5adf-e974-051365214fab"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fdcda933f60>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcda39bbe0>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcda3749b0>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdcda2de5f8>,\n",
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" <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcda2b64a8>],\n",
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" [<matplotlib.axes._subplots.AxesSubplot object at 0x7fdcda0972e8>,\n",
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" <matplotlib.axes._subplots.AxesSubplot object at 0x7fdcda04eda0>]], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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tz9+P02Oi3VW04P2F68o/LHf/gZqpCMBcedub1RtW3qpj1bGrdbWsnq7jyMhIBg8ejJ+f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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdcda93a978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data.hist(figsize=(12,10))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "6869c0ab-6e70-c13a-edb6-18bfe50b2448"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fdcd9f359e8>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Tp6tBgwayWq16/vnndfvtt+vrr79WeHi4ysrKNHbsWDfPDgAAwD3ckdTm5+crKCjI+b5J\nkybKy8uTn5+fJCk5OVndunVTy5Ytq30tj2tqJcnf319///vfL9n+yzNq7777bq1atarcvqCgIL33\n3nuXnDNz5kzXFAkAAOBFPOFGsV8vIS0oKFBycrKWLVum48ePV3ts988OAAAAPsnf31/5+fnO9ydO\nnFCzZs0kSV9//bVOnTqlyMhIjR07Vjt27NCsWbOqfC2PTGoBAABQswyLtdav2bNnTyUkJCg8PFw7\nduyQv7+/c+lBaGioQkNDJUlHjhzRX//6V02ZMqXK16KpBQAAgEt06dJFQUFBCg8Pl2EYmj59upKT\nk9WoUSM98MADNXotmloAAAAzcENSK8n5ibC/CAwMvOSY2267rVrPqJVoagEAAMzBA24UcyXfnh0A\nAABMgaQWAADABAyre5Yf1BaSWgAAAHg9kloAAAAzcNONYrWFphYAAMAMfLypZfkBAAAAvB5JLQAA\ngAkYPNILAAAA8Gwktb9RvDLZ3SVUitViuLuESrtYWubuEirN6mX/uq0fOcjdJVSa/afd7i7B5y0a\nt9DdJfi8MrvD3SVUmjf+HGnmhX/HafhBd1fAmloAAADA05HUAgAAmIGPJ7U0tQAAACbAjWIAAACA\nhyOpBQAAMAMfX35AUgsAAACvR1ILAABgBj6e1NLUAgAAmIBh9e2mluUHAAAA8HoktQAAAGbAI70A\nAAAAz0ZSCwAAYAbcKAYAAABvZ/h4U1uryw+GDRum7Ozsctvmz5+vf/7zn5ccm56erpiYmNoqDQAA\nAF6sVpvaAQMG6KOPPiq37eOPP9bDDz9cm2UAAACYj8XimpeHqNXlB/3791dERIQmT54sScrOzpa/\nv78KCgo0ceJEWSwWNWzYUHPmzCl3XkhIiNLT0yVJMTExioyMVEZGhk6fPq1Dhw7pyJEjGj9+vNas\nWaPc3FwtWbJEAQEBio+P19atW1VWVqYRI0ZowIABtTldAAAA1JJaba9vvvlmBQQEKCsrS5L00Ucf\nKSwsTDNnztRzzz2nxMREde3aVStWrKjQeGfOnNHSpUsVGhqqDz74wPn1Z599pq1btyo3N1crV67U\nihUrtHjxYhUXF7tyegAAAB7LsFhd8vIUtZ4ZDxgwQOvXr5ckff755+rXr5/27dunjh07Svo5ld25\nc2eFxvrDH/4gSWrWrJnatWsnSWratKmKioq0bds2ZWZmymazKTo6Wna7XXl5eS6YEQAAgBewWF3z\n8hC1/vSDBx54QH//+9/18MMPq1WrVmrcuHG5/SUlJbJcZX1GSUmJ8+s6depc9muHw6F69epp8ODB\nevLJJ2uwegAAAHiiWk9q/fz89Pvf/17/+Mc/FBYWJkm688479e2330qStmzZouDg4HLnGIah8+fP\n6/z589q1a1eFrtOhQwdt3LhRdrtdFy5cUFxcXM1OBAAAwJtwo1jNCwsL03PPPadXXnlFkvT888/r\nb3/7mwzDUOPGjTV79mzt2LHDeXxERISGDh2qNm3aKCgoqELX6NKli0JCQjRs2DA5HA4NHz7cJXMB\nAACA+xkOh8Ph7iI8yWP/u83dJVSK1WK4u4RKu1ha5u4SKs3qQf8SrYj6kYPcXUKlLfhpt7tLqJJz\nRSXXPshDjFlXsfsVUHVldu/7keqNP0eaRQ9zdwmV9nfHQXeXoLLsz1wyrjW4j0vGrSzv+kkNAAAA\nXAYfkwsAAGAGHvSkAlegqQUAADADH29qWX4AAAAAr0dSCwAAYAKGl930XFm+PTsAAACYAkktAACA\nGfj4mlqaWgAAADMwfPsX9L49OwAAAJgCSS0AAIAZkNQCAAAAno2kFgAAwAQcPp7U0tQCAACYgY83\ntb49OwAAAJiC4XA4HO4uwpMUnz/v7hKAarPLcHcJlTaxQaC7S6iSuBPfubuECruhgbsrAGpGmaWu\nu0uotAbX13d3CSo7lOmSca23d3TJuJVFUgsAAACvx5paAAAAM7D4dpbp27MDAACAKZDUAgAAmACP\n9AIAAID38/Gm1rdnBwAAAFMgqQUAADADkloAAADAs5HUAgAAmIGPJ7U0tQAAACbg608/8O3ZAQAA\nwBRIagEAAMyApBYAAADwbNdsao8cOaLOnTvLZrOVexUUFFxz8KCgINlsNo0YMUKRkZFav369c99T\nTz11xfNmzpypnJycctv27Nkjm812zWtezdy5c5WcnFytMQAAALySYbjm5SEqtPygdevWSkxMrPTg\nfn5+zvPy8/P19NNPy8/PT/fee68WL158xfOmTp1a6WsBAADgKlh+cHlRUVHKysqSJI0aNUrbtm27\n6vFNmzbVX/7yFy1fvlySFBISot27d2vkyJHOYxYtWqQVK1bIZrNpz549OnbsmIYNGyabzaZ3333X\nedzHH3+s8PBwjRgxQnPmzJEkJScna+rUqRozZoxCQ0O1evVqSdKHH36osLAwPf7445ekvwAAAPAN\nVW5qp02bpgULFujzzz9Xy5Yt1aVLl2ue84c//EF79+51vg8MDNSJEyd09uxZSdLnn3+ufv36Ofev\nWLFC/fv3V2Jiovz9/SVJ586d0+LFi7VixQolJSXpxx9/1DfffCPp5yUKr732ml577TUlJSXJ4XAo\nPj5eb731lhYvXqxDhw5VdboAAABezWFYXPLyFBVafnDgwIFy61lbt26tGTNmqFOnTpo9e7bee++9\nCl2sqKhIVqu13Lb7779f//nPf9S5c2fVq1dPzZs3d+7bt2+fQkNDJf2c7P7nP//R3r17dfToUUVH\nR0uSCgsLdfToUUlSp06dZLVa1aJFCxUWFur06dNq2LChbr75ZkmqUOMNAAAA71OtNbX5+fmqW7eu\nzp49q8aNG+uFF17QgQMH1KNHj8veCJadna127dqV2/bggw8qKSlJp0+fLpfSSpLD4ZDF8vO/AOx2\nuySpbt26Cg4O1tKlS8sdm5ycrDp1Lp3OL+f/Mh4AAIApWTwnVXWFKs9u27ZtKiws1OzZsxUXFydJ\nmjFjhhITEy/b0J48eVILFizQk08+WW57p06dtG/fPn3xxReXNLWtW7dWdna2JCk9Pd25bd++fTp5\n8qQkaeHChTp+/Phla7zxxhtVWFios2fPqqSk5JrrfgEAAOCdqrT8oLS0VNu2bdOnn36qgIAA3Xjj\njfroo4/00EMPlTuvqKhINptNJSUlKi4u1qhRo9ShQ4dyxxiGoc6dO2vXrl269dZby+0bOXKkJkyY\noE8++URt27aVJF1//fWaMmWKRo8erXr16ql9+/bO9ba/ZbFYNHbsWI0YMUItW7bUnXfeWZHpAgAA\n+B4PWv/qCoaD38mXU3z+vLtLAKrNLs95bmBFTWwQ6O4SqiTuxHfuLqHCbmjg7gqAmlFmqevuEiqt\nwfX13V2CLhaccMm49W68fLhY23y7ZQcAAIApVGj5AQAAALycjy8/8O3ZAQAAwBRIagEAAEzAkz4o\nwRVoagEAAMzAx5ta354dAAAATIGkFgAAwAwM73vcY2WQ1AIAAMDrkdQCAACYgY+vqaWpBQAAMAF3\nPf1g1qxZyszMlGEYmjJlijp06ODc99VXX2nBggWyWq2699579cwzz1T5Or7dsgMAAMBtMjIydOjQ\nIb3zzjuaOXOmZs6cWW7/Sy+9pISEBK1atUpffvml9u7dW+Vr0dQCAACYgWFxzesqNm/erL59+0qS\n2rRpozNnzqioqEiSlJOTo8aNG+uWW26RxWLRfffdp82bN1d5ejS1AAAAcIn8/HzddNNNzvdNmjRR\nXl6eJCkvL09NmjS57L6qYE3tbxQWlbq7BMCU4k585+4SfN7Zn9xdAVBTStxdQKU1uL6+u0uQwwMe\n6eVwOFw2NkktAAAAXMLf31/5+fnO9ydOnFCzZs0uu+/48ePy9/ev8rVoagEAAEzA4XDN62p69uyp\nDRs2SJJ27Nghf39/+fn5SZJuu+02FRUV6ciRIyotLdXGjRvVs2fPKs/PcLgyB/ZCeXmF7i4BAAD4\nmGbNGrm7BBX9dN4l4/o1uP6q+1955RVt3bpVhmFo+vTp2rlzpxo1aqQHHnhAW7Zs0SuvvCJJevDB\nBxUdHV3lOmhqf4OmFgAA1DQzN7W1hRvFAAAATMDXU0zW1AIAAMDrkdQCAACYgN3Ho1qaWgAAABPw\n9duoWH4AAAAAr0dSCwAAYAK+vvyApBYAAABej6QWAADABHw8qKWpBQAAMAOWH1TAypUrNXToUI0Y\nMUKDBw/WV199dcVj09PTFRMTI0l66qmnJEm7d+/WgQMHLjn2j3/8o4YPHy6bzabIyEhFR0fr+PHj\nV63ll88XTk5O1ieffFLVKQEAAMCLVDupPXLkiN5991299957qlu3rg4ePKjnn39ePXr0uOa5ixcv\nliR98sknCg4OVuvWrS85ZsmSJWrYsKGknxvVV199VbNmzbpiLampqerXr58GDRpUjVkBAAD4Fl9/\npFe1m9qioiJduHBBJSUlqlu3rlq1aqWkpCRJks1mU3BwsLKzs3XhwgXFx8eXOzckJEQrVqzQ22+/\nrSZNmujmm29Whw4drnitjh07as2aNZKktWvXKikpSRaLRXfeeafi4uI0Y8YMZWVladGiRXI4HLrp\npps0YsQIvfzyy9q2bZvKysoUGRmpgQMHVnfaAAAA8CDVXn4QGBioDh06qE+fPoqNjdX69etVWlrq\n3H/TTTcpMTFRYWFhWr58+SXn//73v1evXr00ceLEqza0kpSWlqb27dtLks6fP68333xTb7/9tvbv\n36/vv/9e0dHR6tatm8aOHes8Z8uWLfrhhx/09ttva/ny5Vq0aJGKioqqO20AAACvYnfRy1PUyI1i\nL7/8svbt26f//Oc/evPNN7Vq1SqtWLFCktS9e3dJUqdOnfTvf/+70mOPHj1aVqtVOTk5uuuuu/S3\nv/1NktS4cWM9/fTTkqR9+/apoKDgsudnZ2era9eukqQGDRrov/7rv3To0CEFBQVVuhYAAAB4pmo3\ntQ6HQxcvXlSbNm3Upk0b2Ww2PfTQQzp69Khz/y//axhGpcf/ZU1tUlKSDh48KD8/P128eFEzZszQ\nhx9+qGbNmunJJ5+84vm/vWZJSYksFh7PCwAAzMXHl9RWf/nBe++9p2nTpjmb18LCQtntdt18882S\npK1bt0qStm/frjZt2lx2DMMwVFZWdtXrhIeHKyMjQ7t379a5c+dktVrVrFkz/fjjj8rOznY2q79e\n+iBJwcHBSk9PlySdO3dOhw8f1u23316tOQMAAHgbu8M1L09R7aR20KBB2r9/v4YMGaIGDRqotLRU\nzz//vOrXry9JOnr0qKKjo1VYWKiEhAQdPHjwkjHuvvtuvfTSS2rYsKFzucIlhdapo+eee04vvvii\nVq1apZ49e+qRRx5RYGCgHn/8cc2ePVuJiYnauXOnZs2apUaNGjnHDg4OVmRkpEpLSzVp0iQ1aNCg\nutMGAACABzEcLny+g81m07Rp09S2bVtXXaLG5eUVursEAADgY5o1a+TuEnTklGtulL+tiZ9Lxq0s\nFpcCAADA67k0qfVGJLUAAKCmeUJSe9hFSe3vPCSprZFHegEAAMCz+XqMyfIDAAAAeD2SWgAAABOw\n+3hUS1ILAAAAr0dSCwAAYAK+ndPS1AIAAJiCJ336lyuw/AAAAABej6QWAADABHz8PjGSWgAAAHg/\nkloAAAATsPv4rWI0tb+R/WBfd5cAVNuicQvdXUKl/e+w9u4uoUrO/uTuCipumv8f3F0CUCPylr7j\n7hIqbc2oEHeX4PNoagEAAEzA19fU0tQCAACYAI/0AgAAADwcSS0AAIAJ+PryA5JaAAAAeD2SWgAA\nABPgkV4AAADweiw/AAAAADwcSS0AAIAJ2H08qiWpBQAAgNcjqQUAADCBMru7K3AtmloAAAATYPkB\nAAAA4OFIagEAAEygjKS24lJSUhQUFKRTp05ddn9SUpISEhK0a9cuLVy48KpjZWdna+TIkRoyZIgG\nDhyoRYsWqaysrCbLBQAAgI+o8aY2ICBAGzZsuOpx7dq1U0xMzBX3FxUVadKkSZoyZYpWr16t999/\nXwUFBUpISKjJcgEAAEzD7nC45OUpaqypLSgoUFZWlmJjY5WamurcvnnzZoWFhSkqKkpZWVmSpPT0\n9Ks2tevWXSXJAAAgAElEQVTWrVOfPn0UGBgoSTIMQxMnTtSaNWvkcDhks9k0d+5c2Ww2DR06VLm5\nuZKk+Ph4RUZGKjw8XCkpKZKk2NhYzZ8/X9HR0XrooYe0Y8eOmpoyAAAAPESNNbVpaWnq3bu3evXq\npYMHD+r48eOSpPnz52vevHlatmyZTp8+XaGx9u/fr/bt25fb1qBBAzVt2lQnTpyQJN10001KTExU\nWFiYli9frq1btyo3N1crV67UihUrtHjxYhUXF0uSSkpKtHTpUo0cOVIffPBBTU0ZAADAa5TZXfPy\nFDXW1KakpGjAgAGyWq0KDQ3V+vXrJUm5ubnOxLVr164VGsswjMuun3U4HLJYfi65e/fukqROnTrp\nwIED2rZtmzIzM2Wz2RQdHS273a68vDxJ0t133y1JatGihYqKiqo3UQAAAC/k68sPauTpB8eOHVNm\nZqbmzJkjwzBUXFysRo0aKSoqytmESj83pRVxxx13KDs7W3/605+c286dO6czZ86oWbNm5cZyOBwy\nDEP16tXT4MGD9eSTT14yntVqrXQNAAAA8B41ktSmpKQoMjJSa9eu1Ycffqi0tDSdOXNGhw8fVvPm\nzbV//345HA5lZGRUaLywsDB98cUX+u6775zb4uPjNXjwYOf7rVu3SpK2b9+uNm3aqEOHDtq4caPs\ndrsuXLiguLi4mpgaAACATyhzOFzy8hQ1ktSmpqZq7ty5zveGYWjgwIFKTU3VhAkTNH78eN16661q\n0aLFJefOnDlTI0eOVEBAgHNbw4YN9cYbb+jFF1/UuXPnVFpaqv/+7/8ul8IePXpU0dHRKiwsVEJC\ngpo3b66QkBANGzZMDodDw4cPr4mpAQAAwAsYDjf8Pn7Tpk1as2aN4uPjq3S+zWbTtGnT1LZt2xqu\nTNrYOaTGxwRq26JxV38OtCf632Htr32QBzr7k7srqLhp/n9wdwlAjchb+o67S6i0NaPc3198+kOe\nS8bte2czl4xbWbX+Mbk5OTmKi4tTr169avvSAAAAplVmd7jk5Slq/WNyK/LhDNeSmJhYQ9UAAADA\nF9R6UwsAAIDa50mP33KFWl9+AAAAANQ0kloAAAATKPPtoJamFgAAwAxYfgAAAAB4OJJaAAAAE/Ck\nx2+5AkktAAAAvB5JLQAAgAmwphYAAADwcCS1AAAAJsAjvQAAAOD1WH4AAAAAeDiSWgAAABOw80gv\nAAAAwLOR1AIAAJgAN4oBAADA63GjGAAAAODhSGoBAABMoIykFgAAAPBsJLUAAAAm4OuP9KKpBQAA\nMAFff/oByw8AAADg9UhqAQAATMCTHulVUlKi2NhYHT16VFarVbNnz1ZAQMBlj504caLq1aunOXPm\nXHVMkloAAADUqpSUFN1www1atWqVxowZo/nz51/2uC+//FKHDx+u0Jg0tQAAACZQ5nC45FUVmzdv\n1gMPPCBJ6tGjh7Zt23bJMRcvXtTixYv11FNPVWhMlza1KSkpCgoK0qlTpy67PykpSQkJCdq1a5cW\nLlx4xXHS09MVExNTbltsbKw2btx4zXOTk5M1d+7cqk0AAADAR5TZHS55VUV+fr6aNGkiSbJYLDIM\nQxcvXix3zD/+8Q9FRETIz8+vQmO6dE1tSkqKAgICtGHDBkVERFzxuHbt2qldu3ZVukZ1zgUAAIBr\nrV69WqtXry63LTMzs9x7x28S34MHDyo7O1vjxo1Tenp6ha7jsqa2oKBAWVlZmjVrlt58801nU7t5\n82bNmjVLTZs2VbNmzRQQEKD09HStXLnyqonrlfz63DfeeEOpqakKCAhQaWmpoqKiJEknTpzQuHHj\ntHfvXkVHR2vw4ME1OlcAAABPV9VUtbqGDBmiIUOGlNsWGxurvLw8BQYGqqSkRA6HQ/Xq1XPu/+KL\nL3T06FENHTpURUVFOnXqlJYsWaLRo0df8Toua2rT0tLUu3dv9erVS88//7yOHz+u5s2ba/78+Zo3\nb54CAwM1evToK97p9lsZGRmy2WzO9/v371e/fv2c7wsKCrRy5Upt2LBBRUVFevDBB51NbU5Ojlat\nWqVDhw7pz3/+M00tAACAG/Xs2VNpaWnq1auXNm7cqJCQkHL7H3vsMT322GOSfg4w33///as2tJIL\nm9qUlBQ9/fTTslqtCg0N1fr16xUVFaXc3FwFBgZKkrp27aoLFy5UaLxu3bqVS3JjY2PL7T98+LDa\ntm2r+vXrq379+urQoYNzX8eOHWW1WtW8eXMVFhbWwOwAAAC8i7uS2svp37+/vvrqK0VERJR7XNcb\nb7yhrl27qnPnzpUe0yVN7bFjx5SZmak5c+bIMAwVFxerUaNGioqKksXyf/em/Xb9RHU4HI5yYxuG\n4fy6Th0exwsAAOApfnk27W898cQTl2wLCQm5JMm9HJd0eykpKYqMjHSmqQ6HQw8++KAOHz6s5s2b\na//+/WrdurUyMjLUqVOnGrlmy5Yt9cMPP6ikpESFhYXKzs6ukXEBAAB8gSclta7gkqY2NTW13GO0\nDMPQwIEDlZqaqgkTJmj8+PG69dZb1aJFi0vOnTlzpkaOHFnhtba/aNq0qQYMGKAhQ4aoTZs26tCh\ng6xWa7XnAgAA4Atoaqvg/fffv2TbM8884/z63nvvLbdv06ZNzgZ06tSpl5x7udj51x+V9su+Vq1a\naezYsapTp47CwsJ022236e6773Ye17BhQ33++edVmBEAAAA8mdsXm+bk5CguLk5PPvlktcfKz8/X\n0KFDVa9ePYWFhV02CQYAADAjkloX++XDGWrCE088cdkFxgAAAPBtbm9qAQAA4HoktQAAAPB6vt7U\nWq59CAAAAODZSGoBAABMgKQWAAAA8HAktQAAACbg60ktTS0AAIAJlPp4U8vyAwAAAHg9kloAAAAT\n8PXlByS1AAAA8HoktQAAACZAUgsAAAB4OJJa1DpHmff9S9GwGu4uoVJ8/V/jAMyNv+Oqpszh2983\nmloAAAAT8PV/DLD8AAAAAF6PpBYAAMAESGoBAAAAD0dSCwAAYAK+ntTS1AIAAJhAmd3u7hJciuUH\nAAAA8HoktQAAACbg68sPSGoBAADg9UhqAQAATMDXk1qaWgAAABMo9fGmluUHAAAA8Hq10tSmpKQo\nKChIp06duuz+pKQkJSQkaNeuXVq4cOEVx0lPT1dMTIyrygQAAPBZZXaHS16eotaa2oCAAG3YsOGq\nx7Vr146mFQAAAJXm8qa2oKBAWVlZio2NVWpqqnP75s2bFRYWpqioKGVlZUmqehKbnp6u8PBwjRgx\nQpMmTdLFixcVGhqqsrIylZaWqnPnzvruu+8kSdHR0crNza2ZyQEAAHgJktpqSktLU+/evdWrVy8d\nPHhQx48flyTNnz9f8+bN07Jly3T69OlqXWP69OmKj49XUlKSGjdurHXr1ikoKEg//PCDdu7cqeDg\nYG3fvl12u135+flq2bJlTUwNAAAAHsLlTW1KSooGDBggq9Wq0NBQrV+/XpKUm5urwMBASVLXrl2r\nPH5BQYEMw9Att9wiSQoJCdGuXbvUrVs3bd++Xdu2bZPNZlNmZqb27Nmj9u3bV39SAAAAXsbXk1qX\nPtLr2LFjyszM1Jw5c2QYhoqLi9WoUSNFRUXJYvm/ftrhqPo3xDCMcueXlJTIMAx169ZNb7zxhoqL\nizV48GAlJyfrm2++UUhISLXmBAAA4I08qQF1BZcmtSkpKYqMjNTatWv14YcfKi0tTWfOnNHhw4fV\nvHlz7d+/Xw6HQxkZGVW+RuPGjWUYho4ePSpJysjIUHBwsFq3bq0ff/xRhYWF8vPzU9OmTfXZZ5/p\nnnvuqanpAQAAwEO4NKlNTU3V3Llzne8Nw9DAgQOVmpqqCRMmaPz48br11lvVokWLS86dOXOmRo4c\nqYCAgHLbMzIyZLPZnO/nzp2ruLg4TZo0SXXq1FFAQIAefvhhSdLNN9+shg0bSpI6duyoLVu2XPZa\nAAAAvs7Xk1rDUZ3f/dewTZs2ac2aNYqPj3dbDRs7szzB1RxlHvNHrsIMq+HuEirl1WdedXcJlfZO\nRJC7S6iSsz+5u4KKm+b/B3eXANSIY0vedncJlfbB4+7/TXHfRZtcMu6nY//bJeNWlsd8olhOTo7i\n4uLUq1cvd5cCAADgcxx2h0tensKlyw8qoyIfzgAAAICqsXtQA+oKHpPUAgAAAFXlMUktAAAAXMeD\nbqNyCZJaAAAAeD2SWgAAABPwpJu6XIGmFgAAwAS4UQwAAADwcCS1AAAAJuCwu7sC1yKpBQAAgNcj\nqQUAADABHukFAAAAeDiSWgAAABPw9acf0NQCAACYAM+pBWqYYTXcXYLPs1r4HgPwXfwdh8uhqQUA\nADABX09quVEMAAAAXo+kFgAAwATsPv5IL5paAAAAE2D5AQAAAODhSGoBAABMgKQWAAAA8HAktQAA\nACbAJ4oBAADA6zl8/OkHLD8AAACA1yOpBQAAMAGH3d0VuBZJLQAAALweSS0AAIAJ+PqNYjWW1Kak\npCgoKEinTp267P6kpCQlJCRo165dWrhw4VXHysrKks1m07BhwzRo0CAtWrRIDodD6enpiomJqamS\nAQAA4CNqLKlNSUlRQECANmzYoIiIiCse165dO7Vr1+6K+4uKijR58mQlJCSobdu2Kikp0YQJE7R6\n9WrdfvvtNVUuAACAqfDhCxVQUFCgrKwsxcbGKjU11bl98+bNCgsLU1RUlLKysiTpmmnrunXr1KdP\nH7Vt21aSVLduXc2dO1ePPPJIuePWr1+voUOHKiIiQi+99JIkaefOnRo2bJhsNpuio6N19uxZFRUV\nKSYmRo8++qhGjBih3bt318SUAQAAvIrD7nDJy1PUSFOblpam3r17q1evXjp48KCOHz8uSZo/f77m\nzZunZcuW6fTp0xUaa//+/ZckuX5+frJarc73586dU3x8vJYtW6ZVq1bpyJEj+vrrr5WcnKyIiAgl\nJibq8ccfV15enpYvX65evXpp+fLlevHFFzV37tyamDIAAAA8SI0sP0hJSdHTTz8tq9Wq0NBQrV+/\nXlFRUcrNzVVgYKAkqWvXrrpw4cI1xzIMQ2VlZVc95uDBg7r99tvVsGFDSVK3bt20a9cu9enTRy++\n+KIOHjyo/v37q02bNvr222916tQprV27VpJ0/vz5as4WAADA+9h9/MMXqt3UHjt2TJmZmZozZ44M\nw1BxcbEaNWqkqKgoWSz/FwRX9FMs7rjjDn333XcaOHCgc9upU6fKNaOGYZQbr6SkRNddd526d++u\n9957Txs3blRsbKyee+451a1bV9OmTVPnzp2rO1UAAAB4qGovP0hJSVFkZKTWrl2rDz/8UGlpaTpz\n5owOHz6s5s2ba//+/XI4HMrIyKjQeGFhYfriiy+ca3AvXryoF198UV999ZXzmFatWunQoUMqKiqS\nJGVkZCg4OFhJSUkqKCjQ//t//0+PPvqodu3apY4dO+rTTz+VJO3du1fLli2r7pQBAAC8jq+vqa12\nUpuamlpunaphGBo4cKBSU1M1YcIEjR8/XrfeeqtatGhxybkzZ87UyJEjFRAQ4NzWsGFDLVmyRNOn\nT1dxcbGsVqvCwsI0ZMgQpaenS5IaNGig5557To8//rgsFovuuusu3X333frpp580fvx4NWrUSPXq\n1dPs2bNVv359/fWvf9Xw4cNlt9s1derU6k4ZAADA63hSA+oKhqOi6wJqyKZNm7RmzRrFx8fX5mUr\nbGPnEHeXAFTbonFXfxa0J/rfYe3dXUKVnP3J3RVU3DT/P7i7BKBG5C19x90lVNqaUe7vL34/7gOX\njPt9wsBrH1QLavVjcnNychQXF6devXrV5mUBAABMz253uOTlKWr1Y3J/+XAGAAAAoCbValMLAAAA\n96jlFadXVVJSotjYWB09elRWq1WzZ88ud4+VJMXHxys9PV0Oh0N9+/bV6NGjrzpmrS4/AAAAgHt4\n0tMPUlJSdMMNN2jVqlUaM2aM5s+fX27/nj17lJ6errffflurVq1ScnKy8vLyrjomTS0AAABq1ebN\nm/XAAw9Iknr06KFt27aV29+oUSNduHBBFy9e1IULF2SxWHT99ddfdUyWHwAAAJiAJ93UlZ+fryZN\nmkiSLBaLDMPQxYsXVa9ePUnSLbfcotDQUN1///0qKyvTM888Iz8/v6uOSVMLAAAAl1m9erVWr15d\nbltmZma5979d75uTk6NPPvlEn376qUpLSxUeHq7+/fvr5ptvvuJ1aGoBAABMwGEvc8t1hwwZoiFD\nhpTbFhsbq7y8PAUGBqqkpEQOh8OZ0krSd999p44dOzqXHPz+97/Xnj171L179ytehzW1AAAAqFU9\ne/ZUWlqaJGnjxo0KCSn/4RS/+93vlJ2dLbvdrpKSEu3Zs+eSpyP8FkktAACACbgrqb2c/v3766uv\nvlJERITq1aunOXPmSJLeeOMNde3aVZ07d1bPnj01fPhwSdLgwYN12223XXVMmloAAAAT8KSm9pdn\n0/7WE0884fw6JiZGMTExFR6T5QcAAADweiS1AAAAJuAo85yk1hVoan9j+eTF7i6hUqwWw90lVNrF\nUu/7P5XV4l2/1GgWOcjdJVRaWcRud5dQRSXuLqDC8pa+4+4SfF6ZBz0HtKK88edIs+hh7i6h8kYd\ndHcFPo+mFgAAwAQ8aU2tK9DUAgAAmICvN7Xe9TtVAAAA4DJIagEAAEyApBYAAADwcCS1AAAAJuDr\nSS1NLQAAgAn4elPL8gMAAAB4PZJaAAAAE7CT1AIAAACejaQWAADABFhTCwAAAHg4kloAAAATIKn9\n/6WkpCgoKEinTp267P6kpCQlJCRo165dWrhw4WWPsdvtuv/++3Xy5Mly2ydOnKgNGzZc9pzk5GTN\nnTu3omUCAADgMhxlZS55eYpKNbUBAQFXbD5/0a5dO8XExFz+YhaL+vXrV26M4uJibd26Vb17965o\nKQAAAEA5FWpqCwoKlJWVpdjYWKWmpjq3b968WWFhYYqKilJWVpYkKT09/YpNrSQNGDBAH330kfP9\nv/71L/Xs2VPXXXed0tPTFR4erhEjRmjSpEm6ePGi87gjR45o0KBBzveDBg3SkSNHFBsbq5dfflmP\nPvqowsLCtHbtWj322GP605/+pMLCQpWVlWnKlCmy2WyKiIjQ5s2bK/7dAQAA8BEOe5lLXp6iQk1t\nWlqaevfurV69eungwYM6fvy4JGn+/PmaN2+eli1bptOnT1fogsHBwTp58qROnDghSfroo480YMAA\nSdL06dMVHx+vpKQkNW7cWOvWravQmHXq1NHy5cvVtm1bffvtt3rrrbfUtm1bpaena926dWrWrJkS\nExP12muvadasWRUaEwAAAN6jQk1tSkqKBgwYIKvVqtDQUK1fv16SlJubq8DAQElS165dK3zR/v37\na8OGDTp//rx27Nihe+65RwUFBTIMQ7fccoskKSQkRLt27arQeB06dJAk+fv7q3379pKkpk2bqrCw\nUN9++60+++wz2Ww2jR8/XhcuXCiXAAMAAJiBrye113z6wbFjx5SZmak5c+bIMAwVFxerUaNGioqK\nksXyfz2xw+Go8EUHDBigqVOnyt/fX/fdd5+sVqsMwyg3RklJiQzDcL7/9deSVFpa6vzaarVe9muH\nw6G6detqzJgxzjQYAADAjDypAXWFaya1KSkpioyM1Nq1a/Xhhx8qLS1NZ86c0eHDh9W8eXPt379f\nDodDGRkZFb5oq1atVFpaqg8++EBhYWGSpMaNG8swDB09elSSlJGRoeDgYOc5fn5+OnnypBwOh/Ly\n8pSTk1Oha3Xs2FGfffaZJOnkyZNasGBBhesEAACAd7hmU5uamlruBi3DMDRw4EClpqZqwoQJGj9+\nvMaMGaMWLVpccu7MmTOv2Hw+9NBD2rt3rzp27OjcFhcXp0mTJslms6m0tFQPP/ywc1/jxo3Vo0cP\nPfLII4qPj1e7du0qNMGHHnpIDRo0UHh4uMaMGaO77rqrQucBAAD4Eofd7pKXpzAclVk3UAGbNm3S\nmjVrFB8fX5PD1prH/nebu0uoFKvFuPZBHuZiqff9+sNq8a4P36sfOejaB3mYBT/tdncJVXKuqMTd\nJVTYmHU73V2Czyuz1+iP1FrhjT9HmkUPc3cJlfZ3x0F3l6DGf5ziknHPfO4ZN+HX6E/qnJwcxcXF\nqVevXjU5LAAAAKrJ9DeKVUZFPpwBAAAAtc+TGlBX8K7fqQIAAACXUaNJLQAAADyTnaQWAAAA8Gwk\ntQAAACbgKCOpBQAAADwaSS0AAIAJ+PrTD2hqAQAATMDXm1qWHwAAAMDrkdQCAACYAEktAAAA4OFI\nagEAAEzA15Naw+FwONxdBAAAAFAdLD8AAACA16OpBQAAgNejqQUAAIDXo6kFAACA16OpBQAAgNej\nqQUAAIDXo6mtpsOHD2vMmDF65JFH9D//8z+Ki4tTcXGxu8u6piNHjmjQoEHltiUkJOj111/XCy+8\ncMXz0tPTFRMT4+ryLutyNXuigwcP6oknntDgwYM1aNAgxcXFaf/+/R5R+7Bhw5SdnV1u2/z58/XP\nf/7zkmNr47/1kSNH1LlzZ9lstnKvgoKCa54bFBQkm82mESNGKDIyUuvXr3fue+qpp6543syZM5WT\nk1Nu2549e2Sz2ao0h5UrV2ro0KG677771KdPH3311VdXPPbX39Nfaty9e7cOHDhwybF//OMfNXz4\ncNlsNkVGRio6OlrHjx+/ai0bNmyQJCUnJ+uTTz656rEpKSkKCgrSqVOnLrs/KSlJCQkJ2rVrlxYu\nXHjVsbKzszVy5EgNGTJEAwcO1KJFi1RW5prnYdZU3Zf78x0bG6uNGzde89zk5GTNnTvXY+quKTX5\nZyIrK0s2m03Dhg3ToEGDtGjRIjkcjmrXXxM12u123X///Tp58mS57RMnTnT+f+i3qvLfHLWPD1+o\nBrvdrnHjxik2Nlbdu3eXJP3zn//UtGnTNG/ePDdXVzU33HCDnn76aXeX4bXKyso0btw4TZs2Td26\ndZPD4dBLL72k1157zd2lSZIGDBigjz76SMHBwc5tH3/8sVasWOG2mlq3bq3ExMRKn+fn5+c8Lz8/\nX08//bT8/Px07733avHixVc8b+rUqVWu9beOHDmid999V++9954WLFigm266Sa+//rp69OhxzXN/\nqfGTTz5RcHCwWrdufckxS5YsUcOGDSX9/EP11Vdf1axZs65YS2pqqvr161ehf0ClpKQoICBAGzZs\nUERExBWPa9eundq1a3fF/UVFRZo0aZJeffVVBQYGyuFwaObMmUpISNCECROuWUdl1VTdV1Odc6+k\nNuqurpr8MzF58mQlJCSobdu2Kikp0YQJE7R69Wrdfvvtbq/RYrGoX79+2rBhg4YPHy5JKi4u1tat\nWzV79uxq1Qf3Iqmthk2bNqlVq1bOhlaSoqKilJWVpdGjR2vGjBl67LHH9Kc//Uk7d+6U9HOqEx4e\nruHDhzvTsYSEBM2cOVOPP/64+vXrp3/9619umc8vfvmB+MEHHygsLEyjR4/Wc889p+TkZEnSuXPn\n9OyzzyosLEyLFi1yZ6n6/vvvFRkZKZvNpjFjxqigoEARERHKz8+XJIWGhiotLU2S9MILLygjI8Ol\n9Xz55Ze644471K1bN0mSYRiaPHmynnnmGecx6enpCg8P14gRIzRp0iRdvHhRR48edc5j+PDhys3N\nVVlZmaZMmSKbzaaIiAht3ry52vX179+/XIKXnZ0tf39/FRQUXPJ9/LWQkBDn1zExMUpPT1dCQoJm\nzJih6Oho9evXT+vXr1d0dLRCQ0OdSWh8fLwiIyMVHh6ulJSUCtf5y/+PJGnUqFHatm3bVY9v2rSp\n/vKXv2j58uXOenfv3q2RI0c6j1m0aJFWrFghm82mPXv26NixYxo2bJhsNpveffdd53Eff/yx87/P\nnDlzJP3cUE6dOlVjxoxRaGioVq9eLUlat26d9u3bpyeeeEI5OTlq2rSpkpKSJEk2m01z586VzWbT\n0KFDlZube8n39Pvvv9fbb7+tBQsWOOd7JR07dtShQ4ckSWvXrtXQoUMVHh6uadOmSZJmzJihjIwM\nLVq0SAkJCc46Xn75ZYWHh2vIkCH64IMPJEkFBQXKyspSbGysUlNTndfYvHmzwsLCyn3/r5WsrVu3\nTn369FFgYKCkn//MT5w4UWvWrJHD4fj/2jv3mCjO7w8/ywIiYKCRi9VQFSoaXAveqIWSauxFlK22\noNUgGEWjNVZsNZQUKhS06FdFbSVWlCosBE2sLV4KtoK2pImgcqkIWgQFL7QgArLIbWV/f5CdsFyU\nFvDyy/skJDszO7OfOfPOmfOe97xDj3borm0EBwezY8cOAgIC8PT05MqVK3q/1Z+6H0fHfWNjY5k7\ndy5r1qxh1apVZGVlAVBZWcknn3yCp6cnR48efezxnobu7vzKrFmzePToERqNhokTJ3L58mUAAgIC\nurTHgWgTjo6OABgZGbF161a8vb31vvfzzz+zYMECFi1axKZNmwAoLCyU7suAgAAePHiAWq1m7dq1\n+Pr6kpmZyeLFi/usUdfB1/Hbb7/h7u7OoEGDurWljs4jhh9++CG3b98mODiY//3vfyxZsgSlUsnx\n48el5399ff2A+HNBV0RQ2wdKS0txcnLSWyeTyRgzZgwajQaNRsOhQ4cIDAwkJiaGW7dukZaWRnJy\nMklJSfzyyy/cvXsXgH/++YcDBw4QEhLCkSNHnor+Gzdu6A35/vjjj9K2trY2oqOjOXjwILt37+bi\nxYvStpKSEiIjIzl8+LD04HxWbN68maCgIFQqFVOnTiUhIQFXV1fy8vKorq7GxsaGvLw8AK5cuYKL\ni8uA6iktLe2SHTAxMcHY2FhaDgsLY+fOnSQmJmJhYcGJEyc4ffo0bm5uqFQqQkJCqKqq4sSJE1hb\nW6NSqYiJiekxQ/dvGDp0KHZ2dpLjT01NRalUdmvH3lBXV0dcXByzZs3ip59+kj6np6dz8eJF7ty5\nQ3m9ckcAAAyDSURBVFJSEgkJCezdu7fXpTlffvkl0dHRZGRkMGLECCZNmvTEfSZMmMD169el5XHj\nxlFZWcmDBw8AyMjI4L333pO2JyQkMHv2bFQqFTY2NkB7h23v3r0kJCSQmJhIRUUFly5dAtpLFGJi\nYoiJiSExMRGtVsuRI0eYOXMmV69eJSsriz///BONRiP9xksvvYRKpUKpVEoBd0fGjh2Lh4cHn332\nGa+99tpjzy8tLU3yN42NjRw4cIDDhw9TWlrKtWvXCAgIwNXVlTVr1kj7XLhwgeLiYg4fPkx8fDx7\n9uxBrVaTlpbG9OnT8fDw4ObNm1JZw44dO9i2bRsHDx6kpqbmiTaH7v2gqakpVlZWVFZWdmuHx7WN\n1tZW4uLi8Pf3l4LwjjboL90A2dnZej4wMzNTb3ttbS1JSUkcOXKE8PBwvU7xrVu32LVrFzExMU8c\naehv3d3RnV8ZP348xcXFFBYWolAoyMvLo62tjXv37jFixIgB09idHzQ3N0cul0vLDQ0N7Ny5k4MH\nD5KcnMzt27c5f/48x44dY9GiRahUKpYvX05VVRXx8fF4eHigVCqZMWMGZ86c6bNGhUJBdXW11EZT\nU1Px8vLq0Za9wdDQkPj4eBwdHcnNzeXQoUM4OjqSlZU1IP5c0BUR1PYBmUzWbd2YVqvFwMBAGoJ0\ncXHhxo0bXL58mbKyMvz9/fH396ehoUHqLese2sOGDaO+vv6p6NcN++r+PvjgA2lbTU0N5ubmWFlZ\nYWpqqpeNdnJyYvDgwZiZmfGs/8tySUkJzs7OQHvmq7CwkKlTp5Kfn09ubi5KpZKysjLq6uoYMmSI\nXnA5EPTUJnTU1tYik8l4+eWXJc1FRUW4u7uTkpLCli1baGlpwcXFhdzcXNLT0/Hz8yMwMJDm5ma9\njMF/xcvLS6o/1QV63dmxN0yYMAEAa2tr6SFmZWWFWq0mJyeH/Px8KePS1tZGVVVVl2N07lxt3LgR\ne3t7XFxciIqKYsOGDb3Solar9R6aADNmzCAzM5O7d+9ibGyMra2ttK2kpISJEydK5wxw/fp17t69\nS0BAAH5+fpSVlUkdTxcXF+RyuXSP1tTUYGZmxu7du0lMTMTBwYHff/+dpUuXSveF7r7R+YB/y4oV\nK/Dz82P69OmUlpYSGBgIgIWFBatXr2bx4sWUlJT0WINcUFDA1KlTgfZA89VXX6WsrIyTJ0/i5eWF\nXC5n1qxZUnu4c+eOlHHV7fcknuQHoasdHtc2pkyZArT7QrVarXfM/tQN4OrqqucDPTw89LaXl5fj\n6OiIiYkJVlZWeh0PZ2dn5HI5tra2T/TZ/a27Mz35FV0HPycnBz8/P/Lz8/nrr7+6dEL6W+OT/CC0\nzz0YOXKkVF7j6upKUVERM2fOZO/evezatYuhQ4fi4OBAbm4uycnJbN++nfLyctRqdb/Ycfbs2Zw+\nfZrGxkauXLnCtGnTerRlb9C1DxsbG8nGVlZW1NfXD5g/F+gjamr7gL29PcnJyXrrtFot169fZ/To\n0bS1tUnrZTIZRkZGTJ8+nYiICL19zp8/j6Hh83UpOj6QoF2/judNq47W1lYMDAyYNGkS33//PRqN\nBm9vbzIzM8nOzu7TQ6O32Nvbk5SUpLeupaWFhw8fAu127NgRaG1tRSaT4ejoSEpKCn/88QfR0dF4\ne3tjZGTEqlWrpOxBf/HOO+/w3XffMWfOHEaNGoWFhYXedp0de6K1tVX63LEtdPys1WoxNjbGx8eH\nlStXPlZPTzW19+7dw8jIiAcPHmBhYcHGjRu5ceMGbm5u3U4EKygo6JIdevfdd0lMTKSmpkYvS6vT\nqDtP3b1qZGSEQqEgLi5O77vHjh3rtt3LZDKam5txcHDA0dERHx8fYmNjpUBYd621Wq3ePdRbdDW1\niYmJ3Lx5E3Nzc1paWoiIiCAlJQVra+vH2rfzb7a2tnL//n3y8/PZsmULMpmMpqYmhgwZwtKlS/Wu\ne287rPb29hQUFDB37lxpXUNDA3V1dVhbW+sdS2eHx7WNjh2Tjhr+/vvvftXdG/rDDz4N3T35FVdX\nV2JjY2lqasLHx4djx45x6dIlvXKigdBob2/P5cuXmTdvnrTu/v37NDY2PlbzoEGDeOONNzh69Chn\nz54lODiYoKAgjIyMWL16NZ9++qn03czMzD7b0cvLi5CQEGxsbHjrrbeQy+U92rKj7o50HJnp2HY7\nt+OB8ucCfUSmtg+4u7tz+/ZtvRrYQ4cOMXnyZCwtLaVhy9zcXBwcHBg/fjxZWVk0NjZKE4ie1zcl\nWFpaUltbS11dHU1NTQNei/pfGTNmDLm5uUD7UKtCocDU1BRoHy52cHBg3LhxJCcnd3HkA4G7uzt3\n7twhIyMDaA+Wtm3bxv79+4H2DJtMJpOCnuzsbBQKBadOnaK4uJi3336bwMBACgoKcHZ2Jj09HYDq\n6mqio6P7RaO5uTljx45l3759KJVKoHs7dkQmk9HY2EhjY+O/ylqcPXuWtrY2mpubiYyM7LXGnJwc\n6uvriYqKkvaLiIhApVJ1G9Dq7NM5SHJxcaGkpIRz5851CWpHjx4tvQlCVyc5evRoSkpKpFnR33zz\nTY9vHLC0tKSyspLg4GBaWlrIycmhqamJtrY2hg4dCiCV7eTl5eHg4NDtcXqT1Vq4cCHZ2dlcvXqV\nhoYG5HI51tbWVFRUUFBQIHVEOj5goX2IVXduDQ0NlJeXU1BQgK+vL8ePHyclJYW0tDTq6uooLy/H\n1taW0tJStFptr+95pVLJuXPnpHpNaK+X9fHxkZY72+G/tI2TJ0/2q+7eMGLECIqLi6XOQOc3h/SG\np6G7J78yevRoKioqqK+vl0be0tPTmTZt2oBq1LUJXZlTS0sL4eHhem8GGTVqFGVlZVI2Xqc5MTGR\n2tpa3n//fZYsWUJRURHOzs7Ex8fj6+tLdHQ08+bN6xc7jho1Co1GI80feZwtdZibm1NdXY1Wq6Wq\nqqrLm1R6YqD8uUCf5zPl9oJgYGBAXFwcYWFh7N69G61Wi0KhIDQ0lIiICJqbm1m5ciUVFRVs27aN\n4cOH4+/vj6+vL3K5nLfffhsTE5NnfRrdYmhoyMcff4yvry8jR45EoVA8Nnv3tNANVetYu3Yt0dHR\nyGQyLCwspJmrTk5OXL16FZlMhouLC/v3739izWJ/oGsTGzduZM+ePRgbG+Pm5oa/v780dBwZGcn6\n9esxNDTEzs6OOXPmcO3aNcLCwjA1NUUulxMaGsrIkSM5f/48Cxcu5NGjR3q1kn1FqVQSFBTE9u3b\nAQgNDeWrr77Ss2PHSTqLFi1iwYIFUuesN0yaNInXX3+djz76CK1WK80y7kzna6rRaMjJyeHMmTPY\n2dlhaWlJamoqnp6eevup1Wr8/PxobW2lqamJZcuWdbnGMpmMiRMnUlRUxPDhw/W2+fv7s27dOn79\n9VdpQsvgwYP54osvWLFiBcbGxjg5OUn1tp0xMDBgw4YNREdH4+bmhrGxMSqVitDQUOm+1pUy1NfX\n8+2333Lz5s0ux5kyZQqbNm3CzMxMr8ynI4aGhgQFBREeHk5ycjLu7u54e3szbtw4li9fTlRUFCqV\nisLCQr7++muGDBkiHVuhUODr64tGo2H9+vXs27dP79VEMpmMefPmcerUKdatW0dgYCDDhw9n2LBh\nXXRs3rwZf39/7OzspHVmZmbExsYSHh5OQ0MDGo2GN998U6+D0dkOtra2vWobHTl16lS/6u4NVlZW\neHl5MX/+fCkY71zi8ix062qBdWzdurVbvwLtdfS6IX5nZ2cuXLjQ5bf6W6OZmRn79+8nLCyMpqYm\n5HI5SqWS+fPnS50sU1NTgoKCWL58OQYGBkyePJkpU6bw8OFDAgMDpXKxqKgoTExMOHDgAGq1mry8\nPEJCQvrt+nt6epKUlCSVX0H3Pvr48eNAe9Dr5uYm3X+9fVOFp6fngPlzQQe0ggHh888/12ZkZDxr\nGX0iNTVVW1NTo9Vqtdply5ZpL1269IwVCQQvDosXL9Zeu3btWcvoM5mZmdp169b95/2flR36qlvH\nDz/8oG1ubtY+evRIO3v2bG1FRUU/qOuZ/tI9kAiNgueVZ596Ezy3NDU1sWTJEhYuXMgrr7zSqxno\nAoHg/w+3bt0iMjKyywSq553+1H3v3j3p9WlKpbLbTGB/8SLYW2gUPM/ItNpnPH1dIBAIBAKBQCDo\nIyJTKxAIBAKBQCB44RFBrUAgEAgEAoHghUcEtQKBQCAQCASCFx4R1AoEAoFAIBAIXnhEUCsQCAQC\ngUAgeOERQa1AIBAIBAKB4IXn/wC8IO1wt8ovLQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdcd9fc8cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(12, 8))\n",
"sns.heatmap(data.corr(), fmt=\"f\",ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "957410b0-3666-d3ee-d6b8-6800987f7766"
},
"outputs": [
{
"data": {
"image/png": 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6HYdvHa7yiD5ZDgbpROW4nXYb/db1w993/xb2DWg8AJ8O/lRve+3RR0Dzh7c8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\nx5p9oucj0hwFh9fMy3R7XGlNGw7l12Htd6dc9vWkzzbDOKwGAWD51WM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wQVGQy2WwWhnY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\nAwAAADZDkQ4AAADYDEU6AAAAYDP/D+lkXz5F1jBBAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdcd9d0a048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,6))\n",
"#Volume\tEx-Dividend\tSplit Ratio\tAdj. Open\tAdj. High\tAdj. Low\tAdj. Close\tAdj. Volume\n",
"plt.plot(data['Date'],data['High'])\n",
"plt.title(\"Date vs Stock High Price\",fontsize=40,color='g')\n",
"plt.xlabel(\"Date\",fontsize=20,color='r')\n",
"plt.ylabel(\"Stock High Price\",fontsize=30,color='r')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "f21d47c9-bb53-b582-fd1b-3683e194e85c"
},
"outputs": [
{
"data": {
"image/png": 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qBEZ7vxAZjckaLFNIeeHjjz9GcnIy7r77bvTv3x+bN2+W7MGZNGkSxo0bh759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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdcd9be9c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,6))\n",
"#Volume\tEx-Dividend\tSplit Ratio\tAdj. Open\tAdj. High\tAdj. Low\tAdj. Close\tAdj. Volume\n",
"plt.plot(data['Date'],data['Volume'])\n",
"plt.title(\"Year wise volume\",fontsize=40,color='g')\n",
"plt.xlabel(\"Year\",fontsize=20,color='r')\n",
"plt.ylabel(\"Volume\",fontsize=30,color='r')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "56f67f38-72f1-b095-0714-009b94524f74"
},
"outputs": [
{
"data": {
"image/png": 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7kfhl2i9eNanYHVnFWeLn90RiaKJDMK7x02DV9FWY/9t8fPjHh6gx1cACCw7m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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fdcd9b7dbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,6))\n",
"plt.plot(data['Date'],data['Adj. Low'])\n",
"plt.title(\"Year wise Adj. Low\",fontsize=40,color='g')\n",
"plt.xlabel(\"Year\",fontsize=20,color='r')\n",
"plt.ylabel(\"Adj. Low\",fontsize=30,color='r')\n",
"plt.show()"
]
}
],
"metadata": {
"_change_revision": 766,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159356.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "fbcdf1f6-536c-8db6-5856-f4258f993063"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "8dc308cf-e134-f9f5-d00c-1e426e7cb03f"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"names = [\"movie id | movie title | release date | video release date | IMDb URL | unknown | Action | Adventure | Animation | Children's | Comedy | Crime | Documentary | Drama | Fantasy | Film-Noir | Horror | Musical | Mystery | Romance | Sci-Fi | Thriller | War | Western | \"]\n",
"names = [i.split(' | ') for i in names][0]\n",
"data = pd.read_csv(\"../input/movielens-100k-dataset/ml-100k/u.item\",delimiter=\"|\",encoding=\"437\",names=names)\n",
"data['video release date'] = data['release date']\n",
"data10 = data.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "17959425-3081-5e45-675e-b7e6403ab270"
},
"outputs": [],
"source": [
"def getdate(row):\n",
" if row['movie title'] != 'unknown':\n",
" date = row['movie title'].split('(')[1].replace('(','').replace(')','')\n",
" return date\n",
"data['release date'] = data.apply(getdate,axis=1)\n",
"data['movie title'] = [title.replace(title[-6:],\"\")for title in data['movie title']]\n",
"data = data.drop('',axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "908617bc-2f61-65c1-ddfe-0422880a7d1d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>movie id</th>\n",
" <th>movie title</th>\n",
" <th>release date</th>\n",
" <th>video release date</th>\n",
" <th>IMDb URL</th>\n",
" <th>unknown</th>\n",
" <th>Action</th>\n",
" <th>Adventure</th>\n",
" <th>Animation</th>\n",
" <th>Children's</th>\n",
" <th>...</th>\n",
" <th>Film-Noir</th>\n",
" <th>Horror</th>\n",
" <th>Musical</th>\n",
" <th>Mystery</th>\n",
" <th>Romance</th>\n",
" <th>Sci-Fi</th>\n",
" <th>Thriller</th>\n",
" <th>War</th>\n",
" <th>Western</th>\n",
" <th>genre</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Toy Story</td>\n",
" <td>1995</td>\n",
" <td>01-Jan-1995</td>\n",
" <td>http://us.imdb.com/M/title-exact?Toy%20Story%2...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Animation|Children's|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>GoldenEye</td>\n",
" <td>1995</td>\n",
" <td>01-Jan-1995</td>\n",
" <td>http://us.imdb.com/M/title-exact?GoldenEye%20(...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Action|Adventure|Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Four Rooms</td>\n",
" <td>1995</td>\n",
" <td>01-Jan-1995</td>\n",
" <td>http://us.imdb.com/M/title-exact?Four%20Rooms%...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Thriller</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Get Shorty</td>\n",
" <td>1995</td>\n",
" <td>01-Jan-1995</td>\n",
" <td>http://us.imdb.com/M/title-exact?Get%20Shorty%...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Action|Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Copycat</td>\n",
" <td>1995</td>\n",
" <td>01-Jan-1995</td>\n",
" <td>http://us.imdb.com/M/title-exact?Copycat%20(1995)</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Crime|Drama|Thriller</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" movie id movie title release date video release date \\\n",
"0 1 Toy Story 1995 01-Jan-1995 \n",
"1 2 GoldenEye 1995 01-Jan-1995 \n",
"2 3 Four Rooms 1995 01-Jan-1995 \n",
"3 4 Get Shorty 1995 01-Jan-1995 \n",
"4 5 Copycat 1995 01-Jan-1995 \n",
"\n",
" IMDb URL unknown Action \\\n",
"0 http://us.imdb.com/M/title-exact?Toy%20Story%2... 0 0 \n",
"1 http://us.imdb.com/M/title-exact?GoldenEye%20(... 0 1 \n",
"2 http://us.imdb.com/M/title-exact?Four%20Rooms%... 0 0 \n",
"3 http://us.imdb.com/M/title-exact?Get%20Shorty%... 0 1 \n",
"4 http://us.imdb.com/M/title-exact?Copycat%20(1995) 0 0 \n",
"\n",
" Adventure Animation Children's ... Film-Noir \\\n",
"0 0 1 1 ... 0 \n",
"1 1 0 0 ... 0 \n",
"2 0 0 0 ... 0 \n",
"3 0 0 0 ... 0 \n",
"4 0 0 0 ... 0 \n",
"\n",
" Horror Musical Mystery Romance Sci-Fi Thriller War Western \\\n",
"0 0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 1 0 0 \n",
"2 0 0 0 0 0 1 0 0 \n",
"3 0 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 1 0 0 \n",
"\n",
" genre \n",
"0 Animation|Children's|Comedy \n",
"1 Action|Adventure|Thriller \n",
"2 Thriller \n",
"3 Action|Comedy|Drama \n",
"4 Crime|Drama|Thriller \n",
"\n",
"[5 rows x 25 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def genre (row):\n",
" genres_titles = list(data.columns[6:24])\n",
" genre_list = \"\"\n",
" if sum(row[genres_titles]) >= 1:\n",
" for genres_title in genres_titles:\n",
" if row[genres_title] == 1:\n",
" genre_list = genre_list + genres_title +'|'\n",
" else:\n",
" genre_list = genre_list\n",
" genre_list = genre_list[:-1]\n",
" return genre_list\n",
"data['genre'] = data.apply(genre,axis=1)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "c496309a-c97e-3914-ce42-5f21fb1c3dcb"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>movie id</th>\n",
" <th>movie title</th>\n",
" <th>release date</th>\n",
" <th>video release date</th>\n",
" <th>genre</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" </tr>\n",
" <tr>\n",
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" </tr>\n",
" <tr>\n",
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" <td>Thriller</td>\n",
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" <tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" movie id movie title release date video release date \\\n",
"0 1 Toy Story 1995 01-Jan-1995 \n",
"1 2 GoldenEye 1995 01-Jan-1995 \n",
"2 3 Four Rooms 1995 01-Jan-1995 \n",
"3 4 Get Shorty 1995 01-Jan-1995 \n",
"4 5 Copycat 1995 01-Jan-1995 \n",
"\n",
" genre \n",
"0 Animation|Children's|Comedy \n",
"1 Action|Adventure|Thriller \n",
"2 Thriller \n",
"3 Action|Comedy|Drama \n",
"4 Crime|Drama|Thriller "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"genre_data = data[list(data.columns[0:4])].join(data['genre'])\n",
"genre_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "76745214-53d9-fcd6-1167-70264e271271"
},
"outputs": [],
"source": [
"genre_iter = (set(x.split('|'))for x in genre_data.genre)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "8b93e68a-eb41-01f9-b0f9-0ead2991f055"
},
"outputs": [],
"source": [
"genres = sorted(set.union(*genre_iter))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "23cddaf8-3eab-d5a7-3883-ef81ce5b7086"
},
"outputs": [],
"source": [
"dummies = pd.DataFrame(np.zeros((len(genre_data),len(genres))),columns=genres)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "848b0db1-684d-8dd3-d09e-17b50d9d8a9c"
},
"outputs": [],
"source": [
"dummies = dummies.drop('',axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "8ea974a4-874e-138c-4753-88584d1082ee"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 216,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159358.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "bc6f50f3-1f12-8a5e-0877-2eb83e788e12"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"asd\n"
]
}
],
"source": [
"print(\"asd\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "f6019991-0c7b-ea73-5559-a3e5f1631f7d"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n",
"Using TensorFlow backend.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.linear_model import LogisticRegressionCV\n",
"from keras.models import Sequential\n",
"from keras.layers.core import Dense, Activation\n",
"from keras.utils import np_utils"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "9b366412-e42e-7b19-b9d1-c0299ecdbf08"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-0347b81ea09a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0miris\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../input/Iris.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0miris\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"asd\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"iris = pd.read_csv(\"../input/Iris.csv\")\n",
"iris.head()\n",
"print(\"asd\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "6b884bd2-b803-0edf-3538-d2a35d367428"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 104,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159460.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "edc27297-e1cd-9503-05da-2505454c145e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"macro.csv\n",
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "501fca61-0d0b-9a65-ac28-8b47e9c20783"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import timeit\n",
"import math\n",
"\n",
"# vectorized error calc\n",
"def rmsle(y, y0):\n",
" assert len(y) == len(y0)\n",
" return np.sqrt(np.mean(np.power(np.log1p(y)-np.log1p(y0), 2)))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "076edb41-1327-cce7-ad54-4a74a70edc7a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(30471, 292)\n",
"(7662, 291)\n"
]
}
],
"source": [
"train_df = pd.read_csv(\"../input/train.csv\")\n",
"test_df = pd.read_csv(\"../input/test.csv\")\n",
"print(train_df.shape)\n",
"print(test_df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "6b63f03f-c3e4-e6c7-dae8-86ef6de21b45"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2011-08-20\n",
"2015-06-30\n",
"2015-07-01\n",
"2016-05-30\n"
]
}
],
"source": [
"print(train_df['timestamp'].min())\n",
"print(train_df['timestamp'].max())\n",
"print(test_df['timestamp'].min())\n",
"print(test_df['timestamp'].max())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "63c544f3-64c9-4872-3981-d552b0e46066"
},
"outputs": [],
"source": [
"train_df = train_df.sort_values(by='timestamp')\n",
"\n",
"# split train / test\n",
"train = train_df[train_df['timestamp']< \"2014-08-01\"]\n",
"test = train_df[train_df['timestamp']>= \"2014-08-01\"]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "4569fc06-b131-e451-44fd-17996dcaff02"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(21210, 292)\n",
"(9261, 292)\n"
]
}
],
"source": [
"print(train.shape)\n",
"print(test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ce3efb64-d837-5b4c-f022-6909f4adf898"
},
"source": [
"**Plotting prices**"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "2f0b4f22-6554-e461-44cf-f63cba191bee"
},
"outputs": [
{
"data": {
"image/png": 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RcBg60vHnp3M6A91h9o+lkYK6+7Phb1JQMEyiFZKHQJaGVs+F4kI3bExLVmjb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6n59GNwlQVJpQ+W6U0PdVi8oT/RCznQfHEfqcw1iLaa7w9PU1RIHA9noHu8MMd/ZSFAUFN4H7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aZ221uGhmGoz1w/xI8NejyPRkqJ76s9Mgs1l4HuTOf6IIgY7MkgEei6YHy2TrnuEknV/qAM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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f969cf7e6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(train_df['id'], train_df['price_doc'], alpha=0.5, cmap='viridis')\n",
"\n",
"plt.title('Price per transaction, in chronological order')\n",
"plt.xlabel('id')\n",
"plt.ylabel('price')\n",
"\n",
"plt.ylim(0, 20000000)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "6b0ff841-86f8-3c89-2493-b5b7f09a002d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>...</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-08-20</td>\n",
" <td>43</td>\n",
" <td>27.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>4</td>\n",
" <td>5850000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 292 columns</p>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material build_year \\\n",
"0 1 2011-08-20 43 27.0 4.0 NaN NaN NaN \n",
"\n",
" num_room kitch_sq ... cafe_count_5000_price_2500 \\\n",
"0 NaN NaN ... 9 \n",
"\n",
" cafe_count_5000_price_4000 cafe_count_5000_price_high \\\n",
"0 4 0 \n",
"\n",
" big_church_count_5000 church_count_5000 mosque_count_5000 \\\n",
"0 13 22 1 \n",
"\n",
" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
"0 0 52 4 5850000 \n",
"\n",
"[1 rows x 292 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "14d15803-caab-d12b-ad17-07237af92fb7"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>oil_urals</th>\n",
" <th>gdp_quart</th>\n",
" <th>gdp_quart_growth</th>\n",
" <th>cpi</th>\n",
" <th>ppi</th>\n",
" <th>gdp_deflator</th>\n",
" <th>balance_trade</th>\n",
" <th>balance_trade_growth</th>\n",
" <th>usdrub</th>\n",
" <th>...</th>\n",
" <th>provision_retail_space_modern_sqm</th>\n",
" <th>turnover_catering_per_cap</th>\n",
" <th>theaters_viewers_per_1000_cap</th>\n",
" <th>seats_theather_rfmin_per_100000_cap</th>\n",
" <th>museum_visitis_per_100_cap</th>\n",
" <th>bandwidth_sports</th>\n",
" <th>population_reg_sports_share</th>\n",
" <th>students_reg_sports_share</th>\n",
" <th>apartment_build</th>\n",
" <th>apartment_fund_sqm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2010-01-01</td>\n",
" <td>76.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>690.0</td>\n",
" <td>6221.0</td>\n",
" <td>527.0</td>\n",
" <td>0.41</td>\n",
" <td>993.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.03</td>\n",
" <td>22825.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 100 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp oil_urals gdp_quart gdp_quart_growth cpi ppi gdp_deflator \\\n",
"0 2010-01-01 76.1 NaN NaN NaN NaN NaN \n",
"\n",
" balance_trade balance_trade_growth usdrub ... \\\n",
"0 NaN NaN NaN ... \n",
"\n",
" provision_retail_space_modern_sqm turnover_catering_per_cap \\\n",
"0 690.0 6221.0 \n",
"\n",
" theaters_viewers_per_1000_cap seats_theather_rfmin_per_100000_cap \\\n",
"0 527.0 0.41 \n",
"\n",
" museum_visitis_per_100_cap bandwidth_sports population_reg_sports_share \\\n",
"0 993.0 NaN NaN \n",
"\n",
" students_reg_sports_share apartment_build apartment_fund_sqm \n",
"0 63.03 22825.0 NaN \n",
"\n",
"[1 rows x 100 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# macro economics data\n",
"macro_df = pd.read_csv(\"../input/macro.csv\")\n",
"macro_df.head(1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "c7e050ac-8098-97c5-577e-4e1e5edf9eb9"
},
"outputs": [
{
"data": {
"image/png": 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6dOlCVFQUERERDBkyBLBOHo6KiqKsrIykpCRbutwBAwYQERHB3r17CQ0NRafT4eXlRaNG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\nW+uR57fSGZ1D+MwrkL+9meymJqI8Pbz7URc9ighQSLlmSRZ6jWzk93j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TzKxz/binDc9ocEhp3U0qArczNoKams54tfDnG0oJpe4V5MGxuLSiVJlBBdlVqlZnzk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\nERERERFx0f8BM9NzJEbnkbgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9697633f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# moving average\n",
"\n",
"# adding macro economic data\n",
"train_df = pd.merge(train_df, macro_df, how='left', on=['timestamp'])\n",
"\n",
"# first let's average per day\n",
"gb = train_df.groupby(['timestamp'])\n",
"gb.sum().head()\n",
"dfagg = pd.DataFrame()\n",
"\n",
"dfagg['avg_price_per_sqm'] = gb.price_doc.sum() / gb.full_sq.sum()\n",
"dfagg['rolling_average_immo'] = dfagg['avg_price_per_sqm'].rolling(30).mean()\n",
"\n",
"dfagg['oil_avg_price'] = gb.oil_urals.mean()\n",
"dfagg['rolling_average_oil'] = dfagg['oil_avg_price'].rolling(30).mean()\n",
"\n",
"dfagg['oil_avg_price_2'] = gb.brent.mean()\n",
"dfagg['rolling_average_oil_2'] = dfagg['oil_avg_price_2'].rolling(30).mean()\n",
"\n",
"dfagg.reset_index(inplace=True)\n",
"dfagg['date'] = pd.to_datetime(dfagg['timestamp'])\n",
"\n",
"plt.figure(figsize=(14,8))\n",
"plt.plot(dfagg['date'], dfagg['rolling_average_immo'], label='avg price per square meter')\n",
"plt.plot(dfagg['date'], 1200 * dfagg['rolling_average_oil'], label = 'oil_urals')\n",
"plt.plot(dfagg['date'], 1200 * dfagg['rolling_average_oil_2'], label='brent')\n",
"\n",
"plt.title('Rolling average price per square meter')\n",
"#plt.xlabel('days')\n",
"plt.ylabel('average price per full_sqm')\n",
"\n",
"plt.legend(loc='lower right')\n",
"\n",
"plt.ylim(20000, 180000)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "ebb9004f-94fc-43ad-1f3a-1d4a7ae07f73"
},
"outputs": [
{
"data": {
"image/png": 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AAIBAajMQPfLII+2+kEUVAAAAAHR3bQai01eUAwAAAIBQ1GYgWrhwYSDrAAAA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\nERExLRUiERERERExLRUiERERERExrX8DEToF5aUzaxUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f969757b940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# price depending on the distance to Kremlin\n",
"train['kremlin_km_rounded'] = np.round(train['kremlin_km'])\n",
"gb = train.groupby(['kremlin_km_rounded'])\n",
"dfagg = pd.DataFrame()\n",
"dfagg['avg_price_per_kremlin_km'] = gb.price_doc.mean()\n",
"dfagg.reset_index(inplace=True)\n",
"\n",
"plt.figure(figsize=(14,8))\n",
"plt.scatter(dfagg['kremlin_km_rounded'], dfagg['avg_price_per_kremlin_km'], label='avg price')\n",
"plt.title('Rolling average price per square meter')\n",
"plt.ylabel('distance to Kremlin')\n",
"plt.legend(loc='lower right')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "beba56ae-6328-74e4-e89c-223f618c4e3f"
},
"outputs": [
{
"data": {
"image/png": 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OzBTKzDzP6J6HX9HQSLbiY6cqM5NOOaHbuRrXzJgKD2vUOIduZgAAAKiKv8h8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+/SzPTaLDJZ18uZaZUHM0u9oUEpygQArDSzdTzynJnR58aR/U5TQTOz7SR9KRY0M1O3SVEze+Ul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\noVAoFAqFQqEoSf4/RmvV49aq+50AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f969cf6c128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train['date'] = pd.to_datetime(train['timestamp'])\n",
"x = train.groupby(['date']).count()\n",
"x.reset_index(inplace=True)\n",
"\n",
"plt.figure(figsize=(14,8))\n",
"plt.plot(x['date'], x['id'])\n",
"plt.title('Number of transactions per day')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "90728499-cb87-bbdc-4ce7-fd71f3750415"
},
"outputs": [
{
"data": {
"image/png": 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hhJB6gwqMBsNyIcs2T4w2GFIj2OKwKHyXjBUDE3E9CCGEEFI8VGA0mM6NLJlG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jAAAg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\nQ0REREREgfH/AcbXAfbDdeUsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f969cf6c2e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = train.groupby(['date']).mean()\n",
"x.reset_index(inplace=True)\n",
"\n",
"plt.figure(figsize=(14,8))\n",
"plt.plot(x['date'], x['price_doc'])\n",
"plt.title('Average price per day')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "64ec5af9-cb90-2407-3066-323819e3e838"
},
"source": [
"**Basic mean prediction**"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "1bf00882-a129-cbbf-be86-30e51224b178"
},
"outputs": [
{
"data": {
"text/plain": [
"6823634.024752475"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.price_doc.mean()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "933d02f2-2773-3047-adeb-204c0af27df0"
},
"outputs": [
{
"data": {
"text/plain": [
"0.59487197437447836"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rmsle(np.repeat(6823634.024752475,9261), test['price_doc'].values)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "6392ed13-73f0-91a8-7595-2acb0c58aba4"
},
"outputs": [
{
"data": {
"text/plain": [
"6000000.0"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.price_doc.median()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "2022cd1b-7d0c-50dc-5c77-5ff4a33bccaf"
},
"outputs": [
{
"data": {
"text/plain": [
"0.60174513491130599"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rmsle(np.repeat(6000000,9261), test['price_doc'].values)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "70798306-11f3-2437-8f0c-be93dda70849"
},
"outputs": [
{
"data": {
"text/plain": [
"0.59421389093242827"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rmsle(np.repeat(6500000,9261), test['price_doc'].values)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "fb45853c-7199-2726-4cf4-e557814a64d4"
},
"outputs": [],
"source": [
"#list(train.columns.values)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3ae9c652-487f-eff9-9fc9-bc63bde27475"
},
"source": [
"**Predictions based on price per sqm and area**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "7fdb72b6-809c-a59f-fe59-f7c50e7ab480"
},
"outputs": [
{
"data": {
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"<div>\n",
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" <thead>\n",
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" <th>area_m</th>\n",
" <th>avg_price_per_sqm</th>\n",
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],
"text/plain": [
" area_m avg_price_per_sqm observations_count\n",
"0 2081627.761 235561.497326 12\n",
"1 2104404.816 126200.303738 65\n",
"2 2641243.496 154690.758971 60\n",
"3 3284350.011 135377.358491 6\n",
"4 3292112.223 128608.032496 117"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gb = train.groupby(['area_m'])\n",
"\n",
"dfagg = pd.DataFrame()\n",
"\n",
"# bayesian average\n",
"dfagg['avg_price_per_sqm'] = (5 * 6000000 + gb.price_doc.sum()) / (5 * 40 + gb.full_sq.sum())\n",
"\n",
"dfagg['observations_count'] = gb.price_doc.count()\n",
"dfagg.reset_index(inplace=True)\n",
"dfagg.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "3b877e66-c694-e567-32f0-6aba758e324d"
},
"outputs": [
{
"data": {
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"<div>\n",
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" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>...</th>\n",
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" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" <th>avg_price_per_sqm</th>\n",
" <th>observations_count</th>\n",
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" <td>23.0</td>\n",
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" <td>10.0</td>\n",
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" <td>6</td>\n",
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" <td>32.0</td>\n",
" <td>32.0</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>21233</td>\n",
" <td>2014-08-01</td>\n",
" <td>73</td>\n",
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" <td>17.0</td>\n",
" <td>1.0</td>\n",
" <td>1998.0</td>\n",
" <td>3.0</td>\n",
" <td>10.0</td>\n",
" <td>...</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 295 columns</p>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material \\\n",
"0 21229 2014-08-01 32 18.0 1.0 9.0 1.0 \n",
"1 21230 2014-08-01 39 20.0 23.0 25.0 6.0 \n",
"2 21231 2014-08-01 46 44.0 32.0 32.0 4.0 \n",
"3 21232 2014-08-01 38 23.0 10.0 14.0 1.0 \n",
"4 21233 2014-08-01 73 44.0 4.0 17.0 1.0 \n",
"\n",
" build_year num_room kitch_sq ... big_church_count_5000 \\\n",
"0 1967.0 1.0 6.0 ... 15 \n",
"1 2016.0 1.0 10.0 ... 4 \n",
"2 2006.0 1.0 1.0 ... 12 \n",
"3 1973.0 2.0 6.0 ... 8 \n",
"4 1998.0 3.0 10.0 ... 4 \n",
"\n",
" church_count_5000 mosque_count_5000 leisure_count_5000 sport_count_5000 \\\n",
"0 32 1 3 76 \n",
"1 6 0 0 5 \n",
"2 22 1 0 67 \n",
"3 38 2 3 68 \n",
"4 4 0 0 26 \n",
"\n",
" market_count_5000 price_doc avg_price_per_sqm observations_count \\\n",
"0 12 6500000 146594.849363 150 \n",
"1 1 4740000 94814.934735 1382 \n",
"2 5 7900000 128608.032496 117 \n",
"3 12 7700000 164124.618508 156 \n",
"4 3 12500000 142740.121594 511 \n",
"\n",
" est_price \n",
"0 4.691035e+06 \n",
"1 3.697782e+06 \n",
"2 5.915969e+06 \n",
"3 6.236736e+06 \n",
"4 1.042003e+07 \n",
"\n",
"[5 rows x 295 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_merged = pd.merge(test, dfagg, how='left', on=['area_m'])\n",
"test_merged['avg_price_per_sqm'] = test_merged.avg_price_per_sqm.replace(np.NaN, 6823634.024752475)\n",
"test_merged['est_price'] = test_merged['avg_price_per_sqm'] * test_merged['full_sq']\n",
"test_merged.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "81aaddb3-c70b-c168-8973-544c61330f8a"
},
"outputs": [
{
"data": {
"text/plain": [
"0.51476313034506394"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rmsle(test_merged['est_price'].values, test_merged['price_doc'].values)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f212d69a-3b5c-d206-3c61-d1ee688755a2"
},
"source": [
"**Predictions based on price per sqm, area and subarea**"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "2cca7043-66e4-69f6-2a9c-0753d9b81b47"
},
"outputs": [
{
"data": {
"text/plain": [
"0.51531739228572693"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gb = train.groupby(['area_m', 'sub_area'])\n",
"\n",
"dfagg = pd.DataFrame()\n",
"dfagg['avg_price_per_sqm'] = gb.price_doc.sum() / gb.full_sq.sum()\n",
"dfagg.reset_index(inplace=True)\n",
"\n",
"test_merged = pd.merge(test, dfagg, how='left', on=['area_m', 'sub_area'])\n",
"test_merged['avg_price_per_sqm'] = test_merged.avg_price_per_sqm.replace(np.NaN, 6623634)\n",
"test_merged['est_price'] = test_merged['avg_price_per_sqm'] * test_merged['full_sq']\n",
"\n",
"rmsle(test_merged['est_price'].values, test_merged['price_doc'].values)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "03819d30-e25a-cd29-ceb9-cbfc0449e199"
},
"outputs": [
{
"data": {
"text/plain": [
"0.5204584645521062"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['dist'] = np.round(train['kremlin_km']/2)\n",
"test['dist'] = np.round(test['kremlin_km']/2)\n",
"gb = train.groupby(['dist'])\n",
"\n",
"dfagg = pd.DataFrame()\n",
"dfagg['avg_price_per_sqm'] = (3 * 6000000.0 + gb.price_doc.sum()) / (3 * 60 + gb.full_sq.sum())\n",
"dfagg.reset_index(inplace=True)\n",
"\n",
"test_merged = pd.merge(test, dfagg, how='left', on=['dist'])\n",
"test_merged['avg_price_per_sqm'] = test_merged.avg_price_per_sqm.replace(np.NaN, 6623634)\n",
"test_merged['est_price'] = test_merged['avg_price_per_sqm'] * test_merged['full_sq']\n",
"\n",
"rmsle(test_merged['est_price'].values, test_merged['price_doc'].values)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "bb63e811-fac7-2027-2ab0-e323996a8ae1"
},
"outputs": [],
"source": []
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],
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"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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},
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| 0001/159/1159502.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f58ae579-4502-4f30-8380-244ac9163e76"
},
"source": [
"EDA"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "47558d3c-efd7-0d10-eae6-b355163dd6df"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"macro.csv\n",
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"%matplotlib inline\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4674cfd5-8754-0000-34d4-cd90e4a753d5"
},
"source": [
"# EDA of macro.csv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "2fcd67fc-3865-4b0c-6b26-2abf4b59792e"
},
"outputs": [
{
"data": {
"text/plain": [
"(2484, 100)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"macro_df = pd.read_csv(\"../input/macro.csv\")\n",
"macro_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "32a2b11f-b7f6-f7b3-0ce1-a3243623c0df"
},
"outputs": [
{
"data": {
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" <th>timestamp</th>\n",
" <th>oil_urals</th>\n",
" <th>gdp_quart</th>\n",
" <th>gdp_quart_growth</th>\n",
" <th>cpi</th>\n",
" <th>ppi</th>\n",
" <th>gdp_deflator</th>\n",
" <th>balance_trade</th>\n",
" <th>balance_trade_growth</th>\n",
" <th>usdrub</th>\n",
" <th>...</th>\n",
" <th>provision_retail_space_modern_sqm</th>\n",
" <th>turnover_catering_per_cap</th>\n",
" <th>theaters_viewers_per_1000_cap</th>\n",
" <th>seats_theather_rfmin_per_100000_cap</th>\n",
" <th>museum_visitis_per_100_cap</th>\n",
" <th>bandwidth_sports</th>\n",
" <th>population_reg_sports_share</th>\n",
" <th>students_reg_sports_share</th>\n",
" <th>apartment_build</th>\n",
" <th>apartment_fund_sqm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2010-01-01</td>\n",
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" <td>0.41</td>\n",
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" <td>22825.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>2010-01-02</td>\n",
" <td>76.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>690.0</td>\n",
" <td>6221.0</td>\n",
" <td>527.0</td>\n",
" <td>0.41</td>\n",
" <td>993.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.03</td>\n",
" <td>22825.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2010-01-03</td>\n",
" <td>76.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>690.0</td>\n",
" <td>6221.0</td>\n",
" <td>527.0</td>\n",
" <td>0.41</td>\n",
" <td>993.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.03</td>\n",
" <td>22825.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2010-01-04</td>\n",
" <td>76.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.905</td>\n",
" <td>...</td>\n",
" <td>690.0</td>\n",
" <td>6221.0</td>\n",
" <td>527.0</td>\n",
" <td>0.41</td>\n",
" <td>993.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.03</td>\n",
" <td>22825.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2010-01-05</td>\n",
" <td>76.1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>29.836</td>\n",
" <td>...</td>\n",
" <td>690.0</td>\n",
" <td>6221.0</td>\n",
" <td>527.0</td>\n",
" <td>0.41</td>\n",
" <td>993.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>63.03</td>\n",
" <td>22825.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 100 columns</p>\n",
"</div>"
],
"text/plain": [
" timestamp oil_urals gdp_quart gdp_quart_growth cpi ppi gdp_deflator \\\n",
"0 2010-01-01 76.1 NaN NaN NaN NaN NaN \n",
"1 2010-01-02 76.1 NaN NaN NaN NaN NaN \n",
"2 2010-01-03 76.1 NaN NaN NaN NaN NaN \n",
"3 2010-01-04 76.1 NaN NaN NaN NaN NaN \n",
"4 2010-01-05 76.1 NaN NaN NaN NaN NaN \n",
"\n",
" balance_trade balance_trade_growth usdrub ... \\\n",
"0 NaN NaN NaN ... \n",
"1 NaN NaN NaN ... \n",
"2 NaN NaN NaN ... \n",
"3 NaN NaN 29.905 ... \n",
"4 NaN NaN 29.836 ... \n",
"\n",
" provision_retail_space_modern_sqm turnover_catering_per_cap \\\n",
"0 690.0 6221.0 \n",
"1 690.0 6221.0 \n",
"2 690.0 6221.0 \n",
"3 690.0 6221.0 \n",
"4 690.0 6221.0 \n",
"\n",
" theaters_viewers_per_1000_cap seats_theather_rfmin_per_100000_cap \\\n",
"0 527.0 0.41 \n",
"1 527.0 0.41 \n",
"2 527.0 0.41 \n",
"3 527.0 0.41 \n",
"4 527.0 0.41 \n",
"\n",
" museum_visitis_per_100_cap bandwidth_sports population_reg_sports_share \\\n",
"0 993.0 NaN NaN \n",
"1 993.0 NaN NaN \n",
"2 993.0 NaN NaN \n",
"3 993.0 NaN NaN \n",
"4 993.0 NaN NaN \n",
"\n",
" students_reg_sports_share apartment_build apartment_fund_sqm \n",
"0 63.03 22825.0 NaN \n",
"1 63.03 22825.0 NaN \n",
"2 63.03 22825.0 NaN \n",
"3 63.03 22825.0 NaN \n",
"4 63.03 22825.0 NaN \n",
"\n",
"[5 rows x 100 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"macro_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "18fe32ed-86df-8a2a-e65f-da3048d13994"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>oil_urals</th>\n",
" <th>gdp_quart</th>\n",
" <th>gdp_quart_growth</th>\n",
" <th>cpi</th>\n",
" <th>ppi</th>\n",
" <th>gdp_deflator</th>\n",
" <th>balance_trade</th>\n",
" <th>balance_trade_growth</th>\n",
" <th>usdrub</th>\n",
" <th>eurrub</th>\n",
" <th>...</th>\n",
" <th>provision_retail_space_modern_sqm</th>\n",
" <th>turnover_catering_per_cap</th>\n",
" <th>theaters_viewers_per_1000_cap</th>\n",
" <th>seats_theather_rfmin_per_100000_cap</th>\n",
" <th>museum_visitis_per_100_cap</th>\n",
" <th>bandwidth_sports</th>\n",
" <th>population_reg_sports_share</th>\n",
" <th>students_reg_sports_share</th>\n",
" <th>apartment_build</th>\n",
" <th>apartment_fund_sqm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2484.000000</td>\n",
" <td>2394.000000</td>\n",
" <td>2394.000000</td>\n",
" <td>2453.000000</td>\n",
" <td>2453.000000</td>\n",
" <td>2119.000000</td>\n",
" <td>2453.000000</td>\n",
" <td>2394.000000</td>\n",
" <td>2481.000000</td>\n",
" <td>2481.000000</td>\n",
" <td>...</td>\n",
" <td>730.000000</td>\n",
" <td>2191.000000</td>\n",
" <td>1461.00000</td>\n",
" <td>2191.000000</td>\n",
" <td>1461.000000</td>\n",
" <td>1826.000000</td>\n",
" <td>1461.00000</td>\n",
" <td>1461.000000</td>\n",
" <td>1826.000000</td>\n",
" <td>1826.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>86.467157</td>\n",
" <td>16993.230075</td>\n",
" <td>1.523726</td>\n",
" <td>407.808398</td>\n",
" <td>480.216347</td>\n",
" <td>110.122308</td>\n",
" <td>15.344006</td>\n",
" <td>16.846742</td>\n",
" <td>40.732406</td>\n",
" <td>50.444801</td>\n",
" <td>...</td>\n",
" <td>480.500000</td>\n",
" <td>8691.922410</td>\n",
" <td>580.51540</td>\n",
" <td>0.443398</td>\n",
" <td>1245.543463</td>\n",
" <td>349902.577766</td>\n",
" <td>25.06859</td>\n",
" <td>67.800034</td>\n",
" <td>36282.434830</td>\n",
" <td>230615.059255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>27.528709</td>\n",
" <td>3187.074480</td>\n",
" <td>2.875659</td>\n",
" <td>65.895969</td>\n",
" <td>70.286366</td>\n",
" <td>14.818429</td>\n",
" <td>3.878104</td>\n",
" <td>16.988727</td>\n",
" <td>15.006583</td>\n",
" <td>13.905912</td>\n",
" <td>...</td>\n",
" <td>209.643641</td>\n",
" <td>1668.967502</td>\n",
" <td>37.98811</td>\n",
" <td>0.016061</td>\n",
" <td>162.526951</td>\n",
" <td>72146.453110</td>\n",
" <td>2.44944</td>\n",
" <td>5.168602</td>\n",
" <td>10761.669111</td>\n",
" <td>2944.879242</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>29.112500</td>\n",
" <td>9995.800000</td>\n",
" <td>-4.500000</td>\n",
" <td>315.100000</td>\n",
" <td>337.200000</td>\n",
" <td>86.721000</td>\n",
" <td>5.823000</td>\n",
" <td>-4.100000</td>\n",
" <td>27.276300</td>\n",
" <td>37.445400</td>\n",
" <td>...</td>\n",
" <td>271.000000</td>\n",
" <td>6221.000000</td>\n",
" <td>527.00000</td>\n",
" <td>0.410000</td>\n",
" <td>993.000000</td>\n",
" <td>269768.000000</td>\n",
" <td>22.37000</td>\n",
" <td>63.030000</td>\n",
" <td>22825.000000</td>\n",
" <td>226047.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>61.283300</td>\n",
" <td>14925.000000</td>\n",
" <td>0.400000</td>\n",
" <td>354.000000</td>\n",
" <td>438.400000</td>\n",
" <td>100.000000</td>\n",
" <td>12.785000</td>\n",
" <td>7.200000</td>\n",
" <td>30.508800</td>\n",
" <td>40.242600</td>\n",
" <td>...</td>\n",
" <td>271.000000</td>\n",
" <td>6943.000000</td>\n",
" <td>565.00000</td>\n",
" <td>0.439390</td>\n",
" <td>1240.000000</td>\n",
" <td>288177.000000</td>\n",
" <td>23.01000</td>\n",
" <td>64.120000</td>\n",
" <td>23587.000000</td>\n",
" <td>229294.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>101.416500</td>\n",
" <td>17442.100000</td>\n",
" <td>1.300000</td>\n",
" <td>393.000000</td>\n",
" <td>473.500000</td>\n",
" <td>108.299000</td>\n",
" <td>16.041000</td>\n",
" <td>10.500000</td>\n",
" <td>32.316500</td>\n",
" <td>42.862200</td>\n",
" <td>...</td>\n",
" <td>480.500000</td>\n",
" <td>8522.000000</td>\n",
" <td>603.00000</td>\n",
" <td>0.450700</td>\n",
" <td>1309.000000</td>\n",
" <td>329348.000000</td>\n",
" <td>23.01000</td>\n",
" <td>67.850000</td>\n",
" <td>42551.000000</td>\n",
" <td>230310.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>109.310000</td>\n",
" <td>19284.100000</td>\n",
" <td>4.100000</td>\n",
" <td>469.400000</td>\n",
" <td>520.700000</td>\n",
" <td>123.661000</td>\n",
" <td>18.076000</td>\n",
" <td>21.500000</td>\n",
" <td>53.752300</td>\n",
" <td>61.323700</td>\n",
" <td>...</td>\n",
" <td>690.000000</td>\n",
" <td>10311.000000</td>\n",
" <td>603.00000</td>\n",
" <td>0.453560</td>\n",
" <td>1309.000000</td>\n",
" <td>398451.000000</td>\n",
" <td>26.70000</td>\n",
" <td>67.850000</td>\n",
" <td>46080.000000</td>\n",
" <td>232840.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>122.520000</td>\n",
" <td>22016.100000</td>\n",
" <td>5.200000</td>\n",
" <td>531.000000</td>\n",
" <td>606.100000</td>\n",
" <td>133.160000</td>\n",
" <td>21.939000</td>\n",
" <td>75.800000</td>\n",
" <td>82.276400</td>\n",
" <td>90.881500</td>\n",
" <td>...</td>\n",
" <td>690.000000</td>\n",
" <td>10805.000000</td>\n",
" <td>627.00000</td>\n",
" <td>0.458880</td>\n",
" <td>1440.000000</td>\n",
" <td>463938.000000</td>\n",
" <td>28.20000</td>\n",
" <td>76.200000</td>\n",
" <td>46352.000000</td>\n",
" <td>234576.900000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 96 columns</p>\n",
"</div>"
],
"text/plain": [
" oil_urals gdp_quart gdp_quart_growth cpi ppi \\\n",
"count 2484.000000 2394.000000 2394.000000 2453.000000 2453.000000 \n",
"mean 86.467157 16993.230075 1.523726 407.808398 480.216347 \n",
"std 27.528709 3187.074480 2.875659 65.895969 70.286366 \n",
"min 29.112500 9995.800000 -4.500000 315.100000 337.200000 \n",
"25% 61.283300 14925.000000 0.400000 354.000000 438.400000 \n",
"50% 101.416500 17442.100000 1.300000 393.000000 473.500000 \n",
"75% 109.310000 19284.100000 4.100000 469.400000 520.700000 \n",
"max 122.520000 22016.100000 5.200000 531.000000 606.100000 \n",
"\n",
" gdp_deflator balance_trade balance_trade_growth usdrub \\\n",
"count 2119.000000 2453.000000 2394.000000 2481.000000 \n",
"mean 110.122308 15.344006 16.846742 40.732406 \n",
"std 14.818429 3.878104 16.988727 15.006583 \n",
"min 86.721000 5.823000 -4.100000 27.276300 \n",
"25% 100.000000 12.785000 7.200000 30.508800 \n",
"50% 108.299000 16.041000 10.500000 32.316500 \n",
"75% 123.661000 18.076000 21.500000 53.752300 \n",
"max 133.160000 21.939000 75.800000 82.276400 \n",
"\n",
" eurrub ... provision_retail_space_modern_sqm \\\n",
"count 2481.000000 ... 730.000000 \n",
"mean 50.444801 ... 480.500000 \n",
"std 13.905912 ... 209.643641 \n",
"min 37.445400 ... 271.000000 \n",
"25% 40.242600 ... 271.000000 \n",
"50% 42.862200 ... 480.500000 \n",
"75% 61.323700 ... 690.000000 \n",
"max 90.881500 ... 690.000000 \n",
"\n",
" turnover_catering_per_cap theaters_viewers_per_1000_cap \\\n",
"count 2191.000000 1461.00000 \n",
"mean 8691.922410 580.51540 \n",
"std 1668.967502 37.98811 \n",
"min 6221.000000 527.00000 \n",
"25% 6943.000000 565.00000 \n",
"50% 8522.000000 603.00000 \n",
"75% 10311.000000 603.00000 \n",
"max 10805.000000 627.00000 \n",
"\n",
" seats_theather_rfmin_per_100000_cap museum_visitis_per_100_cap \\\n",
"count 2191.000000 1461.000000 \n",
"mean 0.443398 1245.543463 \n",
"std 0.016061 162.526951 \n",
"min 0.410000 993.000000 \n",
"25% 0.439390 1240.000000 \n",
"50% 0.450700 1309.000000 \n",
"75% 0.453560 1309.000000 \n",
"max 0.458880 1440.000000 \n",
"\n",
" bandwidth_sports population_reg_sports_share \\\n",
"count 1826.000000 1461.00000 \n",
"mean 349902.577766 25.06859 \n",
"std 72146.453110 2.44944 \n",
"min 269768.000000 22.37000 \n",
"25% 288177.000000 23.01000 \n",
"50% 329348.000000 23.01000 \n",
"75% 398451.000000 26.70000 \n",
"max 463938.000000 28.20000 \n",
"\n",
" students_reg_sports_share apartment_build apartment_fund_sqm \n",
"count 1461.000000 1826.000000 1826.000000 \n",
"mean 67.800034 36282.434830 230615.059255 \n",
"std 5.168602 10761.669111 2944.879242 \n",
"min 63.030000 22825.000000 226047.300000 \n",
"25% 64.120000 23587.000000 229294.800000 \n",
"50% 67.850000 42551.000000 230310.000000 \n",
"75% 67.850000 46080.000000 232840.200000 \n",
"max 76.200000 46352.000000 234576.900000 \n",
"\n",
"[8 rows x 96 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"macro_df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "b4338305-b2bc-f905-c757-ba0f44ea6db7"
},
"source": [
"### missing value"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "ada44b69-4ac5-5829-9098-65b22c2beea8"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>miss_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>466.580000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>379.269345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>273.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>365.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>658.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1754.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" miss_count\n",
"count 100.000000\n",
"mean 466.580000\n",
"std 379.269345\n",
"min 0.000000\n",
"25% 273.000000\n",
"50% 365.000000\n",
"75% 658.000000\n",
"max 1754.000000"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"macro_missing = macro_df.isnull().sum(axis=0).reset_index()\n",
"macro_missing.columns = [\"feature\", \"miss_count\"]\n",
"macro_missing.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "789cb1f9-b4a9-49f2-3d0a-946a00142520"
},
"source": [
"### outliers TODO"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "408e8a61-47f6-2b84-46b1-789768c3f670"
},
"source": [
"# EDA of train.csv"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "2768997c-96f3-c15f-5372-fd1cb11e64e5"
},
"outputs": [
{
"data": {
"text/plain": [
"(30471, 292)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df = pd.read_csv(\"../input/train.csv\")\n",
"train_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "df0b0d9a-ad52-4012-9e04-7b984f7fc819"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>state</th>\n",
" <th>...</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>24088.000000</td>\n",
" <td>30304.000000</td>\n",
" <td>20899.000000</td>\n",
" <td>20899.000000</td>\n",
" <td>1.686600e+04</td>\n",
" <td>20899.000000</td>\n",
" <td>20899.000000</td>\n",
" <td>16912.000000</td>\n",
" <td>...</td>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>30471.000000</td>\n",
" <td>3.047100e+04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>15237.917397</td>\n",
" <td>54.214269</td>\n",
" <td>34.403271</td>\n",
" <td>7.670803</td>\n",
" <td>12.558974</td>\n",
" <td>1.827121</td>\n",
" <td>3.068057e+03</td>\n",
" <td>1.909804</td>\n",
" <td>6.399301</td>\n",
" <td>2.107025</td>\n",
" <td>...</td>\n",
" <td>32.058318</td>\n",
" <td>10.783860</td>\n",
" <td>1.771783</td>\n",
" <td>15.045552</td>\n",
" <td>30.251518</td>\n",
" <td>0.442421</td>\n",
" <td>8.648814</td>\n",
" <td>52.796593</td>\n",
" <td>5.987070</td>\n",
" <td>7.123035e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>8796.501536</td>\n",
" <td>38.031487</td>\n",
" <td>52.285733</td>\n",
" <td>5.319989</td>\n",
" <td>6.756550</td>\n",
" <td>1.481154</td>\n",
" <td>1.543878e+05</td>\n",
" <td>0.851805</td>\n",
" <td>28.265979</td>\n",
" <td>0.880148</td>\n",
" <td>...</td>\n",
" <td>73.465611</td>\n",
" <td>28.385679</td>\n",
" <td>5.418807</td>\n",
" <td>29.118668</td>\n",
" <td>47.347938</td>\n",
" <td>0.609269</td>\n",
" <td>20.580741</td>\n",
" <td>46.292660</td>\n",
" <td>4.889219</td>\n",
" <td>4.780111e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000e+05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>7620.500000</td>\n",
" <td>38.000000</td>\n",
" <td>20.000000</td>\n",
" <td>3.000000</td>\n",
" <td>9.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.967000e+03</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>...</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>9.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>11.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.740002e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>15238.000000</td>\n",
" <td>49.000000</td>\n",
" <td>30.000000</td>\n",
" <td>6.500000</td>\n",
" <td>12.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.979000e+03</td>\n",
" <td>2.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2.000000</td>\n",
" <td>...</td>\n",
" <td>8.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.000000</td>\n",
" <td>16.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>48.000000</td>\n",
" <td>5.000000</td>\n",
" <td>6.274411e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>22855.500000</td>\n",
" <td>63.000000</td>\n",
" <td>43.000000</td>\n",
" <td>11.000000</td>\n",
" <td>17.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.005000e+03</td>\n",
" <td>2.000000</td>\n",
" <td>9.000000</td>\n",
" <td>3.000000</td>\n",
" <td>...</td>\n",
" <td>21.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1.000000</td>\n",
" <td>12.000000</td>\n",
" <td>28.000000</td>\n",
" <td>1.000000</td>\n",
" <td>7.000000</td>\n",
" <td>76.000000</td>\n",
" <td>10.000000</td>\n",
" <td>8.300000e+06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>30473.000000</td>\n",
" <td>5326.000000</td>\n",
" <td>7478.000000</td>\n",
" <td>77.000000</td>\n",
" <td>117.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2.005201e+07</td>\n",
" <td>19.000000</td>\n",
" <td>2014.000000</td>\n",
" <td>33.000000</td>\n",
" <td>...</td>\n",
" <td>377.000000</td>\n",
" <td>147.000000</td>\n",
" <td>30.000000</td>\n",
" <td>151.000000</td>\n",
" <td>250.000000</td>\n",
" <td>2.000000</td>\n",
" <td>106.000000</td>\n",
" <td>218.000000</td>\n",
" <td>21.000000</td>\n",
" <td>1.111111e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 276 columns</p>\n",
"</div>"
],
"text/plain": [
" id full_sq life_sq floor max_floor \\\n",
"count 30471.000000 30471.000000 24088.000000 30304.000000 20899.000000 \n",
"mean 15237.917397 54.214269 34.403271 7.670803 12.558974 \n",
"std 8796.501536 38.031487 52.285733 5.319989 6.756550 \n",
"min 1.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 7620.500000 38.000000 20.000000 3.000000 9.000000 \n",
"50% 15238.000000 49.000000 30.000000 6.500000 12.000000 \n",
"75% 22855.500000 63.000000 43.000000 11.000000 17.000000 \n",
"max 30473.000000 5326.000000 7478.000000 77.000000 117.000000 \n",
"\n",
" material build_year num_room kitch_sq state \\\n",
"count 20899.000000 1.686600e+04 20899.000000 20899.000000 16912.000000 \n",
"mean 1.827121 3.068057e+03 1.909804 6.399301 2.107025 \n",
"std 1.481154 1.543878e+05 0.851805 28.265979 0.880148 \n",
"min 1.000000 0.000000e+00 0.000000 0.000000 1.000000 \n",
"25% 1.000000 1.967000e+03 1.000000 1.000000 1.000000 \n",
"50% 1.000000 1.979000e+03 2.000000 6.000000 2.000000 \n",
"75% 2.000000 2.005000e+03 2.000000 9.000000 3.000000 \n",
"max 6.000000 2.005201e+07 19.000000 2014.000000 33.000000 \n",
"\n",
" ... cafe_count_5000_price_2500 cafe_count_5000_price_4000 \\\n",
"count ... 30471.000000 30471.000000 \n",
"mean ... 32.058318 10.783860 \n",
"std ... 73.465611 28.385679 \n",
"min ... 0.000000 0.000000 \n",
"25% ... 2.000000 1.000000 \n",
"50% ... 8.000000 2.000000 \n",
"75% ... 21.000000 5.000000 \n",
"max ... 377.000000 147.000000 \n",
"\n",
" cafe_count_5000_price_high big_church_count_5000 church_count_5000 \\\n",
"count 30471.000000 30471.000000 30471.000000 \n",
"mean 1.771783 15.045552 30.251518 \n",
"std 5.418807 29.118668 47.347938 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 2.000000 9.000000 \n",
"50% 0.000000 7.000000 16.000000 \n",
"75% 1.000000 12.000000 28.000000 \n",
"max 30.000000 151.000000 250.000000 \n",
"\n",
" mosque_count_5000 leisure_count_5000 sport_count_5000 \\\n",
"count 30471.000000 30471.000000 30471.000000 \n",
"mean 0.442421 8.648814 52.796593 \n",
"std 0.609269 20.580741 46.292660 \n",
"min 0.000000 0.000000 0.000000 \n",
"25% 0.000000 0.000000 11.000000 \n",
"50% 0.000000 2.000000 48.000000 \n",
"75% 1.000000 7.000000 76.000000 \n",
"max 2.000000 106.000000 218.000000 \n",
"\n",
" market_count_5000 price_doc \n",
"count 30471.000000 3.047100e+04 \n",
"mean 5.987070 7.123035e+06 \n",
"std 4.889219 4.780111e+06 \n",
"min 0.000000 1.000000e+05 \n",
"25% 1.000000 4.740002e+06 \n",
"50% 5.000000 6.274411e+06 \n",
"75% 10.000000 8.300000e+06 \n",
"max 21.000000 1.111111e+08 \n",
"\n",
"[8 rows x 276 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "761ad31d-e274-265e-93ec-22fc5ce64aa2"
},
"source": [
"### missing value"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "ab21e52d-cd5e-99b9-3018-03176ae8a740"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>miss_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>id</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>timestamp</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>full_sq</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>life_sq</td>\n",
" <td>6383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>floor</td>\n",
" <td>167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>max_floor</td>\n",
" <td>9572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>material</td>\n",
" <td>9572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>build_year</td>\n",
" <td>13605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>num_room</td>\n",
" <td>9572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>kitch_sq</td>\n",
" <td>9572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>state</td>\n",
" <td>13559</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>product_type</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>sub_area</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>area_m</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>raion_popul</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>green_zone_part</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>indust_part</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>children_preschool</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>preschool_quota</td>\n",
" <td>6688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>preschool_education_centers_raion</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>children_school</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>school_quota</td>\n",
" <td>6685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>school_education_centers_raion</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>school_education_centers_top_20_raion</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>hospital_beds_raion</td>\n",
" <td>14441</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>healthcare_centers_raion</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>university_top_20_raion</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>sport_objects_raion</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>additional_education_raion</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>culture_objects_top_25</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>262</th>\n",
" <td>big_church_count_3000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>263</th>\n",
" <td>church_count_3000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>264</th>\n",
" <td>mosque_count_3000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>265</th>\n",
" <td>leisure_count_3000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td>sport_count_3000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>267</th>\n",
" <td>market_count_3000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>268</th>\n",
" <td>green_part_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td>prom_part_5000</td>\n",
" <td>178</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>office_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>271</th>\n",
" <td>office_sqm_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>272</th>\n",
" <td>trc_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>trc_sqm_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>274</th>\n",
" <td>cafe_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>275</th>\n",
" <td>cafe_sum_5000_min_price_avg</td>\n",
" <td>297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>276</th>\n",
" <td>cafe_sum_5000_max_price_avg</td>\n",
" <td>297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>277</th>\n",
" <td>cafe_avg_price_5000</td>\n",
" <td>297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>278</th>\n",
" <td>cafe_count_5000_na_price</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>279</th>\n",
" <td>cafe_count_5000_price_500</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>cafe_count_5000_price_1000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td>cafe_count_5000_price_1500</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td>cafe_count_5000_price_2500</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>283</th>\n",
" <td>cafe_count_5000_price_4000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>cafe_count_5000_price_high</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>285</th>\n",
" <td>big_church_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td>church_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>287</th>\n",
" <td>mosque_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>288</th>\n",
" <td>leisure_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>289</th>\n",
" <td>sport_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>market_count_5000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>291</th>\n",
" <td>price_doc</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>292 rows × 2 columns</p>\n",
"</div>"
],
"text/plain": [
" feature miss_count\n",
"0 id 0\n",
"1 timestamp 0\n",
"2 full_sq 0\n",
"3 life_sq 6383\n",
"4 floor 167\n",
"5 max_floor 9572\n",
"6 material 9572\n",
"7 build_year 13605\n",
"8 num_room 9572\n",
"9 kitch_sq 9572\n",
"10 state 13559\n",
"11 product_type 0\n",
"12 sub_area 0\n",
"13 area_m 0\n",
"14 raion_popul 0\n",
"15 green_zone_part 0\n",
"16 indust_part 0\n",
"17 children_preschool 0\n",
"18 preschool_quota 6688\n",
"19 preschool_education_centers_raion 0\n",
"20 children_school 0\n",
"21 school_quota 6685\n",
"22 school_education_centers_raion 0\n",
"23 school_education_centers_top_20_raion 0\n",
"24 hospital_beds_raion 14441\n",
"25 healthcare_centers_raion 0\n",
"26 university_top_20_raion 0\n",
"27 sport_objects_raion 0\n",
"28 additional_education_raion 0\n",
"29 culture_objects_top_25 0\n",
".. ... ...\n",
"262 big_church_count_3000 0\n",
"263 church_count_3000 0\n",
"264 mosque_count_3000 0\n",
"265 leisure_count_3000 0\n",
"266 sport_count_3000 0\n",
"267 market_count_3000 0\n",
"268 green_part_5000 0\n",
"269 prom_part_5000 178\n",
"270 office_count_5000 0\n",
"271 office_sqm_5000 0\n",
"272 trc_count_5000 0\n",
"273 trc_sqm_5000 0\n",
"274 cafe_count_5000 0\n",
"275 cafe_sum_5000_min_price_avg 297\n",
"276 cafe_sum_5000_max_price_avg 297\n",
"277 cafe_avg_price_5000 297\n",
"278 cafe_count_5000_na_price 0\n",
"279 cafe_count_5000_price_500 0\n",
"280 cafe_count_5000_price_1000 0\n",
"281 cafe_count_5000_price_1500 0\n",
"282 cafe_count_5000_price_2500 0\n",
"283 cafe_count_5000_price_4000 0\n",
"284 cafe_count_5000_price_high 0\n",
"285 big_church_count_5000 0\n",
"286 church_count_5000 0\n",
"287 mosque_count_5000 0\n",
"288 leisure_count_5000 0\n",
"289 sport_count_5000 0\n",
"290 market_count_5000 0\n",
"291 price_doc 0\n",
"\n",
"[292 rows x 2 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_missing = train_df.isnull().sum(axis=0).reset_index()\n",
"train_missing.columns = [\"feature\", \"miss_count\"]\n",
"train_missing"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "513da944-5982-16ca-e285-167e412f870d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>miss_count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>292.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>893.924658</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2596.124892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>14441.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" miss_count\n",
"count 292.000000\n",
"mean 893.924658\n",
"std 2596.124892\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 0.000000\n",
"75% 0.000000\n",
"max 14441.000000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_missing.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "99148d9f-473f-9eae-07d4-01a14aee00d8"
},
"source": [
"### Is macro date match the date in train set?\n",
"It seems that date in macro cover the range of date in train set."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "62899ef3-b62e-c1cd-9273-b3539969ba3b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: 2011-08-20 ~ 2015-06-30\n",
"macro: 2010-01-01 ~ 2016-10-19\n"
]
}
],
"source": [
"print(\"train:\", train_df.timestamp[0], \"~\", train_df.timestamp.iloc[-1])\n",
"print(\"macro:\", macro_df.timestamp[0], \"~\", macro_df.timestamp.iloc[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d9f0cb7e-774c-3d33-628a-a4504f16069e"
},
"source": [
"# EDA of test set\n",
"see if the timestamp in test set is covered by the date range in macro"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "63147b4b-736e-8dd5-9610-1fe52a981dff"
},
"outputs": [
{
"data": {
"text/plain": [
"(7662, 291)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = pd.read_csv(\"../input/test.csv\")\n",
"test_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "71ab51f8-c211-472a-d320-e226cc529410"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test: 2015-07-01 ~ 2016-05-30\n"
]
}
],
"source": [
"print(\"test:\", test_df.timestamp[0], \"~\", test_df.timestamp.iloc[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "50f6ae93-96cb-3f70-4c58-af5fbadc8fa7"
},
"source": [
"# investment type"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "09ddab04-76bf-e15c-a7fd-7b85f96a8ee6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"19448\n",
"4998\n"
]
}
],
"source": [
"print((train_df.product_type == \"Investment\").sum())\n",
"print((test_df.product_type == \"Investment\").sum())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "a0d2057e-aadc-c85d-31bc-9f16d0d71776"
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_df.timestamp.iloc[0] > '2012-08-20'"
]
}
],
"metadata": {
"_change_revision": 2,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159590.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1e37bc3c-00da-5c42-69e0-b86c50e7df83"
},
"source": [
"# Trying out..."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f2e6397e-462b-b02f-7020-9b5b985da1a4"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"from sklearn import model_selection, preprocessing\n",
"import xgboost as xgb\n",
"import math\n",
"#import datetime"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "a0ec5b6b-3e93-6872-3d8b-42e1b525c5d9"
},
"outputs": [],
"source": [
"train_df = pd.read_csv('../input/train.csv', parse_dates=['timestamp'])\n",
"test_df = pd.read_csv('../input/test.csv', parse_dates=['timestamp'])\n",
"macro = pd.read_csv('../input/macro.csv', parse_dates=['timestamp'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "9f75e402-b67b-319b-8b7a-615408d11f23"
},
"source": [
"## Some cleanup and correction on most important features... let's see..."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "cb8028c7-7471-fd0b-95d7-62f492671e0a"
},
"source": [
"### cleaning up floors and max_floors "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "ab627620-ea16-8615-1e71-de64b88da982"
},
"outputs": [],
"source": [
"# Few rows here to drop not much impact...\n",
"\n",
"train_df.dropna(axis=0, subset=['floor'], how='any', inplace=True)\n",
"train_df.drop(train_df[train_df['floor'] == 0].index, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "46dbc6c7-75e4-0ce8-2182-07a4504bfab4"
},
"outputs": [],
"source": [
"#Assuming Max_floor is equal to floor when it is not consistent.\n",
"train_df['max_floor'] = train_df['max_floor'].fillna(0)\n",
"train_df['max_floor'] = np.where(train_df['max_floor'] < train_df['floor'], train_df['floor'], train_df['max_floor'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a69f40f9-72cf-20e9-f5c2-40f1a0f2a2f5"
},
"source": [
"## Cleaning up Full_sq and life_Sq "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "19c303de-0ab9-4ef7-e5bc-f6f497609114"
},
"outputs": [],
"source": [
"# wierd values corrected\n",
"train_df.set_value(train_df[train_df['state'] == 33].index, 'state', 4)\n",
"train_df.set_value(train_df[train_df['build_year'] == 20052009].index, 'build_year', 2007)\n",
"# 1 Outlier in full_Sq we will delete for the moment\n",
"train_df.drop(train_df[train_df['full_sq'] > 2000].index, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "a6a05e11-b80a-881e-9629-64e7c06665ee"
},
"outputs": [],
"source": [
"#droping rows where lif_sq is greater than full_sq (22 records )\n",
"train_df['bad_life'] = train_df['full_sq'] - train_df['life_sq']\n",
"train_df.drop(train_df[train_df['bad_life'] < 0].index, inplace=True)\n",
"\n",
"#completing NaN values with mean ratio between Full and Life SQ\n",
"train_df['r_life_ful_sq'] = train_df['bad_life'] / train_df['full_sq']\n",
"mean_ratio = train_df['r_life_ful_sq'].mean()\n",
"train_df.life_sq.fillna(train_df.full_sq *(1 - mean_ratio), inplace=True)\n",
"\n",
"# droping working columns\n",
"train_df.drop(['bad_life', 'r_life_ful_sq'], axis=1, inplace=True)\n",
"\n",
"# Replacing life_sq < 5sq by mean ration full and life as for NaN\n",
"train_df['life_sq'] = np.where(train_df['life_sq'] <=5, train_df['full_sq'] * (1 - mean_ratio), train_df['life_sq'] )"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "fe3dc500-9275-98f0-bd87-031c727bb915"
},
"source": [
"## merging macroeconomics data "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "2ff69200-e170-8cb9-2c7d-f74d012c3c55"
},
"outputs": [],
"source": [
"dftrain = pd.merge(train_df, macro, how='left', on='timestamp')\n",
"dftest = pd.merge(test_df, macro, how='left', on='timestamp')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "3f64c194-1726-ffab-3885-4ff70c51f46b"
},
"outputs": [],
"source": [
"#y_train = dftrain[\"price_doc\"]\n",
"x_train = dftrain.drop([\"id\", \"timestamp\"], axis=1)\n",
"x_test = dftest.drop([\"id\", \"timestamp\"], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "a7ad4310-dc6f-7e13-ed67-150d85275537"
},
"outputs": [],
"source": [
"# these variables are empty in test set or their feat importance is assumed small atm... \n",
"# we will revisit later it will grow bigger for sure\n",
"list_empty = ['grp','grp_growth','real_dispos_income_per_cap_growth', 'profitable_enterpr_share',\n",
" 'unprofitable_enterpr_share','share_own_revenues','overdue_wages_per_cap', 'fin_res_per_cap',\n",
" 'marriages_per_1000_cap','divorce_rate','construction_value', 'invest_fixed_assets_phys',\n",
" 'pop_migration','pop_total_inc','housing_fund_sqm','lodging_sqm_per_cap', 'water_pipes_share', 'baths_share',\n",
" 'sewerage_share','gas_share', 'hot_water_share', 'electric_stove_share', 'heating_share',\n",
" 'old_house_share', 'infant_mortarity_per_1000_cap', 'perinatal_mort_per_1000_cap', 'incidence_population',\n",
" 'load_of_teachers_preschool_per_teacher', 'child_on_acc_pre_school', 'provision_doctors',\n",
" 'power_clinics', 'hospital_beds_available_per_cap', 'hospital_bed_occupancy_per_year',\n",
" 'provision_retail_space_sqm', 'provision_retail_space_modern_sqm', 'theaters_viewers_per_1000_cap',\n",
" 'museum_visitis_per_100_cap', 'population_reg_sports_share',\n",
" 'students_reg_sports_share', 'apartment_build', 'modern_education_share', 'old_education_build_share', \n",
" 'child_on_acc_pre_school']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "a86f2340-595a-7e10-dcb2-cbbe924f61f6"
},
"outputs": [],
"source": [
"x_train.drop(list_empty, axis=1, inplace=True)\n",
"x_test.drop(list_empty, axis=1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "e05ec983-6bcd-57e4-3cf8-81169b446bf0"
},
"outputs": [],
"source": [
"for c in x_train.columns:\n",
" if x_train[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(x_train[c].values)) \n",
" x_train[c] = lbl.transform(list(x_train[c].values))\n",
" \n",
"for c in x_test.columns:\n",
" if x_test[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(x_test[c].values)) \n",
" x_test[c] = lbl.transform(list(x_test[c].values))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "f6e15636-d089-ba18-cad5-17d345f0258a"
},
"outputs": [],
"source": [
"def rmsle(preds, dtrain):\n",
" labels = dtrain.get_label()\n",
" assert len(preds) == len(labels)\n",
" labels = labels.tolist()\n",
" preds = preds.tolist()\n",
" terms_to_sum = [(math.log(labels[i] + 1) - math.log(max(0, preds[i]) + 1)) ** 2.0 for i, pred in enumerate(labels)]\n",
" return 'rmsle', (sum(terms_to_sum) * (1.0 / len(preds))) ** 0.5"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "475a1470-bd50-4de3-dedf-ec0746e3dac2"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'y_train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-44cf25495e49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# Train/Valid split\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0msplit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m27000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mxx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxx_valid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy_valid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0;31m#xx_train, xx_valid, yy_train, yy_valid = cross_validation.train_test_split(x_train, y_train, test_size=0.2)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'y_train' is not defined"
]
}
],
"source": [
"#from sklearn.model_selection import train_test_split\n",
"#from sklearn import preprocessing, cross_validation\n",
"xgb_params = {\n",
" 'eta': 0.05,\n",
" 'max_depth': 8,\n",
" 'subsample': 0.7,\n",
" 'colsample_bytree': 0.7,\n",
" 'objective': 'reg:linear',\n",
" 'silent': 1\n",
"}\n",
"\n",
"#y_train = x_train[\"price_doc\"]\n",
"#x_train.drop('price_doc', axis=1, inplace=True)\n",
"\n",
"# Train/Valid split\n",
"split = 27000\n",
"xx_train, yy_train, xx_valid, yy_valid = x_train[:split], y_train[:split], x_train[split:], y_train[split:]\n",
"#xx_train, xx_valid, yy_train, yy_valid = cross_validation.train_test_split(x_train, y_train, test_size=0.2)\n",
"\n",
"#train_test_split(x_train, y_train, test_size=0.4, random_state=0)\n",
"\n",
"dtrain = xgb.DMatrix(xx_train, yy_train, feature_names=xx_train.columns.values)\n",
"dvalid = xgb.DMatrix(xx_valid, yy_valid, feature_names=xx_valid.columns.values)\n",
"\n",
"watchlist = [(dtrain, 'train'), (dvalid, 'valid')]\n",
"model = xgb.train(dict(xgb_params), dtrain, 600, watchlist, feval=rmsle, early_stopping_rounds=100)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "53cbdf83-6817-68de-86fd-9744fc3bdb9b"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-18160254a48f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfeatureImportance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_fscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'features'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeatureImportance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'importance'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfeatureImportance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mfeatures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'importance'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"featureImportance = model.get_fscore()\n",
"features = pd.DataFrame()\n",
"features['features'] = featureImportance.keys()\n",
"features['importance'] = featureImportance.values()\n",
"features.sort_values(by=['importance'],ascending=False,inplace=True)\n",
"fig,ax= plt.subplots()\n",
"fig.set_size_inches(20,25)\n",
"plt.xticks(rotation=60)\n",
"sns.set(font_scale=1.5)\n",
"sns.barplot(data=features.head(50),y=\"features\",x=\"importance\",ax=ax,orient=\"h\")\n",
"#b.set_ylabel(\"features\",fontsize=20)\n",
"#sns.plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "b9659d99-f4ab-24b7-823c-4b3b9d7b124c"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-4b0a14faa46b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mp_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0msub\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msub\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdftest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0msub\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price_doc'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp_test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"p_test = model.predict(xgb.DMatrix(x_test))\n",
"\n",
"sub = pd.DataFrame()\n",
"sub['id'] = dftest['id'].values\n",
"sub['price_doc'] = p_test\n",
"sub.to_csv('xgb.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "cab97d6c-18bd-4bd4-dc22-fa95db41c615"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'sub' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-9c134b68bda2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msub\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'sub' is not defined"
]
}
],
"source": [
"sub"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "e2570daf-cc29-0a75-47a0-97739a745928"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 1064,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159614.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "31ca96ee-558e-bab3-6626-852ac24f2916"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data.csv\n",
"\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 569 entries, 0 to 568\n",
"Data columns (total 31 columns):\n",
"diagnosis 569 non-null object\n",
"radius_mean 569 non-null float64\n",
"texture_mean 569 non-null float64\n",
"perimeter_mean 569 non-null float64\n",
"area_mean 569 non-null float64\n",
"smoothness_mean 569 non-null float64\n",
"compactness_mean 569 non-null float64\n",
"concavity_mean 569 non-null float64\n",
"concave points_mean 569 non-null float64\n",
"symmetry_mean 569 non-null float64\n",
"fractal_dimension_mean 569 non-null float64\n",
"radius_se 569 non-null float64\n",
"texture_se 569 non-null float64\n",
"perimeter_se 569 non-null float64\n",
"area_se 569 non-null float64\n",
"smoothness_se 569 non-null float64\n",
"compactness_se 569 non-null float64\n",
"concavity_se 569 non-null float64\n",
"concave points_se 569 non-null float64\n",
"symmetry_se 569 non-null float64\n",
"fractal_dimension_se 569 non-null float64\n",
"radius_worst 569 non-null float64\n",
"texture_worst 569 non-null float64\n",
"perimeter_worst 569 non-null float64\n",
"area_worst 569 non-null float64\n",
"smoothness_worst 569 non-null float64\n",
"compactness_worst 569 non-null float64\n",
"concavity_worst 569 non-null float64\n",
"concave points_worst 569 non-null float64\n",
"symmetry_worst 569 non-null float64\n",
"fractal_dimension_worst 569 non-null float64\n",
"dtypes: float64(30), object(1)\n",
"memory usage: 137.9+ KB\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"data = pd.read_csv(\"../input/data.csv\",header=0)\n",
"data.drop(\"Unnamed: 32\",axis=1,inplace=True)\n",
"data.drop(\"id\",axis=1,inplace=True)\n",
"\n",
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "86ab7768-64a2-1981-4fe6-023ea68e080d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 398 entries, 280 to 551\n",
"Data columns (total 1 columns):\n",
"radius_mean 398 non-null float64\n",
"dtypes: float64(1)\n",
"memory usage: 6.2 KB\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split \n",
"train, test = train_test_split(data, test_size = 0.3)\n",
"\n",
"prediction_var = list(data.columns[1:2])\n",
"\n",
"train_X = train[prediction_var]# taking the training data input\n",
"train_y=train.diagnosis# This is output of our training data\n",
"# same we have to do for test\n",
"test_X= test[prediction_var] # taking test data inputs\n",
"test_y =test.diagnosis #output value of test dat\n",
"\n",
"train_X.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "6ba144e5-c9f6-3efe-79d7-76db25503c4c"
},
"outputs": [
{
"data": {
"image/png": 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f9MJkgcKIgjLKjmOcuQs0H9XDjzv+339rrLdskI1Qy82HPFKPPJeJqeeJ428H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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6edc8564e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"n_train = np.array(train_X)\n",
"n_test = np.array(test_X)\n",
"\n",
"plt.plot(n_train)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "e7dc6983-a69f-e095-0952-d31b90563e68"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/Keras-2.0.4-py3.6.egg/keras/backend/tensorflow_backend.py:2252: UserWarning: Expected no kwargs, you passed 1\n",
"kwargs passed to function are ignored with Tensorflow backend\n",
" warnings.warn('\\n'.join(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 398 samples, validate on 171 samples\n",
"Epoch 1/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 2/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 3/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 4/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 5/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 6/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 7/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 8/150\n",
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"Epoch 9/150\n",
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"Epoch 10/150\n",
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"Epoch 11/150\n",
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"Epoch 12/150\n",
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"Epoch 13/150\n",
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"Epoch 14/150\n",
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"Epoch 15/150\n",
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"Epoch 16/150\n",
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"Epoch 17/150\n",
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"Epoch 18/150\n",
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"Epoch 19/150\n",
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"Epoch 20/150\n",
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"Epoch 21/150\n",
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"Epoch 22/150\n",
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"Epoch 23/150\n",
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"Epoch 24/150\n",
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"Epoch 25/150\n",
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"Epoch 26/150\n",
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"Epoch 27/150\n",
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"Epoch 28/150\n",
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"Epoch 29/150\n",
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"Epoch 30/150\n",
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"Epoch 31/150\n",
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"Epoch 32/150\n",
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"Epoch 33/150\n",
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"Epoch 34/150\n",
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"Epoch 35/150\n",
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"Epoch 36/150\n",
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"Epoch 37/150\n",
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"Epoch 38/150\n",
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"Epoch 39/150\n",
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"Epoch 40/150\n",
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"Epoch 41/150\n",
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"Epoch 42/150\n",
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"Epoch 43/150\n",
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"Epoch 44/150\n",
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"Epoch 45/150\n",
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"Epoch 46/150\n",
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"Epoch 47/150\n",
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"Epoch 48/150\n",
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"Epoch 49/150\n",
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"Epoch 50/150\n",
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"Epoch 51/150\n",
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"Epoch 52/150\n",
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"Epoch 53/150\n",
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"Epoch 54/150\n",
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"Epoch 55/150\n",
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"Epoch 56/150\n",
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"Epoch 57/150\n",
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"Epoch 58/150\n",
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"Epoch 59/150\n",
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"Epoch 60/150\n",
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"Epoch 61/150\n",
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"Epoch 62/150\n",
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"Epoch 63/150\n",
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"Epoch 64/150\n",
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"Epoch 65/150\n",
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"Epoch 66/150\n",
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"Epoch 67/150\n",
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"Epoch 68/150\n",
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"Epoch 69/150\n",
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"Epoch 70/150\n",
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"Epoch 71/150\n",
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"Epoch 72/150\n",
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"Epoch 73/150\n",
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"Epoch 74/150\n",
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"Epoch 75/150\n",
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"Epoch 76/150\n",
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"Epoch 77/150\n",
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"Epoch 78/150\n",
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"Epoch 79/150\n",
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"Epoch 80/150\n",
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"Epoch 81/150\n",
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"Epoch 82/150\n",
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"Epoch 83/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 84/150\n",
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"Epoch 85/150\n",
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"Epoch 86/150\n",
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"Epoch 87/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 88/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 89/150\n",
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"Epoch 90/150\n",
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"Epoch 91/150\n",
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"Epoch 92/150\n",
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"Epoch 93/150\n",
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"Epoch 94/150\n",
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"Epoch 95/150\n",
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"Epoch 96/150\n",
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"Epoch 97/150\n",
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"Epoch 98/150\n",
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"Epoch 99/150\n",
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"Epoch 100/150\n",
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"Epoch 101/150\n",
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"Epoch 102/150\n",
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"Epoch 103/150\n",
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"Epoch 104/150\n",
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"Epoch 105/150\n",
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"Epoch 106/150\n",
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"Epoch 107/150\n",
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"Epoch 108/150\n",
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"Epoch 109/150\n",
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"Epoch 110/150\n",
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"Epoch 111/150\n",
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"Epoch 112/150\n",
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"Epoch 113/150\n",
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"Epoch 114/150\n",
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"Epoch 115/150\n",
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"Epoch 116/150\n",
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"Epoch 117/150\n",
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"Epoch 118/150\n",
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"Epoch 119/150\n",
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"Epoch 120/150\n",
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"Epoch 121/150\n",
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"Epoch 122/150\n",
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"Epoch 123/150\n",
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"Epoch 124/150\n",
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"Epoch 125/150\n",
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"Epoch 126/150\n",
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"Epoch 127/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 128/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 129/150\n",
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"Epoch 130/150\n",
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"Epoch 131/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 132/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 133/150\n",
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"Epoch 134/150\n",
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"Epoch 135/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 136/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 137/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 138/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 139/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 140/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 141/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 142/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 143/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 144/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 145/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 146/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 147/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 148/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 149/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n",
"Epoch 150/150\n",
"0s - loss: -2.0774e+02 - val_loss: -2.1287e+02\n"
]
},
{
"data": {
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ooWdyinb3wpl8H+H52b0yZc7Uz90yzEsA7PcnJrlo7SVktFoBdOtiSgwCDT1o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\nTbihm5nVhBu6mVlN/Ac4LBl40deN6gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6e82b936d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from keras.layers import Input, Dense\n",
"from keras.models import Model\n",
"import tensorflow as tf\n",
"\n",
"input_dim = n_train.shape[1]\n",
"print(input_dim)\n",
"inputs = Input(shape=(input_dim,))\n",
"encoded = inputs\n",
"encoded = Dense(input_dim, activation='linear')(encoded)\n",
"\n",
"decoded = encoded\n",
"decoded = Dense(input_dim, activation='linear')(decoded)\n",
"\n",
"\n",
"autoencoder = Model(inputs, decoded)\n",
"autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
"autoencoder.fit(n_train, n_train,\n",
" verbose=2,\n",
" epochs=150,\n",
" batch_size=100,\n",
" shuffle=True,\n",
" validation_data=(n_test, n_test))\n",
"\n",
"predict_vals = autoencoder.predict(n_train)\n",
"\n",
"plt.plot(predict_vals)\n",
"plt.show()"
]
}
],
"metadata": {
"_change_revision": 132,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159636.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ae72d061-58df-341f-21ca-4bdfe130b9c3"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "431a26ee-51fd-112f-3397-9f8b562657dc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"macro.csv\n",
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "2873e90c-8fe9-0e53-04bc-cea6121f3ad7"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.preprocessing import LabelEncoder\n",
"\n",
"pd.set_option('display.max_columns',400)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "047d5583-9971-05c1-f878-e1fc3b8c66b3"
},
"outputs": [],
"source": [
"train_df = pd.read_csv(\"../input/train.csv\")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "17a1fbdb-a4b0-f4f8-552e-162745bdf99b"
},
"outputs": [],
"source": [
"n_train_df = train_df.copy()\n",
"enc = LabelEncoder()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "f37e44e3-00e2-1289-4903-6daf7e7b8c10"
},
"outputs": [],
"source": [
"for col in n_train_df.columns:\n",
" if n_train_df[col].dtypes == 'object':\n",
" n_train_df[col] = enc.fit_transform(n_train_df[col])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "b7b68aeb-87f9-0834-d4d6-5666ba9605b5"
},
"outputs": [],
"source": [
"from sklearn.preprocessing import Imputer"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "4f168ea9-0adf-2821-d84f-61c14f34193d"
},
"outputs": [],
"source": [
"imp = Imputer(missing_values='NaN',strategy='most_frequent',axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "298a6c52-1c51-4596-4a96-481b41e98ff7"
},
"outputs": [],
"source": [
"X_train,y = n_train_df[list(range(0,291))],n_train_df[[291]]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "f5c3a3fb-2040-34d5-4ca7-fa3b786a0ce6"
},
"outputs": [],
"source": [
"X = imp.fit_transform(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "53ddff6e-aa94-f47b-f67c-811b5c9e4473"
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "be55fbed-3ce9-aa1d-a08b-073c22f739f7"
},
"outputs": [],
"source": [
"clf = RandomForestRegressor(random_state=0,n_estimators=100)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "7a7178f2-1602-12c6-40d1-308a66de71ce"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 102,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159648.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "69be9882-19e5-2001-1958-87c642fab6e7"
},
"source": [
"**use simple neural network to classify the mnist dataset**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "2038e51b-1d46-4ec4-e63b-91c91f9e9e7f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"#import random\n",
"import scipy\n",
"import scipy.misc\n",
"import matplotlib.cm as cm\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "484295d8-10d3-cf7f-9285-f23451862924"
},
"outputs": [],
"source": [
"train = pd.read_csv('../input/train.csv')\n",
"#train.head(5)\n",
"test = pd.read_csv('../input/test.csv')\n",
"#test.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "a15feda0-02c2-898c-8a54-c67cf7773339"
},
"outputs": [
{
"data": {
"text/plain": [
"(42000,)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_true = train['label']\n",
"label_true = label_true.as_matrix()\n",
"label_true.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "2513ca49-e414-db9b-8f58-53a47785fc5e"
},
"outputs": [],
"source": [
"train_data = train.drop('label',axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "42c91f03-cdbe-c6c2-7ad8-65844dd3b68a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ndarray'>\n"
]
}
],
"source": [
"random_data = train_data.sample(100,random_state=42,axis=0).as_matrix()\n",
"#print (random_data)\n",
"train_data_numpy = train_data.as_matrix()\n",
"print (type(random_data))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "3555dcb5-0836-728b-cf9a-e4b7996cce97"
},
"outputs": [],
"source": [
"def getDatumImg(row):\n",
" \"\"\"\n",
" Function that is handed a single np array with shape 1x784,\n",
" crates an image object from it, and returns it\n",
" \"\"\"\n",
" width, height = 28, 28\n",
" square = row.reshape(width,height)\n",
" return square\n",
"\n",
"def displayData(indices_to_display):\n",
" \"\"\"\n",
" Function that picks 100 random rows from X, creates a 28x28 image from each,\n",
" then stitches them together into a 10x10 grid of images, and shows it.\n",
" \"\"\"\n",
" height,width = 28,28\n",
" nrows, ncols = 10, 10\n",
" big_picture = np.zeros((height*nrows,width*ncols))\n",
" irow, icol = 0, 0\n",
" batch_size,pic_size = indices_to_display.shape\n",
" for idx in range(batch_size):\n",
" if icol == ncols:\n",
" irow += 1\n",
" icol = 0\n",
" #print (idx.shape)\n",
" iimg = getDatumImg(train_data_numpy[idx])\n",
" big_picture[irow*height:irow*height+iimg.shape[0],icol*width:icol*width+iimg.shape[1]] = iimg\n",
" icol += 1\n",
" fig = plt.figure(figsize=(6,6))\n",
" img = scipy.misc.toimage( big_picture )\n",
" plt.imshow(img,cmap = cm.Greys_r)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "0e4e3018-630b-f592-e5fa-c6880cf883df"
},
"outputs": [
{
"data": {
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5S8nOzg7Z2dnIz89Hfn4+7O3tBam3Jo28oRuCbt26hZ9//rnOJNmvnCDnqWvX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BmVlpZSeXk5ubi4SNrZ9ZG3kjUaDXEcR15eXqLLCAwMJK1WS+fOnaNz586x5Tt27CCO4/SWJZSC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\nRCIJcKQhl0gkkgDn/wECyWcahJ1UPgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0063f7ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"displayData(random_data)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "a5ea8ee6-bcaa-a6fe-f89e-dae38c406950"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fa007f33c18>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa00801c9e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#display a number\n",
"random_a_num = train_data.sample(1,random_state=42,axis=0).as_matrix()\n",
"#print (random_a_num) \n",
"img = random_a_num.reshape(28,28)\n",
"plt.imshow(img,cmap='gray')\n",
"#plt.title(label_true[])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "a9788628-a185-8fce-2623-827c137d1c0b"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 412,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159711.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a0736384-ca97-7518-b84b-e8382bb2ea85"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data.csv\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt \n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"from sklearn.linear_model import LogisticRegression # to apply the Logistic regression\n",
"from sklearn.model_selection import train_test_split # to split the data into two parts\n",
"from sklearn.cross_validation import KFold # use for cross validation\n",
"from sklearn.model_selection import GridSearchCV# for tuning parameter\n",
"from sklearn.ensemble import RandomForestClassifier # for random forest classifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import svm # for Support Vector Machine\n",
"from sklearn import metrics \n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.feature_selection import SelectFromModel\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "0bcbb970-634f-b24b-a93b-ee79b1d1a15d"
},
"outputs": [],
"source": [
"df = pd.read_csv('../input/data.csv',header=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "0d397e23-2c04-bd93-709a-eeb5f6cc9e75"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
"0 842302 M 17.99 10.38 122.80 1001.0 \n",
"1 842517 M 20.57 17.77 132.90 1326.0 \n",
"2 84300903 M 19.69 21.25 130.00 1203.0 \n",
"3 84348301 M 11.42 20.38 77.58 386.1 \n",
"4 84358402 M 20.29 14.34 135.10 1297.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"0 0.11840 0.27760 0.3001 0.14710 \n",
"1 0.08474 0.07864 0.0869 0.07017 \n",
"2 0.10960 0.15990 0.1974 0.12790 \n",
"3 0.14250 0.28390 0.2414 0.10520 \n",
"4 0.10030 0.13280 0.1980 0.10430 \n",
"\n",
" ... texture_worst perimeter_worst area_worst smoothness_worst \\\n",
"0 ... 17.33 184.60 2019.0 0.1622 \n",
"1 ... 23.41 158.80 1956.0 0.1238 \n",
"2 ... 25.53 152.50 1709.0 0.1444 \n",
"3 ... 26.50 98.87 567.7 0.2098 \n",
"4 ... 16.67 152.20 1575.0 0.1374 \n",
"\n",
" compactness_worst concavity_worst concave points_worst symmetry_worst \\\n",
"0 0.6656 0.7119 0.2654 0.4601 \n",
"1 0.1866 0.2416 0.1860 0.2750 \n",
"2 0.4245 0.4504 0.2430 0.3613 \n",
"3 0.8663 0.6869 0.2575 0.6638 \n",
"4 0.2050 0.4000 0.1625 0.2364 \n",
"\n",
" fractal_dimension_worst Unnamed: 32 \n",
"0 0.11890 NaN \n",
"1 0.08902 NaN \n",
"2 0.08758 NaN \n",
"3 0.17300 NaN \n",
"4 0.07678 NaN \n",
"\n",
"[5 rows x 33 columns]\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 569 entries, 0 to 568\n",
"Data columns (total 33 columns):\n",
"id 569 non-null int64\n",
"diagnosis 569 non-null object\n",
"radius_mean 569 non-null float64\n",
"texture_mean 569 non-null float64\n",
"perimeter_mean 569 non-null float64\n",
"area_mean 569 non-null float64\n",
"smoothness_mean 569 non-null float64\n",
"compactness_mean 569 non-null float64\n",
"concavity_mean 569 non-null float64\n",
"concave points_mean 569 non-null float64\n",
"symmetry_mean 569 non-null float64\n",
"fractal_dimension_mean 569 non-null float64\n",
"radius_se 569 non-null float64\n",
"texture_se 569 non-null float64\n",
"perimeter_se 569 non-null float64\n",
"area_se 569 non-null float64\n",
"smoothness_se 569 non-null float64\n",
"compactness_se 569 non-null float64\n",
"concavity_se 569 non-null float64\n",
"concave points_se 569 non-null float64\n",
"symmetry_se 569 non-null float64\n",
"fractal_dimension_se 569 non-null float64\n",
"radius_worst 569 non-null float64\n",
"texture_worst 569 non-null float64\n",
"perimeter_worst 569 non-null float64\n",
"area_worst 569 non-null float64\n",
"smoothness_worst 569 non-null float64\n",
"compactness_worst 569 non-null float64\n",
"concavity_worst 569 non-null float64\n",
"concave points_worst 569 non-null float64\n",
"symmetry_worst 569 non-null float64\n",
"fractal_dimension_worst 569 non-null float64\n",
"Unnamed: 32 0 non-null float64\n",
"dtypes: float64(31), int64(1), object(1)\n",
"memory usage: 146.8+ KB\n",
"None\n",
" id radius_mean texture_mean perimeter_mean area_mean \\\n",
"count 5.690000e+02 569.000000 569.000000 569.000000 569.000000 \n",
"mean 3.037183e+07 14.127292 19.289649 91.969033 654.889104 \n",
"std 1.250206e+08 3.524049 4.301036 24.298981 351.914129 \n",
"min 8.670000e+03 6.981000 9.710000 43.790000 143.500000 \n",
"25% 8.692180e+05 11.700000 16.170000 75.170000 420.300000 \n",
"50% 9.060240e+05 13.370000 18.840000 86.240000 551.100000 \n",
"75% 8.813129e+06 15.780000 21.800000 104.100000 782.700000 \n",
"max 9.113205e+08 28.110000 39.280000 188.500000 2501.000000 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.096360 0.104341 0.088799 0.048919 \n",
"std 0.014064 0.052813 0.079720 0.038803 \n",
"min 0.052630 0.019380 0.000000 0.000000 \n",
"25% 0.086370 0.064920 0.029560 0.020310 \n",
"50% 0.095870 0.092630 0.061540 0.033500 \n",
"75% 0.105300 0.130400 0.130700 0.074000 \n",
"max 0.163400 0.345400 0.426800 0.201200 \n",
"\n",
" symmetry_mean ... texture_worst perimeter_worst \\\n",
"count 569.000000 ... 569.000000 569.000000 \n",
"mean 0.181162 ... 25.677223 107.261213 \n",
"std 0.027414 ... 6.146258 33.602542 \n",
"min 0.106000 ... 12.020000 50.410000 \n",
"25% 0.161900 ... 21.080000 84.110000 \n",
"50% 0.179200 ... 25.410000 97.660000 \n",
"75% 0.195700 ... 29.720000 125.400000 \n",
"max 0.304000 ... 49.540000 251.200000 \n",
"\n",
" area_worst smoothness_worst compactness_worst concavity_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 880.583128 0.132369 0.254265 0.272188 \n",
"std 569.356993 0.022832 0.157336 0.208624 \n",
"min 185.200000 0.071170 0.027290 0.000000 \n",
"25% 515.300000 0.116600 0.147200 0.114500 \n",
"50% 686.500000 0.131300 0.211900 0.226700 \n",
"75% 1084.000000 0.146000 0.339100 0.382900 \n",
"max 4254.000000 0.222600 1.058000 1.252000 \n",
"\n",
" concave points_worst symmetry_worst fractal_dimension_worst \\\n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.114606 0.290076 0.083946 \n",
"std 0.065732 0.061867 0.018061 \n",
"min 0.000000 0.156500 0.055040 \n",
"25% 0.064930 0.250400 0.071460 \n",
"50% 0.099930 0.282200 0.080040 \n",
"75% 0.161400 0.317900 0.092080 \n",
"max 0.291000 0.663800 0.207500 \n",
"\n",
" Unnamed: 32 \n",
"count 0.0 \n",
"mean NaN \n",
"std NaN \n",
"min NaN \n",
"25% NaN \n",
"50% NaN \n",
"75% NaN \n",
"max NaN \n",
"\n",
"[8 rows x 32 columns]\n"
]
}
],
"source": [
"print(df.head())\n",
"print(df.info())\n",
"print(df.describe())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "1bd64466-67f9-bf27-c051-18cd20b9f825"
},
"outputs": [],
"source": [
"df.drop(['id','Unnamed: 32'], axis = 1, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "7ed3b383-4c27-dac4-becd-fad08c398fa9"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
" 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
" 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
" 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
" 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
" 'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
" 'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
" 'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
" 'symmetry_worst', 'fractal_dimension_worst'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "d52cdcf0-e6e7-5e26-a769-920f048ae7e1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']\n",
"['radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se']\n",
"['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']\n"
]
}
],
"source": [
"features_mean = list(df.columns[1:11])\n",
"features_se = list(df.columns[11:21])\n",
"features_worst =list(df.columns[21:31])\n",
"print(features_mean)\n",
"print(features_se)\n",
"print(features_worst)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "67d9594b-3a97-198d-2aeb-24cb1174401a"
},
"outputs": [],
"source": [
"df['diagnosis'] = df['diagnosis'].map({'M':1,'B':0})"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "a005bbdd-99af-1230-a4f7-9648a466b926"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>diagnosis</th>\n",
" <th>radius_mean</th>\n",
" <th>texture_mean</th>\n",
" <th>perimeter_mean</th>\n",
" <th>area_mean</th>\n",
" <th>smoothness_mean</th>\n",
" <th>compactness_mean</th>\n",
" <th>concavity_mean</th>\n",
" <th>concave points_mean</th>\n",
" <th>symmetry_mean</th>\n",
" <th>fractal_dimension_mean</th>\n",
" <th>radius_se</th>\n",
" <th>texture_se</th>\n",
" <th>perimeter_se</th>\n",
" <th>area_se</th>\n",
" <th>smoothness_se</th>\n",
" <th>compactness_se</th>\n",
" <th>concavity_se</th>\n",
" <th>concave points_se</th>\n",
" <th>symmetry_se</th>\n",
" <th>fractal_dimension_se</th>\n",
" <th>radius_worst</th>\n",
" <th>texture_worst</th>\n",
" <th>perimeter_worst</th>\n",
" <th>area_worst</th>\n",
" <th>smoothness_worst</th>\n",
" <th>compactness_worst</th>\n",
" <th>concavity_worst</th>\n",
" <th>concave points_worst</th>\n",
" <th>symmetry_worst</th>\n",
" <th>fractal_dimension_worst</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" <td>569.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.372583</td>\n",
" <td>14.127292</td>\n",
" <td>19.289649</td>\n",
" <td>91.969033</td>\n",
" <td>654.889104</td>\n",
" <td>0.096360</td>\n",
" <td>0.104341</td>\n",
" <td>0.088799</td>\n",
" <td>0.048919</td>\n",
" <td>0.181162</td>\n",
" <td>0.062798</td>\n",
" <td>0.405172</td>\n",
" <td>1.216853</td>\n",
" <td>2.866059</td>\n",
" <td>40.337079</td>\n",
" <td>0.007041</td>\n",
" <td>0.025478</td>\n",
" <td>0.031894</td>\n",
" <td>0.011796</td>\n",
" <td>0.020542</td>\n",
" <td>0.003795</td>\n",
" <td>16.269190</td>\n",
" <td>25.677223</td>\n",
" <td>107.261213</td>\n",
" <td>880.583128</td>\n",
" <td>0.132369</td>\n",
" <td>0.254265</td>\n",
" <td>0.272188</td>\n",
" <td>0.114606</td>\n",
" <td>0.290076</td>\n",
" <td>0.083946</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.483918</td>\n",
" <td>3.524049</td>\n",
" <td>4.301036</td>\n",
" <td>24.298981</td>\n",
" <td>351.914129</td>\n",
" <td>0.014064</td>\n",
" <td>0.052813</td>\n",
" <td>0.079720</td>\n",
" <td>0.038803</td>\n",
" <td>0.027414</td>\n",
" <td>0.007060</td>\n",
" <td>0.277313</td>\n",
" <td>0.551648</td>\n",
" <td>2.021855</td>\n",
" <td>45.491006</td>\n",
" <td>0.003003</td>\n",
" <td>0.017908</td>\n",
" <td>0.030186</td>\n",
" <td>0.006170</td>\n",
" <td>0.008266</td>\n",
" <td>0.002646</td>\n",
" <td>4.833242</td>\n",
" <td>6.146258</td>\n",
" <td>33.602542</td>\n",
" <td>569.356993</td>\n",
" <td>0.022832</td>\n",
" <td>0.157336</td>\n",
" <td>0.208624</td>\n",
" <td>0.065732</td>\n",
" <td>0.061867</td>\n",
" <td>0.018061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>6.981000</td>\n",
" <td>9.710000</td>\n",
" <td>43.790000</td>\n",
" <td>143.500000</td>\n",
" <td>0.052630</td>\n",
" <td>0.019380</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.106000</td>\n",
" <td>0.049960</td>\n",
" <td>0.111500</td>\n",
" <td>0.360200</td>\n",
" <td>0.757000</td>\n",
" <td>6.802000</td>\n",
" <td>0.001713</td>\n",
" <td>0.002252</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.007882</td>\n",
" <td>0.000895</td>\n",
" <td>7.930000</td>\n",
" <td>12.020000</td>\n",
" <td>50.410000</td>\n",
" <td>185.200000</td>\n",
" <td>0.071170</td>\n",
" <td>0.027290</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.156500</td>\n",
" <td>0.055040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>11.700000</td>\n",
" <td>16.170000</td>\n",
" <td>75.170000</td>\n",
" <td>420.300000</td>\n",
" <td>0.086370</td>\n",
" <td>0.064920</td>\n",
" <td>0.029560</td>\n",
" <td>0.020310</td>\n",
" <td>0.161900</td>\n",
" <td>0.057700</td>\n",
" <td>0.232400</td>\n",
" <td>0.833900</td>\n",
" <td>1.606000</td>\n",
" <td>17.850000</td>\n",
" <td>0.005169</td>\n",
" <td>0.013080</td>\n",
" <td>0.015090</td>\n",
" <td>0.007638</td>\n",
" <td>0.015160</td>\n",
" <td>0.002248</td>\n",
" <td>13.010000</td>\n",
" <td>21.080000</td>\n",
" <td>84.110000</td>\n",
" <td>515.300000</td>\n",
" <td>0.116600</td>\n",
" <td>0.147200</td>\n",
" <td>0.114500</td>\n",
" <td>0.064930</td>\n",
" <td>0.250400</td>\n",
" <td>0.071460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>13.370000</td>\n",
" <td>18.840000</td>\n",
" <td>86.240000</td>\n",
" <td>551.100000</td>\n",
" <td>0.095870</td>\n",
" <td>0.092630</td>\n",
" <td>0.061540</td>\n",
" <td>0.033500</td>\n",
" <td>0.179200</td>\n",
" <td>0.061540</td>\n",
" <td>0.324200</td>\n",
" <td>1.108000</td>\n",
" <td>2.287000</td>\n",
" <td>24.530000</td>\n",
" <td>0.006380</td>\n",
" <td>0.020450</td>\n",
" <td>0.025890</td>\n",
" <td>0.010930</td>\n",
" <td>0.018730</td>\n",
" <td>0.003187</td>\n",
" <td>14.970000</td>\n",
" <td>25.410000</td>\n",
" <td>97.660000</td>\n",
" <td>686.500000</td>\n",
" <td>0.131300</td>\n",
" <td>0.211900</td>\n",
" <td>0.226700</td>\n",
" <td>0.099930</td>\n",
" <td>0.282200</td>\n",
" <td>0.080040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" <td>15.780000</td>\n",
" <td>21.800000</td>\n",
" <td>104.100000</td>\n",
" <td>782.700000</td>\n",
" <td>0.105300</td>\n",
" <td>0.130400</td>\n",
" <td>0.130700</td>\n",
" <td>0.074000</td>\n",
" <td>0.195700</td>\n",
" <td>0.066120</td>\n",
" <td>0.478900</td>\n",
" <td>1.474000</td>\n",
" <td>3.357000</td>\n",
" <td>45.190000</td>\n",
" <td>0.008146</td>\n",
" <td>0.032450</td>\n",
" <td>0.042050</td>\n",
" <td>0.014710</td>\n",
" <td>0.023480</td>\n",
" <td>0.004558</td>\n",
" <td>18.790000</td>\n",
" <td>29.720000</td>\n",
" <td>125.400000</td>\n",
" <td>1084.000000</td>\n",
" <td>0.146000</td>\n",
" <td>0.339100</td>\n",
" <td>0.382900</td>\n",
" <td>0.161400</td>\n",
" <td>0.317900</td>\n",
" <td>0.092080</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>28.110000</td>\n",
" <td>39.280000</td>\n",
" <td>188.500000</td>\n",
" <td>2501.000000</td>\n",
" <td>0.163400</td>\n",
" <td>0.345400</td>\n",
" <td>0.426800</td>\n",
" <td>0.201200</td>\n",
" <td>0.304000</td>\n",
" <td>0.097440</td>\n",
" <td>2.873000</td>\n",
" <td>4.885000</td>\n",
" <td>21.980000</td>\n",
" <td>542.200000</td>\n",
" <td>0.031130</td>\n",
" <td>0.135400</td>\n",
" <td>0.396000</td>\n",
" <td>0.052790</td>\n",
" <td>0.078950</td>\n",
" <td>0.029840</td>\n",
" <td>36.040000</td>\n",
" <td>49.540000</td>\n",
" <td>251.200000</td>\n",
" <td>4254.000000</td>\n",
" <td>0.222600</td>\n",
" <td>1.058000</td>\n",
" <td>1.252000</td>\n",
" <td>0.291000</td>\n",
" <td>0.663800</td>\n",
" <td>0.207500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.372583 14.127292 19.289649 91.969033 654.889104 \n",
"std 0.483918 3.524049 4.301036 24.298981 351.914129 \n",
"min 0.000000 6.981000 9.710000 43.790000 143.500000 \n",
"25% 0.000000 11.700000 16.170000 75.170000 420.300000 \n",
"50% 0.000000 13.370000 18.840000 86.240000 551.100000 \n",
"75% 1.000000 15.780000 21.800000 104.100000 782.700000 \n",
"max 1.000000 28.110000 39.280000 188.500000 2501.000000 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.096360 0.104341 0.088799 0.048919 \n",
"std 0.014064 0.052813 0.079720 0.038803 \n",
"min 0.052630 0.019380 0.000000 0.000000 \n",
"25% 0.086370 0.064920 0.029560 0.020310 \n",
"50% 0.095870 0.092630 0.061540 0.033500 \n",
"75% 0.105300 0.130400 0.130700 0.074000 \n",
"max 0.163400 0.345400 0.426800 0.201200 \n",
"\n",
" symmetry_mean fractal_dimension_mean radius_se texture_se \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.181162 0.062798 0.405172 1.216853 \n",
"std 0.027414 0.007060 0.277313 0.551648 \n",
"min 0.106000 0.049960 0.111500 0.360200 \n",
"25% 0.161900 0.057700 0.232400 0.833900 \n",
"50% 0.179200 0.061540 0.324200 1.108000 \n",
"75% 0.195700 0.066120 0.478900 1.474000 \n",
"max 0.304000 0.097440 2.873000 4.885000 \n",
"\n",
" perimeter_se area_se smoothness_se compactness_se concavity_se \\\n",
"count 569.000000 569.000000 569.000000 569.000000 569.000000 \n",
"mean 2.866059 40.337079 0.007041 0.025478 0.031894 \n",
"std 2.021855 45.491006 0.003003 0.017908 0.030186 \n",
"min 0.757000 6.802000 0.001713 0.002252 0.000000 \n",
"25% 1.606000 17.850000 0.005169 0.013080 0.015090 \n",
"50% 2.287000 24.530000 0.006380 0.020450 0.025890 \n",
"75% 3.357000 45.190000 0.008146 0.032450 0.042050 \n",
"max 21.980000 542.200000 0.031130 0.135400 0.396000 \n",
"\n",
" concave points_se symmetry_se fractal_dimension_se radius_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.011796 0.020542 0.003795 16.269190 \n",
"std 0.006170 0.008266 0.002646 4.833242 \n",
"min 0.000000 0.007882 0.000895 7.930000 \n",
"25% 0.007638 0.015160 0.002248 13.010000 \n",
"50% 0.010930 0.018730 0.003187 14.970000 \n",
"75% 0.014710 0.023480 0.004558 18.790000 \n",
"max 0.052790 0.078950 0.029840 36.040000 \n",
"\n",
" texture_worst perimeter_worst area_worst smoothness_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 25.677223 107.261213 880.583128 0.132369 \n",
"std 6.146258 33.602542 569.356993 0.022832 \n",
"min 12.020000 50.410000 185.200000 0.071170 \n",
"25% 21.080000 84.110000 515.300000 0.116600 \n",
"50% 25.410000 97.660000 686.500000 0.131300 \n",
"75% 29.720000 125.400000 1084.000000 0.146000 \n",
"max 49.540000 251.200000 4254.000000 0.222600 \n",
"\n",
" compactness_worst concavity_worst concave points_worst \\\n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.254265 0.272188 0.114606 \n",
"std 0.157336 0.208624 0.065732 \n",
"min 0.027290 0.000000 0.000000 \n",
"25% 0.147200 0.114500 0.064930 \n",
"50% 0.211900 0.226700 0.099930 \n",
"75% 0.339100 0.382900 0.161400 \n",
"max 1.058000 1.252000 0.291000 \n",
"\n",
" symmetry_worst fractal_dimension_worst \n",
"count 569.000000 569.000000 \n",
"mean 0.290076 0.083946 \n",
"std 0.061867 0.018061 \n",
"min 0.156500 0.055040 \n",
"25% 0.250400 0.071460 \n",
"50% 0.282200 0.080040 \n",
"75% 0.317900 0.092080 \n",
"max 0.663800 0.207500 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.options.display.max_columns=80\n",
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "d5b7c0f7-59bd-b4db-2c08-7b1e76d6a314"
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fec188ff208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(df['diagnosis']);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f8e7a032-31d2-3464-2c34-1e31b3bc34e1"
},
"source": [
"##相關性"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "6338d0f0-5ad9-4f5f-e6b9-f7813b4bfc13"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7febdaf62208>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Es8pkp6aRnZZByHXXkHDO+7jKrgOHyMrOpk71KnbT61Svwva9BxzKB/r70avrrf96/XOW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EIIEV4yFRVCCCGEEEKIEJDIlhBCCCGEEKJBNFnevl4S2RJCCCGEEEKIEJDJlhBCCCGEEEKEgKQR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dyhUYN7AXoy47jRH9T2VE/1N54DRnGPsR/U+le4uGdE6vD2F4aep8MnPzyc4LMGz6AsJh6Na8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\nD1VNFP2Wdv/dM1PSTgCSdmu6js6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"text/plain": [
"<matplotlib.figure.Figure at 0x7febd36aa668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corr = df[features_mean].corr()\n",
"plt.figure(figsize=(14,14))\n",
"sns.heatmap(corr, cbar = True, square = True, annot=True,fmt= '.2g',annot_kws={'size': 15},\n",
" xticklabels= features_mean, yticklabels= features_mean,\n",
" cmap= 'coolwarm') # for more on heatmap you can visit Link(http://seaborn.pydata.org/generated/seaborn.heatmap.html)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "dd137fe9-a0cb-4e04-89a7-e2c8106c1766"
},
"outputs": [],
"source": [
"#for i in features_worst:\n",
"# df.boxplot(i, 'diagnosis', rot=60);"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "dd92f4a9-dd68-e3bb-5c0c-7ebd7fa029aa"
},
"outputs": [],
"source": [
"pred_vars=['radius_mean',\n",
" 'texture_mean',\n",
" 'smoothness_mean',\n",
" 'compactness_mean',\n",
" 'concave points_mean',\n",
" 'symmetry_mean',\n",
" 'fractal_dimension_mean']\n",
"\n",
"all_pred_vars = list(df.columns[1:len(df.columns)])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "52608a57-98e7-23c9-e851-4488cf52d928"
},
"outputs": [],
"source": [
"#X = df[pred_vars]\n",
"X = df[all_pred_vars]\n",
"y = df['diagnosis']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "5bec3626-8792-1045-e91f-0670fc18df32"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(455, 30) (114, 30) (455,) (114,)\n"
]
}
],
"source": [
"#看一下有沒有出錯\n",
"print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "5c371e3b-b20d-3f9e-dc68-1de54c658c75"
},
"source": [
"變數挑選"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "4ee5364e-d0fe-278f-24ec-b45e2440526d"
},
"outputs": [],
"source": [
"#使用SelectFromModel\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "23de4682-cd45-572f-e6bc-3eb3cb6e3a8e"
},
"outputs": [],
"source": [
"# 設定pipline\n",
"steps = [('scaler', StandardScaler()),\n",
" ('knn', KNeighborsClassifier())]\n",
"\n",
"# Create the pipeline: pipeline\n",
"pipeline = Pipeline(steps)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "efe07afb-139a-ffcd-d399-6a4ca30d5257"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"classification_report:\n",
"=======\n",
" precision recall f1-score support\n",
"\n",
" 0 0.95 0.99 0.97 72\n",
" 1 0.97 0.90 0.94 42\n",
"\n",
"avg / total 0.96 0.96 0.96 114\n",
"\n",
"含混矩陣:\n",
"=======\n",
"[[71 1]\n",
" [ 4 38]]\n"
]
}
],
"source": [
"pipeline.fit(X_train, y_train)\n",
"y_pred = pipeline.predict(X_test)\n",
"\n",
"# Compute metrics\n",
"print('classification_report:\\n=======\\n{}'.format(classification_report(y_test, y_pred)))\n",
"print('含混矩陣:\\n=======\\n{}' .format(confusion_matrix(y_test, y_pred)))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "8685c763-f3de-cbee-b4ad-9c3cdec16ef5"
},
"outputs": [],
"source": [
"parameters = {'knn__n_neighbors':range(3,10)}\n",
"\n",
"cv = GridSearchCV(pipeline, parameters, cv=5)\n",
"\n",
"cv.fit(X_train, y_train)\n",
"y_pred = cv.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "c313a381-ed63-76b4-dd1d-d7b90ea48e3f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.956140350877193\n",
" precision recall f1-score support\n",
"\n",
" 0 0.95 0.99 0.97 72\n",
" 1 0.97 0.90 0.94 42\n",
"\n",
"avg / total 0.96 0.96 0.96 114\n",
"\n",
"Tuned Model Parameters: {'knn__n_neighbors': 7}\n"
]
}
],
"source": [
"# Compute and print metrics\n",
"print(\"Accuracy: {}\".format(cv.score(X_test, y_test)))\n",
"print(classification_report(y_test, y_pred))\n",
"print(\"Tuned Model Parameters: {}\".format(cv.best_params_))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "c7c0608a-65f5-34dc-b6c1-53e9497cee39"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 732,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159764.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "34976c36-7bcb-6bb4-b89b-3e003964ce8f"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "d1d65f52-e902-4a19-aa30-81f92c21bdd9"
},
"outputs": [],
"source": [
"# Imports\n",
"\n",
"# pandas\n",
"import pandas as pd\n",
"from pandas import Series,DataFrame\n",
"\n",
"# numpy, matplotlib, seaborn\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"sns.set_style('whitegrid')\n",
"%matplotlib inline\n",
"\n",
"# machine learning\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC, LinearSVC\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.naive_bayes import GaussianNB"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "efd97b6d-c8e3-5b26-95fe-2ae49ec97204"
},
"outputs": [
{
"data": {
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" <td>71.2833</td>\n",
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" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get titanic & test csv files as a DataFrame\n",
"train_df = pd.read_csv(\"../input/train.csv\")\n",
"test_df = pd.read_csv(\"../input/test.csv\")\n",
"\n",
"# preview the data\n",
"train_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "4746ccee-3fee-0f2e-0989-7de61ec6e9ca"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.6+ KB\n",
"----------------------------\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 418 entries, 0 to 417\n",
"Data columns (total 11 columns):\n",
"PassengerId 418 non-null int64\n",
"Pclass 418 non-null int64\n",
"Name 418 non-null object\n",
"Sex 418 non-null object\n",
"Age 332 non-null float64\n",
"SibSp 418 non-null int64\n",
"Parch 418 non-null int64\n",
"Ticket 418 non-null object\n",
"Fare 417 non-null float64\n",
"Cabin 91 non-null object\n",
"Embarked 418 non-null object\n",
"dtypes: float64(2), int64(4), object(5)\n",
"memory usage: 36.0+ KB\n"
]
}
],
"source": [
"train_df.info()\n",
"print(\"----------------------------\")\n",
"test_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "cc70762d-489b-905f-99d8-cb10bff546f0"
},
"outputs": [],
"source": [
"# drop unnecessary columns, these columns won't be useful in analysis and prediction\n",
"train_df = train_df.drop(['PassengerId','Name','Ticket'], axis=1)\n",
"test_df = test_df.drop(['Name','Ticket'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "298f9ee2-6fbe-ba8b-0c79-cb613202ab7b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Embarked:\n",
"S 644\n",
"C 168\n",
"Q 77\n",
"Name: Embarked, dtype: int64\n"
]
},
{
"data": {
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qXcFKRO4pLjqcR1dl8OiqDC5dqSe/sJzdReXcbGjr7VPX2M47e0t5Z28pc9Im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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4703bc1898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Embarked\n",
"# Clean the column 'Embarked' and transfer it from str to number by pd.get_dummies\n",
"print('Embarked:\\n{}'.format(train_df['Embarked'].value_counts()))\n",
"train_df['Embarked'] = train_df['Embarked'].fillna('S')\n",
"# plot\n",
"sns.factorplot('Embarked','Survived', data=train_df,size=4,aspect=3)\n",
"\n",
"embark_dummies_train = pd.get_dummies(train_df['Embarked'])\n",
"#embark_dummies_train.drop(['S'], axis=1, inplace=True)\n",
"\n",
"embark_dummies_test = pd.get_dummies(test_df['Embarked'])\n",
"#embark_dummies_test.drop(['S'], axis=1, inplace=True)\n",
"\n",
"train_df = train_df.join(embark_dummies_train)\n",
"test_df = test_df.join(embark_dummies_test)\n",
"\n",
"train_df.drop(['Embarked'], axis=1,inplace=True)\n",
"test_df.drop(['Embarked'], axis=1,inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "62a3405d-3755-4b57-c986-6178ec6a7277"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f46fa0341d0>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f46fa1fe160>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f46fa034a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fare\n",
"# Fill missing 'Fare' values\n",
"train_df['Fare'].fillna(train_df['Fare'].median(), inplace=True)\n",
"test_df['Fare'].fillna(test_df['Fare'].median(), inplace=True)\n",
"# float is hard to analyze, covert from float to int\n",
"train_df['Fare'] = train_df['Fare'].astype(int)\n",
"test_df['Fare'] = test_df['Fare'].astype(int)\n",
"# get relation between fare and survived\n",
"fare_not_survived = train_df['Fare'][train_df['Survived'] == 0]\n",
"fare_survived = train_df['Fare'][train_df['Survived'] == 1]\n",
"#\n",
"average_fare = DataFrame([fare_not_survived.mean(), fare_survived.mean()])\n",
"std_fare = DataFrame([fare_not_survived.std(), fare_survived.std()])\n",
"\n",
"# plot\n",
"train_df['Fare'].plot(kind='hist', figsize=(15,3), bins=100, xlim=(0,50))\n",
"\n",
"average_fare.index.names = std_fare.index.names = ['Survived']\n",
"average_fare.plot(yerr=std_fare, kind='bar', legend=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "8a153d28-bd07-5a55-8a91-0a30757e9fc8"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:16: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:17: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f46fa1e5518>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f46f9fb73c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Age\n",
"# get average, sts, NaN in train_df\n",
"average_age_train = train_df[\"Age\"].mean()\n",
"std_age_train = train_df[\"Age\"].std()\n",
"count_nan_age_train = train_df[\"Age\"].isnull().sum()\n",
"# get average, sts, NaN in test_df\n",
"average_age_test = test_df[\"Age\"].mean()\n",
"std_age_test = test_df[\"Age\"].std()\n",
"count_nan_age_test = test_df[\"Age\"].isnull().sum()\n",
"# random generate the age between (mean-std) ~ (mean+std)\n",
"rand_train = np.random.randint(average_age_train - std_age_train, average_age_train + std_age_train,\n",
" size = count_nan_age_train)\n",
"rand_test = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test,\n",
" size = count_nan_age_test)\n",
"# fill NaN values in 'Age' column\n",
"train_df['Age'][np.isnan(train_df['Age'])] = rand_train\n",
"test_df['Age'][np.isnan(test_df['Age'])] = rand_test\n",
"\n",
"# covert age from float to int\n",
"train_df['Age'] = train_df['Age'].astype(int)\n",
"test_df['Age'] = test_df['Age'].astype(int)\n",
"\n",
"# plot\n",
"train_df['Age'].hist(bins=70)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "377265de-9312-0dd7-401d-ec02a7b2306b"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:16: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:17: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f46f9f67710>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f46fa1fe8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Age\n",
"# average, std, NaN of train_df\n",
"average_age_train = train_df['Age'].mean()\n",
"std_age_train = train_df['Age'].std()\n",
"count_nan_age_train = train_df['Age'].isnull().sum()\n",
"# average, std, NaN of test_df\n",
"average_age_test = test_df['Age'].mean()\n",
"std_age_test = test_df['Age'].std()\n",
"count_nan_age_test = test_df['Age'].isnull().sum()\n",
"# generate random age for NaN values in 'Age', between (mean - std) ~ (mean + std)\n",
"rand_age_train = np.random.randint(average_age_train - std_age_train, average_age_train + std_age_train,\n",
" size = count_nan_age_train)\n",
"rand_age_test = np.random.randint(average_age_test - std_age_test, average_age_test + std_age_test,\n",
" size = count_nan_age_test)\n",
"# fill NaN values in Age column\n",
"train_df['Age'][np.isnan(train_df['Age'])] = rand_age_train\n",
"test_df['Age'][np.isnan(test_df['Age'])] = rand_age_test\n",
"# covert from float to int\n",
"train_df['Age'] = train_df['Age'].astype(int)\n",
"test_df['Age'] = test_df['Age'].astype(int)\n",
"\n",
"# plot\n",
"train_df['Age'].hist(bins=70)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "299e1639-9b8b-e40a-cc42-8b2e9147dab3"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "slice indices must be integers or None or have an __index__ method",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-69adeca80305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m#print(\"train_df:\\n{}\".format(train_df))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfacet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Survived\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mfacet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Age'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mshade\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;31m#facet.set(xlim=(0, train_df['Age'].max()))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m#facet.add_legend()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 727\u001b[0m \u001b[0;31m# Draw the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 728\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_facet_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplot_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 730\u001b[0m \u001b[0;31m# Finalize the annotations and layout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36m_facet_plot\u001b[0;34m(self, func, ax, plot_args, plot_kwargs)\u001b[0m\n\u001b[1;32m 810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 811\u001b[0m \u001b[0;31m# Draw the plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 812\u001b[0;31m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mplot_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mplot_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 813\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 814\u001b[0m \u001b[0;31m# Sort out the supporting information\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[0;34m(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, ax, **kwargs)\u001b[0m\n\u001b[1;32m 602\u001b[0m ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,\n\u001b[1;32m 603\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclip\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlegend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 604\u001b[0;31m cumulative=cumulative, **kwargs)\n\u001b[0m\u001b[1;32m 605\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 606\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mTypeError\u001b[0m: slice indices must be integers or None or have an __index__ method"
]
},
{
"data": {
"image/png": 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6frqv9j3T+TDaRftw6kjW7hbzvStRHDvKZu+JX+/ufd19IMl7s3bnto9duVJ5ntusF59J\n8lhV7Z3OXcnaHVZhETb7GX0yyQ1V9aLp47ck+dmWV8iOdzm5Zdfq6urCCqqqz2btjljnkxxO8uYk\nT3X3/VV1c5LPTad+o7s/v7BC2NEu1odJvp/kv5P8+7rpX+3u+7a8SHaEzd4T1825PslX3KadRZrx\n8/kvknwla3+E/UmSD/v4ChZlRi/+dda2Tz+T5ER3/82Vq5Tns6paydof3a9P8qck/5W1m/384nJy\ny0IDFgAAwE6ylTe5AAAAeF4TsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAYRsAAAAAb5H7AT\n/sMOOhzmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f46f9fb7e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Age\n",
"#print(\"train_df:\\n{}\".format(train_df))\n",
"facet = sns.FacetGrid(train_df, hue=\"Survived\",aspect=4)\n",
"facet.map(sns.kdeplot,'Age',shade= True)\n",
"#facet.set(xlim=(0, train_df['Age'].max()))\n",
"#facet.add_legend()"
]
}
],
"metadata": {
"_change_revision": 228,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159766.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "8732986f-234a-f8c9-30e8-5e78963aa59d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample_submission_v2.csv\n",
"test-jpg-v2\n",
"test-tif-v2\n",
"train-jpg\n",
"train-tif-v2\n",
"train_v2.csv\n",
"\n"
]
}
],
"source": [
"#Library\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "4ec74e11-c814-5565-1709-a41826cb7e33"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2697\n",
"2089\n",
"7261\n",
"28431\n"
]
}
],
"source": [
"df = pd.read_csv(\"../input/train_v2.csv\")\n",
"tags = df[\"tags\"].apply(lambda x: x.split(' '))\n",
" \n",
"end = len(tags)\n",
"id_haze = []\n",
"id_cloudy = []\n",
"id_partly = []\n",
"id_clear = []\n",
"\n",
"for i in range (0,end):\n",
" for x in tags[i]:\n",
" if x == 'haze':\n",
" id_haze.append(i)\n",
" elif x == 'cloudy':\n",
" id_cloudy.append(i)\n",
" elif x == 'partly_cloudy':\n",
" id_partly.append(i)\n",
" elif x == 'clear':\n",
" id_clear.append(i)\n",
"print (len(id_haze))\n",
"print (len(id_cloudy))\n",
"print(len(id_partly))\n",
"print (len(id_clear))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "918a609d-9050-5126-bfc9-a1bf2e61f965"
},
"outputs": [
{
"data": {
"image/png": 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j43sk89W5vRf28e5k71HjFgtNbsIPRF/7XTytF/tZG0e+v1G3JoA8jpqWmjWi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\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Trp7ZgWKstcd3MulVDd+T+yc6jqsltwgquNOBL2XahKr46biRgcWJxWD5oknwSz+IA9rx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4ElZ6Hp1vplJWuvY/4Pbn+6uzyIp30WmxALnHnKDD5yEjGJqQG4+Y22a1TmLKRyBtMGY7YqnwUj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\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nM0tRbj3WNjxsjbWFBa5fjPtyj/WGt5XlVCinhVJLxE8jAZw9FAG7fJG23mM4XQuRX5RJ\n2EQThd4bb9w9Y+0rzQqldyodqCy1oItEFYQU2FIVylaq6lUk98j9CGgUjbfEArVFQix4Vbp4VllS\nmjstgXTRrMHLZhgJoGaYkpUb0uDQUqlMFBMYdsgvpxKeU+1O4qRQaE0ZOTEh2MJ4pWkQ2UIdQ2k6\nZd3NkYA9vEKyMC0X4gSId2jrfdg5CITpaQxr3iOGOG8NQ5nWHMoAbfF+LZH/YoxSpCNtcYRk5aPm\nY0uWcZzKdg8FunWw9S5uV6LxlwKNbIqU1UXcozpGkUq3oexE87WqwxbUE+xrvCZh9Mcl445zWt3R\nGhWiIIyKnvkbazeqLmHkKHCqkZRpWjjrwlIr9VQ4i6b/KpLTIeplayYKllKnsiMq6HJCBPq5c3Nz\nE165RblZzuH5AcSzG6lLWJBLSZ4tc0ItLcamlrw09k3PrsOtXyhPXuMLX/g8a2s8e/pG8Oi3SC9j\nSf6HgT8C/N8i8hP52p8A/pCI/I5c+18E/jUAd/9pEfkLwM8QWST/+stWtoB4/tEVzh16c7oZWhPU\n5saqqlSWLJwdm2BMYJSEEVbviIXQON1XRAvusNbOCuDKjQpLITKcsxpCN9LFEAxh8kJJ7dgNs6zp\n6T002KohvLvTW5tB5KJRVmXt4bLs82CHcFeJsAxHo26xR5KejHrHaTkflsuZ6Ds9DbKZLvfacCbL\njdhhmUwpGEIxp1tDGQB2qzghu/F5ZtFGV7Q5DdNK6m7Z5trBbFaxKJLabFqvhg7etGfrWg+lI4Xq\nYCDjDsOeXAo0GcI+FRLVnBvZ3FoSjuZmMplb1JtmguRhFRxgKSy5NgzMOXXxS60RAtAtmk6cynDp\nhyAZ9yWrFuiwtHgmkeQzl1O4kCWXqJvQTakqbLXdbYttfpVpbzEhxOGwRo0qItjWHjrKGUbedGlG\nvVlwXUA6bY2yaefeWeoNeKe1xtoikbIMYZgADBkejhSaZDULjfOjXWZ8bVjONBWxNMHMjPtsWyvp\n1ksXrAypstsj45E3Q8Z86iGm5z6GvUVJUtgGAC5SWEq0Xr2s0RxJfAMe6uF2DOtwABZJhXOLd87r\nil0Di3n/tNnJrKOzPT/gpSAW522AZAQud3cjuiuepkhafDpbaSwYXbZUbCrSs6qHG/eX++BjhGJx\ncz4FwO9gLeofd4PqBadiDu0ieb0wChiKm0R1jtbw1qL6iUuGTgRfsJY7RArNlkyijLXvGnPQ71fU\nwpIFMe9r72iVXJ4AdUWjytEEd2MbZHGBV5WGZdhhesGK100BZOAaSUUlwZkzQwJm/X0n92v+nYkq\ns9pDxsuWWZUgaYQkMSyvscua9ACqrsxqDuOc5l6ejp3oLIFjVD3RPWSSIdN6WVzCAppup+1rK5JN\nMBDBLSWfpWFlBheP8zO+fTWRVEomM46T32ODyPImc+/b5YZ306PKRLeeuDzyqJzwMI6Y4fE18zDE\nbRb29AAPmZczy1ReiIS2oUwLG88TmY+1fzpvHanRN6Lo6EisoJVzOVGWSJ72WhitJPxqXUM2lhrK\nyzCg1Jp8Z4WqI8EzEvdOWqI0QlYWCi45YuKDX47l2HLDtn01LPVbWIdzWe+5tJVnlzvaur5wTb4U\nvaXEvXeKROTvAZ/MP38L8OnfxOG8GT3GcT3GMcHjHNdjHBO8+bi+yd2/9jd6MC9Lr8CZfYxjgsc5\nrmNML09falyP9swe5/XLpsc4rsc4Jnic4/qKz+ujAMl7EpEfe0wZwoMe47ge45jgcY7rMY4JHu+4\n3go9xmd4jGOCxzmuY0wvT491XG+FHuMzPMYxweMc12McEzzOcb0dY3r1giAPOuiggw466KCDDjro\nHaYDJB900EEHHXTQQQcddNADeowg+SO/2QN4E3qM43qMY4LHOa7HOCZ4vON6K/QYn+Exjgke57iO\nMb08PdZxvRV6jM/wGMcEj3Ncj3FM8DjH9RWP6dHFJB900EEHHXTQQQcddNBvNj1GS/JBBx100EEH\nHXTQQQf9ptIBkg866KCDDjrooIMOOugBHSD5oIMOOuiggw466KCDHtABkg866KCDDjrooIMOOugB\nHSD5oIMOOuiggw466KCDHtABkg866KCDDjrooIMOOugBHSD5oIMOOuiggw466KCDHtABkg866KCD\nDjrooIMOOugBHSD5oIMOOuiggw466KCDHtABkg866KCDDjrooIMOOugBHSD5oIMOOuiggw466KCD\nHtABkg866KCDDjrooIMOOugBHSD5oIMOOuiggw466KCDHtABkg866KCDDjrooIMOOugBHSD5oIMO\nOuiggw466KCDHtABkg866KCDDjrooIMOOugBHSD5oIMOOuiggw466KCDHtABkg866KCDDjrooIMO\nOugBHSD5oIMOOuiggw466KCDHtABkg866KCDDjrooIMOOugBHSD5oIMOOuiggw466KCDHtABkg86\n6KCDDjrooIMOOugBHSD5oIMOOuiggw466KCDHtABkg866KCDDjrooIMOOugBHSD5oIMOOuiggw46\n6KCDHtABkg866KCDDjrooIMOOugBHSD5oIMOOuiggw466KCDHtABkg866KCDDjrooIMOOugBHSD5\noIMOOuiggw466KCDHtA7BpJF5PeIyM+JyMdF5N95p+5z0EEHHXTQQQcddNBBbzeJu7/9FxUpwM8D\nvxv4O8BfB/6Qu//M236zgw466KCDDjrooIMOepvpnbIkfw/wcXf/hLtfgI8Cv/cdutdBBx100EEH\nHXTQQQe9rVTfoet+A/DLu7//DvA79x8Qke8Hvh/g9vb2u9///vcD0HvD7O23bn+lpCqPblyPcUzw\nOMf13Jhk9+av//LV+2/nk+3HJUBdFgB+/ud//tPu/rVv462+YnrRmW2tISK8nEdqfEa+5Kee//xb\nJxHF3V70zu5/x+dY/Ln3/UX3d15++F9iXO4gb+E6/qJ7+/V+3D/Jw336ZrdyYg++2fp9qWV90Vl5\n+Exv9n158Mf+cyK8eE85+MPletE1v9S8vtUt+PAeD8ZV6zJ/f2xn9u2UsS/aqy8zzS9//ZzXlzlf\nX+EZfCv08rwt6UWHzq/flgfv+fj7JW/zlcrXsZYv/1gvd2hEBX/huB6+9uvtnJdYXLn+lDx84cG9\n9vtXtQAvf17fKZD865K7fwT4CMCHPvQh/7Ef+zEAPvrRP8cnfuGnQBxXcDNw6OZ4z52kjrgipugp\nXhoCA3FEYG0g1mJ2ioYwdKfjVGLzkz8VsN1C+v5/UcD54Ae+k4///E+iSGwG73HblF5jb6gIpgIu\nuIHgII6KTsFrvolnrQK2bdh5YJrHGPNEmQguUNwAizF98Lv4+Cd+Ij7TBTPi2oPZACoFKblJUhi5\nAyYUDBdw8ZwHwXoCi7GpxoEacCKllOXPooqqIe5gQkf5lm/9B/jE3/opGmMMTpGOi9NcKDk2c0Fc\nUQSvINJjs6OYx6KKCt4cG2MpCi4UyDkd47GcW4s1knhHRHHgm7/52/nk3/7p7bRo/FRTes91wfES\nG0ndwUqMAaOIA4q7cvELInHYzJ1uhoqOS6KkoCfWQ6fDRugJvaw3CsoH/v7fzi984m8Ss6/88X/3\nPxj78pM8MnrRmf3TH/kB3vPV38hnf/X/xXNf+lyV7XwEYHXa2umisd9EAqAByg41uoPFPuzjCLjn\neYufYjbPsDs0M9ydWhZUhN/6W7+J/+9XfoEioKpUhaol9owIgsdKiE/+AcTNJJjuajGOtRtfvDRw\nQ13xEp/xYEwpRGOvqAi1KOKOuePimAmoIAJf/3Uf5JO//HOoCN4tPoNQq1JEMFpefDxbDK674tZj\njlSQHJtZRzXYuCcjEQctJU5szl/N81DFuXToBuZxpr/pA9/OJ3/xZ+g7Hpgck47MNYQ4y0OeV8rc\n2z3nU6ojVlCJ9bx0aLaCFUwcDXZMFQUJXmoGvRk9DiIqygc++B388i/97FySAVRWa2gpCIK70Jrj\n5pxq7CUV2QlEZwWKO5rL3Cz2Tz2Vue7+QC4HrxzK0iYnigtf9w0f5FOf+lu4gIjzb/6xf2/3vcd1\nZt9cxv5ZfuEXfnL3yeTl4yjI9hNgUeFGlKqCqrIsJXidLtycNNfC6e0Se0AL3YzujpljlvtSBXXJ\n9YROnB3Rwtd83bfzmU/9HOZOszXOeQjmBJIO2mO0rrlGCkgonXSqFNxlU3zVERwTEFMkH2w8lwis\nPfaLFljGJtGQyd3hfd/wHXzql/5msAkxxErwagWngTsrKXvNN/kLcd/83Qc/wBFz6pK8yAVUYz+5\n0K2FLHPHJBhNby1OWZ7Db/u27+b/+bkfR0QCt6RwdzMc41QKTQPcJZQInoBTiqK5t2NZnHt3Fmye\nedMFE8V6R3CUwDVL0VQaoGrgkYZzSR74wQ/8Dj7+8Z9EEAxYZcUxxAVrAyMJt6eFWoIxte64gZvT\nLSRIc+dUQiK6gxZBFIooN6cnnJfKaSmczydUlZubhfPpRCkVEWE1w8yR1qnn38KNPmU5FW6ePEG1\n8g/9rn8y1//lzus7BZI/Bbx/9/c35msvQcHkJQ/qKjHhV3YdlziYKUvEmEJUNEBfWGqEeSLZZOGA\nLFd2Jn/xH7qzcoSFI9+QBMB5eWEI8Xjz6nJT+M+vzv/ntXb3mTJJPA+Rb4x7oughRAcT34F8365V\nBpuXuJ6b4+4oFRFFJDZmXi3mlWuKefOrUc+fg4HN9yfKYBjyHEDjoOl43a+vHEdim2DxEMXPzUve\n3C3WX9iug/tzAu/NFMwJ4nLvjHuN1zVH5sR8xRw5YAGCc84FT0DwQAeeoGus1bbq8a9Q8krqJYHb\nq1dsZuwLn3/z3G9XCNTl+lOuOXf2govs4fZGOwzDOHRuIYRMffeN+L5cfRO2EYzN9WCTxIGe3xES\neLlACb4y1tol3jcfwoQJ1MQDPM4byjYFWkKwjj3rea2H1p3tvIVyHMcmQIm4gwa4TpGd+zFuHude\n5nwJQpcwFHRPBU43vui+8drJTvR6vcYb4nrFM8bM7h4TYZx5obtP8DX5gzhFagCHwAqYhbIrcdTm\nOovlYptRSkGGIULzbIokv99vDEE85tQCaQWf9Ovllt3Bv7Lwi+TjCiI+eakidDrPLdYrRCFKxtnN\ndXvwPJMXptwJe1WAPFWgG73n+8YEpwaYbPxbcwMOuT5w3cCkA1SWUlA3upPAV/Niu32jwW8lL+SD\n8SKIdJySoBrwYZLYuPq4PxL7v5tBHGuW+EQYgOQh5wnAL91jMzokusOkBMBLgDyUyr6f0wTJAGvv\nuApVwoCiOKLxuw3IwjxqwR/S+DfG4uYUCq20PGea0snp3gO0E3Ksz/0eDz0NTjleEd3OHeMMBsgv\ngzcO4x9pyR58YRPCgR1yrJa7SsTxnHcleNz4nkga29wDECd2KzkJ5rlvVNEiFBFuzgs3yylB8oKq\ncr5ZKFrTS0diHMPMqLm/3Jzeje79RcfhS9I7BZL/OvBBEflmAhx/H/CHX/bLe5i2h18P0eo1O3z+\nG54WowGgH/qM9jLZdq/I7hqYTjQ9QflkqAMab5hvYK45pvFhC432IfnuI8L2iGODj9d13EkgVOzt\nnfi1Xwvj3VSNG8RTpfaeh+7a3fwQYb4cdd/msoxD7i2vnZq9hLas+JXFCuJgDOvvFK8JSsZLe4Yx\nnz0PJAlk9+u5gYvdfV7wdAPg7wX7+Mx+LbuP5zMKTCbNA4D8giV4MJ78TQlFYLc+PJiXV4L0wU92\nG/gF0DnAjF+952Od50vb55XrWdlmSq7BLjm3CcbGbadVjOfXYzeq3R3GRttdZOxBefDRq73qO1AI\niEwl8bkTJXtgJrGHAy3MMT3cEft7X+2SBKGIhDXedRrHJ1/ZfS/O/xSZO9rN9GA0Dld32+1VTaV2\n7GdNrmnzM5Y8WhJU7vb+jsOra3h/NKxUaJxJJyxzg88P75ju/g2FIwz1+9EwgYoiTJ08MNYU2Fdc\nYnolQh2WXJsNVCVwl8HR9jvq1aOxAkP1mkB5928c6bD6piXQwC14oPtKbYUyPHoyFN7dLh57nb3i\nxTyQc21JhS15gcg4gtu6ymS0fV5fdiP23b8YuM77Br7bnXxLMOeWIC0HMO8kV2d0fDc+P85sXKeJ\n0dMqm9IICItoiAef8wjhxSk2FHqfYxxW23HPYcQbp9XZBG0cs71iOCYtlJkdbIl1G3M9ZmfHcKso\nXYRuI+SlIxiylDkHLkJL40HJsQyjQN2NtRPWbIM0A1U6lg+xYaW5l3yT7ZLGBZUd1gFElaJCUeG0\nFE6nylIqS10oRVhKWNx9XDg9F7Zz7YaxrX9ZR/YdAcnu3kTkjwJ/lVDSftDdf/qlvz/2uIOaIDZk\nbrJaLxRVREKTlQzNCKloW/iClvw73JQuSoHh0JxndWW47q8BUwjwvO24dPJ/SkmtMa2zAxTodg3y\noIk45o2eoKggeFrIi8ngLbEZx8Yp29HHN6ZfSl59TtIAa4RAUcKllT5/pyNeGNZ3QRB1kNDUN8aS\nrimugeKYp73VPff3kOvjaOdwQnu3nu41DQ1QCC2vYLTUthWghAY5xyZxx9BlFdyumKZYHioZTp0Q\nqT3X6kqmJ897DqTsno9q2/qOZ/ChlOQFd+ClV2exktbEiG/RULmvMMUA1YMxDZozVWzOm5cRJvLq\nkQ4BN1yJ7J93v2tiMWpVoAMRoiOeNgf1bVNBLvIDkMO2N4enaGPiGgzeLK2O2z6NkYy/ggfMXe8M\nU1d+sG83YruH5tnqYrjp7kzm2IaumufQkBAVu/2neUwXhYaFhUTyuc1wA63PA3onBJntYEzwHkPM\nWGWz1DQTGsJZr9WLNWfC1jj1VRN8DkuzOnUcaPY/g+dON0sypsKyhyOT39UMGBkWsQDwCqXTBz/0\n4RoG0xbueBG0KG5h3XOcduUVIK1MNQCJ2bRGKqQrm3kIe2ooS9lCRGKZBlooc32DTwcT7j7wcj7A\nUGRtAAAwcUqp6NXoXi2ynCchBDSQ1r9roAzQLSyfMSUCLUJdekqMoqEynTNkiDwfg6nGrxniNETX\nkKOEzADHLQwrVRRXWKRgS2JRdyyNUtaVoqQnVNE8v41d/LCTHrodr5cAsj3lNqoJ0GKQnbCAK4Ts\n2kFwgEUrq650b3RxzAp4obnNUItxHwiQvMHulEdAKWEBHYrdgNHe71lUQQuuwupxzbIU1h7hCMMy\nLgWadNQChzRvGBbXUwn5kzxhyFpHIvRrKJQJHtt6j3mEEo7wS8d5stMpgrdGeInVgmt8uYgkaI4n\nKWJkUAxuZb5O5mEYcOlQcFyEG82QUwlcpi5cAlanPFGWUjhV5VwqT24XlqWiWqnLwlILqo4PC6QK\np3MBcU564tKV2ye3qEbY3YvRwJemdywm2d0/Bnzsy/ru7jmGApJK0vUBzoPmymadmuY/mQsM208l\nF2N3PyNj5MYmi+3EXpsjLzvcP6RrdIBat7in7sIexv1CaA4A+tD6vLmhJsiL88v+UuOx6s7xz/jN\n9wlIQxOXHGMwgE1bY3N57GZ5Ow9bzOHVAb+exg0G+S6kY4xUHFwzTjcBRjK44aq+WpCxuLurjDEM\n92fiYhLf4+X55TU2C9P+Ol/qXAwlfNzLfPf37vo6wO+cPxh2AHzbTw/n52H8x4xF1zKZuErZxvqq\n0e6Bt/3z0Be0kahse5Ex4S+45n5ddv+uFYmhVG2uPO99oBwGbLfdX0PMzCvOAMUX3P/6VlPg7124\nQ3mlMC2N29U3Cb0/Q4MHSYKOOI62E2b7a4y/k/HMDZ0jsYjxDg9OxD834Kzj0Phkh46zWoD0YWTZ\nrKyeZydnZ1rk9+uzCZkR8z9GOoT9sIRdc5cAqltY17hs2JzMewAel/SBJ3iTuZQpqMFFt9hOZwKZ\n4comn3OA5pKGkRlvOcYxkVr+y3kd6zbvm1Md/CuHIoLINdd7lWjwOSFWdKxqqkMv/sL4dYBbd0xq\nWgQtzt4AcOOUDgPObjNMkLwt1/xjxMgPq3QVpaXr3t2x3hMEKyPIZ54fJL0AWxiMUPJmZe7JCEeI\n61UGSB3qzjAajZHtd3ga0qYXbHBySdmwl+xBRa7nd7us7nIjttfdeijOCTpbnmlVoQ2FInmMiNDo\nVNfMRTIMS6t+v+YdMo71iFKWuVLgXNpKkQUhjD9NM5eC3dK5oz0Aj2U88jDyNA9PEQJFahg/cDqX\nDENbdvMik+WLQE1eZDlG3W2ZLXBAUFFqKRGfnIxBVBFV0OQsiRW0hNHgVhdWE2qp02j4qEDyl00O\nvcVs6VBxxcMlUIZbveEscRilhfafDFN9BJY7z1pHsYiDrZE8Bgu1hwUGBC3CSSI4fts+Q6SCuHOR\nNf9WZClx7npHvOOSzqq04FqFZchfwkIoFuDgNOGk4CUtt72g3RJExxerMEH+BMOJPTsF1w1bgtBd\nOGM0y8S9HL+IU0vBvMXmUUmN3ENbZQDo0FK7O9INrwE+9gdBRVJbi12smYgn1GnZNYUiBfFwjZhH\nHJcDRUsIqR6xX/O5UstoYlTXjHUM4eniSCY9jWQDybjQLveBlJN5VIlN4LUGq0vE4q7pXhboiomD\nphXOwaVEfHOCJdX4rknEZg2mbUvGaHXm5Ec0mYYVVBTxNXdQvOZ06IVhIVdGApVzNxInExh1kxFx\n+kqRSr1iu6ohepr1mdwpgBeL5NOumV0c+8rSteZ9uNhiHTUtzYVKy3kSQDLUSGQkam6QF0BqYbB3\nLblU7jSPdbZklKol+EVdsVyzEJeGiNMyBKh7WIXde8SzdU8XfwxIpyQKQWwO3jfVWlqBEt8ZYUnW\nt/2MO0U89h2OlIomn3Cci7U4l64Idbp0O3HNU1U0Xb0NEDWWFMQRORv2JbwgFE7FaRg9g5KtxPXu\n7y+cyini+nAwQUyopwTZGTsoCdRdQ6DLsEqlht9Ngw8Sln3zDAPTU4jKKQAz4S8tgWLhVahFqOnm\nrehcWEswFJZeD2+BOasYrnAW5ZKfLQQ4NgRfG3LSEJxIPhcZON9X0AAAIABJREFUJ32fnrCCdgEx\nii4RRjd93bEfSibpgmO+5vtDQL1aFF6Hzdvp6jMpbcTTO073lvst9sIweLgITaFZQy4RXlQKWD1j\nFMw6w3rkOGYBE00uqEXCWtw3BFxNI0Ekw8fZRARKJlx64KCSMaf9kkle0lH6TEoVBKknuhvWOwVl\nKZWLd1oLeUJaSd3BLz1zmDRd87F31YaMi617cw6Qd7k8i73pFc3k8QibloxJjkTcIceLb0qeu2x6\nbulhxc4QqVrD67qUM2XJBP/8rCG0u4Ja4zQOg8OlK+ZC10a6gAJcu2Na0bQ6uwgLkdB4OlWetQvu\nPTx0LRST8+lM6+nBsU0GtgajEILks4oopgXrIUe1CotG+Agi6JLGQwfphUJ6KEaEjHvyJDhpodmm\noA0lugJYmil7xMCzwHJeKLKw6MJSCjenGnHsBVq/xBzLwmk5oQpLXdAVllPwzRnS9Rbp8YHk1Bpl\nMOYUBjY1vmEX6lNL2lT87VcVqC6Zfx3qa0FYpU1rQeiaJXXfAVTz+wwxHgB9xMoMt3Izo5LJHBJu\nmrBD53JPN96IAcqrDWvN0HqeWzNJi+iLAdNMEEQYsSCCsLJZBthNieS4AnDLtMbuL7/Xr/aJPKmD\n5zVHJNK4/yYgSlqHBxAEdhYArizBRXZaosgWg8awcCTokf2ijnCQqTvkyGQbeM5vxEnt3WS7qOv9\nXKcyJfi8TIwrn9FkJksIsZdGYkaA57hDz0TI6rJZBmSEawgTguXA73Ni1WWz1LuhIxnkFaOeQmdi\nit3+3onhBFJ74Tz2WGyIvcdm//6FHrFwZKJawnE1mZ/Z/zTZ7B8BtVMg09JdG+tkHl6O6sMEPPhJ\nhHOR8Yrdo3pJMGtF0hgzLL7DsLS3FvnuXykveJFUDucuzaQVwFoPwZQwWayjOBfdnPua5wgP4eJz\n1gbUd5p1tiAqCOd4x0VR35TyLYwrkuHGCTeN6iGFutuWA4oEl5rO4mRkTu77TZbP53Vv8WmJfWAj\nfqp3Vs9zLcJNhmcBuFieOaYRPWB6WLpcfHp2CqGoFhWKKEsCLTejFI1EvxGaoyG0Hcn9YiChnFga\nDCL5h+m+L+eVs5wQlJOeMOm0q137apHOtRy2+Ej4nJZWAKvX5zmVQ5MtJGmE7JlDG5sxKw3F+m9W\neZMM0MnQv0qEuDS/BzaDjJG8sI2xgqJZEQWe1R6eAneaO22W0FRqi5A+MbiIc+mdWlKZtgDJmVQS\nQDQ9royhJ39SZMrmU3peb8/vpoTWHWjBwmrdXGhZqcZsC/UBaH1Lxi2pgBQcalQIKTinUlDNcMSa\nq+HO2QPY2w3crYV1hcsafOrSO0vJ6jjzgKYMXBMQKpkgHM9/1y6sveUjR7iEEwYaz68PhUdEcG10\nhuVXQsYB5xIhVe7O2jr3PfMHzFkvG44SGU4HT6+QzJ8q0Ft4ZtnhgxGMFzHITupPGRIZnmhVpdTK\ncjpRS6EjnOoZFaVo5aThoTUVkJVaSxjFJJL73io9OpAc6224a7q4HsaRjA09omlTeOwARlhtnD6q\nQ2ziOK4p23XEtxALv7rMBnhKMpQR/C/JPMf7MuF0Woh8COa8kjAZxtWjMKyHe9j+4CMPqPsWAhKi\nPTZxHLjt+xtUkDkWF8nEFZ9x2RN4yvbUWxY6E+T7zhEcV8vITrEpKAfImBMzpeTuicQfzMMQrqEU\nDaG0AQ6ZQGoma0k+4RWojGuO2w94dqUNbKx7u30y7PGC5ziydtsELJqfa2ITWARgTnDjYQ2I+497\nxPPMMbkk+IqV3zPnV5a2idxe8PFzKDySSuFQGmUHMIWRA7Pf+w/BL74B6vhzm7Xrz24KzKYqydU2\nDMzludiSa7ytGQ9CCQZCEDzdofvHi+/tWe8eD/tUfrazM0K5xp6NVyO+yncPLGRM7y4kZLxvOfQt\nOGo8cSoSO74U44sST40d2NnNow7hIeG6HUqqX119ozreSZA6+E3Nxxoh5uM7I7ci/ojwKxEid4FN\ncJb8Kfm5Ecc4wp3Ckpm8KK35IkJZhrkj+V+WEBQCJAwOP0DFEibEHNkYg871sfn5uMrgfp7PPFz9\nryKNNR1zVfJEqC54RhrHBzRlySZxwvK+8dBxhKJSRCqyD0IOJeWmUudeHmcwWEMavsZ7KVOaWXpc\nck/keKoqbXopQ5HFA8x0s3n9OLeZF7DjMQCZ7jKfQ+bTDPa1k55jvbVQis+QRTfC2+VCKZrl7jaQ\nrMBFyKQ+qMNAjuB1k6qlhHfNEKRK5mpATW+tN5kexzGfy5Jl1fKFAR6DR/bgswnuLUuixRz7AyNS\neHcHy84FiPHLCD8MHuQe12+pRLmHlde7g8UYW08FdkwAYXjTKcM3MDxyoZK9bvkCjLjwuSQxft+v\niUQsfAlvcliUC0UKNQ0KVhShUUrJvVVmjeS3Qo8OJEMcljhX24Gz59IkwhKi8etelk0hMKyCILt4\nJ834xXE4OiN3ZlyK3e8hqHQelhlZnO78ecBkCECFyYCZoGm4sHahc8mAmCBRGYD82rJ2Tbobo+z+\n5+r37ecm9LdosWvQsD8wEwHsJ+BqRrbn3mTeNaSI13TGJm0AaX+pPZCBUfMxjIZRYijOiyZzGwsm\n28RdDeLFM7JXfa7WdoAzuRZ32zfl6huRcDTixZhWpmEXDUydojiHGLw7NuRgTOP73beYL3vB/V4V\nem6/7ay9E1y5zz0y94pvq7IX2oO2ZZUNIO0/pRtM3s/aPk51zHV8N8/w2He7zbhlv28xcXslch/a\nhIyzn/DKt7HvwfFGmVCY4QpxDZ3XHnMVIR3Bn3wnxJS0igxoIts/8W2On7urbnt1ZDFMa75v+y5x\n5pyPmYw8APDuoO3UG0ZF85n4JA+wkWzXnoBsCNvt1NPwdOMHLzffP4kyjv083yLMzEzZrM7opsjG\nDQOZyKylyxTssRw+hb+wKRyj6o+mJ2xsDPEM1SK8FQVFfa8avTq020LECof3s7Dk4478EQiL64P9\n5cnj0rqSnvYEqBJ186cndbtnodKnldmuYt7j3OQey/VZMyxAUmoPL1SEZxh9YoPtOi23q4iwAMKo\n5jKMakOOM70XyGYAY35ym6zJK6RPZc5FcC3b+SHjeMWxsS/Epmd0hOpAXG/ovWNUM38hD/nAHa4e\ncbge9+o25lJoptPgJyIBFCVqrU/u6ESSrG3nV3xn1ksZPQzqUY84+Z1ooqXJPeJ3jwRo3LJ/xdgz\nsOYAReJcO2Spu9wrZGgVYV3uySimB3ywyDELQ5Cax3O479YmJrHI4CESBUpKfLvWDNuqNZUEzeS9\nt0aPECQPRkosbBmHaoRajC25E6wb0phW0JqxdZVRSD6YfyeyZ2fiyhQKQQOKW/5UMU6jWL9knGpq\njnjUVDQcTffmKHQ/vj9xncqsRUg+jbhQzafLdGA/QzbLzgOq09Tk8+8BInfTMO3vLhIJCrKFIUT9\nyQ2tjzFCuCgydGsC+y7gPgI6ZM4QgGV9xilkZYRe7NZmx5xUSmif85nD0qYsEcea4QvLGKtk7PCQ\nkiqbr/sqtSDdR4PJDx4xijKnVLiCEi4YPTP9kwWMDGLR7B6UAKCG8lO90rtl85KBVgRRQ0oFjboG\n3sFdKd5A9LlEmTizA5joTot+tWhaHAdim4hR2MKl0oPBZgUcgnIk0di2RHG9/Hcav42YV0/L4k64\n7n/CYOzkOcxgkKm0DtAz1qFTJIKKYtgBGNyVQqFooWG0ZQ0LygSwmzVlhg7txjHW2k0imN2ZqFFq\niDnNA2seAsBwqusU4J7PE/PbNsA45mkHaIZgH+d/oRIJPFueuEGCyowZxie/87kOoyBYxtzvkO8A\nyOCsUmZjCDFnZjCXaWPeAVMiMSi/vgF7ENF04Ydg9RSgLsMClddJdmi9TzQuEgJYVOh9pbIkD4h9\nFjOpcwzB+XIfiaHUnAVLPmcJfG1uxgESunVqiVgbTXD06lIshM99kLx8bekNG7HHuZJlBzA95QdO\nVEMYMezO6g2jU6rMxLShAIkQSaY+GuiAlGigJFmqzTImeZyeKGkYI47GPCH/T3kOhpU5Gkx5WDOz\nsVNUv+q4RQWsPnIdFGalJQ0eMcIEM4qdSoTvbIo9+bNRMhndyQoSDkvRqMebcztkXxOn1M7aO2vz\nCJUwzxyqyN2I2u4raIumNxoedEEitCviB2cY4Tw3HZZFZ57DMEKJOrUurKtjo4tIIt/WLA1+8bS9\nG6s5/z93b5NrS9ZkCS2z7X7ui0gk/iSyBDSYAzUAcgT0GEL1aFNDYArQo5d06QISE2AO1UGAlMqS\nKisj3j3u24zGWmZ7n3tffPVFZSHFTQ/Fu39+/Gf/mC0zW2Z2+I293N09hX3G0RFqoPJHEmNS192g\n/LzhcGOVjfcIuCspclL03c+EHa4cBmAcfK0jVVZO6+rKxJ30rOelqLKSCHMCc07M+8a8b9zuuO6b\niYFmQE54BMYw/DS+wZyl4swM5/mzIkvz1VH3Zx5/QJBcGaoFJ9j2k72TlpKIVlPbsRu7Cl928oBA\nH1XRsopKWRQs/qikUXftP1LAlKVcApgWWn76JPos3yzFdYyswtv1SbKa/Tdms2oQ1tkFu099br98\nXdNkyfkPHuCj0lX0uV93gZCAUhX0tLv9/uMnRVZN5nWm4dA9dlW5AGINYTnfFBhvaP7BHf3hCZZn\n19aVP5xf3xY40OjbBv4T3NAfL//iddboWBlTMgzMljmcKnWTq0b24rxVQuEyPb4gRu7haU4y8BlA\naDgIQin82rHac7jefh+HBf1KBXElljf00/PIW1F7ae2Q+jlfblfRqNzvKtrD0J4d1MhAA8haoGXs\nLZBaz9RGan5sOpGAT1geDZx3+j23TSrQssL8p3XeDyWkxu/oEftAF8t6u93JYKIqUVbcgLpfbmHP\nrLNd0Z2ahxdJoQJw6DyCW8btGOshFjjViHBAXmTLacziLy+tZwHZmnvJ7do/ETy3ZRT95cc4MJLJ\nTBEsicWk7bqKVkSKq90lvvgkNUoWdHx0KTHd98bEN5jC5UPeuq/JSU5UhScaQofil+8zMFLeNgOq\nFFhtttIRZYLOLGcEATXbrRuqlNhHeUYW906U/FhkrXYTjZHRu6j+LAltNVt8KBOPgtx0RWSNSbq3\n5E2sBdPUi8NKN27qXfJg3XlFnVaUWuteKytQzplcEVNT/WCV+owM4OIeuSLoLALkyeW4HUkM4aKa\nFgZ43oEjGX0sYXEc5Otmd7JLVb81nDmA2LoRHNL8d8kIK+csIhLPSJWhrcHhVLz9BBkLaGyQSNyT\nyca1lw+gq2hWI7dENs2uGs64gV0xnUmRCHTis0TDMuTl1yhDG9KjlRMUEcwVMUMokYqOJskRyKts\nBjPVe0bA/i2s2z8gSGZtXa65oGfPQIt1KLsF5KMAB3I+cR6DA8zekKxwYVRyTBZIWpkwHH7gSBHP\nkTgEOm1Y6T0e8rT6SG40AIHZ2fiHgxm/CKCTcByIgZHcZDekzCALt8CTNk1iIofhVHUFpkPQ+42c\nyDzLxyHuEbNBjwE0XSG9wxYFB2hRMmv8ugL2sKaf9Hs13KBFm4j2qxRfEINKCXfC7aFblvKQ8pkB\n5ClkfTNjGeKxucJkBhxw0VoELI/iknPQ7/sJoBKdKL0qpLcEmMGVKZ0+8aPscr8pRMt5R/lh7RWu\nDoMQZ8uSyZtD1nslojnUDawEZBisNqb2IiuFXCqddfJVQoZdXgAu2KjQdCXqFZgKVBLBjSnh+PVg\ncoUTh6I/U4LyTcJtB1leYMwSlmWuFiBaFRsYmiNT8urC5By1oWbIM+dLtlzZYUojAn+seA5YESVD\nURquqDADZtKQUZcJk4L6NihPqtzYCIY8L3+N0qTLxxbXi8LPUemuwIXqxqWRmI7TDM+4MIPJacMO\nHMayTpXgwwulojoDocB4Ilu5eJq8sFyTM8GSUIdIDaIVhTzW8FQlicRZihFApimqxo0/NYpvGIhx\nN0/0wEMe5hIFqVCvvHOWmIrMeALPG+IwTozzwPBB3nFM5o28s8rLkPc2x8GQbBocZwN3E7HzHN7d\n+2yAHkOQEvDMaPk9ymPmB8wp7ZEJTOqQ4YxoQbKyDHp3wxS5b+SBGYk7A9/GQ7JeHs99jr7YwQjA\nBbMDDme1gQy8nSa9pGoyRnNzZgLw3pvFDR5puCu0n4Zpgwnsd+I8AfNgVB4OhOOGMSlMc0SvMrrO\nsQ85xFTa4Y59jKlfAOC6DG+W8OEIlQHzCOaLON+Bcpr75EpbLbcn4AcTyG4E3iy439xx+MkmKUr6\ntKLa1P2n4RkT5qTxmD8ADMy8lihKGdwJ3DHhp+N0lnyLWx3f3gfen7+IlnGybwFufLODSaUWLB93\nA3mTOvV319Q8mPYJK0lNPDGcVYUiD8RFqetjZT9A6S9+ANf7DbjDjxPpBN4GB/LGROJOx/DEYQm7\nDcNV8USmVSIwcADGqMPwA9NY2coA/DRYCSQjcb1/R0bgp59/otwBsRnZUgKvWxQHRgoGnhcxV9BQ\nGkdi5MQ1v+GOd3LA80RM5XRlwMdTGNCJWZzRQAA4Dsq3+/ZXf8WfefwBQfIrH7ctwxiyVBUeK0J+\n230LgPK3scJ92//TQi9dNhU/MbcikQZ0VuUdgNm7PDxy52SQI4Tly95ya148Pglmax5G4VKbqIO2\n8frZOnjd2dwhFVBDDqgzEc+bLst2rh7rZlDHHQp9anRUF00AgNuBaVMbKfHEhGdipC/LsUbJAMyn\nwiUOuMO8QjGBJ1g+77CBQw0AhkV7rYBKhan5Wm9YX0eVeWsMvrkiymuX2YkZ+C0+oK3koRr7Whm2\n3bW5j3bgmokrFUmQ9XkML1qj7jv5OL7oLSMHDnHFUB60pmgY4FZ5I9v915rZAWJuz/aVDkt6fcpr\nC3H8J87eW8A2F7N8QAytQxloQ56K+swaC/HfXnZHfPh5/8vyCdceBADcXAUucFx7bvQyWnckYDcl\nAOs69nrX3M7lN7Up9ZcUPUJG7P5OloZpgc0fhjAmHLI183rw4dul63629kf64m0aDDNcSzC31YXX\nNacQCcH39sz7y1kN283yk6rwc+cEMHGAXbr4OVaUqLyC8mxpo7SB1F4I7WXI8xzl7QHwsCEjRmUA\nRZ8wG9pWdDYE9I5K3gofLwrQ+FjI+AVHnnClGl7JknqPSaO0AJ9XWR9LvOUDmQQ0GXS2ZLLEJg23\nxGGdMv7ljjSwQkeCIFfJk7k1RKqvsMXnR3IuqpZ8p2smDePoJHjAU7F2q/glvfpdFj2XTCx/8isY\nYX3bGas86TW5bt7enL6Z25CD1lqY4UgQ8ndSqmlt0siVjSo6CA2x07aOlfp6Gr3Hy+QlyHInNSWD\nPO64n3zOFzBN51iAOt/TcTibKI2f3hAZuA7D3z8dz5mY03BN1kn5e9yYczlUWv/mQarHyL5XwPDM\nS826lKwm8JxhBDTKKk8/kGl4D8NPB8sh3iff/ydzNhJR6UaC2IRbYN4h2r4J61BiDdx4qpDrwFSt\n4oFKfI1gPevzZEnJeQeK1Vx6AACOseo5Gwzf/KBR5nQcQjIt03CHYb7/gp+eP+HwwMTEdTwxAziO\nk1xtA8Id8y0wkJgXFficSmTcDLTfc/whQTLwqpAMwC2AvBTR68LEy5LGZ7kvBUwP41JO/fdYCgjA\n4ugGuUqSBKBnJ8ijKuKgFdxeTUPq6FCOEtlSm3N/ttD9DGhgXxSFOnN/T9vuUWFBGpmrsE+Bw9P3\nzbYN3YsEqI25NQMoYK2xk7xBeVlNoRZPwzsqrXI9p6sNdWcs93jso74mi/VZF2yUvMLLbOXyXPrn\n2nlrhMy2sbV+1x0kSx/Sws+7wS09U0S9Bf04dgquiQfCzy/6RdK1vL8o0Y3C6S94XzOy9mu+jNHX\nOtaz51q4n6BtQ7GN/tSeVX3s5TM9Zh+vxJVtP4gi8GMvxImec0soQrWFT5E9w2un6d8fgFLO48dZ\nq3PqM7n+T64jB16SlEpO9JjUOjeFiWss62+29nnLg/p9cT/r8pa7WEInBOl3w7D437aN1b45NsEb\nSIyC4UnvHz3GFWaWV1HjSrunDIW6jvVwVHe9FNBNCZwsMG3Wycs99mB4lq83UJIiSiFDcuFFdvKo\nuvkTlb4oPaDcEvJt15qAB44cKv3Hkz1rvmTQ7ELkCx6UpQOpHoKjmDC7zF8nr/UNaybZWuvrvKpW\ngWSlC5gzpO4c+Y7k197KpfP0G1AX1Mp1pEWvZ5eePWVJFm0Abp0wDEMbf+XRdbem8MAIat0ICA8B\n4ixgbFVJA72Q6vFC+S2rq1/q97XF6EApkMyGBqq3nJQE7oZxOp4x6bWPWlOrP0LtjdpLabcaAPnK\nVcrANQMnDoQ7IljyreVZhv7Hau8+STPKHl/uuWGqVy59ltDeUHRn386ZwNMmch6ingQeXs3Y0J29\ny5FEX9GgkTBcvRp4rbvLi/C+D9FlxjD2R9DEd2VBC1QKJ+csOweqFm2gIryJguOh7pwLp/2+4w8J\nkj/KIA7ofOHKVu3j32saLFG3rl1KFLr23ib604PVgonXPxeoK+8xn3GVZVuKc4HVmtxOhsUHoKx/\nP/JVLUsooJkerRzKw5RFvbAGJLmDNWtbG2aGQ4lKmdH0wR4CA7ILvmoHpJpPZvGU9T697r0V367e\ntxHffgYKCC3n1IK5idr8/+Z2G1cLa36eyQX7y7zethJSlt8tt2f+fOeUcK6s+B1bvF58ez+BhLrP\nqx+07vRFNS6W4uC4rMXzo1lG5gvg2NfZzg18HY0/X7DtcFopWPzZwVazEshUbDuP93WR1J41M/Jk\n9YC7HfTpGXfwsL3ca3e6+mz233pjFgXowyubLe9LldGr+6XRmIPJxBQtAumLKrT9yxYjJvCHz/Kz\n3mGbwzUrJZuqox1H0LZ3L+UO0JDeMVVI+Vet+QA2zvCeeCjpV7Qv/cXqDbbxnIZV03gNbssRx6FK\nMtFj6S0fKynYP485tpVQAHHftflx/r/OUbqA1TvYB2Dnf+Z2HgCCmI1kvN67dletM3n2zWEZTSRn\nJIWTso/b/jX7akv6Qp5+d829Wq2Tb+w9P+S/1trZJIqJ++wrQdUU9RhKUD9qj0iBdwLcC5eGK3gq\nOb+uU55nAF09K3LVmQ4oKmRK/9TpPpT86QBsimtb4/jK9ec7Tcw44Da4rxO4rhsxA+FOLysSVTvR\nk0ZK9L5IpCXGTNwweACHMv5I3+R9KyGx590hyk1NPP/wnsDbTYraVPRhyIBwTzV9SSW4M8I8HBjO\nyHaKC3lFYpSzSxvTARwHk4KRkiVqtmbDmEzoB1bHQhoV+3zdEbA5cQ7oOZiYaL7Q2O85/pAgGVhh\n2vKYcv6L8m9svgDbhGotyxKwZX28fnWF1fdPkCyO9piWsAeK3P8AAVwqmWGIH7RQpyv8NjLx1GdL\n8XPRk+tQ3d1m0NJzOIIMBr5zFOAdIEvTIPo8gFB1kyVpOuvYBbZLECk0FaMZIkAqfGtQqFSjmdvo\nBRiWANiZKiFemTUA6PfR+70pGS8xm79d3ZRqiCpT+cfEefH8cvnp2jBQlYRW8e3q/rGKOsotr/E/\nNk/2/gmH3g0XuWcqDRMVfo7mWvBaapYwwtm4pRW1gMA0dH0tgWgEJVZVUqAQLRCfqk+6jekXVLvV\nrpeeEw29QOXczqv3J6stV6KWV+3uhS+BpbD3Wr/awSjD5UfHjygRvaac3EOAwCqi4gX72Ne+2LzM\ne/g1F7Cup+HH6gUkRMrLZI5RHlvtuUjyJ1w5ALuF4GEd/alnZ0LOSnirpGF4QYpVHaRkUCa9LkV5\nYmdQenXEQ4A31cJerZMNZ4xxNoAkT5XPHr7mFVKI0whqbkVlHNm1wxEmb+8yOMNMAIIXTwC/JDmu\n/zHYV7WqvdVgZKo5iMBMyerc5cH2OpftnR+t5R8f0VuuFUvKYXh2kvihklqAj5PcVRBkTazqwV/x\nuPPGZTcG+9LhHbcqWpS+QlcjYeMdvDipqI2YlMa9agKH6vw2J2YCRw4medmK11RJx+y54+LXiiVY\nxWo04WBEbmhiLR3HQfqGOWkEtzqWusrY0UCe7Ymu/evmONzZAGt4798yKB1ojv8ykygj3DfZU8Iq\nE7DRzTWgcXMk/ICoH6SepG7g88I1A/cM4QDulvepSlca5ylZiZm45w3YxDDgP4/Av/5+k/M/JhMi\nIzBsMFfrNkZICtvOCeRE2g3HQE7gOdmJ2A24IhSvU0lFUTVZuatgO9cBHfYD12T3xAMHcgDvKd4/\nwYLkFKtJvD8vHM6yiQhSO0K85Idka5hwhxlOySgOcShyHzj8RMJwZ8DmBX/S2Mgx8RgPuBFX/d3f\n/z3cHX8/EnO+4V/9q3+F8xj49u0nDP/9kPcPB5JLRn9SdEY+ULc/1UyMfGUw1vf5g/8BfHKctJqw\n/dP6Pis1bKwPGwTcqs5jLQgJ4cxqFARgg+1Z3jbpz4+uXWx6tq9az7RZmSqnU7u7jdkC+fXZJMiV\nPn7VfUYQzkWoLktUtU232AEuXyQXBgBQHbYoFL2Bz3qGXK9R1/oTRlz7evoC0nRZUNLWQOt5fnQs\nrrGusr3D/on6fWRg63/ec5PIlwoczZfdaty+sKsTUJ0z/hD++iDbm358lvX7PzFAf9ij4msVpguF\n8Tmm8XoWgLVGKry7v/bncfnh7G1Xfj1y+8vHvd97z2x5NT+Mef308RplTP9ohuo1PqHMXotCGNm/\n1hrar7b+uDmoVhQIHwKFEhaeoWTT9fSm8anfrf2jJB3kh3v/9uHWlcF7/5nkUC93KVRkKmFovXvd\nzetJ5HFjEDBfgJfYxQtEZ7S3CFGl3KKbO5Xc6xBeDfX2/DcuuJ3wHIpuBaNoucUtqkqDpmAiJMsN\nFfKHSn/xHtbe8q94GMDEJgSQBxJszvHYqHlA6S2sSjSb8WF9xn5IIyRWlYNe1tnrYd9jH3d1mX21\nRtala67E9XV1b3N6Q1fuSXdCQHXR7ChM7Q/tsfygkFZrOLV7AAAgAElEQVQVi203rc2t5NTi04ei\nioAfahcvYDdSBWvTuzJihvayAQ9MzECXn+23r/KjfcslPW9cKEMSAGwk8jpRlTNmVELyIAfXl2GQ\nskbmmXgYk/UuMEluaC2XkV0DRMqGbfNnCAH9wx3vmTiReGhOnhJc/b4Am68YHSNF5nSr+u2c55Gf\n19QeHa9KM1V0ICJwT7a6H/dQTokhPYheEiplCNxXIvKB5/WE4QDy8WFR/XnHHw4kmzGJK8vdrkSf\n81CY1AZLpCVblt+WyGCR7+EABkGVXYnLCKpZ+zrw8MT0ox1+l0DwN+NkjLEAY6gNzYwBHxe4/AZM\n3Og5pKhtbW0DF9+xzUMpuWMkIm4tvLEpQl8APcl/szRtQBULt7u9u54D98y2xjkWiYcPmeizmBBI\nr8oJh0q0TPLzwmF2cSPjAsDSPwnjfeQWTAmgnIZzLCHCFwOQhomJzKvHwVOJGpNJT4blvU69bwC0\nbkN8U1no3QwAWNyrQwBe4V1T15+ozPReMwAMuAo/FUb3ZVEbADYEgZr0GhM9VASf16GX6mYxy1bi\nY7I6xxzqNiZJMFUs88LBsGK/K9ft4YcEzkpsGZnwPMt5DYSz4sBXxMhCFzkqwkMG63MmjqS3Ip08\nhQxylDx25aCQoh+aEf7+EFFpGottWdIzWeWcPA9M3H0dNkIgv7yMG9d9EYk4JiJuzTnLM90wYE7E\nMHV6M5V3Yr3S9CrHJE9OTtzT26MFYFVRqZInQochDvs4HFdwbIZLMfhEFh1CIUCzG4aJ6pJdwFyY\nnt7hkSqICTznjQNADMco95/G041Z6uSCKuLDh8RzTHkFufhKmZJC376+pbBUH73zKGRAHjGQvvjQ\nxaO2ETgNkt/WQIABHu8x6lY6DsStx3eDGasDZQLXbXgcjjEMl03YTNzDgJgtK9wOmBu+X0+cmkfa\nD9WE6EJBNMvAmKzhO91wR0V5ssPu1xU4HkxGpbdrcsxnIA4+uyHZFv03oll/+MMMwMSbzPwrL1Yt\nSaCq9qcR5JiZEtW2/AxBmmFv3RwksCgD7qQQeE4YnrjyxAnDuBL2cKS2JSJVYkz7psOWTJT8Nrh6\np/7vJP4x1NmNnlA/GEH0eXBfaVqqEpJdwUitzgfINydleqPNqeJOV9hBJXPyCeZkfoHDkDYQY7Vs\nZ6QYlBd+MFLmju/3E/ck/7gqRdzPiV+TXmY7XHgHuC1gN2sps44+DbOZgeGkVNxFP3waMCbiZvk4\nRqQNTzDB9NT4IoHzTIwDsGfiugPVUD4s8D0CZwhMY+J2eu7dThx34jkSpw88fOBfx41f5o1v4Xgb\nB9wNt00MDDxwMhE70bWk3UXpfLAt/NQjhUjfIybmcNbRRpKWMRzTAb8T17xEGxm4EpyD6wlTpsRx\nEDvZ/YbL6cbIGXh/PjlmBvz888+4ryfOIe/1P4oScOWl1Uo/tVFjI5HvlujutWmrzxgK4oImhK2S\nRqtiwboO16N3X/GmMGQCY/HdJqDSCQncVV9XJPsOv7/6aErRzQASg4pxKHwFg+etMCV/Trdl1eWH\nCxmQNl8s8AFXVUm9S4FD/cL7ycUD3AT7Dsqq0xgO9GJHCDCXpt4Luhb6/40ONpIZfPSSfQkc4oet\nuiK1YSmqahqXH6x4JPIAlEjbpr0+Y0BX80id6336atBCqz/FH1NHIFvQoDY6u0JRyU9jdYs7sGpY\nG2CuVMRQD7IttMui9fW22b8F2KDFZOkU3/UrguQgw5UlvEBwMuUhuBNdjD6NUUADvRdt0CewZo8I\nMZM0BeLNNWb1vwFMZpF3IsFyThcosPfG7VE+4Nw924tnPoy1de8AwukdcQEkbpUsFEwD1zapY+vJ\n9+ZBtV0Aenq5/Os9qNDYpKp4xOtv+zWw3d/TMW21k3drqNKAtsYGQHsFC8SH9p9rb5f8q3e8u1qM\nYX8a/6BU6vcTHCDXILANdSKmb17ZLfrUntsafa19stC4Z8tVr2czCaXIVHlPgtlM4N7DeGk4H5xc\n2bYC/8DAg3cUt7LKLnoYy3gOU7UgzvmwgZwCfgmMYzQliCFsgw3vdfo1j0QQtoK7kySoO/1T5C1y\nAwmJprgAEGCLjgoUv9RzoOoSp+6WAK7hqgVs0mecrNGVTXgTEbIwbbUab0MMbH3sAsmXPKFM9Jro\n8k0ATHuVCW8b3x8VhVi1cPhKqt6QW2QUO1e6hqGMSZEgb7wY/TdkGD4D17xZ5hHsTmdmKiUqH1Ek\nrkkNbUGjE6McAhzA4+GI74mYhikj4E6DJz1nrY40nufDcMhFm5ozfqVzzqX/oEpWcVAg1N6mzZK4\nRoIl7lgV5qdheLMH7lBPP1nGnnOV1UxuvDTgFgU4p3BHVD6FRso4VkjAgrWXuY4mABpTCTqiZhD8\n49d3jOeFcdDVEYfhOOReyUTMwPvzHZGk43z7Fvj+/Qm3gcfjwoi1Pv7c448HklEGUCWVQR4eb4HO\nvbQxOPddrU1cCSWvqkeekM3zYhK6ldFabTlrI7ErmDaSHJ/0nvgLDXGDni/vslQ7iQl1inUt0aJt\nCCwWnKrFlB8uZiRFVLZuWbat8HJ9xpwguYvC17/iK25YU2MB9Ujfr1GJRMsLpIH8NG+v720tOHjP\nXMkhtpJgysNszUEq/VpaPLbb2XrJHTdtw1QZ0Oul0O9ZdXp7JIwRBFh1NOJnKRRrhONlfqOepwSu\nALhVXNJkEaB4rNuk2GoqYrtQ6mv8ySH9Yx6NVfhNJOd21SJvHIMw4AgTOFyACeB6L2OU05wvVRpe\nzgXas0kvfc2PLb4u9uWRff6ayWwwGxVLrimsO21rvLuQoZTSh/VfD9EgY01mz3MtRGMMJ/se2eto\nXTb7slRgvEJKcXe+ywsJZN2pOMgvzyAgujm811N2RIz/Fj1iw0QvbxVrIBoQs/pFbmMA7I1DCxoX\n7YLg9fPjN7CuYU0q2aJnsCyfxiHlnR+GuYV6qwmCYQBqILJoI7xJlct0N9yNr6z1B5Lyzw9jzdp+\nzIJsX3HDAj38hCBovWDFMeeblax6ITSuQUBtBkMBUP3nNDiKKx5TiWMbFad0WYqiZ9p3gPpEGnn1\ny7RakhRJoyxzNdXxQM9wHx9AcZcVLX27Ywi9XGodlxPl07htZ9ZApGgSfPogLcHYYGdmdn1j265h\n27g6CsS6KHtyaKn5x52h8q3b5pAXu/VHoVtjcGqnYVYEuCJLLS+d9xxeFJLU2Na+SFrX2m3m4gsn\n363ml2tFeMSyOywCcnYKYy1HrmSbK8kxObsxs1jugGsMbI1hIvG8b3gYRhju+8Zhg6Vp1bNp3hP3\nvOm5VzTpvifue2LOiX8bJfuHBMmqm9AAqpNPAJSqrICd7atuO3a/7gtUzpWZ3WrTentsn+dnl1dE\nX7MW3KpliAJ6wG7I4uWjm+Rf4EhhkhIAuUR5x1w/aKl8edmGBq2Aeus2ONwv8OGs/VJ12nw5HZ0Q\nmo7XLJqN7PyDY6ncmjX+v7Li+yp63gVhs5SUZmkBnO2drcZ0u7jVFZeW3pKyJRu1GnI9Wc1FGUxr\nTCnKdgPjt46eEZ1U5ak9oz0SCXHpscoq9+dLqH25w1++FvQvj9M+LrVcqj06DIiuhzm1zgrg1lr7\nOCi8sIluUbQG1i23H/WX0af0uQ/X692grlOpbCU3Y2F/8JmGCJBWL/KytveF+OGxS2b8YHLNdlW7\nq83twXS/aofdHiyrU4pAUlKF8xDN8V+PYyDA3KNoda8qqbjB7Bel/jqWQHfwS7RtWHt3v3bhkt63\nOq/3usJ9tXVf9rVGtsq71UtT4WaPvqEK7a0IxQr2WH8O2AyvBhWN9rYP1kNs4Apg8xEO7qso/IJH\nStivNeU93y0vf6BX+wiNS5bsXPkaUIOdjsbcojCMtbbWMxQ3tzzFVf7MgFzxxFo19VwZ2bXLS0f3\ntuwX4MLqeOWLNxlN+a3oUo1EeazL0Pyscfc9oS6D29qaoqc8YzkNKLPQiYFVKCBdtKBaigOLAz4Z\nzZhz0tCIVupwH5gRin6XsanxsR1j8JsE4IMRWyv8IyV1mCtHY+2oTMNhKr1oHMTD2JSlz9scPTWf\n1y2vchc5AB5DCbLRM85VoCgt35+6kc4qevhnjWXKOWX6qvKCM25W/YiJcsrFZDe+iiEmEvecuOdk\nj4W5p5P/eccfDiQnAJema69BJmyoGxwMLgstIJ34AUgC5A/P2nAgE3boostbC1QC2i1eai0oyGtx\nZ3GmyHutBJWELXAtAfwCxj58TwuOHd5JJyEnMXxQSASFR4jj1enjdREp/+IfubZ2IHDD8AYlk0gw\nuVNGZDhgNyCus+ey0HfsGb3S630oTNiNCJhzJ3nsW+PHi44h9ZQQRVMLapgXmKXgci8OIAFSiK9h\nSHHB+Tzd4W2gK3YkFlRb1TA2r5UeufShY3VdM4XAUEKruPBxLw/8pigPH+uaCXLHk0JLy4BroQRX\n+Ib9rPX1vEN1LQs8/pY2+mMfh4m/KdhjshJjAGcpoQRyAiOgzHFbZRZR7blvQKE6qmyO/ZVs/sOl\nvYVexeEg1kkBmNQ5pVqr4cPAsOX523xe7K6m8c8QLcHE/RXiKwgRZqouQwVBPRY9h/v+L69xbomn\nvcWqTFsjkkRTLtrrqXtoU0xT0g2gPQNcCl3XtSrtrR79xbO6ceVfxKXW/di8YatIH7tClgwFlsKg\nJy8bT4a8RqYAuWueXbkMlrW3cskprHKI0sOviV3JVeWwbhN/lexX1mdX2bhteSR34KA0vIL9tZ/H\nYMjbYskDlfXFMMPA0eCFrbApKzTp2PyaX/KoZljdEARV4xr9WjUXRfGp5dpBzkSHxGtPEwwtuiSS\ndYlPA8Y0jHM5v+6i2iht/DnLKTKor4+C7tzHh/ZQzBvvqKY1lPcT2aUFDcLHXmtRyae2YlHEcXsW\nBFpy3DC5rmrVSMdsQF53Bp8eq6NtGq57a37SUVyBywSmJ6suaaGPQx1LVRkpg3v3Vhk3a2dUlR/V\nmhRX2VwtmGuNx03qkBvGAaQzyjLLT6tx8+CY/JoDnRwHUTSNWOfhgfNwDD+BDMS8MG02rekw5lpE\nUW2OaCopiy1QR8Tku8QmozxZnq9k+ORmY57P7bjkQZ4zcGOSXmc15/QqGy5cdwCDeoR8bm7sUJ3o\n9+8XAMd5/oJx/IYX5U8cfziQTNzq20ZMVXQoU3GxiLKQ7m7q9XU+2qtcQi9OyQ3rhcIHVdLKpAQC\n6GQsG5Bb3zbvxgJjH0VmXX4Hay07sFmtWe8Tm/XsrxdZ5tqm/F8VzaebA21d7r/8cLk+2uG1y/+s\nh9xaEtbv/0SmqIzJBU5144+fWKbAZ0uHY7R7yUhISWRfc5/6Hk/s41wnLNgMCcg9xvAJpKZc6v1Y\nEtddzF2Cr0Hx5xVg29Pt05hGvm4/4257fLFjrSOBqlrXRo4/sCV15WaMAfgUa6crt69o/enPq3WO\nwJHGBLRUBywk8oOPviNKnbG+KAUmiOAyxmBLYe6dMFE6KuXx1gZ5+U9ooZeLrScoGbRvqc9T/XH9\nb5/osPAC7eW5Wp0na30TcFgefO6F6yRbW2y9HHvImKPO+/v4LA74tK/PWp6bpg21d9HaAKh4l/V8\nZK8LJEQP20BYDXxdb71djyJrpNL4tg9hZl6DrpSiurUX29DyHplNt5uZamm94mBsArVNmvVUfNlj\ncdkDptbSBZnXOdAZrzpj/X6dzZUzOK8KfdMraDjdyDcukL3tMy7Q2K5R+5Hgq0ByoMwvQ867tEBH\nHjiXieXi/zBJm+4poPWqzkoqsDSkKWn782jU+bt+32NJxaFPGgvKWUpbVKSKgK/4r5SlcVFmUP3E\nJEh0dxS/ujBQzHtxROt9SqZtG3aXR1POuLJsCldNL4J0eXIlK/JoY9Pc8T4DN81hOjv2sE2JoVk/\nG7qbJRzdXzQlWyxhUZTPlm6SVXR6rDrP+msueQ7wkWckMm9k0gXfWMpT+WfsuDcnKRd/Oh784+MP\nB5ILLK5EGO64jDec8oLeNslvNCMnadtKtSIuT8QEyts0TCk8MeGnigcHN1yOZHvKWJm0AInkpxsC\nbHk6E1oMDrepBVfeLa6sSHp8UtcpATNwsv1kBmCG4zzJhXtO1giWD+z0AcPiX/fimLUgExNPiSfD\nww4lMFBAUKk5F9hNHhh7VZYmCo3qQMQNNh9I1QBlumN5SSwBXBQg4eX60yJT4t5IlpZJob+XhT5l\n3Ij7VIuYiTEcmKlNMmbNXzYbuARXhWQS2ZynNCBO6/E/kx2UfsGFNzykBAOY1STFEPekADMWJT8N\nSAyE0xAjH4/hs9HJAD3s+kaVQJIeNnM2uYmcXcOZHjMlnA55JGrYGrShkyKNS+pLKt00RgRGKuSF\nAwlGLGCQdyKBAcRwHBmwrg0KldcyNmDPRPssNfAPnFxDxvVwR2CmgE8bzVQ0DlUW95XQ4gdJuJYn\nvdxZSov7NOAIo5fYbQOVNtVqmY8zjF2bLgC3OeuAWyIvhkKHT8AGyqjjxxI2JlIVP5rSfzv8DXi+\nzwZ5o71wgNvJBTNuwAOWAwcC6QQORItJeQgpZCTgiersyYy9EL2JiWpmYEUKcQ6Hg55qKcxL1I4B\nY4TATkwlsXWTEobfFFHb5Jx+dtxqX0+jp8BmODBCkjKZKjYzceaAHdmJiIWaMxNvbquus8K3dmtc\niwMpoBCZCj1TRI8cm7gqz7jjSoKIbxM4rJJtHbfc7qYNae6dOIgADosVM7MJS0NsJv5XOjKBO74L\nXxl88F1HTEVu5VFNrvm3h2NOrqNIVwJjwMJxO885ijqYhrQblhNFyTEkbkz4OHFFspEGHGNQABYI\nKc+2jQOnABa9r2xKUTWFK5pzRyCu2Vx1Ow8BPIe7w9WyOisj1D44Ojr8vxxWAXojD7NOWs15SR69\nAT5RkZxk2AlX3ORhg3rvu1pp43aM46bMmYCPgTEM9/OJHAc9vDA8k1V63t5dck7Rb1W6iWQSW+lb\nwODhsGE4Bpt0GBLzIsC+IvF2Ahgk1OSknsTtmMn6yg5HBGsswxxzUC4MGI5hsAGcRyKG4zsAzIlj\nAo888HfxK3DzWcMNJ1J7G4jjwDFYWSdDNdsVZYoIzJgYp2EcA9e8MMbA8IFhHMf3+8LpiW9j4LCB\newbiZt1yH8RXbgeGH3An+L5jwLLiHap8lYnjYIWyiMR1PfF8fsf5j6JOchrbCELKw+g3LVc9Cgjb\nqy/j4+GTlln2fjDxDm3FDiHrLYGJo31R9Jm6lP2WAFNJW5uFuixITVEA8RAuBWDvfOQnAnA1fpZV\nF5OF8VElX5Kby1DZonXYpnzHUihAP8eEbz7XSuxLWXb7wPDLiGZKIc1WP3VxMRsstEJa2e/9tpZU\nur7CVyUW66x9fJACITVY2wBOez15XaODpK/HdUhxcv8/1ZVppOP7vKSw2a3pFhP47iB+iW6+qyqT\ncVkY8YVzuqo6XfPrpjoklUiHV8ifnLdl3GjgtyTRAvpIdDveAnP2YZq+zvHqezIhE7bCWZ59wqNS\nR2fDyA8Nmj8dq4XqNkKaH66n4vMXXGbJJCQw41bVFMfxCc/o/oEVldDcy+rinNbaMIOPxBsG8q62\nu8BhiRw7tWdRKbhuBypMWkshD3Q97xobLptqai/wDIaQHaEVvK2lMiDbDF88XwB4T55xGNdcOPfY\niehKP7W3IFlrLG7IvSJg2n/fZB4fYPG1gRRlpQyUNVdlZFYt9fY4W3kGSYdoZ4JKSLLpw2hKW5Ww\ng1vnNXACea9zcI9GpDiUiu5kQR+eP4x+5dMOjSPQXmZjs6eQUCl5yih6NzYmyBr2RSEyRINxVLIW\ny3ECz6oMxKlvr/59Lbm9OX5xHPfqKJvGFuHajLv+eGrgxnXBH9+oI2xiTpCOlMyKHJK5lYA2JZhT\nxuBK3grcogc4gNO5llxEX64IQy3ZTdS+yFmOgTjFW/Oo4fzd1IY1Z0Odw7lWaUgACAL0awbiJgi8\nZuKXd1Zi8BGIX2mkuzM/BZfh9hP3zZryCSa7jdJFHaEG3k7ukL///mwKQ/H2z5MRs7wSz9KDVePu\nyT1ikUvmZMXYrHch22ezzTf3Ae9/h2S1f8O85WQwwxgHhhn+Iiau2hwu3vDMl9E1VH6A4bouOiIG\nL+xjwH3gIQz0vGXwmnT2oXK+jfccwECYw025J5m4roB5wDGB5o+71mEgb67xX65fcWLg2/UGO/4R\ngGQD5IXabLzeKEvl7kkvL/q1gVY2MT2BzetbiiYXOkH1E5fVA0DMnKXAIGHNs1EqcT9k5HEDVbS+\nlGEGJ1iAMup97GhYSUu9wpF139rSEvPmHB/s45O9yLU8W4B3hu6Oqk0FvvXu5AJKtGR5QnU9rkd5\nhkpD63vxM6qiQIdaAWyP+Do9uYa/Hiv3D+h5161WMlP2/9VemJ9bIImh0pniYAuWpc2+V41z1dy8\n7eW2Dbp3okctmQIWoY1LnS2UW57+ulimANx4feEa15LaPStfk5W8Kpi8vMyncH6JT8LFV5Ogk28/\nfkBfupJDcry7yH3W5Fmjmb1Juv/AGtvhOMO12zxYzU2dYP0eTSGAIe9K/s3tOcfL/LUiToaK2xAy\nsHujns0BcXazIyaxVaaoNb7oZuvq+7sUTK79tAw2/rEqNY7cC+SVK0D3KAEGmS5JQJnbZPYoLvKv\nZCr5KS+2fX0mxemWZzssYakay6Xgd3mqMK6pDCOA9sIPbchdFkD7kOHcBbjNakyswXQBJod3kia5\n8Nl7ehH6TPtYdJqea1uL8oseuSmn4o9n7yP0130HAVh0PADI6lgnfSHdYcViTvQ6og6e7aWmd7Ea\nVJTSAKoON2U6b9afB9dSlJ8suX/dy7DSM75u6K6w0u9ROsal73oPoM/vcoSoZW5IN1jQEZSZuFTv\nnbWHAyEPcrXDSQvW4rbUnqcRdx4OzBC3nq2lGf2g06xaLpfecHcMReaqys55HrjzxpzZuTxDY3Dp\nXRS0bpm2G+bMNyA4jwhYJ3dZ34MJc4qyugFGh5iPA4cngakJICualZIDewUxAJ3EF8AC8FBkUE7C\nU50QRwDhonWUrJEQM8kyA6O3mMp92kDyFEh+4EQiMeeFIUqJ/0YTsj91/DFBcgEVkw9ABLKoGqW2\n0+nximR1lEevu+igFFP/cZmYWOBnMd4CkAf7JVNhB5v7/eq3DsSNT89jEkbLQywlZgVs13Pk54+/\njg+A6hZEuVYepY+fYlj6h1fzxLK6t3GIUgofxqjQYgGD7ZKlhAiwC0q+Ds4OQl9eBtvltuFdKRaO\nj95KwF4q0pl4XAAgKmaD0Wwlvz+TrVFeUmM96/Zs9buXea9r68RukbtdaP93v0yr9tLb+LiSvtoR\nLz/VONiHt1rr7HV174lDP1qmi83Kiw/Nz16EhaB8RTPqfrwdB/rzfOiMbX3vS4EKOdebCB0MJ7Bz\nSC80qHK8vptAmdo4d/Y6f8sGSALPdJ5xk9R9m8ebi7doRYzNtZb2pVlAGWC4+KonMjZPcVOyjD7h\nyM04DnSDkd4riRzWBuja9rZRKgrglEGxG0EmAxs9+rE9syGbI/156kvGL6CKBOsaR11rAaIqzxcC\nfgWoyt/Lp1J9+heZq4S/Mrh6L/9gtW6WTj33Vzwou5YMqgZKVb2jurTueGKf6mL3RLIYKcdb66SM\n5hL0ZfgBsGC926qnvPbZ/mS2+MWdFYhP+qbojcRPSvhto+ezmv4oj7aRQN2keMKL+1Yf5gVvEJT4\nVAh/MrH/iqJxEUO4K/xUK9gUYRXIHcZ1alZUphqLwBgnxhhseHPdiEgcY6w1u3uSk/QYzFzlC1ue\n1PATv1BmbroLqSiA4UpFanQP96GSitV8vRw+cg5YcaTlctoWS+ZEhJwJpj049rXARDyE0XAIca8B\nDKM8Yuk88QaSFNA0/t26P3q0/p1K1KsZLU9yKFoUMRUJ3iJQv+P4w4FkGAC5xEvoAgAiMG2qtiU5\nh2wv+kFa6fupphUVLu3VA+OEWq6NkMYFtylkws6JGZP3k+Q1lOd0DffaZvy7aHMdiuJcuizn7E0D\nY69xJFoBZrXSw5bkItHBDUfu3axNsCnafRBbFRWqqEPcoTFYbJs8YoaQEoDF3E6vetClHDYws1BI\na+JM1XQEuxlW29qCEEi8Fu2wlXUfc3mEoa+JldG81DZ9X3dOJd3UBNDzf92JEye6zmImfJZ/MTSS\nFdQGzsmQLto65X6z0xBsec/DKYsPO5BXsuFKvb4SPYpLyeeX0hivL5Xa3IctegysEqW+3pE9QEDv\nNGP1gQ5Xv/xvSOyefe2kj69fClnfVwSoamwvHcbvDxAo3ZFIHFKa5zauCyav3xQ9CL2MK7Vj3hLu\nUsQZVN6nAW+WGM419Ezx3XGjoLjgNAxGj0gEtDyQycoms+8vVZM8z23grr0O5thHTgz3TdBISZga\noqyR73c7j8HkFylvF4hNrNqzZX4mAIwBy4Sr8k1YhTehda3P5Db2iiLt4Lw8uxUKK+zJ7ogV3QGf\nn7CA9DGNzZXFV0/cE+1Rm0kKysNPysJEjx1gyDAcQs8J4AruqdNsGeaGrqoyrSJpfMgCgKeTilBG\n0ZSKqYz/GmXPz0v2qxyG5Y2D8E1pG7PNSbUbI77tZVI98bwnvokH39VtUFQENVGy6DJoMw3XvJDp\nOOQ9Nbt7WV8JuKoThBmGyn5VhYiK3vhYVezd2KDKDAJn2cabjw+GTs2Z5HHMaODICA+jQXM+cQxX\nF1dGAxOG78/AvJQEFuxWN7HkfmS25xUGxB04Dq7SKVDoA/j1ujE8AWeHyKPW8mBZt5D1zfE3jMNh\ns3ICSsYEzuG4x5RHuihWhgOsHNIr1AJp1gmRhPITbo4Bx+NxYDhLpN25ef9l0Lg66OWtBjLGjrQh\ngHzPUAJdYt4T09QIxOnlTXPEZFOQmJSrNgzzXt3+COQpEzwHHKRtWeULmSOfF+bBbplmvrzCBlSz\nIGR0paP7LBOosJV3FY7fc/zhQDI9yKU+AWf2nZmzhLkAACAASURBVJo+7JbAn37Zxte2gEiXb9g9\nwmZYKfcFc+vDKWCTzbWy8pj+QET2RhSgQgLdUWGscKCB4QsDF2IB5E23LEv8g5KvJKXX9y8V6f1d\nvv7pkyHRtah13/I3z5Whxn+rZV3fNzfkv183i2XwEh4DsDy+2/vV9zXVO59ygaltHj7cMNRStmfL\n+JnIwOkH0op1tcZF5Av9JtvitkarAAIr0TiX16QrnJgjlMRU4fHWNh+OxZuE1lw2yGkb7QfT85WO\n/PDglVDVw6Kvy/P72ePKOXz1SNcRfc7aulkXxdoJ5LPR25Dq7sYqBb+V0az1/RsD32tYN+DeJoM2\nmiLAXIeZ1ozbAozLOFDQsOkM5SWxXjvb0sPxMmqLEbw8YSlcx7/svuSdAJbOpBkDleMUDWHvoEcA\npOs4k19qT5nyJ3yrPMA9pNnKVcmkx7FA8b6ys8ZvyS1D9n3DbKPIFAtR3sfMlmj723bMP9f8T125\naHhVDKCraNg22fZhRWh/ch0tI6B55Fl6acngH3smv8ZRFIWUcJvB5Lu0sQombPJpAvI0LtBM6oAh\njSZa7nS8WIkwNexFVwpxgCPRBthqDAQZdaRchuRqzTvLDFInVUUaRmeZbn7n6M6xHbVxNB1hP6hj\nax2saBGXwpS8qZXJygkRE8+YuGJ2AnFiVYwhsETltCNm4jhV61jUh9L5+wCHBvb0gUve40zgOE6Y\nIlELXPD7yMCdqrVjnKDyAeZ8lcup9X1sa7b2fsDwOA90YxXVGQ4Z1cMEkuGlZAlcY62DG0t6R5gi\nERWBMsyciPDu5MukSsev9+xurDBj4xAEE/O0FqsSBiBgXnvXynSh4G/ks7Z4G4IHjKXqDF1x6fcc\nfziQjIQI53zbW95OnADyQUWQgeMGEo7bEq7Mc3oiaNG4W1t0Dbq06Xx40zAYqggmtfja1DEpGKY7\njuNg9u/jQE7W3/N5KDlEQGnSu3gdjqFdMgFavVaF1aee09VEIXEc3vQGLjoupKmOMeUjLiURccAt\n2k07lK0e80L6JQ5vcRFujDgEFTeQasCYDh+p2pbM7o4M1Y1V0Mm27jnDgVh8UFK8+bcR3mDgNC7Y\nMxN2ZIPgaCJZRWYEztWUZaa3QuPDRoPKlJeLLSUnYA6LA2aBW6T/x8kx9QAybzAkOhCm1peg0HM4\nneuqYXmVS6hCzZJk131hqLLCtAnLgTEHoryLzrrbORK3A4c9gOd3ZCRmMnt3mOG6XzVOJSBNeUsq\n2eyrouQZVamE67k8CgSs1vvMVN4QNpQhTvGlqzA5LQeQ1dSA4NZd8R0Zl6TOUfOlWXP8i5dofiOn\ndktGe54jnjjGwDDHgGHOUNmwA5ZFtAKGMzkFiva0B7YBElvclhdlIDFGIuaBqbCVw7seeU4qlyyg\nBjBUORnJcaM3aRyH1t2kJyQmwiirhjkwlflezxH0LJ8ZmElvEiLJaXTSJ7KMT4PoFMA5To1N7Qmu\n+ZhPch+dnMgQfcHSleRb52tDD4dFsLKAV0IsFZoPehcNg9GYSMAmHk45MQVqHAdsnJh5UQZG4lA8\n2Q0dli8AE3DM23DY6Prt3Z7HC0rR/PVKBzTDYWevQypswO1GjsH7wTCldGMMVslAdq5GwnBNa6+g\njdGRsa94MJoBVHgjQdk8bCDm8qrbkIyOqq/LfVVe9fEYbezQgOJc3WNgXtQtGEoEF0CqRFuAVQrM\nFC6HqfQaGG0zw+HOpL8wVOWjQGJczoStXmMDicSwoGEsnUKLyzFsAqpjP1UUwI1Vqxj3dWQesHjC\ncOE8DG/nWxtCz+s7EsDzeWOOBYbnpME8bLB8oZG3HKqTbIMeZE/HCcfzuvHrc+I82Nr5GI7TD0SQ\nm0tw2ixvpAp555Bxm0yqCyR+jRuWiSMSpxJJ38Nwh+GXydryjkrT43xNf8BwCxc5HuMNpznmpFd6\naMxtsna8H2fLdzuAt5PX/H6xGldE4J6BO1bXxisTgRsPDDz8G2AXztvwa/I97KSeiGviL4Yzej8T\nh+hnj+H4935+Q1WXjwB+uVgz+r6inWFmycpRDow7yT4QvjvNMfxAPMuYTtzCLn79/k37xwPJdeTr\n11NeB4B7e4KhjcqXqtD+UaCYFZnQZqXT6vjo+apjwFQdjaC32lO7yhhBm7XC/bOsZVmH5g64whzC\negYsvIqQoC7gS8AXrIGG8haNyrCJWPfmo0Aw+cP47C9UfpX1M+HH+kv58cwN6dHZ/5ZkZqCNVgqz\nW09w5LoDNqcBYOgGBsByQ4yhEJ0EqCpm3C/h+R5WlVKzZZE3H+0DpUZGQBz5kqjhh1GYHkcT95Hk\nMHXjEL0RrIIHVLjbm/XXwMEQugEHHGHAOyYTKJRFbHVNEu76/YvdthKk9lmph15VHaLH5Otp3VKM\nr6uO/35+G2Oxdyj5pTzrMBqO4rPTw7+r3c1IFEHATc2FVGWC3isst87+LAYYDhlCWgs6bapj3Yf0\nWe15xRvMmq+bplJ3ZRwbBIDF+UWVoqNnpERQHfXt2BomVU0dfl/pKhLwiO2Z9nEO/WuK9vB0LqXE\n7dt6tpWbUZ758i5lIyIQ2NqAm+EBFeOHyun1rFJejfa4FhfYxHMdqHh8x/0sAB+UNUZDY9Fm4jXS\nBD0XgFD5T7apheZhCmjvNKzc5ofyI/R1aNUsjqwkgdpQR9Mu1ocNaK9fcZRZTUxRzQjkOFlN4Ese\nldhdDpjRvwc4bh1wrUlpGlluZ+47dAVN42bCGpK0QC8KoZHqNpPG1a1IQbjm0KoJTeCQ+7kbeER2\nvgz5RQrXmoFp6AEY5TQjFXJApCFzUu/JEHNAzWlyOWY6qUVG0XUjEnhk4l+/vyOCuj2iEvBAQJuk\nEFaJR4/BcqZJYHfJWDPJOTPyax94gyVwRWDeT/p+cHKvS6Y8kx7bs8Y70frl5zFwY3LsyiDnpsHP\nj0HamZTpicTIxGnAU/LpSLAbbAJ+ki5xz8ScBMJhieuXd+Bw+Enp+NSiuK7Jc7sqlxZJJlhcthLa\nJRfPE2/XrCUgGozB78R4HDgMiBx4TkUE7sCtMbgj8P2emJF4xw3PAz5F1Zn0ZD4v4BFoalbIufft\n2xsAwx2OnMCv7++isvy+4w8KkteLCBvyN45XYKJN1iT1/gBQMTJu8lIGuV/65aC3iKfsdGXbYvEp\nS5j82y0b3LZny5eUwn6JV5pEJRFWEHKz3nW90A25LxYZwdY/a6z6ZrvI0n0+yPH1Y24NE1b4P5oP\nUapZv9/GpMOP+l1Zqn1PqBPZrBohWEJ2fxZb+8uKn1KXknejLMH9nXiPXFczNJcOw4F7ynNRlTrX\nOzeY1XNWB7KP40OQxrcfYB1dphCEwnACDV25fYVwu4MfCiisZ+3rmy+F09P5VZXu65Pnp9/WC6b4\n84vXX0c1ZdmsIQArjLZ+rzETSOY+zK7iYu490rWX+OPiHvdcSBDT7PK17rQOu1vWBrBWZQ2gvqlV\n6cDqNpXF8VzPEEVdSLKRK1kmC1DaGoF9fXP0yoy27feLA9yrPLf11Lcu+aLwZO2nbQqGKf9A41B3\nsI2e1jLxwzrNDVHx+tZe7HUb67DqkoFoUPrpgaz2zhapKKC/f67EbwG6XKtNg93FT9YDc6IqOZyX\nF6Wn32M9U8mJ18RvQ9fb/YIHpe2uK9CRTn5veC1piU2GZ+vK/QhsgLZOzy03peoU9ye0f7UmvNee\nePQlU1MUhqLQcAJRuwLSVB+Ikr1OqnU9X4EmOnLbT1afM0D5TlONPJAsOccazY5UNY6qlsUFd6A6\nTk5MvONWlaoDt4jdjtyS9iccJ4oeODVGcWNr3Y3eH0VbqMOMpVrL6Kw5staT3k4+M+vorvmighjU\niMNCBjG68296fmj5raYmU6mJVee+r7X2ASNAA4cPHM7Y30PGC8ckVevd8Cbr0wpXSVfOmUiHDKrE\nHeRLE6o7TI1DptYaIpGxMFThltS8F31nZrDy1e88/pAgeYEG9EacrSQ2MJqvCWutYIo2sYnLH+zp\nl8PApLpSVl0G37CBmdRkbrNRh29fE4sbU6f/mHKJHfgt5qEpnFyWfrXgJOfyReonsNJvXkXQ8lj/\n4KjMjPYkU8SRoqB3tvWKkS1Lto3Z4lXfe9+emJcbvB2GWBvUtsdMFEhuZhM5UvWuOz6q60eusdbo\nVeJJC5lthPdjH6XVJ+yVT7qvm5KjVKLAAkbLQGu/cHuSi+/5o7vT0p65zc2WPPSP4djhLneiguEO\nlTjKBjR1zn4sxfl6zYqEVJ2mpUSLT/j6HK9rbcHPz3vl4/MrZpOAiTeZ6xK95cpoHVJIHSeyJQN+\nODhaJQtvJ/405JKCaBiRL3/7cPG1XvWa/Jmer1XNYY3Gaa6s9fX7lihWO2NdmFtzN//WjklkZ1yu\nsfe+4iuYKQBmkhfbirDX69Z/1SCl/mZGCpWtk9c6AQiU5Fj4XP1EBoA8xs1J1lH86NuWsqx1+NWP\nNasbbM38IIcaNS9gZD9QQdj0b66AzvKgLp1KOcqLJGig9b0SuJMe4DIgm6eu6/E5aoWuFVg7o+RK\n5YzcmRilWyI72GndFc5A3Ox931vl3BJoLytBV22oBSDDVrSnKEflfCnjff2bohKU+wkowF+116tV\nc9elSuvIZ61OFx2solr8Ha90x8SQyTHMVB3DYHbDo2hUfJ9QMYEyQvbVkGMwOTgZPYkZqEz2ki0d\nZQd/4YNUh8MP9lwA53KWSEDxgw2P0/BMcrfvOTGTkds7xnIM9r1VSzrR8qWSulnMwVp2mTnMnV0J\ndY65VRD6dx//IJBsZv8CwN+B3P47M/+pmf1HAP5nAP8FgH8B4L/JzH/5Z1+0dlndQ78LhV/SyF26\nk4v4geXZSUO3JsXgTi1l3LL4N+8rnigEvsXRCUsR9BMzJlxLd4zRmxCg1RJSRB8BeQlyIJAK2RFs\nD4xjCW4AnUzhm4bYN8KLBVEAubX38nDycCYybb8pOB1ZFBNuu+LHunvfozYTIKFSY7vdZQC4lVnq\nlIQwiCKkkCZQ9AZGxffu6WVzzFjMaTOrFmTq31wqr5CJmqFkdtJD6B3suihglI6duXkUYDA7KLCF\ngK5EwesXATFzwsFuhtMChzlOG0xAmOLXZvZGRUaLvcV/rFGs2VgKf7gySpKA3xSJ+GpHvV18+PlV\n+S5/OhvhDC0MVSMQH93s0NwJUhnUWGgZx1WZNRDNiS/QWQplDXlFkgDHoAek/ii6zAB5lXe3EDKU\npylT4V+FES2ZA+C+ZI61Fejs1mlVh2athdytRBQAZW/CAgH0kGR7gPY0uSJ5jfaeMcGlK7qH9RjE\noHxhY4V6CogqJmWRWPKkntVOeabEt9aG7VJbdbZoD7OVFBF4re1SbO3d0pwiyQet6kR8b9IgfBub\nWQA4oTJzFVrNpsPNqGx6sLuXMYw82jmyAeaobntaDziArDqwi1rhTu8cnPI95IF8zvJ2DoIGgFny\nOZmN9UWPAm3710xSApf3WDC69phAY2jtLH4xtFr5/+WGb2q2kmHAAGyw4ykEmsKAWUpBCyTjat2U\nAJ6YOCoHRrLUjV5e6tsC9Kx6nxaw1pMFpqXORi7QZYOd6OLGcLk0jP5dJhUm3r8/tQaA6w5c98Qd\nsg5ctxRSO+zGOVgB4jTHeb4hE3jmhF0HqZ9piLz5jDhw4R3DBmsDj4Fpift5I+4CfGBVCy9ZusNs\n4JmSMinDw9gwyYEG9wu/imIygJ+SemkmkEFqxPV9wIaiSQ4avwacb4b368aMG3Y7HsbSdMfDez96\nZFMYuLuMHG0MPK8LdzruvDBnKJHOMCfndA5WvWFlsUBgIDAw5403deFzGJ4hIG/AzKvdWmeyk+nh\nBMW1UCmjHd9OGv3n+cDbYJe+tB19/HnHvwtP8l9l5t9sP/9zAP97Zv73ZvbP9fN/9w+9SSlJCDAS\nlb++QG1UQ/V2+Ago148/OrLw9HZONKBtUbKUnzZuhSXScvHr8uXu23e1zCtRbNEM9gdxeXiLM1iW\n1SdX2Q9fbjcyXqFX3b1wpwkUVLJEUT8yF8wxbCGzT3eUF9aqdqjGKqZ4ZFAYSuNabn4NTj9p3hqL\n4oGqBMyn8IjmwD4ow/KAhbjf9XOHq+rEGvdX42nBV34ltYJssAATIQ4YLj170TkgIdZj3J64JaBe\n2blaxbazloul+/WOlyHcfvd6bGbjy1ysQvjmpCB02NtI7JsNt189/MXVXTiwFPfrFl/zIhdCcyVM\n64v7eiUgHX2nMsu0E/syLzW6damYeAHH6y61Q9Z661bdn0bJfvgd37+8VeW93bzJWYa21qNtMiy3\nL6nVbyVGegQRVs011niX02WN4fqBctJefs29mJ2k1c9f99vD97lDNI1ZD1Gls9paD4ouVVH03tdK\nigxj057q2r1L2XIMvLzGqHiPcP5Q4tU2JgRnBMyPnhU5avDq9f5qR27fvaqexV//Ux9cEHr93Nkd\nRoPF01jpQF6+Ka49vYtOkAv5QQBUVYla9TO0Xms5aZFk2raHli4tWVEPlBUKKooUSk8TqN85kclE\nXJg41cqNYCI7rx+RTO4nB0B6S9Ihgbd8Y4KvNkEaP3ukqeoHN+SNqW6OA2E3HMw7gqdoILkqKjlz\nn8zkzNLc1Pa5M3H2pBRG4P8PNzyLjpCJVL7GPJa3tWr+jExEWJdi3fcLSzNqr5nB3BvoumRJ5qsu\nNVn9gcRzsqScZbBzqM75rgogj0j4GRiDxo8FtWzmTZkr+VwxtEceeGcxSb6zDbDVOOeh1hNHJHEe\nlOXDD4LoilD8zuP/D7rFfw3gv9L3/xOA/wO/AySngZ1tsEIdnExT6RQACRzuInxP4DImrwzQcg1a\nqpWIBqyJr6IumQnMtfkuAD4q5Gm4k60v6c0oYDxUT3jCbuvntQSTxsIwzXEMJodVaNSSTaMzNsiq\njfl8v3GcUqQKGduoENGmoJTtz5oXu8kg7xtM7Zf5poesLIuEqfcue9oLFDzp7U1LJdM5TgxcrBzZ\nSXHtiR3Vdac0ngp2Y8DEr7phMBVSf4yBZyYOXWOCmzqObLKThWpcArjmAxjR3MmQ5LwHkzbbA1U8\n0aBSP6T18+KjvSPZ2jZKsKzSXIDCXzMbhA2Mlf+BJXaPPFBGEfvcBy6EEqUAqE1tdTO1ZGWBLnae\nXH9DinfDSICpbqxixH5Kun5BvsXhFOGpSMlIejVK6c1SZsE5mGEYuAR+DKbSiGYHRkCJfYJqZhSY\nJb0h9JdQhYnA0n4FRtE1XY/BfX0n8C28eYZpte4Ss1KYCtRJCc5IJquqTifk1eQ7ywMdhusucJfI\nKPHtgA/1QqCiYaUF7o3zAHLeuPFAJe0NGUqZgYfROJ4AEErY85OKrap+pHjUI3HFJMgbgKUjAhjT\nVmAp0N+TonYAum9VRT/N8d3ulhOwyTJvlnAM8vkwgcn7Du3J4h67M7z7zAk/Hh2ByrwVfs+eu8BE\nJL3k4ziQc9IblRsQxcqnCCi87MB1AzbV2NxZeC9kGQWMiVmm51EzgcNulb7zth7sSGW626p/Dpdx\nXM4CQwx6FrnUFAXsWvdfEyQbDOdgQndEApM1vm0kpp2o5HQPtk6OgzxS5mVwb5jA1amtaU7PYBrw\nUwyEXXBn7CMtceDAdMD9wX1kSc9qJvxmB6jpjPJV9PMtaLyx7XFQ/wxHKmmMAWzW+wUYYWZZMFqy\npK4GeyZUInAC930DuFgM4GA98IjA87owA7jEgS0DaV6TttmcuMvTPlfy6nMMIJgcV90L3Zi8a0aq\nTxg5zQbDExM/+4nTHB5MVCOgH3CnDk8EDjsw3JHB/B5yb6l35/svOI6fMQ4C+Ajg7XHAD8fzGXhc\n7ISXCdzTMA1wtfNmWbWqjAX8fNyYx4HaH54TLKU4gXPgAWMVmFQlLQOGT+57xIrIOJB24IqJZ7BK\n1gOJdMPzfcJPMcfnjWHADYdPQ4irnnnhPZ/0IB9v5FDPp0rKDdwJeA68yVExjSmbRzoyb3gysvQ4\nD5g5ntI9w6YceKF18PuOfyhITgD/m5lNAP9DZv6PAP4yM/9v/f3/AfCXv+eCBmwW0uI2dSnj3E4E\n8GYDcyOaT1ARjRa3L6d/sn7/TQGzA2hu4ZkLbE8sbyZsoxB0/JRKnrJ/eWUKKJX32apkE8ozLRyq\nJ12vW73nWJGiAMEEgeVxjC0Llve9ExgnqtIPq4LcBIpjGMLXeRasFBJgw4QqsnFNgmQXiC9FRhDj\nYgykMJ7AaOrdYKh2lAVCxvS9yIAKz4unCmB5d8TorTBLDylfxMbke9aMxhMA8CgPURlHMjygd3J5\nf28YQoyHYnqW18KAzv7fJy2BkrP9+30t7duv1lmnjLSHVJ61a/OapsKFX1Lnar5qqIqqY9s48Yx+\n/xqotDU3tJ0KIC8+7InHdqeCdYljI6/so39goxSBc3oC+I6bVKAsakCAXk/Iu7D4/8htb+tQ7X+k\nIhsM0RvO80QCOO8bEa4mP0aqkQGMBkk+aUDmHDApeO6kqmkRL28U64mQmIuPl8uPPEQJkSVKYyRt\nCc59mqQo4oWxWxSOKhtFoFHMw+mmMlMJDzZ8mJmoMn5l9FXdkeJRpugM0+j1O8RJTpFK0wiecSuR\nSh0sPJbft/j/axQoF7MmoIWlDO5jsBSg0aFSCWQuAF2RikRiTM27V3ibo9zN0jQDrjmCP+hs0C1r\nDr7ikcbE6kg2dxg2aICgohRdPRvAXrt753pbG6PlwTw0J9NvlkaEdI4Mu4ed8iZOrvlgtGAqTGkj\nux0y8c2NzME6w0jMoCFW9MfyJpfOyNsJ8GTg2NAuOk51n8yO/hqAX+8LPouyEZhXtCf3nqqDnMAv\nMVWXv5KF+Z4qqIKZN763gVe+DsNjGOYJ2EGv+ing+F3lyC696Pd5s0RqON7O0ZQrgBRPNjJKwLOr\nNf3Ft59hBswcsHNgGHnT93WzGQ5oNFfECAY6kjhzlF+H6GTnG75Z0FseCTvU5AsHMLNrUt+Y9OJH\ndg7AMRzjUDtpM5x2MJoLCswwYJyGRxwYcoilGcag0L9m4DmBOdkIDAn8xZiI48YYLJ337z++IeG4\n80Y5vDg6Vegx4X7CnbXxR8o4U5jynmxEVAbH7z3sHxIyMrP/LDP/LzP7TwD8rwD+WwD/S2b+B9s5\n/zIz/8MffPafAfhnAPCXf/mX/+Vf//VfAwD+9m//Fu/vv25nlsL8sQWwsMweesVvnP35WG9vLz/t\nvwWAt7ef8f7+ywIyH5ToUm72gyvly5mvP9Wm2k6zl29fr5ALmD/efsL791/18wofJvDynMACMfX3\n3Wn5cQn81t9q8+/o8OO7wIC3t59e5zB/8D6fnss+P+Tnb9epL1fbbvIbE1/P9Gk8/+yV8uMjt4lZ\n4/NyxsvvPs714+0bnu/f+3n+yT/5TwEAf/VXf/V/ZuY//Qc93L/j40d79m/+5v/FGA/M+fx49iuI\n2IyPH41475l9XRlB7f6Boi2YvZ7/8TrH8cB9P/uj0Tvz4wPVp378TLs8sVqjP1qbuZKL9r/XZ2pP\nPs4Hns/3HyyTjzvkRw/z+X0/DVwJD/vwGfv/uHubXt225jpoVM21nn2uHQlBYoJlOQlOFAMJkIZF\n2xISfyAKiujQ4J/QosMfMO0QRCeCFn8B0cCADHISO4oVk8QyROTjvWc/a84qGmPUnHM9+1znvW8a\nOSfrvufdez8f62N+VI2qGlV1/8p+2cfjDdf1/uGSH+5of/QfON/rd28nyfsn5m3ZdrMah8fj7T5O\n2wlvlBpbQ7cn79z0mr3O/f12Vlt5u527zBMehIfH8Ybe11j9wi/8W/P3r23P/qCO/Uf/L97ff3Kb\nzPv0/MCm+sK7Nx1o6/25X7DNxavwWxOAt7fvcF3fb/p8fXSfBb6UH25xv9H7TKeMufver0da1Kq7\nTqt7/u7Tz+Mn3//T+9q/gWF82AAzgayu8frYmR90bG1hL+MvseUhbRNgwHdvP8/5wxpr7oOcCfY/\nxMrct2M5LLYRUNRnnWNravdBvdZT0kjiHH7/+ScfaEhGK/VlkJbRcnsO/XRZXl8uvsDXEq/3VI6Y\ndZnH4xN6p24q+fDH//i/CeCn36//Qp7kzPx9/fwDM/sbAP4jAP/QzH4xM/++mf0igD/4ge/+BoDf\nAIBf+7Vfy1//9V8HAPz1v/7X8Df/1v+OIuBXIkfbkeMNuG38sATKZWrpN7pCdfya3V3z/pPpIXvb\nDV4oxYv51V/9S/jt3/7NmVSgUrmcUKzfI47KCgPMaZkiGEIKqEWnQoPG9sTnoevJveyW6LYsptsj\nh0+KwZ/5s38Bv/s7/ycf+YRCtvQUlweY3rSgpaesOlY/5QNUN6AcLP/T0/A4lif52ZXkN+sNF/zh\nEp0V0MDF2dzxb//KX8Tf+d3f4vu1EVSU/BgNQccpp0uOo1bZp6Zxl5WdaOhguIjrQI0qWkKENwkY\n1VRVCbASOrUJ/9yf+wv4O3/3t3Qvqnuqua17SWgssDni9H3nsmJb7VznXvTLZLhbzS2qZfarJ7lC\ndBn0CvypP/Pv4vd+7/9SZRXgr/7V/wxf6/GlPfvf/MZ/jX/93/hl/KN/9PuktoRUkJM6oG/CqvpE\n0VGAm0A702bdTU5RTqnoW9WFOfYWqA5cegEGCrQLwC/8iT+FP/zD35sRnIFqzsPzdBtIYxg21cQE\nANxj3pdtu+9RTT0MW6mlnP/ZaPjJeM76pKkIywnKg4HAyMQv/slfwT/4/d9FOJNVmBQorysgj2uR\nvoCpXV40Xykir6owWV5xjasfm1sXS1AheX5tBEvgl37pz+Pv/d+/DauOayDdK5K0tiFeIqAkQTD8\nXuUxqaT5fq/Q66RbvCQRVX5H1tN1mB8LWIwBy8Qv/fKv4u/9/t+cijxVdqoPht75jMXn5GY8zxPN\nGdO7Or1zR2twJ1XMrU1AcKSha6O3ZkzEhnGDlwxCIoJyxfwB2MAv/Ik/jT/4w99jCAqBv/JX/iq+\n1uMHdex/99fwO7/zm8jBnAD3RipDkmYzLiN/swAAIABJREFU13VtxmNFEQHMBNh0k46VR3JDV4bE\nmQcygc/J6rztOJSYPkhdSZUcPIA/+yt/Cf/g9/8PUngkW7MSvBDoOdTenT0KqsIJaxUfACqiQn+4\nwcSfTcAc3XyjXIoZ3VKlJMFSbF18Zzdkp2f1L/47v4b/9Tf/Z+RgkjcUxaWIU/RJAMCc3N6HGoj1\nMBzFtzXMTnowUkccBqRNyt77NfDdg82MMkHPqbHJUihibDD8B3/+1/C3fvd/g6OjZ0NzQ/PE9+9P\nvD8D2dZcUTQqdtZ7zRZac7w9SOcw64jseI7EFYbv2oFPzfH56vh+sM2zFw4zxleqmo+549PbgcMN\nv/Ir/z5+62//JkYf8KBbopvhOAF7HqJmkPJxno5rcE6HKCBvJytfRBgeZ5uc5MOZFPl2OJ6jMEei\nmzI1Mrm3jc2QTkWHYIE//cv/Hv7+P/zbBPEHn/cv/+X/9Efto58ZJJvZzwPwzPwn+v0/AfBfAvgf\nAfznAP4r/fwffuy5WxlThls93inwBWz40qh4GqhXXWGIpYiBLQRbwEjHzKx+sQbnc06hr1B9YXF1\n+JoOkPq+kxpixvq6fN/FJVrPUe0zw2T9pYBTVclwKr+l33i9KJA1r8sHiIEVvgbEJWSoZCAVBs5Z\nsqqHzTIyDnaeG+LoQkoewKzbmGMlF1RFijSopExMYyPEi4lkG0jeJ42EniAPl3KSIR8p78zAIgfb\ntsdVdUAPXF2ErmFwG9NjQYFlajJTwAICRgK0VYxeyRPmhtPWlVL/TwGNWdd1gjvUHG0yqH63hUX2\no1lT/Umed6iETjttNoFwGGLkMtu/paOSvaTZKlw5Pb0QjNK8sjeMvMwCnGazHcA0vybtRr/foiRT\nAJRvYa76eZ76bBnHDQdmMXAULxewJsCpbPG1jsYt4yvmFXegatPoPdqBNwycTivrEuiNCJi0YAHH\nowGBA8+kR5IOAHajM/E+J6aFpIA23SyplXUPwHFUiDphAoTRA3hdTgmkLa49lFHAMzPrnu+QkOFw\nHMMxrE8Z5K3Gv4wck1zSOoDNWsM1B4CMDk3Vescw2jFrnXL9lBWUN6MgQFnH+rAFzHQmA+CNRmzE\nuqYzqUcNtvlfVWYwIJPyJqgwUDSbxYc2LhAkmjekVV59gf9vcL+C8/AQZ3cgceWg/nC1H9a+KmrT\nKalrKICpNZqYO3NkEQLBtWwXirQDJXshCdJSerrNRDImB/VnMmR/0NFwNXJW6ZBKjJ6wYTga6w9H\nphwVnBc6oYbEqMOTyusSl7yM35ibp3jHIV4vFVZrDejLwfMZ5BW7r1wds/WHy7Is3T4GO/iaAb0v\njVDVV472Bqgyh1k5TgxvnzhOEdQ/h/I73IEM37ZEIvoTx3HgcfK1MQwZJ46WuOJSp1wq2Gqscr6d\niJ6TtlUpcOQFc5xP0BD9ZzGQnXTLoiClCdsA4mwD6YHLBloz9Eg8r6F79FlOcVwXjtaQjdSHSxSr\n4+HId+79SNJpmhtOb8gYNGLhrGDTgM89cI1YDWGUbDu6I72L3mEInFyLMuaenc7PHANHef9+xPEv\n4kn+kwD+hjwAB4D/NjP/JzP7XwD892b2XwD4uwB+HGzHHXQkNgD6ekjJFpiZ38Xyde7nzPtXbz9/\nSNztr5fOpIq2+f3cfkL3W1ETl6WT2wkLdENgfoUcNsXwkp5eVyuO7x3aQeC9Eml4N9yAmN7eACBa\n3RRy8xkMszpHbg8+iwLso1gYBbahRCryVGe9KkZfx5wdW6chupDgyvtzzquJY3Y3dzB5lWYaYxlH\nU1qVcQOOR26XL8wRidnfvW5nw8zzyC2xb76Vt4/cjv1u5XeQ3g95UvTefq1IyJL5po8P6xJf2C/b\nhinwVJGC1xFw+HaeRDWQ3l9/vf6X9v2Aw2xoTS4vzixTUTDA5ozx+7rXuT5E3KiM+UzX6zZLFgHk\nICcIlk0A9kbVyoL/OQGI4X7t2gX7nrgfS+rUHjYDZhr5Pgi2vrPdxvzcbnLUx12ApkHKXNcg9bng\nUc7n2MewZF4ZKSc01plzHAHQQxtjeoNvK+d235jnq7YRU08k8DD21pue9JnjUIY0ZlgYGvObnkko\nwXGgEtfmaNW4vsj8HxQA38Dh0hXpQPZahfWO/RGPtt6tvVySlmNfeThMGHeNZjOousWWQWrrV0CR\nECV4mpSkaeIIRGWmNkfLTocSvR8UoUM1tHOb3+QerG6YSzYnKvZaz+pa9e+XDNta30q4PVwJpLUe\ntCZYdhFa26yxDACfzoN1gHUBdqLDVDSz1odU1ltjjk/MaCYdTeHUpzQIuMbfo+PEMc9HvxDHySJf\n7lFPaw1pSrhPdkU0AC1OAk7QCPocHd2At+Jga09SptGUHsHKQ8FiM2y4FTJkrKp68HmPaDj9gLm6\niQ5yh9lExZYjUgl55VmvVZYySOIajNRpQCsx+hEOsxCJwLVussQJ10hFHn6azfFy/MwgOTN/F8B/\n+IXX/x8A//HPel4YE8cubb6HHcwoxRO5skLoWTUD0IEOhXYTlgcQpozyxVGrKgM+VkgXUHTNgByk\nJExKUFJQMryu6hB2qCTZYPUEq5DjAjx+GkbX/q+wzRhI1VFMK8+QMor9oQQGLZRDzxWGrZw4qnfM\n6Q09VnchdwqG0x1QgkGAVQeaGa4xuKyNEMMbc9OrcUovxD4MHry2NwqaCABdAmoYzcxKsmDeE/wE\nrkvKwxbotHyij1L6pua/prq32IwaZQsfwbHMUoa8v9PkO5aHLZSxfjZwc2mDldC1SLStBpYX/cZY\nvxEAq4cwxRNjAI+jweAYg4XYEwwveYGZqBQrVVlpohJIkJqxtapneUqZjNnc8B6BNJnjMLiTaxLK\nHEcmrmdXJYGfedf8SzuquoWZYQioGACPlHSx5YZ1MAw3DmZqoyMaYNZWu9BNqAOcxiXwWVWCNgW9\nTNWqNtXmm5266sgVccnnLLDPewUSoTJNWC2GU7V7I3Gx0DUOY8mjVHIrZQSV2yFqVeaQ55Kg+YzA\nISl9HYT1pgjHcwBoHZYE+w2O9IAbIzK1Lgao2JrZojFMTyj3x2EPjDRcSLizmsxzJNrZlocWC2pj\nXNPwtaIWGBA4eY0EqrV2R+B0A5LRkADX/2EHPWtzpiqpSUG9WbaR9UphiTMbu4+ZokGqOexjJUsF\nBHJq+yYT8kxUCZjheXXRoxJpg8DG6S1s3lTGLYHBPQUztPMNUCUMJDjiYXhIaUcaIk/t2wFXcJJB\nBa7fvC50rxr3XLNfNly+/oNeRDofaAQdgAGfY+CT5HsACKf07hFqmCwZqGiLHY4Wi5pxJWkQJwLv\no+ECmPB5Oi6T7MSYkcgB6umqMsX3DB2Uu292YFhH5ICPAfcGezRcz4G3x8nnGB0XgGouFNcJD1ax\neTdWUWjgOkgXw7XoB+99hmsigfFMJnYdgB+quWzLU3xE4ny8YcSFqw+YOY5jAMPQjiYKBWDPC8jA\nlQPD+bCehg5Hd+DnjsR5Alca3gdw9sSbA/0KvI8Lpz1wtIbuCcOJGDkjpMHyWogOfG4dlg63xv3R\nBlt+ewMUCTMYDtV7HWeWpUid9s73/SDVMjyRDap4kvisJODQIDUZxkMyk7ICQBcoTiBszGiMCUP5\npwN/zA9kAB6OgZ8g+oW388BPGvAe7KqHbrDWcHo1bQHMAu/9Hc8BRDdcAzjVXS9heHMgxgAaZY07\naTU9A+5NOhsyLFKxsh93fH0d9/RALqEbFqIj2AzJTetO4QgTkw/i5JryrJc/ACiYmbYRUGVaMIxw\nb1rAMKrBRsfyNgFoEo7TbbPfeyJ74k0CZYBlcbCFUNelq/afTgebPxOAi2awjqWS9p8Ab69fQ9dh\nb3iGMGJVB7k/MuRQWeBczzJaZzFwlaAYEjiUE03fAQo2bhHR23DE9E3Vi6an3lKoyiIxYHwGw5nK\nTiffEORR6RnMGLICivqSE1SXUj0E7utZxz49t8laSQHlu0q9ahpuGeeizvBWl3lS90SlftoxN2NA\n9IGsLkHK9Z5jR/qL1eft23VK2VzNSQ+GePUJKGpBagC550vwlZeAGG5foXXw9+bslRV6qRxQBwY+\nAxjyyBwCx4axDFejMU3wxpqklRDiWPkBIkbd/vVGD0cB70uLqvkKSNe6ALCa/2CtPTPDJ+2vwMam\nMX7qzVi5I5JZ/QdO5MuaLIlm23qtqxiolBwN54yk1M4rSktOPjHX3/L/vojBsmPgdb9YnuDpVHj5\n3r4XUt/dfd/1L6xPKkWCyjcBwNr0aCJXLoABquGpZxUntj1caoBexJ70QD4Ols2LmofmuoZhjBCP\nOXE08hKfnrO1cBojYLBJEKgpQgkYD188atXNXcSeb+swGJrWmjU6DeDA+RS3/RbdVM1jLMfF3I99\nNYKpVQsDnmZ4c66/DlauOUdSY2StANAIAr2lANCdXsPDmvZpikbjQFY1IMfjBOLgKnVvOEeyYgKa\n8hpSXeSUoyOZZFIicx9FwxircQwrMiRGAK29icPuaOlAdFxJN2eARrU1AM1w+sGyUTL4cLAeo2fi\nNMMQLetshtOBiAv/9DOQYfABvBvwEwcsApnB6iDJ9sw9LjzzYki45KgBcNIMPj0MD6d3Oy6Bwd5Z\nptId8IZQIxVER+EI23ZnNDrjKj/okPz+fA3M2ukyGt0APAP/RGP7MOYTVbk7KxoGDJ/eDOeReMMB\nqEBtR0eRlkYHMAyWhpYOTxM9FDhsyYLKiyINjHkIZo7WaPicxyekp+o6A3nxQYbC5mMMVu6wRXH8\nMcfXB5JRLZjFdavOMlrod1SW83MF93yKubIZViZrifIdaBb+bUdObtCEMvKe7MEngsniIy+gXIoo\ngnVUqzQJtIiqBjIFPgTuSx2sbFcKZ8PkRdyAst3DtvUQur5V+SNQiVBJ2oezLOWW8/vVWWvytYyG\nQFFAqtyP3DiwqS5tfhw1RVhb8fX+NxtxAtB65MVzywkL4jYjSxDbDMmUwJUCyzsdZWGXV4iM25l3\nnmXNSYWM9NR6Vb7KKWN8rol9Za3EJUHI+VYxb+0+j9+iGxmYnPi9dTuNjDV2qP1sWAX+bXsr76lq\n+1xV2KwoQ3u/JNf179+qoKrONhNgq5veGue1pnaVwTNVWL5WxtDCru6Yr+2Lea/a0amQY1HB8vbI\n4kUaPJv26NA5mSS35Fm+rOPdQKvfQlQI7ReTJNzAS2o9cjnfXQf5Mh71bzdbviBuXnbTDpzv+90/\nfOr+Q05JbMO0HlhJVfv4raYqkLGjSEBxhDO3eZXxkjlDrkOobnKON3EHi+0eclHSyvJVviOjF9Vs\n4ts86jljUhwSR43jlqTHMU/cZ4dj7Tjveqvqo08jWHog6BRIc1QFBip3rVXXHotEeorKI5BckkFJ\npTCG6rv2UFMzrkwopcWmLqnuuaP2bQBLpMuBUcnCCaApTD/uEsWSQC6SDUhgoG5w7u8+TVmKvcqp\nmvlDqdrSTvl1BdA76LXWuooAWlNuTXGco/jeKMbHhEAlD6d80O2ycRe7c6ZCMhHJOR7BXg2JNRBm\neNSeqH+NuqmiMtqKStYFcCTOorYAjLzViFnFEsUzTkMMwzMDPYLJeoPzO5wPUMSoMu6RAuR63hgc\ng3Cw7nIJDCuskTP5j/MeGADOSqLOaviWy1HxI46vECRzASwvn9TFq7TWUYpksZCW52ZO3BeusQt7\nrRUdu1jItRCp/QBMtDgB3n4wYXARXblp6DG7ly7VxtN5rIj8yUU8Ppx53e+CpuswQ5UanUqn+MVf\ngl/7a2uj+RdeL2m5abbSaAJ6ZStIH/3AcVeeH/48KcQmyJJSnHxzwybgeDs7IK6VsnuCTAJrPc8a\njeWZ+7gWAAr0W4bnfG9svxf3ymekABLMJuA3ux7N60jJR1slfur8Pz6n4F/6UUKnEmzn3pCU2+FY\nCTU1E8eufssHuQ5+IgyzagMSs4QcqQg5vzWmwjhu8za5r9Z2mxb7qi/waS+vr5WywNMOjLg+P4Ll\nfSN01BpYp+b2Ufh6G7cFincwXHzR+3V2jzDlzFpjTRf5sB/n9deO2Z9ljcH9K4b7zHzp99dz5W38\nykPt2t8293fN35euU/KFF9Cd5Uv1EXUmy1E9xEpW8AIN2n9qDnFlgbB1seKRw7Dqquu9AteGnHO/\nt93+Vo9I0VyijAt6764AMsnvXNGR/eemLUz83PsuxwGy8yxr/FmLn1Qczfc2fqkoTQ/W6h5BLl/z\nRfGoyxtMFYRiUm3I2Mip++rMKXlRPcjMSyHqQyWgt/PDgRyOTFXgQBnZalc+O7oKnMOAPgSaqQc8\naZSGaA0RjG4OJ81rcKFhDAL5I0O9IdSYCfJWw9UHQV70bcxCEfCRrCiTAooj6GXNwhaj5jjYRAtY\nirSoNSzFozmxub/YcEa8SoPG3RAn8MkCPVghJtJIW9P3mjZ1JumMIdZoNZYqh9Pezpr8a5u+lcrZ\nmo8cQHUvDEUDPCmZn/FE2TXVeCoNbCwHfi8LRP8MW/YrBMmGK0ORNiK/E46rzMDaBSIqRoaWVC1Z\nlsQXs0lnrKmhgq5Tl8KlkUnWuIkgP9QNyOyAbWbcTFrYNW4uj09DILzJGcylHu5qZbs4rAwfaSL5\np86lbWmLj8yDzxjpHxVfArDG7nrQvYpU12MRH3YMZqB1W/3oyGMynOOB8GSxeQOFkYmflmqAEIZq\nYV0exFeQXHOyfLA1VIV097JBwOEHojoPpXYFDH7YDatO0n4W+4Z/V+bwuCFpAG0DvtvzF+9S6m8b\nF1UR8aU8DZhWKrs5SXlb8aurlBcvEpwoefUOJAamB7loPLm4sw7O1wew9Q0clUDn5vTEtBJgmB4+\nAJMP2tPgXgx1l4ckvgBe+fM9SOeeeT7q6jUuW1nlZuzWh0WHodFoEzCnlRItJZMClAU/+RxDM21j\nVVQxc+UmGHrEvf1u3a3JYAKfeYb9bYH8uTcMOI2taS3VhAGBtAtDHTWXpCuZwZPtEg0w5Dgw1D0y\nLJFoOM1mAx5KBIYyOS6LxlWeG4OADZZzwbaf+7r8aMbcj4Zq8MNPpO7Btc9K6U1Ar9h9jc/ELMkq\nAZPjrhyTAwdY+o8LghGzUqbLoHAp9BEpbzI/dPWBkcBhDbOusi260MPbNFYyc2bxBwa67i5MLXa/\nyR1LPTkwJihqSpjyKDlHet1xcH+NmKS8uS4cwCFKEudTE5jUFd5tRtNCHtcQwKnEvvIY146qJK7M\ngYChD5PsTOmWc/FdlUPkbvgeiT6CzTRU1o735HPuuPWlc3Rf14YckKQvAEA7wDJzyncIeVt8BHI8\nkf6gx3cMxHAmJY6qkk76grkpOQ7sfBfAZweeoDGCNByN3mPgIOjtFzIM3Qa8Od5wIHHB7GDSIEQv\nNeDxOBBqxNGDoHxcHREDb8cDA0Plc/uGQ0BaA6Bxpkb7vic8DC3pwX07oY52iWsM4oQAniR/o7fE\nI7m7HUC6qSU0aRMFfZtxbFqjrDzMAbRZ1rd3Nvho6hTaGvN4kEkOMjArJbUGeJJC20Hj4MiBAaDH\nE5ELaJ8Hwcg1uM6egxixR0ww/WOOrw4kU10AVe3W8pBFu4nnaclVSHIpDdquDYZrue9RamV5JqYq\ndsxw5er5tv138xbXl7Wa9vtRJ5dWf6NI7uUZx/QarScpoKbTGjZo+SVo98NmEEMrkg6GGcd8/Ya9\n/FL3M22ScISa1YbE19IxudEh7mHb12ONzvI8ANhMl/s9WdLLVJy4+p69DsM2f9M+0AsFjKZnzrA6\nNX7hIJxbF6hz2QaO690KPbeZnU1vSxq54J7VEXF93hJo5rAP7oqlQOqlAnbf2lFgoRWIk7BEr4WV\nUykCy1NXCWgGTMDD4z7RociK5TY+utbAMnTdIM/CnSdagcCc5d9W/OC+N1Jrnvu+KeQ8uyLq4kws\n2z3g97vmtWwazs0Wx7c+EwAe4L2SH0ppt7L/t8e8rfQ5YutnNgT7R4reRUN/JvjN79j293plN9Rq\nld537Mfffwgor/2z/p6h1ylROdKZSzZpmUylmNrQRRnjeSXH/dQX1CFMHqLWjiUL5NWDwEuBYZ42\nySv1AsnlMY05Dvs6KLBcJKm5auxlD39Dx5wjrWlvxlKfT85U6ZBKXOzRX6ThRlCSYZLiAmcmhjV8\nSsjDSx3bHItmAUxDiQYgYUhVc1gOl1UBgopqS7sSSDPp9EyCxVklKdfK3C+7/zU2hwuXhRITT9IS\nyqExdSMMI3NSNEtNXacjOsGomcGPgwnaSlJfBDDJoRwIczzc8akZ3rPReZAdCVGwkqXPLA3vfbAz\nL3waf+fhuKIziS4L9BIsN1CHh6JL1C+UDVrcHKPgmn6cS++5A36Sbx2faTanog598FM9oTJsqTKv\noQRFlgIcykU5DqiRSCLdkcmE9S5jDPPepIk171XoqVTIIRk6a+1nylOsIgUwFnrQPn0iybFXss9Q\nNY+hCMmPPb46kEwgIjc/As0DT3MpQEwvRYVrIgipzRaLVb75mczFzy+lWLSEEs4ZtHaHDVQnyzBa\nONwoGiZzcYkNPjq6LQBe4C79wDMTp9MSJwfIpATKd2nw8mEd8lFOklFlpnHyK/HoUoZri0R3n/eP\nVBJMss2rCRZMjs5lXPRg8kOPgbTO7HfYXHje6M27lMfsSHo3FbM6lZtQAsUaYM6s1DdzZYhjJnJc\nPWHW4easFexqiHsceGuNpkgyszzNYL3PxJsMbYqWaGF4amhYQqcR+I6Oh7OAOCAPSKoRC+Q1SKNX\nQvzuVmtmmwUHs5NLQPfGkGxzbfac0wGzauENJfs4WjS0dMQYuJT8465qKmbo+T28s65CGus4Zyar\nOmBMITEQiPwhks3Xe4wyfjaQY0hYa/IAFyWCYPnNEyMribHioIZ0CsMyVUdwPT1CdU8rjBaGHPJW\nVbIGMHk1R9YOMLhX3d+Em0Komv2GRk6iJUZ2VnrAoSoKUmbiRzIrmx7JdhzoQU9mKUVUfVmtLz4c\n/1k2HNbRN1DezBEe5CQnk8/CHMgHqrYnrJQz215jyNtuxZFWRMmfaA7uWGMFgQFgBKtpnDDATfzH\nAT+aZo1jb7Mhgs9w7gSTAOxIYJCPSWBDeUWPLu/Tzdn4IRLpIemh/xTKtWbwPGb0J+X9ziTRdwJ2\nJT8k6MlaNAv+bIjJTWVdBt7nuPhVBhgTPQdbQ42OnsdUwDSiDY6OVK/7gDx6pgiDFQAyyRxjxYMu\nWTk6cByA7wz5b+dIsGkSh9Fhxv3oBrSDMtOQCFWC+pQNeWzGkwOns9xXzUuWI8sAG8CV1fyC62oM\nIO3CEU4PKwKOg/+C1VCatk0O5cYcTHoGSCfyBrgHgIbjIEFhRKAPUmneg3t5pApFpqODMeXDcnKF\nxwhEAI/DkBbonujyph9gneQnAhmMxYQNAA77ZPDrTc95waPBE7jeA4c34HSB6YE2Aj//OPH/XYE+\nnhhsZADrA9lO2BlAc+oQM5xHIP6ZYVjik7Hm9z8OOvre3hqy+0oWBBCX9lkvShB7EbRm6I1UlBgc\nH/NA80QMB4zxEAvDw+mhfprBD8lpEz0lgfQHzDtczYRcPOcRHd4ShztOa+gIpeVRDk0DShzzbuS7\nt2Ywa7iuJ/oYeEpmHW54tAOHuPHRDjyvJ8YYijYfMDS0E+gB4Z1AWOBA4L0DPXzmeFgOHHbAGtdk\nlcTzZIT8xx5fHUimZSlFg5y8oJUZX34F/vxISpDNKw/x7jIxgP3L7cVLIqE4C1ZsbzoqwxPMkNfV\nv+j2E/elum3NhJIte2f5c3in70+CKgpxKn4YF8JAKQoqbf5KQskMk1ZyCxoTCahpxGEyhHXM0UxM\n0P399x1vx+bpehdOfyyLjf9Prt81dGUv62wgBysGFPe5/gHs6Z7GMLzB0YxcKRaCNM0tKTQGQ7dA\nTHDD0AyXwVCVgTVqSJvAsqgS02OIxYYsr0GBJqCS66pJC6VBLwW63X8kS2rV2qhuSfQy+rp/EKxE\noxFjumx91v2Y92NY3FEma1HisbKKGrl8Y8fAbMYM7sdFFShjpOakSYRy7PP2uclcQkoQ54eAjS4h\ng0kgDpWoKq+IeHwAQ8pNhd9i++7Nr5RbEm6tp1QFlo1OVaC1OJUBeTsgPLx7xrefe1H/8uA4gJSi\nrmSS8oCtwmK1jjVaVnum1qiSA/0EvJLyuEu4kxSZoRW2uP0AYA1VqaHWpvvExShsyqpCW6ItILBc\nHL9YXe8KwNu65+INAsC4EiPHBNatsRzf9ObNf3MhEHjr0jWmfeviuP8sOk8W0Ioaf5/rzLKq9FBe\nLLpOUXOqXvc6dw1bl8Lm8BHofYM2LQA+/6QMgR3VzEhVe4hmF3IhnKZKT7OMRU59WHk0c91IBh8W\nN/th0R9SILLhgM9d+pR8f0/SWFQtE2dnqU/msi1HT0PgysQViWck/mkP0moySbfTvLbpTba5NgYM\nrZFS0dFwBp+5ZbKKAoD38Y43lRwEylkFOpxKBxoNjZEAzJUfscskw086WJr1dOo8O9BszGRSCyXV\nJT22ftDJNLQZHp2GQ6/oR2DKjD5Sib7lxYf0mOG77vCD3f6GkXKJAI6gniEXHXhmkqaWjHjPBiiF\nndzlSmD1sCcuDAyc6SyzJ8/7exqGnr4oSQ7KihiGCxfgySRLT5wPhwfgT/KobdCr/9TOy2Pg9Ia3\no6E5HR9I4PyU6FeBf0fvwEBD9AvWRLnUHg4AwzpXWJWk/BkjP18hSAYI8raAvUz7ahENCdqlSO5A\nOQtUFxieSmK7Qgm8/Kiop26qyyyxOsN21D93JVoh1vK2VlHzInD4dvq6wKxIkvxUKSa+ZLcHI6vP\n5j0XVM56gGoiYAlTh56mcUzdWMFNB8rhXr53fY6Wem31Cn0igFnZwyDLGB+TzfQ8bA+d08NP4VhZ\nqKt9dgLkpVoTj7WgQW6TlvU/zSnBVoGhUsk10jtgBpbw9gI6U4xxDhdAKyAr0FLZvJbV9ZrnV7mg\nVZlX95UFVAQ+pLBnNKPK6RR03CgjOAVzAAAgAElEQVRDOzXoWzpm8mHtpwLJxvey5jRzgk6bM1ZG\nzYZfoX2TmEBuoRzMv4veAX1v8IvzGol9/g1m1cJa75ccwVqfVCDiwdrycC5hIU+i9t9WBACVcPoq\nhmss3FRz3Up0rXV3n/VV/yW1zksQ5cs+AMrAtmlkl+zgdbXKzSevgjJJ2iSBW/yx1vC65ATac7hr\nW1bkDEI0Nd9zB6zBMFS4PSSDDKOzkYNVnekPo4aZvIPNCJk5EK/f8Ps5KKmAu6GTs8oH5yHnuJWu\nqCom2GaBP2zKMleVhm+UbQGgKCY10iERv2hTHHoaMu1Fd8oPM6lsTIzS7zV3moBMKKy/juLyV0WS\n3YVhtS+S4X13SXZj9ATpCDzxTALk5yAfOSJxWCoQQR3ZUlosIfqE1q6LjRNlwq/1Qszr5KZb6S96\nIRGsQWxUZuz6V0pw6lZRxIxA9juYOLsmgx1gYWgC5I6YZVbhC49IkxPsDWAmD8+OtrW2t/1QczAS\ndsjZaFAJutXNktExQ5XMmGI2wZrk+t2lz1jmNNVtEcBRMnuV2VtqQJI3gD5cHmh1FfSUI1Dc6kPy\nTIunUm+bJU7VnW5uQOf8Fpe58FUla6fGrCrlrIVW79cM242G89MeXyFI3pfrem12eDJ7HQfsPpe1\nUrgGihBYFu+mW6cCQ67fS/mtw6cyW6kswIKVWCefRy7E9YWn4yf4/cMXt5ebgAvBpQRXBY16OgFv\nrRR10oT3WgyVwcSag+4Mh1SnsvLvPQ4nZ6z4SXo0siIL2k91VUtsCp3lL1wbukaAQNP2EVrzY8uX\nWwvYrJpE6D+ndxWpxKosOAWUQVIeyah5RIGUCVv0z1EIweZTrWehcuBJJt5PyJu+1hJQimBMgwOg\nYCswU/BmscHrjnL9rOnclvi3B43Xsdbu4imuVSHUddsL9/W8nejDzKUFfuigol5QafFD95V4/1my\nYv9MZskUzZGU3Yw2lfFboCJf5quU2oxwfXy2ajLQYn3XwBbJuYM4ALklAO2jBcOMSNU+Atia3bck\ntPlkavNOI3pRuCxGBXKkRP7o1Tdi1bGdo6tEoZJVQLKMV+Ys87XGpgyl+7yUkXLfs+uJv6QFeKzK\nKLZ9I2aHlJIH29nqdjI3QFBc5dIBJY++fNV7+cBqd/2tHibPbC4ggbVL5k72Sq9FFXNYe0KAtPYt\n96uknPStTjdBVIHLBcGLIsePVlnFWp49g5Q5OkLR58wH3gc7xnXVrKaol/xtojiKNuBOQnQ5g8rZ\n3BC4kJO7O2uenw8c5os3m/XstwKmACoDgtx4r/VlokDW2Ol6hxB6JmsFZ/IMV7AqRaLh57AuOYze\n4VJyy3kwVbzuZcXrkFCjGMz5rfP1BFZJTUWmXdEhWyXYWP7Z0PBgfXNnYvrDGzKA723QoSVDGcCM\nIB7atYGc0dcjOcaWiebqYGEE8kwK3GUD15q7wxtdBM9k8mC7UrQuedSr+dK24afcTkzslPP0P9uO\n/epAcgIbMDRxjjEbMxTBu4Z0ZVJzcJrOUkJzOkoqY2zbvLsKPhKLbgGsLkBWQAsqC7YaIXzwJAMw\np+XkCr8U7SMGV/ryn6m9rhV022YXiRhqgzmfkzytAwNX+uwAFxaAG/wg53KS8k3j6ATIIaV2qlB7\nj1XbMbEo0aH4tqm8VsykioEciUiCRHODuaM6cZXyKa+Dg5Yh/5dU2gjykhR6nklXIDcMCvtZ473y\nu4c80CUrmNq0Up50/6hM+MQhkUGPdWOITHO5/Lj0k8R4zvt1YF6nOfle0z8gizhA4MA5SZWtMaS6\ns1Xb8OlJiWqlXGu7gAvQFA5qVuP47cVvTWFJm0aRekOa01On3IBICnVWPxCw0U4KgF0zy4UB7qOl\nkHHfu1BocKkC1IYdttoQMyGOo+rlVdUFKzk0YzD2qjdIfXAq2gqLWiUJ0jitSlKBvRqDSlK9yKMA\nq1c0N/T5vEwMNF/yrCkk3ZHVpuf22OnOLPk57oXOE2GsiFElkQYSPuvm2QzI0LjlmBQDrGn+duVb\ndg35w21eryoPFNgcKaWVpkY63C3VOhe5PLMJghc1IEP4pSdpd0Bbvxkmp3B5/VVvdknMDbgqoq0l\nU3VWG9rkrNZeNgBtVKUD7eXYEqA4oVucipUcxijq2UW6yM/QmOBrOMyYL1N6tivB6aFRbWZ4QIAK\nwLCg9y8BenIFqjymY2Y6aMAGWp6YzSksCRI9DgzvSKdTpAVdHUU3dP1Sa7xhoPeBCymv7gDMmbA5\nxvSOlnd1HFxNh9RN1xbwxtdH5kw6Q7DzZRhrE5/GrnmVN/Je4BsA3DHgaNbxHGxZ3QCYqFoRA33b\nm55KQDyA9zC8B6XE6Z1c5wQsG8jJD4HLhssSRxLMsvU79dFpDj+GaH+gvmok4Juix/S0kpYV2dFl\n3B6q+pDAVlKvJGbqXpQzVWCgIq/OspmewQolSY9uS3asZGWxwbwjffa7xwMI0hOveALGvKMhw8lT\n3mHQedD7gBkpbuUJ/nkZ/XSEJOCB7oFQTkMl1Xq1/W2rMg87dDJy9RDVynMlDv8sx1cHkku6Lu/t\nEriTLAdsFtKy7HaV+apU9eEJCnO9hE0Gf+FY1mNBJl5js0o2T7JNoIuJo+3lPmy7clQBZd0vwUGy\npXMujFCAniGpzSOi65k7EMuL4wIuQ52uCtgXy+jzGCxBo8cYQ0DZZ+/vecW66RCIQC6f+j6O9Wz7\nENSo1RAUrT+MISmGL4ELY6f8o7y8u1e/FBwVWHGNa5a4DX6Y1Vu++/quGr5U8wZbwGCAPLScil9X\n541zdHRvFcmZobyUdb6tvymAdM/Lm7n6qd3H/Ns5vkQRKXHEBjU5J047e0IVQSB+J+cWohL45+CP\nAkTzmlnnX3SbRXSCFusOx/KLG3/d4wZQ9+ti7ccAZv3z+u79tkWl0b3un2MreqAQd5XSWjJs3wv3\ne1j3ltiyJARQyfv1Ai6znqtAjG1rbRNMt/HH9ntyf1Z0hB6oRPiax9yASp1onk/P1Q6boDKT3cRG\nJL57fORr2br0fZ/p6WvPGJa3OUfKyALCl6rYqVQF46g4X6G59Egl7uHln6X498zHKLn1zR7yotdK\nS9CoHNo9BQyGyaVgAJNTGSoHtE8skVk/Nw1Zyw37CLvSuwCW3SOIaeJvFAVkVZQ60AXGEiH6m61K\nGrE3f7EXp0vtPq4DdwLn0k0JcmkbmNdzusFbQ5jjut5xDVZ1mA4wc8B8dm6czEay/2/0G0awOaYD\nqZbypCZ0tcxrioVyXMkvhg94BYIDcFngdmBRK6be58PWWg2jHzABnNovrmd1jekBaRzDRoUDfK55\nE41Bq8Kes3YxkEi7EBZ48zeYOZucCHs0haK9iZJqNfaJnj4Xwo32mnQ7mQR06e5nqEydJawSK1vi\nzX3Jmuq4nImk12yNP2od1HVC4v9n27BfHUg2MxaGlyZoh6uGIzk+OT0TTqDVMOsQGgA7Dp5jVnvQ\neYuGN06GkWrSkpbdQMDHWnSVyBLh4s0lYAMGJWLZE4ea4bKyA1fmcwz2HjcJZIUkAuKy6X6a1SYy\nIlRA1BAybs9mLEAuykhVa3jHGx7FbQTXSgPw2d5V59BXd7wIWDQcVUrJgS4r8wDHrGsc/NBiMPkz\nNXBetYCzlUqURcgnCTOMDIWSHUOb71KCTYFaqraGZs6kP3m2A4ZhhiMOWKU3hzzvCSQ6uoGZvZBy\nS0hAjhlqOeUL9myEyzYDhcjsgBkO8dUywSofGAgHTjxESHkCDpw48Olo+DxyK7Q+A/K0/hWuCpN1\n7abxIzkyhApbAmlsUcpEn0EB0n2CsYgh/PbtBXCPViaEjLrKds8n0Bq7QMKQo/Dyighxk0jh+oDh\nFFgcyuIWAGwc+WJPsZSmiyKUK5CTjADUHj1m7WlDdFNdVCqEs3G90jurKiusd0GlFyVrSthT5T6M\n7XNJ0RHQyEAmPVIrZ6FU90B03n+ZR2cylDiYxg+WdgqkJZ79QPPVsGACf3N60mTqsX47lcYxvdgQ\n4AasHQKIGwhsVKBt9kG3lVRlqjCd23nAyNBAMHmoPNAAol+zIxc1FOf5QEOvyhWATHKDt0SPainN\nDqeH0TNt7edQVYnY9INhv6NAbwL9slnhcpSXWJowQc9ayfT65wZcMWQoiFFpCbfByjlwIKBaq7uP\nbYHrAsJj0GvOx2Wx7smZ/xaPbECSc2yN8zUujg0M9Jx6qnpIQ3SBPQO81mc4PpuDbeYNz8EyaDmY\nbedtI+UlMFrHobYZmcnaw2gz2TMyYHZSTvvA9+8dT63sg5oAFxItGjwI6MnNpXMku8nJU0iSz3Vm\noJ0PZA/Y80md2xqOdCQu6ld/IEeg5xNXp8eIUQUTl5c0JT+qCow4sAhc10Cfbc4BS1dFnAOf43t4\nHjjg6GDyJznCl2QZS8YBjgcaegvkIOLrTsMyHMieSHHEAeCfxcAfOxvyOVSFxOhZzcQzOwwsQzfc\n1e8A8NYweiddCglvTeB7AE79nsmayOaG8Qz4ydyiGIEjDxxmeBdGuyLxhOG7R+JhHW7AQzKiW9IT\nnY6HFygPVttI6YpIRSdYUShq7eUTn/KB6Il3DFwBeDKJL80QeCKT54cD1xU4IA96Bn6uHXhzx/FG\nWfP9pSotjxNH+/E69qsDyQAwvU+QxyB3/0Els9DaGOOJZgfsUEk1odO9R/cuyhouuRq4cooqd2kh\n7VZIAd1mPHcT9SCQONAwMhh+ROkcm92kXxMFPZ0cmhLEEzBXohw3Yl292u1Wl74h6/DmKbXlYW6w\nxcdBFWzHzEo2WZRVBu2q4LDuc3pcYp17/+kKYUwloiH8Dm0q3IQR0JaMej0JMOvMoq5d4NPunywv\nR2LP+F+vbWzUbUDy5ZNCTvOVRGXm13uGhpgzfuhVZ0JIrPBsJYQ0L2VdnhM9g8Lf+xMnQCoMBLIz\np4DI7bOvPPtv6si1Z2oFA8sYKQiN+ZlQKSa+NjwnDUDWyJz8nVZVq6lGyhfVHGbl4dw/v1+1PGV1\nlZyl63yrSlHfMptswnn/RSbpaepwhem6cADhivOZgIfu/YCrFfKSYD/XGi6nMqpKOB1UUMdjW8G0\nF+SR7ShEawAVqSW8PFYlL1NZ9ruluy2uwnkFIn2+v7H7k3/zrodGIOf0cLaOOedrVhLfjwvLNAE6\nVN7LDswOZlPO2qp8aTJIYt6Q1pCMY6ORn7nue7YGv23z8jBLF2S/D4PWayQ2+suKONhhUy7uP0Nd\nDQwMW1ek7ls9vNXaL0UB4MR06BC0Qu+rfBm0voLreyTQBeiyKsKgId1wherVAvAWBKKhjBcBW1Op\nqZai9DkNri4HQliKfIqbZzqzK9mtGuZwlTaclCfApCZkKtqjhcYwPvf4UIm3gcATHYFU22nSc6qS\nxHXR8mTZUyewBY1jYIt0A9OoAwCLrrWiMnthsHCcuLBrx8iiHtEwZ3K71mUarueFNzdxdZcMoaOl\nzYZZOdaDd6tKFg44/dYYgVitSaf8ijBWn0LCMjAC6HC8qRQdNIZQ5PeBjn/sNEz+NWMlq8s5D5/H\nUK4Cm4m4m6ralK6kQQoA7znkXCI1JNNEp3C8i04zEDia4XA+/HfNMPzAsMB7D4zoSDg+g+XpLDFr\nJj9/IptWOqWau/zY4+sEyeCAc1OK76hJmuK1gJmxnFAT2tsTX74kwqbQT58Cs94plDOvO19VKAE+\n+aVmBwXw3MGldBfS3hU2vWy5qgHUc1gpp/tOYyB1KYEUt7OU2lznAsq87P1pGDrB3AwUfHVOm+fa\nYeW8tf0nFqibocgC1tueuyXq2f0kRbgIufdXFYIVKr7P11K8jnUri7WbWJ7XH1JWdyBd4d/88Kkd\nxOg6szB9ClCIZCNua72VGoycLT/XJQ2YCH8CPF3c94v94P1/A0estVUHp1ikgW1vAHy9TBLYDs18\nNpJYYNg+UJXmurD7eNOju6gx+27iK4vHWpA3ameb1p9kTN3/+m5dmRGUlqtHowUpDHW9AqBl3APL\nKKz7PMzR0aenKgPTqC2+Xs4LQmu2QNpi4/J2DdXcBPo4k4bGuuC6KeSMCOnt2kJp2LlNi75SJKVt\nLJK78j7W/GtYzsZMta9zAq4F8lk/Vwar+M4lR1fJzDUL5SxIjecEsPOW9VRz4Gy+bNv352saewg5\nr/Vj9dKccwCr6odt7Lidn/INHXPsqqxbV9SkxjhTgGXNeo2oo5wM/NwIel0raRNmcNPqCyhHI2XI\nriVG3KJQezmfjMDzGYzO2YEZOa4IBO8kCJATL1zznHQZzOeQN7hqm5fOyur4x589QiA5VmLYjN9r\n7yUUKaNTJARoXxUKgRmBuTebES9Gdjk+tXgVx+K3AuJZrxFPACcMx5QxxDfXYAvvnCDGpn47oyHk\nkOs+6Bk2w6kGKdgeCwkcSY9vgWDImRPAbFs+x86S+TeSR2mkeXRd/5JlYjC4M0pAekROrFBy+gyu\nGeY3kLccCTyc8zWS+RVojmHkzDd3NDieWnOhTpzyWzFyGymakJ5zRqRiOQF/xPGVguScgwqsyeQf\n26cSMGOpkEqCGVrhPiupvpzZalPx/DdemU0ROWnGVmp9Bz9ZAG0lvDDMytBThQA3qjKmGpMs/8g4\n3FXX/uA2Hzu3nxMc6G+vVOM627yvUk18ryoEH/Us+zmBe8R/0zXl407D7HRGeZVLmNy/cvutjogK\nU4Eh5LozbcAd9MwheLmlL7/yhQWyrjoHJT68n6gUHd9eq6YHsNttSR6ta5pQXe62jq2py+229nFW\n1aDbnX+LR9wsowWuuAkWxFreRb+t6wIoDNePDejafV3OV17n+/X3F0BX+/d2jjsAmzdf83aDfS9n\nryhI0TAAPXSBTP4rQDUjOtiWzRbBkusE1S2qcTOvxywgM6DGH0t51sZZrd5LZgHL/LgvyNe1tkZ0\nl2cC73r6OQ4biI7t3AVSCqqo4bg+IeVeSbnzVu129S8f9UmGkslo85klshtQFbmoO46FHW57t56l\nki9JHyv2Olbb+3pmrHOuo2TeH3XvX/GxWwGQ4VHrUQA5E5M+EBaMppZjo6I2ajKxR9XmKrsBogVW\na9juemdFQ0YONQVJnFl7bQFkwdrZyXa/Lr39y3iua6NkORZIDjXt8dILyT6zGYnRbdn+qLbKrLvM\netyqS1wgbur4pXsBbMUGAFZF6qhqTmt/1ec57lV7mAYcP3c0JvgPC0WvE/3ZYa2BTTVsVRbJIBVy\nDowp4dj1/KWIbBMNpDwW4D5gt4TrGl/X/k0QsBqWt7vAZ9U999KhkLNJ4ojf53cfEXjKoDJgRmgj\nCZJ3o6oGma2QhGuS81WOhemq0CS0g+f1hmUU/avQTISCtMI3gIk31TOmHuJiUA0+kbkjxhRZe43T\nV2FnR2Mm83Rh0cptiVuYtBbxCUOl6aYxYcPMEDa0UfmFZgoJbotfexZIwGygfKJupnbPiY4DwLtu\npqbjQhg5V6VsDz1dIjFsYdnqUZCo8F9Or7UBgBIBp1bNMiaW/kXWOUCayg/gkFIsub9VHnyBkVlw\nf/MU7z9jDBja9LgXCKaHCJsArfSl5bVDTRl2pbXf7MsNbz/LY7C/Uz7NqXRRCSnFH+UDG2zmPIUG\njvSKnGPYqyNV3b/WUe+DJcAcSLepgMxylvDpljcg/i0dl8r81Ay75DPcUW2FgWVcVGWSoQf2rAST\nhohKPREXOfdFWquC/0UMZCVoQQoyl0cJ2FeAic9MRUDuLM8ZVZ2jvjA9EKxkMBWEQgLpm9czUxw5\nAINdQWttDPGuqhD+DgoGMDnPy5vJG+jpC0DXU2Qic8DspGyDkZOvcORenmzo827HWojbz5arWQEE\nhCoaUtdMYEZR/GY1LwGwCCt8XS2f8Ej6ddYl9OTJBJ+ZcCSZ45M3LmEZO43BZgUBFK8xahXk3K3M\najdU3fLyLgLAw30Z5ZjReyAT3SdBgPvVOCAL5FT9bYj+lorOVZbzt7hjAa6ftWcmENrGJms+wDFP\nl6w0CByzygMgB1XyfOxYqMRGKYMQhSaUwLUqiJwwGC67kAm8d9EczMS3N7Sy+gwwI8P9ioEuzrun\nTb1UXWYnhbA6oCYrulQ+TSWNssSduOxgngmJj1qjuvDh/Exrhs/PjlEUE+N6rltM6bMC2JkD5ziI\nVWbSu2FsybQc4QbkAcRgF0PDbMaSmegtcBgrOwwt4HhPPB/XDCOnqZtuBilVxmQ6Nm3hLvbHcupl\nAtG51i/rvKAiOqnoPYKl2wB5ktXY5dPjRB8xPfmYVgl/lsh5XteUNUcjVbHNvA3D05NVL3RDLj7z\n90/KWTfDYQ0tCG7TEmN0XHHhXXlczQwDhjM7RnZEdGQ8WBaz9IAoa83H3Sn6Ux5fHUh+PWz7uVvz\nqWXvmQvwAkAWR7Z/+Xy2eYYFVvZrrPNLZ24wLbXcqAYYsN1BmAHT2zLvdMZlUwBKcMwKTvv26VKj\nVOkziRBLyRaM2Dm89vL3PFNia61cIIaKoc5zWzM7An5ZTLYe4c7ddgo/CobyEv50x0xywpe/U9dY\no7z4yfnhkz/N8SVSRwlQAOJpU9D2BZyQqHbhq9lLzkEpHvztVmqJRSqyIG/VBojWcP/0Y/a1HVsQ\nYc5X/U7HgD5gCwylQcJ34wSnwcplNM/ww+OS0uGz8oG+et/D9vL3OmtBvz29dF1yN3XXk5URuG+T\nSuCLaqWbtcrIGT5w3HE+pB7nYOmuNA6Rqz9kXcBS5Qy3vUWQHYiWN5AMSSZXo4AP5NoCnds4rtHB\nGsypwqudyVzBepT7TprzngVbU7fPHUu5U8m0QGVnmL5k24mmx3rOp8399KXjPsdZUFmP46W755xB\n66YSQXN+lx6wWlNzbYHreKbubiD8mz3KyMM2LttA1lYwA3zMeCqmx17GPrxqVAAsw8i1TzDEM4Xy\nTqKuO+eSiQWBdwBVL5e9A2AOxDKEhJIJrmr/pKITe1RnPyznvtr3cyaBe2vL4JrBmDS2KI8ll1OD\nk02GmPhRLtfqtevLGrjkTsykAceGKw5D097fOpVmaTWVV517SzWc1LyqZGeCnOGWg/676UmmwX6y\ngJ9aqieGUpZbe0PVj47iaMq1f8hYKHcXd0K/DWphYVcyYETOIa69XLXHAdFRBh0JnjPrZJW9dYMN\nzKYll5Em4aGcLHekqXRmJt4kB0cGnaZw9WMwtBxArnK3SDq0MoGQq9oN/2qA5ExuSvcDOJy1eXuH\nH443q9aHEBcl0ewNVbuYHmZaGDYaUtmt5Ekx472PDqjWphvw8AZ34Ce9bwDG5JbXZpzVzgUGM2WB\nUnElktmhZmjZUJVxi0/D9quPqUABiNNrSOey5mIdsujetrBrbRc+4ekBD/F8EkCwrmUOtRPdFLI1\noD+HkjT0DKoH+njzyYXUx1XRI/jFGVYuM8ymt5RecP7+eVxoQ1m6nsvTZcHsU7D6R45BRdmSFI05\n3xznlmA3o/I0mXxT3ug1UzKANwnLyxmSAQBzHI0gaxg2d5GqsNazSLCmJiABjNFVU3uVik8AXZb1\nUq7a2NaZQCHeiRnxM70ZpUaWV+NQDd7y0tRzD9hU5FUvdBav/4aON/ZIx5A6Y3tWdsdyKJmt4HEm\nGwKMZSDV3PR4wlvD0cg5yzGUXOU47dD6NfRkQkfzEwkqCUsjpy0TvodZIFpAJpqQ6vIT6yjwIx5s\n2AVYwPGoD/D/pQ0GUhU0uCdn4Ztz8w6lqcSYTIXiE9Zec+DNH8jrM65YDRWaImizxjuAMNffjV3D\nZJj5QW/bmzU8+2B7Vwean/A0WHbAj8kjNMmKzMAQfz49p9fVsk2vDxN4uP/CGWmrEnW1kC0MeQgw\nhj7jiQuf1H68AGgiMRD2EDjS97nJYeYI9A10qsqG5AmSEbtx6d6c4VWb7aapQIepgYHmuMl7ls7W\n15M/bIF04GmKDeixhuQNG2G0zYBnRGHEgMm9lwmcqhP/LR6ZmA06QqCfc6UqKUljqCeNtvZw9M7k\nPXdnFZcMIA8csbqouizX1g9cB9WISl1PA/a0gX4ximaPYLOaUaaQPNP6rLmheZvbuXfuj+/8wPeD\nbp5Ta7sL1M6OezDYMJ31miA2ErAj0Rpw+ME9AMoQf1I3RatQPQEfm3okHn2o+52jHeRQ96CONwtk\nGRPOihePJIBeDpeBzAvj+YC3LNyvZiiGHC4jPDEGK8A0T2Sc+D7liVbqvjdWdroa4KJgWAbekOi4\ncOQJFjdwfDqYBNkyYT3xORn5ORvngTJbyZkmnZwDfiSupxJv3WAH29l/jou0EO2do5LTDWiNkdke\niSsAhGHYwLM5ZiRdVJcRolE17v42DJ6O9t0JaD8aQl7hA0jDMxhtczO0wwDVS3l+35HhOO0Nbg1X\njlnV6ymKxWmu2us/7vjqQDIA4FC6phuLf6fDImcJEBjUVUe7HRSWu58wD2xeFEyw07oAj0BqFe1+\noOGZKslmJI83bKATuKGc5QWpC0jYbmFI+Z74/74yYc1yhgAZTtomTsK8stU3VwZ/tDZ5VTAqOigM\n+XokcGvTWZbgTL77wuep3Gc1SxYoT4PHgbC+PqmTmVW5O1FNBsEQhQlQXqLJR9z5k8gVWpUyr9NX\nyOsoqxQbvwwM8XmNAdhvHmZKllClEwAxAsVzZcRBcLfxyyd8KtTymRHkxbqvbYRcUYuRUGLK62de\njmMb6G3QKw6BL/z8lo7NJtPfywxY0RxWDuFKLCDD75QPxV7GsNbogQDbF1SiWC2eFGVj+VDN8uOJ\n9JV9vd9/+gRlSEwjr5b4NJx/CtlaAa1Azv07bHvGTTLQsFbfyDA2BQITkItyZho6SyCHMdStuz6M\nd96nxPFiFaPoDrtHuhKg5vi+DETg2oIh5YtN0Uw+jh3D7svooBFgMOuYkbAJlR3wKRDWCYBJFnGQ\nNsdM+Jj3PCNkslkrguCaoJ6qBGArr2CCZ+zr0lCmLsFwrDEwwMSNi6hOiPqejNtHGnpydIeMoFVr\n+ds6zDDbeGeCnNZSFtOgXeaCfmsAACAASURBVN7+noYh46WlQEMGrlCjGWm5IfmdD1LMltuO+od0\npIFoKuc4nTEdBsPDaZiyAkugDwOir7Unj+kF4DtjBHNQ61DM+jJuCh4DQPpBSKGkXHRSTK4nq0/I\n7wI/qZQfvvUNgOCIARkE7SYPdw5xs61PPOFq1pVh4nHH0r9Gp9HOYy45iDlcRfcptwyperU580VZ\nPFrDoQo3P+lPPIM6KqtCh8YhkbiGoQfXcHPSSLoD53luUCnVRCXp6BBXOQ14rxK800ClrOrzy4nH\nw+HpLPUWbELinZ7eI0LFOSnLx9OQ7iuBVJWOHtERRplGXjSdkZ+HKmXouSoJsSexoYvOwYYqie+V\nMPNwlgb97jgnf/nHHF8dSC5lxvWSGPIWFk1gKUR5R4s0JqWyEqNXYPAWDhBIW8Hf4ojaEr5p9xD5\ndnMLFOTtPGtbVrvSu1p0X9zN9aQbGH4ByitK/fK6wOMMvW7PuZ/5DtS3U8yEyNzuYj1BqfkKYYVC\nQbQuY7Pw+Hyu5Lu502MJ2QqHzdPmPu4LIK9Q3nZHhhV2XZpuA5Ks48mN6hqHnOc18xn6rced9DYv\nMJUs05vFWMu68bmG+GVDJT6Re7cUTI3fD+69nch9e7z7vHyrR26rZr1W6+HjirQbN4rfXDvxIxbN\nOt+EtDV29+C/2TamtWxsfg0vGPH2+7yTQqWTZLjvI8zXvnTckkagyE8tI3st3UjF3sxl5JYnPukZ\nt5zKvsBglbMqG5Ml0CAf7F6ubjdBto1TS1tjITEyj9VwaH/gj8cOksvjWJ5y7vcdovIbH9MFt9vK\nqv66LjirJ6DmLRdbJNbYpOTBSHozX0tFAhVBq/NtQNnWOKzHLSretjvLeHqRrz7P/Y0eGpacABks\n7TY/kLNTmUlG1k5kNJX7eBdtVYL2cCPI0dhVJZTD6FQJ8fjZsMmnYXr6oTlnybDIxY0nZ7wqjQNM\nkk80JFLUIve1jspg5l5qcM/ppT2C5d06gqF+U56QejKg+MPVvsAIVJ8R6rFgMzJhWv9VscKdkd1Q\n45MyWssQNFVzmDQQq8+RBuGp0nco0wO3f7cj2QDmMMfiN3OuLFPjZzhEFezRcYU6DPLiANh9d0qQ\nTKCiSHbcrtVHkKbRqpwmed0rcV+0s6x201o2rc3kk4p2B4y18A3A3M+c3c/CYGb8WlMkqwziKgu5\nugf1aUCtfgplgJO+c7hNIP1jj68OJE8hlwo9RhY3feIVbOC0yrjcvswT6KfEos0/p5CErQ3OAY3p\nN0lj2ZHqb/HxuHtBl0elROiuLHZ/y7o9sx/2Hr5Opd1+05c3L/a6q/05AeHH+6f+OVHCHWjUvU7l\nmzYVFj+m0jEGbf77cxfYzO0eFojCAspflAJQ1u0a2+K1lYgmI0XCst5fo3Qbk8W30uubsAL2MlWA\n3QD6DvLWmOT9rR91vDLR95/f1vHT3HXNxp5St6vjbf5rn1oB5AUW+bMSOuu/UjRA7qO6D+oPYLT7\nHdbVALgJrC4xUiLjh051W9vI6fUMY6ORKlsGrO3X3OVhi4LI5OJZrdX9eoaVBYzt6f+oZ1rrdbaK\nFqC1180BADfP6Mfz74Do4zjsT5cbTK8ImlTNbjXPvfXlk84IkAbTsiB3okpiFriPWMtnOSkwnR11\nT/Nu3W+0r6qF50XlqHUoiktURwYQNDWzu+HzTR73fbmSITlmTWuWyWyJKs1XFA1DoFh/ZsBp4tO3\nlaDMRls5rTvSijSO03DjeFfUYqAswpjKi+CZ13aj99JA4N0P5t40t7mGax8XeHbLGZ05BNaOgwCT\nHkiCZA5JTG97nav8Je0Qh3Z2AqTccSlbNyaKknoI7DuGXFxXv4Elp6pbXYjSVbkWhSOKQnqbucJB\nGtPYDLl5V4oCI5gs18eFHsCJNaYGRVtRMmdp0ZxPp79CCY+1dY2G1aQzppGDnAM9x5zb1thMqNij\nBO+An8qnKMEiykxs45bzTlLguNwjS2FYVsUtDsYjDebAMDaWO508c47Zj4/XfnUgGQDQmBkfo6xI\nQwqtpmYnlVUxIhiS1Fensk15WLbFw8Gu8Erxb5Q+Z7WZKW1Z8lFe6FqjgcrfwvSamIkTWRv+7het\nMtontO9zUS4AeSB3btvsF/8aOuL9jIibNcR6z4adIlGLP7GeYY7ATCDk368ALRvDYBNuZNEcElYh\nczM0kO99CTSjFfBdBP6qEMHe7RRgJSAKKE9l36Bi8wVAFWqxJnwQy8sPA4JJSzRJEkeSc8UOhiFB\ny/up0bXtuUdC3gxMWs1I0laqfM0CXBtc2YAcIDG2ZPPHo38ZBLxCkLi//c0cd6CfU0BzzNhJiv8C\nQGeoGja9fitlck1tyUzOLc2fMcU/P0+KTMw1FOLXtgm+tmkzysYv7Q/WLa/z1JcWlrvtjztqvR1V\nVQIQAK1SSALIqwYFM7YvPHHYAyWTurr2XT5mpYjMRB9cq2ZM4IE43zCGFY9c5mHBgyzDNddzsJUv\nAF+ew9uj5FQ5ela/ceTz5R8cQOd9FSUEYTOMX8f0+A9f7vTpWic9qsgdHDvmdQCcM1JXEgiFoZ3t\ncMPWPXsmHl4qXgBvXnsR8RLLQDmSXviqS1tUg0clPM4NTcDmzXCqysGjsWMivlVOMqhm6HDQKKVh\n+AVgNcsw5QCNzi5pNbFTj7XAQwYDK/hQEV+qz9YDeI7As2rIH8AnZ7WFTHkm+5jlMJ+iZ3XVSW4Y\n8HYwgQvAuDoB3dnwMGf6jpP3HIokVLechNO1bcqTCXHnETgb76ep45yVx7uEcFUjgmT7oKn3sIbv\nPpESlQG8XyQyPC96rMNqxQUjZpPS4rP5DCVhzPJ1ZlAlB4A8iVVVJ9TFMsbmTCu51AA7HCMDF60R\nUWH43GG8zhXspAiwDrEFpCeBjoFD8klJUpQLh35G4nGqZbcZ2hW4RjKHQEbQQKJFOSkCqQpFDuZN\nOJU33H02kXoGS30224tFAjAm332yk51swX0+yhoI7unyPA+j5HAJdwfpk+cBWCbzBgw4T61p8+X8\n+hHHVweSC2CFKTnNgNmStQS+/o/LcXlc7OU8/MXWe5kTFN8+m9V+tRZj4gGo/e3LDW4KZgrhG4ba\nLZ0VBJm1JzdBA+wW5XZHuXHrdqCcOUMb1bkNshZ3T/l2m+u09YZO/PpyjRkrO4hgDyAxFPLq8oY1\nnZfPFZnqlJTTPTq9YfMi6/kP3AgsM9S0QMjrHS2jw+ZrgOUhfhRBQUUezFOF47dzaPMbZHAJazOS\nV5KsDKYxuU4LFG1oY0NNBUIce5OTl+OVXbCN+04v2//+lo4v0y3q7wLIE5IiNoC8XsWHc9ThNFlQ\n/o3VKihegGEqOeXFv/di3+SHn/sd4L7G8HF//JAhMyMYZQRrINotqlQ+zqq4sPa2J1aImptIzjSe\nj8brfU8n1GlzpqtxxPYnNOSMetzk3m19Y0qs9fSvnuz78UOqxpToBpScLY63Nh0F2XxO3v2W6Y8F\nrFf4N1WqLr8Awnk0V34JKvKwV124P0nJUlM8mFtcT+1CjxrkqgXc7Ji4uRyW3yglGTudj38LdAyI\nj12yTq9jGR/UO1xQHYZPZjiN3dWGk1gwxKvtkbiSiVOW1e11tXWGZHbOREFK0aoF3MBwfnHZqiZz\nB/BzRt0xHDA3PDYLLmPdP8yQbip3x/dde2nW/bWkLUSMBjt1fzZvE0jgPBynN3ZdRSKaY1iij/LA\nSi/WF4wA2SCnntZ+KFm35IQDCKf2pTVfcVJKlbr1m+7RF6vsHkRVYFK9o88ITqqXIG+LXnA+c2gP\nRgQKrO71+93I8T0aQfIpy7SPoVyupZktRbMYQQdG0R9gBP65jNiRoSRrrGg8RxppiW42m6HweSXl\nbHU5EKqaCKul4UjDidqfBvOVV0QnQ/uZKFJfHUhOANGHQI08msHQJLCEeaqeZUuliZiRm5PKQM+G\nYYGsyhdapM1iFuXPpHWBtsIVVRc0k1UZGAoS0vEDsIGqxVnAzSAr1gyIE+F9cX+vBCLQ3h6q6xr0\ngpoh3LXhnAlnEcjmyGZwdGQes2A29wst9nqWBGA5YJn/P3fvEqvbtpwHfVVj/v/a9xwHTBTLvgEL\nP4CEGCOhOIGmLbpItBLZQohGpHSQ6Ca0aEVKi1Y6RAIBSrAd0QEhISSQEJ1AUACB7YQ8bCLFhBjJ\nhuBzz17/HKOKxvfVGGP+e51777k8srfnvfustf7HfIxH1VdVX1VhBE1qhogSGF0KQHwgoVA3eoPZ\nR36nqph4ubTyrLIZ2KVTHsCEW5D75azDahFS3gxRl/V7tMZrAHBz3J2eqTO7FFhZdRX+NRGWtA68\nLNwTsREy3Q/ADa/xHkc6fBiaBxM5DYgzJyguo6SAxukrVHYAwhPBiglGsg1D5MxkN68amlyECcAO\nhysKiDI2kmG4vuWpcS0SFp5BEXSAbZg7gBEPWdLc1OM4ngTGp3EcyBUuQyWrKMyq9cWIwg1pN4QH\nTN6GdFU2yIDFHSvXtnx/hkeeuAU7QtGDOqiIHDjQZr5BJBXErRm+lFaxIS+tcT5YRg3iBkrADtbZ\nbKkQuohyj6PDR9C5ozAvkl7LULQJsMnDtGzocc7OXq0xxDng4iuiBglhKQAng0wMPwcVV3X7iqSx\neQCwFsAwWIWyZJ3FSPjRLhB3Vg5RSakV4QLX7MEyNYGA+8kwtDe0SmZzrl3L4gLKywvMSJCjKVmO\nezdyYGTAq84z1SHSRZS5OTxnM3CC2UyVkrraxiZLYMBmxJ3tfYFHttnxMAy4WcPNGWEr7mKE5IdT\nPpG3WqzLBHLgQQg2TYnWFHafgDp1L1wPw5ksaTBkOHrGUsKf2mEQiKqxD/QkQG7GZFkzYBg9kn0E\nHVUFcuSYuOVAOz7HrZFCMPLEyIGGwBcRBF8B3DyZaOo30mKUpzECSHd0p855vD6EIys62Si0NSft\nAPJIvDtkZqcAzJB3xgx2+DRwInledMp9ChCnrgDwmQNfjkT0VPWkQDo9nKNjAeuKwlgghprbG0Fh\n9ECLEy/tBa924pEdrd1wT1a7OocivM42zIhABt9zE5WlKwrWAnAnPkngHIGRAz5kDEABmASrfmHg\n1kzt2m0C0zMTaMtzap10IbvREujbvn2FwQcxUXUGTCVK0UAJmDW01nAIJB/e8D6+RV2aTR5l6XqX\n7I6SdYwMtdIEGfBItGjoDl7bGJQh17uhj5y1qeFAOGUmu4iP6YBrxwGY4+EHbicF5aMBxwg0LwcF\nFJ2iXj+/0rz/6uOjA8l1TGuzDDB86L1IfdCqqLeEm/aCFrhgWNEY2jVbe0gZI8Ys7QRQSWUkwdHs\n0V6VEpYH6+qsUJZ6kfhr00qJvDsOJBrOYFFwy0AMh+Za4CIRKomzLKlUR8E1Avn00w3Twk/Qyg8D\nPDtW71mgKg5Ykl5QRzU3UdeL6XEngcgEXpZFH1UKKqO+LYVTPodc918TgcSBJuBZAFnqvJ9U7hqH\nxuqIyGq+mXWmAUvgPkOC1yu72Celp2MZ9fDgODDZhx+6pYRYeeZl27fydhnEfeT52/ZUmNenIbHT\nncpyR3OW54vAAAHazQ2RL9ObMCLh5+PDBf4JHF3VTUrp1tgc6oJJzplhjGC4z9ZcQoYp1AhioKro\nYiZSVq2GyVG24kkKfNFixiQBY3FFaxe1i/+hQBD//oY7HpqHnmxKVAo4Y/HlyuYtBZlPZ6QKMMkh\nzCDi87+6r2pOQmOOyUeZVc3F0LWXa0m1kNKqE4T+7bSA7RqYSU7X9QrQqDYQUCoupPl7TsZKjfM8\nKSbQRMwEIzZHYufTrPBb7t+hnBgx5oZxbU4XnWri6jKSMtFHzCoUFTE8tlR/dhYLSeONc/d0JLaT\n654OK4mk9VSifOtcWjNVv1e1hxC/FU8j+6kce0Rzj/gZWDnioc8s52sSRAmIHeJsZwI3dFIPRK+w\nBMJv8PuABz2LY6gDYiVY6WrVNvo4+YpDjpEblcC3HsBhrziMZf9e2h1mL+i2d7Jbnu5bkJaZSmZP\ncSCPY8VXgKUfHh2L9pymDnqG8wEgMI32273GwvDFtx7LA9wc5uw54EicALqRDpRI3CDgXVfWkhn5\nikxHNSO53bj4IhpGVFc+4OU4eAOjYwyXg5rnerXAZ+2G+42gPTxxJpuJOBI5irRFHZpKJLzskbof\nJJ1toTl2xZAfwOuN8ulIJWk4KZa9NyUaOg41GTF34LhNpXuYwZrjjECXwcRo7h3HcUeP1y3Otm6t\n2XW+ysfnwidmvMf7caMjwgLv+2AVDNVGZqlRTvTZEw9LmHjRX/f4KEFyVVYoLMXf16bYRROpNOUl\nyanQiie3+LQ8doU5gS4wEwKK8zJWK6ol9LcwygwdXm5c15uCx+iRzUCOQDOoOLZN8M4NlzDP6fFE\nFieWz1Q0AeOpLscFsGVdl6K/QOS+BHM+k8K52CpQwBQuCsymKBJkszQW+J0UMKks35mYtCHFN/Yj\njnmxOr+eIbC810aVVKBnCRndGzBDqQWk6rMuT2ZdYjp7bF2vxgoBepgqFLyB+X4B4SbwAoWmdnC0\nPnN54HkP+33uM1GFuuixYPjv01O6lYTTSvlt+GL3jIc8o4c+s392543WdJUiLfbBegUbj1DXNKOx\nZuts11u5SAzsQLlAbQg0ekWItvvnGWu2/Ao7K1yPXOug7vMiYZ6Wh3gDJS/qtthBqr67wKaFVRU9\nXq0QjLXtSTHlxQLxNvMQOA81AaJ05VqRuJ5pu+krRYph98orUFKiGSMshg9GG6I4DfEfWfFJY2OA\nXTalLZm10U8K2ByGy36/3OeOW0vMbXO9ZH593jC9MVaXvI5HvbGvqMlw/h4U7sd61JgHlow8NF7v\na6zkqGnieo6kZ3CIUtHD0ZIewAKY7kAMmRqWWHREyB6xWVo0nUl2hxP8nG3M6km7nydjc8AkEKly\nhTXf0l0uTkwELuBohHw/nbJ+FZdcmIAR0HkJJMivDtUHNqghWDJBzuSFr+gEk/pkCMz9zeuMDLRG\ncHlzRYexGSYyXg/V4k40ddpbu7DV7JhoDKbkNY3BxBXb3H4VPYgiYZrjhDjcmAiwqkThhohgJCE5\nD4EAExZL4tmU9TBHS+rkULZeIhEHozYe1+odqJ+msartKVy3aBSO5g03J33iy2QBy5GsynXU80u2\n92RehyOnHv86x0cJkvfDgA+cBLvYg8lrM8HhLmipBFKKoXqm17lyE5pLeJbwtqnEvv246m5KQc9Z\n3ifcZghjKekPlWhu72YJ8FyW8VtHvV5+vMo7aXpWbtXdk2zXL15vSdfe38rrRyZgkaAqZSmFucbT\nnkZuabAFY66vVsbqPjr5BBxrs7q1jcm4HuDZUtz/DHGxDMW0kBSfz0RGHZCIPKRDbSHhbQzqvMUT\nzeXgWgkbKEDHLxSDqhIp18iZXvv0tK7PmplVZXqDEtP9X09Zxw5wbP/GB69PQ6+wTE0HbH71A1B2\nucqCytfr8oh53Zyfqs8Ro7mehe+M2L+f29nzAu7emsm8/OT+Xhit1q8klAy3D87zvKXMtUekVOr3\nUVjhDdmxrbXiSbugAvfm6rK3LrtyA+YTCMRO40DRg4raUVHVaJJbjrX7LjODqoBja47MjUhq9ksG\nQc+8U/6964I1LvuLzyNgKhlV604eTLtG6/aTXr1w+1r5nXOwbNY2L6msAt8ksmgCZvxsj0SPwGsG\no22ZuIXIvaLruM5t7k/rCIpGiLp3LEqlJ/CNuyPzRiPKoHkfyAhRIQFylMtxwn3K/zkaGj3EW5Ql\noaTF5M9Zgs3kiEp5RXOKfIzgvx6BZjfJPEMEa4JXLX86Wgvwlt7OuQ+kLhFheLk77reGW2NTr1Fg\nkEqP82HcTxEH0l5nRXSD4W5KaJbjiPeYum4VdtZIa133fN4HNdHGaHk50EoKHEA66auRLP8WKpQw\nQXXGms0kzYL1zuX0s8TNDvQcaALJw0hVcrdFW9S/GfnHU+4SKrmbMqFkpCXQMxgFyCBlKBmJSNa5\nW42m6kRf8/gIQXLOhyfa0wBuyumiCJU5SSErAFXZ3WbyNPv0Ur4OA3PNF9EdYOgkcu4ccXQMXmaw\nMTmhwA5UrQHA0twJeGt7nAoVmnRzvFaB3Qgc6UhnkxFyimmxubFEDasvFBANBBqQhmODhfWzhqpn\neabJwTQs46E2QG4lBGYp+InmAYu2usPV6m+iV5SI0zUsjZa1J1Kpa8v7dkxxu7LO2RHPJrJZCMcP\nVzKNvuMlDgb2R6iojzVH1XNELvkSTyl0F4wtjlp6AtnYnQ0rMMY7pQJvyhM17azpH9/M8V0Pb866\nOSEJzvWtGbKRstE76SLmFeheFvIFf30ix9b4kQpAr9+yofc+w3/NGwu9Y4jfx3/MVzF5E9cAVKSg\nGgtwF5FmYQKDsY+z9nPfFDA9suW9et4x/Ndn0osKyHl5xFNztEVwMudWL+MwpSSY67V5vvUvJqhf\nV++AFCrD9gbSFQBD91MfNAGM4mfq5aJUTQNE9JA5dCpfln2jqeT8yF30hEV5GfP5F/grcyVZ0qpk\nw3wGeRSFJMyVRpnkK3Ld55wzADhcxqklDk+kMdueiWIpm6A0pq4vWZGJ2WK2txPdONYHbOaXrGjf\ndZqXb7CeTUDM6j8aNdvm7wrf9X6iomRmQLM2oyif2lGPvtuwIerOMCw5pDkj57taMnPPGEh9+XKE\nmlSA3dMMGCPwCKyIQXO0g5RDq0Q22NTtn9mhPJWG1VDGcBgbhuQSpjAkjgh2LC15U7rtaBgja7OS\nYiAue7o8rQK8GcA5DGCKC+dSILnlEA+WwzDm+NBL3pzraQxDpuO1Uk9NUC6oL1niLijb06UbE8fh\n5PIOdrDzxsEKBJo89p58PhuJ3hnJZRdAqqBhiXftAGJgjIHRFT1WLgy8iTYB0foGuduBxWWQTLnh\nQIz3wg4up85AeIP4gMhgnegegVtz3EDDIqyoNK7cqaSDrspbjkT6wJmBWQYQgRcERlXEMsxIgTkd\nEbtzzLVQh0rZIRLdBvrosEyc0XEMfjdgwABaMk8pwfM1c7xr9jujmchXHbuSeX6jQpPPXkcA03tc\nXBYU+EFqY+rcbiTwa0KahD+Vlc1zzdDFV9ykzbPXDUtaSykgE66C+AugLkHlqyN0PdU6zT4QT0ez\nSsLIuQ/Weghc4TQ3N7H/epJrJiqE/JTgkQWR6wyGUpfPHidc/u03vcJk18dI3myZ+ACgxAeb93wd\nEZt3IKCp242nsENhcdP7kHVf9VUbBnBhsfJsHQMtUTXQJ1h51Fqo89dc7Qhxd04jpxeKQXsonLZx\nH+vmPkG6RVFEah2tR3CF2Kg4rUFeERDTbMtjeln0zTXbax9V1KBMtdpTu3OkxreO68p+e+PYRsUy\nq9xRg6lsFeuM2zTSFphciGxSfuquDdhvLN/8mTO5d+2nHeyCskeRj2mb7UDQwO5YVW6hlM2M7+7r\nKYUl2UdvkYaq5JSAxnyiGrfnXWzzvPvr1SSgQYrKdJYlCGEmWhGSFRA2Wbpf+RLrSxoiOcjZDjfJ\nt12zfvioXyUn63CFkyss65Lt1x24ZOYu8a8y8BM8tn03c6KTYekpTw2zUkDNJ8rHk+VgMibohq1k\nO2hGZ4UgVkhobhhnX0aeFnUCiOqqF8D0AJuh9xMphW1yUnHJDFF36nr8xc0REWr2lOg+aEyNNtVK\nBJSUB0R2FFWhxiQBYFxpX+XlTLDkWkbVUDHR9ZbUKZ1bcZmpB4y/JAiyI9hK2xy4t1QEic602nVF\nh4wcWp/VvjXxsIHf5XeMoNPBEipzZkAfBPgSCzGq9TbWkt7WgdcYTH3EE8ZKr1KyJf+xup2vqBWc\nHOuaDSMVpQWrWZ3ZMaDkTc3XIYNsspueRd/T74U26o1M5o/UTw9HqAxkF2y5FX0VTMo+2v9HbanN\n7N8B8C8A+I3M/Kf02u8G8IsAfgTA/wLgj2bmb+m9fx3AHwMNt38tM/+zr3NDhCjG7MwKWwcwjpyb\nlMKT1k1rjve9czO0A2YDPQZyNCoYryQR0GqD4YjbBICZgHUgjWVQaqf3wQKlFonDFrfHVDLs4YE7\nVP20Jg4hTEnlNkugoCHPBrP3ICBsylId6FH1mVnjsAknmpSMpcrqJBNrPB3NVikTN27o0E7h89Kq\nd2pRwA8BtyAPK41W1yYcquQnEwrEQw6Dt7vCnuPiLW1OwRVuOAfETwLrWSPxaomWDHsBjvQOWGCk\nw3pZ7+B9pSE7YMeNJOIIsNakIYPpbsS+i4qRqfbTxfNSkoYh6SnQrmuq+EFPVUN14jPXP7wIWFf/\n+YEAayyy5iMFYQj0WWX6WwEBCpgWq1YvEriDAuvEUJ97jotZKM+xo4mTdYRhSpBP7LCQRzzW2kAC\nwwZzZJ0eUnpiOgGoHVy/qXCrAbd0DOMYmEFVJ6CKCIriwJGNKX4Z13I+UbSqjRPvpWik6Xy2RZh3\nzzRWhWhTBlEzoN8a7v2BHA3dD4w0WAwqcN4KyHcMhXjZan6uR61rwOA5ZlJSQtEiVOhZIUj1zhuD\ne/F2gN6sBB9gQAKeHqbykrWTXqgCGRGgB7BqnWedX4ayG8aZ8BbiCXP8rSwZzR+Hx3CaPM0C/Z5G\nL5gRnE8lPoBblspctLY0Jsb2ccKsIeE4h8Gd8RuzJuTFe3WLaRiYonUGVvxBUnEf3hRNDnTxSg5r\nUzco7wjR9ZyoUD2BiIGhaQ6HM3kQLF43BpV5RcSaCShmwME1967dFHq+QupP6kjM8mFlzzHnkmvs\n1Op8B0wrNr30Anm8X0YDbKA11sKviP0jgRaBEFf1DiBH4tYa+oPc0QKmBkPLd7wBo3w4A6qRHWjW\n4XbAceeKyo7mhE0txcdV2cfRWR8/9L7nwbXjiTE6AXQmBhrSDQdu9FqrmcaLH7jB0b3jPQvw1FAh\nHGhnoknG9zRYa9KDPiO4GYHogOGOPJOVkiwpxwRaHz3xYkNrzNnVNslVfjEm9PVgYw5PeqwPb6qm\nwr1xR2KME2fv6EFKqGFDdAAAIABJREFUxOFcz7d3jREBrKZbLYG7JU7cuResq7WBIfAQmj6QOHD2\nATMlwTXHXXZAZMPNGhoSYQ1DZQ/QEx2PGfkBEp6B6I4ejseROIRjYIYXYz34OG3WunZLNJkmgRsC\nQzo6gdHotQ9y1KfBz0UDizsebTBBVCZ0piEfr8REFkAY+uu41jD4Lo/vxpP87wL4MwD+/e21Pwng\nv8jMP21mf1J//wkz+wMAfhbATwD4vQD+czP7J/KtljHf5sjtZ/k4LPigZkYF0RpgifEQ58mX1RtG\nT7BvHLOEPAXWsbx2K8geoSCQQn6VZZ6el/vKoDfrRSBp91wBm2d6+5kARjvBJTn9klQE48lqmmBV\n3zeWLylAwOBqtb5OhhWgUI+1CQ4NNAMDjQAmWU3DzUW6qvJQCkl0jsbtIJieoyMA4bIU616rGzhA\nK7j8ovX+zbtaifPV5kCaYzyALAZLOX0TGKwfpwodrCJC/pvQLIDyrHFwczrpJ+yRkJ/ev81boaGs\n02Bu5wASYzsv58btjsCJijhEsfVsn9l1KOg9fdLlu3fcwQLxk4ig9Tamxzmqm0l8cNqP/rDpzauE\nDf5Zsmjfy7KJynHAr+m9bBBffHks6Git0kRYnLXEZQqWJ/St+9v/eON9yJs0P8Dfb5l4bDvYDQiz\nGXeQE7gSqGFZCXT7EwcWQepDT0ntb2ThEENrrHxTtaFNdZAaqDBCiCYyKRNF5SH9w3Dl/ayxrjub\nNVdRoWifgPDN8YsyS21SFIjbbQJw2oU2R9PnNxbVJZ10sfpOJU21m2qca1Q49fwMATC5jROQVla2\nBrGeZXhqj5VEVyUNb5NPXjxvQHK3YXa2zUwgWPN9RiZTyYbb5M1VpqH+JI+5GQ3VnCXBxldVazaR\neASrPdg5OFb636NTVkX7EiPUMipXovOhmigDBHwDLAcZnUl8yqeb9dLTSJI6zBHpBLoWyJEYTk+h\n2UN7vKNFWyDboDkDzJsMRa6XkR0dQPcB10Sb+KqWie5Byo6xlGQfgcyB6DKsy0lX9ZNL36e4y7Ei\nLX3k9JLvYd/PzLlvYejSk35ryEGOcB8D9p6GYTvu/KoicP3kjfiNiXspFymnj43UDqfMSDiaaCsj\nq9Qln7P295cN+HxQhrzC8ADQPGGn+lJIjlgmMgzvbkHahPa8jSoP22Cj/BHJxjpyGL3vABBIG3Cc\ncDfco+ERjFmx2RcdaIc2cEZKjlLmHy+J89UQJ3f6zdgAJawhJzaAaLV6DtDAJtai8+XVuSa/76AD\nZGxQ4usc3xEkZ+Z/ZWY/8vTyvwjgp/X7vwfgvwTwJ/T6L2TmK4BfM7O/AeAPA/iLX+emag8Lg03r\ndYYUBQQZbh1o1bEGNjcgt0S1F7UFpkrZytIqMFUKhsqghPvCLbX2655iY4E7DK3Ctk/6pv50D2Qu\niDCuumwK4imQc/t+PXPW71M7THjAkFCBcNB6AsSTogKwUrpmAjMrYLiswLqPvADQArN1f+XtLvVY\nU1LF8UJ1rPXX9mDbeG4vNWXyGkIflyiYXFW7/OA85Ay57UDYKhRn1zfreqUfaEWndnuucZ6+MIGD\n+b8nMLYJxK0o4OX5LPe/ElWv1cSH28HfhTvwiRxVO5aFiHJ2i9uW8DwK3uwgeb5XHoKJhfKy3ubc\nayjfolblvLLO8SE2ut7M5SiArD2dC3K1mj97+uK+oFYKO3ABa+sve/pafaeShGGpMOZ+S3IM6H0a\ntTEjE2m2DdsKL9rTWqpTznQJ0QuuSb1vHGlTNiz5tkLBk9eqcVj146/Xd2+oSjnzfiXb15hWDkbd\na31+0eD20mtza7116J4r6l+DXnc2gXPdaekZw6yuVDKGvqkrjWtO3yd4cN5qnuRBrnfkrU+QfsEG\nPVhevRRHN+mN30tswpk/4u2OaogB0TFGnTeve4G6lD60ajnP9U2Ps0eKD14XMpxylrEsqigOJsrP\nMHGIE52FdUmZ23RjIpBGWWXms+JVj46iNOdsK31dYrXfDEtGIZJ1izNnNIv6uKnrm03PLjIxZvIp\nv9tK98RsM8RIa6OedicPPLb9A6g2vIOJlcodcJAfvtSlTXl5KIGN56EDMMoZYaUzg500U44cE4VR\nMh4G5gt1bHsSszzlNHhTdFVj9ImsAFORFPLEPbXH5z1KhyYNqKJA1RyYIlfTpeGa05MdGndnCe+J\nsrS5Io2ezKP4modlfucvCST/Jxvd4v/IzO/X7wbgtzLz+83szwD4rzPzz+m9fxvAf5qZ/+Eb5/zj\nAP44APzgD/7gH/yFX/gFAMBv/uZv4vX1y+2T+/3Z5ccSbB8i0+Vt2t4TqNqV1Vu/10mownj9l5dv\n4PX1/eU8O8isK314nqW6lyrYvpPX176NU+fpKnVPXz69t657ea3meSq5t0H9/ObT15/xwVvfq+PD\n+7p+YXnJnz5h6wNvq/gP7vLbHNfv3O/v8Pr4cr66IgwfQKvtEjmB87pavjkO8xw1n/bWXFyVA2wb\nK33kh775DwMAfuZnfuYvZ+ZPfYeH/P/1eGvP/tZv/u8wPxDj3G2Gt1Ey3pjJOX5rJ9Y4vrknntbl\nfPnpWq3dMMb5be9lnfAN+YGnZ9mvfTnlphSeBPCCiyt9rLUbIs5tHX5w6knZeV6Z84q5/23f4fme\nvvvGI99vLzgfrx/cx37Ufr1cej47COY/PPX1BNvJ385NWM/28vIO70uGPN3Ukvfb1ex6getq2l9/\nQrr7PXzlza/vHrcXjP46z/gDP/BD8/2Pbc9+lY79rd+ijv1w7e1GzxqON9To+t6+J2Z+gm8y3C4f\nnh/f9LABeHn5DK+v35IMqXW+dLttX9j3pW0/p/ypvVMq7+kB1l2UAWgoY+iiewz4/N3n+OL9Fx9u\njA/U61s7x7BTYKfDrZ5PX1/y7W2WO42aNVuff/Y5vvjyizk3z/jngzvJda0rQyjfBi3bJl/nXvu7\n8FVqTgsgf/buc3zryy/2x58/rirzknXw9M7lz/WJN8U0X4iq6lGvG2bU67OXz+e6KmnwzW/+MIDv\nfr/+P07cy8y0Z+3w3X3vzwL4swDwUz/1U/nTP/3TAICf//k/j//5r/2PgIknqz7uaG16fc0xW4r2\nc+C4k+sWwdlzBw5LwI4ardl9h6H0yvqsAtdsL2m2OD+mznQ5yPn78R//Sfzqr/0VMLgU4vq4FqAs\nNABHK8FS4plD01RkfGQgciCCtItbC/E1rzjWZ0tFnUkeyMMdqbDHj/7YT+BXf+2XABg97StzQh5S\nsAPW9NKFLH9Dt6GwmBaaEniO48AYY3LVqnzM0E6rTV0ljZUUjVqyDYYf+dGfwN/6tb+CajdqGOzS\nZw1hOTvT7XQLjAE7KKxm0xazi08XEFPEgBhDYyawq43qZcVrIELx8B/9x34Cv/o3f2kOUROt4hyd\n3DnYTM6rDRUx6HF2V0WApR3KU1fl3QaK+Izt4QyIR02jfuqX4Ps/9uM/gV/9m7+slOvEz/7cv/Qd\nds7fv+OtPfuLf+7fwu0bvxtf/PZvYETgHDQ7Dm9MyNH+II6qJgA2B6TKlyUaW5wDs8RUohJgEqTN\nuOqPBmatZB3V6c7Eq/3+7/8m/s+/979OxT12HtP+E9dwvzkpV5FACzW6KEZJcl/PDnGJGb4b6vJU\nt1Q1Sw84XnPgZo5mhn/wH/q9+L/+3v+Goxm25O6Vr6rmNvTUBV77QARLHFXeQPVFcq9cBCle8Xph\nQEbRBioixhs2BHqvHITESOAf/eHfh1//9b+uEcDlJzxXlAyLfhBpGKPPZh/uB8zYQdFRrVSe1o+6\neUJKn5n9q2JIpsLxCfzYj/6T+Ot/41fkIap9Tw95VTOaT5VscIFtj7XqsGdrkA3a90a+J0zVO8jP\nolTPNr1NgVTYOoFsONzxAz/0Y/jN3/i1ecN/5I/87AfP+bEcX6Vjf/EX/jz+7t/+FRR1po+hrqbA\nb1fydLLTooGexqYayWmGM5z6zyirK/n1ZooU3V6APvBIIM1wJKkB7uLwWwLObrkW9LL+gd/3z+B/\n+qt/mRWABj3JfQwcfkzqpMsra8As2Qcjn5X1/Qdez4E+mHdTdXTNG5o2ZwJ49IGR9KyausgkEv3s\nyEi8ZuJIejL/4D/9h/Hf/fJ/OxH4A4YMgw9y8cMTt8D0mgI2o4qZiXe3Azfn3j8HK1id/RVnxgTN\n7w7e53gEeiv8YmiFQSwwwkVnG/jnfvKfxX/zP/xFtMNVCxozOZrVY1bDrkjgVKWMdibeW5+4gFU4\nBvn+6qaHXE2RYgC3IzEax+IFNww4zAPWSWcxJF5ud6QHfvL3/yH80l/9S3CQ559N7aUnLUUYwZaL\nqkd1LwZufihxcZD+qn07cmAE9+LtblMWxOmIAB6j43GyaQ1guN1c9bYTP/n7/xB++a/996qaFTBP\n/NzP/ctfax99ryD575rZNzPz75jZNwH8hl7/dQA/vH3uH9Fr3/OxqS8Au1WyQFtJ9MxVjQBKIANW\nPnp9aIJXhR3KT0glu/w+dc79KIF+JgevKmR8WOdhfwawlI1Ch7yHWtnrM9tjXb67/4wNfgMMZXCT\nG6CGCinhVGWkCtmmGXlNmSTMJya3srjWi1pQo75+m2OwWXXFBJxKqObCCGqqNFxKpnmu7zMjV2DI\nvawi/V+BlSqHMMd/syQqjj3f5P3MpjCbxQ5grycxBdmK/xVc0XfMQRYVUD4A3lGxjq+TVKBuYuTr\nXc9xnAHFDeCVRf4GrvjojzKWalQKHL51PI/P/vosZYb9Axq1WOdcu/r/7YNCtIBXgNxMoOo/aBa3\neU5UWLBWyvNd2Zt/5f5CPv1aIBAgHxB9kxlLBlbJshDgwL5N7DrOS4Yo4Kz1lgmB3K+YsO2mi4LA\nW9zW7vZAeXnVLq8xEz0w91I6CizvjjBhkesJa3/aXBHr80V32hNqcw1HPb9BduvsWmjX85fFEvOB\nAcn0y2ffGJtP7TAztMYySpHJNaAyb2kM77vqHsPAtskzX4IgMAAc7NlOemKS72vmrNEtvioXK4X+\noknisjBLLZIKABmh/FCmGpxgUW5e7KgMEQFEruWIwBlDtYMNhx0wtTdulpO2VLWFLDsq1StlQM6n\nFAf5IpZLt2KTFrnpnOJ9pMqwqbIx293La500OHrs+lqa11Y94nVRhx+ilmTCowC4xi5yjpE5Zddt\nG17b7vlhwK1xPWfy3j2ZVwHwgWqfwIDjuKG1ziTdch1nzg54KSOysjEMpiQ86oWh4giGK82uxq7N\n9zlfLsSQR2K8cj1WHhCLaxDANznukl8HDkMb0rFpMD9YSUkOh0zAkvkk30uu7fcKkv9jAP8KgD+t\nn//R9vp/YGb/Jpi4948D+Etf79SrVWqI82SIbdNK0KuI/nGIjW3sUJODpH1vBwVwMmuy+KRhTfUa\nVQkhU8k4B5qz9Ewa0OVZaLZ69LVkVx2Y4w5MzxJA3gutnrgA3UooTHRkcANZOqs4WMfZDS+HipUr\no/ZoQIYrIY5jMeRRDn2oFg8FheERA63Jcgbgg5KqO6b3jkKKStE7YEdMhydGoCWQg4lMZXWn3Ga3\n46BjNBkOHmcwMe92AEp8a9bgrrbf0ZFe3p1G71KQ5+beJle6G6teYCQXelUxUP1WtBtyDCYeAjiS\nc/SwwMyVQiX4GUYkjq4MZACImAkChzJLAsbqJZk409UnXuVzjGvQs2N4CWrxzgGcF9rKOkYAbTD5\nxdxxwDGig+b9XNqyFIBxDtxagR7W2XzmcX4KR4/Bx3N2VjoEuXqGQBxjFM0dhznyxFpzUyKXsZfK\ncqaSDFpPSwoD4KQ3RgywpsHVbv48mZnNsWb0AgbY0L7ZMBLPF5jRGlRN1sQ9Hd0UPVBb3Q6uoblM\n5n4AC++HKrqIA1et3pmxblVJnDzAIQ6+lSLgPvPD0QsYBJRVDthInGGzNGVKcQAN6GMhjWQ61MM6\njfjyAqt2bE+Qi2gOb45DgCLU8dPAlq+ZgcjAN+CrWoyDakyJHy0Mbjc26XJmZJmgeCXvFaQwP9Dj\nVeBHFXQs4GCliCVHc2lUz+mpRor1bmxCbFlGOL95uE+uJSec3uzmC9xbCCxxwaGXY8DG9FaiDcrp\nIAdzRgZN6dIGGHw6Fz7Fw0CjJQRMRzB/xD3xLsnp7bmqBPk4MLzPNW850BDITnlbnuGAw8PR8GAP\nACPwc3N69gWEoDXcHay5bJQVL3nDiMSXeUoiskpOGtdkazcYDLcb8P4MhBw+FS0IRUarCU02oHvi\n83YQ2BkT1B75wKMPfN8BPJQ+6CmAmkGv+hYNMRxAY+38/vgSRwKGxnrnMTAURSkOrQtQpr8A+R4R\njoEb14sPWBxoCLVVV5GBBN4ncMgkH8lKFccEfkEfkrod3u/8+aoIUgNL5yUSj9Hxu453gCU6Ou5w\nOG54HCdGHupYCLQbtVqOkzXhnfvQlSzsPgh0Cz04cAfwDTP0I9GjITIwGtD8TtCcxRdn1SYfDV9E\n4t1h06FySr4VkmHpOyCdBk+E4dYqryLxwIFhjp6vsFNypwGpRL32CMSt4ajI8+CavvkJoBJ/gWbB\n9tpf8/huSsD9PJik93vM7G8D+DdAcPwXzOyPAfhbAP4oAGTmL5vZXwDwKxqDf/XrVrbABjytBJIZ\n4NXisDw3zDY/LeURVXCxCORTTJePlz3en7Pu50Ywefc0kQ0sMdP88mFaXbb+xtP53vKiETSo45Qm\nqcqjvaoeYTksDFje3e0cdjnb5tXRM94ME0jyCxuYe+NEAQBj8yFJ8aYKprvGIKBi6DFwKGRcCqOo\nFgGGYxj50QZE1WKt+VDIHedsQwqARclhBKRPwBPGsjXDyscHPGbJsBcM5kHrf2MOSOq+lCWwLP+s\nZ895XzfZteWZqNBhJaDVsW8tm//N+ZPNTeSNcJ6TJJVWuPv6r6wnAMU7eSsZ7WM/DqcZd3d6TxpS\nER1mSFsSUGDU2tgZaWVoCivHTiUwFe3HnIh9eeiUyxjVee7HgUCXB4WF5hN7wkatyYvlsv3OfwOi\nJoFVhZVuiTJba/oX6HYMgGXs+ILqdq85neFGAN2KsFW9ClV3VJ45V2jwOBwIQ5/IcVExZp67lG1W\n8qABh2q5VpQipHBmNZJ9Le6DusuLZDmvnLJJ/zNQlonaRIdjaGz22d1lFZ98G0HK6OAioP24EnFr\nDUCGChVsncnmzxLPsWHrKtFHrLt4tuVFV1EHHLDlFYsKTAUsWWJuJocZSO+rmvEVbfiqkMlHflAf\nyWsLVjMwGXsGtZW2Jg+ts5yjL48//S/cnyx1iUU3yCx7bG01fa/ArPwEcFmYQ3s+FZY5ssHTcUZX\nV1TKCUfA3WXcXGow0T4cgDeX8ek0lhF4PzomCjHDsAG0gRMNniyzZsm6xZGGl/YO79oC3+fjhAeQ\nd0Ozonol7klk1w5arNU46Eju6dE7zgzVz1FTEwRufuBInyVDRcjDcU8cSUhW/Oj62Qp86p8fBzKD\n2sUX9qnkfZk06Ono6aysmqQ2TF0m2fByaoxddLMDHPCRCqhwVzfQjfBQyTkaUokDrApSYjUkx0Yy\nufNmpJbRHJEMg+EclKtF76gOnu8agfyIgZ4D53iwHvfJZlHpgLXlLGgHG7NVxCelzUcegGhdiaoX\n9PWP76a6xc99xVv//Fd8/k8B+FPfw73M45Lqot7tVbGiMiNHKTATQE5RHyT1xoTGui/Cl0kHqNdQ\n2OySNb0F1XfgsmXZ2aYg67W3JmDJCqMAoTZDWi2ZJcQNVXXhoqe2cdmjqvuzLasKuX3PAFP7ygU0\neIEEM3KpAG1eOHLZ0MSZvBvWMK2TGMIXyNg37363PmegiCgmzuiSopYsWxNFbZiKcHsW+3Bsd6bF\nFVtv+a9W194+k1mjIIWfGEaBmLhCp+tZ69H32VnPNdN1J6AQKPmq81wAiuDjJ6hzS+TO9WcKwSXb\ngZq8i1M4W2VRP63jUrApowsc6+lJ1CftaUArlFgA6IMhtCWWryvF1o+t0kEZsZOFs06je1ivWqHl\nAlqg8Ded02x9/nkdLC4/95jNBUv4T097Li6mr/a+KQVV/ONtFJcE28q+7Hz65/FbA7n93JZ4lYic\neEfPHFXrFvsukCzIWv/YnAZF9ijTubjNNf/PxLplQ6bmcI431k/bBmDKT2KXWWZsl69N+yz2KiKp\n6BhAwOgpkMWTkeOcKxm3IgwfzOqncaR0joEAsQssR26Tbzupr2R8SVfOCUutYhoUOefQ9ovNs5QB\nWfGGSEZypxPLJCcysDj1ENCpNRiKZOQmRCVJdC4FDOelR7CGclV0KZZjGQhVoYN2pOFmhgOMegI0\nttyAM0k9CAiMiFZhGx7Z79lFQ6k4dnmb0zB539BcLENeEZBcbX7K/PONmmTODncV/eTe04ODIDYF\nVNlgLGawaRoWmgvK5YqySQ8lcxEw56z+k9NAmdSTwjUAu+4Z1IUXElics1Fzr5NV1LDab08qVOQs\nOJVDEyGnS2kDLwVipK8srEHtX3kEwNLp8YQJv9vj4+u4pw03OUxYwK68TvtxSU/Rhi0vToGPZ7iS\nUkjPCq+SAZYYTgQ2BZcbiCpNvr1Xp3vSMwCgklKabJQDJQsbzHvYv7SDwPq3+FP7pygalprBdeyA\n+V69tgQab8DgHw5uKRyNRf1d/5DaA7Z4SCaJc12KU7QCWs6bGYSqRLyeeYnaga30jT7N16v2MO+u\nSDHDlBCRa9Smos7rGNj2c97x0vzf5nieXUzQt4OQDz5VayQ4TpNfLY/K92bn/v099jsuW2vuKnN5\nRTFBctVG3fcUt+raC3PdpyJG2zVmJB5X3qn0OulOtnZ2K8pM7J++Aq23plOQFPtaXDLjyg+ecsAu\nr253/G3mdd6KzTGo29nv0mu/WQoMlGafunbeOb9THpRSYjpzPp38+tBvvl73tLenDgeK1XL9bHnE\nv+Ixt8FekjUvPzb6q/Zn3X1JkaXq5mxu+8+ALQdCSnnSTvjZWXa5AJPGcfeWz9wGQAl+JknL1ff1\nA7cfx1H7zSeQqswRabs9KXL/DsBxdsr9ZsAxaRXy3KPMZrteUHOwz2QQP83k156DwDkGAWBrc14y\nV7myCsNXuTSCwDqvZLn0LIGW2s8Di+qVNu+3wKNDkWrEdCAhAVMt8qJAkC7i8GRdZwjkluFRSqA1\nQ1OznDRnsq43dFpjYKnZ1WNgOdEwnzNL736FCKkiBGVYy+2GMfm49RArQbYms/ZLqKHHVH1dMs4X\nlqg9V4C6bqqMJytZJDGbACmlIPUBYgEUbYMGCzsxVn5FOcN6jknPKaummrRV5Hrih13flCy0kle+\nZCcYmaoE/69zfHwgGbIq9MRseUm+VJHkLbjwS0umm4oFlCJIHEicbH4qH6X4SjnVCxLioYGTUVZx\nIpmFi4Rlq9gHOdK6y7bmj+caS/8AHwJlZnZrSyRgIu0f5itnSetiDExwWsdS2SvxcIejbgege3YA\nzWmRDktxkEpISNAn1Jko5zjI54fVhz650FNZ4do0abjUhU43hXENXSN0Sohd7QhSWoqPlUic+UDC\ncMcNe/3XSXtQN7/cAER52Z7XDWCwg1z22tEpvl0aky1rxFzPPATQV78u0FMhcFdbasLx8qzrnaWs\ny/TVnaQD2VYflMI0F+PK5nu51yb8hA5WaQGqBMRk90ryE6AEmjNWUM0pah6mkhrGNiuuxCC1gyxj\nroZni+qyyY5+r3B52DK4ABqn3H37bPL6c7L2pSRDyqeC0h6xlb5ZUpjrv9ZB4MCSC0UoYhOeWjNz\nWbJzlaqqMKGIWf+u1YkwchnHKWoFu1vxmlRZZgmVgp1Ui2p+lLZMSKB2R24Ca/uHp5+Xf0sL1fov\nf11KZhTARyrUnQNTQtW6sIrtJaBnCTisSblq3HMWcq7p0b6ueWk+QVgp8CkTttuuPDy3Q5WHeD9D\nlDQfxuYClqKWVWWVhqMtfqmJPpCT+7OA+aeYQwAASBqTQ/p0qOqRm4viUmtHxBJLjHEQ1LXA/YXy\nvgfXn/CLAG3C97pnOf+DA/VZjV41/egExf3BddHEj81mGJ36KwbgFhjOxLSqisNVTweRuaOPU9Su\nykdw5NkJGh2wqMRCJq+LBIlhzGkyM7yeA79dUAL87AnmLb2YYbREOqNlPR3IQa8rCET9KH47cK+W\n2AZ8dnfcnG2TH51VPGIs3UZjRXkZogMlCOZJP9icY70jzXEzjmogZjS9h089zHrL5H17uGoTl+FH\nIRYd8j7wXrIDiMC4by5IZ5IwwaghU13tIrA0c3Wy5OZ7QM+TAcxrqguuAbcbaT0FfHoXBehIfK4m\nLEBDd2Gx05EvBMqWCe+kNp7uOABkkuLp8NlgBgZkAywNL75ypr7O8fGB5JJ42lxVsHv6cHL37Oby\nTElWLyouhWIlRNllKq8XLGW3wuSY372VdZKVycrj2NM1gcuJEx9ep6o11Dsr5QTT+vl2339W78/v\nD6M5UB4TxzYo9aXtZEUXyfoYSuGkFNgSQlYJhLYsyt2rAxQYqudUKGu71+XlXnDfJKDoYX/7Qa3u\nvXDvVH524RjNoPx0JeU8x8yA18tZnwMTOZp2E6kvT+HlN+fgGVlB476EyPz3FYftc1NVPT7BoyNw\n189pNiRwk+cNglQz+r8Brvqb+zrLVp3Tt0X7LvPhWCC0Iq41t+SGL9OMHuQAjH3Ersdbi65ev/77\nKjhk2zdqnxSMfH5/P/tk784H3ARNRUESE0hEgd/67xSR9fm6Wt1voqLSMGzdL796Tb51vIWdC6Qu\nH+960uf9Urzp/drLwF+PD2AlHsyLL4H+VTM114Qt+TzXSpacuO7djaXCV2wPBpQnWTOoBRoTOJKa\ntZV7+OQOAyYfuegupPJUPE6G5uapqX3aUukU8iZXNzZOVZSt9OaERWIWD0lwD49MmEjJOQBzw9Ea\n3Jwt0WvvSf+a5mLuSKtnKVtN1SEEmjnfcfH7ZxhiULg0U2UeS6Q5nSlheD1Iu0kkYgw2/1LWbq2B\noowchRvKs8qmb/XrAAAgAElEQVTCIYiiS+n8bolmfL5ST+XEqucfGCiqy6GE3zOWt3t6yXNLLrQd\nE0luwObYTwqS5qo88EyFsUkrWafg2By5eZ9rKk20B1FgMg2Ind44A6N4JBMz37WK5ur6klfVlKb2\n6pg5KVxghkRGsGlNsGTf3NwrxLDlHZSwu8bPRWsn3WX32n2Xx0cHkgvwUrCzzammlF1bcm0OP4yd\ny2y1PKV5SI6r2Y0WUQ/co2pnOo4mD3OAXW6mB5p2ZVW1OGAwtcnU2E/O5GM02AhUByiXJ3Vk4K4q\nEwDwGAwhsX0kvRo9O2I4Wjry3sGShQYMwJvBDyDSYUNA1UsoG+5m4hmVAFPtigx04TKr8THARsMj\nTxHz+QA5Ao8+4G6qOWpKOkjc7o6+dQ+q7Ph0nxu7atemAR5tWuYxeXvGxqSDrzWnJzp1nwV2AZsl\n4U6pINs2MJAYA6zBqnnJagAd1zbANs8jYZ9A2kD3TTw659NQLZQDhy+JnnA0p2Vrdm6AAOu+UvBf\nyjeE4g9r6HYiw4E82ArVWTeFVU1qcRPaH3A84pxA4bCtC9MndCyhw1+aBFREohkFPp+3ocEwckw+\nbQCra1az6RWF/qamcZhS9AycW4chjfs6YIA5qxBkznrhBsj4IWhuFmpBvvYwdJ9o3N8E4OKspqvu\nsQEzzsDT0elSz1nA0SRMDYgiFYGVXFpfvHoDgIGRJi+3a2k4vZ9HAo8x10u7SbZ1AF4jAaCHvHZQ\nopSU7Uk0Y81xNFaCQSaydyADQ93NElz/ruwLlmySapllm+g9DSUlwRwWociOo1kTR5N7B5bqEEZZ\noSIB5GyGjEjnfh5GxUfOpsxdT1RViiI2sCMX04QNAIJVVIqn7MFarc1vGFLGGUBnyQyM7PBsaHnV\njgOvaPkNgYBqOW+qMbsROqoeVpRjg4aXVeWBT/SImg8QHDYzfH674ZEnegTBawSOhBLLTlb7CEM+\nHKcDL8MxbvI+DqBlgzXyvYuyAawE24ZAuAFZXk8gvAFOfns4EznP0am7bgr5S0SHaDx5ymAx8cWd\nVV+iwnSzoknA0vAwVn7whChCrGkeQZ1poEwYGfQYNwEy1UY7nNWsRgCPW4rC1fBQxDrzgebfwNEM\n6YGzs1ITGnDcG2AdSKDhhrCBYQ984/6CbwxHP4FvxSsrbjWgd+ZD3cjSwCOAF3d86zxVP5rP9kjg\nQOK9PeDStwT7geNoM3ehjB6AustlCEQC/SQqbeZbtFmGdlUlUnQ6h6EbSZ2tFU3EVHGDLZ8zgVdg\nOhstWGHr9WE4DhUXCEfawUpkONDjPSIPAHc8Aoh84Bt5w/t4j8hgkuBIVcA5YZ3VdKr1A1I87xwY\nI9HDcD8ajkYgX9LbGljm8HvYsh8dSAbKQ4IPLH7knML1eksuQoRMmAZkwwCLlyMVlmwALNCKQIs6\nV04vaAE/bMuFvCUexfnhZcem+Fb2ebMuj4aWp+oAmh04WTQGTtYTTgy0ASAr2QXXbP96/o0eGTut\nw7A11wAq9FjWFj/XUWXkDDYrQjC0OKMsUyl0SbRFdtcwG8Xceods0QpZ8rJZeGNKxiri0HQ/YT6B\nIXI5wWKMObb7xs5c/uILhLRtHLbjCJYKZBg/WLZP49jmmbD99pZHSJ64y1UpGEY9v6zsVouUsUvd\nG18LkzEzv485E4HQBwgmrPkiT31Cx3kGXt4BfaQSMGqPkOde5KZqCtPuDT5YaqlapNJYKutOBliy\nW557lTbiO6/6rYVrx5mAVa3hnWQATJJEMpk3hIaL19isgJiA41yYS84sow6wokbVVCl6NxPlsPY+\nAHSoPnBuX7KqdKPPG2ZFm+hlwPE8Y4BKIld2RCVcISvJRq/bSqhlVGlds+JLNDlyW6oEnwNKPrIl\ngzJFH9EczZbFkXDr825KbkHGJ+ePIBiIaQQ+SgaUlSJBsPICavzkqWu1ZwwNBxyJVyTu8sgZgGFt\nAod9J68ZaOiWUEFIFN/4Zqylusp26UpWJNmZfcLXLsSxT/vgWFUYXIYiEu8x0OWMYPlFTtgt/RIZ\nBAIehhOBFOrIBOvSZu38Je8WEctxSE2Pog9kotOWJoU1ah9CJQ4xgyxD1A7HAsj1PMhJDpkOpAJ+\nL23J7EDilJG+GoBg46BDJWFt3r/8V9RzUUTdxM0I/F6OfwBf9AfGGOzTcHPgSNwBtkIGm93cD3oy\nz8cN0U50DHRP+G3VfRnpGEi8N+Cz5vg+A2I0tDuo5INGLumOQX1b7l5RDA4kWAEaSrDkWB3HHAYa\nzuKrnasU+xxvgCVNy0mZxnE8DGje5BDj7VTZW9R8O0G7nYY2AuGBd/cb7o0GcM+VQDvyrqoi52xo\n8wXew0Wz6Zno5EPhy9NxT9JtDjnfCmu8u92Qh8sbTfrO7aaV51zjXzzGfMavc3ycIFkacQpyYKGp\n+tvW+9dQJV8KcCHtXs+0yjIt5arzmJSNzlMgrhbHfixyfKwXJHgBTDAwlSoWYb0EzfTQIGbowea5\nt+tt1sB8vHpE27+z/r6Mo5U1udLUSpm0Cfh3jV/1BJdwSJiSGuZd6np1vgWQkQt88HfMn2sYt9CQ\n7VfOy7sLDiT2Uamn2SN66/M8CE75YnE+190X9M3tezvAKeTSLlB5poQULUAL0ApNJL1iBfQqZEWQ\nvKgI83lt8XMTwLRaPrEjtNArbBtp5C5m7aHr/mCGdwKXpLKCwNdFXOHF6uCWgIy8olnlHO/KyN6J\nEZffUn4zafr9HjmXG83IbFZyuUT/87rOdprzAgJPR8b84kwCq+tZ3dvUuwplLiCTAeY72L7ebF0p\nc67hnLJpW+ubsCizBSjuP69fIKPu4Zm2dh3D8hZjyoD6pOVlhV9+mmTvTJ9M0KvXrve89l89G/9X\noMWdHcPMihl9lQT59PPFDD2DXkhUty9jzobpWW319SwJU+A987kY5O+MIwUURwBnSA9ZqErN5lUs\nGcpvbdS8RC/LTqH3os15Cf4pa2tNsC73SFZdICzMWa5sBnSnTl7SWcth0jmWDrFpJJXHeYlkrlkc\nmEbo0lqSz/O6NhsDwVyNwqC1Ir05jUWuWPfay9wwbuqICyAxWA+4D9zBMTl9wNPw/nXADsVFy/tt\nvPi92arf7aR1nqHImhsy2sQQAaCa/Eg7cX4qD0f7qpJ+q+nK/m/OUeHc3LBRqJKFAEbtl8IsUbIn\nMXMJzASSzRjpN3YjvjXH/WCt8xiDRlEmzmjoMbbeFc5IjZ4LhomfsrEfhSPVNK7wSNVIjypOxHUU\na62H1vr3wpL6eEEyvrNookW5C8knj81+TmDutnIWFRCsBTE/83wsjQFpGynTVSyt/DcrKLd9pRTY\nkwKqr0yQu127SqDUY32ACp9vEdOPPBWiHmze2YLES8lO8Ze1WQyzeOg2dpEFSnL71uInf3A/dr3d\nEnLP42tf8fvzayUonz+Tz59VqHdm6uZeFL6+s9csWckJ6yrAFW6tK2QBsjXU/HxeAXAJnA+U/1Q3\nwCqZl1Mpf2pHeQ4nSNb4TKNpgkN5orLehyq+8EilXNT+mdz2yE1h7f68AFSzFCYcOlHs86rBxbgp\noEwA/tXH5FHrP6UQPpgm4zpba2d/i+NTBtEH0ilJUZrKTlbwNA5LWewc95ldbgDKe3e953197+6T\n5ZvlX3sHqmU0bPdYYKMU5/bMu0ThR1MULmwRFJMsWMk7Hx7L6N13XZ15ETCAm9lFtrBhy6IqFa35\nMgfbrZtVi2Nfd67SWnO1bHuRbJVPb19++4OJ6QUcTkX/bAzycC88cIMlm7JooV/WSM7650Dxdfd8\nmzpPoqofmagWuby1SiyaieG87MxJcP19wTe7J3mub5pg28qHXFHzHMVTL81Y1RncBOwANYNS4rZh\nAt9HJEQkRFXfcjP0OPm55ri1hj7YRvkRrGcOOJoHI8nmOMeJuxLoLduKXicjLtUHAlYAddDwWFYd\n3BqGDXWi4xxE8p578YU1R5PakpgVRYpOuQz/nIO/xtzq5HNNGGxWJBEjchoggMax0QhNgL0Yuip9\nNCbo2RD+UGSKEUUmPptkqR+bxgzW2vdDHuRMyUthr+Y4R2cL+wDaQcrtUB+IUfRRXDHWd3t8fCA5\ncRHcpRwrI3N5pPTxANCKBhCouI2pfEgpGdZSFm9ZDQ7IPYx5XVS4ZkNB7rl0jFfygak95EK2OVGt\nqwyaTqvdmRh4sQPIxFARfvbdYaF8l/CZnusnxTdvoWgXwOT9mQbkovDNxA8WpxoDDq/edxjquxkS\nGKkK8Dac/OTSycW97AazATOV3nBahR6rpisMM7veDs6NeP2rLOTIyxwuLj45xku1xhz/okkUzHBY\nUS/X49bPAhXa8ax2sEKnzyQKv5yllHQisz8BCv2eqp+qr02xrDAt5V0yaxnLC7HOLNqBNTRxRq0B\nPkLNUj+tw712KL0OPWbDWFVvkJfO6umbkll8duVKAGc/lXkskFzVZQCkVnDNBrnNecFpFcAoDjH3\nXGUyB0YE2IlXYfpSLLYLnJxc+GY2AdeEgikO+i5o9VXPVQ91X2EORZVy+ecGkvkItfhLoaVufS7u\nRGlBL20mIBFNBgd8OqtT3hQkO45aqKJpXduAQJtKM5NVRBKYJfpKLpYzcKjbIGsHp+o1M7RdbX6p\n5DozzvOm+9jANYBHBHmwAgEwm7Kx5tVQTYIrB8Sw3BD8bMsDto1x5TZsRTguZQYDgx3fIL+zYupu\nBosuIdcUeg+YMfBd7XT31ie/U45M4Fs9UFGBkYYD5PR6ZceBfl6koUfgM6OaZdMGejObGV5HJwBx\noPkBS4P72EDyotKkBV6Fdj0N6fSGvhMVJky1gatwMZif4y7OvO7dQvQYKRJ2DEwcXgnm0rvKh8hB\ncAozUmwk9QeCuldgN91FteBRGr127t0O3NodNwvAOnomGhqGd7xkQ4tEzxOPSGAY7tFgjxO/x4DP\nj8Rv2IkHDlijXqo6yx7km0QfQCP4twB6Vze7ufWX4LF7srvhMAFN3qcZ0I5j4R5bFXei56qpWfvT\ncjZyqsOrLFtzoMeMECUawthJlrWJ1Wk4yXN2AytIyJQYLZW8mMxdyI4MZ9Jkkp7Tg+sHBtyOhuaO\nqmecEoyV/HhX45kuYd+EBdhBlLSo1aPLmRSZiffqiFTlCr/u8fGBZB3fvUgK7NnslTBQJcm2wBFB\n8uQCFsrWfxLTw4T5NkkStn10hjBQaQ8LIEBgwJCokkUzxLuJW9IuDEXBWFp43RaA5TYr5ZhXnhaA\nxSP6yrnfVUpOIMaEhRL/C8iVciwxVQfXcflrZAHDrl7x7f6ng19A+Gq1LiN1DXm9c7mqxnO9M4Hy\n0z3WpcclfL//vh/7t5ZA3t/fGXWEIhVQ75jSZ/+0ladU5xdAuD7VTiwwVPczsyTG/gQVsc+ugfyR\nGet31BPvkQssQSVFR3DWCH/ljeIeCrRZ+aPGTgA4F7SqC64RX6ZW/dwwJqwULjj2iWUIF4XDtscq\nAGt4OnVuP7/iINvGLoZ/GDtofbjmgMnvz3V6c0Dx6c27XaPrk1a1Qszbyqv1qOfKXHkItc+RV+9r\nGQYJrPDlHB/McHhud5Ea0ZTiX+Fcfnls369xXGNeSnG70DbW+06rdt9Fh3lAYV+7itBjPkMwaQsu\nqk+dya4CZIoEwy4RbZPxv1OOzGqUUfQX25wF29MmaEjlagBjZiz5CcDLKWC1ZwiOqqOqz/Pxt9Og\nrBwC7NTev9X+TUx6UQFdwxQTbBBD9f6htsiljXkzgMlxZZ33OGfWRQ9J0Y82OUTdVkY5N8iQA+mO\nA4cZbtUJMogpbq1hnEDGQLeTnmtrCGdCZIOjGZtWnQDexUGDQM6UQxRHWKC3hWUs6NVvODC9AIUX\nPDCS2pz5BIsG8s6dDVl2WbZFADRCU8bFpqP3w5yl1DBL34pKitT92jK4p4dbTU/KwyBhkkh5emO2\noac3e9So63oszQjmGc+9ncbXqpIJQOMpDcyRuGmsy/OdoeomjEZmcs19L8dHB5JJS5CQclkNAZid\ncH/hxFiKmciN0LQAYIYxDBnG+qTSMG6uSQItv5GoFqNNu6974N6S54DjHAnkUAYzk4gsuCQdHX00\npPVagQrfGb03dtA6GoN8t6PBcOJ9nPIqMVxSi4oeWi4czwWez0YwfoA6soOenBbABAap73kDwifX\nLPrsiwNPjkdLZ3el1AZAKZWNi3YHHOxFDwcGOnIEQvfC7l9KPIqBVM3TpdEowbqNOSYwo3c9yduK\ngJIJgNtBq/V9BNxiCtzqSNjUsSAy2V4TyUoFqXvR5g6FVA4lBhZwWLsVlNLGmtqK1nOnHcdsa5lj\nCEg0rbDakkEPfFYlE0xvmqExYz8lDDKReQI5EF6F+tX+1BNwRSKMmfk3U23gT1AP9w1ekSzhAl3y\nCFeEI2g1JUKRCqmz8hLc2ozs9OTnDMxsNm8rtFoGSAJAm4k+A51RDL/hpnvLfCAzGT85llLOxCwj\nNSSEZ6OM5G/pyVrsYIJINHpd26G1bsX3W4K4ynOYJ6rlm6mGe9VrRkKyKcWiqkhOJb3RM5JS6tW6\nF/cDPVKVJYAqlH/mYAgbUKIVZcAZAzdLhW6dSTFB2VoKD4ZZHmr0xNFKHjmqbIn1IYWoMRVwaafJ\no6OmEslWstkCbTiyxlEeWba7XelQlonmiR6de13h55ZteaLDZySqrn9Ajv+Z9FHyQhpTkZkmj3Wa\nzFyjei9k0CwVgdSJGWfHNXawDALbgNmnF++5HoHEPVOeW4FBgccTvuglmQA6WnO0z50VeE6gn4lw\nINBhXnMmR4IbjiBIK5dKc0YQbnlHxIk+WOGmmeEww5cC3axCkPCD95aPzT2hQugZBKBU39xThRUw\ngGwxAVtFj1F1iysCmJQFtxiANdF1gIgTRwba/QUuykQCcG9objga8HKjXgvc4I8OjIExGk68Ip2t\nmY9oiCTbuvvAqUS26IbjFujm6MY9QC9rw2kNlp1Gew5kBA5raH7gyxy4eSpngVNj5njpQDbALXB0\ntVP3hrM/4JKZlms+Rwasyas7E+sN75P7051zcjvKWTE4btKzZ3ScA7jfAAymwg5wbGtnH36gB/A6\nAiNOmAUyHf01qCsSOAfwmsD788TtIDWjwZEPtvDOmy9OthnywTrZeTgOc5AMkzjjRM8TZob7sKlv\nQhW03gfTDrPRN72XHfg6x0cHkqf7YnoRGCZMN/Jg5L5vevdUAlD9ZxoL5fqZrk75KdaHLxc1XDlP\ntr1TzMVKFUn4rAxUe28mu6E8K7y+N97DCIPnsXlwOJGeTd2ctoRCgLSE7T7q9+iLPFAZqw4p6f0L\nnddpTaHUsvaRcKv6FtuQz+vWUAqIVPTTd55Z+XwC02czeXvkWBy5GiUwamdAbPdZgjnULakqrtdY\nKyIAlbgqY0COHgUMFo+zPFc9A0s7SuG/pd00Vq01JUjx+q28ChGbN4pmUgDw42CosLwrfcARyOYT\n8Jkn4qDSblnlACfRAmYNhw8BtY3vPW/00zkqNFnjNOr1uRZzNhlAVuIFX19eO4Mdy/uQseZ8AWKu\nudnGWBAz5/VXGkf5RQ21KRPMiS7/cYJ30dDqjpf+XGsKC8w3qzVZfDpghUNMHmBb20DXpcTAdp9q\nlTxH8LqnsD17gHVGRyTitS/upmgO5RWupVPMkY2xRaLV9C6jChbM9+v342hz9aU8PAnVg39rghd8\nuTzfGuEC45Qst7zPyjyrl5s8S8axNEB1rUv2LYlczxTpfJ7tljIN6Cn9YOri1Vg7GcfyMpsGGIyp\nzQdbb8I+EBLP8/TpH4tAZpN2kzD0B/dnk43kQgdtJMYj0NMwuuHUwrtXScJaU+KKWh6kH4q3YweH\n924xo6FjrqPEDXSefPbScGbi8Rrone5V08Z0AEfdJ1aHvf2pst3mPCKtGr7BDtYydt3rezmSDtzo\ndAEBPX0ozsZZShJrBhyHYwjUzk2FtU87vqR95g5zw90bMh0xDGcbCHcMK6ed4e6Od27oFjgz0aGk\ntTHg4XM+zgj0ThmS3hYdJROvrx0vfqdRK7mQHAZEsNELDdSBypLqPdh0rdaAs8rd54ZZCaQSYs0M\nGDZ54XBiCQONbowQrgDeHYabnAXeaIBGBPpQqd3cal4nYNGAdBxYFYoSXEty8eu6xH49GYm6jYFQ\nuUCzxRUnzYsPNgB0uGCLKoGUfWRAXNbMd3d8fCD5+ZAMC6g6RIVFln4CQLFXq8QmksICZBxN7CW5\n5gXA80z8hmV08nDsG9IIi3iWTSHBgBEmXmC9mIs4nLZA8sbRnCHifd/H9c+6U7dKNFuPkQUECrgv\n/IEVrOYYzTCilHwFOS/xZRkmvIEd1C71elHthe9ynkD1j1dyRZ1rAQ2dRxwXtrLdxrnCRWEzyWAf\nCKvkOKyfc03o+S+h9+t0Tzw+PKbHgK/vudzrqxO+Z+CS7iUJMjsFGk/uLjpOf5pDebhY13NdZya7\nfWLHs8hZ4yjQOMENphIFquLLWjhlbNY22NdIraudauB2udr8NKs+ao2bAMAEyvsdr8/MS2AtYepD\nnbXuyfJZ6MxzL3aRze9P4H25U6z1+tboZYHUZUBFAmMEa1ALkFfTGxhmV8fa+9wDteO36yXUxerD\nwws9l8FZ3212fQD9bKK9FOFlVSMfkjJrTxmAnn0qf7GDJWO38QT3x+WL2xAZCnzzISlStVpSUTjD\nNM6tBgSGq3zD5ZrzxVqAH07WWy992sc0CtZ/94j+/GeiSRSXdTBHZJION5lPK5dR1ZRMTCTGIGAe\nJoNPa7d0TEV6R8b2flN4nOeJUm/P+g+YaxC5sXYN5JmD+6Xyfkp2RAI3ODvxZTUdK28jo39FzaFR\nuxZC1ljpoH5l7oubTyOW+Qg2a76bIkswk9ENnJ7ILp5/BusI65yRCRvA0RLkgZvklcGiIeUhdzjQ\nKipgjPwiZ+WhwzDLvabTndAUCQsHWhJO1lbZPwvT821qtGmPRHCcelt+qRmxAVuBp+iwwwJV3atl\ncdrlHJzjyEV3pOoi63wFMqhDYu7tVt2DmQ40sYYXBmocs6MwTXxvnQg+SpC8q49iM5arvLLFl6/I\n1u4uzTSR4pNkT+Dpl3m1KZtzk80GrPSh/aAH9BmDLaeXzi/lZZunE1JA9ZAfVJnYVOgVmuvv5qwK\nsIEPKwAyhdV2bxO81hBRAbvqi14fzaUjnr5v+GAE1ig+j7HA4tO9X4SLzZlbAMU3frPuERB1ZCsF\nNM/p+pyuPLP65UiusjdzeczvXB93PmuFunV/+L/Je5tY27blPOirGmOufc69lukEO88EYWxIRGJI\nEA+U9J77SIhOFAuJBoikgaBDCzogofQQdCIaQUEILPOEaKEIhESLRmKBEUn0cIM4DkZBSWzskPDu\nvWevOUYVja+qxphr73PfPQ9QznmZ9+6z9l5rrjnHHGPU/1dVRer8OL3Kp80SvoaEnihsntVYg3sp\nlAe3qIEZobtcb0Uiftae+ASPV9S87XWb0yCO9JoXerD0E86phhJYOsxO0kil9OWdCy4BFN1fsa57\nSg7AUEukiAjW3ovLJn+p22dYv8Z0/YLEw6xV9O0613lZ/G3ndDv9bPeJuUv+sfhinL4TRf6+7XNH\n0tLlLi92GwX7xqckx7/N8/4gMpE1YgRUjUNF2uZ89dcc8M3YFSCqS2Qzo1xf6ll53aX0JEvLRERs\n79d3IRuELP8xZKIZQg1aK7Mrx/tkvDJBP0IH531FYC2UOqiHt417IMtlidDzmyK165KIF8O2HAUB\nuQpf0ZxJ88ZqGLmPM7qp7JxxH4bTDIjchC4CYtwNwKwyfnqhs22XBG9ZdLBWnTyFA1UwGa5DMG3V\nCK9EUWOTopUMyqhild9A3S74UUO12Uvdww2tEZ+rjUpp7m2DEx4kaWRyog0cSMLAYF6RH5qXWs9z\nRDo7y8nKxi8VLjNKssU3hd5plcmW38JxOWK9S/YFxCtgWglNTJ4Q0GwWMwhvL/dK1NX2oE9klgKf\no5tHeWneRyScaAHjyHVyX/w9DSPRJRkzisho+JY7oU6kgaTOlJPGndFEoq33+KHo+qNUklchxFx8\ngfpklqfR66i+MI/ppYJ7JBIJEhBeWlholZeyW9shEoTiWVYIQSQTXs0MZjGHVp/zmJJ0KdxNCuJ+\nDdGlChg+K4Gla+KmJwBlfdkMMcMB2Wsy8KAnMz0x+Ty8rylhDlVbMqSTT4HGnElY8nMaemfXq1BF\nC1spyM28CyIJgk7VL5MWE++7M49USZh4lVkBWZ7F3SEtqWQpA5KtwyLcSkIRQAxTlpUvKoUzHAHs\np9zl9VprgeuOdZlbx71knkldjZ7/Jo34Zmc2PwvNX3WPnBHCahbhRj4Vhik6rOY552j6ZPguGAoC\nLiRd0M7A4nmhYz6543HMqZ/NnJhdD5Fg/jGj6bHghVJgBMOLzTEQFo/sOppATII2vfalQKBC7xCQ\nwpTdN5d2vb8iOk3GGPNHQkkAPZLEpU5ii8M4zohU7gSST4xTqDZSZDwoYHXn11Y7Iy25hwI7K4De\n+qV0XZZgmmxqxaeSIJ/AaTcsD/coBThn8LI8vLetiA1buHoJxofBI+tFLJIKBQZcl1ytPOdJOu6Y\nsWIW2eiB3wxvkSN4b8ytlliNn1SC8mQsHqlHi/JZgUVGho/3aIAvhXp3vwPFSMmPNlPCc5X3mfq0\nDxGgd6mo24yoltvETOXDgU4xhidtuAP0QjYPmctuc9XNCl7NIO72jGEKt1S8ouKNO1pb8IAZTSxG\ndTHtEDVmgzjlh03KXhHHcUhgoBcfAICWhlkX+PRSsBzEHjNPKPJgIDiEfLzpwDEcMMGAYCohEXfz\nitpA4p4InHRsG3eUAqkBAXJP5ZZ5Ny2wUMw18lKA3VjlI/nZAOWYquC8s9GSieEpcnrmAMQn4JNy\nFIZTTsAVN2vonRiZ6QJMxegCnaPgiFC2t57m+DG2EcVJEDa6GxDra8FgUoEfkzJ2Op87Dd6vzlHQ\nFxHA575m+6MAACAASURBVMl5N8e7O7NURhgdNFAmuggghB72NIKlQdRKCDDBjgr3OydPUQcOibwQ\ngLI6IhgZVTJMqIWRI1IVbNMJZZbOmXLNfdDxcSrJAHbRJQjssWdu+/Zpbtr9m6Gn1rubzvfoNHi8\nmz/8vcSgXz5tgpUVun1hMk7C0ArIcSQsaSYz5NGpEshqNOHxjAAWtvHh2JrbAECVPInmVtsHSJWD\nIw4BU56ih80SNl5hTtLrXZXtUtJcZ/+VGdxnjRdyoJRknrmUlBxKWX/BTH2zQOp5BReP/+Un98FK\nXycRuV+V5IcFHj7Dos36vBY1G68F2aKnI26qkQzBCXfL3SGbpy9UN89ZlThjEapv3tJkl5+i8H1t\nzIItryr/yWVJCfzK/kvMYL5CkBFcZBtiAXCJGJW43NQYeW1UCTzKDZ1xqUpjq9fXdvflijX89RzM\nnVhr/ZKPXK//vrX27YSEoIgAohp72cHEXy8+J4nuoraK7E+wz0uSyQ4p28dXDRlytgRX6MPDMaJO\nQQqdEQhAxfGCNDlTO9ZYg75oRHpk8Quw6D614n3Or7/g4l3LuseyKJDje4zH5Tg6Xq7OvnL768v9\n+ikfEmubDXVWBHTbR0CUR0NBlkQCliOUa3aicnMAlBPEulVpPkjCJBBaDsKnwiRoKjBsJ9IbFSCc\nbNPMn1hHWUpQwwtRR2jDERACA9SESmN6TLH2FXeFwOWM2sjpz11QzpvoqtzzWpOnjVFIeLdqDk2r\ntKJHqNcR3Srh1VAldxZxtIKbBITBmUiPUJI5RwaPLqUOx91PdNzIJ2hF0OnmAtEexjxVyMxHymah\n5oltZrv3Q3q1pE/jwkBHx7S1NzQV5Whn73HN4ct7TCw051yD+VtFCCVK8IXMk+v+2StZ7bGC3Icj\nkkPTPB9ODJA7WAYuyTQhnJOf5VwfuvIuPuT4KJXk3lmuR8A2yi6GQwN4UVyck8oSIURJ0etIwuxH\nIybHAxdDkCx7xe8e11QGPXyzkuyVDES1V5CWm4UemmGRgSkRQg+rrTUArRMTGHVB0RUY7JGe18ms\n7a4HozgexI0oU2ST1p6CdXQbLWp1u3RATqiY2gEbbCoqSotLIJh6YqiWWLi1hnkA0yaysD75BxUI\n0azqwIzupoGjQiMziDmAMBt6WcyoqhWh1WBvKC09cEv3YNLO+Y3WOZvmm/jz8E2dBhxaobDi1nC4\nrzaTrfWq2TtH1F50ryLxDgDGuW0aWbQm8FOhnvimtE2T88S6wCGRaDDHrmglfGLi1q2AWW7h6VcH\npsGtBeMw4vMmQ1+ZvDIrpPjpqcmtBZdLnGEU9WxyUVtRDQZ00qshIZA8sKm+4cWErU/pHXKMSeJw\nB6tLqMB8FN3yO6ms0csJECdLE1TDc9ZCCDhcLYwu0g0hOtHZj1yXyYSg54ecXaMOeNTKxqq7aQJA\nz+AtEYZQg5qW1zIN3OfBNBUJL1wDav+zDnnIDfNlwIV0U7MQdKwn2j3wwA7yOmXSEewsWyI7g7kB\nTwIMkywWwHlAeJF3we+ATIZeh/PZVRVmE2aGwzuyM6CDsAntoMesRRtyb7BpbFmetBvfsKhDztJN\nm1faloAsdgPwuYQK9pMEVtsE5qRp1QaNSA7AtWT927hCWaQtrunEZ8fb2WQhrYsyctLZoGDdTF58\ngcU/wcMdwFS0qCThMJYia4APo6MHgDZFU4Gp4KnTyTMnZVpvDffbHbdJK9aUzSLUhZVmMKmwiaI3\nRiWmOZ6nw8LjkIlvrXMdngIvf/YO84YJhw6J3gTcxzcFVG5M+HbHlOQ5Du1gUljIneGTnuFhmNoC\nqyoRjTZYb+hDcIbi/saB+xQAJ+BH6Q/3+0SD4mihcwgrO6hTYXunHdPeMWG0d9zvhtNOPMkNJhaQ\nDoUpUw6nC8ZXz5G0x/11AzAn6aOpQloHzDHnRH+S8C5HAj4EDoWhY2DCxuScpkFiB/mLICpnGAYG\nno6Gu08omMY8pGF4x7grTAwihi4eWhdwH6SbjMZIlJl52w98NSfUDQ3OpMZ0Mg2qO4cKnoWJmIcY\nWutBbzPotqPfBOeZ0QYq1+aR7eDALSyI2VjH/DgU0gxidEaekxVXyL9YdnBCMcJB6gchKeaNVXFs\noFUm8jc/PkoleZQtk7rKKuvBv8BWj04ABGH+fD/bCFhE7qmr0kqECw5snqJNKEyw5zlQAIKCXUhg\ndeALZ7XjTzPS6ggYBpY1nkq7WoO4RQjRS+G8BvLWoYdAB+BhhanzzMwBLI946o16PjhM4vq+sM37\n/O05cgDKy0vXt5ellwFbhsD58O4Oyw5iGx6K1n4uYmryIfFjvtRxweImlqkiAk7rO7FrU6icCjTW\nOnxHoWSns0kmn9mbojUSmjsVnMRa5U/oE/QM3+gJyCYuWTqH5b/S/eHb6z6T+7Ftpu09fjPNjmyW\nADwPW1UMbAfqfVpHNaEJr0HGI8vYKDMpX/OL24w5jbE8ZIPLtEajZFoqRtjmScoztLzyV+C51H2t\nPgUy8WzdPw/D6h544RP5xqZv1YMg9q1nHrmgobGcnMqG34u58tjJea2aCGECWr6R9CRO5VQEaFki\nLfZ2BByrS1rNw2rOI3AcMdJTLPsqQJx6Xz7+VhBte7KGFUdJ+MS1PgdkzYnXL9spoeAYWctaRwA2\nB73A8b2dRncwTfZxg1Gx04A1dbXgxYzu5WCyjQ35GbaFSxp+/ehYlO5AOSTaTvI7I/1ED+5z8uKE\nrz67Y2p6jQWTRQhwNMcqFZHc0OAnIjGtQVXRD3LqEdgxNv8QzqE5Q/ppZ1Skkw4dODAHDcQWGN6b\ncedVDwyJYqy2Os3B2YWxiVCh8gX2SQjerWvhrt3j3ipopmgH3/RJL25XKtQVsqzoEKPZX8LQMHE4\nISrPYOvpancccLqm8T5YrnZg4ozCtYfSCysgdCQ7/fXmbGWdAir6ZJ+CFWWOSEsbCsyJuy4ZnLWn\nh71Dvym0Mep5nI4+FUN9a+ktOCdl5LCvgEhu7CLo0iFQ9BvHm5GGEbLrjREykY1YXAJaIUAPad1E\n8PYmuDnwzu8xbkbYPZwl57NAYs1OWx0D75hoR8PRmABvMDR3jBOwGbG6UNhXZDkBqFmn3SDPsREC\ncjkxCQP8wOOjU5KXPsUFSfwJRSVFLb0Uvm3gxbVSJLK4NsrTGFU7L4kewGLu0x1HMQDEtYGrP4xi\nwkOps+37yTwW0ijuGxotrbG81lJCH7/h9RNhou05UrHbhZmEkpwd3xZOYymp1/F7fU/Wm9vpJWVz\neByLODKZDkARTkvw7gvZ86BQuixh+oocDe27rptJRPS0SZ2PeP4ZjK4U7vzUrEKvpA+rTfCoALhH\nqbKqbiHxXbn0eLtc/4VgzLE9Ss51vm2fMpPalqKDwHN+uIH7cRy+9hMQapnXLsGjQbGHPeHx7BIp\ndReMEX9XlaIBIEKWvndOyv2a9LrW7KUuU+mCRRXkLev8jBNo6qkPyy61gWV/fHqYwLFSaSe9qyQs\ny+siWhOwPUMmG/qVYpmAqnAfxBbm1MW+DtlT4xdQUU2y2JG3Anreyj4WVI4EpNIEAKC8xIvBpesg\n4UL2cnUXw9juGY+nKD6OjS+n/up5K9lpaX+qUKoeeEzyiVbd1qhMZySyOpjulnxe9pWj4jnb5wlJ\nyHF/uJj9uI6MpggQ4egtrbXkVZaCY0UanwknIOGmi4cJm1ayzSAVkeEZUkZnypTsoKYBu5BwT0UH\nZxyikBatq8IZ0oFqFX1mGCR2WEGIJp8lw+6194WQgGlBLLWlFX44ZHLkQOQ7Sceeej69wVxwA8vF\nibAk2fBG+jGr2t1i7FC3GldojTMNvoatLVfIRAVhCaLZsZc1i1WFtfZ9aRfJ58yMWVIpV1vMtRjc\nG3zyPsSKG3zGOBKSlNjtcHQkrMaCKKVJYX9TXsJZpaSMcfG4XvDWTIZC9DEA0E3gTCDiPTUWyxqd\nis75zIoW5lGNRAWqwIjmDj7ZNEVi7FndoiYSEspyyO7kqRp5CqbMffrA46NTkgE8aDObuPU0SMKr\niqWG7T8ANkiCBGFuDDvv8agDvVD0FnMEglm+Ns7tOxOrQFjCtwSAqUAiDJ8owRz7PoQlpLK5hC8r\n9YE7C5YQv56QD+aFTX68/gtdrw7FaiG8rpV1bpdCzb9dpAQefJOppRSUZrBed2ffNtT1DOuhlh0v\nIaZjXIpo4YlaJAcYSqsheMBeCgz9Yqoe35IXf+VPuQreM2+Pn+Uaa2VlJ5Pb+xcBDtdIiJSvu/bH\neexI69xp6W+8KjtrD+7zlAKODDITQ3ellGWCPCHFlyPOvdDxjirdVzP9Vimu8vsPGmZqQqF0/2CC\n2TZ/df70uMRlY69Rb3w9M+5fcIN6Sf613AGez+mOIHAqE0B5qCziFylaS2XxLTHZr0r043T49tY+\nDS+eSLbp36ZxPzHgocEz+MrnCrd2MsuLUny5RdE9BSkKogYDjlvO99qHvJriSlfFYR+f4tV3dVOO\nP3ntOI40AuiNTVwwQ/CSDh0J3DsyIS2T65LfG1SYcA5ngtWYTGw/vC8HVe4nCdYvxbrDq8gQO4eU\n0RU23xAxTM01CIdC/l5YY97DAEYfsXZQSxoOCA/LrMUJBsKBsLbeiNcWCT7GW0XSqwOTirrBCSgx\nQXbxrbFNL7Q7k2sZBV1cMqsmSXiZKUOz5FwqdOoO5uNF6VulgpgtnEUFMvda5IIZk91FgEkPvsEw\nffKuFk17JOco+QHhfmQjDpMoiLfJ9t3hmK2zcz2BJdLvciJrTSfMUcH9Q7w1DSsI0NGWZR4vIoza\npBPEjQ3ibMY9POA0yzsRY0hjJKvrsMEUBGHQhwPDdl7wzY6PTklemzzzpGeEy5U1JhyAsNsTCaHB\nAtmT0AC2km7F+AVZl1Wy8ddi+sEVu3tAziIkGxsXw+pkrgX/qPbQWILGkXCMTekpU9FXEgSwqdLX\nhJl6flPAs6sgQpA6sghE6qwBrcREW2+KA9oBF7iPCo6GvRhQBJQX2oXy/aK3huCrPWUoHLfXSYsx\nYiciOK7au9T5NjfVM4V0KEiA11jsiKSSyTCdYpVSO2N9Ci9oiIxJhxwHLCAW5eWMrOX0hSFfQ8hK\nNAIhw2Yi0Z5ceFnlx1phS/14eJ9HE0UWNVc0ZE1OnYqqwOKAb13lPqXDA1OcuDUPKURvTJZZo7Ag\njVoJtrgCBZHOqHMbamHSotNoY9cuht0oo3dNM4U7mKwRFyfX2HFFY/uMu0Eu2lvuTS/l9eJJllS/\ncBEeSbWEUDgY2GMZ/z4T7e+h4AlabxGFWqYFIRnhyYndRJoggTc0wolibzsoXOmze4CYxGB3noK4\nT5t2iVpkYm4pnrh+idV3cv8LSrzLdpGcG1me4sr1iKmfqcikUE1yykr/eYFdscJKaSVUaVUsyq9o\ntpqWAJYm/ltip+U4X3iS309ru9GfWvOeMrD43Cd6eHSsdfLOZmAUVUI5iTk7w3vcxHBOYui1syKB\n24RY4j8FfVBRNgeefeAcxJ4KFBoeyUMyuZX3mcK8Hh/hNIi8EJkT4oY3qpghXNwdZ4TNJZh5poux\nBnJ4IFXqGWbgz5sA2iY1BKMDyh1oPiF3oDcAh+BugmaATwPkLPpsOmEwPA+J3rsI2XFShgn32Zwo\nzHYTeoDTYcCmJVGyLUkw2FzOu5kRJuEOnRZ7XKHTcK0QQIjLU9RGTnjkKaSQ7vSImxiGsw20eof7\nykMSYZMY6liy0dpyMPrIbrqhvAbjaAagaVWT8RD+AibH0QMsNFqi7vV0VgexioPT4z5vrEbxNqpL\nSQPUhXkTUbGtS0PryQc6q57YCShzMHqWGoPCo+QIdUauP6NxjjFn0fCHHB+dkkyBkDYXQnZ56X6e\nRPOgxsRXV/WL5LebTlN7LK5bh+eybQocNoa/XT//yLyN3fnhoAWY56X8rFsln/ZdqVoDuahlpXhQ\nIEgq97LRS1xL6gF9u0p8yVK0vZjm6x8pO+oa13GxVpZvp0uMw64Kg2zf2l1GqWBvly9M8S60Q1HO\nr/hMIqZFaqHetEi+9AjH5XVF2YK3ws0b3vEyx7nmI4qjK40yM1askAtl7LP1YVRGBSYVjW19d5dV\nbLzX0ekf9+GVOLcvvb/w6qGUuZcePO6SxyD7rrimciYM733NEsjlKnn/5UO+3skv39DtrIfBbW8K\n3rcf9l024y4tlcPtjKTr+hY1XjJ32cfo9Z9KowIeP6uJxj6G5YnXEnwec7DC6ysit76a/G6Pl8jl\n+VJRjs5Zr8z0y8mLt2Rdd+eF+aQb81lMF9fv7F7DGp8SG5mY5PzxmosrH/smR5ob7stocN9yLX5E\nDsPyxj85ay0N9WVDlAADbDpsenQ0BJKMz3ni7go1aqkKtmSz6VHOK6+0e/YBpENCaEIL0nOy9jcM\nGCqlCKcLDO4YHt7C2C7qnkI1aJUPkQVbVQXS+KGIw2d2amNL96ZgiTMn3UxpYYBxk3alUXtTwx0U\nwAom0d3heLNRilvpa/DwvjsWPQOOZyxUfw6Xz5H4ZI/vBw1YeoXXXlYHeksq592nMJINMya4BcHL\nUDRruMvcIKJSNHiBAxafpsMIoYfEsoEwjxMmDRaYtOkPwixkmTmTNRkNDGMhOmEmneZ4Wvx4lMad\nZbE7eld0FQwLRwotpYKZStao3iGdEDy1J4gIjnYA7rirrZyJDzg+OiVZALr7awdxB6sKhrNqgbjg\nZg0qDfMJED+qmkRaLtW/veYkvXbBlMuzEJ4HV4jcSURoJHQ3iDm0LzVZAVow7oHH5WcFtleHWVTQ\nEIZE4IbZN7SkABplUzp6ZROn9xYA/JzREjY2ywirrzEBMIXjmV1kmqN13pfYyAnIwJuusDmLybg2\nJhMOxxH6zTTAjd7Wo6Uqmkp/nKOO7g2eVSuEmbqtPaF51MFUxxkeaZUG6dEeMrL0VRqmjPCa+ZoM\nCL0HGeaLmXY47IhAkoeqKcR0TZloWDg6P4LxjsFraK6rlyINWMBDWERI3CBHiODSsmMu0fDs6QXl\nMYDAk629GnVVICKBlw3PoZFRdDG0YLLmQJuCwxumzhIdorTnV23vT+doGh2rfMQbTPpwPSmA0Ogx\nMLZf9arYwM3uUU2my1HVyAEE8+faa7xjIpiRQeV3h7SW1iQVx6D9VMqbIBKONmNSWWlkjAE3gxwH\npAma0yd7x8AU4OgRInUBtJVBN8xwhOHFZCKWA5SmaOHlNQNkGj1V7QZ61It7wF1DyFl0ARMIWN97\n+lkeGyrHpF16kA0wtrDtQh43dZbBCLAWqjugB3CPbEedDhfFaAxjD5xRaSOwuxIBMw38f0Jf0h0c\n4R5xeh+BxoZGCiQsxUBBrqYlrNKr4S44RDBDcaAeHPjmKZAWEQYj3jNLb7GqiEB6RxOHTIO0zr0h\n2d2Mam3ksSPNKw0f+4LAXA+jC7C8kswRYULyDpcbCP+ATGhnf2VvmRj4qSYSgG18XWDD2eq3AzKB\nI5ojDWcinfvkeiiVvTFOHE3RpEO8w+QeEIVVUebwDvlMYNOZ3De9eOF0KqW9USKZC8a8h3JMwMAJ\n4IQAJytUDKGz4w0UZo7xHI1FAo5wU9bftQPQqNwyBTAdgDhu7UCXFvIPOCd59TgNx42VdmQ65D5x\nn47e3wLwSti/NQDS2MjL2CreJnm5GjAa1agmiOgmk9BuduBLNXRpeBItGawmwBv2DqBD1KHN4XpA\n7TngJQrzBp+OYdFnAKiIWe9sYNIxkLWFdQKHC0RumJNocXGDScfZhHzLEMnB4dntQDvDkBDSQ6bx\nfHmf6FHhJPHFAoe2DhvsxKjqYfSQc0+nR1x14DTBtAbVibetFc+8h3VmN3q9FQJXxfPdYPeJt0+d\nJXY1DKEJjOlwH5CboIlHM5SGLh3dgaEO0wlvzgooLjicyrmJ4Zwjmhm9DrP6uuOjU5KBZblAsOqk\nwtCggVURtrjEQJ+b32XnienRRflv4i998IFQMWF7yrA03Kg0Jj5384qVN4N1h3grLMzuQAhshGCO\n2n+6jSctRAcCKLJGl++3BFGnq7pM/M1irgkD8TacvFIq4MCZYdz4ShJbD+sZ6cjRDbe9XT4/vgF4\nhztoQgg6GhwHLBoE5DO0FE5beHb9TLwcPO+SBcyXXUyTXFvM+4bFAoRdkXxF3TPRqprqhLtxrzud\nrTclzwcAVTLxqNWpTSDS4aJ4ynWKe98cUO2135a3Ci+ILy9/z05VObdh8IZ+cVn7D7dxP4Ij2rDS\ni5QRIGF7djAhVpz7nBAEllPK/ySUJSrLYbDwwtBQL9te7QL0iprq2qMJwcBmdz0cqgobg+emESX0\n6JCXhEIcEAZN4xSL5PLYd3TermdCCQCHVrQnw8WU6ZQ+5gPDO5qtup1UQJ1ekaDdvBo9MB4MP2g5\nlGSHVAgb1Pu4X42KeM7ojHKJoj1mERDX4hVZLSBhSlmJo+uuCC6+nPVUF2eM9r1qa+xpCDtwgl4g\nDRMgPYQms7p/mixImiB5YHjBw+mQ1U0keGOxxXYZYa3T+2hq7dTHd+PJJeuuEkYAyaRBL1zoSz72\niRwCoCMMJa8KJyoa3teIQDQmzc/p0CYLNm5MUn3nA7f2VP1E1AzNldUmABpRnRJXVNACApg1uMUC\n/lDjEkAcHrkm7oZjsswaawBPnDD0A2h60EhSRVT+hNhcdVgc+MwQkYZJZZz2EIYIXIF2NDyPE88n\nE2z77cBTwB0yyQxAEJHjbtzxHQJXwej0lt5aL14ejwwBG1nd3CFZtjWu97aT35kCs/P6osA5ZnTn\nY+KODQfc0LvCRzri6EAcgxCQLGNIvZqVHWZ02GPVG0Pvjdj/MfhZzHVWGLEwAjLpdYYT8HNI9faC\nEcGpSpk7Ex4ZfJJT5vj+u2fuIHWINrgbbnDcXcohQkdntJFPHhUOTnfH/bTAwPNQzYySY8HbVHC0\nhqNxv8EGHQTcyXAR3KMUmZyOGT6c/j6G8DXHR6kkA0kk4OYHl0O8FVuboLvdLEHm/GbibSzCdi8V\nkKsauMSrhcPE63tkzNhYaYrBhcMpJdYzLLTCfR5Kcgr9JXc3ZVCW4pa3L9ZbGlRKAmDVanscE5Be\nnTW+RZiXa2DdMCGZu7B7qcImnRAVTDW4Rajs3ES5RIsUvHI13/7df0vBq2v8vp4hO+Gtpw0lecqW\ngb/NoScefLvXY3w+CVNYPihb3VL+tlLWuwDirAOpvgyevMiu0u22i2DN6cTKTSgluZ5jeyZ/Oe+f\nxpFwi2gzjPQshvK6yRmAHkUNxXoZCFRIdNsjye7YuG+1hMjcgqz0sCvKAApmhTj/ArfyFfTMSINi\n63YoCyYiqXhvivIa2/UQoBh+QrA8NoAJkwjSMw7Qs6qayjhpZEg0EMg461btggpMfFuSb5SmiIQy\nlfGfSg9CGEMi9Js8ao27dqGjnrnCw4+VIWpzk953JXQHOVTYfLHLKO+0FNPsLGjiZSD5dl1sa7T8\nFDFvgkoaaprGycryuCrKj6rymiu5nJ0wOVlOjBh/RnJ3w9tyYJ/gIRIGTe7mYIbJqyBemHkKU4lK\nFwFdMgBG6NNNG6QBFtE+eJRr87iRxl5VQETRDMthEHuueGGsMbd08P+gfgHhGe6O3tmxtkUkwWL/\nnGBEI5PZRmCKmhmGL3jHLsO9pkBqblgWz8vxZdEpzyKCoylfWuJoI+IYDqoWcyeujEb7qsChELyb\njnbGtE++pwaIG68RHh9Vi/rlwoD3XO6ZYfQGKxANObhWIgK3yR4Aud7N6KRrDUvnddbwxzIGlr7F\niE7vivNEeZa5TSQMk4jaBD+uVfRSoVi2NhyPZ94DG58ColcAHXlpSMxpAVfzGA+9eR1HdVOtvRJ/\nSOwlQtFItGckb7EpCvXFfuEF3+z4KJXkXae5sLcIB6RAA1g6BOKl0GbxFio8OxN7jYUu1FsiG0Ka\n8o8s4r95tmupKdWBEqa0WrRFkxEHILYUtgIwv6IMJTN+ZbSPx3THFcS/LrIr9Clw8t+X6upV4MvD\n1fBwLlORGhpo2U8w+aM9nPf6Vd531fxdtg3/MNi6Yj5XPOeC+V4UTJKTb9/y+rZuDz7r/DRr+CH3\njdTvO1MRAGlaX5SJvNsrMrO8xTkkef+5n+JRnn1kBjcfLDPn67yY1baZUQCYKMkL1Vz65SfLJaWi\nzPWi1+Ra0/hRHdqPWvoybFGK+qLqCDliMzJxpZPLtbDTzeNZobr59Tv50aGombiorU4hlbW985t7\n1YEqfxn8cLGo5F2hNCoqilT6nFmpt9ybOctLfa5LCcV6jf6BLt8/06+ddJ21ulMt2H7xx/XJY09e\nCKXg1W8+/v1yZ+SuWe/mDCgKJlLvAoxs8YtuYFOZR+P7EzkEiDr0qRxJdIgjlr5yf0KeqK5233RA\nUeFMPHgapiNUpYJKhhJFcbXMkpSZVTHpYQ9ky/c0AutfQXikWfmB4+E6JK62x/3MqZgNB/qIpiPB\neBsYKZpmQVOsxEAPOUuTWXo2wVB/cnsJQ1ok4BUxT6mn0ngjD2lNWHbNo8mR0In0FVve8WljClKd\nKH4S11ZdyXklcdwxjD0Xuiiqu2+QE9tnZ0eGUIiFpfUSiG7O2use850IYSb2GXWX1uAj4VEeHQyB\nHhWHcm8ILSGICLoqaxHnQ4HNtFKOkqPE+qtEorPAaw0dahllyu3C8XeQ9rjfNG4RZQiDLjP6A5kY\nQbLTo4SerMjzhxwfnZLsTkjAfghALCGWJUIsauCagNo8FkS+ypglO2QIIynY60o8rAm6NeIahaB3\nlbXB4izA7YXinMKQjT8U8PmCAQyLxID4Xr52WYTiAHoJ1RlguLhXMg43wNtSlHfdssKPEnHe2HTR\nrYpf35h9/hgiS3cpdQC2+eY1jzAqGNLywg7vQmolAYRXrCaJ8++VH3xd47bLSw+CTcG9TVBuchac\nX5fKfhx0VVxFcmoIWdUDzuE4gNPYlSmtdw+MYheW/gGc4TdEhyedm7K2RQSq1nKuBV+fjsB8b3qG\nWX/gIgAAIABJREFUyEoqqSf9NOUtssNZen4S220WSSSxT7ILXkIEcs8sqNJCwvOKsXc8zpHYUaEY\n9daJP0+GX4rjwwBjXucgnKa1Ri+0M0lzBKaVAo5YVxfFnJakzdHkdUppqm0EAbGTCW/YeYbMkzh8\niWcSduXswgZJSDahvWAIQArQ5WXzkJjiSc8Js8jmOijBkl7OQzJByckP3IEZPIoaB0xW0pRk3VFR\nwj4yz7KiZShaUl8eu3xmy3tdiI+vNAis1h9RS1erk59F6avFByTC/VmHNsPJeU0RVBv6rIf8yLte\nKshJd3vEa+NGghD4vqUpgrC/SI6fpwMN4fn/9A737KbGHzbaEIgxppIdHlV9wdgs2ypnS2OH4sCX\n572U2NOB6QNvFNCTRhoqqRJ4p0BP+KRIeRzLAHQq7FSEBNIaTmcxwyZsCsJopYL9PmL+J9fnSQW3\n4A/THc+Te/R5AlleMlGStP9awCqobNkEfPK6lTeDNMTZovlUQEM/SL2jmVZkx0Qwgi6/nIavxsT/\nZYTq/W2deCsCiZi/R+OWEVEonQ3nnDh9kq+qAGhQW7k3Ulb3wAAxxg18Jo0JvYng6HQXDjfc74Qi\nzDYjZyMciUHDYq2eUYI/SUHhVvqvDSr9EwZMi4Y+7L6YOQZdOK2CxtJrTmNkBvxLQawxuwe2VQWn\nlHRndNe9VBzqt4JTR1SeUkAUY7C03bO20GXI43yeYPSO9benWsnsHyY5/qNTkgFcPFDrtxYqU6q3\nyeS4kGmpTA+btSTcI5N8TRtJfJ/GwiIImZtyv1apQpEBW2afTqT3Yd0pGfd6P0eTDDoZ8RoJisiT\nYZeA4uS8/gjb9/fDIwuUJc7We7KXyHjftGwfddzAvkFWsIRWq7Ejifcnfbzo12zQVCpKi93C3UEp\nSTDUNDZcsaMY+Hru7bF8/Z5fSd59eij7pXCQaVLx4M1bCIWs75h7j6p/4EIvq7gOTeM9LrcjtR+P\nT1RPvhyp6K6wJ49aebFLs4FMIjgvudtJabL9ve+e5VV9PGQ7d59n4n2lQsjJYxZ4aecbL4Px63d/\ndZ0y0rBzLiq6LPzERw34gEQJuKoAwJJRTaKhgPtSwMKl59tABPvzbUSw2ZN5QhpzCRXyKLOV+EMv\nhTn3dnwnlaP9abcHXDxsHaVDv8JXVvXSxRMX+GKnqG3VRRbdx1Rkua8Wns00rsfDMH/QkafLw98e\ne6CUkZI3xQ42fvThAvfjODYPukTilISjAQ64LeYasINyqCC7c3u0jqYC0j2StOGFw4eXTQVYNLbw\nlIfb/L1cBMqsaI7BoKGXY4mQuOgymRsDVIRRYo28XGK85ekl06bIRsewEx54fdUW9PEwW3Fdi+5w\naWBVDSVq1sjojkUzK0yw25tHEwwxwi2yNJnnONbOt8T6qAcsDRUtSoAZ5wLXruilaAJ3djhhgrIZ\nvjrZtvq4TcjWrj2TdsUy/hrUGc+R4Naaj/DijmmAGZNpVaobZToe8kvZCIq2Fxc16ejKQ0Ojk5Sp\nyRFyJflzIgsWSPAGg/skrGbTb7Ld8pF5LkrtESY/FELqo1SSW1ScsPJGKmCTHWDIuUkwKrEo3DFi\nZP5p/jq0hKH6BL+4PFQBh6EIG7SCMwOVVQtYmWDEwpjm8m2ez/QShqufoQ5+1pCMB2g9NnsQdLV1\nPjowwvJxwKXDRCEwhsDi/NY8PJC3pcAVoUg9mggCo2UQd9ygGE6lRZO5+AR6CwVSCtPkAIafrEwR\ngt2MCY2TKeAL7K/EsBFjpuFJstrY5geaIWpIBgF5YCR2YZPSGIbmnQovDNJJzOd8plcrqgjMSL5S\n68AR+C2h18ONVjkTw/g+w2wkwmHEZkImqxmYootXSZqYDgAObx0S1QqGW3mWfEq0riYLPjEx3dAR\ndY/T0xbZvMNH1G0WuGtkyhsgR6limYH7SR7hxtTY6Bla1cy4RSLKgsE7M7VVnRUKNaurrOZA6SGc\nAnR1aOD6MnIBAeZ5p/em0buaeLQTDomEJGKQtTB97g6b9IJIVkaYE9p6gp25hnyC1ZYeKK8Gkw1D\nsVMwRGtA0w4PDKEYWIlDqXw2y0oZMWXacBPQ5RJKoNkMz1rsiiqtF4LCHU+tYYbXNTtJPvWOeY7Q\nFZiQZOZ426KusntBfaYI+k2AgSrsn55b0wO3mKsIQtHuB9AjLA1lVRm4QVVx2oB4VnwRDHE8aSth\nTrZILt5NYboU/0RydDjuPgNGxnJbHnzWR4a3yWPgCm2OJ+xlpCJ/gH+GswPIRqDDJNIdUuDSmG3o\nyyCPPexw4kirj1wqRILWgHcjqpQIQ/DiVTX3kzoczlB07IunPtHVIDfFtBEyp6PJwciNO57tjumT\ntBvhfdcTrdEj/M6yOgngOnEPeaaTVY0AwIbBjh6NRByiE97YYtlBmqH4CBk1uH80zp9+Y4lPOKuo\nxD6byvbJb8G909yhZngG29lLc4g/QWEQPzGHA2iwfkfXBmn0OJ7ngN0nDmU9ZAnjYE4m5JsZ7tpx\niOAmBkzCOVRZDScjNOfJebqPic9unXjoCXw+G0wdfWZ6KPnJCXrnZTqa8HlbA7QLRB1mB+ewAaYS\nUSWBnIBjMroa1UJggqcmeMYJmcA5HFMmjhuiEseA4ICZwvSknMw+AjbBsngN2hpsKiAGKNf2PukI\nVGlMqoXAhuAefcjdAdG2CgAY99FpE+1gXg9mRP7EYc/POJ46POocqzPC+n2beArKGwBshmL/BPgR\nOqBP1uL2g559XU1FXDsgjtGoq4yTRtAhHUPGB9PLD1SSReQ/BvDPAvhNd/+5eO/fAfCvAPitOO3f\ncvf/Oj77NwH8y6AO+q+7+3/7waPaXUHR8kgCemBOZS0V3LDJQteSEn4SIm2hR8P6C+s2M2AR7+2e\nxzAKIWBb41CfWOIprjjTWgQt5BGv6e9OoEG2iN1bSeedHMB5P8MOyJJF4c2Uq2cyx5r56LbBNsQd\niDqClVQCEtKAcKPHuw0CkY6Ohf26REilIas78nwtZzbq6WlAIO/z6jFqPlNhEYTVuoZTUXWbVozD\nYMu9XhYipbkoGakeAfOYqYDTs5AF7ROLuf1DcZq1vPIpbasOIGXwAjleIBgInziH1eJ6B0hEwxyt\n5z708k5ktQ2PZ/VMyvCXDpRP8bBIJE0jasdo+/aK/DtkW2Iei94WCigMTIm1pPJLmmWVh4gFb+sj\nOIyeEzHDiDtOoJJboicWkAw8jBJ2u9pdG1yVjgwA7u+GR+L/i3lDsjhBJsNiv6MsCFd6T0KvJE3E\nz2k00kgeAnFe6T4Ut0ZhJuJl8E1PpTM8NUEnDavSTho1CftfK5o+r5jrTKr1BX260NE2d88AoRXx\nrA18riGOW3i0XIh1rckOhQjwgtlo8noEk67T9y+u17atrayHgcqJNIcc5YqpdjNXL7FjzjCw4RAx\nlsV6T3m5j/0QAd6E08YhbIUsLP0p2gvy8O75C7hPtOOJ8jIUtMEMK7hZ4HKjrFvna5NsdwwEfhKA\noCurKlXoO8ouQilj37QGca1Oced4rnKRIoIxJ3xONDecIRdUqBSqZC9bh4tCW8Ob2xOmA1+dfxvH\nYUw+hEaHQbrBhhkdGfBVDEBoeCd0kPhfRpnfBK0NJ/SS0HTCgjgXHsoscLoQ4yzkey1hJTEtGXUp\nGIuwhKU6Pc8tmMQBw3NWakBSoEBbJKUFXONQ0tTzAKSxpOQBhzePsmmKfig6My3xbKTEownKNJwO\ntxlz0ShXA/JiQXPd6UyYoH5hIYPdHc8jkg9TFwMgXXEE0nV4lIhzwe2JhsAE4NOqHfdtAvfe0FTx\nFA7QEY4xiSo/hJ1SuDocc5TCgCbtEg06IurfsKIRH3J8E0/yfwLgTwH4Tx/e/w/c/d/b3xCR3w/g\njwH4AwB+CsB/JyK/1z+0AOyVP4FMUikwkAx4F6mXMSCDd375fClNG8S1Nl4J7P22XJ9KThMshcrD\nDH8MvlaIQFYgMQXzS6WIeB0uKt3AUleVhffcbpGJO7sAolcdbKIRn7GGLPfSgoUxkVA3qEXORSZA\n0e0Ss+PxHL6ntqXJsY/g5bHCw9vgN8XwIs8EMW4vRavwmNumDtgpINeOPqlMOJj1PLJkB67zTkeF\n5C8x09vC53VifymCk4WVkBUL1hzkI/BzQXafY1UGhoMyxE4GmSUMX4KQX4b5PomjtsH1mS6KMfZd\n8DpcIem1Lhd7z8wwIYWZ555ce2NXlImTTBUvsc9EzVspm369aZZE2sae31n93dbp+jB+f3jdL70/\nPx5efVmeSDhEco+MIpPRYzNS136WmG+BBWaec8HyWxqGQmZVCXySN+Qk0glwHcNutzr2Pf74FLLG\nJGuse7vrfYJyfVbb8WX4LrgSr50YWPLk/INh6oKB7Hyh6HGndFzofx/LAztaDoU68zGiwy/YVqvW\nYREF+CQpFsBjiF9AdKfSKxcn7F1IkQlisvbGNCl+p1iQoab0bZl5OaMqT0i4L903GrUNdCNsTpH5\nDEnjNFQC/BM8Oo09FdbynYFpdYSyiXCGTQe6FQ8WMIrINZ30kMdGFVle3ipTCAtoBBPXzVjxCK5o\nawPWxk/aEm1wiY7BijCqeH5CJTIK7e7wHvhohPwK6Kg3KonivhLlnUrfdMpOAapvgCtzHoRhZXq4\nxTDU0dXZMVGcygEU1bAu5nREQy0o8yaWa4z/JvyF4xBGtSz/ZjdOxwaHjP1hwIo0InIrcu4M1a31\ndFYuOZQJlbNaDG9U7sm/wpkRxgvE0TTLknIAXRoqwvv/h5Ls7v+9iPz0N7zePwfgu+7+DOCvisiv\nAfhnAPz5bzwian1IJS3SFdFaNJcodpocb4mw8pDoSyXkNcH8eNsSePu+j/AuV1qXfjVHLf6ONd1/\nFKFwlm4l2714t1kbPxNaLBgKG16sZ5QKj+2SSLbrXcrHlRiKucEyGQoT5ngxi8s/x5cMLe8KzHqS\nrzu0YA6lYKbElMtp/Huikm+2ga/nBoowcp8XTm7TYkQVakpzwAnDWM8TfnhZWNDrPG233fWC7VgR\ngcXkl5L18KUwRhIGk/tzv8KnfhDPFgyL2hAAFKW+mMKdNC+bz9f07dsgPk/84cuKB3VZAOFprDVd\nc53l0Ba72Chlu98P4qH7x4+r99pqPirI1y88PGyMdGnG66oETHGAJfwR9JnbezMass5xZnyPwS5o\n7SlcCOlw+AFP96BXvuADqWgu8Y9Xv9eAwKLz02ygdAA4JdRxiRJb8RAqaWCGFy75p0jdY/E/vGeS\nH45UTuSqnO/mAH+2CBYisSvfkfXzSR6eCgmfayQEEIKjWdUxbv2AemejoJi3Yqci5KfNa+17a2wS\nEtFQyMpREDgyM5zkvkp4ZhQpE0FPHxiYeJJlsqSSlqXlmpOmOR7ChFQF8MnorwNiDO27oRRb14ge\nOhscTbMVhUQ6hCKh1q93568RSXbSXiv5hmqaldhbLT0kdICAY6jbBi0Brpwq1sdLfGCKsXMdfCW6\n+dq/Xlfg79pZdSTrYDPJVmDMd6top0rUd5cFi4xac4DHvGyY9BzwNCs4E5BQt4yyoJ43R+Xgxwas\nSFHK7VibwqoKcIfg70NUs0BG3bi2UnyA+w++eITDQsby9XAWfDsao+Hs5/Dh0Z//N5jkf01E/kUA\nvwLg33D3vwXgHwDwy9s5fy3e+7AjZ7a4n8N1MlQdEOQMda1ySJl4Y1FQ32oL5b/cA1rysaxRX8Jx\n5wEQdoTJvczwrZS11SJMqCCzdwgGDJBWmNjcTK8LV4f1Hl2unOGWiMOrMLSwl+kBSOTNnSVrgBo4\nWzFTCDUB1Ij7gRsUtwhXswQEyWBpmgKENwroYtGwQFe4N4DXu3hMO+19SNrEBgKLVEpRfkWfFA/j\npnAqgET3JN9aVGbnHxsWnRU5GgkF+HQy3YIztFhgrDAzwuvIvSQvCBsCNM8arksZabVPVhxjF9Nz\nOjOtRQBhd64pZ+Gl0isPD8z5th8+UT9yCds0cDzW9zU9mALRq3WrCqpqgEVCWTaN8PBuSXij4AzF\nt12jdSc+L0K/pf8GNn4F6wGREmf1LySSS6J6AhxQ1Qq7Cy7b8frc2AX7y+N9inz+qGdiStDv9gVG\nLPx6/dieCkKtBAm1Yn5AWmEaPOPJsvSRVQMOVYUbcY4A9ySVVYkCjzxSAW/w4BNe72ZjeLEegjTg\nZU4ve+t9rbctW0Q067/yYSfoYWtyADgraiZyq7yRQ6NvXtFoRgeWgrr4dgJYYqbTu566iG+voVjl\nFYFIygXzOfLdbUXQMMtAVmh47x53xadxOAgPcxAmNWbOnQOTck1FoxIM4QfjtKL13hpECY8ARig5\nvHKklSET5QCHtJNd5bSXoeTiBcMYUQZyZi5PeD6ebh33jI6aA9LCQOoYRoBjYo7VBbAniN3xzkgX\nby28wGgYwzEnFVVCdxx2ngWh8aSFgFda7Q/mGxiodd5DeTRnVHaI4gbBaY47AJnZ8Ehwa4Y5IkKs\ngKiHXkLdwEM/0NhLFpqxlwIce3N6KatnbPHugjPbazcwX0vYInwGzGOE/DpUYdLR3XFAIEYDAVCI\nHjht0thRAbyhJX1WNjxvKoLgtakjEaqzOo7IimQbYD4gobO8m04DpVFATwd0OHlR8I6nrmwPPsm3\n5giBa6FtKHleCyW/4JNg06NMRKw6aJFEeLt1iCgGJlpxuW9+yF5J4r0nifw0gD+7YZJ/EsD/GTPz\n7wL4lrv/SyLypwD8srv/Ypz3ZwD8N+7+X75yzT8O4I8DwE/+5E/+U9/97ncBAL/zt34H9+d3KEFW\nSxS/fK357rUByRRfezapc19c/7Wzw6J7enqL5+d3db65F4O+joBnXD5KPa0+X79d8Mt5egqE8k5e\nR5m/3Z7e1JiSQS0xsRS55b/JO+a/+4i3Z9gfzNc5+Ztsz/goTADB08O49u+9gjLgi9eDr6v6/iTb\n6XljeTk7OdyaiWDUXL+vrvf+mg0g230d2B2PeLG4AB7pKL1duU9k/6Kvp3p6eot3z1/VJX/3t2hT\n/vzP//z/5O7ffjmyv3vHazT727/9W2itY9pDQsR72MpLgBIeDKbdcgKTZ7b39JW1enE5d2g7MOee\nWPWwty7ve+3L5aX017bFyzFip7xt314Ian2ttQMWCW+PH3/tX/54yYwMydp7D/f07bneN3x34HY8\n4X4+P8zQ5mn2XRlNbfOBPvLSoZX6dgOybXkg7zX+Cw8K78Rx3DDO+/b++up+mR+8HR7W6r00v/O4\nx70SmHgHbrcnnOe9PvuJn/hWfe9jo9n3ytjf+R189e6L9XTbJtv89cU76VTYTtzXMvdY7JEFL9yi\njxkmz3BaXHbtH8fbp8/x7v4lloHjhD7kPXf54Lt0Kwc1HIkl5v0r4vLQgKsuY9sIUrd42FSfv/0c\nX3z1BbDt00vkCXnv63s00LaIpewUIxsdPczjdoX14Ne/fuyzz/Hll18iy6Qh5nbftStZmorlY9xo\n8Q+ChXfa9PzuA+9YsjUf/PrB55/9GL7/xRdrTGu7rCj4wzzl3BWcDgjIyX7ekuVLlm4QUSy+ndfN\n4+2bz/Hu+cvajwLBtz5Qxv5QnmR3/5vb4P4jAH82/vw/APyD26m/J9577Rp/GsCfBoBvf/vb/p3v\nfAcA8N3v/hJ+7S//xTqPoTeDtI5LjCtmy2a0kgY9I4fSwhiRHOZhgXgSgM9KGoKuULiH1zJ3apaP\n0XbD6c/4mZ/+J/BXfuMvwe2gJTWeIVFNPHFRAD0RKq2wLxY1kwWATwvvtaD1BlHBF6fjSRaOut2i\nj/upGNl6OTw2gOONvqF/zB3/0D/8j+HXf/17SAKrJIdS3ADRrCgRP2n0KSsqZOcruEfyQasEDTjY\n094Z8oFZeHu1Mvu59WK2HBADfvpnfz/+99/41QhlocI2gOPADVl1ClgJlGIDOQuOrEfLYum4MFeO\nlf7txQ7Z/8+gKpgW4VFfyus/8nv/cfzGb3yPc2OCETU0m8/qWw+hhe2pZAtQHr0MOdmEtF6wCQ8r\nX7JiRuwHU053eZuWvAXA8PN04Gd+9ufwV37te8Xo/9gv/AuvkctHcbxGs//ZL/6H+Pyzvx9ffcEc\n3moOMgR+sBKIirL+uLOrnEcSGRyBRpLwsgC5MbgmQIVlN+VGhFUvpBCBrN0iYLvpMe54+vwn8P3/\n+zfDQ+tommWXIpISwpRJmUBGWBxL8PS2PF9zDrgbtLUKPWaIFWBZpBZNFwSrlqy5RZSD9YB//Md/\nAu++/C2UQhKh2QwbTgxk3WiPUDA8vJfIkgQeFWvADKGatfWqUHw1njF8sIoFelUE8uzRKsCzG37P\n7/5Z/OZf/3XM8sFY8b8xJ3rwCDOwy5gCQGc1gMm1PHqLjmOr0xni+c0dXW+khcSMJkbQUV1SRTTW\nSfCtb/0Mfvtv/voSolG66rEZQHm/pQFzFq7Rt7bjflEJeP1hEy4jIoENLqw8Y8NYoSjgKC2qawwH\n7G74qZ/6WfyNv/FX4Y3X/KN/9Be+nmj+Lh7vk7G/+Eu/iP/5V/9HTDgGHDpJSV0t2snTK/nUiPVV\nFdxP45zBoLcOUVZPefKEbABwR4dDby06m0nFHwSOt28+g5vGnhgY885k6Gn4A7/v2/je//oXwNJn\nAyKOm9/wxX1G2TXu+WlA9w5TZgwoBD9+dNya4NkU4/4V7gKoHuHhnbGOrF/sIngTba+//OpdJWQb\ngDmMsv0QPAXs5w/93D+NX/lLv0y6cIXNgJAoIQriwFO/YSjpDNPxthN2chplwunsJtcC+6yBe+A1\nHadPOlMONu/YeywIBLM7NOx9EeCP/JN/GH/uL/4ypDWIBY4BynbS4vhMDX4Sr+xN8GO3G56k4RkT\n795NjICX9M51Z/WRqCgDx4kTDgucsFWTX0K7BFMEt0n5OJTRCHXgD//BP4I//yt/rhLcmaxI7jwn\n91HvpGVz4O1NIWOw3bcIPrtRbvap+DvDMYzzeyTk1Qee3ip6RJPH8EBpNLQeFc0NOEBeNFXwB3/f\nt/GXf+0vkB4I0MEvfKCM/aGUZBH5lrv/9fjznwfwvfj9vwLwSyLy74OJe/8ogP/hg64N4GgNl9af\nW+h+ochyLNfON4l5cdvPejB6sP3hj2+GxRVK2cRKvlLWhOAGQt9wyNt9KNlLqHsqW07CWIB3/vPU\nVycZM+AcYVXZXJsTK1RpUdF+s6FA5p+pLdcHE6ewWDhrCqqmWgpgPYWw/FOT9G47ptxZnswOZKjF\nxaPKPhtVY5v/dItPn6XcZEcgTs2yEfepfzQ0y1qERYckvjMihN4De5ptRqvqRGAwPSxeKQiSYqCt\nm8aY1bXOcXiUa3NAG9fXueeqW1+WHkoz2Vhqb3U2efksP9qHX34rj0WsNfl90oNdaW5TfK+/XOfy\n/Xdc35Pte9kFMFua7zkGtr0W3EX4XQ/4hsPRgwdNm3GOANowQogF+V4E2mvjI8yDMLCdi22VokG1\nNIV+GnjkMtmqtWlGJSwUyxBs8npOtJuj9xZ4RGeSKwBIhGHB5JjPEMlHCsgIgw+WIGK8idwCi7JA\naoikxoGuAKQt3LgLCTCeKYPGKo4pkfUepk02fTBNHGE6G5KRvOStaVj45Z2YUZvBYjMZjELeNLt9\nJbglBquSKG/W4nWWfGuaK8QFfoww1Lri1ZjIJ3KsOAmRN/QUn1EyKiXNSHmAE9IbDieDZze6uTDj\nGolYYZSk7CxPJjhf757fwb0hqyWcHnkLYeiphhE1ud++0jsdRQCyqoGI4G7v0Ewgxk6B35/GPEq9\nEWqoTB4UTSNMA+ZFDPJzQLOmT4yRCWUCbcTyFrBoW3sBwiLz4ilReAsNimnKPefsZDcgQDvAphaU\nERqOqGmJvqbsbAEVFCCgjiCthTJ4LHXkYiQeblSwYzd+DqDBcJ+CZ1uK/KkDUyZkCEwm5bfSCdCE\nHfK01n3lSE0lDGsls8d6u7E0m/MnxykCjKhtnSJyktS4LlgwSHFgnoKuE7ejQfWG1jpEDPdxhiMk\nZTgn4I3fouQrDfYRNOs2o2pXKO7RCMqNvOnLMdggDo/85Jsd36QE3H8O4DsAfpeI/DUA/zaA74jI\nH+IWwf8G4E8AgLv/LyLyXwD4VbCi0L/6wZUtkBti9UiHKPGJ64xNSfbywqWSnML6qnSFcvp4sxRw\nLxRllNdjsWtF1ByDRysNefg6vUOLQawHCgU5vDKpM8Sj1ibLW3skLGaYQvI6tgQ0T9wFytroHrjM\n7DqTD5ue4zq3Jklj3BY6Ts4BMVKX1D3PMNrDXO7jf4ivLATgS8HiQNVyrSFtis1S/tc1zlLvPVeG\nTObB6NnDSDZTsU1yR5Q/ihVJhS7O37t4ab3XYspjHh9obn/qF9vqR/DYQ+7rvQ3nvRtFr8Xc6nhU\nkNfCvWZIuSfRx1qmLYqQKELhmOUMH7bF+l1kRXrSwM1oQHXdwjXpaBtb/P8qdjkpU/a7O4DAXece\n4r+cpz2txB0XL7ojvd15dUFieR/v7Qjhq1JKII0AtpQF5NLlklPmVTtYYw5bU5b3222BIBBdWUAY\ng+t7oK/uYHDEt5HLlRjjqlahtVylKO8zt8/ka+u3+K5js4jjMGTR+sUuU3ET7ImTnln87OW97ur7\nXsm5itDtJ5u5lzxtYb3JOyMvoPgan/yGA5KJ8z7x7jxZ9ak9Fd2pRItq7Lt67RmBMOqA5LFpQC+p\nVTRsFLMTq1oTgGVEOeCY1SOAuHbenC2muR9mli8TRMMLXmk1d6IyPiGFdxWRSBJduy3zFKx5RJwZ\nbcpcN8I2+JSqjR3nlHhdjXrRxYMkYBhFxVKRFNJJEvvKY7FNT9BtD8IDKpF5HLGlJZ6HNMrxTXG0\n5B6BbZDAnttWrnXPk2hKwApblq+1EjdG4mrZVstyZ9JE0E524Vz7boeqDI8icuIAGp4n13VMRtGK\nNtNhmQa5gRVLphWUQ4xYczfAJuPQ7MjINfbQE9PZ9iHHN6lu8Vo86c98zfl/EsCf/OCR7NdxpNXT\nAAAgAElEQVRAQiiyQHeKkP2crz8ep+JV5fjr3hMAWJsQwIuM0jxtMYbAAm3y8DKQ3Omh5RocZoKI\nMPJaYQHbPp5NkV9euU1QY1eQ87CXj1hCKIeTasXuidukggsyx/RxBUqAyJqDy5BTuPv1O68qKvn7\no6IMAEucxvu0m2eFhOVy//cdAtDAqFIxqUC/nu1KY0FePJhqw5R9bhWyGz77/f4eO5Zq4VvCY26E\nzep51FjreP+svaYo849HhQrl+alyP1HC0PD6bQmtWR6ir1s7SSUqL6SpgaV36EoPkNf250bQLyYk\nFcL4L7yZO2SJPOQlTV4H6gW9khCeDoYmD1kY0RnXaAlpqlJJHDthHgAiYzyHKaHsZpLlctnL4mWc\nMAhYtSIVCX4v6FqXmvRYuSRXdqlb6919JvlLGDQhNXcPf70NrhN5VzgtQrMQD86eiVWhS+y33bGR\n79/DH/8h2ANfVH4rAdozYXU1WRJIldEydwzRKHG5LXn+IJXQ3TCVuBOKDpM+PaAIAJbACAjSTNgO\nUrZKJFCzqq/FHm9NcWiDiaApa3G7O8aIqtcpwz0UOtXFCyR4lGDRiypkM+5biwYZwkxU0zCSYq+x\nZB15gzZBOxKOuEeMggc52CjJUQZ4D+VfkZ2DBWtyCfXpMX97IYAZnulMeB5wDAC9sYa3ILzCM1hW\nFQXIdUVFXBckae2SHnMOSe6WUDJUyVbxhJilYs3IVVYsgfO7UfQD4eCuveLCPWc+okHSxBwx/+6X\n/QU1TBbeoGESyq+BpUIdNCgsNmdC+LLqSMbbP/T46DruOUgcydDS9mxRLDyXMbORU5fJicy2pUuZ\nzY3npcRebpavS6qtV2EW+rITHe4Cc1pfrRh45tOzCcGsMDMgGb/DGaFXioTpvFbdKn91QAwQbSz7\nktjaaDUJicIoxVBWdu7CFYHZuDFBrN+bm1jj6wQrlLyP6zXvcLPqgEXLWqN7ja+wKAQQw1SsDjsI\nfCcA8baEFUigXIlrdmkx0iSIXIG4pjpnf5X+ywbltj2tR2MA4GiRpRxTlOPxXKUsA9YUHh0b9+x5\nbVHVIy9SSkswWUVAgbijWFpM0Xy8UHUejYIfxeMqDBfdZYfE/XN6IYDLF4og5dXXx0bnpZ947unY\nF7F+7NBFguqtwcaq87kryXkXlVSQpWjQBJhzsn55VFown0A0LLkqbZGYade1zvUXSQa1qurAZ1Q7\nWgpiMu8ZT5we5FSS55wR0YkOhn4nvlH7uheSA/Af38IqvbqQsvOe1KRR6h5o0M76sgA9YpmnfwPQ\nBZjKsVHA3bAyBxyiLMPVtuhLeiMdrFU6a2+kUgBEZddQXIE9jTmVC6mrPM782hMGJc163pPPYQGh\nQXDeFhtwDCuvW7FOkM9mPWcBq64AuHiNMznxUyZuaWsNEkPavMFmdKZFdi5VnGDpLVXKw6f+BDPB\ns9/Bcm+xU8LjyGYgi/arUJoGT4h2wm6UpRLMeg4Lb3GDtMwLojd5gLy8maPLSfy4R2t1D0jGOWFP\nTAYZ0/FuCo5Y3Mx9qAJKAjQ5IJ17+BIK8qzJvKKKnCs2qZgJUxSnPJABHwr15cVm/HzGnmYXyhZZ\nNO2meD6NECZ3dM/6xoIzdAMz5tZUlSxB5fI4iK0exsYqCsIq8j8D85zIDwOmZcBUdoZNw3AO1ohG\nOuXioM/uCnHdMx/O4KbirP7VhJh+QPBWmDcw1Nm1EICfoCMw1RXh310B8zdo0QXXATTprNZh5IFT\nsgMy0HzgnII5Q65r/rBsIL3IAIQRqhkQM4s1ZaWT151iX3d8dEoyDykBS8XQt4EupZfbpZXXtSwU\nzzOxfesH8LTHD5Mn+touqV95sOWlOm/Kwfriw8UeVKgI9aXnGECFEiBLNKQIKC/S7s7Znsy3xb+E\nsDZRktgwCtAQ0SXI0ipVMrEId0gUkWe4NISNSGHZ5mOafvwpnln3OVe+kd0PPtbT+zbXa7YHuHlX\nWab1vX22fZvy7NZHwlJAFT5HVdaQesY18y9W09O7Fyxp8xS+piT9vXlsT3+p1eVXMviA41VFGVc6\nSeUwb517PWl4p6jraIMWt8/ZeAPl3aRjY0GdKpwcG2zBIB7vwQ0n+04q3rYplPmp7NPjZVTQq5yC\nL5IgYYD3y97fHqr2KoAoEYXAEzosFNWW+QLg51oK5YrfZE6AgEKLsAlddVRzNgToEdS9cqDIHSiv\n/5Jv9jjul0/y6hkvOGCEWtMqzs6WO9YvEzMFq6Y8281rKR4LhLxdP4ygHwbP+LEeKwd+OZNElMqr\nhzGLNMoiQ8UdHo4RgwBzSeFLdR9f0dZ9naYsw8+MSXjuXhACs/ByasqYKMfGm1fjKOgJlQPiCnPF\nNPbL65MlDlM59HC7auYFhMFZhp0omtBYYjt3DtiMCUL5dB5NoTRKjMmVwAtmQi1N4JFQ5j2kbtBY\nc2J/p4YiyckqKF9GvhJmlezyaBu95FwBcNdtnpVdLEHcdXe91jnIzsVJyc4oEpuGZLfCpE0t2rxq\nLduDe0Qbgm/k2x3EVW+o69LNctxLXp6YOGLcBmlsHkI6pkElzkIGTcBk5cBtqSBw5wJvDbC5HGnK\nogg+ZzxPmg5RgOADj49OSc4QYxMPEL1XK2mgkYDgEGG/8CknnvCGdQ5lwLThwA1TDIeyvSKLhpPw\nTRRNZil9ighTmOPmiJJTQczCBQ8jiJnqTdC6oNtBkH0qSSGQTAweBdUBlDdHWwMTmcIua7Gv1AC5\nQTx7T7HaMWtGEuxPoDXxOAOGp81165GcMBRMHAA3EGtZCpVhlfKqsvWoA11Xkpp7KZtMktBIfvLC\neB/eAT2R7SBndg+KdfHJepmmFEW9dTyD52swUY5OWLHCmSQHEPjfj856mb5gFGlCiySu0DEiuiBm\nkH6jgDPH3SKJ0Pm8iffwwJUyGkwvdnOghcdkCv0c7lEgfjJJT/WIxIRgEzoB4fUaViKfpMe9se1q\nec888FGbZ+3yKoIxOd9jAp93eus+taNFMuTd5jIsRNBNAWkRDjO4nYA7piq9OaVDN+6xaeGtc651\npDKoa7UehwDaqaR1cQhWcXmv8aQgAESMRfVVMXKdQvJoKKBzeEELgNj7AFx76LMhXCKBWDwaOEcE\np7fEShmOiJJwh0aiiNzC0xViSAStHXAfEQFLPCgzvkUMc7IhgomHi0mAqRC6WHjtwDraHEUjDtaa\nBYA+Mi6bgpcKwpiZzU5+mEk32USAArKFGjEhrphCt4/AcPPG0K5MqAnco2pIaQqDVV6S/wm5ZzOJ\nigc8LJR1enYPpPikysBKN4aMPik8ciukGexu5F1m0VlU0Xo0XBCaJCYa1QwMB5g8SP7GUPtxRPa/\nMxlajFFAC09Y0anH783DoACrjlT5sE/wSG9tsG+N57KT896D+w4DmMAHuKbCo+U97q1j3qNtTeM1\nmwkMoyIwcCZPihIrugDBiqYOlxle18XnVbjtn1rDl4dT2ZmAm+JLCA55iw7BCO2yhfx+547jeVVz\nUm2YCjz5wPQEDwhEJjoMw8gbpIWzapI+7z6ixwDHPwaNw3ZL2qYcyCS0AUe7JQyQ86kA/K7Qwynj\nAfh05rXeHAelYjT7CXiaD1ZRCStSQh8RKKQhorGkkrdwPHegTQ9jYsKlw1Qh94kg8Uisp05gjXJR\n6F6GoREm4obWwrQNmVW4aaGZ5GA1CbjhFvgJF4E3QW+C1oi1PsFI7oy9A3HoIRjn4hNZH3+6QGUw\nsqXcdVMmenNAWdcZNrhlLCqGCNVdFRAWo4I3Qq3JxOGNegLcEQWNCCnxhrYnYXzA8dEpyVQ4Q0FK\nLIA7s0XjBMH/w93b89i2LVlCI2KunXnvKyG1cEpNC6ktDMADYdO/AA9hIiG1iYPRzT/ARcJCwgBh\nFBI+QoCEcMBp3HaQQDhtIFUZUHXP2WvNCIwxIuZcO/O+9+5z6mSvq3Mzc3+sNT8jxowYEUEKQNsk\nsoItWJd9qgQjT1v80t3OWmFoBWAMov73yanOG79uIbpbRJpvdaE3ugHwgz9nKY36rixrFgfomuYG\nDJukjXRQSSlpKpFDL9fBPUvunBM+hpSeXFAJKcTV9zqeRszmjgHW6eMcpkj47BM/AIRd3AhOGsVU\nlL+76TABhO+BBRdGp1PjSLqZIlVLOUEg1iioalChCkE93yE+uJK4GABnsnsqNhYD2OdqnyP2nH0p\n5VuNyJysxCQBF1KmMy8CBquI/AOWwPeytLCTGDYAM5wb/WM9c7Wg1k79/GssK/jDgO8AKrP017oI\nKt862VYtgE9mwgALY3YFWRfejSGw3235CgDtD/GEfbtXWSFm/V7gOflMT1IEaAlRMEsmDuPcbNnJ\ntrVZ7bVudwWElkUtixbQi5QSxas8XC57RcqtN2C44pTSlEDIxIyTFpNPJHbqf3VQ7xGxeiPXOBso\n+LH4hV1la+dASdnxsF6fbvzcYHpZSsuOBDSXLfW99g7ts7V+VsaI+xuGGM1IViet/0nCSCKv73e+\ndmp1AHShzthpXWrmSWOIBalXUy74Q+OQ6kpbx3zIEsb5CrnCMcadi1zK4NhkS36UM1/pMpDGRksu\neiqOUcGqNBicIZe5aAX7OjAjVxfDtE4JHGcCD5hAWBlmpIsnFUUZLkR0wFSa1CtOmDNdIY+7sjrI\nYFMZIhwPAIu4V0Hqwym/i6p0ZMDT8Nen480mDgN8GDAG4APv44mf7Q3HIDj/ZZ64ZmBektmxdBUA\nXN9O1CEeDtiDlK/H9cClYLbMibOYxWEYeLASoZPKF54sUKYiGmtlAx7quZZ9beEn1HYruQjEAB4x\n4G+2yTWal04vTak9Wuv1mshkRbvpwJsFfoLBhox9QTnKQnaGn3zFYKmWCyINv/wNrbgEqsA3JC25\naXjOiYipMWMGnsyJwykrXRlCIrm/QzxvGClZ5btoj7cPwUDH30ySKjOBMyilDhjsOGCpgOSUrDMg\ndNodPjrzyp+AkX88kEw9UIKNdeMjyA0rsndxxgApqY0kXiJvAd2yPNftF99s2QIWkKl/ryK83q9P\nz+2z+/uveTxbEVe/Wv2Ckyne8XJcqcXFay79picw2KF1JTdacoMYRluBw2QlfgUrNVDK8LD43I6y\nau1AoiQEE7KPDSQwcthytFtnH7fiDIekDC3TDjMpQyvKApY1VvfWsKDgAJoXnj1ubq5UdaZDSVnk\n8jaH+8Gmgxy2sfB+xbY1on6XcUwTuAOXAhUdkZ8rjc/ulv2+AbFbmyx7PWMbv692rcNkExBWP5or\nIPCpxWabazZKwW4+zNovtRBrjAy4ZxRQKjOTJQYKrLUN/TWPXqNfTtQ6gO2bOrfVYrcoPpNs2SlE\n+3qt94F25fI4h7Tyn2zH9LWo0F2tcSuFti3cBc5266UoUBURo3GpfN5ZKSZuaA/b2ODD6wVg1yyU\nAFqf7Lnp+9R+uu209XMRsdf99XbRu9BjtgwX97Far8c0VtrUvq091/C6uxfCMuM2hqnPRFbaK7Zr\nKubFd6FRIFL/GmzoRl9xvwKFO+0mY2mB53vM782qfA5gKH+yCaC4LJOGg1UcS0ZrsGpJ2q78dPW2\nxtJjlWK19E0KQPmk18+TVeQmmIfhrbxMvWJK0w9xlUuv8DBAAxBzA6f0BgSu3Fz1DtD6cwRpCLFR\n79i+2PjquxxCB5MtwxNB5ISpGiDjJaY63fLHl76wvHsT69dDk0OvempbTrzZoy3GCSAnn1WAug7+\nNS+RBJVu9JiYy1Djq5ow547ylN7i0v9skZnhOERNMx2EZnSimKkJXtJAnlxLuLOOAY1juMlMg+g2\nIIayrOSu3QJMVGXjGh1JD91rpwjRYy3JKx297vXbrh8OJHOMDTBbkYtIuC8GcB0uaRUE0qOFr5Wy\ny4LDpaJKOZZoLcErrh3uKmXnzP2Wa3jxhfh3WTyKslHXUjE8SxpkDZNyvWJun7LuWwPIl9k2bTh1\n+MZHWh3bVWy9XABRY57Lxp7bcryhif3Kjy/xYwLVfQDwXqj1tfqqge7X5gVXfxow1Czs79fMrkPQ\nizz+2KRclui1KRdIXpG8VJ6+Pa+ieX1bJRwyrcf07suaW/Qovg7RoaEspe2ffOYrXMX1yv6vBOSL\ntN/mZR/DS6CzlFoDnbBW2L2Mbb8HXtbefW3XZ1becpfLnYoVpoOXDmD19V7lTaO6z8pevYsH2H22\n9w2nf25AbhtTwosc18/GE0t5qDGWL29ClqOytGHjgOq7gfXZBrtQkA3WlKx9yN+krpt2VnzQTl3Z\nIGC1/nPiwX0jl0u/3+371HN3O/L92eXVqqZ0dIPZnlADwyE6G3pXlyzsaanX8kJxRflSZYr9dYm/\nwNKvfuTLXAV0uRfu8rbWRugFz0QlBeKHay6SQVZKW0Bwm6tYlCaxjzhdIEgiPaSBmmhbcTKc1yqd\nXULSUuzEUQdPtmP2Ojq2Q+zCCW8PdEGSQMDlUbQxGFNjCTipAEcCTyvjhyRZCxRpivJ69V6b22FK\ndCYDhtCqaawigrl93Vvu1+Gkutn6w9Zr7z5IT0p6SADAIjCG5I8c6ZU8oIAuv69UeGDb3t1Vdtya\ngpRGSqQJ8IR24yxMpbGsoP/xSDyDFJdIALOMkOK1ewUp151WLnTIyBcAAz0Lr0B4j4O1kgb1LEcf\n5Mu4Vwaq7xVrqvYWhawOHD3+n+CmP+b64UCygYuVeQYBV2oPz1gAKYEudxFlN1a0rty0xwxMpRly\n436sKlhk10l5AgrcUNEAfASzJTb5WV4Feg13MK39tinbmiTxlLVQGvAOCR25oHKa+EB8ulXC9s0H\n2MEoBoxDz44AcNLFkHShGLD4D3XVbtZ9/fYyiyd0flgDDo1hOl1iTK/Cdrlbn/6GOFSlpKcBlgwC\nnBDXWonLmZqF1sPKRnLNgPlgjkozeJR1YDs9Q9xuGK4MDAGolJ9ZovHD/PU1OTkEX2CQhwWiOHbG\nbR1mOPPEnhUkNH9im2yn80BlL1iwuJzHQKUzet2bI2ujo9P6fIo1fvCreKb0rDTE0ibdkGebD7cg\nn0ShJXIeGxWufXxddKOW+63AaSmZu3U1AZyw9AaUtPKaMi/wNe5juXo35c5bCAIWSMaaayAxZxM9\n+pDEuwYcBwZGzz2tM0zsRP3JMRjYExEtZVaAoIFBAYpuWjZojMFjfGWj4Lhlcxlnlhn0/tMeBz48\nKIEzA7Nyupa1KwOVE7hWtYOgyPHAyj2E/m3sqNVqHRgwvfdNZraL3UbAlEWDo6XRSMqE9k/U0kBw\nw+ik6w1C6F2qcbeYHPltT9UuRS7aRQcSCaxUbZZaXw2wt+Vc7fmC2xUAbmNrKL1DXnzKvGyp2Aus\nKpJCMxhuGINVKB9v3gegCSCddJjEqnjKOADDcHl4U/srSjRw0CNZHCRjYljgmY6fBuWKG/CuGIdL\ngJDgJXHqiJMRyzpqhrcxcDjwMEOm44rEzMQ1yb0e6Qg3jAOkIcmri9xNJ4APgcCsEE8ApqqqAIGw\nl3mLOtmcOcYtrbNFZZK0Nc9Upd/No5zMPjFqUSVwap2OJGCNimcC4PbAVFxAywihQHOH6sIoOxa/\nfwgHHeLxUuUnZhr+7CAWeQYpo2NW1byAV7CbATDGRuT3ExlMH/F4ezD1nht+fmN6QKZiY7xKHImY\nBkxTXJjkAC4MDMV5VOG2xLAH0qZ0r/emfWhdWQCHPBfPVHxRUKa7GeNVjPxmIDHz6viiP2XP/nAg\nGVjcG5PZP40Wp5K3HDppEUWhmo6vBTZqgZetoE5D+0liHzBvWPrCE/qgzm6H5NvvPB1hPRzoCk4K\nZW3dV/cxIxAjl0qAsN7f3cZ9sswFENCYG8jRBUh4Qi2A97llJGxze5fyerEkG2oj90NEGclu356a\n7X6g2Ed+XZv3tr/nxlyQt3FpyFu9Xp0uebD8AcvS9AKLb1eq/TJ8aM5Wf2s8qURDjlqVmi71vaXa\nyQIUsOaRtVJHWcaW5e42/n3H+7h9tavgDbb/fzgW7H/a5mkxVpNySwae5loPZXkoELbWP/8Xdc9t\nYPlndIvaNmmv7Vsg6F4YYrORmslbucAzf9Tahw6+tydtvWfLrfaW1gu2W65WfX59tm5qPNbvu4WW\nvrU0YIXHbz9ztQFbtxK0tHXeVWpPFoyoAB79+0zJlPwD9gSP+27OHqsCygUaTHtkd2HX7JUlc2+z\nbZvGNjnLA+9dWjSQzbWv11mt4Hjdrsbpk0G39db+na96ZYJB0yXL6lDUg7sBxI3auM+ogxXPSjdZ\n6ShD5z2uYNHUQ3cfU2kFspoWPZKHGga4Q0ASOujK/lMro68uGNR6X6xWGaOuyQD22neVuPC6EjYO\ndGB4svT8Eg5rr1OVLuNWD4IBPl0GD33esw0fA9kp48yZTQYqt1xi6Wbd1P7LRFe0C2NMjm/uYhsH\npizYpr0Vshg/4D3GxJfU9W5lsNL8lpc3t6rF2gae5KSbq4S81brnJgwBHmaZcPjBNH+j+Q9AxgMJ\nB5MsmDKPoAJIwLLwa98n+KyByhhtDAA16t1xJS4ZVSyTJc5tbdnU58pCXpz36H7+aTv3hwPJaUA4\n6RVA5SZ0IC6kW7vLXS6LDJr2RsXk+sWcnA4cyQ2FhOiFhp8SeMZForpVtowJ+MFgNIQWOKN4WR52\nASMuOOCbAUdEb0YftH7P6wkL06JW8mo3hF2bUZck95DQn6rx6JmAAuEMj7a6WBPSuf4csrbDFBWa\nsINW6IUV+cG4gONgtPqFAIYzp+l1cmnW5wM6fTF3sHzJSjzt8HBMXFhbhaEuhzue8zvMHYe78jUC\nNn1F48ZQQnN+vq0LFWRYY5heRnVEMAP6zKpsSOkxpXAtHfP71QrRdXo2H7DrROj07u6MvIcEbgLz\nBGxwjeU5yOHSaXXKhfRmB3lZ6q15NsWWh4ZKT6IZNbokPavyFKlAE+DaUNYEU7SMBRPPU8kMZGUa\n+WJXWmWh0JIZHJfI2QnyM1VeOQD3PauDIcIZ5TxnI9jIbIvWOBaYWQAWq1DI68+o+suFSDnvY5WH\nArBx/0aSV68oc+QTtJg+AK1VWMDtAGB4+F1BF1AdU8DdSBCjR8RxIWE2btXtLshDU8Bb/zdjRoHA\nRYaGM4gWZRU1h9mBxKHnJDxPBA7t+2guH3wwiBjGGAVn/lVa8FTaPq0BA4IWJp4VEvMZHJMDMJ8v\n/U3YVJy/QJCrLxcS7sySYUmyI4flops5U0BEMRBnYLwdiFDemrgwLzrQQ8TN8lS5Ja5k+kmbBAzm\nARgtcDMvpNGexzzPlFEGymPKO/bAsQ7vta4MbBYDtgxNUG4wyBmsaoR/ml3qb/9K0CNiB0HfCAeG\nwUcgL4FKAIdPjQktscSOlMYsgmV4T1p3cyQODMx0PPAdlxHgONZB+oLhzSfaOz4vfJ+B8ai9W/S3\nwLTAsAPhgE3lEh70954XLbdTiPVdwPHKb0Acyu6UeHsEhjvekPj/ZuJ6Js4ZLIU+GXgYfuGaRs8e\nIN0SmJ7aw8DbY7DtIzGnPDlOcGoBHD6oS0YCQxl2IjutHC2fENgD/Bg4JoEeAjhlDDzcqUeStAMz\nBpt5OL5xipgNxgBgwiJwyeiTBtjB78TFzxVofwzHI4EQ737mRaeq3CTuBxiSKI6/vMeP40Jctc+g\nHM6GOZ8AADuUKMAJyM3AIEimzED6hUwGF8eMjpcICfE3G7B4g2WA/gADKwRfmB5kE8RF/YzA8WbA\nfIjewsB6iwPjEHdNMqjKnWeVH9f+TySODwHlf/j64UCyJcnntPCnBraOptiODf0NLgYejVpoh05r\npS+jXDtjLK6ilDUtW7EFDxkqOfYspaNrIjXoy2XnyYG0hJLPWyugWSeuav+rlaJB7eoe8eBUX7hh\npvo44LfiHZ/cZr0WBGt1oi9QzL83XpQ2B0+UVO4VDEEXbKq/VDgueoEDmBiVnAnzxvBdxIddCX2Y\nb/17G8CZLEkJA97N4G74HkpOXydccktwHA9kTLnLU6UogbcuX7gseklkCytGRKLdAOH35ZR7o7DG\n7A/trde368yPkWDifJ7hDxWouHlFPOH4miC59pEV8NUe6gNhB98ZYBVEp+9i/WKvY96/5MfP71cd\n5vpQN/Q5K5MZp/t2Ily/Hz7o4vXyTuhwjHxZFOqXyHL9VrV5HB0IBchSh+QBSd/d+3FridyAmVRu\n0ZGzavfhSoxfC7c8XAXiamHXTjYsEhqfWLHuFsH0cnWo6c5EpzusJP4EzMxBWjKWrlEDaUrV7tWu\nCm6soiNF9SjqQ8s4tS7pdyYVJXlaVSVptT2bozoFoNp/VXQOPQG2LKArkt17ktb6WQL0VZ24PZB2\nUZeYLPM1xq0zgCZGfsGLXVj0l3lRhh4X5f9h7O4sT0lEryMWFaEOevfE2/sbLndmE/l2IuYk9WDj\nHFZAIDzxy/fAdxWDeH8AP70DOUWTGczqdFQQZZA/XLqrCtXgyDWZ1caEYk4qQNRoaYTjzAR85dsl\npYNr/IxoEDV16POYN5Zipf2sAmZmPOSOMUSnyF5DlbmjrPW50SGm9NB8Xhhmoq04Ho/RxS/qYFd0\nJwAYDwYxmmxXmTwouI9e86QaUOc+xsINbIf2yJzL2o1VKS/jwrcpwlMAMwxnYmW9kO6sg/4YBLaV\nq/j9OJTlBzgG4DYQkRh2AZn4HoYJU8rcsqJzf50tDag7qA9N4Jvr7ShdHpDRS5swgRTHvPLxpTBX\nZSYhZuAzrn9u6BZW1vhNQ1kF2KyXOjOC60stVbmKqixpgeb6aUqed3PfAijzfgmC1tpZts6dYF9c\nWP1dYAdlZbDWqxmbStg2Xj//ddbUztiq9iGx3IylQDaN222x+334nB0kLx5SK5klbficUubt6k7p\nv3VY2akUYeiqeLtLo91pDZY/cnNvTzeDKjbwcHNwgzJ5yTZZqUyq54l2A1sJDEMmBUFFugK7dX0b\nHun19EWbQW6gxzZstH391/Rivvysyw1Mdl+WrF3X9hxq9P6EU+7f/qVOuN1diLAO/p4jkfoAACAA\nSURBVOqVJ2HXxl+t2cz6DG7LkX/m/pT1doFi/tFAubwG2D53u2/fZwGnmmt+Ttq9NG//ZKPcbH22\nmmnbvO9AMLPJNvtaJ5Tt3bX6kIS3prFDJuzQhg9D15cFUIT2ygywOlZ7cyeAlIWODQxku8RH7Wlb\n7UlB0VZoth5rW0dDMqH6Ws9vSfpi4CiqkjoK20+KqL1lS9jZelZK8C3nLLp3vVdRsLpk3J4W0rYv\npTIcbMO2PalY2Nzvftc96uPHnf61rg5oRXlugCsNDwWDmQtMAq1va5xTsioKHEfFZkTvNTPpy03W\nufgULsPfcCiupdI2smKdasreqId6atMabrO3ZTfYqR/MVW/tdoesq6UHj8JWgZbByMQ0wyGAlmBD\nHYauNlL9aXrFNpZY+z/kuWFQGm6H79TZLbexYko00USQvdcyycPdwzwYMO43Spipj9esAy7KTrCN\noohN1qQUIIPV/aw6QDT02DS3HFubnqs9YFijAoyusQB4VgasJOCVnC46BKFFRTPIam65xaTJG2+g\nxAymtqy1Wx5vq0Ia4ojnTDl/1rjwkII/yRD1w4HkVqKNDvXqxo0C0LNSFqrSQrvqyZfP8lfT57bT\nH9a9mjFVuh8LHBcH1oCbVWQHU8NcyoBXyPJZbfsVjLUUrb44G2zqhKfvJj7G4tXabn2TrXOhPXB/\ncNSwvqLGGv8NfKTGMykYF3+o7t8iAfcQxn1kP7/qE1T/HNej3+Pp03pb89WOQS8rlnbAygp5aU0I\nmnVmgZcxWJO+xt22IalARHz86mfXK4irW25M+dvr9unY/KGn/HhXg03DOmCg9optC3GNRQNfad3+\nvN1uvO3xX7l2oPx7rlpjxQLfASsF/AKHJbAb7b72d1vi+36miq1As7tkMdyfeetTmYb08Ru3vrkM\npRgKtNwbtv/VwPG2Ig1rptq3ovup55tS7T/6ffT3s+9hW7NXC2rN1+9djGVr5731dYjXAKhIwW20\nrP/XoX0lv6pXlaW7pE8RRPYs8fen3tuxfu/oEv7LusO11tqmk77kZZXqS5Q/FKi7y7vi9FooH41A\n45W0kE4H7FSxh4yuwortHvvPnJBLvo4ia/8v2b4Ota7nU58qlgS4eVLrkdntlftf26ewUhYfrIGm\nraJQvV21uq3+V1JDFd7sYGpVodX2rDotqWV4UW0UwRLVHkh0gLgVJc8pC7NSqG3ldghk2Z6YS464\nqA1DxW32zC1339F63TVAFa1RRsAOmC0ZnBCtSxjDGLu0/FRroVSWn9ShouhM/OlgPJnzmX7Js0Cl\n6laxGliH7Jq/rc2Vum0UIDQe1Exr1/T8iuXFti5almHDCX9iAaAfDiQDgHV+JMNI2mKmZxt1sA+u\nTi51uigr8sd8oGsAC4BC7hnehrWmbN+YFsjpsmTyxDSwBTLkDvKkO6cWrha4K4R35lJbZU0EBKK3\nptZe9U0xLNcIDUpVhKv+eX0H9/tmkg97qSqXywc2I0TUL5BRteoVTtYbw9siX8/owDUjJDjMcUmg\nDCz9XcEU+ylzc5R+mJc5k9wujX9xWKtaXgVQhsuNjQOzOglyJA1giUqsDRebdXqXfQWMb9Z9bAeQ\nmtRac759/5Nr5P140Kp2DhU/0Xs5FddQDLAUH9Y/ehW+wFXA011Wh1rA8mCUO3QVmSnhn21JLvrT\nPr53aPZR4cZmPb61Z86WBZVKstuCfavtQrbkBpYpaAka7IA5ZaneDKT8OQOwiTrSmjOHTkfDo9Qt\n13I0POfjpwR4nIE55OSvAJ8cGD4R4QvUy3LHHon4KG1hSBy5smzwX2j8mf4JDno3ND4hRVcg4pD8\nCXsCNlqemiLGEY/7OBTY8d2jVJlpYpsrzX0pYQQUCr+AcsEmWxC/FObUzipgZSrm05IycXv+1aBL\nVAzJ5bkBwnoqoDiWKKMG28Kltq3A3aT2Ja+sUBMgWTEt3BCDmVtqp/w0ZPXzd8wrcM3AmYnz+8S0\nRHyfSHuQYujAu9zza91ibR1jUQdgYsjzON1JoZAr/3ePaG7sMMPDDvhIXFcBeM7IQ3NYf5uwQQBw\n8YPNE+ncAR6G60zMM1Vgi7o+jVmYCtGmaE5HXJh24HDu10OywR8LBzDIvXSsaFgTtGpS7GEMk07j\nfa6YCAS+48LwIkY55iTXN4z5mbVDuhCYIZGqyNqGhhiA82Bi6oMp+wdzJ28+OAnWcXHU6oryYB6O\nN6XVKG7zYcBzqohb0gvQlfomKYR1oMnJLDiRwPPiBxMg1SSMVursOqgI0b08DYeQggk/cH+yngKL\neVWR7IS/ATMmaRNFn0UiBymfBmUCgRIhiGp3+ZL9409Qsj8cSC4Q0yKphPPrB20TzC+v1R+70l38\n5GU1qvuSLqB0cXVHEYAK7JT+5CJ9sWoDqFiPlux8VQs0X15f7d10wjYIWCfTVgdoQFtf+3D9yhu3\nR2R9NG/vL/vSctIa2J/szy9rUn3e4DtRZbtzvtx3jdjexPrd1desHJJKx0OKsp5aBwMA8TxVPpYC\n6hhU/s/Yn5JYSBcNdnus9p/7nx+x1+fz9PK9/e1WvImO0AVSHrvoMS1MuTIlfK2r530b9hrD1wX5\neiBZGPf3dfzXBj0bjN9ffnmtQKDKndfObeuI3PLFu/+1lH2fNmn7UAM8a5i8Du69M9YNFlxcMiaE\nmFNgA1rfyGWZX4/epRj6bks1LtLFbcxsHUj2ziQ2uoy+n7a+85nXqT/XPXrdBwuJNhvjdRyDB+7K\nsPDytW3G6jiw99Nu/d17Wtv8RPGUdz2yHZpxX6WZA53BJzU79aG98b9HFnyFy7b4gNH8z2IU8PXD\nFKPitAyGGTxSJbkTmIF8e0faABwEeS87Z9c7bwfwfPL3SILIhyuWxQzv4+CYm+MBcVFtrcOyelsa\nyrNchS/YJ9d250H8RjhKFJsPPopysDdyeSXMHdMdaYxvcHdMw+1Z7JPLIloHTK2rQHFGyT2uE6HS\njf6CwChLWQJFysqoOKSeJJhxfnLIgq73czrCr6YN8dwrakeIj6w9UWlh33p2aiAVr+FVojyV1o4p\n6ooW0xZ/gVMGyS5Yc6rBkcCVc3ne02UAAEbIom4rX3H5aGp/1gF2zYbGdYFBUcVCa0EyclfaRY/d\n5NXuZX8hp/1R1w8HkgEgtowG6ZOm9YuTltEUWW7qeeHI0ZaJhOFCYMSBLR1BC+JHclOWtdS1CY/Q\n7GtsGSBmCA/m3QPEkdHk5mCtcC27UEnUA9aEe4OzYiB4qh3jYPYFG8h5AcoAMLI41wOXTcSYeHbm\nDPIgR1mfT+B603OTC3oykFsZAWSRMmNU8mRgAltjeGRykbtjWXYAHj9Xoc82q2nhngYcwQyv5rTo\nWCauM9oFFFGUDG1MAAmnAMXExGQteUWmQ4EHQOD94TgxUdbsoc36DqaiKXqFx5DgTHGcFFQopD6Q\nuFQL3DPwMCygb6uHlwS1x6SQb4GsykzhEiJsK9dcdpRyGNfhyMnxHA+4hYZ0FS3xQShcB7qHcf6n\nTVkueDo+bCgi/2td5eYa8slEKZKo8uESy0Xd8Vw8WJR+MgwfeKrGOoWcI+WOvFTr3d1RJejdV+AR\nAKiIOS4vdpvJTcz1xSwzfhOeSD7HkXKhKlcnAJlaVyuVl46OoZOyybTnQABx6LkF4yyHMtgsYc8x\nurZiItzjXGWJHE63pxlSwYCIiWkDrN6XMIvl6VfZVlTVMrkkZwzY40IXQ9Fe6ewfFdlekezzQIA8\nf7rVlWM0Akcmx9cA2GBwm4O5iBOwdFygdZz0NOVHBYOtHMa8ODO0z3yBMgtccar5Bgt5ApIHSjdG\n1qfRysg5W5bttv57wLfXZe7DT2De1jQNkXz2zqoUUrxApgqKzxPpATeWS4c5whx+TcxByHAF3y8v\n5Fe7Mo1ZQjSlcdAz4ccDdqX2rmG8MQPPmw/MYC2BAPPTeyT8Afz1+Q0Ix9vbgTMTMQOwA49JDT7c\n8fNgqq7n84K54zTK3jcFtY0H/bPTBh6WfcA0c5zP74CC6p4ROPLAFU+8M0IMMw24yHMeNnE5M5sM\nDGBOXMEqfRGGMQar1wFIXAwwG9T/lsBxcJt7/hnMv7O8tgHTLgGxA35ABg3l3wVgc2Kylh1gieFa\nN5hIlUenPuDePB4sbhBBw8iUZT2Zdw2PBzMEXdeFYQO/4MIIb4qBGeBvoUD50fEGrNJLfXgMyeNI\nWHIvjwO4zmB5cHM85Q3+nQeu44HhA+9mmHHimokZhnl+x4iBww7kAUyfLP2usuNpzKncfO5nqFKh\noXJPDfCgFakUbwe5xniWbAXzpatUY+aExbEs8BYIS7xF4sljHR7G7TvBNKIzlUI36JXOBKMIE8q1\nb3j4gfsp54+7fkiQXK41IBQoQ1cEi1flB2NR1f8mCKKieupEU6eUx3b0392eZX+YGNitMxWxbVGn\nEgFZlWEebozwRAFWKf65Wz8Mt4g7bRomSgLv30ys1TpDytWivgM46UQAjgG/xE2qk+W1n5Ba9eoJ\ni89cOSsS6DzDZVHxasAt6G3x/4YVPYBtHTYwMglKdBOehvnziUPUFJU2RSUMNxQ5xbq1G12lR2r9\nju1zdekogOawoXS/mFZJoXGtqfn0CphK1ELuMt6jqdZ1OZ/ApOxWwbTINOV83dpiudxmlUUk17hz\nBLJPwmX8/orZLdYaKTuGDgUCcisYsawXBKZlrYq2o9Jqs3PsuOtWCintoBv3rm0dm0CoJ4bWXq24\nSrlWIKxav091ryWl5Ntgch9AO+YgDWkE03WQTh3U+xBla56LMuVwdLxdsl0uV9QzZtOo9EiNxVxr\nKwkgKKLiZYFzoTWNrAZRdIo6Urzup4kngbERTQ71y3HA4QgVG5mYsDQ8jvfb9qh8p5zJ9dywlTf3\ns7zvbqOpJwvxqs+ypN1au4UYsLdaV5GIprtwXDKBOa7bSim5EknSS3WiLNkVQOXbU+nSXc+lRe0r\n7lVeTD1Gq2Zbl2DAd0Zv2aCV96cx8HBDpuGMwDXlcRlviGE448nsRxGY84LnQIZSf1qteepKN8Pb\n797wkwWmQBYULHYch1qgAEBZNo+YuMAgyzcDhieQoeIcYMpTA44H1/t5BlN1Dh6ih1EHRTjmuXiN\nZ14IAx5jlDWMGOHxBh+GIx3XdBXvAObFA9P0CkYoP4YIBKn0rfJKUZcRL4SCyqgHOX5HDjxDuilC\nelK3lkElQQpWlIFOVpaU9TlkHu1qf+pFAvh+Bd5BSg0zwVJHP2Xxdx1eDuO6P+PEfJKxPMwxVKjo\nsImsoETlS04kbFD/lTy8gqfQAPDtiuYTV2wB9eSlTGWBYU4d+tPAvJi7OTJxnjSSjHHg9BMX6FEv\nsHPmkiVcx5QWs4Os0HQTS2ENK49Iwmz+KRj5xwTJ5WaoSacp3m6BYw0rswAT9MkSb7Q+1fWyDFGk\nhQK5LNeqE13FXJaGaiJrSklBXGUs90hbj0tnrT7QtjLAHKZ1riyxkFvbVi+G9bbC3sMQMN6L4GCi\nrW53ZV+R5gUW6K7Y85sWGBEGbACyQH5Zxi6ESYlauZOljG2BjOxVSEiz1LJv972qNygn0a4KbXu3\ngPP+es3nK4GjAMzYggriNvd33GvYqDQFoFKn4x06bX7oFI/bt++m1Z3sTiHIxC37QIGspMV1d/0s\nWsrXuu4ttgbKcsRgd4gDgKlcebkaucqiQdWmsjuAptbn7TB0O/zUvfPTueZ0JBqZ9VyvljUg7/Wu\n+d2pBZJBS/TUHtkPxtD+QB9iYevRAFf8rDWvBddZaHaAjB4m7u19c2ettSUH+81qVhZwl1V8G891\nrYNDrdtyifJAM+6j33g2b46oenZZ5azarv1UwUuvQLkOV0uO3+kRePlZc7V3IjM7lqHXiA4ks2Rf\niXAOQUtfWzfhm77LFdzG5AWdf9FDLS/hGgBLKgcYUOYOuGVn5clMPOPClZMZCIaMN1HpuRIjUrnL\nbeOBc+EXYHL31hmWBF5cJ3bTQ+XhfUPimw4swwyXsT21lkrM+uDnH3kgxlD1N5cXA/hGxAQE/35g\n8kDlZZ5i290P+DCMAEsma63PUEzMduBf2SEgby/vQ08aZU2BuM6cJSvvdTF9Yq3lKp6UWRz+3HBE\njZVWZesQ0U9y05IKUo+QkUZCrTJpcM+pMrGCRGgNdh5aytLrWgcwFpjxtbcMpr8X7bPkZCbzvA95\nE138EIdL3rGdrv9a4/WwSgYly3BX9pCHjKZXZU/R3K/pWJpz/xkVYxFF5ViH4t9y/Xgg+bUTL4rw\nV3u5y9J8rQRmqLrmFTKzgBX/OnAvDlETEPAGM2tZAXTHLgVcInQJzrw13TftRyXCds0Or6nWsgNu\nK1jhluLlZWxMYKGzCWwwWWFh29CU/fX3XS/K4MNTX9+/f6p+vqjW7ZmboNnau4OjPZr2hgtuT16q\n9K72s+eBJ9f9Lh9BcitnW9/Oz7u4PXYHXCYwvF6i2thCFBvIrFer7De/IID0BZXuZ2uJc3h3RpdD\nhVZ6qkKCQA12LlBVN/l8CqiM8lOTgPc60ad6zTS4ymxL8v5atbseX8Wn64nW79ckl4WkxsEXbcKW\nsk8JeMMrON17s/7tSO+25rVeSi7dV8pnTLtcVuRu95KI+0/ewVcwUq/ewG5TJSxYf+9yC1YHJLw8\nd9vD2iC3A4N1+A8KSGD7vK1mb22+73cADcDqSBYln+1zjUGnvkwCWSAdOvzux+/d8KB1UgD5623X\nvnKz9PVJbHgDvszEL9fFAXTDU3SU4RAVDghZXbEdRJhH+Q58AT6G3ktb1Kt80ah0FWPICIECojaU\npzdFxRy4BGBNgM8NOB5DBXcqiwR1zICqA2q+vHdjAFbFh0gxymSgfiaa4kc9bCqUld2f0j1NK6s0\neKXqQ+tHSDAEkr89ZwdOuhn8oKU0rot7qEBflhTD0lNLUaEK8xRvvqu/wvoeqbbDrNmkNe3lRTV7\nwOSjo3EjdcAnmaOMQQALfmW7UbW/d5Bar23PoZETkvsJgvmBOZ8qYS45qc9NLOyQoKmwvMuj28Jf\nHJD3dz27dVJVIC4hUAPyG68fDyQDyAiETJtMzi2QW57FbVK40CS4zTAxAUsMlGW4Bi5aCH6EsYnT\n5hr8UiiJPhUCVPSJspbMhVANKGtzyE3rJbDbb38q9zHbuZ8et/CCpUwdQJ4oxFY8yzETGJsVM6CK\nN7b1ZneCR8PwaDWfzSXF9h0YOvNCWU7KXc6qYVnGAYTaE9dTKXdkWZYlxpWKbSnUsp3l1sKOd0Vs\nY1DtqQVfr93n7trAz7KVpxvySlXc49FobOBpn+F+kC2ahFUxEsS247OJ8Dwl2yqHGRTIJ0gjuGdZ\nCURROeWKq9Kh6KqSBFW5pdX5SteFxANFH6m8tLkSAMgyW3uIIJkzWx6X1qDrr95STPJvt3XQoaG5\n1l3/zA6HFN2ilErt1QXESrjvh+DKjVrcxSkwXAntL+WAK+XTAULmTOBkNaflKdhFbAE4UhbKozMz\nWSUquc9uwLCDZ7SmrHbypkCxfV7tDlvprbSa0QE4GsN1hxoXX/u+bppXu865PxZIrDmpA0FnBhI4\n3elThrsleVEplv2YYGQjjtW8Vts1hFbT2T/LirnUOe+8ArHLGVhUkPAqOpO9NDJrX1sbKKri6sh1\n6Og2b4FGX+oyw/E4Os1Xja0jEBF4XiooU0IYB4502DCMQZBsEcCbwca7YmECGAYbjp+krUqmX5KD\n15zKRCG+uQBmnmSavh2KydDE/jKyU4qlM0HaAeAaBystZnlASVjwAorqV4FnC+CvI3Bl5UA+YBjI\nYGXLBLm7+f2EO/A0b+5uG1ucmSNmEkwiGNtT1KnLgrI9QpZLwxnauTp0nME19kxmWXAwq8fbJB/3\nlKGkjCUzQploOBEVo8Q9R9pD5NHfMem7wxzwVbyHWSJUiW/ToCHZM4I5kS/zpkd50JMwc1HcKlcz\nfCiDhChtzg1vBhxDKfPccDw4DhEThoEwWojDgcsS5kfXxGjmTwLhsym3mcD3INVsCOgHsIqL1ffM\nkE7jaMU3zCe/x4wnwGNssRy/4frxQHIuIEwXYxK4uczzG1lvt9yV3XRBwqOFID/D37f8Ffqe0t5s\nVA6gdJ0tigFK45SraW4lq4EOhPmVTq2WLdfnzkSulha4XaWwSxUdsHR4zopd6i9VnfjXq8HGJ6/3\n+0v3cOx7nNY3E9woB1awCp08/uG5637RwL56VOSSewvWM1omYwHk1zavsYoP7wieoOxg9aTP56S+\nVMBGQOGzQdsa1oZPgYARXEcyfby0dAHmukn9/85Cx21UvtKVPX/r59prq1+NhwtIyuJCULsOSJ/J\nsBo93+/7Rwm7Nar7etjHel9F+/prMLdZmcsiXMxrSPnV4aDS3O1ruEHa1rNEtrDOApz6Oey+WvvM\nxcYskFitNT1pt6wL+DC4dQUSvhK79vGeUMCqJmrt7MQKd402GphyqPWY2bonCijjviNaXr80wnKf\nm00Gf7IhOhCx31M7siSG5FO5Wm0ZSmz7V9SA9t5sD9t3ZsuSxG3synL1JS8DKQkyetTIHTPwLYOl\n0bModYZ5XXg4GlRDBorHGOiyOAHAsw02lZYYIJCKRFMJ1n7WiIZK20uRVeou1x6rWJpKK1qeGljC\n5aKPAGZcrQwZGOpwB6vq2SWKA0GyG/A8CT1bBkylYHOC5iUMAJg1OIvCJ73uEpfJ4hzJTA4CmyZD\nGg/DBSpVhAUE4CNigT8sXLBbZX3fSPrVB3kUJUOqslxtwqJyMrxPdIXC3KhjJICc+psPEF0cMyci\nHQGGbHpyvD1H241W8onsfcXDj7VXgQW+SnqDdC4E3o6jW0lvLg1Tb7xDdzhy3umJu7xI8HBm60AN\nGOWEbsEKkORh25+waX88kFyE26DqsYw+afhQBGgasw8kT7VVxpVBALSGTEwYDqV3C0xniUqPhOel\naGeRdSLgxVupfevQ6SzJy6mlcBEcmyvfaFZScZ0qU2W1C3w5rcCmk2MJW9cmM18W70jHGdwqwwaA\nB3rxpYTGMJZu1vZxmUaOBJ5pPKEnkEMWkD6RJfN/Jq0uMw6GpWYq/+GQxegJjFHHM0akI4ErEP4Q\npsy2mI9HlY1ebqVEKrVPrNOsNKldA+ZVVpapaugKCrlyuRXk9IIh8bROe66gGuDhcrwIEEDeBHs+\nGUmfnA/kAkIZ7RCGTbHPPejeM5clgl+eYchYp+ISxNdj6vTqLEUuZfkGwPYqibK4AaxbXxM/3g9m\nB5lno/fRwP4Lat3qW4MN/jhj9OHNY534K/K5rtEI6pCylicmWOnsssThrMHFqeE6C43dqr7F/eRB\naxH02SoF+zYGVm0ob5C7QOz9MjBYqALwCl6RP10+DbqfV7cNXSJa+5glju0GwiyZvcXgLCQQBLP0\niNG6Vd6xmIGJwAG2hYczNiwT9GRYVZtMZBAMPB5HB9GW8gYSI52Zaaz4+txZNkk/WNBYMiYrN7Gy\nAc0qR80MAm7rG4COqDnE8WeQjtnAGxy/XN9xqX9vODBSaXmC8RrIqVK4BOU8dIScDIZhCrxrZL6A\n1hOBkZvvStxQZjVhu5gdicG3Pk5cyg7pSQuVRgiOE5UP+jBmLYgTPBEDWKV1f+/x+4e9zIDfHQS2\njGdLPJxje6T3ed8HAcZ1nUD+zKwIOZGPRBzOimenijcNgawrEdOQXqXME8+zgv8ShzxoVL0qCa2s\nJOOgnp2TQPmcCTdHV3aMwDMvZCQOpXuaSJzzSV1tB759D4xheHujXrqehqnUcONwuD/oLcxgDYHr\nG2YYkAciHbCJt/fEtwAe16TenkDihJ0ADsq7nMCFCXNysCMAj4G4Et+uEz/hnbnNB3h4MAa4PRPw\na1IOGtcmOcMGOyf8ACau1ptz0ih20E3bh/cEMNMxQDxyTe7nwwwWUJVB7uJzBq4wmJ3woYp4MgoO\nN1w5xL+mdTwtmdbNBmI4YgZSOZbNjUGQxU6N5fmmJRmlRKGk1/j57R1/g9lloR/uGE5DY3nIrLCD\nJ4ZSpnqSSWCuHNEJfCMUaRxlACIc9pgYznLhKWB/yDNR5cnf3h2P8dsh748HkmFNaq+LbsKTpwLb\nbJGWmDjA8C9edYL1pKCvMpB0u2fdbVk3Svm9nNQAAaBm1/LTTJXW5xieXrYT400hvmrewuDbCXHr\nNvlgeoNCeoFGnWNZUGD7YoiPU23fjaGGRVcotV5ZJAayOYiA3MMGpJKcl/nEi89jshlsp2gUEDXc\n8uuXfR5bO6q/MbJPsK5NCQjZG3lhAEQ9KIS7dU6fu+YaZsH5TwZ8TYOOStt4h8BtcSz53bZFVSS1\n3Dgp4eF4EPQKxA9RLGau9pTFgUA9ZIiTMLmovMMHzikhFqHcnV9P6R4oP87O4wQeG+TfZ8bNPtyD\nbySONFGr6kDT0w1Ark8TuWYDZ7aMBtxHemfnAZeFpmzBuiMC87Zq1hp2bNuq99DKyZLAhzWnh5fv\nMAkkTAVIdqrBrJRjqcM41M+kAm8Zp4NiFTGpR3L/mbje6g/P++T1NR9PZ/XuMYD28CxFus/V/nPU\n9sfK/EAX6ob7N3FXNKxhHH9m95iIDGYEsoOgIcFD+1jBxKjtraeXfAoBJDfgKMslluUNuHuL1tjV\n3+uAFSmgfWXPqfXhAyAarsNDaYtsGg6AYsB92YtrLUXVzM55WyHUjuzxRQD29pP0EXhoOoP62I+1\nVlP7zQA/gtY7Pa0OawPKTBG1n7iX/HfaT666q3TP0WopKgf3qgHmyjRSnOqSKYkYQI7ENFIb5hnI\nAOY88Hh74DgM7gPfvgXO68IMxynleRjw9sa96TC8DfAwbLT+TqUlikuyqVCsymzHAKVJJC5zfEPg\nEEXP9NNDxWpMuZ4SsEh8v5LzMZ0eGkBGtawidbd/ANtg1xN/jYRN5iGONPyNsm6ZgLlboirbD7wp\n9zQnrI7J/hiYF3oz1eEIp8SNdCCz+Rh8oIOq81WmbmIqJA5/marKp/dChwyI2Dt5swAAIABJREFU\ndtZrUoaDx+H4dnJfjgfT8M6wBZY3rAEAVyaO+cAVgdMmjpE4bMAeWo+PBGzie54448JvvX5AkFwD\noGHYhHRd1q9bn7iamx3rA+QcZn9nOSC2+2DBZtufp/suTjFws5aVMmzFWML2RXrm6o1tt+ifWwsI\n1lfE7e5y2D99e8J6/M09WU28gE/v0m5I226ACmrSeG/5uLoS3SLw8W/tBNtvjgr5Wa83+LTcxqSo\nKhzL4hbVfbKFQiGl15/LctRu2NWEF2Vp23sFKvb76DNZg0oUUKVOYVAuTse06LVl2rQl+GGbMNMa\nqjKpSFC4hiknZC2ygNvRIOorXQVWm3t+W9fQe/vf+zv7VTxm3mmWOSs0T/WMzA7yKP5s7e36zDab\nG93gvioMuX3jk+uTvQQsJfXxQ9ue2GIVbgb2zdUXubkZb3cUREnc3svtH+WIuN2l9HpNC1hq/9b+\nqVV+T5+33XlTPPcuWvef28W6cNJtu9r2+Y36EFAgU6qaplBpcy+LQy55auDhed+vqaEr9+4+DnX4\n3wMa7/J99YqtqqwA3Lf1r9QGD+dkWd9G/1Wsrwd8yesM0hhDwIhjI+FV+6fltGGFstJbmlpbuwyv\nA0clNa3XL8k9ziOlgRnkwZOhB8C81I5pqoxnMOtAJMnX2vvohi9Dl2GoImtRHDKAA/QPuFp2IvBE\nUQf17eq3ARa0sEYTPNDZj3KW+jOulaSH21x9TAAXPWBWHoeibMkodMlrWQqiqm26jeZVG7J5ETvl\np/RoJIolQe/IJJUzpTQtoOwdr2gH/Xdii/XYxRfumIJxCLveXW0nZ3w9Z6WZRMuUiOwy3GwvO1NB\n7DWvLWvEDsja4FYHacq6Uc46SO5EAOGISHGdQznr1QwN3nld8EoL9huuHxQkr8H7CGnREtqx8l62\nKNzBlfUS0P+rBGKdfur+uyZreX2/KbTjWt2gXe03sdwa5aVhd6TQgr641dyiu1guwbArjOLerPso\n/zYtARvG+0NXAbp65D3L3dI4Vn2CLY7UOomwNPRmxkktyMS963W9lstYliBHboFv+WEA79ceZb9g\n0R0K1c9+/iJQ9SQHXEGWpvsWqPL+u0qYRqcswv0+uIOnfjuBsC1WvuYni69dfbkvma903YZTvyeA\na7PElqwDau18dp/a86Xw1j6snapAe85PL/aXtXab4rt1G7e99Ov9+LX3P5+fXX5smma/SsFuNJBW\nurlaVZLl9Rb2YZS3Tt5atckPycD88An+t5JLLu/Kdvd1J8dtMAzkebIU7ceusjBLSAkCVcb+sNpJ\nAN3kpe5Ncrr4kSuUr2XT+rUTe1o/j78vNvJno1IgZX2mPBQFkuuNPpz1P63XuU3t66B+sSsS+F4u\nxYmu5DZ9HbjutqFAdmElQxrloQeBZOimFjIfywNnetYpjjNttIEO2jysjTIEaUsGeJKaVanKyr3O\nD5QBDG0kMxiuIGGongtQfhwWyGD55zp/uVMXhcC+YYFf5s8wUSJqv9LrUTSnBs6JxRkz9c1rXEwl\nuHXlsq7ver1+TSe9s7wpobmIKhS0zeHMxIRjgPSnmQlWe+D9XV7pcjQlSGWb6eRxg/TOaLolOq1s\nbkVqy0uXDlErtD91SH91nu9bN315VTdf/LKzdW+sRdpuZKwlSG+Shqnman2NejbkJbJEhLWurj5k\nAnPefMl/9PVDgmQTcCRvUK7BMssLsJmU5COSgk0jV6fShGEkqzcZXFHLgQvkkrbS1mcr0X8BmZuy\n3M1jXko8eIqyUsYMYqjk5L0EtFsrz2EJ6pr0MbMRVpoBKvk5wzA2RVIM3eOmwe6C++AeFkdYPGar\nDaKxhdKoWDZVgiBbZR2CK6rmgH5TQ6bztCouMjlNjnTvwiYAN1gBm/2ZBZRsSiKXBdcM8EpsDzTf\npLI9FG+cA9T0i2HKgCqlGtlhkR8Act7aIZFaxVrkRiw2Zm7P2sFKz9k85fPhCroua79vZRPoh4aA\ndSw6T3oqP+jEGEpb5UOWgN9+yv3bvqYl+bIFRWrZb/zWDWrcs8FvF6151p9/aDlc2jQFHkMTzBRW\n9bwVCOdmPYe04tQ9tcf0XwXG7sJ7/4nar7ivX89dzG4C3Q4ufsvbe0N8wypWkZm4rgunkYrTQUBW\naa/4d8EFF/kvrlAaVO6fssrEpEXLSplrfzB/anG5i2ziAhElV5YC99WV26Gjwm13bZiy8O3ysvW9\nLQBgcFU3DcQw+NXaGiIKI9Mx7NL3XZUw+blUahiOiypt/soeKbl63+toMk1RvIaoYg84wtGFfEzt\nGn4UXO9Vwn6tm8sA/mWBcqaKPiRjWdoFL4BZNIyzjLiWuM6BzAngZNENP+Q5JW8UyqRmw3FZ4FFx\nPGawka2oWGmSI3xqLQ8xaOc8BZBVMdMNpuqpGQl7VkVOiFueCjIUM/0KHB4yPqYsvIbv14XMAzYZ\nIPcTEimPYK1WHu6IBU5zWJAbnQJggHJxx6ilSstxJM7vDn+kqtAyR/PTJgY5GOwbOI4cFR446sD4\npoCN8JOqBQLjkz+Pyv1stWMTZicG3jimZsLpnMfotHbabhdlpYiMPaclk57XWFkiAFyy6I8qhW0m\n8pG15zBAjx4GMA7CtPLitewwyfWQ4Nb9K9PYgUetSIJYUbjOK2GKPTu/J06RuC5t8BKxHfMygRkn\nAslS2JfjmyVsTI0VYOa45sCtbsEfef2YIBkbmEHN9y7+1jHnM1nVQqzvVcrxLkjxyd8vT+BM3OBe\nIcKqTrU/5aUl+fLzs+e+dKte21UY36owtrxxkutEdrNG2nq9AMrehBoP77+gQ0QC6X2iB7hBskJ1\nsbdL4GNLCis8g2XXXVdbfFJutLQu8w2ztqyt0VGrG13WW7Z9ZnOhFr/q5dk7bi26zeobblCe96qV\nV64rNGgJJBCVO5ajFwHkZkyvtdMn5e6XRsWDAmoNPQBxVD+zQv7gV27/7ZdvrzQuAnmGn96np7gA\n7g5gtQNsgSHv2Vwg+H7n9VedyfYWVnvm9r19B++i9H7f3F55WeFtSV470jb33rIeF9Xp9Y47EquH\nbx9ql+dqQmHXLu4B4LMc0qulK+hw/X23uO/fLi9Vt2xzi9ZVMobfd8FjR2U1ZaYNrZXUTlPJ2eI2\n1/0re0B3Pyn7qgjC6xbZR/tVxr2OgqQ3f7d7FENNnSvwcvdl1Tj8wQd8kcte/5D8Hgo2D8UFKNkZ\nqQfFr8dcMnBPeQkIFUGV2WhHMIgBnxCIoQEGScte+pJ9z2uSFqETSIHcrI1vlYWhcuDnxvxLBr0b\n8zwbQC6uAx4M0KauSoLGEHe36AICiR2nk7bto10T18/lAZ7NtSVgy9ABXuhq18XQXjUBcnoyTDQp\nl7GOGMPCmVnvULETW3vckDgs8E3yYBi9otNYhc9kWLgrQm9PryFh4o5EjrL5dHtp/eW9jcPRHldv\nIxc75gLJsLWXa5hoyS/BtFZelpGtTPs1nyCoLmpdFIXFk+Pa/VccbesE0oEqeQIt5MXj4Tds+j8/\nIJmnN462DXJ7PB2XAkAMwCPkNjDD8GxhfgWtMwyI55ltJh0TBw7MPLlYtMIZ/WyY14ljjOagViYI\nzoByAsNxBSuS53iwXWYMUgol9cZKhj0MiGG4APyEQ0FC/G/UmVIb2uQuyWSIoSVWBGlWFZvEU3XI\nO4WRRd/xzFqHBh88+w0DzueJSxbgt+MBc2vBBUu6RbT65gx4pE7xknIRMGP6J24CMvtptZ54i7Hm\nYohvhpBllZ/NGcgZeBxy10GqSPvmMZhzN66kRcec5VHtwIigeymL70Zrw9ngOPBmoZP5g5G+2nBD\n6yfBwGSe2A12sGzndcpeX8BF4zASyIPR9xkT6Q7zgXgInoUymgj9ZzoubUqHIcfByODnLzgOdOJ4\nQ2UdOHnfZP7Q5ld9sYtZko2Ry1lFBQxIx9iCKye0nQbzlbYnJZTDekL7CbKGrLXM//PnQ67NKTJl\n5aptKgMmhrEIvWmRG5wJc2BK2zjXPMM7A4YBogUAHhNT3iHTfU8EXcq46x5u1VNWYVUEM8hb847I\ni9aTpF3zciNv0WkptYP9M9AyVkeMAHAFFdnwcmLxiaFMKoysY2QpwTIzVxyVNSez+w1MHO7iolJ+\njAqQtQBsK8EUBDNXzuYRcx6Ya9QeD8Sci96me1hOzHLj4kIq6t4r80TdxwLptPZlPiT3yBjtpFxp\npAA4LWWXBfwieCJPEbDJA3eO6NzWABARnXFnKBMRLe2UwRdwDxKUb7p09gJ+AgKRCNFC4OUZ+Hqe\nH16JP3sbBKSR5LCEIX+amNMVKMnQRTdj3mRMzGDmgRyGcyZ+91Pg+2mAO2sbzAuHJyzf8PjdQevj\nBcQ8AQscMTGOB2YCzwjEnPBpCt4GZjjCDIcVmS7A9HDiT0+u26cxADAj6FpPhZXOC2kDOQbzDiuX\n2TkTb5bImTin8u0ewJwP5m5O5tc9MJomco61DtNZ4MQCyCMFInmvTMAtMNLxGA/YAcSc+N08MOcl\nA0Fx7TnUcyTs0gHaAGAgw/DuQ2nwCAbt0D58Az2rOpgZgAcOfPeEVy5nIy44zHFeBwxTVKdA5lNy\nyZGPN8RwWAJXnkibeDwuWBxADh08qFl9PFZaOTgzemk83oJexMuAnMwAAiSO48CZgcAFS+DIA/Y2\nSJuRQWkmD1IRF2aWRRs4LsraJwj0y6czRI2BMyXdDGbzOFVZ0Y4TuC64DdigUTPjwsCD+vX5hNvA\ncYxPKWJ/6PrhQDL1QJUqTZ72wIXG4s51VWzmsh6arWCA63nBHgNj6O9r4ozsEodlgy5gFFYZkwlY\nw8oNA1TgkIMp1QDguC6miTMWHLiMwQZvBbawQh0OAPeIn3X6dvRRGEBZZLI3ReirRWLnj3JsAIYD\ni99UGksC3ChY3Iwpc3TiXgVMbDWrTUapzVUbu8DHnkKrtj6k2udygnYFpgPAJRddWU6zs3GkhqG9\nuHXqDUNXytmEUm7DZEiceXUb6bgecirHJkz0HzEQflEZ0kcabB54WmX5vM8MADwtNM4Mb1hZR2RF\n2L5msoxP2bMM6KTzLHO86DJDbZu5b72P+aa/ynVeJ97fEtcl8KTUZT6WKxvYDG9VhUWCsXKXouiM\nue2OhAr4rHs0hunXVoQ1ZcGeEcbudIvMDThi+9a6cWiOSdnkeimZU5af1+v+0rYXwRROkZN5UQWq\n5iQ4HHUKTh7kr0yCldofKE6mYRmkrQH0q7Vzb0GnSlMfK1cDT3UbxNdncouyzRJ8ySyR9UbCmi3z\nSEd7YdpdT2U263CTKblIwJ3YntFyT2u/ZKwsYjCweIUOTW6ch/TBEdA6IQ+09vp9LhK1BnQgx/IU\n9fDoO5U7diIkRgWeiyZQVVTqtVrDX/AabvjdQysigNNoWJon1mGoTIJh+Nkm7DEQY8CGwQ7DCeDb\nM2AReFMmhXm8wR6Gt6TOYyEMYCilaBkRZhCwQkU3LvHUBni4PXWo/F2+QdPB2Czo3xyolC1uCZ8B\nRCDeDNNobLEJvAWY29cGaRom+mFe3B8ClbUGz0hRKyaQlacFxBCglyG06lmNrtZxwN4Iha34vEcC\n9o5DnqVLa8qR6iX/kbM7gZwsoZ4ls1QrwQA7B6aAQIjn+814kH87Drgxb9cv18QVF8aArOAUJKaD\nsB0Mdp46+LHanTElHybMQkY/UlyG6wwuAcnaEPQ4/I3236F9/V1r68otW4qKqSAS+Vh7k2lllYcZ\nCyRfos0cY+AJMPOIGQ6nTjnmxEwefq6ZGLKE//z2BrwRxJsNXCe513oMLjemhzsSw3/7nv3hQPKr\nACtBVOK0ikQvVuEShsDa3DW5IbBZ1hCXe68wWcm5KoVZEePk7drWELSlCzA8g2aycio+FMxQnKvs\nNldU7quqV69WR6VEdvYkFibV793P1arVmV6FBZSX0md/+F7RUXb+EJ9THdD3W/XwAztIWTB7oIJ/\ndsAd5opzpGoqde5Yz7/hCV8Hna0jDQbWKuB7E5U9w/qdGm/YAv97D6S36WLP1f99iOt6CKElAIRJ\n8UMpifbUguqX3GHbYBKT7MBDesdMyro0vU5BXxIolzWtwAR0CLW8jesKuND/Gyyt73bWBG0L6tiP\n48L37vkpCijfZGBFWhvaU1BbrnEOljcj0zqIq9ad4PWiX7w05vM52/a6BRisuMurbGBXnw5x8C29\n5UD1jR/L7e5FvbrTQm5dzzXagKGi96P0s3JltmUlX7/P11Ze1u1nT2y1rXaZqGjbWiDXk6+loojs\npT/7WNKdW/uk1hLfpI2oepS9RpArENte71ftzzXX2e1+uRL0TOQGkmuNvaznxaf6mtfyvCwDRs6E\nDcp/g3SdA4yfGPCkdy89YZHIM1H53ciLJXf2MOY3LqBHL2Ci0jtWekze3lTlFFqj2VSra0u9EUlw\n1zm/B+jtTNCbUG3VPO8efralqBqcW5ZjPpYOSHr0MhMY0aFFwvkAROvbvGAtz0F8YbJcm56700Fr\nx9bSb72TYNtyIqeyW1Q7ZcWdCjJidcAlW8wGiiQIsHDHMPBEgYG8ZZ7QvSp/e9DbAgPcZstPwDD8\nAJRbek8MUKtlpo4Hwhe9poBlGQ5bwZ8dVma9r5DbFtLPSqtg7qpIWHKk6GmiAZlifQyioBhsuIxV\nPOhErOwZxHXMUz3/hIPtHwTJZvYvA/ivAPy5uvyfZ+Z/amb/IoD/BsDfB/B/Afh3M/Ov9J3/GMB/\nAK7M/zAz//vf0ii6v/X8Uq5271wpiRbIEnqlVI4hrpNcvn64UpmkBOddSpru1TBs493tT15Vteqz\n1uughEItyk1Vfvit7rrU8NpM65WP12egrtvcv9wv237uPMi2wGW9kSuAEUIryF6k9nI/AHTh1IrX\nA0robt3siXxte39ku3kXHWtaxg5yiKRCx6V1pCgbfKFZqU21Jw0KMLOmwxy5x/bf++ZW4mUpWUqL\nWDypRHPZxi3dDlA+Aes+26b4Cypmf/Yr5kgG0ANWlbaa24f7XPec79RdLODyAQpLcPrL9+8gyLYx\nXO/vcDIBOVXKg7L2XT8/0YlP9vZyzS+Pya0ff9RF6kOldSwlabss2waqaQsvvVg9eZVZv36wKtlU\n91mf24vWbA+PNW/6WK/vW8S5PkDjw27L3lf0WuMjV2920Npgxgro8gMtm82QtgKKUjSPBYS265WQ\nfB+kltUE06u1n4hj9TE1HwuxlLx8HbaveEUC32Z2VouVISJ6fmGBo8u8OR4Hg57o2aG3biYtpJJ0\ncKOxyQ5aNiMDMwLnnJhJj297BSHDlBveXbZVmWEHDEjHNaP5ywTJ+m7GisExudzj/joMiDK8wGEq\nbQx5fCOS3gGIrpBBekRSB8a0lbIsKUPSoq2kJZzaKID7crI6GGpjkxCSLdMmghQjY+AtI94Hs21g\n08GZNNBsiFTnAcAPXPEddVweKr9cZ5dWrKp9EBDdqEzywHbK3iWpWqA85t0fCYUzWbiHBieet5n3\n2VA88U1iIM2XThfHPAHywV+UP4eWWTtUCgRnBi4DhgwIle/c9fulTDtuPGDNyXzVIRdg5aR+nkXP\n/G3XH2NJvgD8R5n5v5vZvwDgn5jZ/wDg3wfwP2Xmf2Jm/xjAPwbwj8zsXwXw7wH41wD8SwD+RzP7\nVzLzFY/8nqu0BoBKf2KBiQnV6EFW0npUqIiEufLPOlhlBil8JotyJcMvyVeZQ6MsIFmKjNLZogAY\nlYLrhPXzMXD6mtxL6DOMHGVAPMxWI60mbn2cuSgdhgXCQ7SRsqzVzriKxoRyH/IUOPJVe3Dc3MmR\ngioX+kHe9ZlbarLkgkWykl1xJ+v+lgWe+2gCE2NrIHBqU41CJEi4sdxOiifCcraB5ozwJkugXQr0\nkEW1AgIJSLM3h0moLbcJPz/UX1ojsxXb3Cydll45SJAWYBGhjzDDoNryWGFNQ4Sq6RRWBXxHzQtY\nenNV7eMz04scI+oKFxcc3iddirjfB3l+3KuyL9SBZrqp4Ic424UrpEyd5aUaUoWoP4/tUNVLOMFw\nANx3DYC2JL+OWeYWG5DR96+CFbWWXHIloFRLapNiQRoUsSTr3U2/A63+vZuRt5+VmmgPSIt0Fu7C\n2mcO8hHNF58+Ue5f8Xq33i4ry+fgvQKSqmGV2D+LyyHwUcq4SiTVW3XPUmj1yQI6MwM5KurOUPTK\nUYi3PCNqsKd4pj2RAj/t3udT3L2t4JGS8klC15HoqPcae5M8rpiKu3TVOolFs6mMPsUmqKs587Dl\n12eHex6XuCyl8jVRcmYiT1aLG5k4tE7iMDxVrMrM8P5OV/cDP3FvT/b9QmAgEccJn/K+BHDOibjI\nJrVT6zZTQXKUlyXzzYDjYXgMw7sKvL4dXJ9XMGjOYupAnWJXlA6HMgYJUA7ABvDuD1lRtceHt87P\nJ/vkY+BKw8yL5FgB+CsTMMcwBoSdl4t7DpwXWGPmWBQsN8Pb4XA3XBjwAvQOeoOmwXPiKV30BmAM\nxxMgosJJuZSKA9i8WNCPKghyWLQerHXpMMST1TO5T+nlNAsgFBAntFpV9jxlPdbeexzelKYQQEca\norMxXTAcqtDHSaOTWiksBXL74Au+V/q7iGHm1qC9vMtpiSMcaRVbUjK2KvXSA5eZeFrpYQaVWiTG\nZHlvpLNAiBEBuKoR+1H7nllUeMYI4INZ7A9ffxAkZ+Y/A/DP9Pv/a2b/FMDfA/DvAPi39bH/EsD/\nDOAf6fW/yMzvAP5PM/s/APxbAP7XP7ZRux21QBPApX+HmruKXMoiwdOZ+WjwFiHOzzjW90orgsI9\nN0Fbrr4+kKGEoz6x50yBvJdAW6TqPi2su+Xou+0Q+t6L1cldCXffbm2KNSr9iE19m1qu4y8T+ltz\nvW4sEAHIeVO862m7RbnhSeeiLuXIAQ3cnB2oc+qQduoZrj7uB7z8+Osr5cL2/MMi4hhAF1X5V+r7\n2rhsKVncExOXJU/En4z/AAMVy007JXAOJy+sqiiRBwXVlt9hm8bD0N6QXsemka5IW5Pk/4Igudzv\noch4gD8q6Un1qPdRYl9S60V7hbufPes+Qp9ZkrGtkwK566/8MNdVdKNOovv6DgQcuQFng20y4nav\nX2l8pCxXuSkKrYewldDMjWW4S2ksmhm/sPOU10opVvUn7fmklXcYqd9zeTx2wZW723i/su66j6bG\nLysF41r7pddL6u7AedmddwuvLVHS3qHU4QdYJb0KfbPxHz0Eu9RawN/U2X2+6v3WLRVEmNv6el1m\nX2+r9pUJPDMQ6TQySfw8CDN6JIcbxnCMNLrRkZgZuJC4kKzOGugD1vegpRWXrUqZxiDsRIEU3tuM\nIMfdOpf88EEwiGR6tjbMyFaqPdDW1n4GLdvDD1xY1mpP6bPdSadGlUbIpNVxAnCVmvMKoC/DV8jy\nvG0GM4MroG+aAZet2CEY6QZImfS4bJlBonbuJovSYTngvu0qgw5oiQMqfb19JxO0mFUJOgxxvifc\nHljmbr5fBwq3bFUz3JlBJK6mMZVnjSI5mXrPbHmTjN6hZ3LLH+DPOoT3EAN9wKQXyNbhk0h7O6iW\nUYD3P3LimbtBg4f4GALmeiYU1Fu2wWr3cEO6MebTWB3bHFjJHX/bZb8l7ZSZ/X0A/wuAfx3A/52Z\nf0evG4C/ysy/Y2b/GYD/LTP/a733XwD47zLzv3251z8E8A8B4M///M//jb/4i78AAPzVX/0lvn//\n5Y6i2NIFMrau7mrgo0rYF7Ve23iK9bHPVEkDaU3g+/vP+P7929aDG7p8udbd/rCbdm/nKwzYX00p\njgVG3t9/xlNtWn1Y96j9uvOACox8BBf6Tiuo7UCyc4l+Ty8ANu39/Wd8e/7y0qtq00cHeVmCdltq\n9+XlsbdRt5c3bPtZvRQI++mn3/W6+rU1cr/lvaWr9XH/ZEmETyxZt/vaJ73PWle/9Et/9+/+PQDA\nP/gH/+CfZOa/iR/o+mzP/uVf/j9wZyT3+hzwEbbxtT4W3odh+876ZP32eVvWt/eRbz6zD8StBOma\n61/DO/cn7ofmtec+tujX1rXumfu+BMZ4YM7z1qpq9+rN75Mp90VvPVLZz9ub9ntbp5eO4w3X9f3/\n5+5tWrXdtrSwa4x5P+vde1cljaBFjCkhVtmRGJGoCOkI+Q8SEUkjzXTSTl9Iy2YaQlpSIkICyW9I\np0QSRUoJpioaEDGKBFNnn73Wfc8x0riuMea8n7Xec84+Hd/Xec6711rPc3/Mz/F5jTE+eG9+cEQW\nIOyjHr2nFZKU32kRGzSjR7DoFvv09q6z91O2vnvnUdif/hHt+sxBvY8p13yKML48PuHtfO2e/tqv\n/ft975d2Zj/HY//Vv/pX+OkP30tIeb+K9QnjLLb9VXyh+FCuNSk6y/eis0QUt9mnu/aPW+FIgcen\nb/D69j0AdK0DQhmyhd56dgf76okdiC9el9ueTWQHBMPUt1hbcod/lNDWWyKB7777Dj/5/idoK9VG\nByp125o9vpsBcdk5hfv7jVWkfnk6Kbff3b3viy1F6K98+6v4yU+/72fdufkzFft8W5lmsqT7Ngrx\nXmuYayuvzVezPdy1Ft99+x1+8tOfrHf3EuW2HncaVXtkn4f8gO+vUb43pnCN7YnG1Fz9Ss9VtV//\nw78O4Bc/r79w4J6Z/SqA/xHAf5OZ/3ovoZuZaTeg3c9vmfnXAPw1APjTf/pP55//838eAPA3/+Zv\n4R//3u/0MlWKLcSyUuwoQbon+cw6DGSSBk8D9V4yTbdg7XVbE2o6JT6MUbfCm5q0VQxO0q//R38c\n//Qf/w5OozL0jQ9UadlIOU4zceRj29Apw4cBteG1AaKIzbyw6tC1WIrhjrByEgNWObECYER34o/+\n5p/A//V7/wCAw3MLuDNDJz5MQ8yrLTHDDpg501JtVtRRwnMytQvzklLTBBJxKUF5QwawUZS1FyIu\n/MZv/An87u/9fVlp9Q4lQ484YUE3D2BM5TMSMR2HrVLWTNVF2IMjO6C3bCVPAAAgAElEQVShiXVa\nBwS0mdIAzED2pqEFCgj85h/7U/jd3/376/A1LblkLzTQIs2H+mFAXsKLpd5hQDKIpSA7EZzXGXL7\naTqY3i0R8E7Zdws2SwZR/OZv/if4R//o7zHlHQx/8S/+pc8dmX/j7aMz+9d/67/Hd9/+Qfz+9/8C\nSGtGM3woPaFKeHcMTjHXIoTKRJET8EHIijEaueACO6sp0u929y2sGAVafT59++/h+5/8S9RCr7C5\neg69TPQprO1cz3PzDhxiTtiyTAcOfqJgXw644FyLrmiNLRkwItjFv/Pv/kH8v//6n8PBmlwtXBTc\nIyszB7A2KXB1qjZF4ysg9oB3tHphIOnk2KylBqTAX/CxOJWsOn/gD/wR/Mt/+X/jfo757ilYlmls\ntb5TuECrFVSmH4djzinaTGtcROLlGJuwQ2wkn284XMVQisJn4td+7Y/iX/yLfwKOsncOrqwMJrbe\nLUGpC4Vt1qZLVcwM9ZNCWYUqrT3Dc87ncOyufdqs2RJ/+D/4DfzTf/a7PVd/4S/8F/hS2+d47G/9\njb+Of/h//O+oSWL6PccYstglMbrHGDjGwMMgyZJZME5hQ2PSXziDZZENtBB/csc5ZLmE4XgZeAzD\nuA7Az+ZRpkxIJ4Df+CP/MX7v//y7OM3hYTgi8f2ILr1MV3uhKRI2DmQyc4wPwii+Gwd+OE9as02B\n1Jl4GYazUkw68PaauE5gurDIyf0MWZ6PVLarCPzZP/ln8dt/77cx3JnBRx5kM+DbbxyPA3iMF8QM\nzJm4IvD9OVXMawgOqrgeMbE3WaYhGjGG0k56Ce8Ot4FvHkxvO4O43JmBCOA//eN/Fn/nd/42rgD8\nkvxhoTGw9HUIb+1S7sKAF9s8egY8xJO/n4GHFCJPICdlkzczjEOZPcC4GzgLaM1TAZaOLuD0Z/7U\nn8H/9ju/jZJ0qsJh2BTdKKiW4GdIvBR9L9gWgPP1lEdB7XDxzuLRwB7CGzjxchzEZGuvMu924s/9\nyT+Hv/P3/jbpixN+85f/0l/+UefoFxKSzewBCsi/lZn/kz7+52b2hzLzn5nZHwLw/+jzfwrg17fb\n/0N99gs3LqI0wyqV2erhXY8oolv66tKO7hoeH1YSlX3ofmuv96bI7FoOn7IFXclsZUZIRxZGSgQ1\ngS4tueONWrgkJ9l0o2L6u3VoGxvQ3sb7+Eo13qdIrKXA7uvKZhZ7M/UxMRB+UUiGA5GYmEoFpzFv\n68PiIEsBKLfwxp9vfwcSx57mpvFNuspq1Fzc3RXLV+ivzA/hRXXfGleN7n1bzjwevrxdF+vdZkz3\nB2OFPSkQQOFcA5YPdW/NkSX3b/dXPUmQgM5tlQ1+szx8La0CPakLajaN7LEEEEBlXXu82wETFCaT\nEfPwzfWm7/dV3EMe93NZosxSK3H7r1SZ/luIfeLr9HfFfwUqNqCEqux3JFZucwZ2eQehbVojas+Z\niYn00hpzLZOToQTk3qOyWN0gVNvTsf1c4+dzbla//arMTqW38PJP3UXJ98IdNtTI2jJY+PxSBN8d\nquK+/Xjr5+47u1SPtrY1g7Se59Wftfq1z/Y31KliENryElY11N6XHZlpitaPfhJsjb1oLWnZPePR\nPuB2q3+lLWt/yyJoSKX3C1azk1J3RuJyYKBidRbsgfMjmq/gMHfD4QNw8iQ3w8sYOA4KfqnUncCg\n0IiA5dC7lF7NAlMbNUIFN4ZJeEuMYYBzTV0V7swGKgMJAL1Be8ZdqdhAFga6+kcspc/NVG2WF6Xh\nlsKy7i05ok6sIZF5NSQjgvExVntQ741UjIQpTqOFQOUwd2M+aUggtMDbTLgKt9CwNzrIo4TIFRBI\nurQYaMEZDCZeVT97GKBw+xKJ45BAbUy9VtjhOu+W6BzjXX9PchP3Qo1mHZVMKjWX7TFNaGVkHFRE\nq/jHrKIf703NtM1ZCTcrzssADFUmTBDecvW7RTnq+Bbu8Ue2XyS7hQH4HwD8w8z8q9tX/wuA/xLA\nf6ef//P2+d8ws78KBu79MQB/+8d0qkvObgfSl/R3a8XgdCegrAcJHliyRs7yxpc1OAm2NYFYn9fF\ntDVxY10boQ4sImv9VuwURA/YFuYDxuLu614ssThydz6KYIB4m+X6QB+aouOVAq/dYvtgdV0iceyM\nyyCNkMKabEcknHMKm7mF9hjQNSF/xq77iI8WVSp21AcaLVb0AUiQ1u6QshbCd+vxOjtrEtETsrjt\nBy0lHi2yJ+TX7A3APsmCkHNSh9X8lUfAlfliSSFAW0F7EmJLvWRrj8AUIPX1Md3au6UE3vIIV9Bm\nFb5AuU8XM/Pt2pTlhwF+/HhZ39eDOe/r/K09VmE1euRt933E8fK2Ze7yo1IQabu3Mi73MKzSGi4h\nnDvo/Roa7vOy20BbeaqzVDJz8/S8PWff6rfzpT9WpL1JEFxntoLuPt+s+1LBc4vrfebyfQJ3klf9\nsP2dq+dNldwY3CrFChk3uPHzCto2JxKxAFAggu1kPPsZ2J4T29dpW6c/oFa9nl7XL4r8NQvI9BLW\n1guUBZ/BYRMZiZhgyIQHcjhebKVJHZI6SpCsdTqMGF0fjod4kAP4JE9aHsD5JgsjgPGg3DcuiuCv\nCcUABCaUmx6OMmJVc6dV1pIBocNGe0cXTLDNVcg9rmk/bwlUujnL0bwxEp3OfRc7TPur4wME7Tjn\niZgUsjNUj3XbSpm0zl969lF8b10B5Oa1EW07Y8IUJJk+lEpRNC82YltsTk8LAPQ2yytnvrKDlYJv\nZVVOHEXFTXzJiOkdeTX/oleUnvaHloVrofc1vdpIQrP38joVTdC9WLR1l1VsbU6OrTXjFZG1KuVC\n40SPy1Di851v7Gv5Y9ovYkn+zwD8ZQB/38z+rj77b0Hh+G+Z2X8F4J8A+AsAkJm/Y2Z/C8A/AOM4\n/+sfldkiiwRpNHMzx3/QqLklYKws5XKBhk9pTox6nNJCteJaSFOEkYpc1N41eiWLGS8nby+z3PD6\nvO5LMPCm109anGFFN7zj1UsgBbb66iAMY1lXRADscXtIj2kMadSal2tipbzizqnDmv3esp8u4oBx\nwSYTfb/lCbcBF0RjA1Ohwowtg8Sp9qQimXfWs/87XLkgWaIHI2iJhmcHewFlzaMbL3FnugBYrawg\np7sZ0IaEbjHNMVqg5VwWg005vBfEo4qx0pqyiJKZKUAhYQd3RUcZm3eOmeAEc/8I8jFg8GCGkEzI\nIg1EnrdYxYnrveXiK2jZuX0Aek9cDPRNblxuPI6/4Ee5hMMs3N1AZq0II8phwGOUPejZVvt8mCT0\nFlXvb8tJNxB46WuLkV4ZDXYq2AXrLG4CZrpiThIvcKQxIp47pehCbvtoCYFR6Ym4xXUGcMs2kRK+\nazghhlQ5Zut7jmPNwc6Qtk2Oyheac11RJYTjemLQLQCUIFKWZH4+bF1Y7AlYSlH3Kfe5qL5qBWyn\ncLWWJdymfDbe0JUomvZOiF1runwKHPQ0Y8ljK2qpZE+qRggUvjib6fecYdGVKYXdjdADWPmUVg9q\nrj5SiL6GNszx3fGCMy5c0ZyFFeFyap4GmqjOlKuaynwI13gM5Y93wMLwYgcON6Q5jlHuf8LKXjOB\nuJiGa04YEp/8BZ4v+KR99I1DgeN87dsZ+PQwKVqku2MkYhpmXLBU4LVNpCemGealXejAHOK9lyrW\nZnamExsJOwbOGcgQfOfBUDu8qhhKse4SQv0ATHw1E+dU/dfaSk6ehYtHrLJmpOgSBdYExkPZMNDZ\nIIDE9AdhZjLshLxxgaQlfzvwPiCLq7xcJiUmK4j6gjsIYfQBWGDOwBD9TSSuYFDxZYaYyXTNYDVb\nkQJBWsDKf2UhH7mq8Tpp5RSO/JybLDrEz2cumUgKGgzIa9FyA/AiiN00J3wt+U2nR80TUof5qJLF\nhsMPFj9h8CNp6k9/KnripUTkzeD2i7ZfJLvF/1pj/qD955+5568A+Cu/RH8A3C0I7b0eP+OGd60o\nuMh1M4X8+FLYzboF1KSSmBb09QCt0rWRCgNIbYkOXQ/v1CN8jh60Jwzd2hJktw2PnY3UEdK3+SQe\nlMbV+eKwqXD7GNGCMiTL+f6dZP6ZF5COGYFpAXdifcoq2GkxZOpndUIJ2LlZ1D/TOt9iQVWelmJ3\nsRLPtD7ff55LIlmcOnGLQJbE0GvM/zJrQW7/uzPgurXLwHBcScvGddjKk8yJByBwwU0pEfSnxigG\nzTEZMbj9xhR+9+trO4wHWMtBlL5v81RWgGdSUuJqRVAvS3Od+8R7eND7zR0okvqBDAgvmFXfGf17\n0Zvsa58F0IWRN+PZoTVp7Z6jf7v3a5fF2rlkRcY1J4memxIhqwe1U59rMtYVHahYb7X6uwRD0sFC\n8d6p3MfNik48jWh/9/PxQ/VlI7c1oqJlz+9PTVD2+AHSl+jvn9chn3pUHkbCQlJZhkq0IMa7hAlT\nH+kd/Hjg2V6t+9if52Af91fXDMT/JyusHckPowR/MylV1OwymHoLsLYapgGf9KyFwLPGjrpRcclg\nqfU3S/gZWtqksDkTmY7v/QKMRSIuk5s8HRGzHZYVzOdGS/RkYmQ67ywEWXhBRMDN+2wC7L+VN2BT\nwI5hOMX3DMaYEgPydYf1LPZSVXibRiVLYj9AqAfEB6cE8onyQgsWUILyoESRqqoy68xG4eZXPp0E\nYIPCr0N9NcCHI65lZtnP5GFgtiVdW1Z/ZyFyoOMrqAAeOFltMItKc9LTDZX+FmCeYoMhSpESsy58\ncf3eLNnr93wiKlQyBwsXUHYIphh0QMpK8U1TAi27ZQ7bV8aVNSNFmwmqW8W9ZM/7cSLk1r64invc\nTd6afWRqciY8D9Zdd+CKExEKJLABl9U1jNaoxxjIOTeBUWia4YwuVwJWB2B26LBbc5mQRhSO3sSX\nAZbMh2gjkf4gjicT51QN+Vhg9t3Tn34AwUQuMxIIlxYsZit1tAsPhGNOlUm1YACdH6z4Fmu/RS4q\nYhlLJqwTFaFfVyqqmcC3AWAk0pU+R7TjgRdMMEDBwXKeZ1yIySp0www2BvscUwnXy02qHLUG+HRk\nydYGVF1duybdY0lCEYOauQ3Z78riGqCVLS+8IZUGxjCUwg+g+65p3qxzGIDT1WRpiDfl6szcGL8j\ncwJp+MYG3pqp8oIHHNPFEnTQptnK0D1EDNPgwQDR63CWR4VKUMtfd9mFlzBUKsNzBiaAT8dQ8BKJ\n4sDRe/5raiYG68b1yyxLy6GfpGDMpzwwx4mHzsqVPFPcCQokNc7Hw2hZjCjtn+uwnAeFs+PnjVfT\nfgcqZy9JnHsJjRSnpyTWo9YLxZYCJ1S+XgovMllYwRwXgmn/YMoNbBKCDzHh6qlE3WfKzGOMeRlg\nAzYq+l6Kps7v0NwW9OHTOHCFaIMT1595EONvS5gM5TivfesqtsDCDhOHDVwlcGzWbUuaAOqccj0N\nbzY7qBdgFbTQfNDKvESJtj6PcpsBl2rDBqUZuvpBiEVm0q071tmNC8RsJw0RVS7bJvv6ZhMvOGRp\nv8AstQOwA3OyXHI6hayRCeRsLLU2CufKTJAz9vNM0sdjHMj5RlyuBJrDmLGgS9ibCaSw+4K+nsbc\nxa8UIsaQl5DGAzsNFsmgxbzAeNqBGcHUa+Z4yCI6g1jfmSk8sXb+YBCbpWPmwA/zeyRO5PyGzwzB\noo6JMV4xnH36/fMHRCQedsDN8d3LA6/X9/h2fIPvHp/wwwSuk5mBH3FQmbJAagxvZwXWSW1K4XLH\nXFXynJUBYWBJbQwaefwCcGDEA/kpMFjcGgbDY7Ay33UxAM1LKQsJrADsoBU1MzAscAUDGC2Wh+oY\nlF9ebeLbxwNxBc7zQiTPzHlcpHvao8wtDuULJ83p/MfXxHDHm21Qr5BRwgLunxqqZsI1I0kHKz5h\nTjDmaDji7ZUW7pfBXMNvA+OauL5Be9mnJSyA8XIAc+HZXbgbg+HYeZiMhT4O5CKMrTDj8cD5+oqZ\n2jseQAzEyUI2pF2GaRcSk2sjoZ8VHkn3rzPwwwng4no8HlD1QcpmeV7AmEgbChb9ce3LE5IBrITg\nVFkNrMxTk7xbIypD7m7ZAFIFNBYezhXZmBcB7FYZEDKR0+qVfX39PHLTXhKrLnmicTAAmlFdVm7H\nu7Uh5wXDbKEfwvJUvt13zRxjMDoWKHeIo7EMDSLSC48PngE0vphVccTYTNbUBvDRimdpOI1Me2Ah\nRBI8WJGMkjVdAy/hb7fzSYCV+b3PjA7awErczwnX11sZIGq5JBQP+6SCIAX69w13tJ7fBnvTmPPJ\nUl7Xdy9NVsFE5cOtb5Yevx6wbJnrkJl6mjA80uhSNLnnkvbqYSn3JP/tewY3H8XX2tYsSc3Tz7vN\nMqUM2FTdKQeJrLgXy4zKLmi0ZkyxowSenqXS4vrD1sv35b61gPeh9oLjIPue2ufV6wo07DhR/bex\n0jIRV1BXQWX299d8PPfJoOw15drHytCj6kVA7V/9+RZ0l5qUCpd5hDry/fzJlodVrGTriRWUkdeV\ndSzs1ETWkyZgicMGdrYyQAMAcY/b5OjXlT5euFN9PXOd0VICsmmJo6Fr5i0Y9w7aAhsPHLhEJxwH\nEoRWrMiRegf3zgSFn3VuqZh+g2Pl+yWwRTzkuNPFGmBuq5s1m1+fUguIfUhxQs8RFaohlIXDmmc6\nxC/kKTuD59bihD2ozL4g8ZaB1wCOdIRKarE6AR+atKWgAsbmmUAEK6YB+M4eKr0sA0MCMxynAfDE\nD7EyOERok4QjwwFzKk1ZxXsSEXzucchbUbJAE3fHGAeG2OiciYyLfw9Wr3MHxgDOCJXs1r1pzCMt\nodssKGxb8VKwwEmu83koCYH7QJ4nAx9fXDCKiU+g4SCSUKExyWfDec7L8AYzHGMoE84K/67CIqOt\nr+xuSLBJX96UTOCMEzMSHo5xPHiKTlYdHG7AmHgIpgUz2K9I3DipNFJAThZGkkA8fFls37CgM7Xx\nEuUxBOhRNnTmrzDABvxiVimOjDmkDcw2UgGzTarauq8TaYJnpCkwFMp29WAmqh9T007tC+TOIv6b\n+1Sfoslmcc3G1e5C6ZJyU4SthKkqDmLmC0uZEsT1TNNHdRhuoud20LC9d3cx+s5RakQGWth2hm9r\nTO/Gr8/dKSpW5ZlMvWD3BaFe/rGQ3GmqespqPjaWogmqzetY1rtL8+eOxgeXZOIGnhaYRMtNvNz5\nbb9crNmW3ZaCjvVaLY7Lf42FLuEFLOcJEaLbrNl6RkJLi1xMr76vDZGF0c6d/9XRfBLzdMBhzYDX\n57S8BLKtwSZKVCJLr7lV+i4KMlvHPruGX3Lbw+SWa30/s0vATDAinfLdPUyxFB8KUrUXd/gB+in9\nULvfXMS/r32WrGF9uF0/9zKltl1ckdG1N+qsRI+TgjIZYi5l8Ll9tKQSGKsscNGwUiX2iWl384SY\nlcAEhau4TQR65veTlLdvd3Sw3c+h8OTsylx39GcVuJh9fnpaNU8l+JsI6X13rK62wAJb0eeoao01\nF9k0vkbqGJirRAMAxS+gcmbc52Hhmu/EqLHeePr+xtCxaI9pfrCm6GsVkktpqQDxKd7yQBkWrJWh\nLP5hhfFWqlTQkog0PJwFml6Mym7newRPDL0wAzMmPZk6hpf4ybw46cMpjkyFfGQsQ9dMFjKZyRRt\nLPfOd4S8mRZrpyTWXoysvZrN3+vK4axGx6BhCmeHG+bwDpIfzvp4Rz1Xd4+kbapSu2WRF4eOkrUX\n5iYnpOGKQJrDhmOoml7TzyRtmaHICFOKV/jKJuSGnP3klixIG/TxlmVJy4rYrNORDJA0OMbxQsOO\n0jf6SLwcgD+sq2DWPqFADs0xiB+Wlys2orvoyoK5LXpktQlX5hkYoLSAiQ2O2DCP2J5Mb2DxWWI7\nCoFQPD7Fc6H4IiDjx3t/vjghebFXuVP1e5UaBprdvrtvkbpsAp7blzxUfE4Ljmm3++uPwh3esmeY\neiVhuawkgYWdHbbRiFyWw4+X5vNC0Q7VKBzdL9Oqf/33JsTvG7ZcS6NndiH7iFuqedfEuDeT/qhn\nLfSoE+UlKkKSpjUsJrgxSi6Rr+PfWQ8StCRnB9XUFO5CSnt29j2wN1GsRVY2JOv2oB1DuUS/hRW3\nvj+1j/SNFcadFHOhHNFwml30u+/df1vaXfho9pJjw+baJoDmNvf7vSmm1KRVP6OJ6309Pt+blnV+\nRp8N972DYny7DKUPdqLvn3nxvsb7Zx0GZ+iUR0vFWFdXKrrhBWkyyaeGcMNodlXMuHuElQ342epp\nzUD6PSCTuUmx+TSoEpC38VTAbq3pFXGHmeWa2V73py7sAu/NmN0Ttvrx7qv+9z6G4/2pWiftDfFk\nX3bx1Q1db+t8t1tKrEn+OXyVTZIUZ0LAJWMQXF3QBToEsaiUlxOEkpWvgjZ8Cm8PWaEDAYuqVJmE\n7pnjxMRVQm9Q8M2gV8RgyhcsmhvAjKk0ataGIiQYkKpUkZsTApuzecEOAWQHby6KDSTcNvy53PcV\nYJeDVmIZOjHAgMTF2pcwFggdl83gonv2WADmFk/F/mhuxP8s15xyXhMnmJbPszjvzjN2PnWnMxX4\n2sdGOC53kyf8HoM1nBZ1y4vW6QRsCGI0ZJ2VhB+T/a+0lxRnV8/mFk/fFHwLMtsNRnmBxuSduGYJ\nTctQsp/pSlcIrST19Z3DAg3cLiWhLMoVOPUj2xcnJMOoJZbQYc5JKJxOZ0FNUNCxmvidwSYOIw4m\noHmvNFQMUG3LcfoK6NnTNOnxOkDojd9EP9ClqKctAdlsQTI4Hv4zH3pnKlNYtMt2Xbh+utONxM1s\nKlQhC/su+Za0/pk2LXqDm7BkHNBYm0ZCK4Q5mtsmhA7udAjvSfsTXUyMwC0WLDGI65FLQYBRiUhj\nLkyFBugdtiTnZq7WKlLaBWxwC9N8uNtN+K91GjDiTWsNXLujiB9IJKbwWi6LeBX7iEonE7uteDFd\nFTBtwaPCv14j8dKuKcdUYF5M/lx491yBIi3grEwmX1trYW/7hG1ZRthk6XNadiy0xubKsapAPykR\nw2Ltge0pnY1FGNfmkHVt9h9PVuWdcWw/Y120SDHxlL15C98PnVtov25y5hVPDh49yfyZgDPVUwGp\n+Ez0vvQsUXkJ6dw/st7onGT1oWIZen5KeDeg5y6bXfMsrisGir5J5bdNSJRxorxPZUkGSEvILLPj\nKqpfbWk39O+tKm78EACqMlub95xKMWkLx006G33WDhCyE2LPFe9eGeETBaoQt9/WVZRadGRfe53w\nzKfK1/LkeQnF1rhU+6VDgf7NtgRx7MMG4Iy1AIA5p/iLLLAhwS9CfHnBZBgg5XhMwhrezPH49MDj\n4M6d16kzWJCESazzWOudSUH1OLj3Ck8flyFL2BK8LiMZAxOOM7MLvSCSWRKSntfiIQbr2IGyRldQ\nZjrVbvcyWZNfMNDMEeY4YH0uX4bj4YnXOPAGFvTISMwB+KBFc2QipXiUcE0JcDR7izk79uDTC/dO\nYb2dgQqYZSk24DGcAfZe4fArxSUF4cDKgbH8mwEpFfpflNxxGU6PhkQdNlQwpjxqgcMZs3DFCcxP\nmFfCcyoo15gBw0pYBWAsZBJ1Zm6nSm16yyuUqyQEb4WKaAS9AAuEyn2XSX7mFNbbeqykCGwR3GPw\nZVRAFqyO3vRU0TDENz/6vHx5QnIRsyLMazX49QdyxEf6we4irB8k+CLY201pbS9o5lSukZGLsMPQ\nQjfW7b2pPdd9G69e/cmSmcVg88bjn/qvbVuHQe6bzFQn9rn6+BkA8IKCNpBhXkYXxSg/7vYcHr4N\nh4diboYzlxbXS5IGdDjbE9xinxzsH+9C8sIe503kJevDk4bIY+OoHK7d/du82e16/pLb37as2aKo\nZfmy/uXjuVyz1A/u3yKBh2l0tpS2zE2N2Nx+bqOftat4X2P7eX1f8yaxuWQ7eKcSqkIQ+7ax2/64\nP4/CizVs6GfbkXX99n3bk7bjc79bT7R9n23FJXo3sVVmtdqv9Sx/euqyCJWAr7uerbbYhAnI6lYQ\ngaSw7vnxXn22HD8/uWhB0azV39iuHj2+nn8peftn7+mcN63SB1z1W4nw/Z5lkV9C+jPx2OMVGORa\naFoK4GWqWiNZq/28L/jMB4hiJm1s1f7psuX+rvUxNDm5zfHX1sIS7hQkXRhBwhOyBcrmi5HwVp6o\nOHkCP0lgRGIgABUTOcxwwZH2pixAzD8cyM2DRLgAIuWN5XveciKD1UsRpuBpcZUsupHwB9p6WMJ2\nZsrCXC8A2srV+7o8oRqbBDXoLB7iBQVpWOyVhpQX12k2ZqQYzn9z2s0rwpOTbRiqo0BLN9//chwb\n5EEC/VSQn/GMjh7KLpUuurXzwHq3AcDh22hzBfpPtOHQAXkH2LmYPGPkU1Tkv3uRkj2DSQTMmQf6\nqHlB886iUzX1+6zXkX6mFW2E9KRynMJ8WzQf51MZuOd4YFPBtzNeoblFfUWbc5+VstP/eMX2ixOS\nE9x9bVdTdDmORKYrMlaHDxfMBnPtOmBuDBhIw1ucjaHh4jGlzQuyAwFchyPywshDWFdmR3goui4t\nypCJnHLRWAI+FjIuEzMJuj+m47DEm1xyL0ZN/ZrMrZqWwFDew8mDGqiAnMoSwMpu00QMKrjJ+Nmj\noQ6GMYL5hK+B8helJHkDcKUhnFGih3gx8zdOKQybgJ/K4HHD3GojnhejxmVxhYIiBgyzMnukwYTj\n8snNX1Z45os2pP2AMwc8HYc5/DgY7JCGEUDkhbDA43jgsAeu8weMvJRmZiAH4BayfiwK5E7Jizg2\nFKitrQoAZI3gbazaBMRpcLuYmxG0WlwIPEa2q4+zQIo94gGY0t5ZAq58ly8qWJyDngo3Rl2fiUrq\nXtuZP0NuRs3hxkS+prbAI0USVblwDMJakxHeJeCl79kYpqxUBhtdxwlI75K3fgg/LAurkiVQWQJQ\nBLEslRcMRwdEOvNPI/CNHbiiYXDNSy8nlKjxs2KKGROd0BMV/FjgnWYAACAASURBVBJ4GYHEJ147\nKlTGYF5CnI6m4kOuUEn1LM+K4WEOVAqrYi6oUtcSTNoLxvePAeSwVmI7MDXovXDBia+LTBuPV+7x\nxlfphhj4dLS8iysmLdhDamsuYZRLsQkMAAULcxgu0VW+eyQj6y8PILIDvyInIoOlezU77UEoWgLR\nRRiYb2SLm0hgGnOQMG964G0o9RAGbMqFLRZIzxDp+MzAIUtyzXOn6hyg9S9ZRANKv5Xj0D5I5ctW\n7pk0pDPMD87dNvO94P81NB6niXEMHONgdb048XDiTj0nJgI/jSTm1hxZhQMyFaQV+AaJHANwZyCq\nGV4BXN//gOnOKnQJvMUFOPDtOGAX90NYMJOIDVxvotmnHPeSLGMyU9SljDXfjAN2AAccP5nBzDca\nS9gEJmkNnOeDWVMojEfttUx5gxgEeprMMxVAb2CuZfFMigi2ZAPtk8OIUfapstdXdil3S+vKg28n\n920gcU7AD8fx8kDMC68BvIFetJgnmMf9BwAHzF4wPECQCmkZEA07eZsUaGOU95Y00dIwEBiyQp8z\ngUlr80Sw5oGyyRxQyoMxcc0EyrsVAcsX/P514iUHPQ4DOCfDMD/lpOXbGEMQb3wH5XEqG8OYg34m\nLc2mqjOc6wt+JBCBGAMPzVmOFwZJzoD7aEH5ugYCTh6fF9wd4xiIcMxrwoZLPjPkSKSCnw+JB58e\nzIbz/ZyY/uPP7BcnJEMMpQ16UpUq+wTAjW/SFGfSLVPWScXRMhUQSssykV6SaBNDL+ul4dgVkKdG\nUklxoEpPlB10tbq9YjJLxztbiJAQUG5flNs9W9C7aX+bfSS3d1C2k1sJyc0EI2zElrUpgtkrfIke\n/Zxd07P+nbOyFw/YLSXDNrE5lQ5GvWbuaKaAq1kOTVutZaVajstlvdL85wmAeWZDEIXDuEYTr4gM\n2FAiexCXGRlyhy9ds6Kea1wLE509t5clq5ckYBPABI5OrrNWlOSi0kxxPhkBTgtCKVe0GgxaHiKQ\ncSHylGtfiDS/b6ti1tc1McZBZutkzLvr/2tpuc0Zsv6m6xEwwIlpHCI1lbN2tdA6iQmjJmzoeSVa\nLYFyqW+2zCjaZ8pAihLfy8ZQ+c3rZNa/gbydB+jzijHA9hndzA8shaCSXm39uN3AXxjQuSwrr7Gw\nyNVHKkyJMGGPRU2WGiKFXIJjVbQ+CiZRM2IJ90QlwtvhEQBksSn3My16VvOOJTzXzzWQ9Y77TO0/\n6cKfOAXpkBD9Lg/eB21bx4/pcL3AoRQIvFB4wxHZCpCQYzhyp2f87n1su63N0C7D2tP3vX2bAiS+\nSq0WUtYiMM+TE+LiXDPwGIZxHMtSmcnMKrGKaNS+8IMZLM4rccWJt/ONcCqVYVvWSzKCH3DhkxFF\nDzMMQSBT+OIwGnhSSrVIgFLWWVuPv7cTR1I5uiLxOkmlWbvWlPmlsl4YU9idZ2e7+PbTJxyjUkwy\nJV0ACB+CNjBo7joJrXi7ePriYEpIV578KZCCXwFcyyB3gQYiB/DTM3TmkmkMAziMxcssgZc0WDgu\nEBIKe9H2v3BlRcKYrNvyusEwfCB80kCYFDKPKopyQLhmwBx4LbD1CLwYhceyamcmrchCr5knU+ZZ\nYvgDvu37x6BBIXAgLt7PNVFObUg2gM5OmkT7agtyRa/4UF5p5useTpjLYQ+8zgsTLPT0qy8HzFV8\nO4++3yMxD8N5qSiZyzsihTu9uDrHuccg/Jj25QnJH7WNYLfgvNGzcl/KSY9yN+b9su2zggfwaLWr\ndpdK9Y665/6UPaBrNYrPtDJIJFjvt4RH0eGFRi3rUr9hgzvcRTfr55nFNgclBmw90WGVAe42Xfv0\nrefWsA1V3/2JIyyLj7hQ/RwY0rRrPBJNbWMuegsP5rhjv+XvdqQEb4fLJVJVz7raE4yZqSBXkTSp\n6i0xqbEwkj0CzagkHRP22hIC9UuQQFn10AjI+yzVf++QFCAxjFbMyhZAPFR0H/ersTEazq3Qt1+f\njNyKCfec4EAwRNRmX3N2l4Duu+9+WtecdY5rlCUQ766hIpYtKO9ekLWy6/Q+C8q7qLfvenv6wqBI\n+Xb/Czahc9HX1zMNjWvcnxxZqeT4WWONt9lqsICtJ5qYZrk3YcLZl0BjxYDYGdtopq2HS0C+C8+7\nULwLynfJdbmt3wvI/LmbINKI7d7n5V17T6TvRPWp0RpoS0DV4GgpK9f8+97V45+etl6qOa19Zred\nULRMuzV/gY5+wS0BnAFcGfC4aImzxIwLhytvstNDQJ3WaY3N7BRugClboWAOkfAIpXwAZM8AprC4\nRg8d8/2TaM8k0rzA/GvGVcZ44yuV2s20VqaBrCJWhvQF2rOUICpcJUN3VkXehnclH2JIeKoarDky\nQ5bhxFQO/pCwW4SobBrPZ5x70BqaWZ7glEX7ui5cYKaO8lSe9EPCFSIPXIgMZLq8SzVojd+cjmPh\nqg00npVBIGYlNpQRKyvRgGkehQdO4OogTkKwKgWj75o9yrFmylCiTBYGQkVs0arQ+fCNx0He8jtd\nU4iioeMQDIYzAlftByOMkZBTo3Fs4+5uNFUwEwsUW1UEWxULt/7/MhCpL1ZIbiJnO3NDr0Qta5Gq\nRWs5I/dwjQK0L4wtt8lyETcXXI/onti738UYt76uaxIVvAJIUf/o2TQlwXFPSZ/95Ny21zYnlERx\nO5ZZmwc9jiImwD2gaP95lwHqk133W8IACUcJRAlYWcPH/pB92hbk8tb3yrta7u1tHFXlSYenLHBL\n/+N7ofm1lZSVlmSIGNWe6ZdKgOrgK42/D+3aRSWqTMS21mXXhwSfZTUIKSwD0lxjbP0MdEqaHsF6\nY/+TkJO5T+DX0u4wA/5TTt/CAAC9zuNzElONvTWF/cDkevj2bXvocZ/PdTIX5Xi2Zqxv71t3X4G1\nh5YQGT3m2in3IXwkWO80KoH2GOTW08peNW4UaxdQhZfczgv7twUrArIk22Kqdf02T3vby2FzjPs7\nf157fw3pzp1m/tyn/aKvq1fu1z4pob1m+bQ2H9zaz7v/gucdtH7J/ZKvup2CF5jNDuSaWVZY0Vcr\nb6OhvKgUYIXgngtsakEpjBA1tKcik3mV5d9ESOAzA05QwKnAy90sVGxucxDqZ6h2wVJ8hzlGAm91\nyos2y2MTwYI47oK9BQt6hRG+CcEvbMpD6YQOVQGSKlWeU/iRKDmZHQs5N54FgjLSD/Gj01RVcC7h\ntXI4BTRYlCW0smFoQMiGiAE8t8MGjWomf2dVbc0D1+Tchi3Pkaf1mAIUPh383gAVECMkLkO0+ums\nkZ05LmdRrM55vB0Uzv3WWSy6bOq7FQOuw7qt5zUvJGtMcx5T866dsjwa65DbTeran1jvZSG0X8aU\n/EUKyR0Yoc1W+KJKPQNbZZBV2bDJ2rLvrNkQi9FRPzHwCZzC2ny6u5AVxQMHYEHRupzrfN4qaXt/\nB+EWE1AENjXEIWFvJda2m9XHsDRmJHGOfJs9sWNTfXXvpOsRQdfZXJHnANo6kPMmYgJYBvPaYXTk\nVHjquTGI2moG8xTGtO7gz2khTHGgAPdAEl9sYNU9LYxB+Gd5Sy8AOYk5PYN5Mg2Jy97aguHurCSk\nheEBc1xX0DJQZumqWwqg0gWa8GfN4C6VoDBDDFm45tS6Uu/meHc4Tc0U5yIMrHqo8+mKqL1iorRl\nvqISAOWiAxvjsAfLBSaAuE4SWnyFrYSwwgxnnSm/GfwKD55HbLsH65zSrILG5/Rz0Gcjt98BkxLW\ntlzUQ0v4nFgK8aqrx+/L7b4z2zr6BtwCbooGwYA5T51Kh61SP834q9m+LbUfO4VVJK4EKnqY+6Nc\nqYp0EH/iPuJYvKYGFfAEuMfNuu4+kJmiHaSEofthAC7i8V3BdZUP2oxYx2cdJm8AhV3M3CWC7acp\nUFUKb0GWHJ+BXOy6wC/QrEzFO1EL0pmqgFbZKWTI7F7v/9brNkHAiEXfGWztLKaptV6/2xR8ZS1T\nwXHa1/HGEV8+YG+sZOgAHoM0fMAwDhpJrkh8fxLvHbOsewp2cyfeNYQfNaiiq07MGKS0dbaVDmps\n+XIHKHSGsarjFWisb627peOSRvriRgsvGA/EAUq4neJHgwJuUYIh7+H0SXimkwpcMYGYPEM2YKO8\nxqzYh7fYaJH1Pptw9as8vSV0MubBdQ4yDTmAt9PxyYiRDhswNzzAjCCuuU8YMsjfpyce4jPthXUJ\n7x2UbsiDBPewxOucxAKPxCcF6MVlJYcjTPYcF0RL3MqNRZxQCtIoCaBoPCRrDByyvkXMPhMV5EpZ\nyNrUVAtjhs4klQdTW1b82SUe/nDRZD3zIlHH2+sr3JW1LAQNGnR3WFjLWaXns6ougyPdDC+Po2OD\nfkz7IoXkd82wLB5Z/NPK4/iO8OX2T7e3NbUY552+LQb7vuX2xSbtPF289i7hAqodRqyRWPbOpiu0\naNlZitUvCf2+vSSEJw9QdbnKVEa5YnVXMea9b/n0vPXtEpYzC+FdvVPSFd/t3XX1+m+lhalxE8qw\nLHiVftRsy3q7LdQMYqVKhAmVe7axAr+A6JLlc8620hrAgM7ASjEECVy5GF5mpdBxYq/cUDFA+xyZ\nVnKJWwX9KEyjbVdRvTgjeSjLjWVD3Pp53jTz7rRMaO0qL/fX1u6wlvqExC8q8EWK1T6f9zNo24ao\n3cG5KaxbKc77FUXwE0tmup8ZfrPO2W4hXNRhpxfv5J4nQXnFMWgfNcDmg8Wzbf/v85S0xpGh8qVu\nDBTdq70tVUKf6Brbnlj4u93CBFinp3wHC8t13YLgshOFd74P/KOxfV6qJU6xKFFFoP8cafIz0/fx\ntVl+U7ZyZYvAl/GhIPGxydM/pn0su+eHv359LbH7yUz89HLCL2Yy0Muc/OsTDnoSrTy0CWTcPHn3\nqRGkQQpvmFThYfCt4MdhS9HtVktrTwrbxtyrAFQZBsOkRMZQUGG2oAwkzAeuyTJxBgCPF1XwTQWx\ncbPMSTr8KGWxZA6Zxi0Jt+rqmhp+W2L1xoIZDFDAU/VnaFiqGEmoZ1h0DuqoC6DTrzRMDOLNd/tx\nYq6y8kZTFzt+YRqt1W4MMuSSSRCu8XkFNNJLwGcUUaWle9QLAARNx3gLVuSrrNbFarHtKUhOSVuU\nvjjA2ngQrxQP19oe44G8pgwuwHBGGuZ8JTVJtNLqMMF2Up4QGefAwNzeOzIK2EfV3n5O++KE5LYE\nGAW1yAvIiXEBlwMo/AsAYDAS0pYg3HF114Tb0XjZM04gee08LgpI5pgzMaWFsPa3A4fDZgBXwBU0\nBpMFVFHc3DzRG6hy81ocAiyQPT60KeZ0mF1ifQrU8olTaVVg0shVonFi4lVgjTGYLuaMYNxKyLqj\njUhhZG30sm7WAZ6o/JdQflbHhRMG5gR2JIOSkAgfslaLFUteiaSrt1zOVc3oGxuYNjf4ATvWWLFK\nM+PMdXjAcOIhxEYCB8s2P6oqLhyeD8AuGE7kzO2wHcxPiYnjxTpoB6loc1myU9k2kMDhC0tlLtVE\nsmuG8j9DLsfNPDXsxDUHqtoPHgPpiUe7A3jKz6mM6Bg4xktr9jS6B64IHJOuLgcwRhXT4HomiA38\nxg7mevzKWmovr8qFg+dzYgXp8QLm4H6VmiGPgPuBcu1OzFb6xnDhzAJl1rUEKpLkCu7zZqqA8H/W\nn43KeW3Aa6DPB4D2Hg04Jt5AC2gi7cCEY8SFWUwFhbV1HHa0GstPeMXExeT7yWsvc0wDEJNWVXBc\nXPBgdDeWKFwCbSbw4hWgCwQuYe4KvyfWXwbekEVnLQhp4RiqnhWwZJFgA4BPj+5LliXKEsMO4shL\nAJHrJ5AKvuKzOecMSHYTtlPYT+L7aU3sdHVah2ihR9A3Kb6M/K+4imDKTWj9xuhArimjyFGuhWpa\nzyutaRmlEpZlWPCeTdmA4Yq3zWhR88/NMuZ6RWUCCF9rBWDFDn6NzQzfeHl+ynwTON4SrwAhBwn8\nkKTNiAvHg1mIzqwzQWHJB2k8hS8Hpiyc2ivuA99+99DqXuRBCRC0PLvoCEAjQ9UY8HS8vV7IPJDJ\nc5sKnrZxYFhVoU2MpNHpGrGcUADhBwbmZzQDDhmevIxR1s+YKSEwDJdfePnEGFED8HK4zmbiOi/h\nfQ3DP8HNlQmJmXcMhsfLQB6G+P5kvyOBmZ22Ma7A6xiyoTBjCwDmi47EGwid8CP6rFzBSrOHtMG8\nKvnqZjRQmr58OL79ZJjhkktYKMWOgbiI1c5pwEiYB440fPMdsdDnSWPTwwfmDORF7+CE1jSBMR1p\nXCuAAXMFERkYQqQEDpsYBvz0DfCHwUzi5hDN8Qfm64V5cA+NAHIkfrguWEi4dwnWYXgZ3yD8RGLA\njwFk4pqOTzjF6Pn4iMCZie8UEB/GrB8ev1xm8y9OSC5e0pWdkjXgLjCTRZVMdml4BsdlInYGlOx0\nWlkNy1Ikd2alOkIAEbApjc13EliN2NQy8VWmA2ybsxWjkgVw8V0yrVpO/Ry4pWMC0P7oBCr5d417\neAVB8bPKJ7lraxS32dp93P2Q9ijLZsEphAyD7WPoXhnwFq15YWCVFcz9OK4bzyqZLe5c/S9IRQW5\nuO5/BXBE8CCFCFwapkcL8G5AhDBxLQ3k7afBEEHMlSWzTkxplS9giVUY8JbEvlGJsu3uepr3WHam\nZynCiErPt1K2c40F6rEqr7JZC7Aw8QcMr1YWRcM3+u4yh1cSbh+YCk786lrDWWqE3HcXJrwUjGTh\nh7A7DEoP4A+lRGv7T4ZKL2/n0tb+3o2JdSLMgGvGcqnNIG2wdV6eA7IsLhSUalk6JhwrjzVHFz1C\nNFURHQGPy8q7yyAaHh1vwbPOCBXJZZHefz7gElwSpU5T9hw8wzUBBiCtk/i/b7PdqGaV3xUS6iWJ\nps4PQE9M9xOShmWx2XIXh2hbgdWqkJJk521RJK1s0Jl9tTbVpuFfG3XpWTc93Dq90c7mNsFX8K7O\nTatjXdaoele1Wt8dggNRRlg9e+/VgYWX5x75WpsDeCjIrfDDHKoT8660qxSKEjkGfnpaV8lzCwxL\nfHKwJLM8dCdSc0+hbDfWAMl8wjbROY5lEBqTgt8MFtd4y5MpMc+B747AizvzEQ+u2Qm0JxaACkPx\nDOVLb23YKxjoPQLjOBRgxo2RCTyUvnPq+hb6tG+rpLTHQAD4IQOPB9PIAomZr4iZeJscRwR5zLQk\nVKK8OnrhGeRT952ujAwAcgw8hiCkYXj7PvE2k1CX4FyH85xe1wUY1YbaskUTr1co65K+09n55gjE\nYJ0B8qsTGQGvNIApY58rtVwZk1Jkvrp8EE1dtLmq8pHA0Bs2U4GhIASs4jXNgHEYfBjeJguyQGc2\nkhCTT98Y8kSnu6ziMvmYePEDhz1ktHilMJ7f4Yd54jzpKXiMFxzueJvfc3/MCdig1bw9Gb94++KE\n5IYSNMU1CpiWzYDJbJYAXETfbAXo7SKgQYwprd23qDKXsC0aehHucm4UcD6x3kut945BRHU5sTIx\nJPtkvbvsiVfUdUtAKDcSJKRB7293s224yf3dPW/qgzpEp85KchaFVOzDiqenPTGxd/zt/ubINTv7\n3RzWIgTtkEpF0GZSMagF0wnvuP479b5PNJIKRwcl1D1YJ/lOh7a+3YWSfZi2XUtzaBXl1hgqKGUb\nMa2TVA7idv8KgSyM6T6dDOgQkVGAwrthfgWtp3ifd9htGSytK0m9V0UlpHSI+BJVC0/xLLPsgZcl\nHJe7LwxLSLaFRB6ZT2+uvyutY/VXqrXtNt5atwSyAhdK4E7tbq72fvUt0wOy92m5IJ8F5J5HW3Ri\npoKbEg1p2ufxc3pVxopRaIenyeLTi5NrAW8HXRNOSQYdULBdJXK6zVB9YdvzbQnKTbW25wNYxWB2\n6vvcBz5rZX95P+hRwk7dZrX3ejfp2bVC5Utg39aJlsj+0abbD/fXiI3aW82PmCOXzjrllxl6jxJW\nkH2uqh7qcRhyKJ5G+xMmr6Pon1vxZ1tnWl6F5VngylWsBzNsAOdFz0kKFpApD2EtUdOLUmMW3cla\nStDLVSnGzJ17wnLFv+/bHjyDC7gIlCHrITqfoOwg/2wbSojPJi0wGMLiBp9vBd1otS24mEE0K9eR\nTAApi6sp3gMbr505Bc/allTPypnAcWh80eeelfXodY1IXBd63SNXQGZBW7s0eaRkGnaYWWugsUoO\n0j+THu/RV1OIVuXR4nuMTcqVin4XHOr9mq/2BGNZgikHqYh3AqUVZ2+xbQNo3q5kVdcf2744Ifm5\nScciKZPACBRB5AEbZl0SeiXWFiZKOL8dA3k7SB/JYJuElc9fba3pP9bmNtC0X0TWxEDZ9c2S3O/d\nWUP2fqnFLtcn7T+77WPdtXr8xGx/RmveoxlupPDwZSRJNOZvqe77Kca7uevXbryYL+LhG1iMPsGD\nqfwZLXKQZVVCp93OtI8YwjnvGEh6GgqTBAAbpKqfsZ9J2554e0uu7227qLZgiUb8P5moBe5ro4Ns\ntur8JKDAziXMDbuR5K+6lW5rEMMQFXd45x5uD8nzkM0Wk0hapaqKZR2VtiT3PfWdBJcNspLm275a\naPJN5KUlTN/cuvPZ47PW6ibcbp8n7vusoCgtiDnas7OeyHsuJI6NSVeE+0NwkBb+3vf4qWUzbmAx\n2SpchFxi4TYxizYCLZTuAcFFg/tcfDRfu6C8jfK5f9X83Sf1mFWEdq2bf3Dl+jSwz08923r2bbtu\nf+dOR28HvttzbMEvg3T+ctryDbBR3pS6WDzNTF60hA1joYYwXJOWzWgceN6qmROKnD2PxTYsa/+W\nwKd3FHF0U2pO9uMxDOnMCuFgcFbd1y9r+g8gWbWuxyTpxt9oFa4iFyUcVy2FOgj7nl75oFdzF9wi\nFMg3mFd5DBrfLGmpHM5+Fyyj9luAsEFWOIx6Ub/0GIEMw0zD7BLXWKjOZ8a1b8nteLlgpTCV764K\nduaCbXEC3LzTuEUbmlwFwVYcV8lgAXnrYJxr8a1oAp3aLyDdScIM0xxuC9xkJQuYsp1o/dIMYSoY\ntR3IUbJHDBaNaU+4w3Fg5qXqkQxajovwz2OMfh+t7pXZ+se1L09ItmTuP8U8Glikgny1NpbBRiVj\nnFQiXBqQBNE0pbQWvc6yqsZJnJM0qsJxt6WTdy+BMxczKqshQJzRTvZbfkwuZGS5YYloZCWroTHU\nQjFLBRydEHzY0MbT61OCYqIJWO2hnSlg6+/qzyp/vJzF3FpmlUOjrhYLqXr25KhLSF7mOk6ozDbD\nWQ0nTZYyad1zrlK65oxCNXd4GHMgKi0PQBWgcsd2zuWoY/qRtFIWZK6YZZKgmmGyZODSsHMJVstd\nvtbUVf57F+4NhAsAtD4UTINeSFbsGSpbAyQtBkZiJug3LsFNHmnycOzaw8KY1ZJZWdW/slZjWDmQ\nRPDSO0CF7RCRrqpR/QTwnJosJiLIBT8KwDbf6hLc6j/6qewQj1yKZAVtJQTf2d+3ccYQNYhNrJq2\ncmMABVMArqxajynqxL8mpjKc7kWOTQElhYFEMz7Sj+rRzvZX/lX2G8IOBy0vmuEhaSR3U9JtXRw+\nRitimdd2ftFet37rGJ3SKzOBqcp0lqy4BdHQeTEewUZXtAKglFNlEVuMf3WoVqLeWth0v/H9uV2x\n1plkKfuqXYjNfny5m2veAsBDsIpdTeGMzydhq/Zx0bmt/62VbT37Gg+rWgJ4S3o6BkijJphqa0DC\nihtsOIYbvnkYfpjB+BBhz8OBK52CbyYsytPL/TKDxZvMrL04iUtWZ/HoTAAT3z5YXc0fLNZxXDyT\n5sS3Zkhhdgl7GwTLwEJhMIMd/s67kQAue+AyYq89Ew/JChVOUkv5MriHps7geZGGXLgAJ5b3MsPU\neXjBUMo8Mhk+hnEoiUROlugmrMkQY2C44TEAzFAe5ESoCMg1EgdU+TcceQ1EBPG8OlIVhjzE18s4\nuFtzv305gAfvncHqeH4ceEsW4DAFGaTko5kUWIcycTDOIDolYBZNrXNxgrhu40m6ZtJ6neSncAnK\nqflxB4LzNxOIyzHSUBqNmWwbB+DOPMm1KOukJ66kAaE8FEMK1/nieNFczshyMuPQvnooaMNqsn5k\n+/KE5JuUR3XJMpt4Pm8WAtZWNoZK1nZhSnBbbBGgNiuVie/xpemutpbmvZPYtm/vrZQ7FrQgw1E4\n3RNB3n63GpO0N7luMovVciMUK9nfvT9pt14BS9wnI48e/w69eL6DgnFxo9zMsIkFTq4X5qYpZo+l\ni6FkX8LfJdSkEbNVVgtqkFqXIoD9jATsc2UkbW0VWxb4CuCpVgGEe6uP7OmzfTYCgUpOwyK8Eq0y\ne4/14j3di00oZ4TzEtprSq+CZ+jdkWvNvqaWt/2zTgpTHpVCaFvlwU0gKfKX65zdhKDntbPbD/1R\n6wAwX/gmmGftea7Bfm8zUWV2KF9TCbfF9O5qZLmGsT2lhLDYZiKR/fnqyh7lXfENz9bggSUoivdu\n+8p6qEWVPm8XWXu0gklrnstQelu5Xfasl2ssVly4BcOV3fZDe+rPtSTf7ZjPa7JabgQvsauaz+1W\nITGXbv/MFNeqBnbv4r0n27nuC3aFPfF+c35drXpeNBj5lPnJpMAY4H4gzgtzquqeMyXQwIPBmuUZ\nAl37CFZkjY1Gd4U3OPaKjwAQr7TpDgNsOB5jIBP44YeTPtgEK9gNAzzh1xpAYvHOw0E0lOvZjNXH\ny7DNA8LgMrPEm85X6XSdnlSGnkpVlxrXyIExDsD5Hct5h+JVWAYagc5mcWTi3PhDWbPNyWctVQtA\nQuj8wXA8BGsZwHUJ15wQbQNWrn/yXdnYyA51hocNVu4U4zV3KhxXwLKCo6PHtQoLKRRhcg4iKgUn\nT14U5CkkG6CMEaG52pppR8h638pLFk+2+4naBP119HLhocPgNlkhVzLSNKarPY4H/GTua0TSuo+S\nSyAlQgrAvw1Ccls6AwCYYzF9IOMUnkg4w3lxQ3jCi0GlFOeMhQAAIABJREFUAXnISf8AkhkVIgOX\n3CcxHhhyOfAVFxSdglXUlnXOgcCRjhjaDMMwZHZMr4UXNZ6pPLCvEqIWEimQkhUqabErQfZFrKGD\nAqQDZ1lYkvl7h+xSPK6DaU0cPGwgIRsAHn7gp6AFeyBp0Qaw3Mzsz4okPlSylc8Oldr0i6B9WsUM\n52BgASJgY0jLBcIn3EgEMsYirMz0zgMXkNVpIi6ewPFIjEkCw9zITJh3lVipZKe1bgBwGDMdcOo5\nnszZOK5I0HQNwA8n9kxYpUQQG53UUucEbCbsYCBJpmEMZSsBOg3QYcrHiCADNkbKX+GYSAwJgNdF\nC/g4KqRUgWKeGJZ4C0OlvQOiresDx6IQgeUJ+craKOqnhPYBEtLDHGYvqP11gcF7n8bAmzLEmKwZ\nMOIJuYJac5CwXYfYdvDzCVqzjnwAQQsrBcHdtSbCOGSRTp7C3itYShtyZcAA0MFKno5LmVkSYL7W\nTDzsgSm3nVg+DIZP9lDAEfv5UNCiPxI5vV3FgCkYGSjaYdD6g+fMyQ0JHzMJfJVvVopmpTh7pDcd\ncjCVYgCwQeV4GQWgno4eKHkVO+B5LRHU0VW0cFYucqYqfAxWZJtTHZOoTChINMO/VTRDIo30Qk5k\nBanyPJhoaeEFfRMETJGJzEzDPNXL2Vpu7MRI7+NUAhPAdFs5uYru7B95KbkxK7CRHkdOuAviUUex\nNO6crekmHJb3DLBfU6tzegyHu+G6JuYMRDCTkoNBeFN1lP+/1yAuFYnLEmkhIfOnAA2HGGb4ZAcc\nA69BAewoN/+g58PHA9+fZ69pHlBWFfL9T48HLbyTvOjTp4Hz7QdcSJg7PvkLLbK4FANCQ9iLHTh8\n4OXlG0x7Q4J83wcAN5zmAjwmMhLnddIZegU6VacxSH8GENeJeQw8Cgc7Wfp4pqym4C3TZE09koJl\n0DAwLTHCcBrLRh8HM028ToMfiU8+8GpcBc/EIyn4xa8wtWnOKes5c0XPK2FOKEYmNYArT4zHgF2q\nkHgA5hd5ix8UhpFIH4h04OQ58EFPVqTh7Rq4ApjxCrxUAZWzlWLPUlDIO32Sl46H4breaAH3FzwO\nh8uYdSEEfy0rNPDAxCsc00m37JrI68J0b885DJjO9Xlx4PtJwPTDgcfhhGzggA+lg0OCqakC55tq\nJgzHy7DOqFVByUOe+E5f+CPbFyckA0qID/RGbiVlJRcu6kjBqQVGLO0BjHQvl4YpaTfbs1Ov7F3r\n3kEbyi2StiYr64ZOUqiLHNLEF/nMcjVtjLje8bOobLjBJpO+zyTo/iEtKwtXJc3PE3jFiRdppqd6\n8AAxsR+1lY95fQIA+Ri4XC7fyB4asyZuQQiFA/MUA+EjCj/lhyHdW7hJU6HnK+E+aBWAKRF5FSDY\n5kgG7azIXwk4U8LGaAuT2pZlfc1/Yl/rG56zBPl6Tq51+Vmy6mEM+BnVKas6VE4LRsF3Si3Ou1u3\nrChddlOvrn59bW3Zw2sMy8Kwt/JDnHF2ej5eX4LH+m8J1pnAsImF2N2wvIrYpnG18HRSpKz2i6AD\n2cau21tKv62lhH4uYSv1zmXDXGCOGjG/mRkSQSsPTXRaKUPK4sa7ZgS6+vvWFz2Ov+6bAyATNqAC\n3VLu/ysm0ose2G2u9nGsV7WafBvjcPabHgBayDiPo/sUNY+2SPDezQSWMKLBLFv3fcB9zG0CW155\npqUsyJyseLLaFY56f8aduq012se/evdMNN637mWikRdp+0xpLjS6r7EZiJstpHYFQJknYJM0PAJn\nnrgscRxHxy1SfztgAL5xk3JBKvt9SimLubxnnhTeAMQPzKbyANMHhhRCK41G9Z+HymKPgwaxc1LZ\nPX9gTb0cC1pjBhyDSlHgp0LFGBJHB9CP+aoIF4JsLu2jKxO/+onKAgD85KfAOQMW5LWHGL+9GB5p\nOE8awkLEqayqbzkx5kXDDgK/r9Len2xgFG4bDGbDCbw9LtEr4gJO+SlfXgcNPOYsqCKVezqZKpVa\ncQpzREycCCqTkwYLS+B60HDDnMGGU+PNCPwwo2MkZJvDy+OhriSNZSEmPgzXBKGPLiXYCDnxoWoQ\no84yPa6HP5qBHjohcyTynKhqhVVe3PkLPcGJFsrfzkRMbrhZ9D0Dj8MJG90hZmkwO7kX0hSgKWhf\nspbC4TScXpY/5/R/3L44IZlWFW+CSFN7dnWbmzSRS1YFagMtgajc40hZaqz8MFgPKOtfk7zdgex4\nwwq62uD29+dsklXjpZonpi617hev27qw0+9ilEWgkzge4v82yWofg0Gafj3CmpnaZ3bFM7tojC7u\nzMcTeMRyTfVwNRYhv+9jwSaAFvYxJTpkYAxqj+nWqaeOyq+7dfAGDdSz25vuz/O/j6LFLNjWqV0I\nvwnDzQh/fivWz3mvvyT8+mLOJkEwsQ2iGA1WCYpndv61tSVs1X9rkHNJGyABNsgSnAtXSM+8bdkt\nsqPPeY7WSq523xx1vAx5w5ev4JttlpfkWBfdaW79x+okbWP4QMiru6KYFeRKrlKxYYrs1jkVTdpI\n1f3lZa20bTaLHmjuVlYKwwx5wfwODMutvzupuYv7uiZT7khI0UhaomLrYM9pH7Vb99c7So2o/y2R\n+U68+fuAd2xCduYNra154yHLnVvveRetkPso0e9+P9E/R1DO4h1LQF4JQdbcxocL+JU043lMo0Wx\nIHIVuVJ0kgVEAp/sgfApiFgqrzKF5aobgFT8AFQcqQCC+s6hqqTDlCLO+hiHl7eGlucxildmSeX9\nHEso7dxy4UcK9x8XvdCyVMxYXtIpeWCP5zFV6uMREPY2s41ubPKMKE3FxuLIc4PjRVKQi4ZAGA53\njFEMkQenjE8oWmHZXql5OfBQDItxjzHl3gYn2ngdAFSskYcJHghEzuVBSgbCMaZZEmoZv4ZtWOY6\nL4mSng8LXLC6pQ19rhoTBmu5LLUeJcTT61AbIABMel8SKP9byORR/iXTbA8kHqJFTCOYG71ZfKJJ\nav2pNTajQF8p/Yo3eDZV+FHtixOSu5kBCt7J0tp2oQnr9z3IrrYxXX678EcmEFkiYEEsVvzzetKT\nJP7UPpzmvvweoFLFEWypXD28d1zm9pk9fbEI9H5Q6ywQd1mMhzNwIT+7wDUrz+zD2hoA2JbCbRON\newD2buOimbxpzUz4xHy6bH9UAnsoLVtHJD6997YBnnv/+W/rEba6378nsFUExM9shbItJaJIAH9/\nFqKeHmYlFP7sd3xN7XPRwly6RdBq95YMVIFvBohRCFaxbbkbzvSpjefKSbkLyKIBm1k41J11PRbh\n3ehKC9hYe/sehZBPe+zpjIjmMJWbMHUlWea6xyEmX4R+6887UgD0hlvf7fSFXzh24ZBXfvSs/GAs\nJWB3lXcsJcarX3k/idWfur/Owj4jy468+9gWGvjAgS5hDrryK7fxijRYY9mtxDumv/cX7jRyUc1t\nEn8eo3zmM9jWSL3/WfjoL74lGus5nYJvCTZhWiunRa4Fo1heSygY2XMhu9faiBttxhEkrbglgEPV\nTvnalQHizBLeaRSzqDStkBJHBW5IYqu82G9T8LeggGXJoOvC/b/tlrRSABKwwxATeLuAcpG4G3Lc\ncfxMBe8o6GJqAkuotlSxG52ZTzbwsEFo0pi4qgpgC8mMd6Hwpj1exoDEmjtZ+2+hrcW/6ixVqfQE\nBXYjZMOr35M0oor5FCgQJvyxFzWpeQZ8cP2gYlfrnZy44wBGEAJZY8pYcUR8bAqXzuiFqnbnSCll\nytVs7+mTG+tEVB+r5gIy24q/dQuGg7tLdRMimfu+DGqV/z1iIm5v+sXalyckm8z9CYGw0WUgmzIu\n2VYHrQQz6N7EKcvxEnqmasDvdmGgSO2OV+G5lqsGC3t2YwKrHqRWeROvWzaIhhugApdsMZoWznZO\npp/lKnE3vIAH8UpDKYNVQjakFTJXRaVlAXFjxaw/muaNpPFv3ldFPKqvlwHprBJ0g5LUICYjki2j\nrQ+AIeIChCXeIRAxXVNCDTyNEaiVimaPl2mmbNZY03WilnCwOKbBfKIqeNVRqtvcepk6n2NryXrv\nrXrjB60ivEsQ4SFPWl1moKRt84MC8RjN3nveq/JST+PXy3D3EhtlIQcShx3K6rAInGFhv4uQRlnf\ndQ44P+igW8e4EbbKJ1EpLzrwTtL3lAZD8sGFpUXQiyd3f6Fr9rVeNLhKwS4SXjbSXdCbKI9NMbrs\nc2wJVg0sF63u82HKFb4xh5bjrIXlphGGZqKo+an+jEVYpiYykhhRmQq2nbf/XPNgMOQMZsLxlSuX\nAchoaMguNw7Du+dwmZfiXZQJ24wtG51OZdFQ8CcRTBp5Ft5cxZVgQMyem489e7IeNtM3QJVLt17i\nc62s9tjWJkGc9O5tc7PPPOHLb5mJeSYrtTqg8pj4pLM2tXcPScivQStg7afHeKGSFxdyK6bUIJQA\nPCfd5wBycu7o9ia8LrKqRzqglF1vIQxrEL/u4wEky77YhosnVINW1pmJOU+McBxj4IzZtIa4elOW\nG5PCZ1sWFMN1BqFFafCDWSMOF+3J8mapmJlo1BB9ekNgGjPquB0wG/ABfPcYeJgD44Fpibe3ibcT\ncE8an4DSI2BmeOABIHEcQ8oid+83IwE3ZIyOVSrOdljA7aAEGeRLgcliI5cDxjR9EwB8wh24LgrK\nhGAbCkg6DA17jFIoBoOdfVSwodFoFsDDrelpWiqsi0TXrpSQS0t1uCFOg4+BcYjXXrEKb5mRlJcg\nbIY5g7EBUoIoX4j3btasUUVM3LmfFaV/Jcc7Ltdep+L9Nq+Oc/ox7YsTkouhtkVImt87CG1JPv0r\nqfYOUXAJV+UCqrLUi2AusdeeYN25EcRb357fv9PdrC9STOYuIGC7fLty3b990K4+8IA30dbny6XM\nzy9LfKvbC4U7sCNG720Jbndi75rwgey8hc9uphsDT9vGjWZ4abEFo20TFRJqUG7nOtDMIbVHwZJh\n3pWL0oJv0bQtSayFKIGghOR90ve1SMOtmtC+hB/OW26ucwl17B873KkBfROmGhoQqPKdyKd+fbVt\n3xm7rleZt5eACYAelRI2zXo+Os/D/9/e2cXYlh0H+au19u6+N7b5iWOGiX+IHUKME4LtjPKSKHJe\ngPjF5CGSLcCRiDAPUQAJHpzkJRLiAQRBkZCQHAUIJM4IkSAsBEIxQvBCfsZmbM/YTGInA7Fx7OAx\nJBnf2332WsVDVa219unTd7qdcfe5zK6rvqf7nH32rvVTq/6rxn0tEQ/cv9/Un9WChUDlNto9xfBy\nkah/vr8W+/u9vw4W8E4FTVBoli+F8GWaEjbM0SWueh0RGYXQGN7wcQzbCqkOFlSNuXrQ7pJhxn1U\nHgJiNNHpNjLmRxJT2cOjozzMS3w27oLxSnsn+qLGDaMEld2g9vND+v337SSXnXEvtO6XfWdch0P3\nCEvywwoKHkojvtYVkcrEZLWD4/B1oUhkAl1MiEItwVEiVMDWO5L3BEvStZJwfrBqhNJlVBcixCny\nN3II0y3Z0y3I2bpMJj8jKp7MPVmMaSvERARSRJdM292WPGxq9uioTM5QRBMp9cpP4tUPZrW2xi30\n0N2OdaiiCDTvB1q9FJsJnCpQk9FXqZYsXooJj9K2ejzT2nkn1CpQUBoN9/DDUbnE31Gv1mBKchVP\nZPQzsDofN6t8glRbg4/w0CTE6jpPycMeMWtsrVCFNHvsupmgWwe8Fl6IewjCpE8vX4n2mtSWDGj1\nlxNYB8BaLRFPfHRNbHLstOcioSAe2hK02X1ksfphZcbPK2Gp9lpcRknhBr4mHJ2QHKe9ohSpvskz\ny1JgcsIKS57ahJswBiAUopOSRtKqMeOanOsmajynCjXaUmf1QtthnbVNe46RHuCxVrHBwjwUC5Vd\neBvleXOW+Jc9NtGuL25dzVLdLQEUP2hEqFmRxazfRU0rs9icCkv19pZmmVNVThCr7RoSZZqoWLk1\nqTsn7mhJW0CzU7mToe9GzXZA7nwWJ7Xs3jzP1MWelbz8i1RhTpXFkUkSc6WkNPtCGimJ1SuwLFg3\nT6WUPImgWCH1AZ8klvi3lELOFgM1idDi/zV5bWnfN55pDBaf1Y7EsJYAy5nFUKVJKFXdrVvc7zRK\nJ2oldPzQACVhJ2SeT9jtdiwq5GR1L8+0Mou0LO2U3WVFxMALK84rFo/ddkl0zLhqYPQRQRZb16jM\nsaonLJMreJaRru4uSx7VqF78PxvB2wEfU+RrW3WHpMnL/liL0aq+ZO3EE99fIHnPcukMuGglUZz+\nrIaoUiBPKCfOvJyZSsJLIhjO1fNygwF68iVY/FsSpRargIGalXWajcOVkqjZBIxIap0lsXgr36bI\nSXiBlMLShmZVFCrTdBISsM2zX59EW8ULxDLpBZAllDefCxc2iy70EoZx3Cpzmtg5vQowp4SSqVpa\n3di2v8HLUoervTjDtB1Ql4KVuBLLP5DEfc44cUNEQa2usd+tVhu14ZOIGso6zHNAEq+DUrtwr658\nVpkQzPun1VzgieKd0BTVAtmbsnhmvEWWGf0lxGqpNknZ96ZAqd3pDRHj+vDRK7ii4UIVFe6IWT7P\npJCKeGijuJdHqXqfqmZ1TJinQVU4nYTfKwtnNTHLxFelxM6rmOgCVTLTnJBqlus0ATqzQ1mUiKDl\nvDifKEa7S7JnTWf3zYvgeCvCnDJVJ7JXFyktsVS4vxTyYlZ/i+NdkAzZq2ZA6K32e9VzK4qUxRLj\nqyBFWGRC0pl3QhWLNV4WpExMUtmpeVxxPWJKczgyURF24jJAfZ6zcxPnppzY6UJNyimTJSeKuLkW\n5C5MSannSsTVVqyajZ1Y63ClKVky5SzK/XrOojDfmanV6LWEGueSpi5ErAxJrJrQ5JbkOXtZOBE0\n22dJjI+1gpcVdLEE2ntnidPUO+eKliZ/Jbc618npqigmuCmcO80mU3EreA3oxISFppxTrMJWqk3G\nCy/arkTVMSvzNqlVKqMWlpTI1c8dJuaUOccq9pzXc5MVSaTWuebqcHxCMj1IfAVWTWrki/6zp/K7\n1nnSgj8H1U+EWgpRwF7EsjS7ABOCS/wUZiYX76xoeTy+eu2IzmaG6DtT4jxcQPqgLoCyeETWaBVR\nzLWRJbX6shZToy0OqJs62oNojQVCBWvy4bpgP3Sr1xjjF0w1YUQZuBQ8qSLcWIIH7sN5r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QohSjC+vY4bqrMNGK+mLZLTDpjl1amGUmkdgtBa2FNJ24UmR5aolETtdntFexJH878JeA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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8f07b4ffd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[38023, 5541, 8232, 20640, 7493, 2995, 3397, 29371, 19184]\n"
]
}
],
"source": [
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"import random\n",
"\n",
"new_style = {'grid': True}\n",
"plt.rc('axes', **new_style)\n",
"_, ax = plt.subplots(3, 3, sharex='col', sharey='row', figsize=(12, 12))\n",
"index = []\n",
"for i in range(0,9):\n",
" \n",
" if i <3:\n",
" l = random.choice(id_cloudy)\n",
" index.append(l)\n",
" elif (i>=3 and i<6):\n",
" l = random.choice(id_partly)\n",
" index.append(l)\n",
" elif (i>=6 and i<9):\n",
" l = random.choice(id_haze)\n",
" index.append(l)\n",
" \n",
" img = cv2.imread('../input/train-jpg/train_'+str(l)+'.jpg')\n",
" ax[i // 3, i % 3].imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
"\n",
"plt.show()\n",
"print (index)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "64014afb-7369-1968-5b92-2ef2976610cd"
},
"outputs": [
{
"data": {
"image/png": 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keXh0d6I7t60ttnSSV/TmKpMBjKG5oyXqyVwRpGFKRMRUUlbbR0zz8iZ9mkvO1wtbd4S4\nyokjEsuSsdVR6/EBlGqqFxAiZ91gYPkWBbNGfn1ZXF/WltWmfcojO4O9Pn5WDofDkbBDotZuShZU\n1EL9uG3altCs7tZiQ6PuUum9TTQsmi5WatqUQGoCvUEEkepuZuuY7xRAHdnSGYIy6rS3WmR0rnOS\nytHQgmql0hJJO5jQ4Uk/ikmOfK1Dns5ZL2u3XOtu6r2JtQEIYI3vbeoAMHa3RrSvea2HrWlfzpZ0\nK+M5LNsqHfd6/NKGMTp2OByOhJ0R9ZJW12briClrc9ZRqp253sYKM+t3pyX3htEVo+xq6Xq+9y8u\nhNfcaE3ENgd2x2utY/PQ63kdN81VBsx1xrohlIAZLfFqIBKBTi8oXdL8beXtW84OBUZ17LHUpre1\n3ZHVwUaPU6lbWWVwdB6qrLn3+vbSwL18V3qi3h6eOhyOy4fdEHVdnIViOmbX0RMBAmKGpCU4QA4w\nQsR8T50Lbju6EEPRKIVaxAxIxITQKY9JlihEWvUlCHEu3f4sb4BgWiiWJpfrAIGHlMqu0yWTXALM\n+TyJstxIqxyQlNwZM87kDKAl9tSRJ+P1BPUOl7wkKgrAUAOrpIBs1YUtTIRg6lol5jnrLBM0MHZd\nmpVaNwXnFJxBILhGYmaKV14d3g5ATMdRHboIhGw+j5BsJajtJRCE0C6z02OGNwAGIIZ8iSncqgke\nU40O6TmBMzRn5BoR3wTECDXRC/XdqU5exASyxmsXqc+FxmRiLTaSBy+1rPoC6RtYhjnS1rA8A9P2\nYIQ10TNGaLDXAIFQVwY4HI7Lht0QdULquEj1gLY+0r0hOyzu3MLSMWtQ9MlS1rAY65m1Bs+q60cj\nDc1VldPmkYjALjKqWqKabntdf0Wr3YzdvcSxXO01e1by2vfyXQZf9cbiYIZM4NDQIbr2Pd+njmDl\nO5m2RWOiqy+A6F29pNV8UHTVTPpbz0+Jdnn2Xmu62fOBeQDQGAyWEh6L1edwOB5O7Iaoe6Nj6lBn\nrJNPqJ3vIo9xCePlTl32JqNeC6pGWxu/qpvbbPKqWqLu2LWIKz1MX+9qS1DStgOY3rC6QtQy1sbW\n6GqNpmAkYtfyNkhHuWYKkE60OpQR1efLnVFMibQ32ekN05ICkLW9W9l1t7JR+/bmaP3VL6Q75S1b\nac21kKu0g0clazuwqv8vOEU7HJcXuyHqu8UxfTGaLrjesd399SStCMbkeZpk1bBfvaR7UtXUK0S7\nKG9JPJu4h9tB90beKk/GgC8ldHxFezDSVvvtWUx9s3m+5c7WgtEgIJvda6DPNpW1bVjLRZ/f+awS\nxzFqkDoNYd+Fe63LOxyOtw72QdR10i79LfO2vXg6R9mescdrfGRmwLN+a5YSlT2tW3nW9ldWotY9\niTXPdE/fwSeZ55yLBgbRjrg6d9F8J+PvLNHUh7nsurVGrbESeR9JbMImgWHdnGoXWmn9a1sIJmNX\nUDuBatQBodxY5qul8qmgmr8PrDLq3xmxLu0KpvSocdJ7FZM180U981SKhqMVlLnyNY26atYrA5FV\nnHNahYdat+I4IQBmMNQlcDzntIXD4Xi4sA+iXoWNhWy10C1KPgU2H+Be6CrrmzJYE3k1WzPHiWap\nYzvz3ZrX23MtiYw08L58rpPFidzS5jy+ybZBNTd3t3Qi2o0tai5mXhpAWnKlpuFgzMJDFR0Xea4t\nWff59ZVYw9r1tcZf23TlnNYSh8PxUGNXRB1lTtqYqO5Z5wl1g4diGpSYNFpJ3rDCtAMGpW6+0Xwz\ndfCEauxlA0zErI1pcE1mIojGTFqiiEEQKUi67ZRlOqslSR+AJXW6EzWa9kEFSnp1nJNczN+hBhmJ\n85xyMWHQIgQMakqvYUdjchdH0QsJ01651WImQG0ZEpF1NzAaC0FKVjW6YMmRKaCqlB2pkpl6KvyS\nyTaqJ0Hi3KLrEyVkiob8rBSn9o5DziMW/R0ISTsOGn0ulLZUmXWLkYikpAYwbwKSZZdYVggo0iYu\nSs9nKbugmr61SFQbghZo19FLN0deXQb1GXR+AvENLDEBmBDLeySYj2wFOgLJ/wzAf5KF/TyAvwzg\nzwH4eQC3AHwJwIdE5I9z+h8D8BGkt+iviMg/zOe/B8DPAvgmpN3w/qps7afqcDjuOXa63mOkGS5T\nWCznHXvETMRJq5VsJpYcFNRugth2yG1HfVzTWUkj3SeLEhGyZPlbYpbumMXAZgbYFhlZSqnnj1aj\nvTC2uvZlHn9eo9xH+nC9bs34rWlfVv5t43QZU3LpPt2Su+a9SN/jf/0kwv0HyRsA/gqAJ0XkO5HY\n/xkAzwH4jIi8F8Bn8m+QfF++/h0APgjgp1jWq+GnAfwQgPfmzwcfWEUc9x2+McdbAzsl6uOISGuH\nNexmQESQdXJj7nCJmLXeGSLpExARoNHD0/polvW6fTStY022woQrnBbJTNBpCjYKEEUQRc3IrVNV\nuj3tGEacGZ3fatHtNwkwZFNy/hYKJLSuVdXdTi0YqlGjfNf50pY8kzxjkzmAsj+GfiR/+tjnNXdd\nxmb3RUt2FhFdJ26/+5GQ+SjJFo9qbdJaWk3NqmHnd6s5zmFUW6IOQJm1B6q9Y7RkrLaMmHboP/cA\nBwDfRPKApEn/AYCnAbyQr78A4Pvz8dMAPiEir4nIFwG8COD9JB8D8C0i8tmsRf+cucfhcDwg7Mr0\nragd89FEKPN5zeYLS9RgHsi8JoYw76Ulz86dmh5XpyNDm1TnqrWHLo5OVO/q5WIkNaSqMbYvqyfr\nCmmOq+MUSkmNabfkUcn6lLbqbRFbUGv5mMqqudjWedGIht7XS1m/Xiz2FmH0wNLO5muIpdZi0lcr\nTVvisNR7AhG5Q/JvAvh9AP8vgH8kIv+I5HUReSUn+wqA6/n4BoDPmixezufeyMf9+QVIPgvgWQC4\nfv06bt++vSnjN77xjaNpHiQuozw//Od/GACOl/PtP/HAZDoPLpM8OyPq04hAUwK1A0+cLWu2WlSm\nTF+93tVKMF7EY0s8LtnKpc6SHlQYvVPqspysJzflLg2uSmQ0u1d1xTJZFJgzqHtl6OrldpPKFAt8\nLHwNRqOQJkV7xhyzflna1NAt/f1tK9e7BDoeG9n3V56N2AbG8ljzRW1v+wTaG/pvMen6N2prGLLx\nquqtFwTJtyFpyU8A+L8B/K8k/6MmexEhN0a254SIPA/geQB48skn5amnntpMf/v2bRxL8yBxGeX5\n6AsfBQB8/i9+fjvhx55OMj31qUvXRufB/ZRnZ0RdUbfkGMPuXFi6xiMdH5k0b+nYoobRzCdWNZ5T\n9MQV7W5NqdMdpwQogi2WeFkyQHfcrzfOKXqtulQrE57k4UCjKfcDJWv6tsQSsKzIdrtY3ZQ4Je5X\nXfmeJBPUKG0X0UTzi5JGLV0eWoI5KysPrOwQZs+rJ7/dKAaoz6bdTrRktS3t3eDfAvBFEfkaAJD8\n3wD8awC+SvIxEXklm7VfzenvAHjc3H8zn7uTj/vzDofjAWIfc9S6/paJpFKH2c3NiiDGiBjn9GGK\nf4xAJIfuAOKAKwg4ICRn4vwJkryb1YVMIGAAQhAcJknaphBBTc8yI8pZngnOmh8lBc4IzIQXAZnz\n9wRIACRglpw/k0wBIW29SDQzliIREucyLy1MYa1lImKYEMMBZ9nKGkL9QAA5i6noGRB5DYBgYjIT\nC5MHvDD7qcsMiW/ktgpAmMAwgcx7UzNpz4Gp/YMKGPJaaEaIzADOQJmzqX5GoGAKwBSSJk8gW+9T\nnmRAIDFldg+BEJ2Pj9XMz/kMUhzocn0FSHO+ybow5V8HBBwwIZAIDAhhQggTwORhQNaY2ETI0Ufn\n1C6SPogAo8ZwR3637LuYPqmNI8jaXtD77HiNBHiA8EqRVwdEgghhDhFavrPVh3kdvTDJgzRUoAgY\nUzxyZo989Q04x3rq3wfwvST/HNNNHwDwOwA+DeDDOc2HAXwqH38awDMkr5F8Aslp7NeymfxPSX5v\nzucHzT2Ohwy3nvvFN1sExwp2pVFLc0TU/ZC1Z5xNqgkjpD7SzHCz3UqhL21Lc7fOTa2j071ANrKT\nYJFX8u4aMS+JyrxhCgzM+pkSf9k8wpqkz6N1jmpjg360KS+iy66XA9RduWzKGr9N77RG5LpaW9BO\nDYyM5xeTOOXGLjvBnPNtc+03FrlXuKDsIr9K8hcA/J9Iawf/LySz9J8H8EmSHwHwEoAP5fS/RfKT\nAH47p/9REdHJ+B9BXZ71S/njcDgeIHZC1KlDioYe7L5Ey7RrnX4bVqR0cySCZP/hovDosZS1vr2x\ns6wBRibFhgi3arIlcWvCFntOkCTPLtKFpNX0nLVABrP8h1XGVoY2/MhC7CJGX2ui1nitpiv1763J\nJ8EOgYCl61wrYSsZu3vaPcrruT7P1sy/pqmOBiYRNTpaDUmjZdY18NsYmd71vtEA4/yELSI/DuDH\nu9OvIWnXo/QfB/DxwfnPAfjOcwvgcDjuGXZC1AltFyVITqc0Z21H2C+RSZ1flErWFoFVg866bF11\ng2VXuDgny+5+Dey+ZfGrpUDJ1VHNOpnGBQyh5VLNhjU6mY1eNurOSxSzgdBbltQUMWzZvuPB0ykY\n30NMA120tph9ynplas6lJ5gnT/K50a5rNv16xTWWh2iAHak59cHKbZy48YBydFzlWGuRNjt9dvda\nW3c4HG8V7Iqouejceq2jd0Faqoi92Rt5bk+UeEo0Le13l9Sjx7O59oaZyNwmuDXprRbYkkXVmk0A\nzjx3PHdW3GT6rl7pMV+LEvPez3aXsCmXOy34iaXJeo1TYI3LyDmeRNB1++taXrFqjxuNnaOVlpXi\nqS/v6oY4+W+/09WoThh8L1GJOnvTSyVpUjDhUMppvdU5kAPd7/Ytk8V1k1uTtB/6ORyOy4RdEbVd\nddp2pSPTYA/TrYs0JuGkman9OCW1oR9H3WtPTGJU6mM+PeuXe7JW+aowRCiDC0hMDlF6t+SxRx52\nNCEsRRalSPMLTRNVIXt2HZGaLG6/d7ALtCyWNLZ4lk1qrcPSWN2+N8vBUprfH71XLMl0O+xKmjV6\nXL+5R5V2613tfy/tCgtZHA7HpcROiLo3DuuRxkLWTmwyqWyc5I6k0XZ99ffS/DzeSKOVK0lgqft4\np3lqt6oEWzTpQGQ3doQYG9N3dnJPDsQwJC+S5uBXC90iDXZp2jr3EwnHWmwd6xp1O6SqJds77WdJ\n1D0J15QXHl4Mtk6rb48OChJR6/Yqh0U9HA6H4+6xD6LODBNL36zMVDWt1P3VqFAaYpPZazjk+et5\nOgNQ41kRyXwZxJJNa8xt5xpracEsuA5IREhYwl92y6JLhGjqAUKiHSTETNCJlKnnmDaQhEjd8GIy\nZlAB5giEqBt7AAhpowpOybwtCEnfljpVkDbBIART3tFqTnLiapYnLUQLZQ/kPFHAXNcSJDy716X1\nU2Vzi7KBhwRjK44N2RVxonHqIxB4JT0DY+EgAAkTSujPKS0VS6FCY3YMRFrmBYA8gIGYY62DGtBn\nRByaOmQCLyFlkZbPdRt86HtyJnPxsJ844SAzIiZEHDDjDRAaejYkvwIEBF0axzdSWeFa+o75/WUA\nGCCztfKYAVFuyhjz2yKCELAxEHM4HA8z9kHUF0DAVeSVqqlDxesAgOmeLg23eU2FkGPRq3XplzGj\nruZl1LKhkrdypzXJ5oAtmlQ5paboPZ67YYRdACxE7xy1Ls/SXHxR2GmDdsa21/vPDHlpJHaAEiA4\ny81SRzA1Wlk0eahnwrY5v7qDibk25b3F1RuA2f+havvVlc9YZyhJ9pLvlAhakG3n2ghH2rJ77KsG\nEYfD8dBjV0S9NGhu097onnsKoxVXguHKUGBbokKqqj6KLp8akaVqqssWaJYSGR62hvlRAqEloUwv\nJZZo0pZb03c/TXB3IJYaodKjwpZcon4KcztomyWrgwBlXlkjYbIj4GJRaUq1dZTyVS0smbqLim8m\nTETKEsJUnp02QB6HmXaW7LWvS+6aBpCjvg4LqZ2sHY5LiV0R9fkwQzvIvBsygFafuntYL/Oa75bO\nvl26IRLLRkfu6OhgcOIUWK9ktQZYotbS0J2z5+8OagmnbJtxk/uX7gCu1BjLFIHIoW0600iVgi0h\n24TLgZGnDoSIAAAgAElEQVQsrrfPuvyKVSlG2a28plhujZrLKNq/Ga2dEGbbhm5t9pBxOByXCrsi\n6vNo1NZXW7tJveteC0QkTbYEAF2Z725u6rNiNZJDtUNag3nPvlI1atNj92StoqTcbRzqRGpVoz4A\nyCZYpHCnzHPVbUxqJZRRPG+snDuOQtI0PKWsbXItuQuQvN61HXLkNSGinEEJsLZ2CjZad6e2AUms\ntaBfY53KqebvdF7X3TNoKFJgjjH5BOQdxJP7WN0OVbVwkRSuR5cK6px1s/MWt6doigsDDVE7Uzsc\nlxJ3RdQkvwTg/0Hqgc5E5EmSbwfw8wBuAfgSgA+JyB/fnZiDslEWKQGoa57HgUUvXkpFjT0upsMd\n62hrGGl5a+VtpOPgWEal9+X1GvRapsPM7xrnm2pN4WKNod7kUDX/NMzQWWSVtTdPr1kMlvnZtDo4\nq0OJah6vMtlvW8uaN5md7HSQoGu0T24LHH91HA7HQ4t7oVH/myLydfP7OQCfEZGfJPlc/v3XN3NQ\nzTJrjjr3OAVLLC2JFY/ffGZqvs3cYA4PImw70zagR94CJGtEs37PMV8HJKY5xRCA2WpDIoDUNcip\nnDkritlLmnPWslTiqWhsiDWeeV32kygo8ICq5abOPm30YMvXKqXgJ0pZwUyJJkU0gvFgvKsTgcxU\nTS9talLnY7Vl1OSsWjYRuz2Z6w5dczEHk6GpTyxWd02c1piF7vmqg2A5RyRXd6L8Zo7frlnlJ4w3\nSDB7dBNACm4TMIXq/ieocpVzAogkU/ZhOoAMiEyrpCULThFMgYiIYOMfPyNml0aWgDP14RDIyxkI\n4Er3zMzaddH90qXeF2jmxcM9HoQ6HI63Cu7H7llPA3ghH78A4PvPm8Fya8bBZwX28gnJU3ld2kId\n1tSs10tnerQWJ6QB6npczXPKn9EY6lyT0os7F3XZzLfXNJd3rZfStyawDPma6rn+vKyWjMGV5RAu\ntE8PiZiXPt9lMNHEFqv6+FYNgxnkSRlUscSFvzuYPOxyMp0ruHfbRzscjrcQ7lajFgD/mOQM4L/L\nm8dfz9vjAcBXAFwf3UjyWQDPAsAjj7wDVw7XcOPR96Rrw6JamrmXXVZvku3Lv3K4hhvvfM/KVQzu\n3vp9ijSjfNdzu3q4hpuP3jop121JbKo1ubfqY03ONdXVwzU8/s5/3qQbDQLuDmtS2aFCmzrruIer\nePxd/9w5yqlt1OZthxSnPm8r9cg8D1w9XMWNR96D871DDofjYcLdEvW/LiJ3SL4TwC+T/Kf2oogI\nOVYDMqk/DwCPf9tNef3sNdz52ksAqgm6eNQM9MG1bku7z0QF1TlqS+OZJM+I5iQTa1cuAN79yHvw\n8qtfAqAxuNc0TUFqUvVI1+szNMb4gp6aith7CN1UO51RY+sMCaYuAtx45BbufO1LiGIWDFGyo1iW\niqHEDykSUzcrsXOtaoptpDTot5a03xNK8JaSLuDGo7fw5Ve/bNroDKphR0ywbl/qCpbM/ml/Zkis\nCiaQ11K3rZYOZgimEiwmOXPNaT/pkrY6BRLAux99PD9bm0/6I6hvmgbEOfAKknk+7aCt2jRBpPgl\nUtu4RJ1be/es9aFz6Mv1v/HIt+HO11+CThU4HI7Lh7siahG5k79fJfn3ALwfwFdJPiYir5B8DMCr\n588XTcc8wpoV8CK6rGhs8MxZoowdiJGrreQBBA3JUedQpY1xpjK1Z7g8LMXEciy0G20u7mzOHevD\ne5Muu28xqcweXl3Ox1qzN3dbIjoz5ycsd7pq77LDGrt23K48Xz7r9onr3PqyrjZsCdB402vtRwo/\ndWa7JeDe77+VasP+oJHvmjL0nHQVdJZ2OC4rLjxHTfKbSf6zegzg3wbwmwA+DeDDOdmHAXzqeGZL\noqnOR+eUa/B9rItTl7GAEh1zsUVFycsuzcqHhaS7slOuvQS9ZLkUXa8kGJQ8qtn5MGpOuwnK0jS+\nbrM4rZT+1ertCaGRoP+Y3Ubydw4XyzH5pdyrE6HGEE+OX1aaZKXQf7bUQvOjdVDUu+tWHPrRHC8G\nVutFo9IP3j7naofjUuJuNOrrAP5e1nYOAP4XEfkHJP8JgE+S/AiAlwB86JTM1BSbjrVTUr2nroxF\nNjbKJLCLSycAIOt2lCWz3F0HEzOqW5SaPINRO+M5XZ4myf0jEaaQNSCA84yylRVaj3LiDMlDO3e2\nUj3PWyNo7u6nvHRHkOJvR1EhMdkg30XnTdfJRD9kpY0pqF5JJEet17NOOYE5zncx5eYFuiJtUBel\nbcntqdomY47dzQDI3NFuJlGzxaiGdrXPl5Tq7Z5PBglIUwVajwDgAJGzkrtwQjE+CxA4N7pzkUOA\nZoDA7LVuHLOiEMV7vtQipsBhJTNCpvIY2kJCkcTQqcpGIMY6mJumtK91VC1ZzdrpeiivIbPXeRab\nwBxrwVGiWcfvcDguGy5M1CLyBQDfPTj/hwA+cDdCXaQ7UoMr+zPGhDgyyAIwgSqMDATGWqGVcs0Q\nfco5zdJo0UWzWhrL2+wIWK9gAKD6PNchzaj8e9LVm81KmtPdN7vjYctQtevWDtHmbrevbIOujszg\nizK6+kt3dhRY55Sn278NwMC6YtdPW7ZHNb7bOoxb1uFwXGbsKjIZcHdEkvarUOJCY1LUTlB30RIS\nUWpXWebFgRIJaiicwCTsBF7EtTwBxcZv8kxq2zqYNLi0j3LWjPP+1WudfesaVbXCe0kKo329j4Vb\nrUTdWQ+kp8TWAmKv1O8tal0bumzc1Y0C1NpQXoPFt92YA+bsbHLVZWFpcxFbTFt+v/mHa9MOx2XF\n7ogaOJGstT/Px1JIVk3Oks20KUGU2pVr0EcNnFFiT/Ua9SggNcU4N1kD6Ej6kW7Z10NHCJ1NgLpV\n5bjutPlnG62lsiphv4GnlemCGGrUNSRqb91Qo/+otWTYRhFtjDlBX95oSNRqsz2Vdm0GbaUxCVLb\n05A0OzGG9hWiTJG09Ru3+EibXnsyTtYOx+XEroh6zdy4iczJqs2FkDTLsvMDCSCWCFNCVs2PLO5M\ndtq6WJaldxBKF6jLowoxVPN66vxtcA+TZljpXuPLXbXIWKsHwDnmMYQOOQQyxxzkTFB9xat5XLpZ\nY67JcwqK2b3XZy0htuRj/ddtyTG2Uc5UOnKNqFvd2ZYxLZYKVD1X82BDnGkYEwZtEe0pMyAMZkBk\nSVXHdSSKz4KUbTr1f7OYpUlz8rqIr89HZbNSOkk7HJcXuyHqcRe7AUOsNn1xRBvYgAVImjVZoo6l\nJDKYn0aXCe2F1vy9Mmd7Qi1STNJiFtBs8hKtlY0bRGNQZ+1Nsjy6w1SvM7dDB/3LRZvfLeyWIIqR\nOdy2VmhszCrRhJYOLfqtQSuWZ8eaf1/ztScHYDBYqkMxTdfWu05byOpUyHrbc5Fq/bfD4bgc2AVR\n993PKbpe0ZFMf153o1rPfdntY9WZbGx6ZsuAi5yPdb/9JdXIibJNk1Zs1Qaqg4aA5EncDSJOJLIL\nd/vDG6UMGqr+uo5K1Or1rUhe37O8frT43lwsi1Stpj9qAcHa1qhdgBqpZ9c0aujgyTyLspc2+oV6\n7UajC+18WHOHw3EZsQui1g5xbU62blxoNkOYgElCvieWfTE4n6EsI4IA2WFnCn3c7rplxCQBh6BL\nqEKRJRCoW0TMQN6+MOTlQlmVTRkybciAeIbUrDl33UayzFt2y6HmWOQQZHM8UzceFxHOkPNKcumy\nIiBFIZtkquIwpkYCAIk5TzucyJttsL4C1vwfOCGaccOBk650S2ULTVAQnZ9u58bVuzlF7YoIIZOY\nMBkNhHlTkFC0ZDULC6ZS87k8/0qIU0iWhRkCmWdQBJFpo8u631Vy46JYvT6gTja3ZvT2R35frOUE\nwOs4A/ObOGWq1U05QmngJClzm0i24ugSsLQQQSBSzfsMUkYD6a/ZnCWkPC4aW8DhcLy1sQuiXkOd\nU627BYsJUjGmdbvRgn63u0a3pmAtaw3L2cNWe7b61Ui329aPLBX3uyZzsGyptkUvZSI3K2lreI9N\nar2ie0lJJg9wZO5dQ68LTqYGfXsMPLthtV2WK5WOxyWCktZDa2rW9O3q7XUz+UVhg9FKU4fjFg17\nlYt0g3v6BnDLt8NxKbEzomZzNDZJ5q5Y2k3/2giLtuNM3eKIWvXqukl4TLLjHbRGJayZXGv+xQRs\nUpeNLZs56kpylHbuOutvw/JsrOq2p6epTBe9m1sy93wxHha0ZJ0vlSAwUspYUmlZ5GTqtiwpmxXy\neeYBRrtRxkBfPgHbz2w8hBgNCOxwYQkZHA3l0GkOt4U7HJcWOyPqhFE32J6X5uvBYasTP68w552J\ntHTVO5n15zTvXrPX73qcjcMDgjsv7FBjI4dOpJ6k1VR+TIalk1rV2EcObNvoBxlbSOUsW7a3GJzy\nfG0glw69kn7e18XhcDw02BFRtz1T6pcqRVdTYV3f2vRjVkEcaDfj6FTVXDrQNdGTWslN+m7amnJ1\nyRQw2niiRV3l3HfxqU76y5qYWTcNMfcI2h2o6rXexNzeqe2VstR5WWzav8catZa1xSa91WG5qluf\nt52THvFVCaUKpHCqJCTG5kmK1uhcSrW1a4yu1idWn5UGQRmbv0fDl6X5e8vOzXESh8NxKXDhTTke\nBNIK1wkon4ASVxlrlGC305DyW3VO1fvs8XpePSxp6l3S/e5Vn21VaDIpZpPD1ORvy54geYtF/STS\nWO5C1ZaqO1bZqxEzJDlkqQmcbaiRbfRaet8eVtPPz0XMeU7ZUawONAjmjS/XS0tz1HnAJChxy5U+\nI+pTv9fcpptaJtRBEBdtu/7MW7N332aloPbYSdrxAHDruV98s0VwDLAjjdp2X0nTmjRwCZinN3PH\nKEC7zXWohNWq1k3uKbhFNfSW2VsSkFBumRjynGc6TzB5lTPtCdyu/W3LiRJzXokIhar26S5OlVoB\npOuS6aysL8v1xVkmhiqvoNO0yzxtBMq+y/kuapAX5IAvJnBGJrqJV9pWkqqDd9teFz4RdlpqPh+n\nuRAxswd6AAAyb76BLHN6DiIxBTbJG12ksULEPEcE9UtD3rzCjAkCAkKoz3iOc24X5L2yWi1fYoDo\nxh8kmLcPrZtrGAcxSRaLEukOGrBU8kBKh1MC4lAGB4IZZcmc1pVz+qnRU3RTF6kDTTvUaWJ/m9dL\nYvPkHA7HJcOOiBpYaqR911S1j+2ubomRLtx1582vZadYZYp30WUOdW2zFzaKqdbq/TZ1K/kgN7Rt\ngsHxqJVOsymUtKtZtOqfrKU9pWhr8QfG1V5kZLeurJkTWFln3xe4JRCwNIl3wvVTBmtbZp6nuR0O\nx6XGDom61ZSX14tPNEbzemsdsaU525Wra1AbxsLOkC5Nk1u6Te+p3qe1uSVFO6iaXL+zwBI7bbep\nzei8daOyH82238XJhu+w54+RqzRftWJVMyUle2EL1odPgHqdN/nlZeeLu04i6iXZjsZBahSoNdah\nX7+nVVc4bf6dqVu3syyxaFELa8SUcww+HA7HZcfOiLrHuqq1dPVRo+/4no3ZQCid2i56nM/Fe9We\n9s9355iALpLbccI/Mb8VfgeUl/KA5+ii7JgJrhUnAv0mVOvGhAIdvB3RqHvebFIfk7dfK6459BuJ\nKAYCa12so7jD4XCsYGdE3dsER8tXqubTnwNgZhtb9OE+FAIbF8ueZ6Hvelf6zKte3PXOVuLW/aix\n5kr+pYRWSKnXdq30a7ZTdmlQ0rGsyW7rE5uNR0yep0Y9aUg6Fu2xSnhkcCP5j5rNTfWKYrqi3I7B\nZQI7DjBF2GRtEWuDGWK501n3fogRWDcJ0T3Rzd7oZc/xU6ztDofjUmNHRN13sL2Rur2+7Ku3J/4s\n5fcG7dCVu1zMZeU7rlXbHLbM5EnxHJiRTU7Hy+wHElbmi2jcttw1sC263JZ9rVnb6rhGbUhaBy0B\nkLleBiqvQbDhlj6q78q+2w1H2icuizeg3ACg+uYD6X8f816wU5GpGv7cWgSsabwX3+FwODrsg6iV\nq+QMhICcwEBEuZY7VLMrVN74QUoIyVZHFRtnu4ApzjQA2Lx0uVfUfaeyf7VEUAQHBgh1vWwsm1oh\n2A0k25n0KKkrj1R9UjBJ+p0W8UwmVGhEiIeigUme100XIwInnGXXtUP2Zz5DTF7pSoR5MRMZMCNi\nAnO08tQ+hIYJzabopsmJwEpjuuQpAmBoBxt1WkCAmGQr231K8nqepuQ5PwsQYrsJhYS84KwEFM/t\nmrcdjdkTOoCYohngMHn+16XjRH4U0HnlVGdgljNMeeetGWcQRFzFhLOy6UcekrHfQlQQZS7XQw5R\nSqBuaZ6V6SB2eaC2cwAxldFFVZol03QESUxhKpaSGAUym7TdR8xWqrrTmzO5w3E5sQ+iHilv2ZRa\nO/sasSpRy3gf4zG2bKdmDhNAXbJzOprcV29cs3EaAlxIUu8da3njEoZylRJ660Gl7tYGsE4KZCbu\nohhaLXoNdohQBzGjmOuxSd+3Wx2U9U+RefGbQN+WsGofaWvYTEYcqYfVz+0gkRhPsOTyZTm9cl67\nhy/QcjguJ/ZB1A0sNc+lw0WjJy415nG313b22mmrJiRdp6+art0FakkPo5yXJaJLq6UsyUH9xG3u\ndT7Zzm8rBdXoa0zrsHNa60o1lrO2nm0Te922Qz1TtWqCYCBkFkTJC9VITNN27JwaSUzSOnBJZb6R\ny7OO0bFpqeWztVbw2hJpDbX6D2jwlDhYTLdNin0LrNdo+RboBEtYpCkySNLm0y5i7dvQD6zaoYOv\npHY4Lit2R9RWi6ydUzEIGh2s7yT1/qXmBqDb3plN+rEuNXblSmb3MZEDgAz4KpnTxxq1pR3V5WvX\nHvMgBaiRx0Lq7Etgl5pX21LLktLftoa1/jUMpqVzvdL8ZiJAnX9OgcHChv8ZS5uVWtLosNJSWyQQ\ndL56ACmS2prkrTnNNpSFqKXmPR5Q9Rq1jIsuA6TmhPm2nt+qYddNVNJgZVu7r2/16M10onY4LiN2\nQtQdrejGx5zM2YiIM+SAl+CKR1GNN915Mwu7jq/Ous6oEZz1ykhrq9m1JN6sTh7G4Qau5K53LjTM\nrLdZmSpxaz1CNt/O5feEaDaOrhts9fPPPfVUE62U9JIJDSVlNby3hnCrzSXSTUxbLAWbTmNinJxz\nPXN2h7zvt5U3NL+6vaRRn2NbE0AopTbVUnBoPNvHUg4oc00N5+iN0MSTOTYDmzwvLVEA1mmCrZza\nlQKySOdwOC4PdkLUiqrZJIXKUmDSpqthc83UOjZ9V/oLRh+LmdKlRHAW1NLWNOqt0lra7dP31N/G\nh65Xar0tX0TkpWSqbTau0Hp92fHXFO28tw4EqrZd655SLM3eiXDSMizdhpMQxBgRwrr5m6aBhKph\nAmQ2VqsSyxSiVcrmJiqVkmDIT7Gi6rN1YCP514RDeWtse6xDB3lbqexQaGSPWdozUttF6Fx1CMl5\nby23fslgHOTpcDguB3ZD1L1Ok2hE5xdrfKvaRVff6fbOrePOvFnyZklFrHeHPSFbYi7XBlZTAVDX\n367JYnV5lQvmvCllVPBA7tZg2pZrt8Ko91ea3qQpEQSGrBmmrEV6t7Aeyxyrmb3eGRbpxXxUq54X\nOWr7hEzwMW9YcugW39mArGPZbFkjjIZBW5MO2Qkyz003Vohu2NTT/rJUJ2qH4zJiJ0SduqWQTd2S\nXYIoQVVrMGu9AYCQYIiZIHIXFpPWFHQJjOTlQGpWDapVxbzZRqVBluVWdZctAUGZUYlNHbdUJ7cG\nY13YJRDJM+ulx801koheS58REWn2XhI1h0uOonUFMZPSVHYQm9OGFSoDq2l4yuVqXWYRzNlKHiYg\nzjOQlx2F7AAWYzdFwDQUmsk6dyxpyZIgLZWapgBGScFacrhPxFCWWJGm3UvOSVUmAUoEYloCB+ZJ\njBI6NbdkAOqOVOo+OEMQqw0gm6GnrK6L6FQJMeVlehGvIRy4GGVJFq76QagmnZ81ZjC3uZ49gEmz\nLZvD6Kgs+QxE807VNwPF9B6Cto8AMiOEqv1Hkfz8dW7d5EIiSDwyveBwOB5W7ISotZtUTaY1B1dT\n9SmaRas5bmnIipE+BQBzY0ies2RrQUrH5R9Da5ruZa0DheM6V3ZkkkqStjJxTuRcA5QJ5plFK65S\nJ01Pt9lUEtN1yyvK/FEtHDxD8T0oiY8boW2KWvNqms5DvM18Yu9/qDcWe7w9Xc396eisSCJH/ncZ\nyVp+13GIab/0fIMZHuoEz7Sf/zUdDsebjF30BmuhMq3fa5seWAsruXQDO76oxXoFt+VYWq4e2NtY\no5eVsgd3VslHpN8b3c3dqi329cnqI0MlDAGyc9NSvjZ0qh0aVeuCleqYsbgV05iwCaAJYTpI3shl\nv8/Xzo2wrUFkkJTGrF8ZfsMRPd23co2LA01bt9nUp53m1l1zdjgcFbsg6gpLTDpfWjWcdmcjYtyf\nnU+jBbZWzfZ67sl01OH0e1o3pvN32NmavAgksjrgWSljTcvXICLnR28N0M82Ufc4Zai0uKddGN6I\noG9ZlZKLo2RWBy7yPCxKC7D9PbJQOBwOh2IXRL0kkZ4igtGtMwV1y6CqRp3+WsP3MXO17pWw6Mtp\ndUUaYtsi3lFOG2VjPBxI1wZa8+IuNKkFVaO2omiAjXIpmyX6gCd9uTYFDa2MddmtVs5saZvvhHkJ\nW+NWEW5N9kcHUefWqHUwaGon20/UatSrgyRj+q6+DnUA6kFNHA5Hj10QtVJDvze09Qpe0/CWaHXS\nUzq+3vys56ay6hmoEbPH5vjjua9rrkC7EKnyiFUFxaTU1rKShHLzYjpWgOkAzHOKay75ZJgAiWme\nNN1aHas0Iro+ieTIl67PxWS7VbNBRVMw8XRH4LqteCXftl2qvaUGwRm/ztGaF/qBwqKMfggyIbW5\nfo4sQcPyfS3XTbkEECTFJY/5XypdXSYdDocj4WiPQPJnSL5K8jfNubeT/GWSv5e/32au/RjJF0n+\nLsnvO1UQgZJV3oCBxMSAwFD69EBBoGCirpOVQi7VQ3gCGRBCSKEuybTdsaQ52fSprlGBgIT0QQCm\n/B2D4RUAkBS0g1L8u82/khsmXkWkdrxAEEAkeRBrKFRgguQPW+8iBCGmvP43t2j3CSBDjgamQ5nk\noc0pmXlDMDwYAJmAsyjJaz0gz1UTEpnlSh8rF2IsG2gQzPWImOOcvMsDU7tpW4QUyGP4AdNGJwQY\nAsgDKBMY0xIvyc+TACgCkRl1/ralLoEgMm9hXT4BwgOIN5DCzuoWK+nuKRAhMEVPY0BE3ggkpwmc\n0kYwDBDOAGeIhPT8OYMUTIHglDy7NXeEtDFJCMxe8dbyQxBXQfwz0Mces2RzDDiLIb/nAYEHTOEq\nQjighMolaiAYQVre5cZxh+NS4pSh+88C+GB37jkAnxGR9wL4TP4Nku8D8AyA78j3/BTJ1U0Jx2hN\nrOeduRPzAY5aObs7+zN2pyQ2v7cxkl2GH8GM5FCVP6Wff1Cdcq9eyuJKD1sD6w3+4NG/JxycH6XT\nodVanheVplB4/uiQYOXZjxptpXiP9e1wXF4cJWoR+RUAf9SdfhrAC/n4BQDfb85/QkReE5EvAngR\nwPuPi1HnopMKGAaCnUK9hCVpve983dvdUU5vGletf62zTlRntUCdCH1QnbJq+RaxauoDOWobs/u9\njRI+9FxtPJrvVxu/vi/ajuvvhX1v6lE/kGol3bCQH5FVkZ7t+rNPop/E1w/0nXA4HHvCReeor4vI\nK/n4KwCu5+MbAD5r0r2cz21CmgMb43iRAnV+VjvmtgPXu+1VO9ta87CePz3VpGM7XLDxrbb8lKUs\n47JGcRtFzc5zCqKx4WZDLTSOtqwsXRp17CPK6XXKMXovgFx3ah2WqOEt033nIekqo6X6ETboS1VR\n0oiugUWXte6tLG25+kwUofzdItCRzSQ0sb7rLDVX/jcr1chVsaKzqwZlKqZwh8NxuXDXzmQiIuT5\n7bQknwXwLAA88sg7cPVwDTffeevUu7HsdtdF2Lp6ioH96uEabpws27gUrpDeMh0MHx2TOuHK4Sre\n/ch72s5dyp+jZS4hg/OWms/HGFcOV3HjkVuNPHYw0Q8sFk5Yi6Nevr5tW/k0euvI0nLlcA03Hrll\nZDgnG16EPNcr2JwSAFemq3jX229esCCHw/Ew4KJE/VWSj4nIKyQfA/BqPn8HwOMm3c18bgEReR7A\n8wDw+Htuyutnr+Hlr34hXaR2o/28opppCTJ5K6sxU/XXqrtKuZI6vZg1GAJinJwIQDRQp24FoXcn\n7erb3vkEfv/VLxbZueGtLBAcMEF9eQXAAVfyFXUyq6ExgTQdzZwvc5hNkJjnNd29Lf/GI+/Bna+/\nBFqVy+ywtbzDyjtKQUy0umZyk9KZdA3P2eeyNhS58Y734A++/lJJY9MFsDre5faOnRm7n59NgVrM\n28HtWecQNGwsc4jXJAkJ3Hjk23Dn6180RH3I74XWvGsnfYcA5Hio5ULSqK3unp9ziajTfkt+D8H0\n7IuPvQgOU9p3/PrbbuKVP34ZQXprgcPhuCy46DqQTwP4cD7+MIBPmfPPkLxG8gkA7wXwa0dzyx1X\nsQJ3OlRA8oRWwo2YUVx/RYNNp44sxtRrq/dzMiGnzIubWq61QBCj4CwSEiMkCuaI7NsVc59r9L0c\n2itQPZGzt7V2zkybQkSR5FEdJwSZEvEwxclOVYwIEhF0j2YKIgUzI2ZEzJLkSsOSgJD9wNMn1bf8\nKyFAW6elRLIsHwgxC3AmgrMcAxzCsrlGkiF5yTO5tydvZwoCCcme0ROnVBdI3mgiEzhHs7ypBqn5\nUySygOTZrqQJ49UvBGJyDUdtYRu1S3lSJzTyMEyY4mQz5pUBKf73gcQVhvQ88zy2TVPfslA+6e2a\ny6CkjHfyicAcr5yp1XNTpTrlIZ56G6h/fzu5Uf3YpwOrh34eJAYBpvzMhDrY1Djx5zZcORyOhwBH\nNSbbGxMAACAASURBVGqSfxfAUwAeIfkygB8H8JMAPknyIwBeAvAhABCR3yL5SQC/jRQk+UclrbU5\nJ9TAuuyYjusUI7visQ7Oau/997iIZFmuDkE6IDiuudbfSTK7KUTVHHUbzvUZ6b6k7ZbRO9pgJsvc\nTmmti0G1bjt3uyXzlhTj+wjrpT/K48FqpKs10CZYmwR3xdnhcBgcJWoR+YGVSx9YSf9xAB8/nxhc\n/JJCXDnfZUmj0s15m2fdGvG4DBtEbQhZzKRnYx5d6WSrIX5UJvKuSTVN1YvXKX6rR19eWYbRELAx\nMq+HUr17ENnUi7TTFKVrN5vuwmW0DnDZTlPaNf1dI/mVFj3e1AVHhx+2cn1Fh/tvmnfx/o2gHA7H\nzrGTyGSKSstp+m/cM23NECdbpLHDyvFOjr2LLYAUGcRqf7UIJWnVqiWx0JHOvPU9rzlX64Fq1pVo\nxkRtiWe7xCIyhNaIrCRd50XtDOj5om8PChyd7wY1IFcfixq20V0vM78rPgJ2WCflW0pZY2GLHbtp\nr+aO9sIG2B1pFDi2949MGXoTzf05XXkNXdN2OC4ldkHUVjvVjpNM0ayaucmaAiMCSxeJ9vLxHo6M\nWPaimpftxKUQsjCNCUqPSkuxK+UsLAeE4Ax1htLKvJXXsjXGOnT9FvaznNJeNzlfmA+OWbJpTP2Z\nPGOz83LNpjfRN7aSVatFX8fsk0CbV8eMrBna9qCNJd9e2KijNb6rRLn8kSGkyRdN++kSvdIW/fot\nh8NxabALou7B7IA0vnbkrHaKdpHqUR3RxnEe0UZGUqE7DVzLOYWkZXAmdcCJruoK5a7k7teaHtre\n294h2Qe9vcOWuBUy5G5BpJCsOsgJ5eyYuy4CyUFBbZns2lTLW2vdRfnnFqw+1bg83T6m5UOqCGzf\nYekTOxyOy4IdEnXtGWk68j5F7Wx7vbAnxPNM7Fm9bZRXLapsriQAqZrTenFLmtbvNuhkOhNzesGy\nN7eynt5xK3HYvbB6c3cxLZ+c6/lRzcrHViyfn5TsxIKWMDaw3x/CGz2RzacUTKL+Jmzd6HA4LhN2\nQdRq1WOcIABmChAiImfoMp8gzAFA0urkPnKWZI1DYs4wm6R1H+Fm2lpqdGpVjokr+Wqs1yQ2fWay\nhoY2JDdZdkIUVMW6nouACCLTGlsaqtINNurdc17uVOeN7dWUgjgwFBKaywYWAsR2dpawlJi23rDX\nI3T+Ni8aysutdE5bBxB280WWaYpQ123ntm61Qyk+Btp2LPef1SERVevVeqbyJxjTr+VawWJ2o1gE\n4lSGNdbEH3kw8tdlbNUHQOk0aMngNEMEiHO5EdOEvIbQylpLCvWlgECXoaVFW5D0u5qxA2Q+K/fq\nBia1waqFhzgA1FCkDofjsmEXRF16eOZeMbOqJTdms6agN9jWzQ71d4XtiNf1RDW0a0o1WW4YwasG\njaRV1ynNGWJceOsiM/WpzrlTAImJyFHJFHkzyUTKZ41ObZ3Bzqm75RTt7K00tdU8NKXV8LU8MaXY\nIK1GptFjaNDKOCEMa7JFSmv2kqk7X+0jWwRnd+bS9cqou6SFKnWU5Vu0sAuw+UoEnwOhtFu4zilN\nIefR21aHZ/UNcDgclw37+D+/9POSVVJrwlTaUppuTcFVP9QucG23Iq5+RkQhXa4wMkiOeGElRdFG\ne/N5LsGqkApaQmwlaPXYvq6tlKdg5OjGjTYB2hZik75tk3I8EiUn0SAh6ZOdBYn6zPvPJliy1roJ\npAYiMR/pm3ZYT/s+RSxn81HHIOZWu5VnZt2UWxak/1ebwzTKohxbCdvOo/fE4XiAeOU33mwJLi12\nolEDS+2sXVra6W0ARrwQzZXTO7ae0kd5a45t10ksybIdaLTnR9IHc0VXNqerKVAnjd4rJYdevot3\n4da4TvOt+VZmOjky1lGtupY9yvO4bQCQQSv00tfjNStEvXNtaxGuzSN3CAiI7Lg3C2SHmsOMVrVq\nh8Ph2ItGraDt6ZZatNWuW2pcnjOZojUjLj8jvVSpqTWx13W5VqMWIM2fy3KTSyj5NrG3VSXTzvu8\nGvWKRruBi2jUrT6N7jyWaTfEuZcadbsASmsn1TG6aLz6Oq21Uc+qNbynninR5fsqDzRq257lnWVd\nP9+vo2/ed0vSi4XTowHhNkj+BZK/QPKfkvwdkv8qybeT/GWSv5e/32bS/xjJF0n+LsnvM+e/h+Tn\n87W/w61A9w6H475gR0QtSX3JMaEhKepyhOTdmqf8LYgmyph1jmo16dB91ona7hiM7m47bCjxn2ES\nop5IX8EQiWrIui+xmf2mlnCGtJZarwWkjSEOmMHmrgCU7TC294saY7nCGF2t4uJD86nn+xZa2TBi\nyC0tm2oMc/2nv09BT7/22ahU0yJ1LwOgUdWZ46pPmBAk3x/yhykO97I2xrRtOddwcCJo65ho3eds\nZv1AVWXUzT7OFY33bwP4ByLyLwH4bgC/A+A5AJ8RkfcC+Ez+DZLvA/AMgO8A8EEAP0VSm+6nAfwQ\nUtz+9+brDofjAWJHpm8UF1vJwbOrYpXIIXnVhpxGO+PWuCl4A9VMazS+PoCF+Zl2JtI9syzlt4bU\ndJQ2xWj6bBq9XaTMUQJMG3SIIEy9/zZN2fWKZbeQTcNVEyMEB1DOkB7dlIcvQJS0wcNS+635TrrX\nda7ljLl4y4f8L20EkfbI0pZv+ZY4UCCMaa4eyDtTpT2sdY+wNLxi2RGrxlOzZmYlsJ7oxTpXp7YN\nSr2AxNh4xM/6pISYJpTlclEEb8zANE1A9o6HTAjFaTHVseqIKo+A4QCIIM5ZVhKcmHbu0hbRKHjq\nna1h7UXbONc5/54YTQsETIjG05vV417yoNW0xzIA7DpIfiuAfwPAX0LK73UAr5N8GiluPwC8AOA2\ngL8O4GkAnxCR1wB8keSLAN5P8ksAvkVEPpvz/TkA3w/gl04WxuFw3DX2RdRD9ObJ9D0OYnFRWE3F\nsrgukQGqVn5By5/0B8fmTZel9QbR5RTAOixB23NLU/3xWo78xNMxy5ljq6SHGXQSA5JJtNcyN27p\n8qz3r5jrhzdvyS4m3/PigVmNnwDwNQD/I8nvBvDrAP4qgOsi8kpO8xUA1/PxDQCfNfe/nM+9kY/7\n8wvY/eWvX7+O27dvbwr4jW9842iaB4nLKM8P//kfBoBSzl/7rrPmd8G3/0SS6dq7L10bnQf3U559\nEjWJsiXVCURtDdlh2CEvDZ/2vC796s2yy3nC/u6B6GoGNb+XEtjS18281sisqatONp7FXIPqtb2F\ngEVGu8xtu46WqPuUunpbcznugDYqK2uiGtqTbbr2qM7lV1NzPd84ZEtNUzNZI/ENiYsSbNP3/gkj\neR8YDgD+ZQAfFZFfJfm3kc3cChER8qh7/cmw+8s/+eST8tRTT22mv337No6leZC4jPJ89IWPAgA+\n/xc/DwD4S8/9IgDgS/9hV+7Hnk4yfftP4KmnPnRfZToPLtMz2xdRs/shpgMlWjVGqvanBJaIergN\nEaxrlt0eI/2dYMmZA4Nvpac4IN9Wx106gQlSAAtbVjb3cn3eUbCc/Y3QTSlSrQs5kJszu+Rym4u0\nn3ZriG5LH6NfQW2pqtWoT3CAEmBJkFICwTAv1xvum2JKIggEMXujpPNT0IGDsjjRsjewzHxd5krS\n2dzdv7ND2JX5WwO3tbzOTfUvA3hZRH41//4FJKL+KsnHROQVko8BeDVfvwPgcXP/zXzuTj7uzzsc\njgeInTiTjTqtrFWLXrfzguqgU89XD+2RViSwftSVZMoKXFRKJHpv7/rdltXq3+162bYmZqDRfG+3\niqU6O7Nd1u0yr+nO+Y9sAqUYc4+2YV3rW1tpbKdoP3bw0NJrdefSiHLHK2kFZ/nUSF0mYlebpNaB\nRnM2EgBs5OwUcyODdJ8j4g40ajINoOoH5dPWz3xvN4wRlt3vbYjIVwB8meS351MfQNoj/tMAPpzP\nfRjAp/LxpwE8Q/IaySeQnMZ+LZvJ/5Tk92Zv7x809zgcjgeEfWrUGm/yBK1udPv5NJeUmw24mQim\nj082oqZ1LOeE+8jax/PpYpkVyXQwETN1VzPzsbz6GeVapza37Z2p+32+KqKZfki5n7Zl5pA9F7mz\neyesg1o60IFdrVegGevlLKV5lNJePPq+LAeDNfInuzRqwxhNodx3fBTA/0zyKoAvAPjLSI/ukyQ/\nAuAlAB8CABH5LZKfRCLzMwA/KqKecfgRAD8L4JuQnMjckczheMDYBVGXrjG2WmwINdhH6mwFQTKZ\nEIZYSoTm7GssCNIS7SG0mu9sKLCakmHuCYhzMsESxMSruRtP5tVYli9piMjsN667HsUcGpRM9aAu\nsek19anpwlv9PbdDIaR0ZQqCKARjgEiKTU4C8xxzeSGZuoWIUbInekDyxD7kIKVpg0nJZFXrwfw7\nANbsLJm8RSARyfM7BMTkuwzydYRSR61BXoZEOyUxIsKIsnY4s17AGRbEqR7qIaWoAdWTR3ecU3ur\nw7QgeYSHaIiTAjCUqwBAKbPcxpaSB1PUQVtiePLQaNTq8S6CvH7LDsZSnkGu5Pc1P1VdU0+zS5zU\nJ53yt0OceO49wkXkNwA8Obj0gZX0Hwfw8cH5zwH4znMW73A47iF2QdTFfGgIyRpbgV5HOT/OCqmG\nsmKWmDJdnw3vsU5bdYFUlXg0o2uMoeb8tv5/XpQZAaDdEMSYYnUzkkS0o8Cm521LqyGyW+0mZSFZ\nm76ReiPPtbZYa7PRwEoPN8qlPeifXq/xXvRNG2Grjg6Hw7GNXRB17SLbziwOYi4vu2duXm2vcUEn\nWwjGAUuKv3XIxFTp1w4qJjUds5K0IKJdB3t3nXYE6kYgmbRFNTICIszfmaRlTNR28dkSlsxagozU\n+f7qfR9EMNP6qev91QlvTLxrz8wuh1sj0nZ41E79Zt04twvyYCUr+GiXkqHNpzGHWzlHz+3Ut+nu\niP9uBqkOh+OtjV0QdY9emx5d7wnmWCd2MGEh0xAg5oGAoG5x2SKZbTPdUrKXdHIQGtFO0mqBFBCl\ndu5C65zWEc5IgStJR+TAlniNRh2MNlk1apzqu7aoSyUtK2pylqobUWRzeLlrRettVgIFc3o0dzuS\ndm2CwKRnel666igFzhEI7cAh14ImT9rHYMl6UI8G3SDjJCbl8HCRSrCQwHVyh+NyYhdEXfe9qltP\nVKJIsO5NqdNqI1rVQKDayU8mddRNBaEG7RS8suq8Q7lYrwSqpHVJlJWt0U5pV9QSxAFY06h7g0Cj\nKI7InWWJecinylWykpPUYCE0S7fY5bYGO9Bo/dkFV4OUTShm0SFPHxaza1P2i8fYVnFRzy306XJe\nIcuYRderkapDa2NpukzLZsstrSMH+ScyHo2o8rGs2CeKpaGr1/YDWAxdHA7H5cQuiLrqakstqnah\nSy37fObA2lG2et96DtGEeGR3vzWOqrE3AtlhXWpHy+SAFI2GW3ExPUkV2RJlUhX/rE3GqBplcmSz\nZazNzK6X1MucPcR5KNeSIqutoDn3g4xj21CN7BP9t9Utbb3MEykDF5apgSp39e2vf5Sga15LL+2t\nt+yiBunTW/5877nD4XjYsAuitrquPdeve74bHDCVLjjpxboRhiCsNIPq09YYKpQmSIiVt3ao7cIh\nLuwB9vsCEBjPY1N+no9OGnXVFFXT7sNubM9R25q1iMZiQCJ74jO76/XEqrBzzls+zCOtvCdpnfwo\nOjOGKuhiM2o2X9tlbqW5CM5D/vemRIfD8XBgF0StaA3fAnKqGm/2nioE2IRvjAiYQCFmvpFz0WWg\n2Vwc3qgFJcaF6LIqyabdbCINTB1/ZMybZhBhTgQliIicUfXotMOVOiAdGHBWDPHJ/DwxQuQMBMsS\nqGSQF0yGTNLqo3QlHYayX5UKTkjeoCJFFYtClGhbMRmfpypauU8ITHKlti+BK0EgYkzSItAIatYc\nnMy9edhCQHDAmW1fTpAJiLE1HYuxa1OXQhkbRtoyNC/p0rRqmm7U4c4GImeoEeJCjhIWy/K+xupC\n5iVs5U0wR/X9sVuxWOtJW3YKMJMeWURnt9f5kfwgpzJQYEhtVzeSSf4EWsO6J1kdGIrmVR7B6U6Q\nDofj4cJOiLo3aKs+vZ4C6mxjlJS0CdbU3VXNwPb+VpOcs8NRyL9SHgGHLInkddcmrnSn/euZOUZI\nrC5JhOR1v2lGfM60W2d9ex28zg/XncGkKWutw95abRsWQUy223h59rgWSFM3peRWj7ffWa5eC86Y\nh2FcNd+A1qahVDcOoto/qUrIg4KP6LH6RFoNX6/Z/JWCUaTVlhCmMYV9IuPWGdfC4XBcLuyEqBW9\neXBMDTRJJVtBq4JtO8uaryRV0GhWJmX2Ci6+y/nWiQdYR7eqa1VSteRddKLchxNJo6samt2VWDvw\n5UxxJX7VP2udTjOaLtFvGaElAGtUvNryGwJUbVUHOO0Nvel3zRTdlWfytXLrtaINi5kTKFksn7ve\nNS7rFKIe37O8U61BdhBWT41m8rdLdZ3a4biM2BlRj7HQprHUtWq333d56YrEtg9nk/IAnefMRkpr\nhDQy6L7Ao3nmnFsIYEzMUPTKEJLj72rNbHc90vpOM3uGjRnnkYuU/e5rc1HYGoyJq6ErxBLjs0VY\n1TePDFUsWW/sRTnOZTt06npOfSvat1MWtdDfpwWS7eVzOO49buWdsxz7xM6Iuu/wuPILbd8tNc2y\nozV5mEuNnxFDccIqmhmQl2e1+SmBc0CexZxNmJVVdU21pV4Vuz1rJa76eq3XRfVp1ah7fbAd2rQl\njHS9bY26Nf22V5bDKvO9qjCuadQ2bE0oz2RNo7ZvkdWol9rx8WFLe3appw/rrtWkteKsD8+WOB+d\nOxyOhws7Ieo1jWRqztiU0nOGIC9XtWtWTRco1VUNBGaTFzEDjGntMYAUf5uIeL2TUOlZyUFgF/0I\niBAjNManAMb5KKVKzmSJaKKJbWY93CtJB9h128dI+nhE6KVOrTrk+jCgJclt6GysoBr19T5t8TpE\nAZDjgy8FqHPUS7mn4l+u544t/WoN8nqndFdPMTGPTeZ63xnadkrX9CmXzVQzSZ91OWwPxZysHY7L\nip0QddaJMouqhpTWJAdMYAquQd0II4IymQ4tEycTiRLWSJhSRbtVYikrofojZ/LKxDrJVKhvyvdr\nING+41TijpASuasarnVDjGQSjwDAkOKNS5uH7aQjNTRLJT8iYhaU/ZAnHpIGT0mhQ01bSt7EQpDX\nU3NpFba7YTflS6vTCycghDygOAMRULzBRRARcCXXP4Wiqa5wuQWTb7UAEycEpDXfc4z5mdnIcZIH\nEPVXr/3XrAWBybs+6jBBtA10A9O6+UUUNo6Fmk2tqwbOCaXFixc+AljIuH3LAOb3xWyeypC818sm\nIf0u6FLyid2USm/9YVj6GDgcjsuBnRB1xdJAatbswpiXT8jnND10LcXIuH06qml1TUc6bsoO3bWq\nC26ZaLf14uMp9aKYi2qVkGUmR2B1Yv2M52aThCM57xbWUN5/1x/2DNuRXGNz6DVvO1lQhzolmhvP\n+x4VQ76VxuFwXGIc9U4h+TMkXyX5m+bcx0jeIfkb+fPvmms/RvJFkr9L8vtOE6PVvXRetn7qlpJp\n36ttsY91i9J90JhZVZ4wzOuUTnNMk2vH659QdUSoZhzLAunWhGyNzLWU1uALGsI+yrmaoCemuKzG\nCY2SNN36I9rfm1Q2Hm716beeudVy9V/bdqPKrD0X2/42XbIsWE04bbk5apx26DmqAQepnLAdjsuJ\nUzTqnwXw3wD4ue783xKRv2lPkHwfgGcAfAeAdwP4xyT/RbMJ/TZKuM6Eaias2mnIn4hxlqrJLqjR\nOhlJJYlqMte7akfcm6KrXOu0oJRapegl6o/XBx2zMbQDZbfiJgBKzSuU7SyrHFoKTdtKlVHZe4Qm\n4Imaua1m2adH2X5zRIEUYJJaa5Z7JEtoW3VNr7ZvxKL4VRzVaIcataVGNUtvzYXnqHBMwVqYpxqE\n5mVbSLy2gmBM5Q6H43LiqEYtIr8C4I9OzO9pAJ8QkddE5IsAXgTw/qN3mXlTMs0Rhzy1F4qpW7Vu\nG0RjKHHRmBrtqevxiobXkJXVUo/R8drHGkLtnOSoQ94mkNGsZDt8aIle2y85sKUPmbd/5FLjliGB\nmMwWWiPQuCu3gi1qOSLT5jEoh6kmKlKPhxQ1yrVeOQXEQA4ijSSo78nI8HxsIoX6X4rqJsgadj90\nGUmClTQOh8Nxd3PUHyX5gwA+B+CvicgfA7gB4LMmzcv53AIknwXwLAA88sg7cPVwDTcfvdUYwa1e\n1Wu296RLs3y3gauHa7jx6K3FbWvYzG5jbe/J6BTOK9NVPPaOm5sS2ZnUe4PTn8KV6Sre9fb8Gghy\nG2zpxIJ2g029cb3UNRrt0/etf5iu4PpfePem/KuF9eKd+9EeI39gmq7g7d/yrvNm7HA4HiJclKh/\nGsDfQOph/gaA/xrAf3yeDETkeQDPA8Dj77kpr5+9hj/42u8DQOmi7aKliGQK1nlbyV7ffScc81z2\nwlQQiuutKm7lZk7WxxiLwcLNR2/hD772papvb5CtOvn22mvRn5hN11nD3fL6noq/t9XRkbyZc8hT\nMuBdb38Mr/zhlxFYg3aqQVktEiHk1ce6RlxSboHGP17qdUw5znoUUHSJWJonJ3PglxyXXJi8vqeI\nodf3u97+OP7gD++kuwVI235JjgWeTMqLaG+srnTpPVgStYqqlgRI24bEmKjtc370bTfx1T+5Y/LX\nRVRnGHl9pxjfOReGapERInAu72sKO5vmrafQvln1O8c5N9G+9bq+F+/41nfhD//0K27/djguMS5E\n1CLyVT0m+d8D+N/zzzsAHjdJb+ZzRzJMX7NuCJG7u6gLThHTHCcBYEp9pGjK1DWnfixAGPP8Z17d\nnAktYm7mpRkSnaZNGwCidrKx7KrVLvLSD6WakZOpuaYJUk30tnKSTdLMxMiePTIm1vnbOMemdtR4\nXZOtd26zoHtSlK0qGuqbGyKo985SqUXbTQBMsVBmIVYodYmgrldPkdgmXRKFtNWJ0BBals8OwbTi\nQWzrVkRpV7o3pG3ksajx5Pr7xKTpJx/StqAajIRMO5cvNt4qNxxy7mmZYMoroE5zpFwjYnoGecDR\nG7k11XJYkeUORko2r4jD4bhkuFBMQpKPmZ//HgD1CP80gGdIXiP5BID3Avi1E3MdnJPVjwx+C/7/\n9s4/5pqjuu/f797nNYnAuIDJi7EdTCQX4cQqKBZFIopcRQ0krerkj1Jom1A1iqPEQSBFak3+wSiy\nhKqGVpESJKdEcVQCsgQoqPmlhGJFkZrwqyiObVGsYBI7/hFCinGrAu/d0z9mZufM7Ozee58f967f\n5/t5dZ+7d+/u7NnZ+86Zc2bmHIurrlP4kX5YS+uvMB51rEuby1Jdnl9baLti1g8vuG2zTcFLNpZc\nvdfbeV85qp8Uns8o5RNkjGdQj2dS16+yrkZjxDvTqvmTlbYb3s990oGYyR4Bymc47pgIIc4HGy1q\nkh8CcCuAq0k+BuDdAG4l+RqEluNRAD8FAGb2IMn7ADyEEHjpjq1nfBf4htCqfeHd6Kda5ZnVXcrB\n7BtzQ1zP2ri/4b3cmgsvUc/jLt/b580pg2FOV769UQbFSoD2Pu8LLi5cn+DvdSxZEKOcWz4uhdV2\nvW68VKZTowVz89k2s20XaVO3i8NRpe29qSxO7N/u+mXc+HRcqD8b1a8Q4ryyUVGb2Vsbuz8wc/zd\nAO7eTYzSOqndo2MLJijeDjm7VIgnxcEla3Yhl2Y9wDpSVi4xJ7jMTuDaZepZox12xDDtophualel\nXnWub0xZ1bOKOtbXyGytHbDhIqshTGsOjkqgWv7mosUhO9enqZ8XJ/XYlHrbTjVta2VOl5a6dWN1\nOaeoW9a037/d9dtJRtN7+q9JTKXvFEKcDxYXmSyQlHVLGWS7jeBwRD/szWtd+/CGYLvkqNzeXiHG\n4U6S+3uqsa6drnNN9Cb8KihDqaiPZ2zuKo13WtfKwndthlFscFRjwLgG0/llyadD7Qo+vdK3qz3f\nJUu/oNO6eipz05CFEOK8sBhFPe0wNozVYRqDztG081aaSfuteGy2l9vjiWlE1jvb58M+nnRcuigr\n+rjNW9InLncbdZPqK99/ml3to26nY8uuzbjG2gMBVS/k1GiN2c7d7261OV97p2lRT1+DRblS1EKc\nZxahqJOyYprAZCHPlLfc2KVxztDo9QwzmS8N9l4ajv1m2EpZmeIYbV+06a4ZNMQJZ10IVAFE97nB\n7AKyIzQrpW6YdMwga5z6TUOc5bt2dmSw7I+six7pJG+6D8OQTomEJUHNsCKwrhZ7dTCs2cFsDcMa\nsLScaIWVmyk8eMLjro6rMPM9WX8MdRzKGaoJtChhFysdXajONNXAAGDlLjBUakgWEgckuko59cNR\n5VQ9nw60eLfhD+qlS2lIYBjMMKI3DPMTMqFDYcgJXMaDKOEYxmneXZwFfyn5VZhjdnfosGbu2oT4\nKMGLQzL/bsK3Mdgt0ffr6CoJz8rivTFmbEOsymFFAIm0omtYWueXzgkhzhWLUNSJoPDS+trSnVjb\nTtmFndyxm2zcafdkqaTTG4FqnHZ45yorKVpsoBH3BYs+j3J3MedSe3rQ3EhvWqDkA1eGfbkDY4Mt\nvJ64Atxx5X6L64OHwtPXBhDr1IuJ361Dx2TwNrRcs93gicgejtN3T6fnn2RvJ/moj28paTQ/WSz/\nOG6Tds34Dkf5TbmdvUdBcadtKWkhzjOLUNSFW5UEbQjtkfZWx7cZK2vvmp25/mDMReUyWPhOBidE\nSldZTteOB9HnYs73146zNU8aCfX3H5JC9oM6LL6xDlbc81gB+fdgRTckYbCsmwrK/HwAfx0rOk7T\nQwdNKXZkXJvj+sh07nmMlTULSYb5C67jNki8QdRinT7ydqbuLNbW//wFpKyFOJ8sQlEP7u3UHk16\nXgAAIABJREFUyPuhP2CikS0VwniZUW1dzjeCdJq4ThExJruPw3vn5C2t8LRMzFvO2za3tSrL1lqq\nCbo9ISKYv4uxzLWC2zAJKlrQ4TLMn1E9oOF64zsr6/F0rOvaN5Dq5ThBAWrp2r+viU7LhvLy55ba\nHj9V/8RrpSwlLcT5ZSGKOpIUQtz26Y+9sk6Hhvfaidly/25o5EbrjlEksWB9CC33JlgqM+PaFZMz\nVfkVs4VKmxFtqnmn2zLfMbBSMXp/REtdAghRxvzF4rZZvlI4oe4C1F0nHyylRWv/ZiuyXVLZlcrh\nVtpl1Qv+6t9S+ZuKz8yFMB2qod0X8Rcavk6eEBs+1U5xND6P9xukpIU47yxEUbtmlmmyTXJEBlpN\ncG35ZgdzbUETmEiLGQgTz1i4Ow05FjOq9yuS3zi+4pgxgbpRrtXkLo7fWi+UgwFdIV9a/Zzu3bvt\nA2lSlVf/bnTXG+kAzNYI49NdFCRNJqPzPpR3Y2h5NmpaCms30qx0v9p9g/5sKumy65FH3lMdh75X\nvMbwvOepqjF+9r+J2hsyLZmsaiEEsBhFXTZMKcZ2b31QCl1MTtH36DpiRWIdg3tYDGQS0v8a+j5F\nwQ4O0tS0rfoVjGGimsGiuzrYPT1Doo9k+aa8yT6MJ9HlmN4pLClD3Gtbe1uynGqVyrXUGSjuOG3V\nTXuMNW4rYGVZWRiANfAtEh0NIcxLN8zutsFsj25qIpQVPQD0k8dAAEexLsJn8tJwvqWZ3YOZ6NJp\n2jrMUI6zlM0MvfUxwUeQ37ACjUh26cqSjdkjBK0L95wkSrO/OfwGcp0Y+1gHMT47iB6X4kg9sYq/\nmJw6JHePDMSl3tX7kMEjq8SO/onF59iHZ8A0Kx/xd9hn2fMK/HzVzt3hUQccEVj3aTUDBvmBGJvd\n0j1ZeMAW0ppglf9rmoV59F1+eEKIc8RCFPUMzuc85br01PuG41zIUZKDhZSb/3z+eH70TgJP7J+w\nhmrNndz/Lb/3Rk9x7UmoL7K9+3lT/cZu0sYr1B6Ek1O74DeX72tl3FHyZ7v39Bx2ktx7UcLn2bNt\n+JNljR2J1lORPS3E+WShirpWA5HYgFlcxoWGPmsp2Q7BhUmGtbYE0TM7bP34sVfUo8CNrvU0IK9c\niuLkq03dU4PBlEx/nCpJ5bq5a9ahiANeDg60rrGLsimP9XXrS+hRR6kuz5lW8F7W8qr+uOyIrgcO\n0ucUaz3U18SvZSBbooz9vjw7fXx3bnt4Jo3vGtggUf7cPiNKPDx6S86JuJZ6PJ3xtGKfCSGeeyxU\nUQOABYVMxkAW0TWerA1WTahFRyTb08mCC7iLwSSCY7VnSGjZ94V6zKq2ZZyaV45ZYeQ5cFNNc3t/\nXq9ryGPCwXVt7EfxTZN32efsHgublE/tVk/f+WO87cbGvhYr5BzR2dXvg5yU3wDdqMysZGvSuWN1\nnl6hLLr7thmZV93QK2t2LkpYvIXZ7ml7U36ZMqabxcjped17ZT0P14qdToZfHxlc7LKohRDAohR1\nu1kqVE0Vvcpbln20bv0yq1RK0IPd8LkfmjxGq7i02XwbPcK7K10bniNqTSnqthJhKmQwz224cOyX\n+INH1rS7k8pqLa+C5ndeWacBgGzPtkoYZHZXT0b/amQH1tcaK+qWBV5b1VP2d86k3Y9KH0mfOkOD\nwrby+3p7EMzVnc1fpay7shvRoosBTfKYeTzf+ma9zF1ZCHH5siBF7YlNXbSOu+jq9nZf2kj6Lb1W\nRQmBECSky+EaB9dncIOvYshPf3xxnVKsrEhsMH7d/h1d31Y73q34aER2iyaNONtib2sVz+PrYGyX\nez9FDplZXj/dgu8+jK1/VmdYcV7+pvQJWFHLueyJ+2UuxaLCrW3b8bs1tjd1B2yoDyD8FolUl96i\nDmV2DB6gLFf2QtRXOlbieCHEZcFCFXXGW9OMmnGzTZMZtuMELb8IhxbjOCM3oz1mxqi3lngH0sSx\ntGjch7cC2qHJhs8tq7Blh7Xss3TslAegpBbDH5NLmQoVM+f4P65Dt/wdzKnQlsWe31tK2lm6dQ9i\nFr/SIHgpZn+rXawBS85xGzoSxTruSkIhxPliMYrakKwLIDVJXfQ2Xlr3GFbVWA+zYGUHpVq6yPs+\nJlJYRUvVeli/Bjp3q8bYJOZEE6suji/2fZhwRqKrpvAkddcN8ciTfs2JG3r7FoijqIDS2OTRkDBj\n1O4zxXFmGEaP4+fsgi1mboy0hwFdHxNnhDtegbFu+uhdCOOcQbM657C1FC3RdVfE7RBf2mJSDm/B\n+Y5M2LFGsvxT+pQVid4uOX0Xy0MXyowdEgJD8gnAQlISZLWbVNsaPn94ypUWaqEbtnwXIYwHp6OT\n3GFoIfduCLrhhGi9upC1wwCJxXKYn1sfaruoP58kNN9DfF59dMqzDH+S3r81jHkPqT3CcWSRRCbL\nJ4Q4jyxGUe9KnRF57JZtf9sihN8MW8FNbjEHVBn9KpVkQ8Nfdixs5Jd2Uk2KMHb6tizgsau2LjCc\nQyB2Ipit9EK2XHbR+Fee3pbjt3FXxf5B6cax15RkxS1d3pq6o1AKOWejwx0DpzrT8f4Z5XH5cZz4\n9vS0sSxT3oo5atd6LbkQQmQWpKhbbtjpxqs1ahiUVNhIY9dBZ3nF1nSMO7soWEYpkYN3iedS0ngi\ng7UUs0xxmP1VN8KGfqvGvFQNtRc8H9GuF++p9YUYkqJOFmH2KLSiXYW5AdPykuXirLgXabB+sFrd\nbDzWGxv0UrszUHol8hHzv5Myz1Y6OzvzW27maSXdqpdNSnaXc5JMbs+uvRwhxGXFghT17jRtUCZ3\np7cxPSNHblVa/mTwDbi38dYzaqxWYuFzP7EQNnjcvTylkm93K6bl9t2RMgL2uPNQlpJd0PVkrTbj\nDklIFxqUTJf83N5aPRXjMfg8TpccQW3cKUpH5A7bFMeXSla1EGKahSnq7S3qUiHlSN5H0aykpeQK\nyQ6etqjTmGyatXsJ5ZXD+GE+1rgKY7DpykNiji6WdGk4K7xfKlzn5Z3VStq5picsagwhL/25fWkZ\nhgHnQVmn+0u+gXCVVWlRp9uobNS6W9ONLLxw4ir2jrxFnYzqFHRkGHJv9xdG+MzeaY55GqMe5yIv\nlW3uRqTM3umZeD8JQObOTJ6lPpWPzao9Y4u/dRe+hPbvuvwuVzFlUQtxzlmYot6eemSwbj7LYzY3\ndH4CUo7gbKPvg5JhqTBHnYFG+adgVLYc9bXCZuO47P61SqXNuYzbXYl5+YaVwPC1NxpP3uLma/lq\n5bsbY3e3d4OnwYB8dP30UUlzVkghCyHGLGZ5JkGw78C+y2aX03+GENQkvb5pPS5Zj3W0go4IXGCw\nrNdEMOFCpg4YDetVj35lwApg12HFDiuu4ntIbNEbAHboQHQGcE1wHe0oY46Uhg4dVuisA60DU3iz\nfg1aSGwRDNoevfVYWwf0K6Dv8vov/0q5nhmjkiWt3hnQhfCS7IKbvOuCvdthhQ5H6HBhqKS+Z3Cx\n98GjQHQIc7KPEBJwdMNM+TBmvUbX2fAik2KP1rh7Mb46M1jfwfqY1MQ6p+g4zPDu+7wNpBn68XNv\n8d4ZZQzRuMgO7MLLj293BI5AHGGFC3aEvKgukJY1rXtgbbljwVilq26FVddh1TH9JJBq0QCszbAG\nsQZhFu6Nceb/0EmJHopLBqyNWFtIKDIkemE/yFX/66zP6UQRni9JMO6nEWE1wzrWt//pW3z1sRaF\nOF1uvvfm4vMNd/72gSQRUyzIonZKYgtCgscqZ/SG46NnFkA5rai2wLPX0buFfSM5MZkKYfTaKxFG\nV219dHGdHfGSmNuXl5ONXelTo86twCbh7HX1TQrfAXQoc26n7X72jsrS8766Jk5SM22PxTB/27JH\nxItRe5bLz6n+wuj9qm8cN7y3Fen4d7atn0cIIRalqL2L1NAaI/SMF9R4N+ZYVXmdmxpsi+8rdyHv\nGg7ZIUMHIrt087VaUrWUfzsX0vFVUu3yL13TYyU9VpBjeWuZy/QS6fzkEm7f/9xQqh9z9e/t0KrH\nc3DXwwtAfu5+EGN0kDs3J8oofgnwyno4vypjWur0a63i0M/Ul3+Kx6sNIcTlwqIUdbaqpxRLpm05\nlYp28hLISnrquC79aQb8zrJmKfZvH+3eiLes2SmOUKqLbK2nkCP+7reTo2VBny7FsqZ4qXbUVQ5/\ny6VQWVmPj7f2Au/hRzc1ktQXhwH5Vy6EEJtYjKK2+LedDapFqR5ybKmJ8i03zKMLu/a1+N7i7GXm\nc204qS1TtqDhjsz+gdoaPklj3T63VoZJ9rZF3Sqr7ZvIgwVlSeVY8pRE7ZnLKdVja2BgdwqFm959\nh4zOpt1g/efEI/6plxZ1KnMLyYpLDjPLt7Co620hxPljMYoaSA2YX9E7d6xvTH2wzrGtC2SrKrm0\nYeM2djTkyFWWhimpx5QdG85KY+d5mU+YtlVX9Eka35bcafR47PIOR9ThUFv4u+tQp3Ts3FZSPP6d\nwOicKelrD0Q9WHEyRoYu3AarTkPS225XtqhzPQ7P01VjmgOYil5NjFH7uvPKWgpYCLENC1HUoeHu\nnTIhgDL/b924l+E8mNyxFtbMjtbWdknp9uBwZsyeZUHFpMlQKX5XnxpewzCDOQUfY1FWVg3sV8DK\nht5AZ2HmuNFPznJ34SOZmQ2xtoFgza/7PsQd7zp07GB9j3WXkz2k2cStGNbhWjnOdMtZX7h5me4t\nvxfFedLsvB6hA7MCaoXk77Rce+2UPoEefc5s1od76dHHNM1BG6Yx89DRipPZkqfDlV2L6u3iMAc+\nOO97l+eK6KPGXYHoQa7j2La7i/CDRLc6gpmhtx6wNbqYLz3kkE7WdqX9OVRV/FivjU5uHXcfrkdg\nZqP6FEKcHxaiqLMd593EmV2aqNpiI7I690dlZd5yts9bO/UZuVPQMyiAZH3Xbt/abuRoT7aIx12T\nbeSqjzpju80bwycoZHAq02LvYYXxfRz3Qvk3kQOo1BZ9/WozuNAN8JncBvt4aoZY7eQQQogtWYii\nDtC5btujp9uV0mps84pYNmYH0+mbqTnac9fLBOuwjy5Ri6k0p8eo51y+XcNhPR59rr89AyVdX6gu\ndjDPj1N4WO+dZ3GFGOQslsD54CShQzeeRT5HTr6Rj/flJ8+DP75kuL0hiDzd2mgM+/IJ9e/PvSd3\nRXvWmhBCFCxKUSPmrPJ2T2CsmsiJRm6U2QoYlMGwN7kew0C1WbLk/RmoElbYeN1sQ0MN5buZRmQX\nwmtirJbJem82vVJZpTVI1ConX2na0t8VXxvJFZ4+WD2NeoviJ0dkGYOexHCjIYhrCMYS7jF5PoYE\nlKgnxg1LqiaN2ToBZtqXtlfuCaSaHY83+8ceApbEkK1p+KGoKLftU3kyP1mNUgshtmFjZDKS15P8\nJMmHSD5I8h1x/4tJ/gHJL8b3F7lz3kXyEZJfIPnG4whWxnaqHZMcAnjVr0DLZeoVJZ32cWPC8K5M\nDFG5fDkMA6f5/OI9jMUSDJa0BSWU8lu3X5h8t/FdN3wF45rLFuLmo6dInYKhjCRv42lsk8fSJv4h\nZuoK8bbjmD/p6tmGukIcC67jt5th1jjNzzTHPp+Xsu1oZ/oTn5F3fQexXe2kbbdvUNLHyfsphDi3\nbBNC9BKAnzOzmwC8HsAdJG8CcCeAT5jZjQA+ET8jfvcWAN8N4E0AfoXkahth6NTDWMGMreqpUlqv\n3PyGUKDhFecQFSp6LsHj1PU81vA+s2j8S6u6LS/AweFb+hbK2infp8s6EWdQZIkhJBUp01Cm8KL5\nmHAc4CdjbSNIpdhdx6ekVeMTV0nKGky9Ovcdq/dxGfNrGoQQIrNRUZvZE2b2ubj9dQAPA7gWwG0A\n7o2H3QvgR+L2bQA+bGbfMLMvAXgEwOu2F6k1tQsz+1rH1GOS2W06dnGWV/QW9XYylmelsekUsxlA\niAWNUlGXKSHa72Mlva017evAl3nKnEqxHKzpXDtEmEzm7yEds3u8a7rfgzV+F6n87abrjbtX6ext\nJJGKFkLsyk5j1CRvAPBaAH8K4KKZPRG/ehLAxbh9LYA/cac9FvfVZd0O4HYAuPr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QAAAY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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8eefc487f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"plt.figure(figsize=(8,8))\n",
"l = random.choice(id_cloudy)\n",
"im = cv2.imread('../input/train-jpg/train_'+str(l)+'.jpg')\n",
"im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
"\n",
"r = im[:,:,0]\n",
"g = im[:,:,1]\n",
"b = im[:,:,2]\n",
" \n",
"plt.subplot(1,2,1)\n",
"plt.imshow(im)\n",
"plt.subplot(1,2,2)\n",
"plt.hist(r.ravel(), bins=256, range=(0., 255))\n",
"plt.hist(g.ravel(), bins=256, range=(0., 255))\n",
"plt.hist(b.ravel(), bins=256, range=(0., 255))\n",
"plt.show()\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "85129a0e-a491-326e-475a-7f4c2a7a722e"
},
"outputs": [
{
"data": {
"image/png": 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FsLBURcICXcIMQm8t47dLIpc4+ptu026Oe9jmElIVv5V2PDJsgnoIqAiWIt88ZM8O6QIV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ailb+jahVDIDjPwtt2s2arHjflaRVrv4MqCFJUl67G+SVM3d1/QbneAAeQqwg2XRhUACalQCNPz\n5igpp741EKK9rKtUNkmotb6SbaHZsGgwCzXoIlTFp36Odp06v+dRdpXaN2vR7vSySefOaJKa3hdv\nkhQ787Hl9Efv2XM0jN7vwzNQnFPotgoha6OyRgx56rolb1YciyfQEENXx4Up105JViYllNtb9sxe\nvDYg6N08O0+YHxSzNZ5Yc3g5eQDZcjJYm5gkxcm0HhHcNhrZpMKgHMu2BOvkjOW9YsMq5cwnRHEQ\nCrFIMBgugHLNHXMjPCBKKrgQl9pdlXvd2mBYywilZxDSO7uFzBTUXYS4OQOfN0MaHYUS1N5ICZTQ\n8wQ91mM91nu0XkWhXjNmBVxW3nPJn9asFaxsQ7UK7nE4ihUUPBzNxDCO4ZgaWWZUDAzE2OMtOUuG\nBUVMGnYpmFjesr9cMR0MlN2PE6Je7awjkI5IlG2ng8rGGBeOPAoeFZDdeXMpxu5xHMzpZFy5XD4g\nvbpqEzszkGU/Tqg7JxypRE7yONBQ5I3RkVs4wUHwhQ+fkFTSlel7NePNqD4iqmvN5KLd7kowI1A3\nJByTCcOQyzMXSY6c7HHAXudL2+w8FbDCC4hBu4vz9PzEjL2KNw21p7C/7FxMudhgShG5bMnHwsk6\ni1j36hPKvz20giu0bUombJsRo+b56oUITE2mwiC5jK38s6UCNSIh3147d7yIhcoT02HqRIfyRp8L\nYo6yPrXebjzbEy4lA5wxefP8QduwJscIYgTiCdPwtzuHl+vc89MzbLNGEYegqVga12PnJQ5sPGOp\nPG/t/b572cmKldUpjoag07i+vK30Lx1cxqgNVswT4bnmJB9cssd6rPdyvYpCfTJfNc7mLCNwmSwq\n0JqFgpxQa3bgg5bzcnW24QwzUgM/Dg7X9muEu9bvTKMC2hWrOuNKTlr1uXKlq2ddpLbV899IVJVv\nXHrtpOaNFZu5UpmKHiZSU8kbu7lJYNBZ292lqYEoYxhm6xiq/SuyHbfOrFeSxAkzK6aQW+uaaWa6\nCKkrN6x13Un/WZS26uwrdpITp63+1BG8DU9Y0MD5voxBZnKko2EYijRFuoI5qwO1To/K5htkLFZ5\nIxxqbbHas/ks0lhGd8KyCG2NJPQZPvlW5xfqzd3GKidXnzWLTyp4w9djsjYdml6f7UJxAnDliB3v\njaQmiGuZk1BxmOsDNoyUIqKRyuzze4/MFDO90J1Y8Ze9MT2Jfef1f/9pP9ZjPdb7tF5FoV43pNSG\nV2M5ka0hcjaZ6kbkqqjAsjxRGhosLBFrqPNwJ2ZgNuqdyu31FotZMtv3uwtgs4sr4ri9H0kcLxkX\n9dqVKiXoAJkJHWcparcZJp1rnIaId0Fr97DMW1E6NdmNJGjB7ZexIN8uMCKYDaLn2HCzyvQMHEWz\n5uqqQm5ScYyzf1JqjmpqkMnIQNZjuhSsMEXreX6ZwkDmAZSzVzBKIhZ91oTuUK3jLgMLqY1Lh1yY\nrEjMev3Ta7u93YN6I2bJMCmmPIqEMlehRsDthNQLSg7W9mQ52Z3zjv7Ojau9quBt3JIJe+w0DRuQ\n3nR5e3AvuVSNTzIEDGTIyk854f3RCMY2ygfgzDqPKv6ebfxCtJVs51wvQcH5i3CasfaoxpD03vQ8\n1mM91vu2XkehXkVCqIRorVmsshjP60/bfXanFEQLYrTMNQg0tEg5kh0baCRG+uRUFEl1yLRTlntJ\nqlbXHlakp9HFuwqht+WnVMcugQ6wkSXpmtQmYRhBuVYlWcWu56bniDGj56Mtr4obdasSm7rz1OrI\nTpJTgtnAm00sdzf38qP2KrwINoQweq5fwqohXYCpjUJ6nlpxyTXdh/NZ+0WLmFZoApLMDI5MiBYN\naXXm9FgCtM/X+mRp0t/iBHBahGbbpWa2TE1AL1Ha4awZr2ex8yVqLm9WI5Eq8HlLvloCafrv/emJ\nFbkrzvcmrPQxGiko0l1W6Inc5syFioCWlyqDC6HJGDUeCJ8s7pyZnuegOmqY7ox5yzWPWRGrmvV5\nZwoe66jqd0F643Mj4ikia0v2WI/1WO/behWFOhe8mIU5K8LWN3s55TTSpKYqXNWdjROqnL7j6RzA\ncXVGwuXyxCZPHMfk+NgxSxigQ0DjhDSnKG+2y5mcFAL7nHV3Fuom7352deFVKCwqmjIOiCngwmHr\npg9mRcba97rZru649L8OqTxL9vNWIVRVtEM8fM6SkPXriihmG7tHsYi5K9QB6d4Sp4rulAjUE8uC\nnc2Diyhm1cV/4sGRR3urL1Z3df3hq4OrDOr0LrQ6yKNkUGq37h1Lgp3BBcnBt+JjUpyLXihxeY8f\nJMmY7b1dRiiyGaM3CkIyAzIvRbSKrFjPhullMa8RJoJLualJUrD3gPoQs+t0IEedk+p25Y5QV521\nUX6mstXnYJpkWnXTHasqJKKBPhs6CnWJJhaqKKYXQktKcIamdGjLJ2/3QmqGQnoVfmiJViFElzFY\nAR6YVEzpIvdpYD13f6zHeqz3b72KQr3KTTl8zUKi7yQ5y/krF0yagW5b3biy5FLVOVYH+qTaFpLS\nxTcqhlEgm/ErSqeA1I0eFbxv4CsJaWbcpDTd0Vb/Vf/Qpf9ebPK+eRd8Xczxgo2dmQXlntZTdIfd\nm4NlVNLW2D2zt5rBnh13aczPxld6A7Dmub0ZCKXCN2a9F1OtcxhZOditERYamu5ZfG1KboVsDX5P\nS9Zm4uO1CbFOFFOpHO+LC8sWpQ5P2+Cl/TsWQzpLq17Z18FmVjK4qPSqwwOVkrQljsgoV7B06O+L\n2jnvZiEONwrCucGCYmKjlFVo241Nb+tTIMNK/0xgCFsOvIlvJy8CioC4aWdgd6HW4jRU698jjSjr\nUcUYE46WZm2Lkd/HGdEIglCsASm2f1pdm4s1IK0kuHeOe6zHeqz3Z72SQl2ramfBnJqK5HZKgpQy\n8aiECshtZQpVJELpmo0gMN242CACwrMbrFuxKy3VbZR5Fmqv2jRSsIBJnDdHWZGWAi2ELghTm8M0\nVrEU1CraUm2FRjieE6A7o+7ksmeta3NAw5xNKlIrGD3xDupIIrzPVEPfdzdvkWZPW4PY0WdOYbgz\nswt1h0/VKwYnmU9u1pZrZirn7H4iGuU9PtshrIMmCsYWnlM5aAg+FwmuZ+xSqIBCRW+2R3jIjppV\nIW7fbvfADFai15BR0DlJiHOksbHg9Dur1+C2iVmZ4n3eEpoTUHN7Ms+vV4a19TUojByEVpY3J7Gr\nztbTGHgKEUrIwRRvExPniESiMqtDkyFKRhnvGMLTiSrUebt2rvYac9QmSwjNHmssqLtc3iQfhfqx\nHut9XK+iUJ9+1R5obkytZKwPrG6eIckuk4uW8cgRA6Vyp2NCupXLlCRjjNLgqjEUpiThwni+VKeI\nVvLSLIkMVsNV368N8VZkpmzl3jWyy5UJHIEkHFQnJwHxcqBm6DACxY+dLcskQ3tDMAb4vhN2IQ9Q\nMfT5ggxhev2siVSq1NZBE53iVRXU0LbLzEvA3CGN+ym6GIXZexCHEwI62vUrK1LxaRuIQUq5eokK\nT3Mj1BENBlu5qqkwPDgyylUrhScpHfuexfq2HM0hqCOQ3fgWFRohCBfbzoIkgM6aLzvBJxIMMzYd\nhF/YwsrTHSHHRuwH1/gEGxe28QFwLZvPVIQL4S+8hPO0XXi6DFwCDmFmMr1JemKYJlMcuQwGwhBh\naiElaCWXAcUZCAdLtueBpBMv3szzopG7BCqTGR3KEY4YPOszxkDSOPKTkqHFRA5gDHTUvk6s2PHF\np+gCfukZt09eKNmWpWNzkjqaUyFobzr9MaJ+FUv+DSH/6OPDeKzPb72KQl2roGNaGpVZ2ckFCdcv\nRXSPkQrX/Voz6yZWbe17vdyrShbVYiqB8VwxiSoJM8ijx5gTcshpBgJ9Q5R6ncyFBt9CPj7QJq9l\ntn91YJ5sEsxt4FIuVhxl9xl7Qbkihi32eTp+UOSlfv+Z1VXSnZ6izGMS02t2rMbFLkwpTXN1WB21\nKMbMt5goYzOGjRoFzEnMiSoc6cWy7nmtdQfuUaOBN88bmwwik10qYvNJRh1uTEhDwzioFC/JCpxQ\nLSkZftKhzo90QejZs1zVwbMfLU3a0afBdU6u+1Ea8zEYoqhtKIHkW9Jr+KwSiByIWZ3/SPbpJDer\n1PP22eCHoQVJ9yw4myVW2vw6zrGtayfwY2c6BfNndfVFthMQI44DM2Nshj1ZnbvjWiiAXFCzhvtr\nfk0EYaM4AAqS1eZns+9UN5StbbyjRywDy57bL2Z5CMvD7rEe67Her/VKCnX2TFZB77SxzZTtSWNp\nTfsGW7GT/e9md5sKRx44cLQuNxsBVS24VmI9ZkGpNfsuA7K7AgOla1r032I+kS1/qjl6a449b8EU\nZM3EaUg2oBKqWjvdoSHRVpQqy0C03p+swAxAvAt0Q6+LIV7ypJobLxlYc5crW1pBjYLyszp0NT2l\nRpHZI/V+joahk9oMpEd7rY8urjATiJJLmUbPUGvAqqoMNa4+e6R7unnfPmGhNjWeSwVVMZERzThf\n89vyETPr4ovSvW3PyaVIel6apohiUd/CLTjPZ0H6i1gmrQTok0sV9fW8QVYmdWajAD2i6K57delB\nIs3atxzI+T9AFg+iiGQLhk/1jgy7c3NDijeRi4Xf7mwpZNR71B6D5JrWPKDvx3qs93K9jkK97j81\n7KtCfUqCODugs/Okil5Sna5kE3LkbL5rFrugYwGR0U9frOKKWWzgeC5JVB2Lr3bsnWOsb8qSFVGv\npQlnwacCOlApG9DuJG0bLWNOToZZ25WueXOeFpScx+xzlkPWuB2MH9GxiutrsSo/nEB4rLLd58t6\nltsN+yJ1oSDRc/Ei6q0pRGYW0WzNw/sl7889rPn6kp6tc3321OTylBFheoB3XKSWVn3uk8s2Kptb\nikke7qgViayg4mtlOouSUgQ7k4aUswlctOVqfhqS1N5Q5Tp41qGu6+0s0h5n1Kn2Bbe0y0vzfrSe\nHg/kWIS2gZoyqYCO6r7LgQ6EEG+70Rsv4t1La117rQXXUjqc70XWtf6AWx/rsd7H9ToK9Vqi1ZV0\nlxiRpzkI3Rgt7tSw7tm6pkp30NYtTXZiUd3/lGj/7gpRuAUkLL1tJRu173YTeUStGdiCmCFbdX/Z\n0Dz0Dy/Wbz+4PKpunZMNIw/v7uxs+c4id98FEi3R6YdXR1sjgYggjihNbXdnZyFpWZVkbQBKTtad\neBlrl2FH1rHLmfQBpobkIuvljblMo9mnxzfv+HKLrLFAENkBIMI7BencTqRzRJmI1Gx77VdaarfG\nD1bcgNRV8AeiR82NgdUSL3e39QoiFWBSKEud1OxvLA+65OYMlwKxkGSpDUUGncRlRV2QbFSjiGum\nRZYrE5PEPDGzzteu0UUFitSTiozWP9vdddz6aZIx7LwGREeFyOjt+otz2FMns01XH+uxHus9W6+j\nUJ839CpxRsGxrnYSfjLLIrPUNcLYLpUDHdkEsEKnrfXWHtFwcdlpnt2RlFxHF/v5EMRg0y7yUnNF\nF/CjjTw4Lb+7CJXpshO4ZKVsaf1J04rjXOEaWY5V0axmX64r6V1s2ld7bToqUQQQLs8Xci+nr9WZ\nqgw8PgExRAeqowq6CqOh+jiEfWoRmTTQraDa0iDX7b+6N4MQLnYpFvJ8waejBuqBpzPzYKjxJBdC\nHRdn6FabGFXE+pT5nduInHsWFg4yj52U2rSMMPzowv0spMzaWFDmppZGuNVxqJPyjPuGx07wAjRL\nXIp2HuKEJBc15l0oBn2tdB4bQXKh2eImJ/qyqVY6GuVARyZje0bcmV7ojoowhmLFtcOauFgWsE7k\nwRt9w3VO9jyKrCegFuBWRMZcKV1FkDTzMnxxBz6gLM8CdBKuRBxAIi64BN5kvcd6rMd6v9brKNT0\nXM8PImCnboBDkuvcIar7iOMgRdkuG5uVvvdl35kSPD8PyMn+4sWoVcWsivDMkjddj4J2NxPChQPH\nbWJDyCxCj5iSVyf2Nq+wtYVQ0Avuwcvca+7cnVeRnEDEeDkmMkqa9LQVqfztdLcPTmkAACAASURB\nVIYLVzHMA0OZtpWU7FKVbk4nYucyILUY11sqYasL7g7zWeA6iOmITIa9YRmOS2hbe0Yfc5PrCIYp\n8wNlxCDcOXJyUWB7KqOUGQgb10zyOLBxIdgBR1Px3EmCIZA2GayC1XPXTRjhJ3Esogw6BoYgbFrR\nmhnCwY6osxkM+RAdyRzCjEAOJ6Zy2QIRY/Quy3NZij5zMBEdzb8q3kAKHOE9x+fc3IxhuGwMJhtB\nShuwZJB9vPssKZaIcNGSgl1UOLKvoSginGMcLx+XJe020HQshKGC6TP7PovxHVLEQQ0GiW9lcjJD\nzvGLeOKzJHhmGyBdsOtaKvShLU7njmyG2dPn+jv5WI/1WK9jvR4aad4K0oJnVcry0lasY3dx1Q1W\nyEV9T9iPyct1tud1k7koOZY0SAo3FrKwHKUaZ77TyorQEZN3JhP9jdN6cumrz6GjrLfRjObq6mdE\nMYObPBYUNJrr9fqATgi7j7b01uVAJnfout4da71enu+rxps93+x58prjR8hNZxzVuJ3HlMmMZHqx\nv9fsvOb6F1Q2VA3VgemFZbKy3FzrAIoEVWSo9fU266CiJHVs6BjFsh9G2sbUqIjHI8mj2027Hdft\n86ygE1/Qwjvj2rWZqmjT9T9b/9YV7pEc82A/Dva9/ks/Ula0ZYWttVFOj2EyCJ/M49ooQV+kLaOL\n/lMWs/0pSkeEZpCzZ9qnTWpb4a5ZtK5Zwd1ndvf+zhHO9zmiFhETkf9BRP7z/vcXReS/FJH/vf/7\nw3c/+0dE5C+JyP8mIv/o3df/ARH5H/t7/548XFce67E+9/WZhVpEfkFEfl1E/qe7r/2ciPyaiPzF\n/vOP3X3vO/7Cf0/rbuxLE5SGKcMME6t5oHWh7jAG69nyvk+u++y0qGDVpcyKH1yFjmzeFdqF2lg3\n6jWHXcYYZTzSPuTNuK258CritDb2fPddvJJIZ0ZwhOMdVFEErZ5hNplpBTQIPaKXVWTkNH+pwy55\nWKzOmtvNfZ26c5PRhKTlg51Srmssj/DeDLiXNGt6cLhz7Ed39tm2rYbphunArDzON9s4c75PyD57\nc7WKNZ14Fec8drGmRYrAVjNixbU3CEeQsxnhGuC0tWzNaoN+vozbK/eOYH2u5ybv/vplTatvBMWM\nFT3Sc+wsI9E6tkIhfDrhS6JVj5v7UXB6XRCkBzEdn73Z6LHCmtEvCVm5u5ST3fJLj5U3fpe13UdZ\naoE26qnLRIo492kW2mevfwn4X+/+/a8Dv5qZPwL8av8bEfl7gB8H/l7g9wD/gYhYP+ZPAD8D/Ej/\n+T3f70E81mM91g+2vhfo+z8G/n3gFz/19X8nM795/4VP/cL/LuC/EpG/O/O7J+muojes5sxFdmoC\nj1kTqWCMjaV68UOrGESQ7lgKl+1p+ZeQVqQjd0cjsbHmg8ulahVFKSc0bYZuFAlLxfBZ2dhkMuc8\nOxppglcR0gpiXTPZiwoWQWSUHKmQ59LZam0IOK2vhYiJWUnIse7sGsp+ySvIuHXx3c3ZWKYbVQiS\nDo4wubl1RXJ4E7V0kKoMjrJTV0VS2T2QmUQX6v2YhVw8GTIqhcxE2cRImYgog9HRj+UatohmhCFN\nBPMI5qyPfEV9HMuwJEFykj1fHm0VqgqiZSXqXjalSTHwHUhv1zcpbkCEl8+3FrSe2TpxXZ1sFfDK\n9PIukhXzuVknW92hICt845he3e5swpoZqFX8KYlKNF+hCnNtkKrgZsyW4Wm9j77eZCtNnkhdy0kR\n16RICnXMp0SuC3LmieZoIyU3XOizl4h8FfjHgT8O/Mv95X8K+Fr//U8DHwH/Wn/9P83MK/B/iMhf\nAv5BEfnLwA9l5p/r5/xF4J8G/ovv+UAe67Ee6wden1moM/O/FZHf/T0+33f8hQf+u+/2oHX7WQlZ\n59ea5l0e147aVjfFLlDSHVU2K3ezUbNapcIpGpL0CGwYd5FTZyJV0rhys4U9EveiNcVdt7ZY5XWc\neXau5fkcq82qm2rDnwslrRjONTOujq3tU/COmFyJUuV50oUmOeVSa5eQSftcazGwFwqxyGinNGq1\nl7UZyZb7FJu6rEzx5JYmuvzJF0u8wkLrtYWg3OBanXx2t/XyVfiLsdys8yhr0pNZrVY2qancMkcV\nWxuzqoelHD7KvjUT0ssreyEaZSXary31HGf1w05EYx2poaBlSVuELme6I3nLAKe7WGIxxSkWO33O\n+nMxhURPXftK2dI1ieiLJfPmTJdipLaZDTdlQUHxwcqnXmY70jIGPU3fuUMkvq9J1b8L/KvA77j7\n2lcy86/23/8v4Cv9978D+HN3P/dX+mtH//3TX3+sx3qsz3H9IGSyf1FEvg78BeBfycz/m9/4F/7b\nloj8LPCzAD/8wz/MF7/4JX7ix3/6Jnu6/SDr5idLBJv3U+Eutl2k6jE36HE9neoCDldlu/37fha9\nHiNUMQf40pe/zE99/We+/bDujuB2QHVzvxvTssw/Flydsrqm81z026yfYm0g7rqqd15L9DQ6AeFL\nX/oSP/37v8Hd2z/nnCdELmUksuIUhTJruT/d69yLrvOxes6bOcjtue8H1PfHx7ufocCXv/Rlfuon\n/mDXsTsjFJHT/WvhxYqced7349B6/fu5fI8L7tCGhZSszdv6ft49ydKrr+f80pe+zNd/4hvvvBU5\nX2P9Xe7+/h2WrGO7ve79KUne/cKSpp3PvAgFn/ppgC9+8cv8/p/4xrk5+G9+9bvuefv55Z8Afj0z\n/3sR+dp3+pnMTPktDLi+/33+yle+wkcfffRdf/7jjz/+zJ/5vNY3vwlf/erHfPObH51f+4gbWPjN\n3/Xtj/n/+thf0/lZ67Ud0/t0PL/ZQv0ngD9G3VX+GPBvAT/9/TxBZv5J4E8C/I4f+kL+jb/x1/ml\nX/6Fk+AkUg2FDsXDue5Xnrc3mGzETA7ZSxI1Ng53rm/3igq8GMPKocv3ZB41S3z+wlPPrh1IYio+\nC+58+kB4Hs9EBPtxcLQf954HRvKNr/9BfumX/sMzfCLzQG0r9i9CzCtnKzaM4c6LJ3uApLKlceTk\nSYx5kZI4kUgWPPq8GRct2P1AiJnMwyEPzMaZFFUF0tkubzj2HfcgVfnpn/wpfv4Xfx6TIt+pllzq\nugdjDMbYSo4Ub0vyZsKWzicvTkTBwYHg17Ia3Z4N3QQ4sAwsN/Y0NJMNcFWmX4mcPRcuD26L6hI9\nnBTHhjJM+ed+8g/xy7/8C9Ue58CRytiSOj/z+gIqXLbBxYzrvnPNiYliYkWEk4qWHKq8zMnkYFO4\njA1yAw8OvDdkvckBnsaFazgjFTB8BCZlWjIj+anf+7P84p/5j0gCCRiLnDjzdJmbvakZSHfVQkgz\ns4XiUJiS4e20BgODYeVTv5e3mphVhnlvQHy/lrWoGaQ1ia9IkuXT4vzk7/0Z/vQv/SmetjcMs2//\nRfrO6x8G/snmjjwDPyQi/wnw10Tkd2bmXxWR3wn8ev/8rwF/593jv9pf+7X++6e//l1/n3/0R380\nv/a1r33XA/zoo4/4rJ/5vNaP/Rh885sf8Yf/8NfOryU/dvv+z337Y/L3/Zbtcb7jek3nZ63Xdkzv\n0/H8pljfmfnXMtOz2Dx/ioK34Tf+hf+sJ6z/SClZDakbHcJxlFf1RTb2VHYmuu08XUBHlqTFQTbr\nOWzlCw8Vti14ek6eP7zUTDAEdSEn7DPw1fZO2I/JMYNE2S7GeBKeh3Uxrrn47GK8yVNlGGcjlraB\nWJGoXiYvXgQq02I9Hzh7vHDNnS2Fp2UVGokNYxNBIzjmJCPKSXWUQ1YRyIKIlv6gpBwIjsyDmKWt\ntW0QahyRvOzOcThDq5cPn8BE1CqPOowZFwIYYoyodC0VMKviekkhPXk5nJfdiWNy+M6L7wXdiyA5\nwAe4EnPHxWsGPozLZXAZg8t4QlDcg92DqxSHOwPChcmBjY0ZJWO7RnBM2HSw2cY2NraLdhLZwb5f\nMZJn3RhxIQ8lmSTOpTxcT0vTwag5PLDLlT2/hVwn88XJa3I5SsNecaXtCpZZMjGp83+5bLzZLjzZ\nxgzhRYIpAgwWYUI80Sknb8DMkG20D70i2m4qnowsYzlLIC+4D4692OEopGi9zh68HIUWbGp8+Hzh\nb/vb71Hs7/r7+Ucy86uZ+bspzsh/nZk/CfwK8Af6x/4A8J/1338F+HEReRKRv4sijf35hsn/HxH5\nh5rt/fW7xzzWYz3W57R+Ux312pX3P/8ZYDHCfwX4ZRH5tyky2Y8Af/57fV4VrTtYlPtTxOzZnrFs\nLuvWVYlGbW5ZlpL9B68ux0yrEwUyhT32d6g41g4mAsw5GaJ09mMNmbVG5JMmDIlW9rGc4+KGtm9z\n3lxG0gvZvXu9YRUIoj13LGvMGyxbz7W8zNfceIB20RA7vaf98Ar7IGqwu15ssaSzxGnIIpOVMYxZ\nM9gj2hGsc6gzEb9JwNZBhCfzqM5ubDVfzia6le91MKPY0cMUuX/DC2DPM1y0vibLWpXqklPfZWGv\n7yWnvCm0gkTO51juM3eQtPQHcx8Tus6sZ6eFNQNbyCZ99TNGyfzqePJk5yNt19LcBlmz5obX9ZTo\n1dciejyTA3IQOU/v9vJYWU57Nyi/4PlFiEumO+7O9J5T98zh6fLMFy5f4Adc/ybwZ0XkG8D/Cfyz\n9XHn/ywifxb4X4AJ/At3BNB/niKUvqFIZA8i2WM91ue8PrNQi8ifoZiiXxaRvwL8UeBrIvL3U7eR\nvwz8IfjMX/jPXIucBdmhFUFUQCHLl7pFzp2ivAhDCRJsUgzuIuRYz/+WjrkeoS3BWQVMSJqg3JPk\nNpzMJYeKdXT92IJD6/3WCbjNer9tKLnG1gyxLvS12bCeHUcX+Fjs6byfpY/KgJbaKEgIwoHvK9Tj\n7uT1wSxJUxJNQGpCUtHmS/4blflcrPUlraLPZ7mYJQJe2c2TYJOEbBjaExlaCVFRz7cNu83DV0FG\nbsfY76HcxGKNzZE0ZiRrM5bZpL+cJ3v7zJtGz04XOG3O7/cHenf+V4HccjQ1MPphZYO6dly1V7kn\nC7LG3ucVkbJs2uV8IX1nZ5IVfdn+3pnSgSHr2k7GSoNbx7aeqzcvESULm3OW5e2CusUYY3DZvv99\ndWZ+RLG7ycy/Dvwjv8HP/XGKIf7pr/8F4O/7vl/4sR7rsX7L1vfC+v593+HLP/9dfv47/sJ/xquU\n6cYEUW17CyNlUA4UTVVWIVAcxZjVp4mSGgSOS7KNjUQ4XBpGTHafCJ3NvGg/uujOyWZbsXCjJEfS\nOmJJuc0GogoY7XxVN+9OzVqwKbzDODoDuQxkPnU3W4ES1p27dsdVyVl60z8H1H26ZUSxkrKouXJ2\n0feuKDNa+1xOWoliKpVxrYALx5wsWS9NmvMOB8mEMSrdi2aHX2xDQzhyUjIsBS27VvGqkmY109Vm\njJ+NrvSmoPXj1husVCOOtk9VYQZ47gwbbFZ5zG5OZG1MOIllrafHqmGNqLjOk5AFiGL983V4Ha2Z\npauXIWd2dLmIV+FPSQ6vjYFtF8QUaVvtGSctsN5jWnuxJ6Sem5xj/ZgmKd7SQa8Ze5+fIb0Raztb\nUT8Z3xKVb64haAobUR7sIpg9s/vOJ8ff/P5+rR7rsR7rt8V6JRaiTa7xdvuWxU7W7pj8bGSLbgMy\nVswCZ6+UDUlmlJdy4YzVgYnzDvS8Hpu0i1QsM4xk20onnThjFfYMnETdqsNbBblbQ+n/Rt6Dst2d\nKVVoBVw5NbzSHdZp21EHdEK/SLBCseO0+xJk0z4feWZAA6XLbfMOsjTQyxxFVDjmXhsCKd1vxMQa\nORCBsbU0C8Gl4GxjnDady+AlRCHaE1u1nbXyJlGSGkwkq1Nd3W3L07hB39nsclMYUp94fR7VlUq/\nbbUKxRhqlRvdbMMbqlAnWph3rPPq4D3iTiZW51lidbRrAyQs7D66eH4KtL+lhAndkfdxsgiQHTai\nQfHMSo+vRG/aboiLINjoazp7Q9G6flMlfVZqmtSYZh7Oy/X6vf5CPdZjPdZvo/WqCnU5YlWhPieP\nSwYkWV1krkyh+v4KtaibnJ04dObNAlRVkThus0xWTZR3pFgR1WWLKmqDYN48uJa2WXQ14mc1Fk1A\nz7nm+QL974JW89059Krg6bfC3ODzKg8iQcZoVLvmzsmGjI5Ycr2Zrawc6iiMVrLNTxqOVqQNOqQh\neCk4XEYdsgo2yts6AxyvOb/AnML0eh5RcEDcb520KL50x9zex20GUDGg2kV6fbqVvFVQsfZoQFJ6\nM8DymlmGpW0ju4r9TTJ2+yhu0rPbRyBkWL2qVRee6Y0stHyv5/X3c/J3eAgs9fjSRvc44bw2OIl5\ntbOgn0BZmdJLznebBGQlbkkVaxXtjYBgoRyZoPUKKkXsm8cjPeuxHut9XK+iUJftZ4LtePRsNJ2x\n3D6kLDBlVlcsksgchARTHNXE1Eo6JcLI6uTKNCSxqG4npQbnZw3tp7/i2KxpeA4lRJkTrlSXh4AM\neAojPTmsOG9JMkV41vIT31NQvZJR8Ls0kYpQckuINmAhEQ0MOCQYVMKWb7C/XMmO9wwVLpeJOPhM\nZkI8B1tN5XFR9vAeG+xIbOioCFBNQTcrWN7hSDDdeoLQbHAbxB7EVtnQz/OJwcYhDvn29ExHNyRq\ntBCi4Ht1elImKCHBZpfKZM4kwuG44mtDkwK5EcVMY5OtP0Mv2PtyQc3IURurcVSH7SiRzsiDLd5g\nAchEVPB0jqwM6YuWPGrG0daxgnuWcYwldoERGxo36069DeYL5ciojYfVhiBEiBnM3emQMSSd1Avi\n1BUkgWiiDuKJXPIWUxmCDMF1ommQgXuCWI8jBGRivamZRMkKaQKbN3kPcCbq4DN4rMd6rPdvvYpC\nvUIUBls5ii2mtdrZoWpWB1ohHJVYFVm3Ne27bQIvWRIYrLova/7Sx5+UfnphmJvJac1oKYxtFHQt\nNbeVDD5Ybl6Z7B6YGJh1R7/iFEvSRSaaynF0ClJrwc2kEpzSqkPOkmxF24oeWcGeQ62fr8IyPJKh\nAmOr8AYLxAM9AhnVl5nUz4iU/nfbrBy+pBu7vEU+Ks1g7xjNSGF6Pd4T5nS+lW/ZwhFLpuX5WHrD\nMwJYAwGjiFPSlp0OOatQomCb3gJApNpGkQEShF8Xco5eLpVCJYllfZZPMhBV3JTYsvkF1jnPybFP\nDi9/7Wi+gGZyMWGYkj1D1iztdczazDjJoD5bJPE7ODyz+Q9Zmy6Po57XeqMmhURkR5xKdna09ehE\nqVzuAhYKYY9kzu6+c/X3gUV51cmIE9mJ1JM74EW9uDHE3XmJNf54rMd6rPdtvYpCvdZCarXnqugN\n6hOkE5wS7bZleSIDBXcD0+uGaH2DVemCrLNviutxdbMVEqfh8R4T10w2kG2URwdFNDMJhiq+IOos\nc4rT0StLtuRdyGknsKQjmajiKtq7j1A8rkWaEl1DdsgVKAKnRaZKy7Oiz8ciONXew9r684xSiFvG\ndb0nuKdgyzmSvY0ddp+gymgEobyxVyCJ9Cy+JFVLNtannoiEOXGtTtLGnVwrs95bB59E7pVTEcIG\nDAXVRKxHGOPSMirBpaD4+lOBIUcWix9uXurpyUGgo2D90CbdUYS9WFh2LO7D6Y/WrPs+8e7Qhign\n7CwLglacRaxbhVdO1GeNUtbnQrPiF6N+RYNKd/NzBrbSYxoyP11ubx8QGcFcHfljPdZjvXfrlRTq\nLqHpVQyoG+Tptb0KK2vmXH7cuZhGkWdSVoPo/f9LGwNj2zjTk5bBRRapy5k3T+0urSKJDmPZk0bU\nHVQkChrtJ15FP2mYfQwSPzOhk8BztkNm63c7mSuAnCvOsYpOkaOlPaC7wGlNylWjitJybmuiEbIY\n17fp7NkNr797FTyaQL8em57nWfaMZtwXY3xJxVKE6M3T8iVfVL7MlZQFzElYbagy9TyWtST7GEWb\nMS+YRIeS9EZDBdsu+P6CUFrlnEpEacd9wddyex9a7EFmBltFO/fnS3MWlmd5QdKedzyFrIK+ZH2T\n2tCNHmeICqdAb22wbpfsjZNwvkB/zs05ECnmuNHEu36Yk8yd3nByQ0L6+3EWf5qImDwa6sd6rPdz\nvYpCvRrJ6GK38o6zjU2WBkbu/ldFYlGKqj9y+ua9nreLVASYlslHSCVuRQc5LO/rbFFuSYmqQAzy\njKRc8ZQB2F0/pqsgZcdVShW5FGU5fkcmQ0tOFtkEuTX7LkZVm2UUxKpjaapv3tyLbiyi51akNMVN\nozr9v6v77APlPBurKe7NjjTSsHe4RSEW/TN6fwY5NxLVJLbRxzIGyQpMyYzWKi88ua08W8Imi0KW\ndayqtRkammzWzPHOZtZNmVNYmnMxqc2Y1qbI1mbDV8fZpDAvG1DR21Uh0Zugvi4O5yTg3dvyaVft\nWDK8rUNKRMlsGZXcLtaVu52yNn3Zjm29fWv42gSOuNuyrPEKnJukVajvLtwu1rcNhTay8FiP9Vjv\n33oVhXpxdRdUmRINg5e0hXSImku3pVRZM0LPgrMKhAibRJHJpCRFHmtWGJUpHPVfNe1kJWXMLOJU\nRx1uT4YNwfcXsnU9qmUnuo0Bx7WOl9LierGHIJPjWrnNdvpzJ57CGIJ7cH1Jju7Yi1g1igQXAMEw\nATPSjNiPOjtJaa6tGMxHrqhFTok5Ju0VTd/luzNfCOozzOO2OclQ5kz2PCqW0QYXE2wzdBjfmle0\n90hzBvv0mhSrwvMg3TsaMph5EMCbiyFqqG2oDpKWRQkIB5k7iSI6GBYgweWyMWxUoQuHCHQekIY0\n0mEXJYYgT1o65tm50J74DD651uenAYmVTEo5FQNvU3jqDdi3joPDg5HBk9xm/YsncU0jsrp0o2Rs\nxZYPUhPihrL4rOIcvWksCVl9XhGCjfqzHd7SvJv73SDRizCyNk3emwsPmK1jz7NQ1/VTfuWP9ViP\n9b6tV1GoV7enJvjBqYMWdrC6SZkZlqOIPyKIOKiREfjhiCZjKEwQMQzF5+SgLEU9rgjGkCqCObpz\nFCW0JTcaqDhbKiOUqYbPHVK42AW7CFgi8sRTM7ojsy0wi5xkUfB0IBUtKcqTVSd3HMk+d1RKBiUk\nEa2fbewztkAzsfAqFiNPfXSEEKoljcoy/6hutgrqRcAnTK/M5vTERFvC9sIHH3xIuPCyH1yPg8CQ\nCFSjtOO2QQZzHjxF6cVnBOmCejHD19RdBGaWP3oAl6dmUzej/DgCXaS9/owFO1EAkYGwobxBh+O+\n17jBNq7eWddtEEJbpaoUN+GyXdAU5pG89UTGwbDANsVGGcVky5x8BjYd5AIYJp/UccnGU7PgNymU\nYyakCk+ALtLgcSAyS+OdA7H1uTs+AbQMUlSw4yhDeAEboAyYiox2WVMvyZWUVekPPX2AR3JEMeAX\n72Kzlo55IRo7yYd7cSMe67Ee6/1br6NQ94pOzoKV29s3/pNjFQ05FvQqIa27XW5esTxOepbNbaar\nwVJdg9zg3hRmNuQqBfMeUuYm6YLf+3Rmw6zNfn7Xx7q+dtPZ3lzQJjVHF7JJa7UzSdoBDUWWViwb\n6s5yFaNh1JOkJMsRrTq5k18UrbTOcypd4R7a1iO64OpmZkWAw2aGam0W7scIkTdGcqEY1l0ebeZR\nxLByJoMxaMIYrJzq6kxbE94BI12xb4j8EJ7HhkswfZZ/uK/RRr30WGr2hQ5oab81kjGCi/QmYi4L\n0XZd8+A4KoWLlutp495DtRCKukTq3QhL9YwkzHSm74ye2U85MPR2DteQugb56+2fpunrDNLnUVq5\n0JS63rR05y+nWel6uvPKqut/jXke6/Nc8nN/q4/gsR7rlRTq1Sd4xCklMil3sFW/oLvEZtEq0sWv\nnMdIJ9zP+bVqDZVrflxQc1l3dhddd9IqpDF5GiUJypQOcSjdap6z477Ze7YbFZyMoryVa1VlSrHB\ntavLzmQ0/XeFPwiQWcViFYklm1rnRKymviRtxhIN/9fmY1JhE1B1V7rDrzlwthkJyHpMRFdyZ9Hh\ntrEhdnNiW6VirqGs1KapZvU1J9YA2jJUemdko6I11znJnkmvfY6YceaDr4IriQ7hebvgkrxIsGfn\nZH/6GpEVxSItyKsLxayY46LdzNZom/BgTudlv3IZl4Lh0zEodMa05sJZM+SRq1hLk9yUIyceDYGL\n4ZLo0pW1X3ldBu0NvzaV/edexgeg2LkJ6IF1/aXo/OfGKJqfsF5A+1w9CvVjPdb7uV5FoV4rmrA0\nVNmGFWso180ucWouSiahA/U2qDAl51GpUlJsboUqqF5d8HZ5QnWgNkgRdj+I9Jpfx8EX7EM2q+jC\nT66OZ3LsfhZQa5OMSMejY42E8+Z5SnJtSXTyJFxdwysEQ0rrfM6QKSc20QpviBA4ZhXV/uM+e3MQ\nSDrbAHHFCWY6YW3VqYBqM6CzHb9GFfJOywqvmbeZYmNgOgqhqD1LwdiRPRuteevZacqsub8Gpk+d\nvHXr9tyDIaM2BaeUqCJHQRA10mtjYWSxnFUgroQ8Mc04OpHrObSQC1n1LPl/2Xu/kPvW7b7rM8bz\nzLXe3977tCYnJcSmoGBu2oIVQwnoRaJIKhbqVYmhSRraBmwvFCz456peFLw4eCGoECwmxaoEVBSp\nUCk9oGBbVNTSVjFQBUtUiJRkn/1715rPM4YX3/HMufY56Tn7lOb0Jb81znn37/db77vWmvOZ653j\nGd/xHd9vlqFHGljB7UmSLY9edN8cyySm1vs+Bq/7XSxAD7wlH3/0AmnsMXidNzKT1/tgM6NjzBrd\nv7dQ756NO3Lg+oSPuc33QCEKRdZb8q/pRQ4zXY+ZZeeJxFQaKdIa6se///Su2WsLvKE1skbi3GI/\nEJlmXeT/J/L9jGd8kPE2EnUuqNbk1VuVWthiYotdC1VkFLw7S2p0CVcufDjL1vCYby5JTfV51f+e\nIeMEVbky/0hUTd/izoiUwIkrIZ9+EwWpVzVkC/pcp0IC8qZex7DEWqySrc9khgAAIABJREFU2zpO\neTrHQ6V0zmSvYuuo2muzknql49hrKqv67fVU12tLk1vrESGBk2bgvdG2xtY2bvd7rVmNrunAcI9D\nEIY0LDfWCJocopYZB0Awd1tybVol0zCXIPXVMlDfFdc6NpOM6T129hgi5ZmMQVqNwM0F/55AsHrQ\nqyVgMsoQymHHSi2t99YbS3zGXZu/iGROqsfMYTs5LWt+2riPnRZecqpSaJsjDjg+a8TucNPKNXD3\neNnEOndfVfQS6akrOac+160+xXZ0P2r6oTaB6aWM9qyon/GMDzHeRKJet5/WxILtbpLBXrBh9Tmx\ndTMz9WMLMrT0YwbZbfX7ovSvC7otCJtyUmKNGyEoHGoMKYKYmoPuvok8Bsf7mnkJeMIxVwbHDTsI\njIssOnOSOWnRaS4xD/1sJVyzU+zkELdQNWZIjc2NkrCsErs2HRZlYFHbB8dqFls9aUdJPF3JP0Yj\n56TyRMllghGH+pjWpHrm65BqrWKuRCj5zlbfPLgECZH7eVGtVrdmvnIGOTQaZ71Ve0PXfsRgxtCm\nxYwkaK0dvea13GvvEgelTQ9K9MSIch4LE2vamrNtG5ejv2/MenamEqCQgyYJlIIImkmtbZmvWHYs\njTGHmN8PrYoFY9fcAY9HatVqaL6xtnBru5Hpx+w7yyim0KNIoScHip6aHHB7JupnPONDjDeRqEE3\nYNkIrj4hR7/1JNUctbOes4hVZJUhdsznJpKJVCXl7HOX+FP1Wy2VCA0rE5CoyjHpKfUtb86hGLUq\n6nUM1WtVMjmPSj1lZUNZHUp9qlcveklZriTjstOCVSE+nKEIcQtKF55q5tKKRod2VPqhJOSlloZ5\n6U7X6FBKNEQLGp//QudhNavumKDz2gjFkUD0ozNqfM5XxSxm98g454jr+I9ssyrqal0cSajmlC0m\nHoG5c8+Btb7enkOwpJb9FqPW3LT+M8iRsHXVqvUta0ansT0kU7Hkdd17GZJIiCQZhUw40K2THlgz\nttxK/7uU4tfru/gK68AWxrIq6nX8jab1q03R2vBYM072Y22ocpZG/Xw4Y839n65gz3jGMz6keBuJ\nekGsrUnnOJwcieXgdMkyLEUCCwNGcCmLxbQkrGE0MgYT2HzgKWLOHpDFWpYjUbKFiD240eYkBkfS\nb5u8kN2mSGTUuFW6pC3Nj36hdMAHVYKRVyNdEqRNdTG+adNheIljzFIdS+xFc7tzTvYxGXPSw2nT\nsavTtybt7yEWc2+SLgWnt5IXTUgLPvZOtiu33Hk/PuNlGH06eQ/u40bf3pHdmQYx9FgzJ0I95UX0\nmjFJGzr2GnW7mGQsIwBrGMGcxmsKTZhzsnElGIQN0jvafFR74eKwQU9VsFgnzMne8GkYW51KcjV5\nkmOqyNeGzWMRDbMkSDV3PHHoRTNLPWcyyH3w0fVjzWcXbO9jFuFdhh1mxuXSGTHRbKAR3mgXw6YR\nMwjfsdJSxzsxg5xRs/3UHipPx9HMwxq0zMBFjFyWoUwwabyTCBlJydj22giGGXsI+J85cWsMX/qw\nz3jGMz6keBuJmuqD6halSjiT9LqRPfRiwxa7Vo8tJvCdYGRybfOYX+boHwsKdf86mcYDAuVzfeZV\nDSlx699zPxnN1mq0ilO1K5cyGWVGka0ESFIOXzMLKegkVfktj+fVhy8danentVYs9HFU1JaOxUQW\nngXb+7F8Z0/eVHnvY0C0kmOtfu/aIJRDV5Y/89kJ1mmOssTcCg5elpaJpDCZU7POoZWQBajW3YpA\nltQ8cEH0F9d5R7UnLNfo3QFPcJbgs6rmBRinZEyzNierz0tqE4WzWQPTTL1aIevn42z1P0qz1sXO\nJSurbzOt2g6s9oD63d5K/CQLnj7wB6sjruNd0rb40VbRpbLSbK8Pc7U4rEbFTsTIaD4/J3XqzauN\n8IxnPONDizeRqNctcyGla0bVbas+dRRz9tTKVg+6oOIUKQuC7o1wShyjbnSZ5a1cyeRhBOjogT7k\nipU4BUdWUl8Wg5Y0f9RyPJMfJA31z4/+avWCmUeaVzKvkallbLF+fo2beVOyi7jXcnTAydwZlIOY\nl1IYgHde41U9fms4V173nRs7ZlMd1IdDtYJgJzCzmMsrmaRByEgkWEm6sZnrA2OC0XNpfBf8vdji\nVt7OIrFpAWdA611z4L4fjPGZoyaLH3JnVZg6X05N9/X3xZg/LpzOrS3Z1iK5SXEsj0Gq1Qaoq8ba\nTIyCmxeyY5gsNXFogSU0Ty4uW1Gqjxzp5UVeJ/P4mSAf5uVFBPRjX2WHlosV72JtkKI+C/LutuP3\n4lKbt2c84xkfXryJRK0ocpeZbsYGzaKMIqLgzyJzZWO3w3AQR2xxs2TzixTCoG7CIg911k1zVdLn\nrfoxT6+wh+MiUwkHvceMs7W4CEfLqaLTFl0JTAIn3a10qquaM5He9NVKKMMeasq68ZuJ3BarWlZP\nNpZzlacqLTM2a7w3GWS0NJrK2YP9rGSohGhHRizSFNTscM0S5xIZEcIxgdaSXj3pO5BjfA6FMMTg\n1kvrTNQCFyFwjKTX3LP629X/nvUcOzcLAkL6mag40QxYIipVldvaysm0ZB4uXU5zPZbB8fPHxPsi\n74EQkUqsDamG9SjnsHJqc4OrGdPWNTonpM2WDv15jOfomtWoWRYRrVbL1hicjm19vvSAiZlfzPLu\ndlh4PuMZz/jw4k0k6qXw1aqaWcpgLTWyM2NVmw+pNktSI6FZ003M5Ies0R2r2WFpJxvKrr5ujrUB\nSEqEZBXgq60IHIO8hvqZABhjzmN0SSYSXTfzlKxm605vDdiqfxm0DuM+eb3dRVDaVCFJOjToTaSo\nPTSzy5xSTZsrSY8yyzC2qiYdsJTm6jZvbH7l9b7zfr5WIjS2bGxpjGaChJWptcHIxrYsHJd7VfHL\nrIQ9shK3m7y9m6MZaZeYyUzB23Pf8RAaYGu8LiRjmgk5gzHuWDo+nbDk5kPqZR2aay7Zm9Hd2aeU\n5CylJ7eqUzBsmzUipvPYynDFPIkxILqkRdzp75y8aV5+5iRtHpX/mn6Px3MMwyIZbeLe2dpF16Kk\nPt17OZIus48avzJxCFbboZrPkiYdO4kkYhc3IdIO33IItu703ti2jdYa73wjE1pzvvSlj+iXje5v\n4tf1Gc94xnc43shv/upjVjVZ6XiyseQaPQPYCapnm/OhklHlOD2RFIjhnEpYmbBn0JKqK8uakWCa\n9LBXJWdn0SOryVU2HpWipCsdVaBQCbtGdV7HjatdaNWrDVLKZIhYNaeg3ujVa13tyqMCq01IqIpb\nRiVWB5ZmNRutSpCQK9eYd1prgqQnNLaCVHetiF2qoqz3qP82Cgywo4WrfnbB8E5TnzcgSnPcIg6R\nkUyYkdxnsJnWVm0HHnrfgo6NKGxX6zI9mBZ4iGFuBWi3GtGCz8u+KrF6+VbX2qeY5GFrhC2AgWVT\nVds62OAwel5D07bwi3VVH2riWtsORQhUgt0DzWEvZbv1mTHB7Gs4bQ3NOZS7Vtam56zojZopr0Rt\ntbm7bJ3eOy03vbwZl22jtyZuwDOe8YwPLt5Ioq4qN5K0LtJQC9pUVZguD+GISiw4xpVIqt/b8Ay2\n2cgu4tD9EBIJLJLYg341LpfG1jqvr50575ADC5HBcCuDDsGVmmNWEt56TU+ngb/S8wXPCyPg1d9z\nic41Nj6xIPOC9cBaYNbpexKjrA2943RanLO77k7vnYnRUjaWwwZzT6z67Wf7Vuxit0ZLrxc18I19\nGtk7tGAy2diY0dhjVAK1w4jEMmDeeTXHUyz3kRoBaplsly7SWoL5Du1C4FL8KqWQRBseKYFNvF0k\nLdod80mMKTSExLLxsl1xn7y+RonbGK97kCnBma3g6qxROWiIqj+PRHd8WsqC0hZ8H6p6I5yXbJgb\nt03NcW24XKV7dawdEzwA7DFFBExovpGtMe47zs4rwT60gfikA7Fzcce3K7e800zXaFhjH/IdbzW7\nb2IIcr1cyGpjZI3A4Y177HR3Lv3C1rvmucsm1VzMejPjS1/6GCe5jfe/7r+Jz3jGM95evIlEfdQY\nZsAdiyGDDdOIjxJLI0xQsePk/qrnFvyaGYRX4hPqyDBjVi+3b+B2IeaVQZL9U81Mj42Ieah5+UNp\nved4OD6NW8n56LsYeWcPHcMLF2Bws0/ZtkbajkURoiKI24QpMc2X3mSx2cQIHmOoKsxgTllwqsVb\ntW/1k1eHuaWVCIsXqUvV8Z5wtYZEOFYVLpGMZjUrXDrjgg2KtBZbvV2eKmkFcS8m8gz4W59OMu+Y\n7Vx7L1nTxVzfeHl5oTf15FvVwd2ht1JSa5N77uRI7hHF2E9y5CFSEpbFtqckUMtesuBkrURZRpbq\nW6NxaVvJv8L7cedekPIYyTbsULWjNir2wP8yM67bJiEWJCySiTZtay1rFGx/DdI3/BCzKYIigdnk\n2g65mILS+Zzup4RZqrqeydV7rYNGxHp3fe7mLge13nA3Pn658qXrhRHXX49fv2c84xlvPN5EooaH\n3JQhaDWS2YJVbceCZx/IPCyouKDP43sPySqLWEZpcEeEdJjdDzg71wEcrOWHAzpo3wUU28lONrGU\n1LOsRJ/4IQjipkRNAi4cYCvmullCyPyic84FnzQ2EblOyN3O/5mVypkd7lmRaL674P1Ds+xgQJ/w\n/nLEcnMaZcGY6udnvf7VvVDiZO6D/Za4B70LVl7uKFbmKX3rrAElYyV8W61yksmQ3TRjhpTN1tRU\nbVQiwEJiN8t6OSxYqd+WMI3Z0dYIkt5knznnkExoCHJuKUGSrA2c8ubRr1DiRkYt0ohf7QRtUtR7\ntpoWkJ55YLRSnKPwHc3Wh7gOha6T5+eEPPTI9P+orWkZcaxLLCc1Le3WOt0LeK85RHuSvp/xjA8y\n3kii1u19LtWqpRiWNcp0zJNqLAY75RoPq0lbN94H9TKr9FYuUp6COAnH7IIxwYKxkvxKZOteHmeP\nNadYu0qfNwy5KgFECaM0Luop5ygTiep6mkMr+RNbYidrg6ERKe0J1g6g5qv9HPFRlaa/t4eznEtf\nOtYQUL1nnslurcFhcrGax2aM21C/OJOoGW63pkp+wpiD19vOnPJ57l0NdQsR8MxEitu2LoGO0Fyy\n3Lb8gZg3mVObgYiUqtr0WvfF3F6z6ZwjWJaEWHMso0yPYmrneq7WZc8hQqLEwAmWV/fJqHYvha+a\nYwZJmWLVRajN2VaOYiLZVRUcs3zSZzHuKlHXh8aOcb4TptcoXB4bpHP6W2NhzZa9apCx1t+4tFaE\nxCWIEjjneTzjGc/4cOKNJGrFnANYtoIra7rITLkMAHWzmizimYhXS25TD6mi1AhVCaMEuIQYVfHM\nfgiOlDDkeRNdUpclrHHeH9dP3Pmch7QHZg1Lwa+qthaUr9639YJcs4hSGWQ6ka7Z4iN3qooL55BT\ntZWkiyu1NhQic6myzRQxayWDo1YreBfjIYkoLOF2d6JGsBYTvfXOPWTpOfbkvpcaW3e8G5mtGM1K\n1FYe2+kF9Wfpr9u6RnYQADO9rqOY+ap2T5pV5FKBq/Rrjlus7Yeq8+p7U+cTKeRlMummahgBFrx6\nrA9LbYAeEJP6kiZ6SbiuS3pMCJxV9YygzLi1frbEQJe6+uBzkqs1O98s9Rle7Yc1asesNshCKCbN\nnK11Ll0bIzNoOemzHeNcz3jGMz6seBOJWjkn2ffPgI73d7TmvI/3WHi5I0lmE9DIzsVP+DRCUKW1\nYn2LXLzmsnHjfh+0ttHaJlh9fKYZ2dZhFCyeShpxn+RM4rJhC3528KYvu8NOsrekufFRa+wZ3LnR\n2ovkJetubsxSxxqVbE7TCHcj93FUuQacPs+G0+nk2i4QqfddymwQ7CmZyT0msAMiikn4YzsEPBas\nmw8wrAHXjxojgJhVKUoqNWyoK5+dl2ZcrkrS2OQ1R40hmdY7J2O/Y5cXbXSGVt+sH5V/WtdatE7v\nIZjXNmLcRfbzkifFiVTSxVq9zyz5OAcaI8Svbqaq26eY7y/bl0h7ZUYl+gkZnchxqKDZUmJbiRvY\nLk7P2rA1wyeMuQvhqAQeJPuYXC5iwstycxYcL7U5j5RuvNVEwVD+7V2fw/aAMGQaL3blet24XhvE\n17CcXLfOJx91NjZ8E44+/JUbUcIvz3jGMz60eBOJelU2fXthjoIESTYaIyb3oST3rr9Un/kcefFL\nI9PY9x13GOb0VM9acLQIWVvs9E2MWnJCv9Dd8AzuzYm9TDkcvJvGpxosre8lIakm5ItGd6ofuR8j\nYRDxikc/8POwjvegmxMWgodLQjJxbBuQDQ/NR09Tn7XhRA6idcHpAzIm1nYs+wF/79bBjNYbcatd\nhO1YbvjuTNtJv4FfqsYu6NycaE6MHfOg11x1ZkDsdOtgE+uJ94ZvV4KdmVJfu0+JkPTq404a91/9\nVSXbNL7UOj1hujY6EavPrHG44mtzm7C9gF9Mx5NGvA765pozZsJFx5ozYJ/Qau664PxZze7YbzR3\naYEjudbeJvfJQeLyYr0f/REzvG0ip80d60bbOvM9eAtu9zuRjdYubNtdZMMi6Y2EW5HG3IPsF1om\nxM4ed/YxIZzrJj/psB23RvdG887FLowc7DH58pde6Dm5OLRM9u1W3i5Gny9SkItnon7GMz7EeBuJ\nGgAjQ3Cpe8HKOerfqkZ6O3hdDDgg4WVo4N44bmViKR39wBuqsjcWTWvJVHLCoIugVjO2lweBlEan\nFbR953bgz1YqYIvudbz1kRaVksaQvEpopqzOS85atuZsS2BE/eLgYu1MKgbpTnoXi/nh0FlrYam+\nqbWC2ffqm15o3tTBTjt/nqouc9XXZw+V6pkvot2CnzVXvZTiOPrGKacLFvnJNz9U0wzoJeWW2EH8\nE8SctF7uYiXLPQxi1Dq5+O5rVK6G4Wtsri51lJRpX4Kh66SEHSwoPFMM9kV+X2PJq13dnFK5M2bX\npiJqY+glPxvrAp90gqO9IthfxEBLo/maC2/rE17NmmQyCb/jllxa49K6Wh1N7QCL1eQppGcG83DU\nesYznvEhxdtI1JVUIwQl1+RSMacd62Itt2WG4ckYlb1CCae1Lj3vQD3QWD1MJeWR6iS2UpIiFjWX\nyi51W1wjPBS0WolGBDeAYObO6p+3RfmupOeWzIIoF4PakM2m2NW6sbdW4iC5rC/VVfalb83E/FJE\nqDO5a2HmwWxfWuGGVzLZqnc+CW5kbhiXIjmvbUs9Z+WytRQlcRqxklgl1oTIUVCxWM7+8NwoFrOn\nKlxzsKZEvewoe7OjJfFYF7ohMY8m8xApiJVMa5dZRTsSdWK9VvbggpVue80n59qglQ65pelzU05b\nS1/OXRuDRVhzS7q5vlw958gkPksiNPrXXLPQR5O7ErR7qaLNUtGrGfzurrn8YxtU27Z1Ldqd5o3r\ntnFtm/gYBdNYJDZFfBtzkjmKw/GMZzzjQ4s3kaiPXDGrwipZy0szoqlnmmHc9+VyVDfqPHu8l5eG\nhW62A9QcDGMqbXM1QcvbQUxaN9xW9131p80Ndyld38ZOtTSZBN3l8XyNrfSjz5GplSDcLoSJTX5u\nE1JCHmm4KMyqnsKI1pg51SdO42IdPEkL9kxilMzksWlogqXRSNHybrKUGUkzwz3AJiMW0WoQQxrk\nQM0BnzKrCjtg/kyje1fCRB7Ocb9Dlk1nkypcupHNMGs4vbyzAyK533ZGm1VFJzN2XUcWvUzr9W77\niHeXd7QuaH/YTjRpgm8vznbtWMhWcpa4SA5tttK8qmzNWEfNgmcx6pNiikVj0RG8nLXqomJm9J6s\nCejNNbvcW2NM41fsXjPTsv3kuKJGh1Joi3JRCw2omarobZNU6ogdM+PCBa+NQGvGpW9ct413l04S\n5ccOts+j3UHA/XZjjp3YnxX1M57xIca31CQ0s99mZn/BzP6amf1VM/sX6vHvNrP/2sz+9/rzux6e\n86+a2S+a2f9mZj/6rQ9DWKjjZHiNOHHoaYOqtrFPzbJmqT+5qhoz3WhXlZxZlN+F2y5FLs+SxzQG\nybQkSgAjF+0aDrayhSobLZTTbGPzK24d900J/SA8VcJG401uHbeOWSeKDrZUq1oJiSQyhGw4rV5n\nGLW5qBQ8XeNk2TBrglHzACFOAY3lFU1gNc8sMpYQhjk1xLxETVZ1vXLWYkT7YkEfIHjUmrrWrtSy\nWpmNtBphahTrm5LbnMF9Bvuc9e/UxmGNQkfimVy60bpg4qacS7828QNcLQ2BFsucpWDu1ec3K4Zf\nI8sFa0H5yRrN0+v3pq/mNRFQ17sds+Z2IA+tIPBL84LlC/GxU740S3f1qKQLYljIh1oIyWZbba6K\nDW6T9MnmG9e+0Xur80qRI3dV51kucjEGOYInl+wZz/gw44uIBw/gX8rM3w78EPDHzOy3A/8K8Ocz\n8weAP1//pr73Y8DvAH4P8O+YfQuphkJjzYIIyVFGqAtNVnE89T2zrJuua5yoy0aymZWv9Nk/Pbqx\n5qSb9MBTMHjCkTjL5olkQs1aG0nzXsIiKMGlLA+zZEZXo9NNmww32VBapeZVpUVWj3qGNhUFlWos\nSWNILWVAEcvis4hXdQYnJG/nZkQ396l/x6hRLfBstYGoJGUuAwkE8Tav2eE1W10l7toYObDnYLIT\n7NoMta0GqQ3rG+7ll41JrGUouSz/6fA8DFJAvAJB6JXMa/0ih655lJNWgPvGLEMV5oKZTSsaTVKb\nXuQ0hLZEmnTPSy0t13s02Co5rxaGA92NrS0RFA5kQh9HtU4axtadrasdMyN4mJz+XJIeM3XsKXOW\nxOr483jttfnRe6qS3zbj0mujZ0IsYhacv/Td56w2x1Pr+xnP+BDjW/7mZ+YvZeb/WH//VeCvA78V\n+H3Az9eP/Tzwz9bffx/wH2fmLTP/BvCLwO/+pu/BqkQG5CDmTuZ+iIAcohWmBNe6+pqt+aEg5dVL\nZv3sQcVBldfpE3HcULsl3SjiUYlYHGYQifuGeasjHETe9VVylycXrTybzUluGAM/Rnd0cx57Msc6\nT52LZErjEO9Y+H/LZEtIG6RFjXMF6QFeUHhBvCyCkWjh1UvvOO1IvM1kUGKmDU2rmXP5ep9rtGaM\n3ZSoR+6asLbE+4ZvDd+K0Oat+rgwQqNLMZSowyB7VdiloCZHMz8SaJYa2H0O7mNy36c2MwPIrsQ9\nIEd1dq2VQlmvDVQ5j62eflAqcGstK50aVUVztiio3vgi9C+BmZVMU4IsVn3mZc2ZmWUFysNnTW+r\n3rTEWOzh8ZzJtMm0ebYedBaYh+xDm65R1fkHKrA+O2SWJOkzUT/jGR9ifFs9ajP7B4B/BPhLwPdm\n5i/Vt/5v4Hvr778V+IsPT/u/6rGvf62fAX4G4Lu+67v4ni9/Dz/9B//5I3GYfgbgIVGv6hJWrfZQ\ncyoWLHx8B056cJ4w78Mz5U/9+ffUj+tnvufL38NP/IGfWQfOov4ezG47MjZHJniI9ZAd/3n85nkW\nx7kfP2PH3POZSM6TXMf35e/5Lfz0T//R+q59bo3W+9t6m8+9vx2v8Q3H/PA3HfdDksh8uDZ5zKCv\ntTb7/Nt8+ctf5g//5M88XIs8Diqp6v6oZs+N1hJ/8YcXTDi1uuv1shjzi5W9IO2DxPVwTR+Py4Df\n/Ju/zO/9p//Ar3n+ZhxEtTUWeC5mfS6P91jtjXUeecxuLxrAckg7kI52thvy4X0gj8/MJ7/pu/nH\nf/THjuP62V/4c99wrM94xjN+Y8cXTtRm9gnwnwD/Ymb+ytcltLSF037ByMyfBX4W4JMvfZy//P/9\nMj//H/wpZhHGWjdeLi/yOp5xjjU13eQi7rqBu4DiZg1LsXJnDmYOmnV663hzZgYxhhJKa/RVgZtz\niyGHKdvYXCIjWXrWweQnfvwP8nP/0c/Ksco2mBL5iKpgm28HAUxsXk05U5Dw+/tgqzlsb15Vm3Kf\nRdcNesmneqrlakb4hXG/S3GrNTbv7OPOSCPmIKY8k//wT/0M//6f+fdkJRkFugtPpwZ4ubZgTDlv\nHeNRoR74or3ZscVJbGsnXm1ZNHA9MUeyXRuZMEZw3we3+43rtqkt0Z2tO8uu8yd//A/xc3/6Z5V4\ne8eKAU9zbhF8sm30dlbn9/sgcrA153rZ+PijF81M16Zq5lQvu6lHPkcwR9Ca81rQc0160UzmFgIw\nlByhknnAP/N7f5w/+2f/Q9bWpGp+9lR75TbgPiYzJ599FuyvN2giHOYOe+yYw6VtlEUZE533JY3N\nnHvxHV7eyQDkXe9cL853f/kjrVUz7u/v3O+TOfT5vw/tRH7kR38//82f+wVB4t/OL9gznvGM3zDx\nhRK1mW0oSf+ZzPxP6+H/x8y+LzN/ycy+D/h/6/G/Cfy2h6d/fz32t4+l5W016hNi995zcih5lZey\nlYC1lfFCdUCZ1aMWm3kRpgSZmqnPpy50YjFprVVaCvWU7ax4vKoaUchUo7ZshyymfJn9NF9Y5DVL\nsj1UtRR9KFfCRD4Oj5WtPzwXDghfxhqnJaPVAmWkRtJyEeq0KejeMQutU83zehmBLLJb2jjh/1Sp\n1+r9Ao5K+PAGqRluWysXOm+ajEciUollBFueI09eELtobaUmt8rghWSs1nNBGYLEESluRs3SL2hb\n73tcFluWkRJUsSKWxQOKssa3vAhuek8/Kvokz9J8ff9ho7LezCjSYjqX7ky/w2Lbu2tdRI7Aq8Ww\nsPhAEwjHuF9zend6FyO8b53enObaNYgMp2tzgDO1idMG9Jv+Fj3jGc/4DRpfhPVtwJ8C/npm/psP\n3/ovgJ+qv/8U8J8/PP5jZnY1s38Q+AHgL3/z99CfdbtaRGaNB9U4Vpp6n8XPqYR8wo+ZUzdNYdu4\n+eEwJdhyMjMYGRo3QpXliEXAUkI8jmQRtWo+t9NlwpGhfYUXuce8SFMokR7HVX+yEgBncj5gWzCb\nmK/mZtS3q1+cQxWerUulmeaMlUAk8oIh8lgZmGQ+sKNrfed6bupQQvESAAAgAElEQVQ4LU3Epzqs\nlVybGb00u4ksn+ZquKoZS28QYzBud/bbndhn6bPnsXaSQV2r+fVa3mdV70W/ftT7JlUdtyLqJdp8\nZEypsyXMAfd7cr8vNNrYA0aen5FpYkJG5vF5Weu/PlPrshwCMGsrU5svy8AzaJZsDr2rZ91c6mje\n5CFttXYtjS21A57AXtwAN/18P5J11ybLFiFPNqdzJnOaBGRKiSynWPtLQvcZz3jGhxVfpKL+x4Cf\nAP6Kmf1P9di/BvwbwC+Y2R8C/k/g9wNk5l81s18A/hq6T/6xzC8oqZQBHawjRaoox6RS4pppR8XB\ng7rU0RtNwCfuja1tuqFPQYrMZHqT6ldMrl0OUXPsBMZmVqxreD8HTMN8iiBmOp4gJDUZkyWCGWjU\na7F9rTXmgMhZiTB5ueggXSZaxwbDgOZbVdzJ9DgSSMLBHk6qT5tJzMG+J61vNN+YqfGnMSfNOpmD\nPMBsVaiZkzsXyKhNgCpVYx6e2yDxlmVE8ToGllN2E+YYV21SYnJ/vUk9RDwz+Vpfe/WaQ1rkW6PD\n4Yi1+siwiFGAyyd7zmTMwSx2vyXkKATEIGY7+/aZzJrnpvq9TL12r35+r3GrCIMGt0g8gw0vtEQk\nvDX+tKa713jdMWN/lrWaNmjOJx9f2GNKPGVAq3FCC2fExEJIgORpS/yEENu+QetO3xp9a4XvNCKM\nfQZjDsY8N1tCUagWgiD1ZzzjGR9efMtEnZn/Ld9IgVrxT/5tnvMngT/5hY/i4dVXFaa8VJlgVZWp\ned5MeSMvYo7IU6s2vGMm8Q2rfnXEpIXGlciaj87K9OXm5NihthWVVFr6MRIjP2NB4XPc67hanW8c\nhZjGhYp9XMfdmmDZhbyuJA3gtun5ptdIxqorKUmNsyJH4pOa7+6VyMu6c06yLYFUHmCKB5Wuzy21\nWNcRUa5MK9EtApTV9Pd6orS/seTT+50XbzR3ttL67k1rlBRc3oTxu+Td6jKu9gUHqpClzLb60xmJ\nhzEWGmFGLAW4Oq2Zo5xDa+xqFDzMaZSVVVEn5TFdPV6tw0PVXD9fr85qXKw+yKq6qbZIcydMiMwy\n9lg/MNfrFTrRM4/3+/wvUL3YYR2ja7uOJW2B7uuCPUAwz3jGMz64eBPzHquvJ4BZ/2vu+KXhW6dt\nndbbCacaMmnYXkguzPAacdoJyhvQBHPf09izc+8dy2SLKU/imDDuqkBvQ2IeMRn7oLPxctkOUpCR\nbJekXza8bww2bhHc4pWZNzbvmDWmwbwnzqR54t4x23QuaJ5ZcG5B/MDI98zxCq8Dv0sAJWdn3D5i\nxGSWmMdmxmUzaFcGQdqO+2Tzrmq9J2GvNQbkMDs+lAh3d+75ykyDbFJFSxGm9rjhnly7SG54kp6M\n+V4jV9loTYS3AcytcW2O9YbXjPGlO96vDFdvfH8/ef3aDTfn5XKVypsFewbbhLE5oyUtgku/0NtG\nb8ald679Hf2lSGdm5Jzcb4P9fidn4LnRmzTbfd6J+3tuYewljsK8c99vvN4n+z0ZN/DRib0MNuak\n0dj8wtauAEJKbBImcp63Kz1OXXQHkRXdaHSu/sK7dtFYuWuEyxq89I1Liar4xZkdpk+GTWYLPHTO\n7qWsNluNrDnd+iGjyj2IPZlDrZRJkw7++GKJ2sxezOwvm9n/XCJF/3o9/m2LFJnZP2pmf6W+92/Z\nI4v0Gc94xnck3kSiVqwxJ5VBEci1caDSKNQPNjesUZCsIMlEFeCMs8usFp9mW1VhacDVu9Mu8lPO\nVF8wa8aWJTpRidS7ySQBGCOZUz3SRulPF/x99NMDscuPOevVd86ziFrnWkVdZGNmsNuNwc6kMTLY\neWXfT6gzyn5SzktFkMs4xrbgQGkPslppiqg/ypoS1hYhrYRWqrdq1bcVL0DiLocQTEwidmIOcg5a\nv+Iu1y71nQdz3ojXQe6TnMEYO/t+Z593QfmZjJQ6WQvNKY91japvvjhc6vmrz66jDWb9dyLLykT6\na8MkZJMs9TMYEzHcY41VifgWuQRvUjwH1jqdsqaG+tKra11eIkXoFgHt7Dk/jFytZV9a9ctPu4kr\nIQU3fX557LnnBKLaOBK0ESFPHHeKfzFzMOIL96hvwD+Rmf8w8LuA32NmP8TfmUjRvwv8EcQ1+YH6\n/jOe8YzvYLwNre+CiVuJjcxI5gC/oJtyVJqRlZE0vd91mqmXlzG5pXraL5cGYewjmBNi1i3XUwzb\nzYnWyL0IZSkNaMwPNa9pkz2l4GVVws+5uq1BMyetk9kKms2DAd267uLpenxlzTUWFDOLte3KFPdO\nxF3J0aCNxkyNi9ne+eRFCmR7GDNhM7DrVeNhM6TaURuAudyrOHvAXl89jcEkLZhWjlRm9N7oTWz2\neyw/6qT5VVVcNjKMObIMKYLtKvtQShEso1oPLrev5sb+OmQ72XSebjqQyKQPA+uCsUeUqlvT6pp4\n4lbEuqAgca+kZcaFXoMCxozBPsQouxTrfNES3eC6wXi/evZJ7JIZ9e6HV3d3r/aFDDgAWm2I0tUZ\ntvZIPEMjWhg7yZ1kmNoGfWtgkrp96eJKeBOJ7JN3Vy5bucDlYA40ldD0Xr0XYx+wGWVrmdznnc/u\nkzG+WI86RQb4tP651VciMaIfrsd/Hvgq8C/zIFIE/A0z+0Xgd5vZ/wH8psz8iwBm9qeRsNF/9YUO\n5BnPeMbflXgTiZqjf3kaWYCqLSvNZDGaeSD3yPNZEpSr8hB8bvnAcEa0KW9eydKqmhGxqrcGthjS\nBf1WZTf3oLeSfOyySIx0cplsVjnlRXpLS0HX2Q6LKBWLqpRqaqfakaqsYtzUy0ZJP+MGOSF0zG5K\n1FH9ec9Bax1mljnE2b9eGXtV0rmqvfV+q31d/9Exe/Wo7exjp1jk6WVj5nLjWqhHhPy1WefmiP1c\no2fq3jdYA1NmXFrHXEl7ptNNm46Rpa+dVD940syxZlXRG4tNf+idujytLZ1M5/V2Z07IF1WzuZji\nqTVa5a6x2NVVtTb/3HrEccQlR8vSPaeESXRyC8VoDtPBLDELXcMmepqn89Ia1+60Te2Pl0s/7DzN\n4lAwk+ypA6u9U4S50NHM0CZsfhuD1FUR/w/APwT825n5l8zs2xUp2uvvX//4M57xjO9gvIlEvZLN\nSpAAWMo9yLJmpwJrSgpWN+xHjWlbN8A8lbFWwlIasuN5RMKUilRrRZkqmNULgiXFQKYY55lxsM41\n7KQkpnnfiS8IV2Vh3f3rhbzeo0wpZLip877Ha7GPGuYQeSPTcDptq0WpjYgZgt5tA9cc94KOl0Ro\naW4UlA1HnrLa7NSfYqwHSxf7vBjFfzbtKqz6/WZRBL6AvBRcXGfh0D0gnJEDSFqXkIo1rWFvHU8X\nWjF0ccxrvQ92Wc2000VCK3OWpd1OyDErmzKcHMhg7JMxJC7SrxpXW0x6m/K/lgmK1k82ko/jY4+f\nu1qLKHZ6WhEJaxPEapNQeuWVqCmxGkMseNt4acalG9um3vO2TExElddaV6KeM0sHXSYkzHFcP8H2\nx7TWF/udEqb+u8zs7wP+MzP7nV/3/W9bpOibxaPS4Pd+7/fy1a9+9Zv+/Keffvotf+Y7FV/5Cnz/\n93/KV77y1eOxr/IVfe/v/7Wf8+t97G9pfVa8tWP6kI7nTSTq6gwyU6QlfNJMSWS1rd3AF2ScRjCY\nQ2YXYySXrbM11w1YeU83ZJS8b7eB941t67gholaqJu3eGHNIuMLAsssLuKnXHJF87Wt3ZuXcSw8x\ny022l1HVchi8z4lPr3EtaTm3TcYRmpF9UB4zCO9sPbn0Bq2zz3e0SFo27m0y7nrt3pWEYlzIO7TW\naOa83+9VPQbmrYhPHPoqUT366YA1oQsmORVMULdHaW9TLPd0LO5HIrGk+tileObBVkjHmhe3bPRL\nY7wOxpx8tF3J2Yis9/DGSGPPnZZJpPOe5NI2JvfaLExgYLxgm5FzkHMy55AFKjIuGblr1I7OfRpj\nysZzNqd/7HjTfLwmyIxWphcExD7ImeI5VJ/6lqXhbXAxoQb3XeIxqxJfQjuLuZ5W1AkeyJCuX6h3\n3rn2Ru+TtMmLacPjMSuhl4hKCabMMdjvWciLjmNyfj4PZ62/g8jMv2VmfwH1lr9dkaK/WX//+sd/\nrfc5lAZ/8Ad/MH/4h3/4mx7XV7/6Vb7Vz3yn4kd+BL7yla/yx//4Dx+PJT+i7/2JX/s5+c/9Xdvj\n/JrxltZnxVs7pg/peN4EmUy1SuqG7FH6j6qAIrKqZpgN7j1433fGcOZU6di7rBajQzRZCLqbSFfu\n7Ck/4xGDEXdm3rkhf+PW0I2wUQ3dRjaIbdL8IkERKFvGQWvJR/0TLn49e5xsGI0os485bmS8Qu7M\nqZvynE0jNz2wFjIXwbi+uGa0A3oYL9m4eONyaVztwtyCtnUu26Wqf2f2xj2M1/udZkPQvLeycWw0\nLrRoIoEVEtAn3Ca8H8ntPtj3yQDatUtMJgba22gyXGgDmAcRocTWDWsuz+doBE54MtvknpOvve6E\nN3q/MsZg9lnd0UpkNmnetIsyJb+MCbHe1zA2zHYRwDDC/CAKjpjs+Rmve/Dp6533779G3l65+IV+\nccxnJTuntUZvUpMbWXPaudMuF9r1hegbd7RFlP+5RsteJ3y2J9mULFvv9C4f6UsXQmCW9JJSff86\neb8nI5f8qDHTmGG0MC44eU/mHtxzcPPJvSeja6PGDGwErcsKM2fy2bgT08ugBPabhvbiC26rzey3\nVCWNmb0D/ingf+XbFCkqmPxXzOyHiu39kw/PecYznvEdirdRUVeT0HxbtTWgZECeylxm/bCgjFII\nS4+SoVT1kzULvJytFqaZB1B7PgbKIa2XpzQUwWkwI2mXpNDiUp1qtBTZKxALW6i0et4L0pRgWfVQ\nEVy+wO54ePPVv17d+YBDiS0Kdl6bmDWrnRlsU+S2MY2sWWtcM9SRdryar3exybRNdOjCT83BeyvD\nKUHyjzVCmmZ8FxveC8pPJKG6s2PpNDotBaMfSi6U0MlxPQqit3yoDE+4PSJPZykTPLx+YlX06n2X\n6cZBABCT+2idGCJjWY2apXrB/jDf7GblUa1rdDxv9fgN1jS1+vZ1/JxscfXZxSqP9aGypYZ36oUf\nL961WevWsTB8yCku+lrfLnW6RWDIxpg7c2b1qIu98cWR6u8Dft6Wsg38Qmb+l2b23/HtixT9UeDn\ngHeIRPZBEMmsmCPPeMZbiLeRqKk2rXn1QJXSNFutW6ZbiZSkqts10oKLyKV0FYdQhjyKOX7Z1LN9\nMDbIdYPWe7UmMpdlIt2xpB9+1GUVSeIpODdreiUCoqQdmzlhdlhvEmJEz4BWrOKF5cvqUT3QqA7o\nGh/K2oRsrdEqWUROfMqvusVeNozI+tGgtSCmVLXi0AhvpBXlrCyaLGpsqCDe+fn8Wj1Xq3G21e9f\niVqsLGudPXc8jJ4aUttjV5UJ5TutRG1lHTVmHOu9kv8icI3IcqaqEaUy9FhXJ2szkdWTFsFPSX+N\nWQnK1/jX0Zivz40fp2aFtNhB2oKTQMbxOkCeW0aJqYjdLbg/a7Lg7Bu7weYye+nGUaWDLFbdjE1C\nqywv8YiJpZjyZjtLAjfC1dKZeYz+Rea5+fwWkZn/C3K5+/rHf5lvU6QoM/974Hd+4zOe8YxnfKfi\nTSTq5YN8aXcW8TvtSrs0slyqkmSOWSNXSV6lLW1uRHbNS2eIsMXyhi7yl5m8i00jTgdBy0wQZw62\nfpH6VQQ0Ixz6fi5PtkZ6MBw6F5GJgIxd42L9Qusbt/tNhKrs9TaTuVP+zcVAR5BsIDpvVuWUSN50\nqYFZg27OnnfGnvRoRCb3tmFdsqfXi8aMLjgzYWQwskaKbCPMmQTXHFyuTYz0hEmQc2gempLI7HUt\n3GBfRKmHDU39GftkQ9KnN16ZOdUCWKbfRbRaxluZsI84ZF/bEqVxIyy479KyJjWfrrG7ydIOTyui\nVaxNxH5YY0bt5JoZ1jZut2CQ6uE3xy+Or3lzpzZRfswqZyLf60IMPMXGjhLIiTI42TO5ZdJbbQ5C\ngjogP+nrZnx0udJ9oS8cifqS2kxNgim6H2mdOSbkwJiM6cRo3O47t3HjNke9vjgQZvEwh/+MZzzj\nQ4q3kahX5QGVERoaVdFISsYi1HBg15eqaK1sHJUVagZ1vfADUrg0rEEVkSXFbDYu20VjWmWpOcao\nJL+j9Cn4eMmVZjo55ShFZs0hS2HKpaRyjmHlqvxNJK3jfFU5i/QkmDmsoFWroi4Cb60q3iCjS1Cj\nXK0OwwpgD7GxF1wdJRACEoYBMdQF2RcJLPT9R0T1XKX2sJJxwL+ZsOcrzrsiVunJPTfCzkFuXwl+\nOaN97rWPC/lQya8qW0Ixzc4sXxw/1jIu1AQrqxIvcRJ3xv1eH5HEfDvQinUq1Vg4mPJQiPPB7i+d\n9arQ12ZB7+/aSB2Qfh7XyuoztqYMSInfZEJsF51XxNmLT4PWyulssI+NObShuZcd6zEFUa/7jGc8\n48OMN5GoAUjdgMmUGYQvxbHJCN243MpG0ZNt9UwjNXacKRvHBUlnHiM5FHR7QK953kyTgtyxA47M\njBLnKDeuuml6uU5FkbQiQ+pUvYllPXfMG1kzzmSSNvG2brSrb7vmY+fhmITFAcUKai5jhhIgUTKp\nMjsmGeUUNZU0xoyjXcCh0h2qUHGxvuNURMuCzVkIw1EJr8vRCkE+IegFDvdWJDCMlpoZztSY1kJH\nrKD9mWfr4XFcTgkZGJLq9DpmrdM8EYs8W7fmxX4U8lxMbTtm7eVTPjRaZYmnE/RzFlopuvreD3yB\ncwehCr12hMsQZm0QWhpjF7FRxe5KpprzjtD1soLrZ07S4NP9DiZtGqnE1XlYkOWWdR+DOYL7nOw1\nM73Wbs25f25H9YxnPOODiTeSqHVjnCXhcUwZxzuplIUUrQ69b1PPMKt3FyHIN23SFxHJVrVmZ4Va\n/wWKgKYb4ZyBh9jNmSmhkdaZu5HHLV4uSQBUgpVWmarpKPvM5tfDsnFVY96gVQW2alCNck/KtxKj\nPVRlSzhlZc/jqDGT6MqcShiCgg3Lzp1b1fSrK9totrGxsfuOj2RZVwJ47xqBWskbyoJaqIHmmk8a\n3tK+fteu3GyH8ILjjfd5Y6Mf1bQZxFEJWiXwlWgeEk5QyY06Q63HdDuu74jCXM79xMMGrHrtC6aP\neWxkMlttV+zoPR/ksJSrFcf7Uj3zrJnzuob1vCUYu5L06utnnmNwEQ8EwJlqQVjy2U0ubO+2K70J\nrncLtkhxKaIxxq2sLCV2os1grdZx0s+y+hnP+BDjTSTqWInodsLE0xotVmmx2MB2JGDB0mXE4BB3\n8L4xh8oWTQBJgMIdopkYwCgZpV9q/lcw8bi/Muq9u52a3avK1DyyEta1d7xHqV4lk6E+eHNgsPl2\nyD96k0So1KqMeZ/QEu+dnp24whYNj6KXb1197IARxvshQRTzxp5yAWv9qjEzJtcSUxkhF61kMm1q\nvZqRfmM3JdEojXDd+UMSodg5c21J86CZZpBXn1xd1ZpFrjXvaUwra06DS+/Mm6RKvVczei7d7FXh\nll2nqZ2RkUIjXgeRE+sd7EIbdzK7+rlp4JO9qugrRrtsePVsEy/ziuD19srl+jEzBulGtk564rNR\n5b0SfrHy070g/VPru6e2Aa2aKAMrrfCpcSmTst24T7LB5cXpQAt4jdRxFQfis9tkRDJt0JszP062\n7Fxa4+JGjmr5mDGm8bqnLDRJ3t+jFMtEkLtPSdQ+4xnP+PDiTSRqeEBf8ygyaeWU9Wh9ucK9k75g\n1oRdFVAXS0iVN2eldMRDD3VVkd0Nm5RtZBbUWMIhVW1KznShzwUDl6tDxmlhSYag7TK0iIQ5xey+\nuJJUzDWCJbEVnWZV94Fcu/IbEQFVeZMMeUWLw77g63WuD+d9toLVky4UYpltnIAwh9zoMdrmfihu\nad0W/C1f5Dyq0XV80ki3ABuBb6U8FgUdc46htSySIBObfkD96/KErZ7/OSi1/pUpX+h8WKMIJbVe\naqdZx6RRKQqxeBy2KfGWeuRALgCsq3gtItday0SzzIf0gOcBHSj558HOdta1X5yDRuKMEpDxTJHN\n1mcr5Sc+Itjj3AAeVz2dZVX6jGc848OLN5Go1/3n8wDoCR/7Y8IOkbCcd7oZu6o2L1/mldRX/1B3\nQjBrj5jpoXimd3RmrArGyC7mc9s2jd+YaeZ2HYMvcpGRIcGUloKhg1GJ8Owtxmyki708CcYEqXVx\nlqyc1dM67EtzaFYVmrKSRZK2Q47qoa7zCtlRwsMqnue70lSyIP4Qu9raCUQXVL0g8MfQvwUvxNyL\nJFbSmulkNryHlOGOnYUdmucWybTAzNmsgWnTQQTeSimtSbjmcRJZ8HM8bLqK0Cel8mOMywr9WGNn\nx99TPAd/aH9UpxpJqT6uk66zmTFmnokerX1UhZsoSa/Z+8d2ypr51wFEsbW7Nh6H93YQsZTdtDlb\nbP3lGNb6VhKlhvdOI46WxTOe8YwPK95Eoj4pXwWLQmULDQubl+ziso/MJOesn1/NQipp5kMiPXua\n7u1MWitJ1597ajzIChqlN5pB986sFOdH9U7VhbIvDCR2oqTsYHL6Mg051w3aabljyG1pZNJL0ETq\nZjpmEeNWjxTapatnulQ2ot6/nKS8Kt86KyW5o4/7+Zu6pRLHquQzEm/nTz2SvQBpqy8SV+HFVok6\nw88Kvir0TLhcHA9osQwaHyKXtebSR5eDR2bQu3Mw/GpDIOESalM2j763oOdHDKIQjoVA2OqHnwl4\n5iAXbpFLyMWLC8GxOcEWZlDz02sdFzIRyYx59LmXUcyaXXfzIjOu89mr/VKbl5pgSEvCz/cYmYxK\n0Fk2mhq+Xtc1aZ7STX/GM57xwcWbSNRQkGdZQaYt2pcLPgXNxLr6hSS83r9G6yIzZRoh1Q/iPqpH\nbUciZGmGPxCA8iExjX3nvk96psQwTCYKc5w1WGceM95jynSi1ajUHsGs5ODbO5oP3AO6krUPo8Um\nclNAb8GlO6132jSRz0w9Ys+aw3Vj68nrfbDvQRQ7uve1Eajqr19YpX7vqxo/tLqqCq0CV4Vs2Xsa\nm2n0q7oF5ZVcs+clhG1Ticq6KkYSGhu4LDNP8Y5By4sQDk9S5Ou6uEbbDPcNTMYcSvSdtEG/tAIF\nijyWCeP09N6sGPzNydYgoxjrBkymGenOvEsfnqXLHS5WdeRxfqIhRtXT+jk3K6gfMiezYHSrufuZ\n8rq+35LXcdN1d6enqvo9J8ngnXXSJtTY3sSYaP6+VW87ZzAKhXgtQZMRyW0mEeIiuBuvt3tNByQz\nb9rc5Jv5dX3GM57xHYw39ZsfR4m0/qyRHVSZrn5gkjLOSBG0Mg03uU/M1d5b0OaqsPzgUH1Dr8+n\nvrlgcOmcOVHvL+h7jUktX+yV2JT5Vy/bbBQSUN9vhkeDnJCNVj7JvamhGkPs6MCYVQcfXesZxCiG\n+RTDXDKVOknBveW6YYH59rkkLbbcw5IeOG/9w7wqTG1Y6mxrjdvDrqYWDmrTE0ItFrIdopvFntDU\nfsjQbihXVdgbVjzrWUYdIvKtye8qa6t9Lob32VnW9a8vxjGpVIWupFPXdVhRKIutz0Ie7/QAfOuE\nfVXrqTG3rbD/R2nXWS5XhklwJU8EY+oSgE39JaWkJ2GbcltjTSro5+WYVbyE0OdYttcONo7Pvrvh\nuT171N/hsD/x9/oInvEMxZtI1AvEHHMePeHenLBJ905zIzyImpcOC6ZKEbLcstxd1eFLO27GUcIW\nli6lrQe8Oyzqzhr0a+NlbESOGvNJ7uzklFIVaDQs0ohxJk9sgCXemswqWtN7mh93b0GyxmxGjykl\nNRpjJDbv7FXiKufWvHcmYyaxT2BjW7TiqmhtdllOtiDHrIRkUgy7aFMxQ6NJoARz6Z37/koO9dDN\nnGCDy6yNRhaEr976nDcliIsX2756wq65dYtGayapzCZDlTAlX5t2aHC3uhYtVc1a8QXkX+3YXc8d\nsaRCl5r3ZHPHvGNzNTFKZAYTI7+wcsuh1sLlSnS1OczF5va8ECMZLbE2+ejyUuhMybiYMbzhaZpv\n9kZYY9zvmF3AOhGv7PvO196rmq6szu4yRGl0LnY5WPERVuN6ajHYSPwSDOs11w7EZI95bDTktmr1\nkdS5td5qE+E1Hn/ssp7xjGd8QPEmEvVRKIQdMpFmHEe3kogqlGLzFrkrj6JIr7JIQ6sWE0zMWV0m\nB8lo3ehHlKwk7ag6E/XFF9wtZTQRkdzVG49iEy+yW2b1kVfdv0a0cJh5EIky8+iT50o49YcvK0lT\np3RJl8DqrVdFr7NluUI0A2s19ZzFUveSVB3Ja3zGHpORkqbMdIgbfeoKREECaUpEl22TEEmNVGU1\nrNOSGePoGfuRPKovy+PkdR7XdsQapxIBL019Xe2d1tVbZXIWa76u0/o8mB3vcV7PPF43bNJpdPPV\n6iZzcpTfaG+2SGJr5rkHLJ3tWUQ2eV9PxgzuezLKG92tJtUfzayrdE/rD2dcyINBlOa7bMq11Ugz\noirqxWCHRUajZssXBPImTO6e8Yxn/D2KN5GoTwKUAEJLzREbXk5FwczJRmk6u5PXYtDK/onmS7nM\nDwjU3YvII//pVfGYrfEtJcVbJluXPOdMuN31up2dNWZzu6n37RiXrTPmYMyBu3G5XkuYIxi3ss6c\nwZh3jODdtWFjmUfIwCNWBd1bHVOthdvRP48ofnNB3YuElLcQNI3j844lXDaw7oyEcZdm+LttEiPY\n36eUvLYXOi80VwXtwoxRhWpon2KaPIq1SUlmBvfchXjMKNjeDiiZajHI0ay2KVaGFbEkTiW16u7Y\nhBiFVFw65DzFTCJrTn7iUTyFvqnvbbE+LkqCB6OtPjkWSMg1TvUvSYqVOYeS4yQLidF7tVDy3DO5\nF0JBGmPc+NrXduZU8m4tcW/SEXfprs85NGqW4qfXDkCbrgPoP4QAACAASURBVCbT67krEXtohGsN\nnd3n2p6c6X2h8V5cCaXrBjUU94xnPOPDizeRqI/bz2FvmGeBVl9r1HURwLwZOR+0xqoP7e7MOc/H\n1nzuMYt99rzX91rLY66akHdwTIlcrGJ3Tt3oJZKhnnJUn/VxVClyln6zEryh/qajBNwOtjMQ8sxe\nVZnawWXEkIt/vO7dD1XhoqufeQrMiiS21NryeI6bsXPHbavKtJq3hPrnlWDErNemoR2e0TW7nGub\nEZjPYuJzVoBwsKGP2eLj4CiSFDLCiDVwpYu8Envx/vS6uZjdhWysyjuX8Oc5F70unRgA1RbRmbP5\nRj+kWZM9gtItUaImuWeS5uobx03cgOzMEcx9FsnLaZtOOItTMKLeK7M2CLPU21YvvY6zjXNqoB5L\nUr99eW5I4eRPmJ2mJGbqmz94vz3jGc/4gOJNJOqD01MQsu7W6+aqmVnDSVviomfld96TTVaOBb3G\nkRwXYeoc54G6VRYb3I1DfWqfUTKRQQ6priwyEkFBv3FUN6qqohL//9/e+YfadpZ3/vO8a+1zo8Zi\nYkqIRqqFOGD9IyXBEWzLvbRTbSlomY41UzVt1UiN0sIIav/xSgnIkDpQWu3E0Rqn1UyGKoZSW1RM\npcWouSLVKI6hKk1IDVaLxib37LXeZ/54nnftfU/uOfcm95x9Vu79fuDm7LP2r2e/Z2d91/PjfZ7Y\n8rPMfuERdi8RhiZO8H3XrfZL20pMWzFbGxRhkAVsLfSdPbGrZUIzvE9rlfLYWueqDFN7zEfue3Av\nhPPecsFZ7ewjbREsh304znLMyMYOJ84s7JrSE7aqN6t1lQZYjcUM+2KsJKv9zs2vbFV47aojm7Jb\n7aY0xmpgRlsuP9Wg/KsXOryOjNRpi55Py+tZdJah8BIvVt15ZKzTWo9jxccRsk1rmBSftytdXjBE\nxGUYQ6jNMnefDWhs7Yvm2DQDu307mwftUzQgP8YpA0zWuwpEikXFZEJcmMxDqJPwmFahwJZaLFao\nFnN7q/k0f7h1eYqTXlPczNE6U0VxnPhbBfdK+LAQ6xFgGLOoKZthlJon2LUtPNQQAmtePIAzDktK\nv6CUjr4UTg7bALGFx7ppf3d1Y0EJgc6COBuHNGtN0LDVa7cTdOpYZ+BdgVoxH6ekfMUYhhgS0qIG\nUOi6EIoyOMW6zOU6nvuZBzu55umlV+zO4EvMjc4zKlFWndnInHKIczwnCqdixZv3u8oDW07wyi5b\neG5j8hSnVWQkmpWUKAijVbZH7twrUS5d8n1oqYz4xoyejVFq2FdLNhIZ01b3LParq9d155HlQNZt\nMQwwjFAsctZWIvwcHnGPZf9zsrd7pWYxegsFrCrVPesRpggDqwuHSvSoN2+y3P4naD/KKYK/+gsJ\nIS40ZiHU7WRV3MCXeB0j09iBFacrPVt2hO1xO7o30bHAsJ7owVydLTdsdLYHyxNoTx27nM08MI62\n6vpYopjJas3itEItJSZZLUc622KxcPpuCwiRPrLVR8/l5cgj4zL2OXddVHp30d3Lx/Bkn1S69FAz\nNP4w1MUQjUvKIrw5W0KpjMMWEDOJzYyuv4jSRSEY24XBI3+a0zLABootGCxE4qLSRxbTCsv6CKNH\nM5G+6zDvpj7qZs4jyxj04OkdbtkCrzWqtzuojDFWlIIb9F7oS0/pCst+xIZK7+A+xPAIfArpdqXS\nlah+p4Nx+yQ4dIuY3d2XEoMrHLo+9guHro2w3bPeoWTw3LNuYccw1tzuFVdg1ufFSPWpKQsGXgdK\nbxldiAuwemQbf6RychzZHkcWfWGxiL+bN+/9pMWoyTqwXSuLqS2rsejzoqHE96rb6iKyMpLrXNjq\nYdFDV0sMTKkrkTaMxWIB+DRWNC5uold6Z3HRmcPIsL5QOrAxC+GSLsa0bOT/RyHEvJiFUJMnK3L8\nQ3TQijuif0WGKocx/o0VjmSF7TS3mAgfZ2cny0KpOPdbNtMIB65gMXqS9HC6KPDpCnFxALSc7+TH\ntApkb2HJsnZvhprJPCtMoW+cafxje0x4gfHP6zJenuitPY4xwMO8CxHzVfV6BOE7ShkoNuB1ZGid\nXBgwD4+Z9l4+0AKyQ8apSxvb6A51pDmpO6PJsWusMtYhem+nIPq0KqvpYG2CV+my0C/tXYXAMxoA\nU9c1y/D36NG4ZAqDT6+5Sk/42rpj5ByrZvRqXGVr/xmeu01/tyECLavhU76KCEyv4q0dKWCO9TGf\nvBKGm2VqpYXQgUX7nMAyTa9tq1tupYsq/JEpE5Bf1TEjQlPwJHPfbaSp6ryFEI3ZCHXTlzjRRlMP\ny7BoNYcyAhUbKrYEX6SgTznPrGS2MWcO++QxR2x4ZGwdyowIQRPe5VCXq2IyLBtcGN6xaoqRottE\n0yazp4xjFF7lNCuqMzZl74yuWzTXNoSA6G5VbYQsRYoLlJFKbBUzy5nITT+ySKlCio1x0uP3GAXZ\nTcsZ4fAhe0dXnItyW1K7ksiQcEsltOYdtgqxj4yxFcsLW2UrRKpdeWQDzAjPxkVLKWUaaEJuM1vV\nHeTfKUPpTax9zD3CU3OW+LDFWnvzVqHdhL81L1lJdPsLdCXDzi2KgOFja9W5dnGU9tT8246Etz7W\n1ba5vi/UWmMKmoF12XR0HCeh3rJIKwwUBshoQ4TF4wIrPnytY+5IWBX8jbVdXGT+upRpqEc0rVn/\n5u1sCCuEuJCYh1CnSvfdMvbXumEWOco6DNQhXLNiPSyiB3JdaxZhbajFWCleKSPUOkBXsT7Cll3u\ny/USnt+RLit33fjRSWeLEc8K6Ni+09Ev6pQzr3UZoUyvdGWgMwsxpcu8q+UJ3OisiwK2OoDB1laH\nlS6qv7Oqu4/SaupiKwV1CNkpF2VhmtPbRVC387OGb7yEaMRSR0p20cJhGMZ4zQxKlOzEFh214Emd\nU3tLDzWKoXxc0h1p0QEYt9PLtMpia8Hg2yzrSHE4UqJcK4QkJMRsFcGIdas5lzmU0owpZD2w8qSJ\nFD1UGMZt6hCXPjYNuugpXaYKWgOQnIhWikUu2laRjc76mGLJEoZW5xA21REWCyNmYsWFjhMd7FoF\n+eBRQDYsR1gUvJAjVmOiFT6m9x3tRQ2nNxhKwUq89pFaGMsYkYopBBCqP4xDpkK6fO8oSCyZhy55\n8WC5Hc7HGlGYSZ0zsqQ0tRAXJLMQaiNOSoviMf/Xo51m8chNjmPkbG2rw7J/duslHfulw9uaTna0\nns5RhNQmX2VRODm7Aghd67tC59lExeN91z1PdyemBEPbTrQK3zJ5cQBkztvNKV6AGluyClNnNTxC\nsWZNhyqjD9mOk2lLFP1JnGHVYc0KRkefH8S80KXn2nfZBjUrjEsBr116os4Sj6ruEnnzDrA6MngK\nbnqcno1dQta68OgtR1S6T95vE+ep+psQ/7hY8exmZlNF+GqoJLG3OsU0agwyrG8x1CT+fh1k0ZeV\n1XSsFqZuL2Z5kdcc+SnknF8qr1Bywphb7hcntp2tplGttrRZvvb2GKI8ZsrBHXyMdEZnsWataj+s\nzQ7i1j6ntw6jk91t/WxKn7RvUn5xMkLxKA/aVv3ghRAXHmdMhZnZs8zs02b2VTO7x8x+N48fN7P7\nzexL+e+X157zNjO718y+bmYvPqMVHv/pShctQ62bQsyR74vRjJMb5mOesKPj0ykTNqIP5OSBts0w\n3rbYEEVlo2d7x+p0mfU062K/bIHeooDHcsDDSE5zmhqldFNos51VPcPt0wAISxFvYXY8m7dE44vo\nUBVNQjwFisxnmneMlgVRLbjr4cl13gE9Iz0QxWR96cKNLgUrUQDVplQVuhDkHCbRlULXlegxvowL\nCHx6elZoRwOZzno6K8Q05nH6e5DCaqvYdqYSQmy6bG4SwkuuQazVOMaWtnHIiMWU0635+bMNq5W8\n6MhBH/g0sMUmrW5/55xGlmJZOouta8VyHaMz2HSB5hGqtlxXUqjjM+T3w5haw5pZPi4uJCvQm8fA\nEGBoT/QCHlGW1c7nTJJkS1FqpiBalXiuQcQsoqnPqXuxWpJFCHEhcjYe9QD8N3f/opk9FThhZp/I\n+/6Hu9+8/mAzex7wCuCngGcAnzSz5/p6CesOam6xMqKvN14xhuYWRXjXarolbSxTNzUNoay8Ne9S\nwFu+MyuFK5Yzo+NxY83ia3e8FpwIuVezLLSy8IzzVFtpk6jK5E22OHOcQHOEhKcXmKLitFyoT14V\nnruMHPrO48ReC+5jVE+bUWyLwULMa3r5caHhUQRWR7bd2eqyp7ePmC3Su7TMJsSlRAw7qRGoz/sG\nN5Zm1GV8vo6MOLThJTTvMaICNbcr0Yq7aGuw7hGu8uNdyUEdGeFo+WA8O82lV21ZoBXzqUlpG3H6\niF7kW0wtV0/J3q5k2o2cfBaPrW0/d651Ww/PNSn5mSebp+KveH6LvnR9F21dKwxEcVvNv/ZF+Z0Y\niYhF57b2nWVqD2o53MXbZ27etLVLjTpFAlpr1lGqLIRIzijU7v4A8EDe/qGZfQ145h5PeSlwm7uf\nBL5pZvcCLwA+u+sz8lz978uazcnihFisYyzhDXbujMslo0UXKd/OiVKWwjwWFvTQF8qwZKs/Ar3l\nVp+RrUKKbJn25FYP8Tdg7KJAqq8OtsB7o7JMQTHqdqHrIrlqXQkfzkaKOV3Xk/qJlZ7BBwxj0fc4\nxujbjNs5bMKiytnG9NzGCmY5lAEYnKVtgy25yBac9CWY01tkxB8eK8W6COFmv/F43Z5aY6Z2XTq1\nZOv0avgYncZOLmNbWZ8em9foje2js/QYvdlbRzVnOYx0XewLH5eGD9EfvI4d+DZlUSLBMOZ+Y2K+\ndVPVoTowTvuVax0zJ2uUnmmLkwN919Naq0K03Sz1EaYOX12fFeZkGDg6gNUxtkwNbGPes6hbeO9U\numwtO2BlCxvHfB2wZcpoNvyu1Xnkh49EtGHL6HEWy0q12ENexwolLnLG7gijD5QCfTFqH552cdjC\nqWNLvsdFgBmMBuNAetoeH9xqfBeptO3/XV6iVCKwYKXGOFAc9xhWM/pwpv9dhRDnIY8pR21mzwZ+\nGvgc8CLgTWb2auBuwuv+PiHid6097T5OI+xmdgNwA8AllzyNy55+Gdf/xm9maDM8DWM1hSndLaYb\nOzyO1voSyxBjHvfmCa/e+NTfW9j6VMcw86vxy2VPv4wbfvuG9BanxHR4ae2Yr5688rhXb+K5V7z5\nndNDTglxrn9OO8WGnZ9h5cUbT7/06bzyut+aWpauP36nY7beRKN1TJvWZe2+aavS9GHT/jxc2tSL\nyWxfPXTtjQx4+qVP5/r/+lunGpLr5ez4bPkkb8ndaTke/chW6Mepq8pqnT1D1vn81UMnEy+99DJe\n99rfWcu5r/6c8XHWmpXAtMZmrAaS+Ck/Hs2j7l/7LLb2qdYfl9/1Sy+5jF//z9fnejj/8Km7d3sX\nIcR5ylkLtZldDPwl8Hvu/gMzew/wB8R55Q+APwR++2xfz91vAW4BuPjiJ/u//ut3+bMPvhfvMjdK\nD7Xw8LDNyeUQoegyRJGWFUpZxHk384tP3lqw6Du8M4aTy/CYLUZnmle6Lr253Gtr7blEvri2MulK\nVJeXQt3ephbntde/jj/9X/+TDug6IBtYYJ6VyF3s065Ob5XSF6x0QBcNT8Ylj9TtvBroibnSKYx1\newrphtE1nmuFvuvXOnrl1KxxG44cYQrrA6+87jXc+n/eHxXJ7R+5F5gskKvhoZWSoePqPPzwwGLr\notCEYpF2SLU4OQx0XcSPx9Fj7THGMWx/0tYictfVGGsUw7lVIpftWLmIYh1dGXnlr1/P//7Qn2Va\nwCJiUWvu427bmIYUpx6ssr0NLUhcSuv+1XK68Tq1Rl/3kW2wji1W4faaf5/FkajArx55Z1vW5vSC\nwatf9Vr+9L3vZmurp1/kDoGuZ2kjXSXWrkRYfelw8uTD9F3H1qLnyRctpu9RFD3mpjXP0HpZXb+5\nt4vO3PVtMfms1c9ZzT9/pMFzW1vlN/7La/jQ//0Aw9CiJ0KIC42zEmozWxAi/Rfu/hEAd//O2v3v\nBf4qf70feNba06/MY3u9AwDeGX0JIaujU8cx+mq3DlS5v7qjw/tV0wrDoDO85NQqKv2isOVGVwtD\nbe9hzV4mX8zbSdFX1cTZErI1r2gmViIHOqVV26v6qgJ9zB7elvlwy2gnNUPeZE3vlH/NjmO01qVZ\npZ6i1BViPrRbVpIT4fdcudFbX+/KgszPZnVzjGTMoq80uDVEcVL4FuvRi/YCHpPH0mOsnhXxLZKR\n+d4pumCWn62ts9P828nHbUrUSvOtxuKM2ehj3Vt2sh1pfi+sTrlxo5t2mre/ZecdRg8+MCXZPcPb\nNZ37rFXAQhynRirTBVfk1d0Kbh2VkW7t00wREU9hhmyqstbgJD356d/01fG1qIuxlp6ePm/7/qx/\np1sh4/Q93NVlF0Kcz5xRqC1U7X3A19z9XWvHr8j8NcCvAl/J23cAHzKzdxHFZFcBn9/rPVoNTnVj\nGCNTN1bHxjjpdh10fW6xcuhqx3Yd2B6XmBWO9FsUnHHYBtuiJf7aDOBaoOuasESOeqzOmCfII3RQ\nW5vK7BBuhpcQRsNYbIWXHd7wqpY3CpJWjSoGjG6slHFJ9IWO5HOf79vO2W0Pctf6l097k6NiuXQd\nnXexLSxP0gbUkluraCfxsKU4LIdH0r4ogCrm0Yy7jtQSIx4LZAV8R7fVYV0Wkrkz1BbabZGC+Fxt\nr69l2HesMPh2iHsLM5tn2/EOvMdixmVUUjsUopNczONe4pl/LmaMFgIZCm1YNfqt1VapGCJd2sJF\nG7DSLnayuQ0ePcCLQWtB6l1WWLde5E7ft1Go0e7TitH3PaX0dGYMVnGLvfvmEUGp6Rnb0llYl41P\njJPbUTjYwu+FtUxGfrcwYi64O2OGsy3XqWSe3MvKk256HH/H6VKFVXsWIcSFxtl41C8CXgV82cy+\nlMd+H7jOzK4mzivfAl4P4O73mNntwFeJivEb96r4htX5t4xOJUKoJQLNUXFr2d6zkAVIHuIzDtGH\nuVV214GyeBKdVaiwXWPGsJXKlmXhz1qKOR0spg2vrW90IduPDhQPD7jvKrW0/beZvVzLL7YTKlm1\nHr9mV6tpzGJ4YZM3lSH42GoUQj+N4SRC1MOUVw1Byo1R03jN5omaGydxOnO2itF3IUKtE5b5quq4\n1Wi1QqhWBd2GYMRWqFVqoO2cslSfjkXzZ9c85kIUhGUDFZ82laW3nduzGKlT4dip3cscJg/YsVXd\nAHEl11amzTNrM7o9O7qV4kwtsaMHKlOKIF3VNovcCbEGmwZvxPNGoolMt/putr+vxx759tjavkAt\n3dwKAlOsm3fc9g6sVQTk96dM1eyP+n+iRV6mA6nkQogLjrOp+v57Tnsq4a/3eM5NwE1na8SPfvjv\n3/2d17/hR8B3z/Y5m+TuL9x4GTO1DeBzf3di1vb9w2funq19n73zs7O1DeDTn3iUfT9xWLYIIQ6H\nWXQmc/cfN7O73f3aw7bldMzZNpB958KcbYP52yeEOHg0pEcIIYSYMRJqIYQQYsbMSahvOWwD9mDO\ntoHsOxfmbBvM3z4hxAEzG6HOBiizZM62gew7F+ZsG8zfPiHEwTMboRZCiE1j2vEmngBIqIUQQogZ\nc+hCbWYvybnV95rZWw/bHgAz+5aZfTnnbN+dxy41s0+Y2Tfy5yUbtOf9ZvagmX1l7diu9jzmeeD7\nb9vxfZtVfm627TZLfS5rd/Cz3oUQT3gOVajNrAP+BPgl4HlEt7PnHaZNaxxz96vX9rC+FfiUu18F\nfCp/3xQfAF6y49hp7dkxD/wlwLtznTdpG8Ss8qvz318fkm1tlvrzgBcCN6YNc1m73eyDeayfEGIG\nHLZH/QLgXnf/J3ffBm4j5lnPkZcCt+btW4GXbeqN3f0zwPfO0p5pHri7fxNo88A3adtubNq2B9z9\ni3n7h0CbpT6XtdvNvt3YqH1id+wdSm6LzXHYQv1M4J/Xfj/t7OpDwIFPmtmJnJsNcPnaEJJ/AS4/\nHNMmdrNnLmv6JjP7xwyNt9Dyodm2Y5b67NZuh30ws/UTQhwehy3Uc+Vn3P1qIiR/o5n93Pqd3qZV\nzIS52QO8B/hJ4GrgAWJW+aGxc5b6+n1zWLvT2HdO67efuXkzuybrNe41sz/KaXpCiA1y2EL9OGZX\nHzzufn/+fBD4KBFe/I6ZXQEx4hN48PAshD3sOfQ1dffvuPvo7hV4L6vw7MZts9PMUmdGa3c6+/Zh\n/fYzN/8e4HXEuNqrOH09ghDiADlsof4CcJWZPcfMtoiTxR2HaZCZPcXMntpuA79IzNq+A7g+H3Y9\n8LHDsXBiN3vuAF5hZkfM7DmcxTzw/aaJYLJzVvnGbEvv71Gz1JnJ2u1m37mu337l5tOOH3P3uzLy\n8EE2WJshhAgOdXqWuw9m9kbgb4lJwu9393sO0yYiX/nRjPD1wIfc/W/M7AvA7Wb2GuDbwMs3ZZCZ\nfRg4ClxmZvcBbwfeeTp7Hs888AOw7ajt06zyc2S3WeqzWLs97NvHWe/2bM4uN3/X2tNa7nuZt3ce\nF0JsEIsLZSHE+Ubmvv8OuMndP2Jm/+buT1u7//vufomZ/TFwl7v/eR5/H/Bx4iLhne7+C3n8Z4G3\nuPuvnOa9bgBuALj88suvue222/a07aGHHuLiiy/ej495Tpw4ET+vvPIh7rtvhz3POLHnc6+54poD\nsmo+67PO3Gw6H+w5duzYibMZYzuLedRCiP1lr9y8uz9wlrn5+/P2zuOPInuS3wJw7bXX+tGjR/e0\n78477+RMj9kEx47Fz5tvvpM3v/noqXceP7bnc/26g3Ny5rI+68zNpgvJnsPOUQsh9pn9ys1nmPwH\nZvbCfM1Xc/i1GUJccMijFuL8Yz9z828gus89iQiHf3xTH0IIEUiohTjPcPe/B3bb7/zzuzznJuCm\n0xy/G3j+/lknhHisKPQthBCn47gKbcU8kFALIYQQM0ZCLYQQQswYCbUQQggxYyTUQgghxIyRUAsh\nhBAzRkIthBBCzBgJtRBCCDFjJNRCCCHEjJFQCyGEEDNGQi2EEELMGAm1EEIIMWMk1EIIIcSMkVAL\nIYQQM0ZCLYQQQswYCbUQQggxYyTUQgjxOLB32GGbIC4QJNRCCCHEjJFQCyGEEDNGQi2EEELMGAm1\nEEIIMWMk1EIIIcSMkVALIYQQM0ZCLYQQQswYCbUQQggxYyTUQgghxIyRUAshhBAzRkIthBBCzBgJ\ntRBCCDFjJNRCiAsS00wN8QRBQi2EEELMGAm1EELsxnE/bAuEkFALIYQQc0ZCLYQQQswYCbUQQggx\nYyTUQgghxIyRUAshhBAzRkIthBBCzBgJtRBCCDFjJNRCCCHEjJFQCyGEEDNGQi2EEELMGAm1EOcZ\nZvZ+M3vQzL6yduxSM/uEmX0jf16ydt/bzOxeM/u6mb147fg1ZvblvO+PzDTGQojDQEItxPnHB4CX\n7Dj2VuBT7n4V8Kn8HTN7HvAK4KfyOe82sy6f8x7gdcBV+W/nawohNoCEWojzDHf/DPC9HYdfCtya\nt28FXrZ2/DZ3P+nu3wTuBV5gZlcAP+bud7m7Ax9ce44QYoNY/D8ohDifMLNnA3/l7s/P3//N3Z+W\ntw34vrs/zcz+GLjL3f8873sf8HHgW8A73f0X8vjPAm9x91/Z5f1uAG4AuPzyy6+57bbb9rTvoYce\n4uKLLz7Xj3lOnDixun3llQ9x33272POME6c/DlxzxTX7bFUwh/XZydxsOh/sOXbs2Al3v/ZMj+sf\nt1VCiCck7u5mtq9X6O5+C3ALwLXXXutHjx7d8/F33nknZ3rMQXPs2Or2zTffyZvffPT0Dzx+7PTH\nAb/uYBydOazPTuZm04Vkj0LfQlwYfCfD2eTPB/P4/cCz1h53ZR67P2/vPC6E2DASaiEuDO4Ars/b\n1wMfWzv+CjM7YmbPIYrGPu/uDwA/MLMXZqj81WvPEUJsEIW+hTjPMLMPA0eBy8zsPuDtwDuB283s\nNcC3gZcDuPs9ZnY78FVgAG509zFf6g1EBfmTiLz1xzf4MYQQiYRaiPMMd79ul7t+fpfH3wTcdJrj\ndwPP30fThBCPA4W+hRBCiBkjoRZCCCFmjIRaCCGEmDESaiGEEGLGSKiFEEKIGSOhFkKIx4m9QwPF\nxMEjoRZCXHBoYKd4IiGhFkIIIWaMhFoIIYSYMRJqIYQQYsZIqIUQYi+OH8woSyHOFgm1EEIIMWMk\n1EIIIcSMkVALIYQQM0ZCLYQQ54CanoiDRkIthBBCzBgJtRBCCDFjJNRCCCHEjJFQCyHEmdBeanGI\nSKiFEEKIGSOhFkIIIWaMhFoIIYSYMRJqIYQ4R7SXWhwkEmohhBBixkiohRBCiBkjoRZCCCFmjIRa\nCCGEmDESaiGEOEBUaCbOFQm1EEKcDepOJg4JCbUQQuwTzXuWFy32Ewm1EELsAxJncVBIqIUQ4oCQ\neIv9QEIthBBny2PIU6+LtARbnAsSaiHEBYUdsGZKlMV+I6EWQojHgqq/xYaRUAshxAaQpy0eLxJq\nIYQQYsZIqIUQ4rFy3BUCFxtDQi2EuGA46EKyM76/wt/icdAftgFCCPGEZd2rPi4RFgeDhFoIIfaD\nnaHwXYS7edV+HHAHM+w4+NsVShenR6FvIcSemNlLzOzrZnavmb31sO15wrFLLtuOh2jb8fz9HRYi\nbsaJB05Mt0/5Jy5I5FELIXbFzDrgT4D/BNwHfMHM7nD3rx6uZU8ATgmL7+Itn8brtuNw89ptCO/b\njoM37zuPN48clzd+PiOhFkLsxQuAe939nwDM7DbgpcATSqhn64zuJuA333nKfQZwfMfP6Q5fO3C2\n72unf++dx9vvx42bn3szx95xDFhdODT8OI9GFxH7hrkWUQixC2b2a8BL3P21+furgP/o7m/c8bgb\ngBvy1/8AfP0ML30Z8N19NvdckD1nZm42nQ/2/IS7M82shwAAA2lJREFU//iZHiSPWghxzrj7LcAt\nZ/t4M7vb3a89QJMeE7LnzMzNpgvJHhWTCSH24n7gWWu/X5nHhBAbQkIthNiLLwBXmdlzzGwLeAVw\nxyHbJMQFhULfQohdcffBzN4I/C3QAe9393v24aXPOky+IWTPmZmbTReMPSomE0IIIWaMQt9CCCHE\njJFQCyGEEDNGQi2E2BhzaUdqZt8ysy+b2ZfM7O48dqmZfcLMvpE/LznA93+/mT1oZl9ZO7br+5vZ\n23LNvm5mL96QPcfN7P5coy+Z2S9v0J5nmdmnzeyrZnaPmf1uHj+UNdrDno2skXLUQoiNkO1I/x9r\n7UiB6w6jHamZfQu41t2/u3bsvwPfc/d35kXEJe7+lgN6/58DHgI+6O7P3+v9zex5wIeJLnHPAD4J\nPNfdxwO25zjwkLvfvOOxm7DnCuAKd/+imT0VOAG8DPhNDmGN9rDn5WxgjeRRCyE2xdSO1N23gdaO\ndC68FLg1b99KnIgPBHf/DPC9s3z/lwK3uftJd/8mcC+xlgdtz25swp4H3P2LefuHwNeAZ3JIa7SH\nPbuxr/ZIqIUQm+KZwD+v/X4fe5/sDhIHPmlmJ7L9KcDl7v5A3v4X4PIN27Tb+x/mur3JzP4xQ+Mt\nzLxRe8zs2cBPA59jBmu0wx7YwBpJqIUQFyI/4+5XA78E3Jih3wmPnOCh5QUP+/2T9wA/CVwNPAD8\n4aYNMLOLgb8Efs/df7B+32Gs0Wns2cgaSaiFEJtiNu1I3f3+/Pkg8FEiLPmdzEW2nOSDGzZrt/c/\nlHVz9++4++juFXgvq9DtRuwxswUhin/h7h/Jw4e2RqezZ1NrJKEWQmyKWbQjNbOnZEEQZvYU4BeB\nr6Qt1+fDrgc+tmHTdnv/O4BXmNkRM3sOcBXw+YM2pgli8qvEGm3EHjMz4H3A19z9XWt3Hcoa7WbP\nptZILUSFEBvhANuRPlYuBz4a51564EPu/jdm9gXgdjN7DfBtoqL3QDCzDwNHgcvM7D7g7cA7T/f+\n7n6Pmd1OzAAfgBv3s8J6D3uOmtnVRHj5W8DrN2UP8CLgVcCXzexLeez3Obw12s2e6zaxRtqeJYQQ\nQswYhb6FEEKIGSOhFkIIIWaMhFoIIYSYMRJqIYQQYsZIqIUQQogZI6EWQgghZoyEWgghhJgx/x9c\nU+4DMqs7OgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8eed7ef2b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"plt.figure(figsize=(8,8))\n",
"l = random.choice(id_partly)\n",
"im = cv2.imread('../input/train-jpg/train_'+str(l)+'.jpg')\n",
"im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
"\n",
"r = im[:,:,0]\n",
"g = im[:,:,1]\n",
"b = im[:,:,2]\n",
" \n",
"plt.subplot(1,2,1)\n",
"plt.imshow(im)\n",
"plt.subplot(1,2,2)\n",
"plt.hist(r.ravel(), bins=256, range=(0., 255),color='red')\n",
"plt.hist(g.ravel(), bins=256, range=(0., 255),color='green')\n",
"plt.hist(b.ravel(), bins=256, range=(0., 255),color='blue')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "e5d75eb3-2b7b-ca49-e02a-7ace0e5dca0a"
},
"outputs": [
{
"data": {
"image/png": 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mf5DBx+6BZy+woR8Ewf9POA2h9tY49XFkIg2abMLnVnQFJOUjI3fqQ66tqU5m\n7G3SW9jnFvW79Xdb01tsXLlt0lMhJ2/8vfjSX1Xn7Lb5xCAyE4rdm2usdx2WajavqISn2pO2LDkh\nVZvf20rqOaJdWmurH/U79MCxsXYuSuvc9WB216e6qnfAOhWS3DXtIjLIlXjf6q7ys7Hn/f12nJpA\nLu0ZQnQtaF8qU+yCvs5zzsk6ZT1GQX8KFpg1uLRlENM5qrv5IqC+Fp0rLvDkO7qu1mLP9Vgd9w6M\nX6H1gpqQtysQQ2HGbpItRVNkiHuUeYg/CIJrxQn+37+PVcq0teNObzmyh1u9Ll8qNsNeviLVFfIm\nE6kHIaGAItjd5bitte0FT9RELr7AQrP6rDxLJnaLikApgv2+YgV05aeDSx7zAHSxvrs9tz1X/9v8\nESKAC6MHOlkSmed2Hd+vj7Ny2Nq3HQaXoZXBXNxjCtDBC++/iwppS4pqnu0+VNH3HlO+yvDS7wt0\nfrsLayawcF7Te0z7yc0Zjtw4xstx+NCTmQ7vp05FdzIFwYvOz73qpS5BsOE0LOrWIo2R12zjw+Pi\nFxU8IlxoxzUXpahlmkD4ykh+DrvofOlh29zc9oAxF5FxvLfN+DKrmGaBdlelObttLHmSCgGKVOSs\n7tuc+vh2FcGKqnN725h6F3a3Sbddldmm63dz4frOm1oZZZJjgVuZxeYnj7UDyLELb8rWXfg+ROEl\n6GVo3QXx+hoD9CxFyIEb2NzU2yJ4qPvmWW/nT8/n8Q6f+UNoPgWx/etYzn7U+BfDvY0KvXX4U+au\nZhd6i0r3Toh5MkKng+B6chpC7YiNKXrr1VpeDqaqvR/Xiobu0xKEevrMUUTR05roUDibK/1IQezy\ntQkkJWm6UJkbZMCtNdtCD4iz/QgU1pZ0YxPTBZEVrAnCRQPNEpGXCmRBkbUv+iW9Ic82vac2OfB7\nv7cRY681DMe3rYtZfW2xEUGiIEOD2Cqluc31Xo9nhjvkLoru3w7DFWLq2JOfdEu6rylt55V+DgI9\n6msQ6+N9lrnLo12JofIJoEUMzPHXfS8NLjx6zo1BP1rUAFr5W9a9hEGoL6qpIAiuAyci1GZHDdnD\nBJrTGeKxsRV9KcqKUj16WEzQtTEVWXT80sYUSxv2tkAgi9qVIm2m1h4rFuzgTulitl8WgUhuDbMv\nP4lUmwrTzi3ekai23efKmqUNJnN59oZZ7AxFAFkrUtXVvwB1te7SGYAVqLW5ewuBO8XHb9mEoUoG\nUx/LTtSW9kWFAAAgAElEQVSxcbdDSy3IIn28lrrEptSiq3ImdRcnZGRmLMtO/RcW4FdEgFoAVKzF\n78GvpUMK5+uqHaS2zWtulDb2ydsC7BYdDe7Gr9b73hZI0fWiLQLbOl1L67jp8/T5bbqoikZau3BC\nCmjzpCuso8TeqRAAUkv7/RQBUCvy2ENqXcCMPc5hxdS7sb4l2x2ufuVWlira60k2/50UCEVjHaxs\npWhpdGqeYB29JVPnMAiC68aJCLVzWXM0miS1JT7RdtSsKbq9I+Cwvze1h9dxqTk2WvocYX/jNvxs\n544OakvqArUMddpXaedIOR0sL7m5xMY27ee1k047HK0B212s1yKkeQX0AlLN6VwJEe8UzEF2Ag9K\nGyOux/L46bxjY8fZ42sibYdUDN8fM8CPWZgcv5DNFz3DV/OqbI8fV8U6rGmj9uEALxu2VvoRdzrR\nd/Z9ROZN4xHbx+j/hGUdBNeS0xDqYw3xQSPW7VoV6nz0OGF3CANdiC5q4+ZLzReVgy1Dv+AYg9tV\n/6bhGjJ905yclqpyfIHQMWvLvtWFmu1o/R+P+EW7i1fYpxV1K2/aq//bPAFESgmopa8VJkCtOtYP\nVBQRtRUHISnS77fTXfNiY66jBhFoEe0ynMtznjcFHNOYHqvz6SGO9WwdsSGgcJpW1yp29EVvFXF8\nX1TsvaNxvEQ4/LXVoYy9+zYdMd6mlc3PX4VIl1wtCIIHm9MQ6qZwPg3Lt2wb/tkd2XduA5g9C7e5\nffsiB7Z0pn7ZzqFzr/tKRffUGG53HUy1PpNX/1vb8r5dRIjZBatOAiJ5GqsW3TxYlyItCGq0DKfA\nr+ZZ7qJNjF6Fbu0nW/VLRAUtWQKWKisg1aZEuUVtgxBiqWJcTMwKzkxDuTaj1l4vTa3NHS3dOe4R\n5UV6x2Z0nKghOoylz30fHAQA2Bf1yDM9lD32DSJDQhU/p3ZSWvnHE7Vns3kQ7bcg/QbsmHFxkFaC\nSaz1lwlYBlfKxR3EIAgeaE5EqLulOHkwL1RC0VQkrbWVZoqRfh4dH9S83D2piYvVKFnJxXDznWze\nt1JsNx7RhzE4iK3RFfgsaL+2FFtgImUdV86pnfNbd87b/aglJ6hVkJn7hZv72zssvRzNIoNmvhon\ntPl9F3Y7mCLIUpEgSNhhj4JaC2rVceAKnS7G4bxjHY0rWx8sA2Jl64F81lVp85CsfC7SosFcycIC\n1I8yVXD/2+ZHecnGB0OsdW3pO73wbbnydq7xIY7eh9nSPrDI7Sv1Dvgz4bRDO3NbQgvW6bHffe+X\n2Xe6xKkeSyTq7HhhpBANguvIiQg1ZoE+cGf6l2NLO6pRb+HGtpLtXLJtlSfB7lLdBXbLVpMv+7IX\nuw6fvaqL7aoiokHJqiJMqc8TlopVBDsOVqWM8jeakm7IpW7hwaWvl2Ru5jWuuSXuIEAb4y+1Ykk7\nE1X9Lw3nzR797uLCcWJdv/JoVYugDQSoqM2V1d3o0uc8m9Wpi1rY8/FQ/mbBXvQw0HaYppzh2LPk\nfMxg1c4X0ajv6SjZ/Han8x3ryQ1j04PqH7i+2Y/KfrtHf3xBEDzonIxQAzjSoM4uzP5KoDSH6SDG\ngEpN6t+hB5Y1y3P6CwzrHl29sBftOrku/a+7q2V4aadDxJdfVEd4rbrYRK0FBYJFuIkq9roY//on\nm9LUtKXfcU8Co1u8rmsTWY+t1wxgZ626xzJ7Pebu7mWvfk779ClxY92oIHXxBsTmE9ux0uvuyrrU\nPNTHBNI9KXNikuHXc6Qux57ieK4u1Nsr9JON7vLtucZtuHCqWBuqYO+elhDpILi2nJZQMzULropn\n1V5Bm1bEVPoCHSQEi7oQWZqltYhnI9PmuDV2zHPAVrbGm7YEojWECUC1hCOULvjJknM212wvdG9t\nRd2f2dNtAnYvgsXEUWpugpsILOb+3a9rG2pPgC4iQk2yUsRdpbAgOl9LSfoDFLQ0pT1BTG/ohUlT\nf7bkKYKUijvVtaQkCgSlFBTeQbNIU7blTQQ7du3mrGEo64qFukb0ba4oLEh2LdpiKFIJYNfdvdDj\nWl1a/XMH6LQ3T3BjKVLE+2S1BQ7mqulkS4JOuwOBZN2Gcg51BySNWvf0sTRXMoGapE2n09+dWrH2\nJfpCG7M/w1c+axTrDpFImci2iExty1vWfiASpNbButY6JICcASnJouK146S/wBNMJBgEwYvOSQh1\na+uGeb5JiGJzW9H82RisPKDnj9Lvt/alnRRAt1otaqrtogZcGUuBCy3szXSn4/dyDp8/Ozp7PXDI\nc4C3U9Jc4d5XsPvVvoN+cJtQLK0qOYjxNiBpU55WU1IxRSOxLw/S/bdo5nGtpaXebJ0b6OeLspzV\nbIFbUsAimi7VBoZ1+Ni6YXXtnR1q5LevduV/L/NtFD/aLVJ7LtUfrXlX/FyeU85r0h3iHmCY4L87\nIokNg6zDBY9ZvcNX/j4lXZqSiSr0Mnbyhu6ddQ4PU/YMLP1nX931fUmdBMFVePy9v/ZSFyF4DpyE\nUDdMGPz9sdFE/e6w2fKUlKN0T9h4p5hFIzT37+S3HI/jLGwYynWJWGsQW5p2GrQEGKYpjSLrhtak\npXZHnkSEoC372Je0bKtDYVPcg5J7R2G+xbkeu2dAqs1IryYxLcPHxqMw3Tx7ljJRgaILtbu3xToN\n8M8bUTaxvvAaVr52H82zLK1OWz27NB9dbWyePe+dIrH92zKlw+RrexqHtz2ekZxc7LAyzU/hsMN3\n8FjS0J/kQX8sCIJrxEkJ9Wi5AZiSWY0icsEOG7ypPPxOhr/3NvR3Qcdhuupus7+N17pFnVK/flNn\nPWWbo2uHujCo7afhz5WlWeUCtHzo/fqzcbyVYRcBj7w+fiTm59A1eqqFg3unpdgUt1lbjwQ9uFtd\nyG1M2rN16Qm6tX6pMnnu7bmAPuYNdLHk5rgjpxp7URfe3FV/Jxx/VTLOY9eLjb+9rexz+DtMNBxs\n/yAIriOnI9QESpU+T7a5qV3IrNn1NFb0Bh8QZFTLzZym5rmLdbMQfSqQQKduiVvBYzNoDehkfjal\nAdr0qKO34XujW22CWivEVtByd63qGZu15vFmLtZJgMIVQNIFRph13NcFTQTnMtiIh4ZaK0sSHTel\nCZLWHiHY91L3ydrzNCSre38E286Bk3wtxlRBWawjUSAiKKu09KnJhiF06c3UXd+wGIG7mI+eFEYX\nKGm2ucYyVNFsrUOkFu2exuldaeqUsIWI+WpbWPqc8LGjcrTj085TrUz6Oy6lolRgyeZy9w4W/Vyc\nzjOer7rrXS5+rkEQXA+el1CT/GMA34DOOVpF5AmSrwHwrwA8DuCPAbxDRL522Xlc1Eb7QvVkFOrD\nI0ab2O2zw3Cb4cAm0tI030VoPu987sPvLkbDseYCtzFqDp/tnDLsfWgJtwOaFU37x0Vvcn0fKc/o\n6DW5bdYjZXQxz0fT3NhTf6ntclw2knUyZOpUDdZuO08Xfe0AcFNjl1PtWn6AO9LTkTM0z7jV6lhP\n4xpX+rnPNW9ZVGs7wV3RRyyDF8E7glv7fnSD+7Xn9zJMQ/fo+hDrILievBAW9d8WkT8bPr8XwEdF\n5J+QfK99/oeXncAboCVltEwUBKRaaymDyNjeVQq629SsNSGQl0ki3UpKklpDXgis0CCiLBr0VDEs\n3AAVk+Jig41xD6Ki2kQw4gyLfS4ozFYWK7rfWwaACqlqwfq8ZZYFQg2Q0oQtKtwpaVUs1IVICgsk\nVZzlHfbnt6HrWOVWIykJakV3GQsA6lpXYMIqpYW4qXNAo7vTktyHrhckLWK6NC8GCV08oup9pcWm\nKbVBVL1GLRUiSetxbwFjnmVtSe15jTVTWNTSt7FqEY3gX0xS1QOh6UtL1UQ3mh9dI8kJtFW+ql3P\nvQ20tKge9Q1Q3w4JVgC0qX6a6nTR8q4eVz92ZCqYetrTZJ4AAezHoduL97SYkDNQS7UhD51+VzEv\nY6neDbRMbwCwR+mZ32pFGmYSBEFwvXgx5nu8HcBT9v4pAD96Lwdbm3vxnOFmSLJZeTRXMEc/5bgz\naE3u0DgPNqYaQLI5RixD17i//1Wnsbpqaf9mJOShM9Ff0o5Qd6cM99RWAHOLz4zQWtGivKfo61pB\n5iZgrel2t/OBF8Cc3BzP7+5tGavomNHXviQJpgQmmpYPC3CI9A5WH4weXi6T1Z5EXzpFB8x7RDaE\nOi3Otk2nBPq4wAVsY//1Vvo8bX+a43M49M9sSz+/P+5pucTkvqK+Tha2exuG4y8NsAuC4IHl+VrU\nAuDfkywA/pmIfBDATRH5kn3/ZQA3jx1I8j0A3gMA3/bt34YbuzN892N/Y2qMNs7YI5c+9v3WlXtZ\nA2pjhpsx0e0RN85u4G++/rsvbG8vsnSOXfkqbfZxZ7Rfa/5ORLDbneH13/m6K5z5Xtg408lh3vSQ\nXQvdkdvSgw5bAeiz/a7XAxc9XRm2HG4++iSfn23Zjz7b7fC6se7uFsx27Psr/NTGnTY1eylnuzO8\n/tHXzacIguBa8XyF+m+JyNMk/zqAj5D8g/FLERHy+KQhE/UPAsBjr39M7uzP8ZmnP2cJJ9QNudDc\n2UdMvcPT2jSglkt7ngtdZUVznpNTvmyxgKdmRbm7267/n7zuu/Hpz3+mjRP2MVXdoO5qtw+X5g0Y\n/ybbf2EPdyOB81TMfWtzfWu3AJl3gPiEM7GAMmLZ2f4CnK97fOfN1+MLf/J5SFr0DprrtbunE2EZ\nwXo9ajDdEEg3jsVWGyRldz+3Z4eiq2lVdwNXVKzYFUJSsgUn1MImEx5/7HF84Uufg6dpqeaLEBB5\nsPC1OnVREvVAuDXufgzrCND8EzZMIO6GcQvfOzMeTDblV099PxDfdfNRfP7LX4Av6qK/C1qSkdGO\n1v0TaSle3ePil9b6OIZ+Pbi+pbZYg3Gf8a9VNF736GOtfHL8/0pBEDzgPC+hFpGn7e9XSf4KgLcA\n+ArJR0XkSyQfBfDVq57PHKFdZ8yV3aeh2jxoF6O5NPbvcVdk8/I2kdWsVxXQ8dd+U1qSIWrYz8bp\nrG2pjNY58BHx7f5d3GcHqseX0QSASEDqHQY3Mf14F+oMX1Kj76OR6Fsrby7x5st27tYDMbGEmKgP\nLlcZxt0rxDzdQ1mHfOlboezq48MEfRIToR0icaEmwESL/rb97FEkWNKXA2t+vpv5ztjd5W1ohEOZ\nDi13y0V29BqHc/i9ki6qa6CN0YxfC2wu/0wvC1sni3b6Y279IAgefJ7zGDXJR0i+wt8D+DsAfhfA\nhwG8y3Z7F4BfvfI54Q2yFYxuNfn4omBes+mi17Fzj4szjNN6etx1szjtb2ppVLw5nvOGp3YmmnTm\ngxzSvp9fzXNuF1hglLXhibq85JITlkwsWQOfsol4ZtIlKHNu96Lls1JQLc0mtq3QbvW3Gm6vHvFu\nr8nC87qAjZlXlFpQSlHL0SxOmsWfmHrVJfQgstRrbhTq9jToy2ONLxsLT5w2p9RXlXKOd0NmQavQ\nYEFfUrNShpHyfp7RJT3GaY9+iJ5OdPD2SPvFXg3pJT/erQQEs3nOljwmCILrxvOxqG8C+BWzmhYA\n/0JEfoPkbwP4EMl3A/gcgHfc9UymLSknrKtG95KAZtc0EXWry0O4bH1ndwe2fOBLVld21ahwz+6V\nduayNHdthQZkJRK1nqPaWsMJyRKCCJZ6A6vlksykrTZVdelF9FcdQqWIFSKLuXM1iIv0z0StUHGj\nljVL0ejzJNgtZ8j5Iax1j1LuIOUFta6t0SaIJAuk3Gky4zm4QY3qdnHWQDTv2AAa7CbNzUwQLES+\nkTRzVyVKXc1aJVbUQUV6J0P1fLDA9WoAiJTPAOiccdg3LcjNRaZ1vpRaSj+ndZbWFchZmpx7Ck1V\nU+n10TwSJqtZI9PV7Q7LSAb1UojXREWy/kMaLP0sSXPKJV/WRbtCtf0nlmlNurveOie+FOe6t9kA\nZvIvtKVLd0Qpgv1ek8HQl2NNrTr6Epxt+c2N9VzD7R28cLzie9/7UhchuAees1CLyGcA/OCR7X8O\n4K3P7axmpVlDNmfyhjl7Bd1xOk+e6csqHroIte32qTTWaA+Z0EbLxp2YkgtaPHKblnMsGKhb1YfO\nzPHsw8nbxwUkseSM3bLDsixIBVhJSF11qpR0S0pqsXUPRS1E08RadTGQo9f0Mm2LRoBVp0bBxKe2\n1B9bu7R7IWYX/thluTgU8GLGCjlS5ol+3dFp0JwFzfMy7N92tCl8gziP+7Vflj3cIhVbv4vP1Jbh\n9G3DJbc7xZ0N+w1FHpb51PexpGUQBM7pZCYDzNoYG+FRdLtrW1DR1pAeW82hET5wg5ugeTIOAVq2\nsm2b2I7MFYut1KVzrQu8O+CC1bNLjRO35nNN46LD4KzLe04Zu+UMD53tsOyI/T4jpYR1X5Ak2Ziw\nWYW1gAsAIWoVFOlzd3syzq2CbDoKToLNNRaz5Kp6IwAA+YhYjyfYSuVY5/3aB8trHBiG3HzhNTZ0\nTpob2L0ndqTVYbLLV2yeuZ3aFx9pT4r9dzBeubnCxVbwsg9DmCE89q4lR7vwvoZbHsrbk+zYe/ei\nj7d/F+EPguB6cTJCPVpiydykycYAfdx3RW82e6oTPdqb3TQ0qv3MopYjfSyXGOf9jusmj1YWCWSL\nxl6QsEIt8MqC2ZofW9W58fdPLYDKjdv2d0XOGbvdgrOzBUvWudK1ViAlS4Shlm4p1aKN8xTc1U94\niUW9NVa1slD2Rcsv0M6AH83xbNwcOoZbDTXG7UW27zaHcPvt8BSmrGn+XRdSyy/S3M7jI21Japon\nQfoxdmFNbjJc2wPjfK1sT/YyqOYx29+CxS/0BYife5OalezebBEb4d4Kdi9aEATXmBMRam29aiVS\nMveiCNZhvgvhhTWLmFUTaVkrxmyt9bBmsGY2tVbOpi65uzGbSSskSnWLuAurQLDfryjQ6TTnq44V\niwhy0iAuF+hq1+xJTfoUMbE92iIS46pIUOsqpYxlOcPZ2SPYLQLgWRDnWFPG+SqoRZpLNmUgFU1J\nWou6cwlgl4E58ng0y3ioMvb+Tj3XsVdxt6+O05eNi3aqnXHlkDHoyVfKsqMuGgiY1W7bsdJjq/SO\nwDZaPrngsouYu4y7ENv39AVNRkubGEcJsj3vKgCqPtmUMnoHrC+Nol4HvWBzjgzW9dFuh3lDukjb\n72boQMDLaweOxQ2hDoLrzWkINd2U0BnHzTW4aei7WKQmhl0UxqUlx7WGtJEtlilTF/0Q3cc6BayD\n2KC7SSl5igymBRON0dbmEB0aab2Pvoq0NuGtIR+MPAAokmzsuwCs2O0eVvs7ZdRnv6lnE2nJLDV9\nJ9o8Zm/Fe8PunRUvyyVh0hVDvekSmoAJyCjyQ+1rKfabE3md+/djlrTN5OIDTT4Uaa3Xefs09WpQ\nsWZAy7zQhn9HeErRechCXeBj3Xknzlzj1a3yXveAtNi4sYa2NXFoUdv5N72WFtGO7hDZdkCCIAhO\nQ6iN1ojaYF4efKQqfy7UuvSFHjM6lzW2uQt1t4Sqragk0hePaG5wyHDmfsUsOxSL+k7Mtj6Xl6IL\nipdkLM0sdN112v7ah1ppK0ztAa7IS8YZz5AScPvOt+BzmD2SmCmh+HVHq7cCki6xqN0E3HiT3TNA\n8x1XuqD4CPxYK3rfFeuwzYU+ATaGP16z18dFHIq0vsqwDdM+Wxn3YfY0mqTwe/BofWD8xXgK2HaF\nQazV0O1dMI8zFzvmwP19yd0d3mO/jwQ5EGrYbzOEOggC5ySE2r2cIsBaKrhati5W6Gi0N6ljhqlm\nF4EAsgkoBEisKKyoRd2ZREHK5qqm/RWBlBWEYJcyCjOSTe2pzNhxh5pqs9JWrtYRgK1dIW3uM5FR\nzC1e1jvI+SGQSa1eqUg5Azmh1oK6ClJKtlRlRaZgXStu39nj4YcKHr6xoKzAHSlYdjewnK+QXDWz\nlQB1Ve8DRZAqkIpORaokUE0oOUzPEnX/1zZAy1bXAgFqalYjmJB8AZSmSN6rsfpnQUoLxNzvNLHJ\nOWEtYy42s9ItA5mWRVqSFH2IhJuoTaIJ5CQozPosB0X2NTZEst2m3ktOw5zsugdqgVSPiieWlNUr\n4X1AEiURqz/bhCbbtVjutNT/r6E53LUgpZiYk5NF7P0in8ouEKypQEjsjs6xrliL6DQxaCyEVOtQ\nCCBSYFWGWvQ3FNOog+B6chJC7asONTuGPu43urMddy3bSk/02cJqTWV6xi5rQW1w0APTvNWf4r0p\nYNIkI2TCWgVJKkTGIDNr1GFBbgIUS8aS2IPbCm8gMZtFpFHUVQSlmCBRo6mrLT7x8MMZC4mHdwse\n2S04yxnfWiv21ToE0HWomdS+rRSLSKZlMXOrrHpXpVVTtxRNw4fP/l6S+yZMeMy6niOtN9a5+fDb\n1YiDfOkHjxiWbARdlMcsdCMCYBFYYpiegjVDhXtt59B6SK18VaPzU0KiAFJBqZDUvQk+Fa2Z4X7v\nXsr2E5kryjf3a/lwi+9bdX61G/VIWKaUrV4L/bwLux+mNItex8+reXpAT/RivYwgCK4dpyHUAKwV\nhbb63VrubuV532SW42CetWawCwi7a9EEZuwQ+HFiiSvolq4UQOosPrJZmlhcnNjmfSez1tiu0COJ\nNT+GWas2DS1T3+/OdnjooYewnO3wjW99C8/ePsedO+eo1ZzPZrq5nOTBmqyq5kiJKG5Qb6sUF+fL\nkDYeLVZfbP2bQydvO8i8E/1Cd3PVHjqw5yGA6fSi4+QtBbi0yw4ubJfW8ZciEHqKUh9zHnLFDb+X\nOf1pk2k9iv35bcs8R8N7Z6h3ZgQusjZhT7yLMv7qpJXaf1faCUDzUHhRR7f4BWnzgyB4wDkZoR5T\nfI5Gj23B1gZLbs14+K60pnpzYjvDEAy+lR9BsnFgjToHfWSybM/Wmu/WjEtFJYfxThle3vhrVHUS\nz+ktyAR2C/GyZcHLH34ZXvmKV2B3doZnvvEMzu+co66aQWuB9KlCtIAvT7sJYq3mkiexHggquoB2\n4/GiB9AD3Q6+dJt2c8C0x72JCIdXHZ+LdEEe9wFcwP0MU5esPQvPFtZjCNg6XHSvgbmme91I6/j4\nXOd59sBY7k0nZUii4h4Df/oeXsfNVDb/jdKjMGT4PbVTb68tR7YFQXAdOBmhBnrUrie9tOZ0+La7\nGlNr0fyY0ZLF5PpW64SzajXXrTa8aykt93a1+dVlzFom7UjIEEEOu2YfAx4tMbObZHDh2zGZxNmy\n4DUvfyUefuQRPHzjIQiA89u3sZ7vde50zk0IPKkHRUUZ5MZKnpOL9I7EJRVOW+gCQF+4Yszu1i3F\nCcGQRtSsU7lgKtZ8uVl40adaTTUmo5j5Z7Qgq76SV087AwCoFZI02ac+H1gHrjZLmePFN122VoPN\n8O5JUtq/w0BxE2DxT/6Q3FoWm/c+XqW/l6EwbfoeuhemCbjMdRIEwfXiJITarZ9MndEKEVSaW3do\nRHWU0pvQYkaluRsLp7mq/eTacJbaZz2z5Zr2wLKK/fltIBF1t6AmAlLMnvYTDo54Hz+EBnUJiUKi\nJiKX3CKoCUEya61Y419E5/Uy7/DQQ4/gVd/5WpzfuY2vff0ZnJ8/i2f3d7Ave6yl4JV8GETVVbOS\nLf8oAqkrpBKlVuzPzzWIbV+Q0zDjWNTd7Q19nkKc0RQzVa1T90ZIS5c1KtpcDzo5eR7Llno4/WiL\nd1dGj8TSnuh8tQVafl9II0HHpzNHwaL5PUxIi3lBaLnIRAWP1azeJJYhRZqIuu/GLWVxIZZ+DRVs\nF+sF8+i5/y6TJWBhE9xabfikLak632mrWaHlA+iR/NV7JeIrjkoEkwXBNeUkhFoR7FuqJtORWqzh\n8wZ5r2PBCZAiKLW7LzOpyyMSWAVYRSO0Pf+1yLlaVxYRnZaEJCqkdZ+wnJ0hUVcokrJiL6KWqxdJ\nAE/ocWbR5J7dKidd2SolNkuKAqAKpBJMCWSxBUAKCgrWAqxlgdx+FufPPotvPPMNPPvst1DLCkkJ\nNRE1C1718m+DSMX5/lvYr7eRU0I5E6AkpHOiyqoW/TDX2vo60/CsJDf4PTm4dlZKys3KVcnmkN3L\nto5mMAgkQa17FUEAZMJZ0hXBRn+CR6b7NPlqJu0oW752tWpSsrF+XxZDF8DI3gNLsBgAu4ov1VVW\nACvW6hOodGiAJNIiKOc2BU2GFbTEPRJswyhFL4AkRJFVLePkiW1ov0ufqKU52LXc5vBOGhTWujQW\nV1BYZkcLvd/Qj/d0uFLsVpN5YwjkhWgJWa4IyT8G8A1oj2IVkSdIvgbAvwLwOIA/BvAOEfma7f8+\nAO+2/f+eiPxb2/5mAL8I4GEA/wbA35e7RQ4GQfCCcpJ99OYiZVZrhFlFUmiBWaMTdXaouryM8eLq\nsszTi5IAyYDkZgl2i9IaWQ5LC1IAVgiLim4SpKQdhIUJOybshmxl4qtEJY0K98CzJS/IKaEKcOd8\njz/9y6/ha9/4Or75rW/hzvm55lJj0nsvgt2y4KEbD+Ghh16G3Y2HUZmRsSBJBqSPfpYh+KoFYRF9\nOBuzJStgy9u2fR3lCk3zsea7z/W+/Apbq/pu19GXtLnxun62rXlFd6nbmZpH2qxir5/pQrOtz+E/\nL9Vzdz1f8Ht1N4FP/W+vuw0iXJm/LSI/JCJP2Of3AvioiLwRwEftM0h+H4B3Avh+AG8D8AtkcwN8\nAMBPAHijvd72QhUuCIKrcRIW9diEj+Ka8q5FvErVhRJAbZwztyN/uuMKXe5xgZoGxd4jF2ucU7N0\n6FZnTpowRCoKdM7qWU4oew8NApIv+0hgpV2POm5eJemiFqxAJSRVINWWxewsL0hIWJLOuz5fV6y1\n4OvP3sa31q+ApYJVp4MhL0i2cOZ+Dzz08Mvx8I2HwLRgSTdQSsX+XLBfK26fr7izV2t0haYWdWsN\n3K7d7MlZxmxq6nL1ep9kaPb8HuwwGdlAm//rX05SQ2BOhjKG3vXTjpdsnzgqqhZCpO/VC5cBEguh\nU8HLrm8AACAASURBVLPMjVwgSEtfA9s7Mkm8BrpjO7V6qcjMrSKlTQGs2kG6AF9UZgrgE/Se0pZh\n2livW/U6qPWuG8kx4/jz4u0AnrT3TwG4BeAf2vZfFpE7AD5L8tMA3mJW+StF5GNWjl8C8KMAfv35\nFyUIgqtyEkLtbLVBWHtj6RaNu27po5uT3TxFC/v56vAN6eORvhiwuVO9hbV2kuCca1y6fdVW4DJX\nepHaAtAWz3RGdDcsEnY7zZlW1lXHmYUqJCLI0MY40ZKDiECq4NnzFX/5jb/C7dvPWgKVirI/x3nR\nZBl39gW1uG+bU0Pe3a9j5Y61MivvKNKTIIwifYlStJryYYutkm+ek+98LHdZv9Qo0r3ccwdkzMON\nIf5Aj/FIb3vQdgaZOhN+vamqhmJ7mCIvqwA7cNLluxngPpdbzB/feqX9NzgU417teQHw70kWAP9M\nRD4I4KaIfMm+/zJ0TXkAeC2Ajw3HftG27e39dvsBJN8D4D0AcPPmTdy6devSwj3zzDN33ed+cl3K\n87Nv0kyL+aGfPPju1vecDx8Or31d6ui58mKW56SE2mlNs8+pYpdcnbN8zA4DepPqn8Y9PCWJZyhj\n26lKHdzcXfhADgto9DnGnCbTWsCQuWFr9ilYSQOERKPIM4haq0aXD/OjpRSLLidyIpaU+vSgIvjm\nM8/gds5AWkzEV9QKrFWwWqotgrrK15H5Vb0uRrGRtm3MFDZqscgRJfO62FjMzSiU48ajHpYOthz7\nNIv04blmWR2fdLJMdphW8eLwbzv6SJ9h7oeoFd07ZmjvL1dL/dJnbN1VWLc/41YIWkq2sZx37SZs\n+Vsi8jTJvw7gIyT/YLq0iPAFnJhtHYEPAsATTzwhTz755KX737p1C3fb535yXcrzP7731wAAr/je\nDxx894nPfr5/+LG/um9leq5cp/KcjFAfM0Kq6PjuaKl0C2m7t34eczFn9AZzFc9OZq5vcyciAbXs\nsSTdm2xrJeo2P1lim9/dFnOgTyRzV2WyaUotLAtVBGtdUfeAVEEpOu+6zeeVYf5tFWRz7es60wvO\n1xUsml1LmCCyalCUHZNTNjf3IMGjAHm/Y6qtUaznmpwk8C5W9ChG2yfRvSK+ZRyqOK4P0+UEG2va\nfwjbFa6Hrkhy13UXaPovosU2dK/J5mLtGJ1mVZo17d+ku8hvKzKHe7mbFPrcs+3mTYcKuLcRchF5\n2v5+leSvAHgLgK+QfFREvkTyUQBftd2fBvC64fDHbNvT9n67PQiC+8hpBZPV0lNoJXMjC1FLQik2\nBQYAq8XgSLXxXJu+REFKGTlljd4GUapOkwF05SPJQElAqRVFVhQ5R8q7FvylaTorVq5IxYLOANQk\nmooyo2WOcvd3FU3EUW3+kFRBrRrlXJlQq6DuV8hq4+Q276hKBVNGTgtyWgBk3C4F56VglRVkQsGC\nvSS1xNcVZU04t7zVCR4drMtfVhOIIsBaNfq9iIX91qJR2NTOACmoLU67i0qpgrVqBYsnfjGPAUVn\nKI+ZwtyFvHh5MGu7v9cEpxWZ2rlIKSPlResA2tHIFqCHlAEuIBYQGSLJvAjAKlYuVvtrHShWS8mZ\nUCRhlYQK/U0Isz271fKhZFAyWAcBts5RQdUfmJwBdQfWBBYgVUEWQZUVtVbUQpSyoFYtW5E6rEO2\n6d/4ZPFa9GXZ5CDQmIZK+61AO2TZM5kppcXzX02qST5C8hX+HsDfAfC7AD4M4F2227sA/Kq9/zCA\nd5K8QfIN0KCx3zI3+ddJ/jDVXfPjwzFBENwnTsai3qLNEru7tX3TLdup6TJXtZuQiX015J48qttZ\nPTynu3+3NosLFYDu9r6rU1O0obfGX/W8HrVsZ8eAfdNCt/saUb3s5t5mX9BxvEfgeMm2RvHz9nfe\now/WDxHzYvREMWOhpD+/5pUYLuceiEsuXqen7NvGa4xv5HDztGtP+7l1susvRzOOiVvoF5YK/eFs\nxgVEDn8LbW3rg+U/74mbAH7FhkIWAP9CRH6D5G8D+BDJdwP4HIB3aDnkkyQ/BOD3oHGJPy0inh3+\np9CnZ/06IpAsCO47JyvUwJihTBvG0ZXackEDcIklEqQWm4tLO0efLzu6MfuIn077Gq1LjxEW1qkh\nHcLJLixzTyBqn2lJUVxQp33tHxkaZZ+/LD52ji4Enou7ZVXrdTAtqT14jDkUV7B5P5bHReMqg6ts\n/wxnufxAH2rQ3B3DvU6qOgTvpW10NW2a28XFEuE05ap5lTnc3CUnGKVbLErdn4ze3fgwqnvRccSG\nnvGgsabGWg5fSGb+jR26zHnk3WWIyGcA/OCR7X8O4K0XHPN+AO8/sv3jAH7gShcOguBF4SSEmps3\nvnqWpwXdSoJnI5sTPMLSLtp0quTTj3SPUWi3dhfByfLSFBhsKUXhY5XsIVkXUWpqhlPLNyJdpEeZ\nmBrkZkn3b4uN0VMsmYsdxNQ9Db5Oda2bzFWuLuN12L/y/sFReb2rHhzb4XJ1J7vngmO60TbJexBv\nESB3D8PY2bjMetU0KZhmc9Xx81hOGStkXL2tO+vdbtZvvDJ9SpsHNfoP5OKOirTMaPMDGPsMU4Ce\nWAfPvTni+d6DILiOnIRQO0MisCZwPkbqU2o057UHCQGefkvTjWa4U7t6rmTCWmpXscFqs4aYCcjI\ng0Wt56Hsm8Sn1lAODfwx6jl8ELvb7HLglm0dEI8om06pgl2t3GRqQUqUCrZ6wfCSWYPQ3ai9IzOI\ntBf3mADeTRPqMVG6vF5KrdN1ty7f5vb2POZmBTf9G0K1527VWIQyZ++aOiI2giye5oXwtbDFXA1d\nrjE8bz+Vfus55rfDJwAvXJjEk900d5A9iOF256Q0AkD8F9O7DndzdARB8GByUkI9GDj9o4mL53t2\ni9ijebUZ0ylSwtpd3tUFSQb3s51wg1ulg0PZLKY5N1Vvri8WpERLW2rrYnsXoZbaRGNycU7JPHwP\nfeXh+t7foNS2OMXoVdjW3eSNZvf+tvWSpZ9j3P9Khhv9nwtl94A6JFYh0MarpyOGCdLqCJHpPu4m\nVE3Ch+Lor2RcSNJ2kPHKcuROZvdEE83WmySm0LmLDerDa9rSl+OzhT9Tv+Wh83bXUwdB8EBzWkK9\ngcPSla2hGtzQbjFVl1Y1hdQadSvJo7kF4NQc+6g3LfJ4DNDSb1wo7bJDlueL8Yhmjzp3K2wttw9E\nmna+nuiC0IhjAKyae7wlWjFZFmmiJ8NUpYsa84vK+rwa/gsr4HKhBgAO5qOM7uI2B1y/92AuUIYZ\nTNupWdurWyfNPm8j0JWLFH/+bXhHkNNvZexabP9eVrCNRe134x6PjSfp0MEiiBU5guD6clJCLVUb\noyzW6OY+LcUdj9poEkXK4DJM5rguABe1pIoGrXoeaEmrmZNJXxSQOj95kR0qSxeHmpGEoJTWQErT\nlAW17gFmXbAhEUlWDfQWFaLCisyExRa8OMcdcF1aIg4yqTu8ruqJpV2GhBRb9oE6Ti7JOhpwL3mC\nj8O3IlFQKW0qGbRKILTpRgB2iahI8BSZYDWPgfQD2hnN+k9dmrKgpVwVEcsT3uUzA7bEJK2TUoZ5\n5kA2a5kiNlXL57zrXHVWE0i7sdGDUm1bu07RlStIX3zD6qBo4pps90ERiBSbAaC/D/H5dejCDi7I\nqBBU7O0aYNWFV6rll2tZ7Oa4hS6wolnipELMnaMxE0ReFutcrXpPTOpx4V6XNUtZO5ZiY+yJqKu0\ntbdX6zbEMHUQXE9OQqibVHi2EvbEkGPixmaBAtDZwePajZawxKzN/XBeAkiy2LV6jmcgt3V+21UE\nLeipVB2P9K6Ci4WY6Eh1URNbSIMAChbZARCs5VzPLUDOWrZiSx8KtOHN7h0gpzFkFyqgW1hbV/Gh\nYbixTv0iEKwaGtfqo6VRnRKRlO2ZJobFuKZyNHmvBUSyTF61dUwEMjiSe3yBO4D7XXerdRvyNwe+\nLcM2QrBoDnesmK3ui2pMMN1hW2bMY/Y3h7VvZHoOW3e5bl2G3NxiwxTaKcp56ZESYh6FNtdwY7Fz\n/G2njas+CILrxEkINQATQXRh3LSlHJrIyV0MYJSQJGYXNzH0Bt/GtKW7M/WiBGzK6GQhSbdibUNr\nnWVcNEGqRejCxl01XahY8owqBYssSOomUMupumWtQUskW/7w1pVokcV2ef9WjrfZWndbYfJqmQOd\nulC7XduFajjbfO7NHtu9pNW5TL7braB3ET4mPF36ptgAmW4F3iHrQxpoaT5HkXZ7n+3osVPQT3xs\nxpb7cGb55KFbejiTlwPime/qFHGeOEwxa1HnwwMedbr9HwL2m8Nc+UEQXBtOR6iNw8YROJQKwZwg\nFGg2+NCQejM4Wqa+5zh+OV5rzOg4tosuRL0Y2uL2hlvauSvUFV2ktk4HoZ2GnFLL9AVYdPHQ6SCm\n5nvKn32RSMyMYtvvcJSF8RyHkcou44f3v7X071qGVrEvoDVo0WJingx95j4qPf4+xiJcfP3Dux9m\nFGz221rSW9lHs7jds7H9JW07C/Y0/EcyPuxLSx0EwXXidIRahsWEfNNGdM3+gUdkH3NAVjue4lHb\ndg5rzNW9TLPApFmBHrSl+8hgtds1h3m+vblly/stZl1r2s/ajqdQx3pFkFICc0aVglo8WIrNsPfG\nebSt3YqeuyqzW7jVz5hoeqhXv8As0m4zbh3ax63pfu1JYqbtfX8O+szp+NlqbiXHXGifODcdOFzD\nwvrE3cuallMXQ/GQv4vuYLymm6/brh2HM2xqeTC/LzJwSRur30xUr1IxB5Sl/nYTrr9dL0OOmf1B\nEFwLTkKovenyKVVuFXublgbrwiO8Oa1v7HZzwm3Zt08+Xi0pIYkGk1m2amsYi5nQGpjUXae6MEfK\ng8iICXg1RzKzJh5hgkjRYCgpWPIO59T51zsuyCmjoqDsC1LO2O3OUGTVhTqk2BKbgK/URdjCSfRO\nBVpUcMsqOXhLD60u6Qe4SAMgMjSyXb/Q0KqK3Mb6EzCk7azTfPPZom71O1y1wucesw83+Gf0SGwO\n1rDW9ZiBbOymrG2L1397T8B/Fd4p0/v2c3mXwI/Y/lZm34JYulkvLZEgWDcdk+434eZv377Af6H6\nGxnuXwTrWoZgstHXg8FbQ9s8uk9Gj0EQBNeNu3oxSf5zkl8l+bvDtteQ/AjJP7S/rx6+ex/JT5P8\nFMkfubfi+MIPGgyLVCGsKCwo0EjfzB126czG+jQ4q5QK1IokBYkJCQnZsoupRbrqJ5qlQk0YWoQo\nNQEZWNeC8/2KdS3ITNiljFR1MQaIzoOuVS3rwqyLcIhug2QViZSxZ4WILfrAhCUnPPLQy5CT7r+u\nd7DKOfbcoyaNNKYksCTs12oLdQDMVad6AdqDsYVFmGiLO+g2t7RUKJONjfqCGxWUiiSCJDvrqJit\nyoRMQLCDINv2Or1YgVQSUDKq/VQ8LcwKwQqNSNZlURaIJIvEr9DlOIvpj0pfJtSrAFvLu0KXDEVF\nZUHliooVIhVrWbFWQal9LnmFroJGTxUqFZDaIvv3pUAkQ7DoDAIRLJT/j723jbUuPcvDrvt51j7n\ntflIwZCRmRl7xmMnAoJKFQsh5Y9VGsVKq0L/IJBaiIriKkUtlZAq0z8gRZb40aZV1YLkNhWO2oZa\n/RAopK0SNyNUqQQlbSRqKLLxYLAxRmqowXjmPXs9990f9+ez9t7nPe/YDCdz1j2zz9577bWer7Xe\n57q/b3BrmtuETNMiEkwPAWjExrYISBgsK2gQGieDKNCiHBFe5rnkzbObhNCJ0LplsBPSdW8q6S+N\nsFBHo67jZ4DkqPeQzQOfFkAaZB3KFLl2SZU1YA7k3mmnnR4Q3SU482cAvH9z7IMAPiYi7wHwMfsO\nIvoWAN8H4Fvtmp8iom3S5juQgs95K53/1ucX+ec8q6o/XRJHQFWRl2ata+mjfKaNHLbV95qAdCKv\nme3R9nOT3PUFUACZYSc8PSqLK7+BDEvzI1U6PL9qVQKMOPNJPnT4kwsvL6AxL02+ajIWez8VVufx\nybmWvnKk7EWdraQNfqtdv5XOx2vLxS9+SC44prkiPxkl/a4V0xAOfQyV/sdpIzvttNODpScCtYj8\nIoB/ujn83QA+Yp8/AuB7yvGfFZHHIvIKgE9C6+De3od/oC1YJAicu+hku6f6lhZRl8Yqrp5LhlHB\nLX2G8xpPtGI5VLLv0qDnsQ6QBrQsIrlVsqlURd3ibF2q01KdLqlxuPl6naZmhSLdttmKLhjYpkb1\n2slc/qt51fQcj4c799Lhp6nB5iXIz9P6bG+Il5+01fR856GOd+CaMW9mJfLoXaC9xtzX/86iJ2wy\nsX65enUcFzo6c92Zi9xHIe5hvvPJjKpd48wsv/J8zU477fTPCL1eG/UzVqsWAH4XWlYPAJ4F8Evl\nvM/YsdspQqnE6gWbhLmB3OoiJLKqajDyjRqIRmrRGZY9mcnZbFVi13k6aNFkF72ktPSqVQBpAYzS\net1zB9diHAIeqqbOAkqEpS1gIRwZIPE60ZpUowMY5timON7KrFVmbJkAO+ZZHMlLxqsqhW9LnBgU\nXgCxDnXLUvWrYNFMJ3bM7dp1JdVZLoHOi3BY7yymMRbVKhBZ8haP7862XJm+4USQmogLub7hFct9\n3XSOzKk5Cck/7x4mdPUUtMKbUZmlewvS5VIPq7OBIL3HR/6OoicJO7UDdK3oXebog96BeqedHiR9\n2c5kIiK0dVG9AxHRBwB8AADe9ra34frqGu9+9qXL/eB0nzrRWBt+yfQrlb93p5oE5frqGi8+967L\nJ894VQYTQ4Kny/Bc5KpWdlmrMCR1/DW3ZBzb9GXje9ezL+J8BSeazsVmv78obG4wnKayotv2tzHr\nhYECcH24wruee0cZThnBpf59/khgUy6qAu1MU+Ka6Xh+2v5+dbjCO9/+jluuyXt4K508jOduVUrw\ndb3yjNOerg9XeOc3vfPJ/e+0005vWnq9QP15Inq7iHyOiN4O4Pfs+GcBPF/Oe86OnZCIfBjAhwHg\n2eeelcc3j/Gp3/mUOheJp6F0CcUSlfgGSARI07SMtr/1rt7TvLrk5+FPDY2apYiEyZazllZTbHd4\noqghq6aO7Gpef+nZF/Gpz74SV5C6fudYSqWsMVi9zElBmUQwRLCAMAh4dLWgt4YjA8ebAZHHWOgA\nAuHIA6I+c2BioB1gOTtDqNJ0mC6hqY37xWdfwG985tOqOo8ZilUBU2/vIZq1q6bUBkgl/jN0RR1H\nEay2/ocuYBYMsXSaWJB2VwEw0Js57IGwumMcgJfe8QJ+47dfiZSvKnmb3Z0HUrHuanVP42kaAfKw\npgZIB5GPuTJjhCNWXLVFtRFs9ummNcrhKnsPU7PBPP/25/Hp3/mtomjQca1jVRmX8hkRwJzfNt0H\n/gYCgxYzQAgBoh7kLGZIcC1Fa9B/ggRYVjWVvldAFggRXvimF/Bbn/s0pFXNyk477fSQ6PUC9c8D\n+EEAP2nvP1eO/7dE9DcAfBOA9wD45Se25kDHRWktjGYglTtii41UwLHxAwKxMoog9Tz2ZI16JYOl\nTbHQDgIgzauMzuniJYv9JrkJS27a1FuodVnY/MtNnWs5tlsz6VkIdDPwGr2GK+pY146VBE0EHYzH\nTAaiAKBewyCgo2GVo9nDXSomCAmYaopN8iUEs5fltNKY5GpVy1luntbalEALf6TkXLOejcYgESzG\nJK3QnNREDTJWABJrr1qCBQdRdTeDgVZV7QSqlbHYbrbNKdTRBBA1tAaM49F+I1Bb0vRAhDEk8oAD\nwBjKnBz6QT3NQ1Wu3tXSW9zHmrwts75RagtCyyG6jqU4B2HBkDUx3RkMA/GlpSMbxK8mjG4nuKOC\nS/a8IB0enMlqgBw0aM7XRgjgZLJ22mmnh0VPBGoi+tsA3gfgG4joMwB+HArQHyWiHwLwaQDfCwAi\n8nEi+iiAX4WKCD8sIq/DhdXgdUoukfKwbnNbP7hUMj81Fe2wt6BaZynHxSRxKXW26ognY+U8FAJI\nGoQoQoC82+AFCliWr6fj3Kqup5+p/HVVsJ+zVaumtfS82ZU2R3MINeVLbddbnI0O87UnU9qo2Ocf\nzhyflcjRsCc/Oe2pjNYFXjtQn65zRPGb97nxTUCaNPzcegfCgyCKpRBAkuM8+aeR49haCG4Z5k47\n7fQmpycCtYh8/4WfvuvC+R8C8KHXO6Dc5uV0M6ZME4qyJW7harufyXRUpqN146ftYalgLSmJ395U\nnK82aOcCVKIdlkGrgUINq/14OFS55ql5Dosc913ewsIIkmsWbep8uHQyr9uWBZgHU90SdFkEjH7C\no/hC3jqVk4WPTqa2HBKdkZhuA+XxCtjiA6zTPuFBznIK+dN2WOXdn61U3WfQWrAuqZixW+P3vQL1\nZoVcGeJvl/3ndtrpK08/8aeAn/jCn/QodjK6F5nJANvSthunNFOH5vGAb6sPmRugpQg9gZRLveVZ\nkcirbOJuw41zBHUgMQ4SV32ekaincxXg1U6plmMCFbV2XiaWhssTVE1EKYVvugn7cB2Gmw7kpCE/\nfn61NgFOpSfeqGDtV8lQLj0aimEfnA9/fi8g5g+ASLVZG8OBSUYNcI5GpjFudA5+vfd1B8l0nqL1\nWaRhj0GP7GhFmnaPdfYobtegbNbgVFeR7x7uNkn9u0S9004Pku4HUDv2SlELAiXMqKmJVcReNSVl\nlWAAYJyXqKNs4HZTtw3UwU+s3CYBTXpAGUHtnARgYEwg67ZvgQBNHZnAMDuwwaGlI/XQrmGbMLVM\nMdliXrIdYqi9Haj95wpN3aXKAuQeUgVB1jHx77VSKLbwNqajHuhFgKbc3DBV6tQ3l/ioAF89nCsk\nEYnV2Y4VVGBtlAyJFJW7OGCn1oGara1IMEDe2jCgLm/zegLzIsTPJW2np6KNSmmp8K6MQ7N26j0k\nAK0t5Tgs5ek53N2skZ2ghVDkdWhYdtpppzcD3Q+gLuDpkhMAsAGiSyiCuSDjOaXtVqKeN8MK0tXG\nipC46sU1WYYkWsyqUPsev9t1AeJ+InnCEArQGKTx0zlndTKDaai5tH83SiD1RZqtqltZbgaGuRv3\nMj79BaApXMtBNvoHnSxT7WfuOce+HVt9FmJNyY5Rzo1IwS8t/7W3Etvszd9J9T2DZWgmnPEKkJ5N\nLXqWx+wbi9c0nakUgD4NaKxsF0BMUzlTaU/xGOy0005vKronQH2OCNV9C0hJzhN4zApHwOWap+7J\nECW2SsOmCA3b/E1pad46w61oi4MErbQhnqkqpV2085JmIvXtNAvd1YpLAVynCu5EqlMIrr/UHGQT\nS1N6T+7FXOU2Y7tNbjw/oXoP/DuVrqyOSmq+4fdwu/AF/LbDmBigu49xyxJu4BXVWOAuj43E7gFP\nz9Qpx1c/L9PQ45nYaaedHhzdC6COpEtU5SfJ5CDMmoLSY2AbcBxWeMOOiQxAxIpuWHUiturQLn1R\nbobDK2kJYcFQ1bhF8fBQdSy3EYrUBg1tEgg8fTnDVLc+DyEsIKwNGCIYMtBEizGsrCB2QNMQK9E4\n38Ea3tUtfIr5qOpVMomsUQldsjUiS0cppFW7AAxmk8BcfVxwC8BiYU2ZpUxLcay8RP2qQYAXDDuK\nh6s14yUsj7YIwot5IwSLeCgTY9g4Gw46eGdQDGjVdADQQcDrEcuij+I6Bq4OV3h8o4VUGgiDBphE\ni5RQ1yIWVnAEADxWvFkhEo3C7gAEDZrBLlCuJoURXw0ph/RD99A2R0jy0L14avUYucZHU8XCMq+p\nRkJrkuOoRTd61zVYLbYe/nTZelQNRSOx2QMHdAzwFAWx0047PRy6F0DttsMhLg+bAlVKPQ9BilAA\nDiT1KwYITFDVccrgGyyhBBkSuNetOBiiSI2UKtx4hZq6xASXeRCAo0hpR38/elYWmMNVSa2pQJje\n2gRnSAjSKqjYQoHBnj9a0izQW4uynlvNrgAYWIvuQVmPUeY2dQEUxzSXi7PlSzqLtblHu6Za0fVS\n8GLxhB5kTgC+aATqHWLq4dYaGjOurhXINSWIWp6bJa95jT1ljbN0bhX29KKucdE7kfbx+l7nk09L\ntTufo1jbsshC5bnJlYzvntKl2bPXLE/7eALwThEHU+877bTTQ6J7AdROLq24ovWsGc/PdbytmuZJ\nZ1pV4oRIYVZbDwl7ibaiq2JzBgykQyqcwbgOT+DMgl0ETVCSHtzpMKdylxfjnMEjS1GhiMbWg+Qc\nHISamQXOK6XzPX9PyTsmU7pvxUA6A1cda/ZVJflgNCbbcBlcNK2wqKUvi1ZFBG+5egSIZkNbh2YW\nY1GJfvIUr8AaQdmuXlYVSZv0xvOqUIxidhK7RJ4/fSK5Ddp9pbNSmmtoxh2rZN3W+k477fTmp/sF\n1JhLPZxuY2m0zdxUufHpcSrHvUDHbBU83/ecKETK8XgPhEi/3g2+hbqSDLXERkEhBEoZJSb0FJHw\nKjaRfqZgZGZZ9y5ylpgqtsKqGRgCYKXwM+eYkPm7rxQwry5tbOu6ZsFQbcBaxsBhWdBaQyfC0lS+\nXfo1VmGsGFqnm0cAdTNl/Sy32kqTM1I+ma88VUZydn88L/n6Hco0uC3MOreRQKY+dtppp4dJ9wio\nU+LKjVBOTnGasi6iYGgAYcjSAAgs1mIFIUp4dnAl+P5uVZ5klkQdCM5JrkCq3rWJOhNHKpXk6+hg\nVzCZ+xxZPu1w+87xAsmUQMRCfSTaritW5XAK9S7g6mGCnGgO/KQtkzN5jxOV33L8HsIWIylASaEh\nqGut5zwyoD4sC66uriAiOD4eEFEb/mAGs6ZrZRk40HRHYi1bU3NAHvUKXrkK86qk7I3N+yWpOiTq\naaFlZkImFiLXxSuXTgL+BZpHtwP1Tjs9ZLofQG17ENPmQOtlX62wACwC9aC2jXOIlmQEHUHoMPev\naTvXEB82rGmIwGKTdsOpzYzmreFEtSxQe/h52Um/rz4WiNlYCcfYpRWcPCvZQhnyM2RgkJd40D7F\n8gAAIABJREFU7GjDKnSktx3QGro5SkHYHOYAYQZTbu/1HQA61jJuZVA6CAM8pS2NhC8hI9ZK3qrF\n6EihuUISNR2XgDGEzcZs69eWyqcEWH/VV3813np9DT4ewcx4/PgxvvDqq8B6gxWMozAICzo6Oqlj\nm5fZpEmrojHUEtnRXOJdzQlrawjIFeJJU3Ppzs73OMqFC8oV56+lcr/c4e9pcDdj+Xew3mmnh0j3\nA6htG1oMqYV0u1xolJAsIwPo0RgUKUUFNAY6C2RZDAgtOaZJzU3sMxFAzbY+BmQowBCls5gsJrF3\nrdAF4Dgc4AnUhvl1UXqjm0PaGGl5dJepo0CrdxEwXBoUl2gNZJkhwuiEqLpFVw3E5iEuOtw+gOOi\n2cF6A2hRD/e2LBAMLyCtm3sB+IgEi2wpKk13Imufzasa5pSmmd/MioyEY7aKWkWqtW6YHweYHMz2\nHjA6JM/tLRiVtzx6BPCKFSu+dHyMx8cVjTuOKyDUAVogxBhQD3qSZkxQKp+tTou7nOkU+wosV5BX\n1WVubcZCDMGgjt4IB0ugqvO1MhwEEBirPS89KncogzWqWt+jCwJGB7pxOgMAS9ekOU2LrrTmhVL0\nObxetO75iFrdBPF/A7LCE7xgNDQSe9532mmnh0b3BKiTZmV3kYfN5Ki4Myt4Qz5yaSX+0lSAQYWg\ntGpve9VzXK1LsTmfDnKbzquKVlViO50Xbd5Pic58ls3XolK2j43Mmzx09yWa1wOPz3anTIGW5UwN\nwhyTXCXQQPyTcdYUphWkHcgjW6pzXkT4o9deQwfjOI44rgOD/V4QRIyhMlOBqrVpyjPuigYCMHjg\najkAxBgikKE6BKFU+5MBXiqV69xa9K3NVpVz/XbmmfAxBwNT78G8pBlh4P34E5fmBPdf32mnnXa6\nV0B9qrJtoU6MLcyA0ZNNQFDskAU8whhNk2o3YaeC9Zi25Oy/AIKBvXILzUVvRGBwcBG3J1yh6ZOc\n5QVO8nZsRyPVBu9sRzIbQhmk5G2chxaEloEEkX6TCQix9eIsToHfw5wmkM5uEHeR81586dUvoZNK\n9OtgMKvJQcQzcxE8BhsO1C1vaMSYQ2uR05V50VtJUOqkJTpFY8fRfFSRiXuaVVZm4zOLVtTn9WGJ\nNW4bk8BprrRgCsUdD/Opr0/6eVZyp512eoh0L4EaSGWigo2lj2ymoAyvXlUdRrhUSJOqjg6VObkq\n10HEISljcH3H1SO6ITcZORZ3oBKAatUjKlvqtJtupFCjOeM4gbn+bg5cppbWHNESKnmBeoVDzDlO\nWkpjwlhEbaFa9lhUAjW05gvy2Yg5qMrdJWzpNTH4LFWnRDuzNp1K3DtSYtTPEqpclpTwaQxwEzO3\n63iZV5NMGyy1CxyVRchShiL4BcfzRg2vPn6MR73jq9/6CI/e+lWQ44ovPL7B48evYl0HqD+CGEPA\nPSVaMmnY5VoSDm0A4MqVDIeb7mssRzOWz2VyRmppgJkxEzTX2Z9hUePZzyun33faaaeHQ/cGqOt2\nldt7xhyLFW/wndmdol196d64ZJKtRF5oSa10tO2bHiM8yaKn8m6OZzlAG2Xq4MvAi379ZEPdsiAp\nvclGxTxL10VydUcxUslN56QAB1E7ZxOXqFM289zil6KBlLfROdVc1DmPeqHUoZyZ53bWRTfhYwvu\nycC6dTCGzosaRICVLCMcue3W79dcdASSt4UEQNMMbav5Diy949Gjt+I1+kPwuIGMVVthn4MDpUN1\npgA9DZEzRsMzloU3GWIw2xLaJ2BrX4hynWefbmOY4JXIsvnbVO477bTTm5vuDVCfI9oqJ6NMICG8\nwEVOY399Z0vBDQKXPqMF+xSRznFEz/cGEFfk1mmqbxePSYqq+G6q73PRzxRVqWbpKb5ZTJpA1brK\nqegarQyQVXeqgEqsamB0nCUykGaIZdBCrm2M2FfuNqCQzbfN90lo9BumNyhSvEKrpHFjdRzzNKQ1\nyQhZdbKTxQFEGLR08GA8frzi+uYGb/u6b8AfrivGa4+x4oghumYzR1Tj0q1RaVN5VfP6QyzktCQu\nN1eJ+LTVWBl7KJk4mM1kyVwWzzWsf3faaaeHR/cDqEXlh97d8QkGsg3gAXe1Hd0TXbikqPmdtQJV\nh9AwyVo9mdVDu6E14IBS0ENg3twEkQUA4foaAAHD8mqq3bSn/ZtUNcwMyM1QrGYyN2GTyFWkh57e\nQMEcWPpM1tAtABDSOS8Wgibw8DIKYZ3HMfJdtyLdyQGA5dV2BkGaYGVV3LOw5h23c8RCvoQlAMoB\nkIUAixeHXR98kK32DNICMRV7DVlW/DsC6LquOIZsyiJYXfVO8UeJjyBWdTb6GisXfYvnuC7MlIWB\neT01V+tf9UdoNLAcgCENX/qjG+BK8I1f9TXAq49x89pjQBiDB4iBpTkEdmNw1PNeu9PnqDt8kqqq\njxiWUQ7oLXXwAsEYDHdBZzKvfCLQ0PulAM0Q994ezXoQjHHUaAMAhAFp6XfRqAEked1OO+30oOh+\nAHUlKZAwSUv5+YIW1y8/PSaAuHTu7VN6hEfCCpmvmVss77PwHUQTwM0NyqaGZpWeXPAKb+DQqG+T\nvthVF4Xbqi7NDNhzwoxqWEDayJ3JMJX3XL/7dMXlzOfIwGX9uJ/0bfdLQ5y8i1B/oNgUUNXTdWVS\nkrV+5Qi0DmmEwcoe3by2orEleDHdtDStLJZx+3WNaFqpOnuBFkHLGHNj/nzsmQxevzbTWEQhcDe1\nVHVM9rVR4hTietZOO+30wOieAHVu0EK5aYrvglNhXqTm9HwrMMG0fLPKRsgfCAgPYC6a0BkDZfM6\np8q0fdlBVmqQTm7pUrb9CTI3ds2GBGoWt1bW8pE1geZm0nX05PDjV5+m42BQrguAsL0T0ENVfrrK\nW9Zl7rkq9dsTDAFIoPZW6lIXhXD9TObul6S9DD6it4M20QSDCDevHrX2FA9dC4LW624SQO1PnMJn\nZTbmJwHQHOgTUMfcySuZ5snsjE8z7qr4RISZY/NUxRcH9rogT1/Cdaeddvpnn+4JUJv8tSluwGWf\n0hAicvG4ZNBSmqU6g8mqQRU2icpEVwdsUrVmFGSqgnOpujQlJQ3JcyOnlmOzbOkVlCpIK3RyEds8\nLaqDdRQBmeRqd0CboUpbZ8voLTF/xe/KIGykVDoXhnSJHbl8nkyn5Fy3OoST6905y2+q3d/Z+W0D\n2MZQxGrayQx9RkgY1AnL0jDWgdfW1/B4PWJltvKmfv+y0la6LfqdGcBmxQQSQO7JZCoDJFu+wq6h\nWOOqFSjn+ny3nFSu0mYNdtppp4dE9waoQw1LaiNtZU+i1gJcqaQpq9tWlRIJpZJi2GRLuS3v0Pb8\npRFW25ebAaXieQ+IFJNjCQJpHFsnxMzUPsSAc4/HtVENjusTxChzlLswKaqSdV5Ag5MczCzDWd23\nYzrJBjTrm0hThCoTMOsKXOoPydnVENbmae3jebXPqb4VuFy+3HqvXQDqybN+Hl9eN3NQ1VGekLeU\n6Bq8DjRmvPXqEb7qrW/FDT/G5//p7+O4anEP59M6YQqzm9OZVGjOOQpgMdzqzNdAVjda7+WN+ypM\nVwuAI0BeI7vWuUY8P6E8OrtKW3Z0p512ekh0b4AaSMBqUHtiAyYJhSLvNaPIt/P1QErLSPDrvVSJ\ngkneJugcOsLuS712WRW6LlMXJ6qybyZuauzvVgrc4Gq07nu2g0+9shXw0JY1pcYAJbAWqpHijVrE\nfrsUfiYtTJnAVmIbZ87cSphJqjbONZMY8RMcoGTjZBZdZs3puUeNc5+VwNYbq9OahrYvaHTAl179\nA4x1hfIrxiaRJU2ReT2qR8CclQxwvUbxdTxRTG+zv8Xx5sVK7Ao9mJ+LRF2fuJkb8x532mmnh0b3\nBKgNAKQm09iqA1Pau43CQjrt8QIi8662eOyM67VEH65+nrqoUqjkq4yrSnQgqAdzysvxw63DtrZq\n7QihapFMoG9Q9e1Jg4Kw3Ho5klrxamY5gJT3Zw3DWS7kBDDPS9TzLGnzfoGksCZUbsJJerZcf9n0\nb+wTmggGGEQN0lSCfe3V1wDWELxGxkK1BjRRaf6kl9n1bjvHyu0NSq0HQdBkHldK5KlZqQDsnguT\nwuAsNSjjtEvUO+30EOl+ALV747KgecEG2ylZBDwYTQSH5YABkxxDEquboOarXpqGVx1Zq2oRCMIr\nQJbryhzUWgPaAqwsFuKlGb+ECNK04lT46A5gIfvNYmxD1W3Az6yqURkuwQ4QCXoHhqgqmBugjINJ\nc5zJNoloSlayiqC1WrtY02GuY0TIlpDK3UKCxfZxtRSwhgNxQ0fDEJPISrKNIc3cvVgnCAKk6Vc6\nWDiQcTSsnIM0AXtCEhuVq35Xdo2C9U9i2gXVDnh0mDSEqaCZR3TVdqgGoCHNDrXul+BAGYsMEGho\nn8erjn5zg7e0BZ0HXv3SF/Cl144ahz0YwsDSFjShKF6iNu2aeMa8HKzYirh2Rlzr4tnihj1zXmCk\ngeRocrJ5lRtAH4+M1gitNaupDRANKzCyAsLovYGauRwyoUt65Asf0fsCuif/XHfaaac3lu6FGylB\nszotzaKPhYChlagIhKUvIBAe3zzGuq5FQVnaiOQYqcUlUtBSe7XbCKuEptsp+/lNKzH1JlgMRJpF\n0rr3NIdK1qNpaXofQDiq+8tlIcuQWVKCxgJcFDxFBGzVtZgZ67rGuPMFaAxwh0gDMzCGaCpOOULk\nRmPR2Tp3qbVzpLQUi0v3wQy4hZXyHkFLSR6woEd1LF2ZgRUdAq9LluphvX51LUFXhqcL0Njvy6xi\nRrBjtcSmr3PHIAI3gIkgjSAdkA4cxsBb+4IFwPG1x3j1S1+CMGMQYTQC23ncBdyA0Wqfd6OBIwSr\nLp+lotURM44AVlDc77ytG2NByQJXR+BpSr0ONxsz6s+A8MDTEBF1Ivq/iOjv2PevJ6K/R0SfsPev\nK+f+GBF9koh+nYj+Ujn+54noV+y3/5TSw3KnnXZ6g+heAPWWBABLZqDSEpKEMUZsXk4O0PE51KO5\nUbb4VBGxbJQkxYs801YS5uQbYeUNNTxtXgWAratQ2FI5HmOfv0/aYgLqnlgB+zypEhbIXOFSOYFz\nulwGRAY8U1c4P1Em0nSIDkkT5Ihijn0UUulW8Vu7Iky5Yewebye9NTOgvBcJ2qp0ueaFSNteoGlD\niQgrM25WzRme/ocJm1rac/ant9aLruDMXEJTkOdKAK8zNf7cJeMSaxM2lzpP45ZMpZCAXZ/S07He\ngX4EwK+V7x8E8DEReQ+Aj9l3ENG3APg+AN8K4P0AfoooErf/NIC/CuA99nr/0w5ip512+vLoXgB1\ngpmqGlNhinnPdjqzjwZguxpaHCAcpLa2zmwslcFbiJg7Ome7PEdVondQOhn+Fjtp834rbaV5v7Am\n1ED5van4Wq0EDGD1TFxc7LVypg2gzmJY5ak8g0rEtGyu1es7lbAzwibhyNMRSfrUx4us3nPTzGhD\n1HTARBD21zx///508Cc5NSAB1nQHbfPqZy7Vginz4a1EXWOs50IgdyMieg7AvwzgvyyHvxvAR+zz\nRwB8Tzn+syLyWEReAfBJAN9BRG8H8LUi8kuinMjfKtfstNNObxDdO6NXlSIz45RuXn2Z8yxPknQa\ntXXTU6NzFvI4kdKKhC0ML4sIk5A0POr0GgV+KW1Oo4+3tvm1+n9Vv7hLZaJ1WHJ2jlJPqnBJbOMv\nKxhMyiZe2vHYE8yQt+aOa9UVTX9xqZRFpnPjDKqe2vOqxNGC4QRgTgJO5b1ePf9OMo/MM3lSI5OU\nnVHTbPHNFQWobJlgQsupxTrQze+kKv9sRSL0rYWF2ttKOT7yfhdpulYU8+x4GUWXfhrVZ+Mp6D8B\n8O8D+Jpy7BkR+Zx9/l0Az9jnZwH8UjnvM3bsaJ+3x0+IiD4A4AMA8Mwzz+Dll1++dXBf/OIXn3jO\nG0kPZTw/+m0rAKA/+msnv738Z282B+b+H8oavV764xzP/QBq0/F1yz9NpI400jqYGUMGWmu4vr7G\n4IHBIwUboum1jhQYm5gQKbD41yotFrlH2JgB2zptV/Ss1ak4VclxTB64FVwUREo+lQCNOMtB2sB8\nrXiwVYWX+Tm11nAcx3KGi4kDvV+ZU1vOtTVNuDFuvKCE7/4CdAaPRcckBJGD9cXouCkwk2pXDY1a\nkFnPdLXFgMrjxFPO1/64GQhZvelDU4A98jYMrGoJKojna/AhzQaW9Iugdu+bmxuUKHfwOtAPh7hF\nrtClqjS4s64E6FhsZcUC5cRU3ICEY9w5kC+MiAQWB2h7/W33Gcj7TujNC4TcTaImon8FwO+JyD8m\novedO0dEhGqIxZdJIvJhAB8GgPe+973yvved7Tbo5ZdfxpPOeSPpoYznr3zwFwAAX/PNP33y26+8\n8lvzge//whsyptdLD2k89wKofbdgUTdt8gwijc0e2cEAbtYVjYDedHdutQWTnoU0TKaBgKYe0SwA\nDcEgc+iihgMRmjCwDhANrNzAQiAmHK4aWgfG8WigTSaZM0YDunn+OuhzyJ8EkRtzMioa99ZB6zpB\ngcCczLjENxMsMYvbSQUYgBjHIRDIYC22EeDdoj0eRxCRFouAAbYomlHzlKyufxWINKBz9NexBvgy\n2NDVQGMhQAbAmTM9s3Q1eNB7VIZym3UoLgRi0iggGMJYjUFrlL4AIoIhK7CoU1gT4MhzfDl3DYhy\nWzXMQ5vXVdXyVg0MQjg0TVrjmgVZc72bRQyskkyYVpRmsCXXISB87MYKMFZ1oyMApJ7p7NoX8aIm\n3r4CdzctxxDLgNb02RkD8HQ2qwwN82oNrRGoVAhjdlfGO9NfAPCvEtFfBvAIwNcS0X8N4PNE9HYR\n+ZyptX/Pzv8sgOfL9c/Zsc/a5+3xnd6E9G0vvuMUrHe6F3QvbNSVUv13+7Yk5gzEIhiiG/8Qq2vs\n0l6xv1IjK2yUcc7haCQSHuKhXnYP6TO5SufUIe79bJt1s9gjl6xEs5Ix6OzrVIxOyfGi+xBtxmRf\nPAVr1R3oNIqCmvL8bSPbVKU5nrODuPXouV+rSp3qEUFRV0MB0J2pIimJa02a5fdyRy2Jyl/D7bve\ndlVt3DKXNCi41DubGGh7cm22Lng5IY0CKnkzRmmvaVja1pkBdIvQfHfdt4j8mIg8JyIvQJ3E/jcR\n+dcB/DyAH7TTfhDAz9nnnwfwfUR0TUQvQp3GftnU5H9ARN9p3t4/UK7Zaaed3iB6cs0Eov+KiH6P\niP7vcuwniOizRPRP7PWXy29nwzzuRhU0gEyevHkJhYpwBmr2n9WjVzSkRXgYUOdLKmCauNYMrFV9\nTKV/IDyhJzBU1XnEDgOg1swhzk5isaTlG9ps7DMwTjv/Kbn3VCVGOiIBCVjmoEcmBdIEIhVWq5q5\nQqkzDZdoq7bOCdZZqcTqK1ZUzdRsiRjMJp1Sg4cwsRltFXMzBC/s0g7mHtI0rR7FOmVMtr9XkunT\nubnGXQp7sYGqu/MzlZWbfceHud/52qYrXAXphvk5OIXmJ6TNuQv9JIC/SESfAPAv2XeIyMcBfBTA\nrwL4XwD8sEjkV/23oQ5pnwTwGwD+5y93EDvttNPT0V1U3z8D4D+DenxW+o9F5D+sBzZhHt8E4O8T\n0Z8p/+jPUmw/4tgmAHFkEzuBMQJIsja1FFDorWsbjCKVi6ovfaszA6FfKaLq4mYGbTbQawWoFXiN\nO+gVzMhlcz1rHTlGACD1RG7nNASSccbTwQImKXkWkI1MakWVDVUh+6iECM1Ut03UKS3soNZyXTdf\ny+iNOlIuB7Sest2gC3Floa6HA3xhBATleK6cxn+zaTUUiHvv4FXHwwbSLWy2DSJW3drCwzL9qiYg\nqZKqkESYnzMP5zUVyZAIKugmqUImw/Zqdrsqf7uC39tLu7YfszWgVphBVyfoPSUqbBS5LuLpSURe\nBvCyff5/AXzXhfM+BOBDZ47/IwB/7nV0vdNOO32F6IlALSK/SEQv3LG9CPMA8AoRfRLAdwD4P+46\nIN17bbPyrWmrgxSHRz9siVKgG3rsyxZXTSTm2J3qzcn719JkSdlgg0PwLkuykLDNQko7NkCWyC4G\nl76IDETKXLafJ1nOR5HlLSc5S+rGXshUvVVOJlcdu662xPumJ3rKmSFtEyFjq3O2DiKl09qbfbL6\nz7ZaPj2BVhHrk5ytWbucAWmxZk2DwAIJ835NlTnrIriAipxrMFjnzq+MSaz/eSVTzG5+LPJHSx/q\nIO0si77XuDgfhQQA51CKNqWaN8J0sdNOOz1E+nKcyf4dIvoBAP8IwI+KyO/jcpjHCdVwjrd9w9tw\nfX2N97zwkv3qMt+ZrenMoa30sxFwbie5cI7kZnl9dY33vOPdZeznm7lE9ITft+eea/PSNMTG9+53\nvAsVo047nEch586h7ZdT5uHcep0qa+eRXx+u8MKzL5wc93N13JtGXVoNViWvmyT/s3RpFU/p+uoa\n73ruxQvX2hGq62XMDG3XsLKN/tGYi7P5070r00OIzEtrv+naPf+6pOmddtrpzUGvF6h/GsBfh+48\nfx3AfwTg33yaBmo4x7PPPSuPbx7jE698ygJ9VFHYWovKhya0FIFSf2CTaQ9Q71q2PNCrqXnVSUyA\no8VJE6MRoaNFZioJFbsrPs2e2LTDdz/3Ej7xym/oZtkaDktVC7vXt3trs2ZVE7OCi6ALaYpoIPdq\nV9dWgZAQXt8AsMqKJi0c4QhQL3CxmCQwhBgvPf8SPvmZV0CtpcqUGWye5kSEpR0s17bWs4rkGkCs\nN8hVrg3UF4BXgDWZqLQlPL6ot9BqwNpHAxpMPe4StTl/vfDcO/Gbn/00Vru7S+hACJCh+bdbhqEx\nBDKAFYxVBohX15dA0MCWSjPjjHXJqBFIXJ3sNm4GKPPLReyy3YKXnn8Rn/rMp+y3BoGVNiVd4k6I\ndPK8puqbCJrKFOr0Rvbe0dDJ48p17Y7Som9T3oMAcCezsxNW1rGSPU29HwAQXnj2nfj0Z3+76G52\n2mmnh0avC6hF5PP+mYj+CwB/x75eCvN4Aql44jV+fUNWoaKoA6FvJAQWK0aBFQBB2gFMQ3MiwzJl\niV5OXMKHrI6lF+sgEEgG0lpLVkxBRTq7DFjSQYis4tOc2RqAhzVhSQGRzA7NWZzRGQQSirAp1/AS\nxFJtKpTN2cnVcQ6dc25NQWMhDd0SIg1BEwJhcf03bsZq6+oKaAB0BRDDQ7hq6i4+3sQECIQ2LHac\n9DSilgBpMKLOfIuyTLRCk3DrxERqVW13biO0q4OGmwGaTYwHBq/lXgMi3ZOjAjTsDjnUW15yEbRB\nFtpnZgwSEAuY0s4tltjdndJAhIYDyIqESjPWy5ghaa4+JyscbYrrsG6kc5mI+kJ4ZLjHszcZul5N\nC6AEO7iqTd3vcycyRoEi1FAgYOLIxrbTTjs9PHpd4VkWg+n0rwFwj/CzYR53bbeqNDPkKYGqHo+x\nlCNpz91uaHPYUk23fNtLS0Ns2842L70r7JFtwdVa6eqAfJ2f41YBW1XQl8jaolkBzdD48WyhupBJ\nSGr5comzqrw3hoVb8aJem5/JHPE0tposjK3jQCo5MgvWMbAyWyQzJTjbKDXeukXOcGfkpvWSMpMI\n7Upf8zRvM5pnayvrc+Lzd2HpNwYB/Vx4Npl+9J7LS3i7qvaLRSJEA26/V23QTjvt9PDoiRI1Ef1t\nAO8D8A1E9BkAPw7gfUT07dD95TcB/FsAICIfJyIP81gxh3ncQkW/jQRjLtvqZL8r0JwgoqCTe6W2\nkA27ZATzGi9t2vcYSZWWSgv+aWNNLL8LWKjoAzwMbKjM5Juv27/rzn7S4ums/ZgIxfhbLtqkCyYg\nqoJp1rBkhLJNzVaWq1dWk+ra+loWZmXDXaSknJ9qjxVi3IPbV5eZLeMcm7w5ayrSzc/XzNc2V0m1\nJX4g11Qd5ipTYlJuuXJaErhmY0MyvU10coekrDP5Vaf3sZo5IgLBrmOLOnCoviWqfqeddnqT0128\nvr//zOG/ecv5Z8M8bifaiJHibYVUqFtdqjvdludlD/SKrlLSySae0C2SoOy45iHJvkFnQYSIdkWV\n0mub3m5Kjh2CYcC0kYhAaZS2vJe1FIZCFDzIaOqtkguNVBaNSGfuWKa47eFNYmFgA+mXDMCybCVM\n+KypALXPo5W+OA+frMSwM+s6aElKlYg9E5ne2VePNyDmyNGtV3YMrNqye6lTfUZ6KJDtf72OAJHM\nae7KC89Nnh76NKUZrdJvCOSVlynTOytJl08qlduIgnlaMD3gnpGtRcqWssom/VPqNdjWYVsac6ed\ndnoYdC9SiCZVyJP4VuHiVJ7yT1zOnMkBcDpAucHKhfc5eYb3VqVLKe9+ZYfAa2YnW3FWJUyw/OLn\n+z6FhUSRrYzm4JomA094or+POE9C3leAzujeaGda8ToOW8VbVbBy8lkA9IZgs0TcSs5Y14FO81iF\nB0BcCqpUMJNgsBTDs0iH2nZL/wK12ZvXQTWbiLEKAEqYGgrwb6azebSqvsGJCBGzLWWc8ePZRmn6\n5K9B6d3Owpab/PzzvdNOO7256d6lEFVSQKrpJvN1aoGuMdX+/aQtO+S+aVWqFpx/rUiAS5n8dnI1\nJdsrFfLWiuSYst060lPmYEtblW/t3f/6qypXT1uVGKvCpusp/MraY2316alTakO0L80kJ0OcZ7E/\nDMGYAdf7JWe6ssTlNKpYZpfEYdfkiuUKbZLN1KnJ5vNWyn4SSZH0w5i+Wf8yv+390lzieV04r+1A\nvdNOD5LuhUQtptpLJSwAEbTedUOLeCJVIRORFlIg9fnV0wkiR9vLOIQ+sQ1+ZVWSN9JmVmjBBxKA\nllBaQgSudAXx0FAoqGqZbFzcu2muVYJjtlAgCFof4LVBwGhNU5oOaeDVGQYPoWogIRzQgK5JUoYI\neOWUivsCkKk8PSwLhKUZEDBBVp0Q8YIr8xIePDBEPdDJvZzZ9AoEAAMgoKGDueohUpwMwc8fAAAg\nAElEQVR02bxZpi8mQfNqUdQse1u9iw3LQhBZADDYPJ0bdL4aUqVKfWdgBAJqrsZG9M0kYFY3vhYo\nJpkLHAIt2mKairECMrBIh7SeWgrR37qUxDOWLaVWJWsCABoNQM1U5+741hbVOtCKBQseUzIJgqpW\nh6WyFaCpOaYz47A0rGQeAuJPVgO6e+DDJwiBM4b6DHRjRRbqkD6V+9ppp50eEN0LoD4hk4iaqPdr\nI1MFU9qsJ2WybZ7hnFX2MyIK5zGUa6rAtN5IsesmOFBD2JSrXbaPFi2JaIpKVwY3ZgVFEmUWPMva\nBWFoYITVuFv4lFgJhw5XcVfPbB+9gjtryjQMOaZ6AM4IeM0lQaceEngAFAiM9fzAmlcIszmAAKsG\nBeIIVQopnihsqA7gkTNGoDHeIhAZuj4mIa5joHX3WKd8Ndef+DDSAazXzGwuShMhTRItrw3HtK2W\nIKV0QOw+GwMAUl29sOYgjzKoHUsbIcl7sdOQ100zUH0ehmwFYYr79CT5eDdJ77TTTsA9AepzG5ZL\n1Q7IAQSUckich/QkjlDg0qpu4TTZff2vABgsVujBQMfSRVN4ZntD9juXVqSOx2J3af48q7/nWboG\nYbafbiF5VnfrMbNhEocqGR5rGyBACjYQdbKjVtowFfclDiImlcUksqSmqSXiHJpVzyggXc8Q0dKR\nLJatS0JN7JwS+TqHITrH43cw+031tkrqLpufKrtPGssL81gGR5smoSjHjSlpztCVV7aaecABYIWX\n29yyh5sLz9DWcYwu3aeddtrpTU/3AqgBTLt63fhSMoJJcXJyXZXETqUQijZN+Zxq7tKhCXgqTAVo\nFIcjcUk0pblQ35aNP6pcqm5Z21EktXZ8qhWGc5xJdXOn6VisSFkOMXEu4ECy6AhgiTsiUQvFfxeJ\nDaCJc9AuQfs6J65N46kSdcymYKTEgdvQ6vLYoiCHn0d+j22u5VztpfrRV7bnslOcy/Me2sa3r1bt\nLEFV9L7koz3D+pNpF6l32mmn+wTUTrbTZ/pHk1E8ptl0qe7kFcrM2Kztqx+zzFEkLhtKXJfSbErP\n+lmzboVV0DdcByCkQFnbyy61dc9a5ZWhzky2AGYmGnFpeC7zkKBTpdfKSKjUmfJ4CIiu+zfpXkHc\nwf4C/PA5m6gBvabvyutFLjBJZaZu1C5CtGoEKOaiQ/Mfdba+/smg1DAlA75oo8XsfYDn2L8T6dbP\nDJ7IRifKrAFAI/UcZzkP+dNc51bTMS5unNxJop6VObtEvdNOD5XuH1AXGjJUldgAoo7mZQFFsFq8\nrpYe1JrLElIf4IkxHPTMJSismIt9HoDFDplk1lI6H/zYQEXA46hjoQampYC0qc1dLcpkDly+Lwsg\nPIcUFeqFZRjmZpXpMRNOfF/XFCUwr/IEEkhDpMUQzxtNlnqyobuKPARZn8EFx//mJ/raeO8SJogJ\ndMxLb2uf9u88RrTHpPZ1EUFvDWRMhIOjiNn5QWAiiDFOnjGbUixHSNQEdGmYLcfKjhy3ccyxokVL\n4DptUYbiOLwfTV9KpKrwVRDPncwtxT0SyvtPImhT8hjkXM+vfNBuo95pp52A+wLULg0LzHtbDx/6\nIwgzZGV1r+pa15lEwcwlXDDbZgisIRUDMNUvi4DaChIt3AEhrOLFKQSdGMwN1LSIxspau7m1K4Sb\nGOm2zyB0DBxFsFrRjSvqUBtpww0fAyAxYAxERxjPhVODDMI4qPc3MbCIaL5yMAYxGjd0S5mp6msC\ncAXGEQrRLdKUdiKAjyEpN2nwqk0OWZI6gpQrL6ABGRByZK52WV4Tl6i92dO6qG/A4EhDgl7s4Zqv\nejFpmlW6BgHokOUxQNcmMXqbBGodaVYYgDiTRSDqCu4ylPFgQusD4bXlkmszZzwaxuXoagmp5M4W\nqzdkLcVfDIYNXDtpERFgUQ0Pr/EcpOe49rNYLXQMddDrtk7izAzMQY+N6WHvR2ZpPhBfX1qiNQPt\ndtppp4dF9wOoN3R2O6LzP4ZUU7SLVXYKJae42tcvUKA/X1t5bp1OfnNXrFRKp7167jde1W7p55hj\nlR+Y1LYMEKmEnbnWMgmMUs1r5r7w5NMrY53nUBO5vL6t/4zaGG7RzbSXNP1eGQKXhE9HkOOqiWJq\nKti5xbrGjSiYIG8LgDJNEz9CCCH+UmuSa+vMxnyHQ7afxo56hkn6ajbx+WZ34Q2+fWhOdOrOgOwi\n9k47PUS6l0B9Fgf8XRyIbNMUlcQbo6hHgdju3cnK9l2XoggNrZnUx37+tnPeHHcISiBKOVUh1VOA\n2tBCTYrWJn2wl5vso0hUopt6eEd3ilSSZBNtpNWUYJoBVz8ID0hr0+ipbcCyxgnFWr6ezb8CWx7z\n/86zN5LvVJGotpHHaLpynpeekmcQLOc5NZzMSNJXPSDfGTTHzeoN5+O2dfV889U/35kBAdInDoh7\nYZyZ4TSpVii0AzY3aiU3eRlv8CT+9EBntxfk2OnLpBc++At/0kPY6XXS/QBqz40cGxYVz+iyi7nT\nkO2U7pSk2kTRBCbTjp7tar4U2aSONls0AFi5zNwcGVwkuSyy4QBMpsicE3gsrgp1iY3Uhi0O1Gxl\nLkVb69ZiwEFxlBJqJXuWycsCiPRcN2tLgLCNArNWgQBLFpJQEesplwDgNgDfRhD7TXFATWYmNRpS\nkA0hTbepkGfONBNo1lIV3pvbtSlAl4Qw+PyYe4tHBtTcjn8Kztqbjcs0MBny5QBd9Sg2uhmfMUci\nADLYF2HqMxzntlK0zCsSTOEuUe+004Ok+wHUhcj+0LR7SYK0nyUmLRe1sUqytok6giMwwSQZ+AXh\n0DS7Bvl1bkedwS9BNbZuzDWp55higea19rEG5JDpPos05kVIyJza+IzUqwlW6nhNYxBq1rLvW5+E\nTBjj1ziTIlXKnug2UCj3ZXPc47vrmswwi3qTUW9IbXW+G96anut1pLfnu6d0bV6XkuIAW9ga8xye\nV8cAiDE9s8NXcw4vrqHp+mYOackCSErvVOZZgH3qf6OoiHY8Nn/3/N5ppwdJ9weoq+B864kKcmL6\n5QBLcpnOYdZB2ndRe5OUjCOdZEBZZhzTTbqVTTeBJ63BNAHIhD8Gykz6WorNEw7ABFAzzQAbsKoL\nuTpCiW3SHrRsmcaEe1kon79E/w7QEvZvdUSjCPlxtWyGuT0dbe+Qr+mpJ3NiIJ2cj7KKCdN+XS3M\n2Tbt2r0gS4ES5SBbdd7Pd4GG3KFBiDBkYDS2rG76jGz79+Qwlc3ooNRwbKRggQH1NHkbvZlYNPkJ\nWZYzStytl3m3ozJod4ri3mmnnd6kdH+AekMXhYeyQdZTql0YmCWyAFBPWiKk3sewECHqwHajrqLN\nRKcuRfo31aHz2R5SJSeDIhgoR+UPBwECNcIYw6pFOwyybtlVFJy8hmcNg0rfdl6Jwsqkmq93878M\n1FUy1p6LmBhajhR7z48hman5rswha/V3ArCYqrmRugQ0MrX3MA956rq24ZNgY5N5xADCY7z2+YQU\nMeYNv5HDRUAG1J7SVaV/l9DLUApwVyF7sm3vtNNOD47uBVBP+OVOOATcDJOEmh3ndAgiEnPSAYCm\nMdYQDFHUi1hmItMe95CyIIJx9K1XQMRYDlchRGmVxSXUpCDBIA3/ESYQM9Ca5qMmYB1a5MKTfhy8\nX+tB7Z0LVAkuYVMlEYBXPaepTVpg0jULFiGgsY8SQAOxgLoCi66Vh2fB+vGXi5O2/7dmmcl0xdnA\n6jhWrWxli15lWpdSgdBB6O/cIF3VyMS61q0DaBJqZlvmkCqBYXyIhleliljC2crv19JEQ9BE59yk\nhQaAKJPHkDQ0ETBreBXLEdw6eusgamABXrs5AjzQW8fSGa0tWhODOxYbZ+9qAhAgCn8MUZm+N1j8\nvUDkiAVLaA7E7OR2Z3AcI8wXaGSMg4btaeJvA14iYGkgHvC0ryvYlAQacsdeP8X+jLMJaHbaaaeH\nQPcCqAH4DhZf1ATokiIQdkP3BnPBrApCBI1NdQkpBJAGYYG0FGCq9NI8yUkdS9PN2lXFrfWwe7u0\n6hLwnJd5lnpSeeksRWYF28pxzoRABGLCtmpLU+SqVZ+2matuT5AhCP9nyVG5wt+X2UHJ026qht5A\nnmwmDcDWCYx5UzR1k0ilaIwnyfFU0TAJl8kwlOORvQzTxQIGpIF5YKWhDBk3sFi1LzC6aIwzSZoK\nUkSvDooyS7twbYxflBqXTKuzJfIli7HGqvDmOdx8rofl7NGddtrpodD9AWojKWBN4Cwn6SgChJRG\n2Jg+BWoQBhB1Ll15KKwSYHEsIuItAugVZOrLEnvTWotylqm9ldjPgy+gHEpVX4oxDzWr1SBglAlM\nfmORQlOsbZfeKJiHk7XzUK16kArwIJOgAAIRngKfqhp5tj1IDgmk2gyCxgbbD8lMbDov7578q64R\nl3PmTzWOeW43vmUu0vzNQtY4TBtLnMpDIOzaD+9HpV7TWScTRmJStj9PzpQRNiscY84o8or/dn7V\nBJCuYfgHnGGwQtsQf3faaaeHSvcOqDWTk21usT+Z9CoZVuNq6tnlB4l2mlsUvuWxSIRpVUAhSFwi\nBUACfSckd6l4LqRY4ahKyCjHM2eXx9EigCEkZZjbVBWNJadFILN3nm7cFvml/U0cgp/B09xVDc6R\n28xlaGUmBIJqt1cZW2KEK6jMR2XVc0A9r0HckmnMpWSonal9ee1mPxYrF5qG6UXQjGXGmehhvaKR\n1x4XDY0jHcg2KUwkmT1hOjLcDNauxKe0HZ9xLZxvSHU22y7VE7F4l6Z32umh0v0BaoGllkyo8+IY\nDgZRHUkFFLVd+9mOUt2k5LZEuxCAaVh7PaUdk1Q9Bjskx1JK0yXhdR3Wt3as+addly5wwzoJn8MQ\nNCs3OQwY1bFba26LAzelkAjR+Yu485o5SrWGwWusg9tEx5Ap2qtgkLVnGcJL3LSIR3Gnw1SMucTs\nSlEJCwTEFseModcQnSRbmStTmU28pZrdPd1b5xKr7H00NPSNPOlMAgE08uTSDfeeOc3t0T7QUdfF\nniMO0M/5ahlQXdtOWgN9eL8lHEu9w73wx7zSGRWwSTIDsofT8tEDcZPDh3FL4rXCxVbS4w92sN5p\np4dI9weoAaT3rcFHr45MlKe4NhKnEho8EcYU72rgS8Vju6pLHQqmzF3iScRU8mMbEyEKgLD1wQ7S\nROinwvDURwVvBSxFV2dRVhj4CvDozL5MoGJj9smZh7erVk8vwxx65ircNiUedYWzIG3U/pIoA8Ka\ncEWmBVTG5QR0sj9bvmlN1WzABZLyKs3KXgO+yNgbQrjJb/rTED02MLWB0Qp2SduZPmf0fHxS7ogz\nh5KBa85YQDCbWsrv9XP9LkCE5Cfzp597vfhWiXpmDXba6cuhr/nmD/5JD2Gnp6R7AdSuUBzNbbAG\ngK8d0aijdRXFqIl676Ljph8h6GDb9TyLmF/bJD14BILr9kg3Z1EQ4qaSG0tD4/Tu9jbUgWpFMxXp\n0iQAlLGC5KDVmgjgxiplcsfAik4qNXfXABBDeFFlMglurK8Otr4s8xkrimnFKC0awQQsppxmOYJX\nG5sH+oiCV5NuFafKohaGRrqAV1d/a0UsGoKDOWZxQQKyjGjasoAwAHKYVg/7qDFi+NcBk0YN0kXX\nsblXendPcCtpQSpR3vBAbwezX1sIGg+QLKmiNvsGiRbtONCi929Rj3GtFAaQDDC0JGpTdQkGrgEZ\nIcmqC1yqst0HQpqpwtcVPERjos2ZsLUGdMLKjCvTcjDUnOKx3EQEGepoR7Hu9vy1FcoUWYETUn/z\nWskcEYgHRPKeYDeUhXqidnynnXZ6U9K9AGqXu9L2q99vFrFUkQ2NCAMDN7TiRlYsNTtXFcShG+k6\nVCJqZrvksUbWKj1mqtpGCTqlb900VV2co1NpruEAjYzW7daTiAKCg3mHsyUwYTCGMJY+rM6Vy4qA\noFuwj6UVBUM8dAstArpqBmsBoTePxbXsZSoIxqhvXeh6WhWlN5f3sFwb4xOgIVPWUVdggIHh4ypd\nZR70GXa8u94WNNHQO3e4or5AYKFLoklqGhE0DLqhtw4ZQ0F6iDEELnFX1zNlCgZSHncF/aS7YQ5T\nyoLMZMZl3A1eGpVL296S3Z/t8geyNkg8S878IDUQsrktBA07Kw32sm477bTTw6J7AdROqoxN6qay\nZLCCklQZed4Ya8IREU0RSYDaThuZyKidiHjccDOJqGbUTrBGlthARbTtVh81oUNWg3lBy3S+bvgJ\nUwrQLkGlLTpjkWXTK0Uvis4JpDpUOgULp0vi2BakXRqUXNP0Z04WZqvmpqK+d0Vt9VauFSh91ALR\n+HfTJGg76oUttKpfF8iAWgGUWkn8wa4/dxYox5vv/ns+X37X6lkJkoSIwgIFtPp/WaLDF9VncmHh\nBaVEZT5Z+kOq1TPNbRlHWVxnNHbaaaeHR/cOqP1dABykmxp12JbYQi2N4vBVJcMpptltxwUec3Oe\nN/ensQFSUUySjcuPChOEzGWpadGNTqTpPOFbeva5brb8BpeeHOi2G/wMgNNczzuEX5rErROuP8nm\neBXMT88Ll787yIBtc06yJBN4bl4SedFPbbdnlAPl2vlXsY9i6cyIXLPh8eSZ8b2+b9s/6chJprfT\nEXpyFDnV5mzbpzus5k477fTmpHsF1FVlCgBNGlhW8woWLM3AlRQMqycZNRWDhC0zWTPv6sh01k53\ncckNmzbvSrPncvQV39nk6Yax2Y7FkrV4eFDWm5rl41ETb/h2bOMTOZ8/mz1feQk/c3C8iLC1JvP2\nnPOTnyXiIgnWMQIIk0Jlg2g6Mt0qUBkzwgnOG7ESn0Kh+jaxFB6DNjCCIfNe1FaezoJS+mlFOq12\n4fi9NSy9QRUvw9zm3MWNYkazjqQyIrNUPtHmB2cY1Clw9nafLqvPNtXwuZ122umh0b0A6ioz1ppV\nK27ApPbDTg1LW8CdwTQ8x1YigEsvY2gaymUJRyYA6N3Cmew/FpWBtbRzbrTzvjqQtukMHSMcoh1X\niIZFsy2abIPtVyGwNBw6Y4WW1FSHJgWUjqFtiIZ7MZGFBkm0nJK/vh9ZYrAemktizIvTiWR9Bkbm\nac3CHnmYWVXz2imq0CgMhh2DQ1bK/VupeCsbCh8h7aDOXN7PUGc970TIXKmG3rOjx4QTgJ7JTcmd\ntaIl1ab0MoNahsXv7KNlwWHpaA0YgwExXwFoKVNXOa8gdVaL+15V3gRsa0aHRM3RJ0rPrimaNBat\nziA/V3v1Tjvt9LDoXgC1S5pkVZA0vSJhoAPUQU1zWS9txSqCIYBAY14BzbAl3ABu6HTQMKkIs9H2\njzePdS8lU5+TwiWDIDI037WpyAdrmxALvbIRNlog1LCOVbdbk/YGALSGLog83R7fLI3QesPjMUAk\n6F0lx1VMurNsaWSqV50La7EQi+uFgWaTBpEbzUMtli3NtvlVGJ1N6iNA62DnOBotOBJpxaixoreG\n634Ad0BkgHmAebEQNQEWd6oyoPBkISJAWwFYgQuQAioYjVsAz4CUlKtZm6xGzgHAkIP24XpodGVm\nLJRO7bf6ecWpc5rABG0B0MWYs/QFUOe3VBU0LxlZ4HDpDW99yzV6I/zRlx6rbbwluAszhIGVgIbV\n8rQDq5s5yFwL2zZFqzkPUrPnVj3/F9ccUDNvdwaz1kMnWkCtQ0ZhLhgg4oho2GmnnR4W3QugVpqD\nVXwrrvLXgBf88/q8fvY2z3XKWICqkKk1A+qUTwUKAlVYuWSPfeLoJ8OtS7/wnd4c1mgKx1JNbpWX\na8/YHNuM5dKmTUXtK3lALAeYX6vmbM2NHeeZhiLVxBfUubGu1rwgmKxzYy6rUeaRQFrPz77TBi3G\nbYV077zBdpz1mJ1Tk8CcnO9jI5eP8yChXEvTba2PXb6FYmdjAimz3cr6MeDgXig5j1va2mmnnR4W\n3RugDrUnUlLyTbGZ+vNom5uXq0xlqUlvorpgISsIaVIr0DJ5CiWAklTVZfafG+g8vtjcN1LhPBGx\n3NHelIBkFJu6qePNtdgrenn7LhELzvuGecxuANomv/d8fguNAsBW6jrXQSCmokexIkgB+83USh/R\nD2f/NYtcVXQDpaQjEEle/OxzQM2T/XZ+LqbENFJAW7y/2up5dqOOsJlXtmpRTot1+L2ODKD1+DQU\nmZ0Z43RXtVe7tukZ/GEiY5hYQvUuW8DecXqnnR4k3QugrrKWS00MgCwtJRHMdotIv+lyaUpdah9s\nvetGJ8Msgwp3rXXti4Dwtg2p3aHMt/nikFQ26dinS2Ku7b6sCS8amqOtDAgPHPoSoUeAAhGDsYQq\nnhI84TbqUxAWqMqbrHNPwgIRSKvsjjtA6ThYGAuWSMsqBAwakSwEsLSgFhgdOdVP7hFydZzREQPf\nXq9JWVrXvTpjIWzcan8+pWAgbO1npomijRmzbWLGBFUJeUshzxIA0oIrbCaH1lqmkaV8DhRsk5ki\nS4ka6VWl6kcANpt1T/YIs4xtBgGyl6fQFYG0Fjc8HOd21fdOOz1IemJoJhE9T0T/gIh+lYg+TkQ/\nYse/noj+HhF9wt6/rlzzY0T0SSL6dSL6S083JN/sTbLzvRcWtmS5sTX/tO7gBLKQaIl4W1XpulrX\nJJ0I69HtkpFtp+wi26HYnIrkWyRqT4XpVNJ+2/6raneVnKsi30dAF1+T5Fp/KXm1txKc1E+WUDvy\ne2+7sDKW7nW9zZXl7Z2TEf3NzRCtEQ4EHAyvO5G9WzuUbRHcs1tOxuT3O0FUF5w8hEpvsC6wfY8X\nXNOyae9k/GVeokzBYMEY5uftEQSoIGxTDvC0Loy3SfV/+c8emK1uwEuJxiLmwhg3MDvcBfjvEvVO\nOz1IuksOhRXAj4rItwD4TgA/TETfAuCDAD4mIu8B8DH7Dvvt+wB8K4D3A/gpUs+ti5QSRpUYTC4j\nV/cCBxSJxnHIJCgHRgdEoIOk5KJiAyw2r17UTdyhu/YuAcp1RFugPjcZBS9/qRQdW7ShiNupTxvP\nQ+ckTfdUngdQJG8HKGsh83N75rNhHt2c+c8dDDctnsOFKmETJITB1pRBisQkfh+otGcOYqpe1oxu\nF9mU6KgAMSnTk8538+vkBpX1rFqJSgJNjsNDgVq7pMzxLvmsQaCO3Q7aSOD25CwkZBXJMuLAHeC8\nv1zbAtLOCTQAnQL0AWMAZAbvnXba6eHQE4FaRD4nIv+nff5DAL8G4FkA3w3gI3baRwB8j33+bgA/\nKyKPReQVAJ8E8B13G06VR5D7mO1P3cBNJWo7wTczUok6N3uC24NJLOEIp6o49sfEEp3vxZFtP8w0\n1cmwVFPpI9TANEIV6jNsaPOmvZGkL0rUFaQ3xTHqTObUHQLByJcjDm2vlTPFNbbNlzWHMkfNbANS\nJFmmLA9eoZKmVzIfE6/iyWo2UvUMyPOLKkCfrMfle8umdThRMVepuoL15uZQ/Tw/wXAntenxQL3n\npYO6qAC2wLwD9U47PUx6Khs1Eb0A4F8A8A8BPCMin7OffhfAM/b5WQC/VC77jB3btvUBAB8AgLe9\n7W24vr7Ge158qZ6BSQ1bwEkLO9DJRnwrbTOWnfsqHvtrbds511fXeOH5d1s7yH7r5833u4D/KZVO\nLx4pR31819f4M+9814VzTw9uQs/n8y7N7cw5pz/Ps/b2r6+u8dKz7zw3sgihO2lGTr+4qvqsNoOA\nKCxeD55pv15+dbjCn/7GPx3f3+aj3zof3Mk+HE9PjtcX7EkPwQl3pK1cH67wwvPPnz9lp512ehB0\nZ6Amoq8G8D8A+PdE5A9oBk4herptREQ+DODDAPBNzz0rjx8/xq9/6jd0WzMJSsv1WuqPpipk5gEe\njN6LExYQuzhHUmmKfRoEDCvSkfhOsfEthw7hFYMHxmAQXaO1BSQrQMC73/kiPvlbr8Rm21qLmGK1\nz3aT1NnyOmcBDoBwxEC3PNzuOexSPYtL16ZytYpZAkEXV/VL5ARvAqAvWrubGRDCu194EZ/67VdO\nNKnCCUrCNlZTT4MIRwCkQeuxhn5bvTJUrGXxnqtOfmWpoVUz7D5YeyKCl557AZ/89KfzbprDml4j\nk5rYZmtOdyqai2gmMjb/hB7u134/bIhM4YMAW09qDSSZCGWr4H/nc+/Ab/7Op+NZaLYWnkpU7AFy\nM4lWKPNnj22IphfgMcnTPpIRonlZMOs/hH83QcAfZW3zxWefw2/+zm/P1z+BiOgRgF8EcA39N/7f\ni8iPE9HXA/jvALwA4DcBfK+I/L5d82MAfggaBfnvisj/asf/PICfAfAWAH8XwI/IOdf2nXba6Y+N\n7pTnn4gOUJD+b0Tkf7TDnyeit9vvbwfwe3b8swCeL5c/Z8fu0JHvYQZx7RSI/X1WMVKa+ay8Jbl/\nOFERRah2kqDmmy80O5gnbKyOSFVdrsUwiurWnNiILAlKqEop3l2HW53gnFmYtl/X3vpQ4bbytJlr\nmchRXgDRmHS0mQvbwRGTQ1cwRD6/zfsTb9QJuVnBvdD9zLx/EmtDdivJAFEnHDHuUqtLpaniducA\nH1r1oHfJOAeUqUDLMSFlAtwWHDHhNOl1HCvDIzzuIRVHsrpK+kzVvi6vIZ055mM2/4K762YeA/gX\nReSfB/DtAN5PRN+J1+dX8tMA/iqA99jr/XcdxE477fSVobt4fROAvwng10Tkb5Sffh7AD9rnHwTw\nc+X49xHRNRG9CP3H/ct3Go15Jm38gBJuwvZLmkmsAO4QFQVAmhbFEZACVNu8z5cOxhgQ0UKGjRa0\nxmhtvTxOqSUbjDEgQYOgMUBM5WeaMkvWq4AM+wpgtu8RDlYHTLkmVMDNv4ckHSBZltaHTrNgljPI\n1+vNKe1CPm8bnMZA0/uUgrN8djDNtK+2BHT7I7uFO+bLs4lfznIpF/pRg3zGjDEBbBXAgq3KpihG\ndA6Eg2XavOpsttc8mUTpi/b1YC/BU/qVGAP+tSLySyZF/61yzU477fQG0V1U3wXKOk8AACAASURB\nVH8BwL8B4FeI6J/Ysf8AwE8C+CgR/RCATwP4XgAQkY8T0UcB/CrUY/yHRWTcZTAJOrrluYOPWG7r\ntmgccBQPDnLJCEBvAW4qlXkhD8lCB55aM75qvWsFKZP2as0PKUK5ajhLJjIKfbagobdtaURgQZHw\naqRUU2nc92MBUsIWBaUmDqyWTpMELAcANYkKQbBorWTK9lvJG81DG67S9KRsqPcBT4KEOae29aYh\nR+JtV/lS1dXiN8nSpQIEah0YCqihMegdw1XPxnm49zxRU7X/BQqHMpPumRloC3zmEoFROmb96JIr\ncLT3JlkZ3e9uBwDJKH645zxcIzOH93W9At2yjXusvzhzZ9cpzeltWlRnTybsvCbj4jp0AP8YwLsB\n/Oci8g+J6Gn9So72eXt8p512egPpiUAtIv87Lu8Q33Xhmg8B+NBTj8YBVhyoU4ULpPQIZPEO6C9o\njoDNNtLAP1diJq9QFeF6SVe7KRjgAWYFhdjfbUwJqK66deaiGaDA9tZZjootv0q7ZMcz1HteikBT\nu3CWE0OdGpInZh167ZeoSI+bc7byW+FNbiEfTwUata8TAsumRt1pzL3sDadBZOVMIie5akoy3n3D\nDhBwJ65vojqryDoecxCSSJ7iNc/PycAEWCY25XCkvACE5sSf13RNzBlwyVZOp63DV74+QRRrfXcy\n5vjbieifA/A/EdGf2/z+1H4lt1F1Dn3mmWfw8ssv33r+F7/4xSee80bSQxjPj36bagn7o7928ZyX\n/+xN+TL3/xDW6MuhP87x3IvMZEqCIWuAq8sTYurdRoTGus1phrI1onAbyDKPEcBaC5pIz3WJk7AC\n4kU41DlN1cuiyaGsEAhoidAlYqjERwReOkQsXzYNqxal0rTXzmIR8GAsnQyAGEMEGAO9HVSSWsx5\nyjQCR64hZbkdM2De7ZmEhCwOiK0oCHm8MgCRgdbm1KUcDmAAOqEjc6IPdqX3AapHEKAJhNSu2seI\nwYTpwQbZ0EBLt9yu0IQhxxW9d0T2DzSEF7aIZRq10pyU2gNFbrYiGApsPBpaM/s+Ab0temtFcBwr\negOYm9n6Re8tGMwHEK1WHKODqKMvun5DtHiKVmLTki9kz0Yzc+wEldKMgbBnQfRJ00QnDI/Q9/KT\nYs9aA00uEUJmC4i10xETKDmaFosRoxhDIFgBEazMukZ3F6jLYyD/HxH9A6ht+fNE9HYR+dwd/Uo+\na5+3x8/1E86h733ve+V973vfreN6+eWX8aRz3kh6COP5Kx/8BQDA13zzT18851de+a388v1f+GMf\n05dDD2k8d3Im++Mm2rzPFr7wiYZDWqYO5Xi5WnOWPGqhBSodSDERUjo/ubSLTTN2DcWPDrTeYFqe\nq7kzm6DpnOlVg3Q3L3IALSMIH2l3vArWpuziTyUnbSbvnwv3oKaDhuav3oKrSFX6HR6lM0AzS64F\nKmsss3tyE4pWJZPKAFAnPQBwE0PM5fw71e/z29lxObtUp7A9/+zFUg/Mr9AqbNqIGtzTE3ROxj9P\nRPSNJkmDiN4C4C8C+H/wlH4lpib/AyL6TvNV+YFyzU477fQG0T2SqD2ZiafpYDTqIALc/bRulgp9\nntYEJtfUKsizYw+13PgrSOuh9FT2cosUP9sPxVYKahaa5UlN0iNbPZn1Og1RsqIPtCABukXfhDFt\nwS5V67xc/evj1Fkeeg94DuaFeqYKRarYHUznjV5CmmQZSHWrvzg1BnDJPW2zvQuOY2Awm4SpGg2+\nzQ3tBI38TZA6hZLfG2qs0OIcah/WdSQr++hqbJhk3dCbFUSx+5bmCJufSHHSyzGILZLjo95LthG1\nMq60Fk8gXfmANC2rh4ZA/SbOLMFcHdu5BddIpHFH//Zy3RPp7QA+YnbqBuCj/3975x9q61Xe+e93\nvefctGNT0Unnovl5naZiahhLgyO0DBnKtLb9QwtDMUJNGacp9Qct+Mek/aeBIoShdaBDK6RUmkJV\nhFYa+hOVXspArUaRxug4pk3U3EaDdVAzoLn7Xc/8sZ5nrfW+e+9zT3LP3fv1nu8Hzj17v/v98bxr\nn/t+1/OstZ7HzP6M5N/huc8reQva8qy/9B8hxA5ZiFDPvUKXqoTJZKdeCNgVOuj9lLHbo52p94W2\n+UHNna6yMdG2Fpok+6rIBHiIGEduHl47c6re9wavKDXPvJ8HPV2KE6+98EisnJkkbln3qPvI9/Sq\nnTBsaY/6XbTUYHWb5Qwbczegy2nVkm2sK9XGDXWGf3wl7O3aHi4YordUz9JJIT3gXDtbrQOEdqk1\n00Kso+u3ZvJ642LjV7eB0nTm4/asE+ds7eAWNzkOZvYPKImJ5tv/Bc9xXomZPQzgletHCCF2xUKE\neurR9dNs6hgsgL6Gw1x+4yzhnxmmD+w2XorZU5kwXzPdCLXuQo9+8bZbjB83kS2CEjN627kS6OlD\nS57xehdmZQy8u/sQ6fLgngp2dD1S6HOn0zVaitk2xG1zEjYubcua5a04g/N1wKxC1E/tWo05lqt3\nDZ+Bo8LfmzSG/V1Pw9WWPcoBesGV8lmfOrZGx33SYRSzKF91HwCf/j2t9xistl987Za660zk2ia3\nze64/juY3Denb4Np/KE7r7X53vNjhBCnj0UI9dSn62bJMtX5ubnO/i4PsZX710Q3q3rtdydMuXsN\na+msevVtTjVCctespAF2CHAEbPTDx9oPSGRZUuUediIwMPn0o25ZU3WSozSiYe5Rj5yWTaSPTyOF\nuBqiHGXmNPpQk4u45YNvq8uiQvgmYtSEIXVeZr+G3QCsRivDEinGWQ2Z47pIbWItqGCTjeUUGXlV\nsrzRx8SJ4sXnnGEYSqlPlrB0ziuYZWQeoAwYxLKpjGwDDn1qPWnVo7Zpr6t51X4PbRFXqt9BH9zv\n+yn931/nxrc/zq4j2v/OvpQgvvs6BFM7BX0bHR1NEEJcvSxCqAF0DzV0oduMi53aJJRZwyO8hCI2\n+EaWwSGBA0toNnvIM0U4tXiWgxcSNnPNnazhDm8TTf0OBs9AlXARF5FAHAy+/tpKUhMiZqC3SU6w\nMmZNG31NdEKO9c0ZSNZ5usXJrsLIzCJIyW/WALsIfCsbDjzpy8rHcJEzxliL615nkZjiyScAYzGk\nDJEDGDJwWPdoEYKMhFW9j7KWfbUChoEYBsISPYVpsZkkbDjAIcayzpmpJhoZIimLVQWcfGmjjT6u\n66LvdaHBhIMh4eCgFP/OuczCH0q3yLOqlBMlr+l8MRsSV4iZC7RS9iStygxvJmBMLGu0s5fZNJSK\nV96OiQcYOGDMK4DEyOxrvA1DSsh1ORl83Jy1wzdgxIjSSSvLvUpbpRGTZDttWKSNzqf4w68ueqpR\nE5A4gHkJFyHEaWMRQr0eiHQ/a60wQguFH0k/e3risfV+dlv/O6lGVU9hSGwj3slGRGHKMxjaOmj4\npDbGnKVJpWG47KL33Kfh5fV7j/d97md2D/feU+7j26xLfOLziAs0/zB+RzC3u2PUZUPu34Wi9BPT\nYC3cv2Zvl3O1evTTPB5rtKVKfauk7vNNbPi+UDplFgWiPWphZsiR7c5K3u22LH3+TfQ3ur61fhLB\nFf97rKlZ87Q9Elrbze8johy9FdbtN1+KvukcQojTwSKEuqflYsbE+5oU4KDV8eoNJ/DwYy/WKKHu\n+TH+lE0ptXFOoL4eDs2FqXhz8fTti2sXGezSjXWzukMySyj2oF5/0wN304O82IJJGUXABSGZrwn3\nYLF5qcl6NCfnKbOnW4clFn9NxaCbVd8JTBT3KO3AmkPL64xMgiHVDXRRt3yMgK2huZveoWFMOu6F\njt7FmIlfHd3tJ/z1ouvfb0bGmEuGuoTUVgJsEelNZtavOvpdXR8w52mnKj4aN5y2DKm367eOVHSY\n4gzNunmKFCHE6WARQt37L6z/ep5mshbnSB4WDD9x4znYPzUtnogRQSyPQaKMb/pTlXW5FcsYaGRE\n833MiNFiTDThYh7rNcvSofJTvOABwNDSlXrikchFUpK4+EcEVrPOg3U/KRHMbKt14J2J7JWkYiDZ\nPUeypFKje5Ql8u+ZzidCEZYXi4rQlouESCSW481zWA8+vmtmSIkYQz28uRMBDCUxzTTEO+tlrH1v\nXr3Lc7ZGiJpeoQoARsutY0aCnfddmwaGlAYkRmWriCIXYS4ed3mfUIY+hkkkpe9u2GRLWB8zDOoR\nEc5293eAR+RLs5Y04H7N3oGP/sToHY9+hkCEwPNke4vNCCFOH4sQ6t77a3NsUQSHqGt+EWHfOiGo\nUUOOMUU4RMIVuoQ+rc0fiwpIxoknPQmDxwwxdzFrQhWf3RxR1oGGwYVhVZ/GvY/WQrldHg90u24M\nfwKsAlW3WV//qdlXbrnLIV0rOoWUHcx87DaPezIZKv4NZfHrp1QEJso/Rscn7ql8X+xczen5tmGd\nDMWJa+TErQzBnwbd0e+B7mAX0DAOPh4NH66Iv4W5bdu96ubRliXRfc2RiD7Uv1lDXdbX2ncaAmhX\nmuYFmF5x2mnrBVsIcbpYiFAXOHlANS84nmTVszTU0sdrMpA6ryhCqv3s7uZ6lyuSyOPUP48we8qR\ncgRt5ZFfGy7SsEjE4adcuaB33tOGofZ6P/MFTeHBGVDzYrdeyFzUbSKWuROMvtRD1OieemepikVE\nKXpRsATYWN4VcevXJXuTWrttY7tPVm/eottwBEN/YP3di3V0Kjj5g2gSHU2Ua2P7TGpvvJwj0Qlr\nalLAvLxpa4O5XGK2xVCzptbUNT43D4QX9Gj9jdLBib8Tduea3G4Lc/c5wqfB+5abTwhx+liEUM8f\nhvFwP5x7n+7hbh2fBjqXlZMH//oRbVHUmicNFwo7aIkyOKDGrNPoHmu5TmYvIu0u4l7mnlM/b2ru\nTfVeVJ7bHA94+u31HQ+WIDzRZjTR+rXmOSS3XjWWBMWryYCCV+aK24phA4v15NHJ6Po/kf8k+euc\np/e6mb5r0tqkv+de/I6Sqtz1itpdmh9fQurRaOYh6e6G1+zYRAupT/pPxZPGdFJ7f2f997zJi7bZ\nTx9y768jhDh9LEKoq6TNZhTnlEr5X5Sn/sqaFzyGl9h5lIAv4colvSXpC5RWhnEAgOzLjYhxFSk9\nzT3VJotEifOOeLYspSJwTTrAKhdvmRcP3eqMyDle72UAzCJFaAmzDmVtFAAik9UVi5XV61OD/dic\ni8Ppn5XwbVk+FWLBVF4fpgPkcawzkHP4zCGeaUDULUGMwRuAVNYrG1MZNM0lU1byQhgYyr5jKfaN\ngSzLx8maUQtAqVOSijKPuXjsMbmN6CZOpVKgI0pilrSfqB0lM8BG9+j97yKlqawZM7J7wyklHKDM\nMUieqrNfUG4AcMDaDpbzJBIBAuMw1uVwSAPyQODZkv+TKeHAk9JYNjw7rspfiLHMbUi+XMyveXDQ\nLjyWP8j6N0WWNd6w7Mv5SlTDDKV4Syg+gMTI6l4yruXJX5kQ4jSxCKGe0jxQSx56NgOsSE8fyt6w\nqqrs18eb/eE3juvVh5oX3T8Cu/NjLJ40UEoh1vSgIdDhOXfCYKmGX4s6rGdQKztu2nhEq3T7llrJ\n7fZKTyO5CAGtxifXRGtS2DBi3GFrjeUn5H4/dB6g1X8A0BOLlOMT+ntt+c/7s5RoiX9HnW9pNV9r\n+UlYTTzM/tNVNaaNR9dO3txNZctuOvdip55vRGKsVDtlJKtpcybqBLo4NbvXxZoaoUB9D9QCLt04\nDqMmt58vQur076xdtfuOL+HtCyGuThYh1FO96h5GqXifUZu6lGLk5CFWj4oHKJun2/YyL+RQrlbG\noEtWL/ZPy9kSIbCfB2yAZwprMj0V6ckdRGpOlhFG44aHrB396D1wr7UPyBratLApbIPUQYJPKNt8\nvUTAcpshTeQa241CFYhTWuf11mYq0lLahjVEvYmMDHBA+faSrzG2KtBrnagq9+tCHdcuXyeR2K8B\nn+G9kxBra4fXk9XjrN23kbXEauugdP2/mUhbd75pX4j+59QtO4w5Dd0khDr8EB0s9N+5gt9CnGYW\nIdTzx3AIbcIBEs0fqt2aXxr6SlE9ltE86l54EZt8JnX1jjfkp+7MqWuG4R619Y/77rqdF1UThcDK\nUqLn+3ydi0J9rlvfXIibq0LZX7LbZ/64ZyrtNd+P6LNgxpwAtvFphFj7PTNGuFntoIt3CE9my3zW\nxsqjZrZ7jp16TkS1u03OXvXzBLf1EvrkIdvoK2CZlU7FtO3aOvu6be2rZd17YmO3td7h2tKwiAiU\nY/uIRt9RE0KcPhYi1JuJUC5TpMYsIXBk1GQV1ZPuQpKW6bu3rGPD0HcGMsxWaA/7rkwXDUhFpjIS\n0ljCtBdHwwGTZ0vjmmC0zoB7mBbrqj2sO0uPWkX4iPtfhbfrP4Oblz203T/AMww2WJtkxkjd2QnF\nzD1NCbBhLOPcTOA41J0iRXXN/OmdJTNg8KgG2VqCBJ6tCbBbYRL6jR/yoBoR4+NludfYjUGXSEcZ\nhy/nnXejMoDBUMaofVZdRB1a2tb1tmb3d1KbwV/kHGuzDUwjkAwHOfl30EQ6R6GQ8MQZ0ZP2vp8A\n2Kq7ZW9Lj8D4OvFcM762yERU9uJYhgjgnyykdLwQYg8sRKjn/oIXTXAHNgOAJ+CAZZ9xvP3BFWHv\nOBcAX5ZTxMC6p+tkxncd2/XfltBqHqOKY4xFNr+vhV2t/rtt3e/8TrczzELJOU6dpkcZSgdhEpjo\n+w9HuWP1nrudrYlQhHWjycKT7pN0RHi4T/laC3v6uQ8wICbeRUGV1lZx7fgpk6423UII7DRc3Xmq\nm7BOmNECEq3t2nXiRYkklEhATYDTTz+fX3O2vW/RamCkmfPvqt4TSySid7KnX+Gm2IIQ4rSwEKEu\nT61sK39HDPDUnnCvyYCEDKQikjmP5QFmAJB8TBFIngWMHtLOY5HUISUklvxhxhJyZe4yaSVWVQmn\nMNHKxCWU19l8u7k3mczXD5uvTSLSkEthhjbdF+AIsghP1LKIj1JXGrJPYwqU5Cm19GQ86C3jcDiD\nbGVme10jPRjyqhPOVGZym40gRoyrVDzybnJUtlJYZPTZzEwGDmXC3rCK8erSwSkpSEu2t4vZcHh4\niOwFNMzfp0SY3z1YZt1HgpFVCGqkVvUxZhtixJr1FhMyLJdKFuU+6B5t9sxokbhmAJCQ7SJgKyD/\nK3AYPem6e8EjcGiDh+ZLo8fcrhZc8M4YAbIUXylzAuOaZb9hSMiRlc7gqcVizJ141tOeTsfyzYdP\nooNZ9k4gsPK/RWQYa+Xs4oMneEcRMFuf3CeEOD0sRKgxdb8Q65sjLGh169xzsn4m7YbPUd/Ho7qr\nMOWKRXhotwthtuxVqEdzesL2wabtMyJCsPF8aF7d9Jipvxmvx9ln8fncmW7HsXlyM0OzRYSBvhTO\now4xWWvTjc+89CpMnbXhb7fpdrG+e+aub3T3j1alaWnO8K4TyLGE1mu0pA191NMeV/B6F74PnvRt\ngL4lba3didb+EWOB/zZYFfKY5BgRiyiR2lPD7EKIU8dChLpTSH+8JRCjtWSNUUKiel91WVCr5Ezr\n1ppGJJjhyfm5bARqJmWAPiN4XHloncDhgecVty5c6j/1fJ3Zcb3+5Vyzs3WebNw1pwI9H2/vL2Hd\nT87rkggzHzt2Wcgt53eZfb4C7MA/bWP+5oY0Py+29zWbOzEBMJjnRkeJVFQLPWoQ5yornfx87olG\nLyjSjba1803sLyVIGUPtUBSJLgvEhwFYjV1xDJaynLmmiIUnOmHtoG2lNrZ/n9X9bp2DxLYEzTAt\n1hKtRQAjkt+XR4H8LmxAKbEJwrxNs2VkM59T4aZw89+UEFeU+14I3Pf1fVshsBChbmLk9ZxRpiDF\nStr5iHA8spp4cK0k5rpXae3hG1tThBZDIFidbWtGtfP5SeeroOZs+syqR9ntRHThTUx+H0W2fo65\nv7LwhuMkXdpNRC3vuJ8+omB1W/Zx4XL+yerijhBha4I7uaU2Lt9n4Ua0L6bpMfsZ2e3WL9UIm2Sr\nFR1t5yhJZuIunotH2scebLYNQFvSxvVj1qIls9fTNlk78+Q89dpSaSFOLYsQ6opNH8DkVNmse4RV\n35tesnGDwk2inuG+1idfGf0lCYsqXeUybRzVbPKQ7iPAvZDPVoFtFvE+VNz93ibMZm3O2CzS7J2S\nFgCu50/t2Mn1WFKgrg0lWC8JIWpxfKodnNIu0TXwNvdB06hslvPYxtvdk+47PH3UGDGBz9qEu6lI\nHy2p0/Zt5TtjQl1tXw8jt05Q/7d1DOXzL32y56QdOptnX9K0Y9m1eXfiiErEb7AMr9vMtvhvoXnf\nQpxOFiHUTbhaxmmDgTkec/5ga74xypyn8OjYiiSsn7Scw7orGADPOAb6cVX527Zy7UI8JOtsZXfe\nGDs1jVoT6hqAnulPm1Pe29h+z25hcv7WUek+9ElX4U+zU4vEA5TR7VztCXGLdeWpu5pNGjNOz+7Y\n7ixuRGQmszpTur9ZdkJmra5zP+cOx/N62/23yAGQkHMunbsUAsi6Trx1vCLsfolrsPueu85Y+6rb\nPP9eWOfeMzo70R1HsNXqZhNx+tK3lX9P/T1fymYhxNXJIoS6PvTy6Gtr/cE1hLgRYCozcbPPCE7X\nlJFmy2D2Ihk0zylNAINLSZH+Wv3JhSwZy8zwTKQMWEqdx0fAiOHAvW0QI1KxJecyacltKlcYXdlK\nkmojSq1ouEeXgWt44DLpy47MMJphSNnLZRIjiIteh3moumZtjNU9t7ZcCDgYymfjYMjj6CKU3Ov1\nIAKBAwO6OeSoNToNrbJULiJbalcUoShpy5tCJABYrTxvuCFjBC2X8o8W64VRijETdbJfyc6WS4a2\nCJsbYRiwYouVJG/7nLOPzZZc38m8RCWIlY0YbCgrAIYEG0dwzLAhYcwrkCUH+JBKTUrLZU5Dscfq\n+uk+GY314xksaUppCYPPyoYPBwxA6SUOCXk1enQj+TBKi/T4V+x/cxdRqpQPZZa+9wKyZSQSiW5r\nNJ7lMnYfnQyzZrcQ4tSxEKGejd1NvMTY2G/g5N38PNvOP3+fEZWxyokmoUz08e1t5mz6vOW77r3r\n+RSpEMxwXPvjEsy97c0uVD8JbZ5idGJ/16o22av97keh1/Zgb7//noUM6rex6fSY7jdtFf+pnZAi\nylbfNOEM6wkX2uhrcHqpomvh75YJZnnWOpzcb7c9Eo14KL8tk2P3q/v2p7Ht2jb9pvoRk+/fnYuz\n8flujMBmJ6a/Pc78BSHE1cdihBqYPk6BNo7ZPp0GHXtCbI96lpVHNuvr8tvK7N3iyKHNIff9rT8K\nnSpNzxrb2I0k9rKUq2R0qVDLiuGJPSHSIWzz8Kf1b/r7qLoWtubu+qVbMrV5/mq6RxxZOg5F8EoZ\ny25+wFoTdGdbGwOOkG9C/z1bznXYoO+Y1LBy3BysToYra8HnXQh6tIL+HaS6DRzLVp+XUNbR55p/\nvebjdu+WicjjuGb/pOcS160x8fXZ6u29R1miN0gAnoGtiLCHz23alPXvxP8bSKiFOJ0sRKjjode2\n1BBuPLqqOrCUZPTILdrmIrTWjybXs2GVuwerX6cP1RJebhFWfzKshNbhYtc9lKf+crUCE1FHG9se\n66SnsjWE5sCFKwp9wK87wmpay/6J3a5SXgwesR8Qi86ayPWte9Sip/ndFOcxyk+6rQYgqoYltqVI\n3sw5oyyBI6tt0xnhTRTbojvvCMUkgWmlDDeuZDMDUMSMwGFKiGIqURPcEpGx6pZt+VCHL4mqHnPz\n2yeZxpIPF6x1AtgZ049tRwrTGrgwwDj7q4vDPeFKlPT0c7dh+1LdzeIaAMxYO2wJXjpUdS6FOJUs\nRKgL8Uwsv+OBaVNvmWUsup9w1XuuW1JqdyFmuNfV0k9sCmc2cQuB6c+37i2ifr5pSdP8+Dg3XWCn\nFIEh0iytZLn9LmzAFkkokeL+7qfh3ulppu/6tmp9guaL974867KsiZa2hDEltRgiXtu+xYxY5d0s\n7JaQVWGne5DtPltRlub5MqqJTtKWto5dXC/1Haza7l1bzojOW2sNtuPnHbVYQ+7ubkv80lq5OMxW\nOy/JOxfxvi4NjIboQiZrs9U3/WEJIa56Lrnig+SNJP+G5GdIPkryl337fSQvkPyU//xUd8yvknyM\n5OdI/sRxjQmhJVmD3P3TqYlDPDCnx9LDi00EmtSWB2ITghIcNSQzDCxTz1L1IuGTzzYVGOSWn3rG\n+oPud0yE6hcjxRU8OScM4WunuiZ6Hvqu28LJnTRFeOp9eL1vw3Wbpy0828M/CNva99MZ4hGJsYqm\nTY2ufaAuYxhQ77tclNWbjclVpXIFO3tifX3UJJ/or4vggbd563rU1qennqWXQGUkz+lM9ZShedzg\nukaDTMQ6PijvN7VlAsAc7nC3lr444HW+QRT+ANq3F+Fu63uZQohTx3E86hWAd5jZJ0leC+ATJD/k\nn/0PM/vNfmeStwF4A4AfBPBSAB8m+QNmNh/0W2MiJ5wHl9clc+1ATJ6Da7/nHnUclvyJOqmzXD26\nacg4/MR1CV33pjoHvSUj6ewpnvNUCMMLLLnYNoer6z1xfp+9gvatOZXuuc1z+vaf+Oa2fnTfEaoz\nzTi3oUUQWpwivMyWbjTWtScQY+ddVksnoWmbmU8kHGCMMDmiLRNaypyIeMzbpG23XCxLk/KnRyhk\n99H8b7baXTOjEWAZ0oiOY+1M2noHqdoWzSqhFuJUckmhNrOnADzlr79J8rMArj/ikNcBeL+ZfRvA\n4yQfA/BqAH+3/ZAqmeWa8cMRNaNYRhkIRTysu/HQXEKhsAE5l0pZaUhl3DQbaIYzw2SVMHI3Fnkx\njxhYxpDN3ZxEwwES6hIsA8wLUxwkT/cYXhANwOAhWQO+5QUtDhJsIHAxYxXJOLqcpBHKHWgtyWSs\nPzbAEqsn7l8GzAwHHnIlvV0iYUYaq6ABLMvbOuHsQ8VkLmOkKQpeADXHk6bjtwAAEstJREFUdAJi\nnXlJa7kq7QgARgxMGL1AhiGBBIYzGbT4c/I2CcEmkCzqO5cscimX3zHI3oehjebVtgDQx6ir+Jdi\nKqjlJ4lS/CPB8CzOHA7NMzZDSsnnGhDMAAciE6WdQ7x9WVp8NylKXsJq1hkzlqV54eUakcaYIV4K\nh9DHlWOSWrYyAY+DL+vy73aFyILX8qyjDjW0rhc9eJC8QIhmkwlxOnlOY9QkbwHwQwD+HsCPAHg7\nyTcBeBjF6/6/KCL+0e6wJ7FB2EneA+AeAPjX112Ha85cg++/+ZbZTt3rIxzqtpnxCCwi3nnEva+5\n+d78c39w9pe55swZvOzGm7cf21+B6HKMsrqd60H0aZizbLH+o3aOCevFM685cwb/9sZbNlrVjppv\nbfWy1yMHm4617lOu3w9j8lu3r9t/zeEZ3HLDzTgSTl/a/Fxr+3DWXkSfyW7jdzVx8otoXnPmDF52\nw03dhWe3NdvUNxXZtk7jB+txmH7+/qXkto/GnDk8g5tfev0ljxFCXL0cW6hJfg+APwbwK2b2DZLv\nBvAbKM+U3wDwWwD+y3HPZ2YPAHgAAK6/8Qb79rPP4vNf+McW50NCHor0hovNiP/Rw8M+jswMDDwA\nSazyCikRHFoZRtqIIbVlQX041wAMBNLgPpQXbSCBnFcAiO+/8Rwe+9IXfX8D+yxb5jOGfezUuAJW\nPqiY3CVy7xMAarUPK+OWTAfeHvET+xowTItP1ICxFS8/vK1z19+Ixy98EeQKLV85YbmVj4w6HHTv\nO3kxksxUE6h033W9v3m2tDJZrNSLBlBm4BMAM1IOTxQ1hksQ566/CY8/+QVEiLuOVxvqEqXwqOP9\nOFrt4BhyGx4mkPNhN22uFWthGt2DRvX4OQwlw535JK4oTZqLUL/s+pvxj08+6ddGTYaCwWf7p4hI\nFG95HFHbaxgGxJpr6yf+9X0MA5AG1GIpQHvVZ3CzNvEO9GgDgZtfegOeeOoCcl/eTQhxqjiWUJM8\nRBHpPzKzPwEAM/tK9/nvAfgzf3sBwI3d4Tf4tu2EmzJxKK2KDjoHdW0AMHR87tI8B8oEpnIOjyQf\neao21a2Nt5Yq1P7J/ODqIs3cxo1veh+uvOfWz6Zka35bCUuPtX0OcVBrGhvK5K+WmMvX81Y/rl2n\nXjvaOpbGEZPvo4/Kmls9XSzWOhy125Fscjv91wuMtW0BdsdHmtRifHzaOgisnRGrk9G842TlG6LV\nvSfXnnv1cS/zlQTPD85eddEfTDuPa3/r8V46LcSp5JJCzeLq/D6Az5rZu7rtL/HxawD4GQCf9tcP\nAXgvyXehTCa7FcDHjr5KqK37HSwP2fAy588ruKjETOB46GWLetNAyWvt50ZJz1kPxfQ5GMuL6lht\nLl5XyY9diDHksnCqiHJIR6sPbRjs0PNa+xXKiToNjHKbQE10vWZZbeMmpN290N2+pvsxJlvEq52J\nJbslUIeMrTU1zIChnm96/TrRmBG4Ze1rjKOPn3btZxk1bWpr6X5hGNuFk1VPddsUw+Z/RgqaJtaJ\nuS7EKgaWuQM5VgvEDHIzDGSZHxB9gvB8qzISgzcSB1RBTJYRFTHh7bfqOjrzqEzrVLRvq9472559\nK+fuHO2+/Xf/PVIFOYQ4zRzHo/4RAD8H4BGSn/JtvwbgLpKvQnm2PAHgFwHAzB4l+QEAn0GZMf7W\n48z4Dg/an53lId6JdJWSTpzoUg1E5i8D1/wfmxzf+4xxumyG5GIdPwlA4mr20A3/bOVb08SGSm90\nfR+P5txt3OR6r7O2F9uJI2Tdh0+LmJdJVuEtl4lwU5PWz9t5xtywX6e1nN3jpeY5bYpS9E6izbZ3\nR2LaZkVao0mtO2huQnR0IuNZxD/66mP1rH6j9CGClDsxttaZe76CWabd9Y1rs1fTO56/3vyXIYQ4\nDRxn1vf/wubnxF8cccw7AbzzuEb884V//uob73rj/wPw1eMes2Ouw3JtA2Tf5bBk24B1+27elyFC\niP2wiMxkZvZ9JB82szv2bcsmlmwbIPsuhyXbBizfPiHElUdDX0IIIcSCkVALIYQQC2ZJQv3Avg04\ngiXbBsi+y2HJtgHLt08IcYVZjFB7ApRFsmTbANl3OSzZNmD59gkhrjyLEWohhBBCrCOhFkIIIRbM\n3oWa5Gu9bvVjJO/dtz0AQPIJko94ne2HfduLSX6I5Of994t2aM97SD5N8tPdtq32PN964Cdo24nX\nKn+etm2rpb6UtttZrXchxHcuexVqkgOA3wHwkwBuQ8l2dts+ber4j2b2qm4N670APmJmtwL4iL/f\nFX8A4LWzbRvtmdUDfy2A3/V23qVtQKlV/ir/+Ys92Ra11G8D8BoAb3UbltJ22+wDLqP9TrKDQvKH\nvdP6GMnfJqkkaULsmH171K8G8JiZ/ZOZPQvg/Sj1rJfI6wA86K8fBPD6XV3YzP4WwNeOaU+tB25m\njwOIeuC7tG0bu7btKTP7pL/+JoCopb6Utttm3zaOa99JdlDeDeAXUHL234rNnTIhxBVk30J9PYAv\nde831q7eAwbgwyQ/wVI3GwDOdkVIvgzg7H5Mq2yzZylt+naS/+Ch8fDc9mYbyVvQaqkvru1m9gGX\n0X4n1UEh+RIA32tmH7WSVP4PscMOqhCisIgUogvkR83sAsl/A+BDJP93/6GZGVtljL2zNHtQvLDn\nXav8pOF6LfX62RLaboN9J9Z+z6GD8tHusOgAXPTX8+2brnMPgHsA4OzZszh//vyRdj3zzDOX3GeX\nnAZ73nH7CgAwfNcvbd3n/MufnW1oNpyGNrocrqQ9+xbq5167egeY2QX//TTJD6KEF79CL+3pnsbT\nezVyuz17b9MTrVV+mXBDLXUsqO022XdS7bfLDoqv934AAO644w678847j9z//PnzuNQ+u+Rqt+eW\ne/8c8bi/9hXv3rrfI49/cbrhrq9fMZsul9Nkz75D3x8HcCvJcyTPoIyTPbRPg0i+gOS18RrAj6PU\n2n4IwN2+290A/nQ/Fla22fMQgDeQvIbkORyrHvjJ4uIXzGuV78w2n/i0VksdC2m7bfadRPsd1UHp\nrnGpDsoFfz3fLoTYIXv1qM1sRfJtAP4awADgPWb26D5tQgkHftC9jwMA7zWzvyL5cQAfIPlmAF8A\n8LO7Mojk+wDcCeA6kk8C+HUA92+y5/nXAz9R2+7kidYqf95sq6W+iLY7wr7LqvV+jA7K/VjvoLyX\n5LsAvBTeATCzkeQ3SL4GJXT+JgD/86RuXghxPPYd+oYvPdla23rXmNk/Afh3G7b/C4Af271FgJnd\nteWjjfY813rgl8MW237/iP13adu2WurAMtruStV6P8kOyltQluB9N4C/9B8hxA7Zu1ALIU6Wk+yg\nmNnDAF55ctYJIZ4r+x6jFkIIIcQRSKiFEEKIBSOhFkIIIRaMhFoIIcRm7nvhvi0QkFALIYQQi0ZC\nLYQQQiwYCbUQQgixYCTUQgghAAC3n7tp3yaIDUiohRBCiAUjoRZCCCEWjIRaCCGEWDASaiGEEGLB\nSKiFEEKIBSOhFkIIIRaMhFoIIYRYMBJqIYQQYsFIqIUQ4pRw7Svu3bcJ4nkgoRZCCCEWjIRaCCGE\nWDASaiGEEGLBSKiFEEKIBSOhFkIIIRaMhFoIIYRYMBJqIYQQYsFIqIUQQogFI6EWQgghFoyEWggh\nhFgwEmohhBBiwUiohRBCiAUjoRZCCCEWjIRaCCGEWDASaiGEEGLBSKiFEEKIBSOhFkIIIRaMhFoI\nIYRYMBJqIYQQYsFIqIUQQogFI6EWQgghFoyEWgghhFgwEmohhBBiwUiohRBCiAUjoRZCCCEWjIRa\nCCGEWDASaiGuMki+h+TTJD/dbXsxyQ+R/Lz/flH32a+SfIzk50j+RLf9h0k+4p/9Nknu+l6EEBJq\nIa5G/gDAa2fb7gXwETO7FcBH/D1I3gbgDQB+0I/5XZKDH/NuAL8A4Fb/mZ9TCLEDJNRCXGWY2d8C\n+Nps8+sAPOivHwTw+m77+83s22b2OIDHALya5EsAfK+ZfdTMDMAfdscIIXbIwb4NEELshLNm9pS/\n/jKAs/76egAf7fZ70rdd9Nfz7RsheQ+AewDg7NmzOH/+/JHGPPPMM5fcZ5dc7fa84/YVAGD4rl+6\n5L7nX/7sbMP5K2LT5XKa7JFQC3HKMDMjaSd8zgcAPAAAd9xxh915551H7n/+/Hlcap9dcrXb8/P3\n/jkA4NpXvPuS+z7y+BenG+76+hWx6XI5TfYo9C3E6eArHs6G/37at18AcGO33w2+7YK/nm8XQuwY\nCbUQp4OHANztr+8G8Kfd9jeQvIbkOZRJYx/zMPk3SL7GZ3u/qTtGCLFDFPoW4iqD5PsA3AngOpJP\nAvh1APcD+ADJNwP4AoCfBQAze5TkBwB8BsAKwFvNbPRTvQVlBvl3A/hL/xFC7BgJtRBXGWZ215aP\nfmzL/u8E8M4N2x8G8MoTNE18B3D7uZvWx6nFXlHoWwghhFgwEmohhBBiwUiohRBCiAUjoRZCCCEW\njIRaCCGEWDASaiGEEGLBSKiFEEKIBSOhFkIIIRaMhFoIIYRYMBJqIYQQYsFIqIUQQogFI6EWQggh\nFoyEWgghhFgwEmohhBBiwUiohRBCiAUjoRZCCCEWjIRaCCHEdu574b4tOPVIqIUQQogFI6EWQggh\nFoyEWgghhFgwEmohhBBiwUiohRBCTLj93E37NkF0SKiFEEKIBSOhFkIIIRaMhFoIIa5ibrn3z/dt\ngrhMJNRCCCHEgpFQCyGEEAtGQi2EEEIsGAm1EEIIsWAk1EIIIcSCkVALIYQ4GlXQ2isSaiGEEGLB\nSKiFEEKIBSOhFkIIIRaMhFoIIYRYMBJqIYQ4BVz7inv3bYJ4nkiohRBCiAUjoRZCiKsUFeS4OpBQ\nCyGEuDRPfWrfFpxaJNRCCCHWuP3cTfs2QTgSaiGEEGLBSKiFEEJsRF71MpBQCyHEVc6JLc1Szu+9\nIKEWQhwJydeS/BzJx0hqMe53CJrxffUgoRZCbIXkAOB3APwkgNsA3EXytv1aJXbJ7eduwu3nbsJn\nzpwpofBNXrU87SvKwb4NEEIsmlcDeMzM/gkASL4fwOsAfGavVoljc9IZyW4/dxPw4O3Tjb7tkce/\nWPf55mfvxxPf9cY6zh3vb/nWe6tNj9z9CIDi/T9x/0/XKMAT9//0idr8nQ7NbN82CCEWCsn/DOC1\nZvZf/f3PAfj3Zva22X73ALjH374cwOcucerrAHz1hM29HGTPpVmaTVeDPTeb2fddaid51EKIy8bM\nHgDwwHH3J/mwmd1xBU16TsieS7M0m06TPRqjFkIcxQUAN3bvb/BtQogdIaEWQhzFxwHcSvIcyTMA\n3gDgoT3bJMSpQqFvIcRWzGxF8m0A/hrAAOA9ZvboCZz62GHyHSF7Ls3SbDo19mgymRBCCLFgFPoW\nQgghFoyEWgghhFgwEmohxM5YSjpSkk+QfITkp0g+7NteTPJDJD/vv190Ba//HpJPk/x0t23r9Un+\nqrfZ50j+xI7suY/kBW+jT5H8qR3acyPJvyH5GZKPkvxl376XNjrCnp20kcaohRA7wdOR/h8A/wnA\nkygzyu8ys51nOSP5BIA7zOyr3bb/DuBrZna/dyJeZGb/7Qpd/z8AeAbAH5rZK4+6vqdsfR9KlriX\nAvgwgB8ws/EK23MfgGfM7Ddn++7CnpcAeImZfZLktQA+AeD1AH4ee2ijI+z5WeygjeRRCyF2RU1H\nambPAoh0pEvhdQAe9NcPojyIrwhm9rcAvnbM678OwPvN7Ntm9jiAx1Da8krbs41d2POUmX3SX38T\nwGcBXI89tdER9mzjRO2RUAshdsX1AL7UvX8SRz/sriQG4MMkP+HpTwHgrJk95a+/DODsjm3adv19\nttvbSf6Dh8YjzLxTe0jeAuCHAPw9FtBGM3uAHbSRhFoIcRr5UTN7FUpVsLd66LdiZUxwb+OC+76+\n824ALwPwKgBPAfitXRtA8nsA/DGAXzGzb/Sf7aONNtizkzaSUAshdsVi0pGa2QX//TSAD6KEJb/i\nY5ExJvn0js3adv29tJuZfcXMRjPLAH4PLXS7E3tIHqKI4h+Z2Z/45r210SZ7dtVGEmohxK5YRDpS\nki/wCUEg+QIAPw7g027L3b7b3QD+dMembbv+QwDeQPIakucA3ArgY1famBBE52dQ2mgn9pAkgN8H\n8Fkze1f30V7aaJs9u2ojpRAVQuyEK5iO9LlyFsAHy7MXBwDea2Z/RfLjAD5A8s0AvoAyo/eKQPJ9\nAO4EcB3JJwH8OoD7N13fzB4l+QGUGuArAG89yRnWR9hzJ8lXoYSXnwDwi7uyB8CPAPg5AI+Q/JRv\n+zXsr4222XPXLtpIy7OEEEKIBaPQtxBCCLFgJNRCCCHEgpFQCyGEEAtGQi2EEEIsGAm1EEIIsWAk\n1EIIIcSCkVALIYQQC+b/AwwVIFd+PkCPAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8eed7a3a20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import cv2\n",
"import matplotlib.pyplot as plt\n",
"plt.figure(figsize=(8,8))\n",
"l = random.choice(id_haze)\n",
"im = cv2.imread('../input/train-jpg/train_'+str(l)+'.jpg')\n",
"im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
"\n",
"r = im[:,:,0]\n",
"g = im[:,:,1]\n",
"b = im[:,:,2]\n",
" \n",
"plt.subplot(1,2,1)\n",
"plt.imshow(im)\n",
"plt.subplot(1,2,2)\n",
"plt.hist(r.ravel(), bins=256, range=(0., 255))\n",
"plt.hist(g.ravel(), bins=256, range=(0., 255))\n",
"plt.hist(b.ravel(), bins=256, range=(0., 255))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "e5035d89-1917-4fa8-6b28-4f0653ff6066"
},
"outputs": [],
"source": [
"im = cv2.imread('../input/train-jpg/train_'+str(l)+'.jpg')\n",
"im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
"\n"
]
}
],
"metadata": {
"_change_revision": 404,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159793.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "aa681bc8-758d-6f66-8a39-d0422e9293b2"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d9d9c239-6623-2d86-c33e-badaabc2826b"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "5cfd0e5b-5b73-e256-2c43-f22b63233b3c"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f37f0c0c-7918-b080-71a8-ad44a9fef3cd"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "3cab39ed-1def-b64e-00cf-84951f4d43d3"
},
"outputs": [],
"source": [
"def plot_correlation_map( df ):\n",
" corr = titanic.corr()\n",
" _ , ax = plt.subplots( figsize =( 12 , 10 ) )\n",
" cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
" _ = sns.heatmap(\n",
" corr, \n",
" cmap = cmap,\n",
" square=True, \n",
" cbar_kws={ 'shrink' : .9 }, \n",
" ax=ax, \n",
" annot = True, \n",
" annot_kws = { 'fontsize' : 12 }\n",
" )\n",
" \n",
"def plot_distribution( df , var , target , **kwargs ):\n",
" row = kwargs.get( 'row' , None )\n",
" col = kwargs.get( 'col' , None )\n",
" facet = sns.FacetGrid( df , hue=target , aspect=4 , row = row , col = col )\n",
" facet.map( sns.kdeplot , var , shade= True )\n",
" facet.set( xlim=( 0 , df[ var ].max() ) )\n",
" facet.add_legend()\n",
" \n",
"def plot_categories( df , cat , target , **kwargs ):\n",
" row = kwargs.get( 'row' , None )\n",
" col = kwargs.get( 'col' , None )\n",
" facet = sns.FacetGrid( df , row = row , col = col )\n",
" facet.map( sns.barplot , cat , target )\n",
" facet.add_legend()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "88117a3b-d3f0-af37-824e-429f4770afe1"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "b9d073a1-6120-1243-109f-77190adcee1b"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-86dce884597e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"train.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"test.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"train = pd.read_csv(\"train.csv\")\n",
"test = pd.read_csv(\"test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "a48923aa-bc32-0400-27f5-1f2d2792a9b4"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-c84d6445880f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfull\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'True'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'train' is not defined"
]
}
],
"source": [
"full = train.append(test, ignore_index='True')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d42c9115-54c4-d2d9-9172-3c7186678140"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "93d4ac7a-34e2-b261-7c2f-8684f73cbacd"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'full' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-cc0567cad29c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtitanic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfull\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtitanic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'full' is not defined"
]
}
],
"source": [
"titanic = full[:len(train.index)]\n",
"titanic.head()\n",
"del train, test"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0a02febd-7d39-a185-328d-c633951fab25"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "c4d6d5a1-e7d0-28cb-37fb-afb164d4ae9f"
},
"outputs": [],
"source": []
},
{
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"metadata": {
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},
{
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"_cell_guid": "9c0cb9eb-9e50-ff78-ac3a-e1c376bb4bd5",
"collapsed": true
},
"source": []
},
{
"cell_type": "code",
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"metadata": {
"_cell_guid": "299e6b68-f1ab-ad13-dfbb-a3f4ec73f79b"
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{
"cell_type": "code",
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"metadata": {
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},
{
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{
"cell_type": "code",
"execution_count": 5,
"metadata": {
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{
"cell_type": "code",
"execution_count": 5,
"metadata": {
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"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "ca6d453e-e810-71c0-5644-9f6b240ad1aa"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-6a49d7dba805>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtest_Y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mtest_X\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mpassenger_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfull\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m891\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPassengerId\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m{\u001b[0m \u001b[0;34m'PassengerId'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpassenger_id\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;34m'Survived'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtest_Y\u001b[0m \u001b[0;34m}\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"test_Y = model.predict( test_X )\n",
"passenger_id = full[891:].PassengerId\n",
"test = pd.DataFrame( { 'PassengerId': passenger_id , 'Survived': test_Y } )\n",
"test.shape\n",
"test.head()\n",
"test.to_csv( 'titanic_pred.csv' , index = False )"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "c81b6d75-592b-795e-edcf-6f5d81f1174b"
},
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"source": []
}
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"metadata": {
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"_is_fork": false,
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"language": "python",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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}
},
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| 0001/159/1159873.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "6cdaab1a-6883-4db1-d344-41e4e6403dc5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"charlist.csv\n",
"dataset.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "c6c823ab-12eb-844f-5f2d-e0eb4c105b59"
},
"outputs": [],
"source": [
"df=pd.read_csv(\"../input/charlist.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "02d634e7-ae22-dc6d-c2d0-55f4a3974592"
},
"outputs": [
{
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>6 rows × 1025 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 ... 1015 1016 1017 1018 1019 1020 \\\n",
"0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 \n",
"5 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 \n",
"\n",
" 1021 1022 1023 1024 \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"5 0 0 0 0 \n",
"\n",
"[6 rows x 1025 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_data.head(6)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "c7f91548-fd8d-e7b3-2a27-1fbb69580050"
},
"outputs": [],
"source": [
"level=df_data['1024']"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "367f142a-9c3c-f067-39d8-99c09bd7eff3"
},
"outputs": [],
"source": [
"df_data=df_data.drop(['1024'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "1381ce31-eaf2-0a08-9a21-03fa9a033735"
},
"outputs": [
{
"data": {
"text/plain": [
"(92045, 1024)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "3b3ec6ca-f356-c876-3523-29f863ef8fa4"
},
"outputs": [],
"source": [
"\n",
"# convert to array, specify data type, and reshape\n",
"#target = target.astype(np.uint8)\n",
"#train = np.array(train).reshape((-1, 1, 28, 28)).astype(np.uint8)\n",
"#test = np.array(test).reshape((-1, 1, 28, 28)).astype(np.uint8)\n",
"\n",
"data = np.array(df_data).reshape((-1, 1, 32, 32)).astype(np.uint8)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "243b6478-2de2-a8e3-59ff-f06f588c1e6a"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f0defbb2f28>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8efM0ePBgLVu2TDU1NYqPj9eGDRsUHR0d6LECQNA4w/LIkSM6e/asMjMzdfXqVU2ePFnD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\nN6T6zyYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0df119bda0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"plt.imshow(data[2][0], cmap=cm.binary) # draw the picture"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "7f37243a-f39b-9a59-bb4c-32f4dd2992af"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f0df119bcf8>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SAQC44DTq4C8tLVVGRoaeeuopxw/qTZo0SQcPHpQk5eXlqX379urWrZv27t2r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\nBwDARP4XfFjZxquKnEwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0dee9dfb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(level)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "c691a4b6-209b-f6f2-52c0-27a9a4d13976"
},
"outputs": [],
"source": [
"#Train-Test split\n",
"from sklearn.model_selection import train_test_split\n",
"data_train, data_test, label_train, label_test = train_test_split(df_data, level, test_size = 0.2, random_state = 42)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "7e665ae4-cd47-4e84-86a3-6da41f35be46"
},
"outputs": [
{
"data": {
"text/plain": [
"((92045, 1, 32, 32), (92045, 1024))"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape,df_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "0960996b-9b8d-a272-37a2-199dfa82fc16"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training score: 0.999022217394\n",
"Testing score: 0.786517464284\n"
]
}
],
"source": [
"#Random forest classifier\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"rf = RandomForestClassifier()\n",
"rf.fit(data_train, label_train)\n",
"rf_score_train = rf.score(data_train, label_train)\n",
"print(\"Training score: \",rf_score_train)\n",
"rf_score_test = rf.score(data_test, label_test)\n",
"print(\"Testing score: \",rf_score_test)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "b56a9dd7-fc56-1bc1-96dd-4acb599fe8c3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training score: 1.0\n",
"Testing score: 0.592807865718\n"
]
}
],
"source": [
"#decision tree\n",
"from sklearn import tree\n",
"dt = tree.DecisionTreeClassifier()\n",
"dt.fit(data_train, label_train)\n",
"dt_score_train = dt.score(data_train, label_train)\n",
"print(\"Training score: \",dt_score_train)\n",
"dt_score_test = dt.score(data_test, label_test)\n",
"print(\"Testing score: \",dt_score_test)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "34315913-55e8-0774-4b04-ffa8cfa3adb3"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 2,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159890.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "aa681bc8-758d-6f66-8a39-d0422e9293b2"
},
"source": [
"# TITANIC SURVIVAL PROBLEM"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d9d9c239-6623-2d86-c33e-badaabc2826b"
},
"source": [
"### import necessary libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "5cfd0e5b-5b73-e256-2c43-f22b63233b3c"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"from sklearn.cross_validation import train_test_split , StratifiedKFold\n",
"from sklearn.neighbors import KNeighborsClassifier"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f37f0c0c-7918-b080-71a8-ad44a9fef3cd"
},
"source": [
"correlation map function - check how corr method works"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3cab39ed-1def-b64e-00cf-84951f4d43d3"
},
"outputs": [],
"source": [
"def plot_correlation_map( df ):\n",
" corr = titanic.corr()\n",
" _ , ax = plt.subplots( figsize =( 12 , 10 ) )\n",
" cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
" _ = sns.heatmap(\n",
" corr, \n",
" cmap = cmap,\n",
" square=True, \n",
" cbar_kws={ 'shrink' : .9 }, \n",
" ax=ax, \n",
" annot = True, \n",
" annot_kws = { 'fontsize' : 12 }\n",
" )\n",
" \n",
"def plot_distribution( df , var , target , **kwargs ):\n",
" row = kwargs.get( 'row' , None )\n",
" col = kwargs.get( 'col' , None )\n",
" facet = sns.FacetGrid( df , hue=target , aspect=4 , row = row , col = col )\n",
" facet.map( sns.kdeplot , var , shade= True )\n",
" facet.set( xlim=( 0 , df[ var ].max() ) )\n",
" facet.add_legend()\n",
" \n",
"def plot_categories( df , cat , target , **kwargs ):\n",
" row = kwargs.get( 'row' , None )\n",
" col = kwargs.get( 'col' , None )\n",
" facet = sns.FacetGrid( df , row = row , col = col )\n",
" facet.map( sns.barplot , cat , target )\n",
" facet.add_legend()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "88117a3b-d3f0-af37-824e-429f4770afe1"
},
"source": [
"import data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "b9d073a1-6120-1243-109f-77190adcee1b"
},
"outputs": [],
"source": [
"train = pd.read_csv(\"../input/train.csv\")\n",
"test = pd.read_csv(\"../input/test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "a48923aa-bc32-0400-27f5-1f2d2792a9b4"
},
"outputs": [],
"source": [
"full = train.append(test, ignore_index='True')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d42c9115-54c4-d2d9-9172-3c7186678140"
},
"source": [
"#Data\n",
"Combine all, save train in titanic\n",
"delete db"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "93d4ac7a-34e2-b261-7c2f-8684f73cbacd"
},
"outputs": [],
"source": [
"titanic = full[:len(train.index)]\n",
"titanic.head()\n",
"del train, test"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0a02febd-7d39-a185-328d-c633951fab25"
},
"source": [
"# EXAMINE DATA"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "c4d6d5a1-e7d0-28cb-37fb-afb164d4ae9f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Age</th>\n",
" <th>Fare</th>\n",
" <th>Parch</th>\n",
" <th>PassengerId</th>\n",
" <th>Pclass</th>\n",
" <th>SibSp</th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>29.699118</td>\n",
" <td>32.204208</td>\n",
" <td>0.381594</td>\n",
" <td>446.000000</td>\n",
" <td>2.308642</td>\n",
" <td>0.523008</td>\n",
" <td>0.383838</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>14.526497</td>\n",
" <td>49.693429</td>\n",
" <td>0.806057</td>\n",
" <td>257.353842</td>\n",
" <td>0.836071</td>\n",
" <td>1.102743</td>\n",
" <td>0.486592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>20.125000</td>\n",
" <td>7.910400</td>\n",
" <td>0.000000</td>\n",
" <td>223.500000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>28.000000</td>\n",
" <td>14.454200</td>\n",
" <td>0.000000</td>\n",
" <td>446.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>38.000000</td>\n",
" <td>31.000000</td>\n",
" <td>0.000000</td>\n",
" <td>668.500000</td>\n",
" <td>3.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>80.000000</td>\n",
" <td>512.329200</td>\n",
" <td>6.000000</td>\n",
" <td>891.000000</td>\n",
" <td>3.000000</td>\n",
" <td>8.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Age Fare Parch PassengerId Pclass \\\n",
"count 714.000000 891.000000 891.000000 891.000000 891.000000 \n",
"mean 29.699118 32.204208 0.381594 446.000000 2.308642 \n",
"std 14.526497 49.693429 0.806057 257.353842 0.836071 \n",
"min 0.420000 0.000000 0.000000 1.000000 1.000000 \n",
"25% 20.125000 7.910400 0.000000 223.500000 2.000000 \n",
"50% 28.000000 14.454200 0.000000 446.000000 3.000000 \n",
"75% 38.000000 31.000000 0.000000 668.500000 3.000000 \n",
"max 80.000000 512.329200 6.000000 891.000000 3.000000 \n",
"\n",
" SibSp Survived \n",
"count 891.000000 891.000000 \n",
"mean 0.523008 0.383838 \n",
"std 1.102743 0.486592 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 1.000000 1.000000 \n",
"max 8.000000 1.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "9c6cbc3c-9f07-d291-fe15-f655ca850cf1"
},
"outputs": [
{
"data": {
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e53DMZg7HbHZoPxzBnJGR7xjw8vLElHHrYwkgJT2NLXv2MLjHB8wfOYr4xESm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9e00a5a278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_correlation_map( titanic )"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "9c0cb9eb-9e50-ff78-ac3a-e1c376bb4bd5"
},
"source": [
"According to above graph survival rate is highly related with Fare positively and Pclass negatively"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "299e6b68-f1ab-ad13-dfbb-a3f4ec73f79b"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "slice indices must be integers or None or have an __index__ method",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-2debc65b0cc8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Plot distributions of Age of passangers who survived or did not survive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplot_distribution\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mtitanic\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Age'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Survived'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Sex'\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-2-2798cfae3423>\u001b[0m in \u001b[0;36mplot_distribution\u001b[0;34m(df, var, target, **kwargs)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m'col'\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mfacet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFacetGrid\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0maspect\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mfacet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mshade\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0mfacet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mxlim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mfacet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_legend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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]
},
{
"data": {
"image/png": 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L8tRTT6XRaHzn3nvvvZfly5fn0ksvzZIlS7J58+ZMT093ZVAAAICq6RhUvb29ueiii457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\nAACAQoIKAACgkKACAAAoJKgAAAAK/T9cbFUNhMYb2gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9e005816d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot distributions of Age of passangers who survived or did not survive\n",
"plot_distribution( titanic , var = 'Age' , target = 'Survived' , row = 'Sex' )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "2c5b1095-c683-f450-99bd-971308ee6fb7"
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f9df6809f28>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f9df6707eb8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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RGRkp41R9Z7PZ8N5776G+vh42mw0LFizApEmT5B7rDgyNSAC+dCQSgKERCcDQiARgaEQC\nMDQiAbhniAe6cOECUlJSMHr0aACA3W5HVFQUDAYDwsLC7rh+WVkZDh065PxGO4nHZzQPpVarsXXr\nVmzduhXFxcXQaDRYv3693GPRXfAZzUuMGzcOJSUlOHbsGEwmE/z8/BAeHo7c3Nxu16uoqMAXX3wB\nf39/dHZ2YuXKlYiOjsaWLVtQXl6OwMBABAQEIC8vDx0dHcjMzATwz4ZhnU6HtLQ0OR6ex2NoXqCz\nsxMVFRUYO3YssrKyUFBQgOHDh2Pz5s3Yv39/t+u2tLQgPz8fkZGR2LhxI7Zv347s7GysXbsWZrMZ\ngwcPxoEDB9DQ0IDKykoMGzYM77//Pq5fv45vvvlGpkfo+Riah2psbMSsWbMAADdv3sSTTz6JGTNm\noLCwEMOHDwcAvPHGGwD+eY92y+DBg5GdnQ2HwwGLxeJ8n5eWloY333wTzz//PFJSUhAbGwulUokd\nO3Zg6dKlSEpKgk6nE/sgvQhD81C33qN11dTU5PKI0Ha7HRkZGfjuu+/wyCOPYNu2baipqQEALFu2\nDBcvXsT+/fsxf/58ZGdnIykpCT/++CMOHz6M3bt3Y8uWLSguLpb0cXkrhuZFVCoVBg0ahOPHjyMh\nIQFffvklAgICEBgYCABoa2uDj48PoqKicP36dfzyyy9QqVRobm7G119/jfnz52PmzJlwOBw4ceIE\nWlpaEBUVhWeeeQZPPfUUJk2ahBs3bnjtl2OlxN+Yl8nLy4PJZIJSqURoaCjy8vKcB+IZNGgQUlNT\nkZaWhsjISMydOxdLlizBoUOH0NbWhrS0NISFhUGpVMJoNKKxsREGgwH+/v5wOBxIT09nZPeJe+8T\nCcDtaEQCMDQiARgakQAMjUgAhkYkAEMjEoChEQnA0IgE+D+lA/T3sWlAIQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9df66e7da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot survival rate by Embarked\n",
"plot_categories( titanic , cat = 'Embarked' , target = 'Survived' )\n",
"plot_categories( titanic , cat = 'Sex' , target = 'Survived' )\n",
"plot_categories( titanic , cat = 'Pclass' , target = 'Survived' )"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7ff5bc60-359e-5348-c943-009f62bf7fe2"
},
"source": [
"Categorical variables need to be transformed to numeric variables"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "7c41ee43-8053-3dc8-db7a-e74015c809cc"
},
"outputs": [
{
"data": {
"text/plain": [
"1304 1\n",
"1305 0\n",
"1306 1\n",
"1307 1\n",
"1308 1\n",
"Name: Sex, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sex = pd.Series( np.where( full.Sex == 'male' , 1 , 0 ) , name = 'Sex' )\n",
"sex.tail()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "484556db-37e3-20b9-827a-0a598e687a99"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pclass_1</th>\n",
" <th>Pclass_2</th>\n",
" <th>Pclass_3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass_1 Pclass_2 Pclass_3\n",
"0 0 0 1\n",
"1 1 0 0\n",
"2 0 0 1\n",
"3 1 0 0\n",
"4 0 0 1"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pclass = pd.get_dummies( full.Pclass , prefix='Pclass' )\n",
"pclass.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ea1d419b-da8a-973a-08fd-6a4b11a940ac"
},
"source": [
"Include the variables you would like to use in the function below seperated by comma"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "b938fb0c-c643-c931-c230-2f1cb41938f8"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pclass_1</th>\n",
" <th>Pclass_2</th>\n",
" <th>Pclass_3</th>\n",
" <th>Sex</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass_1 Pclass_2 Pclass_3 Sex\n",
"0 0 0 1 1\n",
"1 1 0 0 0\n",
"2 0 0 1 0\n",
"3 1 0 0 0\n",
"4 0 0 1 1"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full_X = pd.concat( [ pclass , sex ] , axis=1 )\n",
"full_X.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "5cda8577-9af9-afc8-c3f6-9b833c9dd8d7"
},
"source": [
"Create datasets"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "ffe2080b-dec2-e0ba-496e-196ad73c21b0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1309, 4) (623, 4) (268, 4) (623,) (268,) (418, 4)\n"
]
}
],
"source": [
"# Create all datasets that are necessary to train, validate and test models\n",
"train_valid_X = full_X[ 0:891 ]\n",
"train_valid_y = titanic.Survived\n",
"test_X = full_X[ 891: ]\n",
"train_X , valid_X , train_y , valid_y = train_test_split(train_valid_X , train_valid_y , train_size = .7 )\n",
"\n",
"print (full_X.shape , train_X.shape , valid_X.shape , train_y.shape , valid_y.shape , test_X.shape)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "11825f69-5cc6-a2c8-8648-433384978d11"
},
"outputs": [],
"source": [
"model = KNeighborsClassifier(n_neighbors = 3)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "868f9095-1044-e0bb-90d5-7a7a3d38fdf3"
},
"outputs": [
{
"data": {
"text/plain": [
"KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
" metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n",
" weights='uniform')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit( train_X , train_y )"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "2a768204-142b-de50-02c7-0dc3295117c2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.796147672552 0.764925373134\n"
]
}
],
"source": [
"print (model.score( train_X , train_y ) , model.score( valid_X , valid_y ))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "4fbac2e3-aa3d-c939-0042-0394133739eb"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "ca6d453e-e810-71c0-5644-9f6b240ad1aa"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0\n",
" 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
" 0 1 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0\n",
" 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1\n",
" 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 1 0\n",
" 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 1\n",
" 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 0\n",
" 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 1 0 0 0 0\n",
" 0 0 0 0 1 0 0 1 0 0 0]\n"
]
}
],
"source": [
"test_Y = model.predict( test_X ).astype(int)\n",
"print(test_Y)\n",
"passenger_id = full[891:].PassengerId\n",
"test = pd.DataFrame( { 'PassengerId': passenger_id , 'Survived': test_Y} )\n",
"test.shape\n",
"test.to_csv( 'titanic_pred.csv' , index = False )\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
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"source": []
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| 0001/159/1159903.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ee5b31a8-79e6-d526-3f05-c6a2d0a5136f"
},
"source": [
"Titanic "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "1aa06bf5-824c-99bf-21ed-22dfc6aaa503"
},
"outputs": [
{
"ename": "URLError",
"evalue": "<urlopen error [Errno -2] Name or service not known>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1317\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1318\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1319\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1233\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1234\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 963\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 965\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 935\u001b[0m self.sock = self._create_connection(\n\u001b[0;32m--> 936\u001b[0;31m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 937\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 705\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[0maddrlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 743\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 744\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mgaierror\u001b[0m: [Errno -2] Name or service not known",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-06a6264500a2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'http://real-chart.finance.yahoo.com/table.csv?s=YHOO&amp;d=9&amp;e=17&amp;f=2015&amp;g=d&amp;a=3&amp;b=12&amp;c=1996&amp;ignore=.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0myahoo_csv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0myahoo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myahoo_csv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_dates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 543\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 544\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 545\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 505\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1344\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1345\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1346\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1348\u001b[0m \u001b[0mhttp_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1318\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1321\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1322\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno -2] Name or service not known>"
]
}
],
"source": [
"from urllib.request import urlopen\n",
"import matplotlib.pyplot as plt\n",
"import pandas\n",
" \n",
"url = 'http://real-chart.finance.yahoo.com/table.csv?s=YHOO&amp;d=9&amp;e=17&amp;f=2015&amp;g=d&amp;a=3&amp;b=12&amp;c=1996&amp;ignore=.csv'\n",
"yahoo_csv = urlopen(url)\n",
" \n",
"yahoo = pandas.read_csv(yahoo_csv, index_col=0, parse_dates=True)\n",
"yahoo.plot(y='Adj Close')\n",
" \n",
"plt.xlabel('Year')\n",
"plt.ylabel('Adj Close')\n",
"plt.legend().set_visible(False)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "46db0c16-65a9-e747-05f6-7443767b5a78"
},
"outputs": [],
"source": [
"from urllib.request import urlopen"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "832852a4-fd96-9f72-3080-74c3a068e63f"
},
"outputs": [
{
"ename": "URLError",
"evalue": "<urlopen error [Errno -2] Name or service not known>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mgaierror\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1317\u001b[0m h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1318\u001b[0;31m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m 1319\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1238\u001b[0m \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m 1284\u001b[0m \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1233\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1234\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m 1025\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 963\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 965\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1392\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1393\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 935\u001b[0m self.sock = self._create_connection(\n\u001b[0;32m--> 936\u001b[0;31m (self.host,self.port), self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 937\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetsockopt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIPPROTO_TCP\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTCP_NODELAY\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 704\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSOCK_STREAM\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 705\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/socket.py\u001b[0m in \u001b[0;36mgetaddrinfo\u001b[0;34m(host, port, family, type, proto, flags)\u001b[0m\n\u001b[1;32m 742\u001b[0m \u001b[0maddrlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 743\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_socket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetaddrinfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mport\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfamily\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 744\u001b[0m \u001b[0maf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msocktype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcanonname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msa\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mgaierror\u001b[0m: [Errno -2] Name or service not known",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-cf7965408664>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'https://www.kaggle.com/c/titanic/data/gender_submission.csv'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mURL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mtitanic\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mURL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtitanic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m 221\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 225\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 524\u001b[0m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 526\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 542\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 543\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 544\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 545\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 546\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 502\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 503\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 504\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 505\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 506\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1359\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1360\u001b[0m return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0;32m-> 1361\u001b[0;31m context=self._context, check_hostname=self._check_hostname)\n\u001b[0m\u001b[1;32m 1362\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1363\u001b[0m \u001b[0mhttps_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m 1318\u001b[0m encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1320\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1321\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1322\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno -2] Name or service not known>"
]
}
],
"source": [
"url = 'https://www.kaggle.com/c/titanic/data/gender_submission.csv'\n",
"URL = urlopen(url)\n",
"titanic = pd.read_csv(URL)\n",
"titanic.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "9c1848ee-e805-d15d-3fe9-25cac30abae7"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-0551d9d9029f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mURL\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"pd.read_csv(URL)"
]
}
],
"metadata": {
"_change_revision": 56,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/159/1159929.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d560675f-ec41-ae27-c9a9-6249c43b459e"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1da3803a-21cb-2409-7998-48aa2e7c59b4"
},
"source": [
"Hello everybody,\n",
"\n",
"this is my first notebook/competition and I hope to have feedbacks about what I'm doing (especially wrong things).\n",
"\n",
"I haven't seen other submissions, as I want to start from scratch and see what I can find\n",
"\n",
"I'm very fascinated by ML and I'm eager to learn as much as possible \n",
"\n",
"Ok, let's start!\n",
"\n",
"Besides the results, what I'll like to do is to establish a correct general workflow helping to work with all datasets\n",
"\n",
"The steps:\n",
"\n",
"\n",
"\n",
"1) Inspect the data to have a first guess of features, relations, instances quality and draw some graph helping to visualize them\n",
"\n",
"2) Do some preprocessing (get rid of nan, categorical feature encoding, feature scaling - if necessary)\n",
"\n",
"3) Further analysis\n",
"\n",
"4) Build a baseline classifier (Logistic Regression in this case) just to have a starting point\n",
"\n",
"5) Do features selection and engineering to improve results\n",
"\n",
"6) Repeat from step 2 with another approach (algorithm, features, etc) until complete satisfaction :)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8d1d83d2-45ab-e492-ace6-871efa666e32"
},
"source": [
"Update: putting all functions used in the notebook at top, "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "9ef30e8c-68a2-9285-68e7-cd93b41ca34d"
},
"outputs": [],
"source": [
"# Importing some libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import scipy.stats as stats\n",
"import pylab\n",
"\n",
"\n",
"def rand_jitter(arr):\n",
" stdev = .01*(max(arr)-min(arr))\n",
" return arr + np.random.randn(len(arr)) * stdev\n",
"\n",
"def scatter_plot(x,y,x_label,y_label,colors):\n",
" plt.scatter(x=x, y=y, c = colors)\n",
" plt.xlabel(x_label)\n",
" plt.ylabel(y_label)\n",
" plt.figure()\n",
" \n",
"def pair_grid(df, target):\n",
" g = sns.PairGrid(preprocessedDataset, hue=target)\n",
" g.map_diag(plt.hist)\n",
" g.map_offdiag(plt.scatter)\n",
" g.add_legend(); \n",
"\n",
"def box_plot(data):\n",
" sns.boxplot(data=data)\n",
" plt.xlabel(\"Attribute Index\")\n",
" plt.ylabel((\"Quartile Ranges - Normalized \"))\n",
" plt.figure() \n",
" \n",
"def prob_plot(column_index, data):\n",
" col = column_index \n",
" colData = []\n",
" for row in data:\n",
" colData.append(float(row[col]))\n",
" stats.probplot(colData, dist=\"norm\", plot=pylab)\n",
" pylab.show() \n",
" \n",
"def corr_heatmap(df):\n",
" sns.heatmap(DataFrame(df.corr())) \n",
" \n",
"\n",
"def cross_tab(feature, target, kind, colors, stacked, grid):\n",
" cross = pd.crosstab(feature, target)\n",
" cross.plot(kind=kind, stacked=stacked, color=colors, grid=grid) "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "b5badc38-a151-c50d-dfbf-c8093e112fe1"
},
"outputs": [],
"source": [
"def roc_graph(y_true, y_pred):\n",
" from sklearn.metrics import roc_curve, auc\n",
" fpr, tpr, _ = roc_curve(y_true, y_pred)\n",
" roc_auc = auc(fpr, tpr)\n",
" lw = 2\n",
" plt.plot(fpr, tpr, color='darkorange',\n",
" lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
" plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
" plt.xlim([0.0, 1.0])\n",
" plt.ylim([0.0, 1.05])\n",
" plt.xlabel('False Positive Rate')\n",
" plt.ylabel('True Positive Rate')\n",
" plt.title('Receiver operating characteristic')\n",
" plt.legend(loc=\"lower right\")\n",
" plt.figure()\n",
"\n",
"def evaluate_classifier(y_true, y_pred, target_names):\n",
" from sklearn.metrics import confusion_matrix \n",
" # Making the Confusion Matrix \n",
" print('Confusion Matrix')\n",
" cm = confusion_matrix(y_true, y_pred)\n",
" import seaborn as sn\n",
" sn.heatmap(cm, annot=True)\n",
" plt.figure()\n",
" print(cm)\n",
" print('\\n')\n",
" # Report\n",
" print('Report')\n",
" from sklearn.metrics import classification_report\n",
" print(classification_report(y_true, y_pred, target_names = target_names))\n",
" roc_graph(y_true, y_pred)\n",
" \n",
"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
" n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
" \"\"\"\n",
" Generate a simple plot of the test and training learning curve.\n",
"\n",
" Parameters\n",
" ----------\n",
" estimator : object type that implements the \"fit\" and \"predict\" methods\n",
" An object of that type which is cloned for each validation.\n",
"\n",
" title : string\n",
" Title for the chart.\n",
"\n",
" X : array-like, shape (n_samples, n_features)\n",
" Training vector, where n_samples is the number of samples and\n",
" n_features is the number of features.\n",
"\n",
" y : array-like, shape (n_samples) or (n_samples, n_features), optional\n",
" Target relative to X for classification or regression;\n",
" None for unsupervised learning.\n",
"\n",
" ylim : tuple, shape (ymin, ymax), optional\n",
" Defines minimum and maximum yvalues plotted.\n",
"\n",
" cv : int, cross-validation generator or an iterable, optional\n",
" Determines the cross-validation splitting strategy.\n",
" Possible inputs for cv are:\n",
" - None, to use the default 3-fold cross-validation,\n",
" - integer, to specify the number of folds.\n",
" - An object to be used as a cross-validation generator.\n",
" - An iterable yielding train/test splits.\n",
"\n",
" For integer/None inputs, if ``y`` is binary or multiclass,\n",
" :class:`StratifiedKFold` used. If the estimator is not a classifier\n",
" or if ``y`` is neither binary nor multiclass, :class:`KFold` is used.\n",
"\n",
" Refer :ref:`User Guide <cross_validation>` for the various\n",
" cross-validators that can be used here.\n",
"\n",
" n_jobs : integer, optional\n",
" Number of jobs to run in parallel (default 1).\n",
" \"\"\"\n",
" from sklearn.model_selection import learning_curve\n",
" plt.figure()\n",
" plt.title(title)\n",
" if ylim is not None:\n",
" plt.ylim(*ylim)\n",
" plt.xlabel(\"Training examples\")\n",
" plt.ylabel(\"Score\")\n",
" train_sizes, train_scores, test_scores = learning_curve(\n",
" estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
" train_scores_mean = np.mean(train_scores, axis=1)\n",
" train_scores_std = np.std(train_scores, axis=1)\n",
" test_scores_mean = np.mean(test_scores, axis=1)\n",
" test_scores_std = np.std(test_scores, axis=1)\n",
" plt.grid()\n",
"\n",
" plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
" train_scores_mean + train_scores_std, alpha=0.1,\n",
" color=\"r\")\n",
" plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
" test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
" plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
" label=\"Training score\")\n",
" plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
" label=\"Cross-validation score\")\n",
"\n",
" plt.legend(loc=\"best\")\n",
" return plt \n",
" \n",
" \n",
"\n",
"def plot_precision_recall_curve(y_true, y_pred):\n",
" from sklearn.metrics import precision_recall_curve\n",
" from sklearn.metrics import average_precision_score\n",
" precision = dict()\n",
" recall = dict() \n",
" average_precision = dict()\n",
" \n",
" precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(y_test.ravel(),\n",
" y_test_pred.ravel())\n",
" average_precision[\"micro\"] = average_precision_score(y_test, y_test_pred,\n",
" average=\"micro\")\n",
" # Plot Precision-Recall curve\n",
" plt.clf()\n",
" plt.plot(recall[\"micro\"], precision[\"micro\"], lw=2, color='navy',\n",
" label='Precision-Recall curve')\n",
" plt.xlabel('Recall')\n",
" plt.ylabel('Precision')\n",
" plt.ylim([0.0, 1.05])\n",
" plt.xlim([0.0, 1.0])\n",
" plt.title('Precision-Recall example: AUC={0:0.2f}'.format(average_precision[\"micro\"]))\n",
" plt.legend(loc=\"lower left\")\n",
" plt.figure() \n",
" \n",
"def estimator_scores(estimator, X, y, cv):\n",
" from sklearn.model_selection import cross_val_score\n",
" accuracy = cross_val_score(estimator = classifier, X = X, \n",
" y = y.astype(int), cv = cv, scoring = 'accuracy')\n",
" print(\"Accuracy: %0.2f (+/- %0.2f)\" % (accuracy.mean(), accuracy.std() * 2))\n",
" precision = cross_val_score(estimator = classifier, X = X, \n",
" y = y.astype(int), cv = cv, scoring = 'precision')\n",
" print(\"Precision: %0.2f (+/- %0.2f)\" % (precision.mean(), precision.std() * 2))\n",
" recall = cross_val_score(estimator = classifier, X = X, \n",
" y = y.astype(int), cv = cv, scoring = 'recall')\n",
" print(\"Recall: %0.2f (+/- %0.2f)\" % (recall.mean(), recall.std() * 2))\n",
" roc_auc = cross_val_score(estimator = classifier, X = X, \n",
" y = y.astype(int), cv = cv, scoring = 'roc_auc')\n",
" print(\"Roc_AUC: %0.2f (+/- %0.2f)\" % (roc_auc.mean(), roc_auc.std() * 2)) "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "f7d125f2-4586-2345-f37b-2fb52adf063c"
},
"outputs": [],
"source": [
"#Feature selection\n",
"def evaluate_rfe(classifier, X, y, step, cv):\n",
" from sklearn.feature_selection import RFECV\n",
" rfecv = RFECV(classifier, step=step, cv=cv) \n",
" rfecv.fit(X, y)\n",
" # summarize the selection of the attributes\n",
" print(rfecv.support_)\n",
" print(rfecv.ranking_)\n",
" print(\"Optimal number of features : %d\" % rfecv.n_features_) \n",
" # Plot number of features VS. cross-validation scores\n",
" plt.figure()\n",
" plt.xlabel(\"Number of features selected\")\n",
" plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n",
" plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0283623a-548f-f5f4-a847-5472a6539260"
},
"source": [
"**Round 1**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "0bae55eb-e8e7-d034-15a7-072e98f1e297"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.6+ KB\n"
]
},
{
"data": {
"text/plain": [
"PassengerId 0\n",
"Survived 0\n",
"Pclass 0\n",
"Name 0\n",
"Sex 0\n",
"Age 177\n",
"SibSp 0\n",
"Parch 0\n",
"Ticket 0\n",
"Fare 0\n",
"Cabin 687\n",
"Embarked 2\n",
"dtype: int64"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Importing the train dataset from file\n",
"dataset = pd.read_csv('../input/train.csv')\n",
"\n",
"#Some info about it\n",
"dataset.info()\n",
"dataset.describe()\n",
"dataset.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "140bfddf-1270-6437-0208-a527cd58be8f"
},
"source": [
"Let's see what we have \n",
"\n",
"PassengerId: meta\n",
" \n",
"Survived: target \n",
"\n",
"Pclass: feature (seems important, based on position probably) \n",
"\n",
"Name: meta\n",
" \n",
"Sex: feature (not sure how can impact on surviving an iceberg hit :)) \n",
"\n",
"Age: feature (maybe target related) \n",
"\n",
"Sibsp, Parch: (seem important, an event happening to all the people in a group) \n",
"\n",
"Fare: maybe related to class \n",
"\n",
"Ticket, Cabin, Embarked: not related, just meta\n",
"\n",
"\n",
"Rows number seems ok respect the features \n",
"\n",
"Age is missing on 20% data, we'll see how to deal it"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "1fab8237-5f0c-f923-daa4-29b794ee79cc"
},
"outputs": [
{
"data": {
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UlpampKQkSVJGRoZqampkt9vlcrl8z3E4HLLb7R0zNQAAIabNIB8/flxLlizRiy++6DtB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\nBiDIAAAYgCADAGAAggwAgAEIMgAABvj/6BiUzDVNhUcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1547d9f6a0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f1547d9f550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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PJUuWIDY2FgqFAuHh4UhKSoKxsTE8PT0BAA0bNkRAQACmTZuGoKAgODs7Y8aMGfjtt9+Q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\nhjQREZFMMaSJiIhkiiFNREQkU/8PwfvvkIu2WFQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1547be75c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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IMgAABiDIAAAYgCADAGAAggwAgAEIMgAABiDIAAAYgCADAGAAggwAgAEIMgAABiDIAAAY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\nlOU7duzQU089pT/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"text/plain": [
"<matplotlib.figure.Figure at 0x7f1547d9f5f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f154769c5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# Let's explore the data visually against the target\n",
" \n",
"cross_tab(feature=dataset.Pclass, target=dataset.Survived.astype(bool), \n",
" kind='bar', colors=['red','blue'], stacked=False, grid=False)\n",
" \n",
"cross_tab(feature=dataset.Sex, target=dataset.Survived.astype(bool), \n",
" kind='bar', colors=['red','blue'], stacked=False, grid=False)\n",
"\n",
"cross_tab(feature=dataset.Age, target=dataset.Survived.astype(bool), \n",
" kind='bar', colors=['red','blue'], stacked=False, grid=False)\n",
"\n",
"cross_tab(feature=dataset.SibSp, target=dataset.Survived.astype(bool), \n",
" kind='bar', colors=['red','blue'], stacked=False, grid=False)\n",
"\n",
"cross_tab(feature=dataset.Parch, target=dataset.Survived.astype(bool), \n",
" kind='bar', colors=['red','blue'], stacked=False, grid=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8863e75e-df51-a70a-066e-fa63ff78cbef"
},
"source": [
"So male, with 3rd class and alone is the victim type\n",
"High SibSp too seems very deadly :(\n",
"\n",
"Ok, time to preprocess for further analysis"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "d741e8eb-0606-c225-e7fe-cbd6838be2b7"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.530377</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.518978</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>0.571831</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>0.691897</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.254825</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.506214</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>0.365167</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>0.348049</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.365167</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.503850</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>1.674039</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>0.324648</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-1.908136</td>\n",
" <td>2.677117</td>\n",
" <td>0.666862</td>\n",
" <td>-0.257546</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.185937</td>\n",
" <td>-0.551703</td>\n",
" <td>1.839619</td>\n",
" <td>-0.445544</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>-1.081480</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.087435</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.770360</td>\n",
" <td>0.524570</td>\n",
" <td>0.666862</td>\n",
" <td>-0.340278</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.949591</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.154013</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.668153</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.503850</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.640719</td>\n",
" <td>0.524570</td>\n",
" <td>5.357890</td>\n",
" <td>-0.064663</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.081480</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.507552</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>1.742927</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.353515</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-1.908136</td>\n",
" <td>3.753390</td>\n",
" <td>0.666862</td>\n",
" <td>-0.105320</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>0.089615</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.315695</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>0.365167</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.164414</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>0.296279</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.410245</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.012592</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.504243</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.117049</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>0.015232</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.494808</td>\n",
" <td>2.677117</td>\n",
" <td>0.666862</td>\n",
" <td>-0.257546</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>0.571831</td>\n",
" <td>0.524570</td>\n",
" <td>5.357890</td>\n",
" <td>-0.062536</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.737041</td>\n",
" <td>2.677117</td>\n",
" <td>1.839619</td>\n",
" <td>4.317274</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>0.709607</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.131873</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>2.500694</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.457520</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.117049</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>0.897780</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>0.847383</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>0.327248</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.599265</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.503850</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.805929</td>\n",
" <td>1.600843</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.315695</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.054047</td>\n",
" <td>0.524570</td>\n",
" <td>0.666862</td>\n",
" <td>2.461566</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>685</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>1.467375</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.154013</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>686</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.392601</td>\n",
" <td>-0.551703</td>\n",
" <td>3.012376</td>\n",
" <td>-0.291900</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>687</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.778495</td>\n",
" <td>1.600843</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.389287</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.599265</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.438610</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>689</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.260711</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.165753</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>690</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.392601</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.410245</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>691</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>0.847383</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.410245</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>692</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.185937</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.394014</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>693</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>0.089615</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>0.298804</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>694</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-1.770360</td>\n",
" <td>0.524570</td>\n",
" <td>0.666862</td>\n",
" <td>-0.445544</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>695</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.254825</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.506766</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>696</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.191823</td>\n",
" <td>0.524570</td>\n",
" <td>0.666862</td>\n",
" <td>0.337728</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>697</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>0.227391</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.561526</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>698</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>1.191823</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.485885</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>699</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.117049</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.202234</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>700</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.012592</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.519451</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>701</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.668153</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.469891</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>702</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.737041</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.506766</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>703</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.811815</td>\n",
" <td>-0.551703</td>\n",
" <td>0.666862</td>\n",
" <td>0.916454</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>704</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.323713</td>\n",
" <td>-0.551703</td>\n",
" <td>0.666862</td>\n",
" <td>-0.164414</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>705</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.227391</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.506766</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>706</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.530377</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.457204</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>707</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.117049</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.457520</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>708</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.323713</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.522760</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>709</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>0.640719</td>\n",
" <td>-0.551703</td>\n",
" <td>5.357890</td>\n",
" <td>-0.105320</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>710</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.185937</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.410245</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>711</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.737041</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.088774</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>712</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.254825</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.088774</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>713</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.158503</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.509523</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>714 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Sex Pclass Age SibSp Parch Fare Survived\n",
"0 0.759051 0.911232 -0.530377 0.524570 -0.505895 -0.518978 0\n",
"1 -1.317434 -1.476364 0.571831 0.524570 -0.505895 0.691897 1\n",
"2 -1.317434 0.911232 -0.254825 -0.551703 -0.505895 -0.506214 1\n",
"3 -1.317434 -1.476364 0.365167 0.524570 -0.505895 0.348049 1\n",
"4 0.759051 0.911232 0.365167 -0.551703 -0.505895 -0.503850 0\n",
"5 0.759051 -1.476364 1.674039 -0.551703 -0.505895 0.324648 0\n",
"6 0.759051 0.911232 -1.908136 2.677117 0.666862 -0.257546 0\n",
"7 -1.317434 0.911232 -0.185937 -0.551703 1.839619 -0.445544 1\n",
"8 -1.317434 -0.282566 -1.081480 0.524570 -0.505895 -0.087435 1\n",
"9 -1.317434 0.911232 -1.770360 0.524570 0.666862 -0.340278 1\n",
"10 -1.317434 -1.476364 1.949591 -0.551703 -0.505895 -0.154013 1\n",
"11 0.759051 0.911232 -0.668153 -0.551703 -0.505895 -0.503850 0\n",
"12 0.759051 0.911232 0.640719 0.524570 5.357890 -0.064663 0\n",
"13 -1.317434 0.911232 -1.081480 -0.551703 -0.505895 -0.507552 0\n",
"14 -1.317434 -0.282566 1.742927 -0.551703 -0.505895 -0.353515 1\n",
"15 0.759051 0.911232 -1.908136 3.753390 0.666862 -0.105320 0\n",
"16 -1.317434 0.911232 0.089615 0.524570 -0.505895 -0.315695 0\n",
"17 0.759051 -0.282566 0.365167 -0.551703 -0.505895 -0.164414 0\n",
"18 0.759051 -0.282566 0.296279 -0.551703 -0.505895 -0.410245 1\n",
"19 -1.317434 0.911232 -1.012592 -0.551703 -0.505895 -0.504243 1\n",
"20 0.759051 -1.476364 -0.117049 -0.551703 -0.505895 0.015232 1\n",
"21 -1.317434 0.911232 -1.494808 2.677117 0.666862 -0.257546 0\n",
"22 -1.317434 0.911232 0.571831 0.524570 5.357890 -0.062536 1\n",
"23 0.759051 -1.476364 -0.737041 2.677117 1.839619 4.317274 0\n",
"24 0.759051 -1.476364 0.709607 -0.551703 -0.505895 -0.131873 0\n",
"25 0.759051 -0.282566 2.500694 -0.551703 -0.505895 -0.457520 0\n",
"26 0.759051 -1.476364 -0.117049 0.524570 -0.505895 0.897780 0\n",
"27 0.759051 -1.476364 0.847383 0.524570 -0.505895 0.327248 0\n",
"28 0.759051 0.911232 -0.599265 -0.551703 -0.505895 -0.503850 0\n",
"29 -1.317434 0.911232 -0.805929 1.600843 -0.505895 -0.315695 0\n",
".. ... ... ... ... ... ... ...\n",
"684 -1.317434 -1.476364 1.054047 0.524570 0.666862 2.461566 1\n",
"685 0.759051 -1.476364 1.467375 -0.551703 -0.505895 -0.154013 1\n",
"686 -1.317434 0.911232 -0.392601 -0.551703 3.012376 -0.291900 1\n",
"687 0.759051 0.911232 0.778495 1.600843 -0.505895 -0.389287 0\n",
"688 0.759051 -0.282566 -0.599265 0.524570 -0.505895 -0.438610 0\n",
"689 -1.317434 -1.476364 1.260711 -0.551703 -0.505895 -0.165753 1\n",
"690 0.759051 -0.282566 -0.392601 -0.551703 -0.505895 -0.410245 0\n",
"691 -1.317434 -0.282566 0.847383 -0.551703 -0.505895 -0.410245 1\n",
"692 -1.317434 -0.282566 -0.185937 0.524570 -0.505895 -0.394014 1\n",
"693 0.759051 -1.476364 0.089615 -0.551703 -0.505895 0.298804 0\n",
"694 0.759051 0.911232 -1.770360 0.524570 0.666862 -0.445544 1\n",
"695 0.759051 0.911232 -0.254825 -0.551703 -0.505895 -0.506766 0\n",
"696 -1.317434 -1.476364 1.191823 0.524570 0.666862 0.337728 1\n",
"697 0.759051 -1.476364 0.227391 -0.551703 -0.505895 -0.561526 0\n",
"698 0.759051 0.911232 1.191823 -0.551703 -0.505895 -0.485885 0\n",
"699 -1.317434 -0.282566 -0.117049 0.524570 -0.505895 -0.202234 1\n",
"700 -1.317434 0.911232 -1.012592 -0.551703 -0.505895 -0.519451 1\n",
"701 0.759051 0.911232 -0.668153 -0.551703 -0.505895 -0.469891 0\n",
"702 0.759051 0.911232 -0.737041 -0.551703 -0.505895 -0.506766 0\n",
"703 -1.317434 -1.476364 1.811815 -0.551703 0.666862 0.916454 1\n",
"704 -1.317434 -0.282566 -0.323713 -0.551703 0.666862 -0.164414 1\n",
"705 0.759051 0.911232 0.227391 -0.551703 -0.505895 -0.506766 0\n",
"706 -1.317434 0.911232 -0.530377 -0.551703 -0.505895 -0.457204 0\n",
"707 0.759051 -0.282566 -0.117049 -0.551703 -0.505895 -0.457520 0\n",
"708 0.759051 0.911232 -0.323713 -0.551703 -0.505895 -0.522760 0\n",
"709 -1.317434 0.911232 0.640719 -0.551703 5.357890 -0.105320 0\n",
"710 0.759051 -0.282566 -0.185937 -0.551703 -0.505895 -0.410245 0\n",
"711 -1.317434 -1.476364 -0.737041 -0.551703 -0.505895 -0.088774 1\n",
"712 0.759051 -1.476364 -0.254825 -0.551703 -0.505895 -0.088774 1\n",
"713 0.759051 0.911232 0.158503 -0.551703 -0.505895 -0.509523 0\n",
"\n",
"[714 rows x 7 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get all relevant columns\n",
"workingDataset = dataset.iloc[:, [1,2,4,5,6,7,9]]\n",
"\n",
"# get rid of age nan rows (first approach)\n",
"workingDataset = workingDataset[np.isfinite(workingDataset['Age'])]\n",
"\n",
"# feature/target selection\n",
"\n",
"workingData = workingDataset.values\n",
"X = workingData[:, 1:]\n",
"y = workingData[:, 0]\n",
"\n",
"# encoding feature (sex)\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"labelencoder_X = LabelEncoder()\n",
"X[:,1] = labelencoder_X.fit_transform(X[:, 1])\n",
"onehotencoder = OneHotEncoder(categorical_features = [1])\n",
"X = onehotencoder.fit_transform(X).toarray()\n",
"\n",
"# avoid dummy trap\n",
"X = X[:, 1:]\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"from pandas import DataFrame\n",
"sc = StandardScaler()\n",
"X = sc.fit_transform(X)\n",
"#y = sc.fit_transform(y.reshape(-1,1))\n",
"# rebuild feature's dataframe with normalized data for graphs purpose\n",
"preprocessedDataset = DataFrame(data=X)\n",
"\n",
"preprocessedDataset.columns = ['Sex','Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
"preprocessedDataset['Survived'] = y.astype(int)\n",
"preprocessedDataset"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "15ad6bab-8f23-226d-ec7d-39c93e41de02"
},
"outputs": [
{
"data": {
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XHTX81N2reco7lMxnxoahqtC57jofH37oY8WKY38NatQou9d4X3hhgAsu8LNk\nydHroXXrY/coiUjJK93pcjxcLnL69iP7safI7XFD3mUx7rvuwdOkaZGz5l51DTkPDT00wGYr9cEM\n4Fy9Iu+hDkdybP4ntMWEgcMBY8fm0Lmzl+TkAAkJAeLjjw7hU04J0K9f2evW/49hwPPP53D++T4M\n40DvUWwsXHmlt0zfKU/EyiJiz7koZkI50j//kpg3X8OxZjVRhklurhezXHlwOPC2ak1O75vKRBgf\nKVClauHjSmtX/XGqXt3kvfdyyMyE3FwDnw9Gjoxn4UI/ubkG557r5447vDRsWLYPvJ5zjsmMGW6+\n/dbOli02OnWKpnbtnGPPKCJBYZimNU73sMpp+xF1CUFuLont2+Bc90f+4VFRpL/8Brnde4WnrjBL\nTk5g584M/H6Iigp3NeERUd+DAqj9kd1+CP+lVGVvd1CKLyqKzFfewNOkKebBbn5/9eowdGjEBvN/\nHI7IDWYRCb+I79aOdL4LLmT/jLk4F3+HbfcuPB06Uqn2qRDhW80iIuGkcBYwDLwXtwl3FSIicpC6\ntUVERCxG4SwiImIxCmcRERGLUTiLiIhYjMJZRETEYhTOIiIiFqNwFhERsRiFs4iIiMUonEVERCxG\n4SwiImIxun1nJDJNXFMmETVzBkaOG1+9+rjvGISZVCHclYmICJEezoEArm+mY1v/Jwt/iObDP5uz\nKKsp8UkO7n3IpOu1AUyz7D2dKOrRoXzwno2F5g0EsNFszhJun9sdz6RPMStXDnd5IiIRLyKf5+z3\nw5dj01j2xkqiU7ZzDVMZywC+ogteDiSxkxxiySHG5eO8BjncNzKRRo0ssapOzh9rGXjZDqb6u+Qb\nHEMWXc74lSe+qc/ZZ0f2s1wj7lm2pgmGkW9QxK2D3Fzw+SAuDojA9h8h0tsP4X+ec8SFs9cL/ftH\n87//2fnvkLsdL36cRc5XO3k/0+baOeUUS6yuEzb95m/p/213CjvdoIptF43Kb2GtuwbZvmiibLm0\nqL6Fu19K5pxWFUNbbJhExA9Tbi5xzzyBc9ECbBnp+M48G3f/2/Be3hGIkHUAGNu3Ef/kMJzLloIn\nF1+DhrjvGkxit6sjov2FiZS/f1EUzgeF6oMweHAUEye6TmjealVy6XQV9O3r5ayzLLHajtsDl6/n\ng9VNjnu+JFsa3br7qHVuHCkpNq6+2ku9eqVzHeD1Ej3xYxyrV2LGxrH6vBt5Y8H5VPj1e05z7uTM\nwVfRunP7v6fdAAAgAElEQVTZPlcy4ZY+RH89Ld8wf4WKZIx5D2+bSyPjx9nrpXznDrhWLs832F/1\nFOzffsOeGmeGqbDwi4i//zEonA8KxQfhqadcjBkNPk7sILIDDz5cJCcHeO65HK66yl/CFQbfY7el\n4/zyC76kC9s5rcBpbPgYwaO0Yx4JpPM79XmNe/ieNoAJGBiGSZ06AebOzSY2NqRNOGE7dxqMet5L\n/+nXclHWgrzhGcSzh4rUYBtO/GyhBhta9abh1IfDWG3wOFatoHyX/8Pmzj5qXG6nzqRP+CQifpyj\nPppAuSGDCh7Zvz97nn0ltAVZSCT8/Y8l3OFctncPDjPsUQeTR6fio/h7zdFk839Mw8aBEG7IGs5l\nDXv22HjttSj8pSib9+6Fkf03s3fGctxEkUG5QqftzQc8zAtcwArOZgNdmcZErqctc4EDxyZN02Dj\nRjudO8eEqAUnJzUVrroqhjMnPpsvmAESyOR0NuM8+Hc+ja20WvwivP8R6enw0ksunu6+iTUth+Dv\n1J34e+7EsfyncDSjRDh/XFxgMAPY/v4rxNWEj2PDn4WP3Lw5dIWEWWYm7NhhlKrfs0gQEWdrz59v\n56OxAdxULWIqP2DPe2Xgpy8f8CIP8CG9+ZeqXMtUTudvPuF67vjtLZYutdGyZSDo9Z+stWsN+t8S\nxYaNDYAGx5g6wAqaMpIHeJAX+e80oWrsYDiPk8GL3M2bbORAl98ff9jZutWgRg1LdMAU6sYbotm6\n2aQFPxZrehdevnhxKyNGxVJn2yI+og812H5g5HJwzZxB1rMvkNv1uiBWHRz+GqdhGgZGAZ1mZlIS\nubkwdixs3Ojioot8XHyx9T/jJyJQ5ZTCRyYnh66QMMnIgIcfjuK77xzs329wTdUf6HHBn7R8qi0U\nsUcnoRER4Tx6tBN3ESd8Gfgw8NODz9hNZeLIpiMzuI13MYCW/Mi3dKIxvwBwK+/yKw1wzT4FLmwL\nzqJPJgu3F1+MYsPG4tZo4zca8igj8OJkGM/mjanP7ySSzgT6cjGLMbHh9xusWWOjRg3rbnavW2fw\n+3IvAWKxc+w6F9OCIbzM8pQLCGDHQz0mcBOPMSJvGvu+vcS8NYrcLteCrXR1QHmuvBpf4/NxrlqR\nb7hps7Go/gAGtY9l3TqAKFwuF23b+nj33RxiSkcnSbG5b7qFmDGjsf+7M99w02bD6NMnTFWFzh13\nRDNrlpMz2MAn3EbLf34g+h8PWTMrQ7+e8Ojwo87iP6bcXLDbwVF2osU142uipnyObfcu/DVqkNOn\nH77mLYP+vqXrV+U4Gbv+JeHugfzzw66jxg3mFXryKT35hArsoyopjKM/c7mcr+jCwIPBDHAuv7Ke\ns0ilPAB2THoyiQ5vd6f8tZ0x9qeFsFXHx++HlSuP/8/sx8ln9MR3WG9CIukANGMpXfgSAKfTpFEj\na+9ZTX9rN9kcODC+jAuLnDaV8lzPpyyjGYGD2667qcoIhvEJ1+eb1vHrGux//B6cooPJZiPj1VF4\nLmqOefBH1J+UhKdGLYZOaMi6dYf+5h6PwaxZTkaMOLGTKK3M8HogO+voEYEAjBwZ+oJCaOVKG999\n5wBMxnELl7GAaDwAxGXuhjfeIOb14h9zdyz5gXK9rqXC+fWp0LQhCbf1w9i+LUjVh070uHdIuONW\nor/5CteypcR8MZly/XrjnPlt0N+77Iazx0O5m64netKn+I/IjmR2UZc/2ckpzOFy9lKZvVQo9Dhs\nJvHspjLewzoa6rIOOwFcS38k9rnhwWzJSTvejd///ENtUqgEHDgN7D92TM5kAwAtWvgs36Xt+Htj\n3v+H8xg/HRHQvoNfg7cYyNn8yVZqHrWMXKL5nCO6sJ1OzOjoki84BPz1GrB/+kzSpkwn4+FhEB3D\n7M31WOE9t8Dpv/++7OwJ/Sf6ownY09OPGm4A/PADxs4dIa8pVFautON2G7RnDs1ZUuA0UbNmFGtZ\ntj/XUe7OW4maNwf7nt3Yt28j+ssplO/XGzyekiw7tDweYia8h+2IDTj73hRixrwV9Lcvs+Ec/cmH\nuFYcuETiTNbnG5fMHu7hNRbRlr0cOLbkwcVgCt5SXEQbqrGD7VQncHB/ujIpeeOdP1v35CC7Hc4/\nv7A926JDtQr/kkgaKVRgNu3yhufgYhkXUrmynw8+yCnBaoOjR8PfSWQfAHtJph1zGcZwPqcb73MT\n1zGZQbzKg4xkD1UKXc4+8l/n7W3SlMAZpfhyG8PA16IVjq1bse/cwU6qYh7WU3K47ILPHyvVjAKC\nOY/HQ9TM4oVTadSokZ/oaJMzWY8LX4HTGKn7irWs2PHvYt+29ajhzlUriP7kw5OqM6yWLcOxvuCT\nBh1//IaRUcTnpwSU2XC2/7k27/+XsADbYR/ADZxJLvkPoJnYWUoLFtEqb0/Kj8FiWjCI1+nADL6k\nCzbMfHvQBya07vFWgAcfzOXss/PXmEQKPfiYO3iDevxa4HwmBo/xFI1YzSBG5Q2fRzsWcin9+/tK\nxWVU1e+5msFxY4nlwBZwJgmMYBg9mEw/3mcpzRjFYLKKOIMd4HQ25f3fX70GWY8+EdS6Q8X+94F2\nXcOXVKXgvcVzzrH2oYsT4W15cZGbp/5q1UNWS6g1bRqgRQs/87mMtEI+9/5atYu1LNvmfwodZ9+w\nvtBxllehAmZh926OicF0BvdQT5kNZzMpKe//e0kmgD1v78lbyOVUm6nNQtrQjSkMYzg9+YzWfM8O\nTuEnmvMuA1jLmTiP2NL0NToveA0pAWefbfL119m4XIcCOoF0XuIRRjOYn7mIi1kI5P8B/ps6vMQD\n7KAGfhz8RS3e42Z6MpELLvAxYEDp6LIyk5O575l4vk3sxe2MphPfcDGL6G6fzDMM5WeakMD+Yywl\ngAcnn9ATb9VTSV3wA74Lm4Wk/mALVDjQI5DMXvryAXa8+cZXrRrgttu8Bc1aqnnbtMVfp07BIxs1\nwtvu8tAWFGKjR7s5s/MZzHRedfTI8uXJ6XNzsZYTqFT4me2B0nyv/nr18Da9qMBR3otaQpAPadmf\nfPLJJ4P6DsWUnV2yP/S+s+oSNe0LbBkZrKER82hPeVLpywQ2UQc3R+/yVSCFR3iOh3mR72nNH9Tn\nwBEogx9pRSL7uIkPqERq3jzeuvXIfOFVzMTEEq2/pEVHQ0yMycKFDsDgCZ6iA3MAcOJjGl34k3MK\nmPNAN34WMXxOdzbVu5IO3WJ46aVcyhW9o2kp/oaNqNK9FR3LLab7BRvp/kgNrnj2IlpV+J2Mdf+S\nlWnwc5Enixls5AwujltJvZE98Tc+P2S1h0LUnJkYPh+XMY+q/IsXBxVdWTS7sjxPP+PloovK3p4z\nhkFOr964Zn6Lbd8+DExMwH/W2dg+mEB2Utm+nCo2Fq6+2kf5GzrgTs0hxpcJTife887HMeIZMtp3\nKtZyAvHliJr5LUZubr7hvlqnk/nqKIgqnedlxMVFsb/2WThWLMe++8BJxaZh4G3WgozX38q7D/vJ\nvkdhyvQdwpyzZhD3/DNk/r6FyuzBh4trmUwcmXzI0VuF1zGJ53mYOvydN+ws1rKeuvwXUpcmreSb\n9i9jS9+P76yzcd9+N2bF0nPP6QcfdPDxx9F85OtBTybnDW/CclZS8G09E9jPJK6jI3PIGP48Obfd\nEapyQyL3hbfJfmksVzCTv8m/J+XAQ9XYdGpF7aTX2cvpNbyO5XtKTkTM6y8T88F47Nu2HrgGukkT\nUoc+ha/VxeEuLSTsq1fhWrwI/6nV8FzdleSqiZF5h6yDD0E53rtjRX84nph338ax/k9Mux1f4/PJ\nHPoEvotbB7HY4MpbBx4PUZM+xb51C75z6uG5umuJXT4Z2bfv9PlwzpvDNSOas3BdDZzkMId2vM69\nzOZyskgghizasoAP6UMiqXzATfxJXWqwFQcebufdvMXFxZksWJBFrVqWWG0nxO+H3Tc8QsP5o/OG\ndWY639D5qGkN/HxIb25kIiaQPu4DPFddE8JqQyA3l3/P70bmnhxG8jDLaYILL7X5i6ZDmjHkodL7\ntz4eRmYGrln/I5CYRGL3LuzZW8BlRhEi0m9feULt93hw/riYQFwc/gsuPPHLRCwi3LfvLPvhfNCm\nTQa33BLDH3/Yacb3fEUXtlGDpTTnApZzIcuPmmcbp3Iuv5JGhXzDv/wyq1TcGawotn/+IXDJ/5Gc\nfeAsy0lcRz/Gk018vula8R3f0QYD8DZsRNqshQdOAS9jvPuzWd7tdZL+/Jlsj4P1FS5i/233cNvg\nstfW4lA4qf2R3H5QOOcJxQchKwvGj3exZclOqq6ezYMp91OBDDbaziQmkElVdmLnwAVGq2lEC34k\n54hj07Vq+Vm4sPQ87KEogbmL2HTLKzRw/4yJwVCe5X1uxs2BYynV2cIPtOQ0tuGrfy4ZI18uMydB\nFcbjAZcrAb8/oyxugxRbpP84q/2R3X4IfziXvTsLFCEuDu6+2wN3VwR/d2wLKpK+P43yHTox7Zso\nrrq/IVU82zGAX2iI44izsh0Ok27dSsflQ8Vha9eGrK/a0vjqdDLddvZw6MxKp9Pk+WHpVPANIL1K\nVXKv6Wb525SWBJfrwG2V9+wJdyUiEskiKpzzsdvzXSrRpSe44p+Hxx+Bbdvoy0fEks24xPv5u2IT\nkqsYXHWVl379Cr5gv7Rq3DjAjfcnMXq0i4NXmlG+PPTp4+Hy22vi5t7wFigiEoEiqlu7OJJNN5kv\nvYZtfxreRufh6XpdmTzGeqRt2ww+/9yJ3w/9+kVRsaI1/h7hoC49rQO1P7LbD+rWtp7KlXE/8Ei4\nqwi56tVN7rvvwLXmyclR6tYVEQmjMnuHMBERkdJK4SwiImIxCmcRERGLUTiLiIhYjMJZRETEYhTO\nIiIiFqNwFhERsRiFs4iIiMUonCNcIHDgEa4iImIdCueieDzYtm098DirMmbFCht9+kRz/vmxXHhh\nLHfeGcWuXaX7+asiImVFRN6+MycHpk1zkJtrcM01XiqsX4Zj5Qp8DRtB5w5gmsS8PJKoSRPZsC2O\n1MRaNLoiGfdzIyE6Otzln7RNmwxuuy2aLVsO3TN882Y7f/9t46uv3GGsTEREIALDeepUOy++GEWN\nTYvowUTKPziNRDMFgC+5moWsxY+NW/iWc9jBGfjZsNfHvE9McmY9xrdXvE5iEvTp46VmzdLZHzxu\nnDNfMP9n+XIHn37q5P77w1CUiFiSkZJCzLi3sW3bSqBSMjn9biVwWs1wl1XmRVQ4b9tm8OCD0Zye\nvhoHXpqynEpmCjlEcQ1fkEMUPfmM6/iCCqTlzVefddRnHd4UaPvxFHZQDc/bsWy7tAXVxz4EMTFh\nbNXx++efwo9m/PmnwerVMHWqkypVTK65xodrz3Zi3x6Fff16zIQEcv+vM55ruoWwYhEJB/svqyl3\nWz8cmzbmDYv+8gsyXn4j3yN3peRF1CMjr7wymp+X2TCxE086e0nEhYkfMA77dzzcbS4jc9IXYCs9\nh+/vvDOayZOdBY6LjQ1gGAZZWQfWRP0zsnkztz+XbJ2YN43pcpF9+91kP/pESOoNBvuqlURP+wI8\nHoycbGz79pFNDENShrE0oyF2R4AmTfzce6+HU0+1xFckpCL9kYFq/4H2l7uhO1FzZh413nt+U9L+\nNxeMsnueSrgfGRkR4Wxfs4p9oz5n7nQPc2nPNK5mIW1oydLjDuMjmUDGyJfJvXlASZQaEosW2bn5\n5hgyM4vX+sas5GcuxIE/b5i/YiVS5y/GPOXUYJUZNLHPPU3MmLexZR840c8EpnANN/Eh2cTnm/aM\nM/y8/76bs2vnEvXlFGz79pHb6UoCNWuxcaPB6NEu/vzTRmysSdu2fm6/3VuattMKFanhZKTvJ+bN\n14lb/we5hoPcS9uRe2PfMh1CBUmuFM/eRUtJvOb/sKelHjXetNtJnfsd/vrnhqG60FA4H1RSK8HY\ntYuYMaOxb/4Hs0JFAjYbMZMnYsvMBMCHjX+pggMPVdh70uEM4K96CvvWrCtVX+DRox0Mf8pFgKOP\nPR8twPM8xK80YhWNiSaX1izikUd9GINvD3qtJcnx81LKd+uCzZ0NQDYx9OAzvuFKCrt4oT6/8qWr\nJ2d6/gAgUD6RXe2603r1aDZuOvzIkEnPnl7eeCM3yK0IvkgMZyN1H+V6dcO1cnneMNMwcN/Yl6yX\n3whjZaHl+nY65ce8ifnzz+D3F/gbadpspM75Dv+5DUNeX6iEO5yDesz52WefZc2aNRiGwdChQ2nY\nMLh/SPvvv1Kuf998x0dMDnVV+7DhIEB1dh73sg9fzpHS/nUT89hQ3M88d9zLDRW/HyZPdrB6tZ34\neJN2l3kBPxQrnG38TFPmcDnpJAKwkib8NvlvPhlUqrZJsE2emhfMAA/zHN9wVZHz/MnZfOTpztM8\neWAZ+9Oo+sW7vM9vdGA22cQdnNJg+nQnt9zipVGjQJBaIMES88Zr+YIZwDBNYiZPIrfXjfguuDBM\nlYWO45dVxD94H+zZXeSOi6/x+fgblN29ZisIWgfcsmXL2Lx5M5MmTWLEiBGMGDEiWG+VJ/blkfmC\nGQ4FagZxODjxH0wDKHgbEiqSxpx3t+H4+OMTXn4wZWbCddfFMGhQNOPHu3jjjSh69IzFoHidJons\n41keZRYdSGRf3vD5G2oyY0Zxwt0aliyx8/VUCGDwDZ34mSbM59JjzufDxQRuZj/l8oYZQCt+5BOu\nzzdtdrbB3LkRdZ5lmeH4ZVWBw40cN66ZM0JcTXhEfzAB+57dRU7jr1KV7MFDStdWeSkUtHBesmQJ\n7dq1A6BOnTrs37+fzINdy0ERCOBctaLAUfsoTwIndyORXBzkFhHvZ7GeD19Os+Tttp57zsXixQ4O\n3/d3u234iSrG3CYtWMxZbKQZy3iY5/PGBEwby5eXjnA2TXj6aRfPpt9JFXbSmW94jKfZRJ1izb+V\n05hBx6OGt2cOzfgx37By5az3GZBicBZ8kuQxx5Uhtl2F9yp6Tz+d7FtvJ23aDDwd/y+EVUWmoIVz\nSkoKSUlJea8rVKjAnj17gvV2BxgFN+dDeuMuVhDl58WOCayjDtuoQSzeQlfYKexk+b+nYaTuK2SK\n8Pnpp5PZkzPYRg1yDq6/C8i/AVS+/EksOoSWL7excqWdtdQnhSqAwSw6kkNsseZ34OFUdhw1PA43\nzVma97pmTT+9enlLquygyMmBUaNc9O0bTb9+0bz/vhO//9jzlXXeVq0LHO6vUJGcXjeGuJrwCBRx\ngmfuDX3JemYkgTpnhLCiyBWy/rdjnXeWlBSLw3GSe2EtW8CkSUcNXkxrzuV3LmPBcS0uk3he5R6q\nsY0BvFfktFs4jUxHBSrVrGq5u4idbIfFL5zH9XzKVK4lF1fe8Jo1/Nx/fxRJSce/4RNqfn9BnRrF\n75a7iJ9ozfdHDfdi50/OBqB2bXjpJTu1axd+kke45ebCDTfAnDmHhn3zjZPVq6P55JP8PZVFnaxS\nJj32CKz7DaZOBZ/vwLCkJOzDHqVikwbhrS1U7h0Es/8HO4/Yg65Xj/gH7yM+Pr7g+cqocH4HghbO\nlStXJiUlJe/17t27SU5OLnT61NTsQscVl+2+R0ifuZba+3/JGxYAKrKXwbzO13SmNpuLvbwfacGb\n3M3TPFpkF4MPG5/TnfPOSmdPhhcyrLXnVK5cDCf7p55BR76jFQsOHqM9q1o6jz7jwOfzE+wOkZJQ\npYoBxHH8V7IDBOjHuALHZNS/iAadLqN5uRxuuMFLfDyWXh+jRzuZM+fojcfPPze54go3HTse2IWO\nxLO1ARg1FmfnbiSu+JEsv0FOrxsJnHEmRMq6OPV0nC++TuKYUQR+Xg4uJ94LLiRr2JP43Sa4I2Q9\nEP6ztYN2KdXKlSsZNWoU77//Pr///jvPPPMMEydOLHT6kloJ11zmo+WvYziTDeylEv/H12SSQEf+\nh5sY9lCRBI6+f/RGTucM/sp7vYZzuYFP+J1zacIylnNRge+3j/K8wv38mNiJD3+sQlSlcgVOF05T\np9oZODCGEwumQx7iWarGpmN/7jGu6Rogyvo7zHmys+HCcwLsdp9YP3wsmeykKmBQjkyyicF+aQsy\nn3+VQK1aJVprMPXrF8033xR8/PSmmzy88MKBy8AiNpwPivj2V4pn76/rMZ0uzEqVwl1OWIQ7nIO2\n53z++edTv359evbsiWEYPPFEaO4mFW338AIP573+mo58T1tG8hAvM4Q1NKYVS46a7zbG0Ig11GQz\n/1CLMQzEffB45CrOZziP8hj5zzj3YzA24V5c3brzwVOnEGWt3uw8Xbv6+egjPz/8cOw/dyV2HTwm\ne7Q6bKJP039I71X6LhOKjTHpEjuHd90ndtvRRNL4l6rcxAfE4Ca6zqlM+KxGCVcZfI4iPgIOh05k\nk4MMo8jjzxJ8QT3mfH8YnqBwZbN/WbQ6Cd/BY6P1WAvArYyjDx+RRcH3wb6QZTzP0ALHBXDwPjdz\nL68Sz6HudyM2lltW3IyZmFTgfFYyfrybRx6JYvFiB2lpBi6Xid9vEnB7yD1snWQTix0vfvLvXTVm\nJTfZPia7b9HH3i3L6+VV415yyeAb/o+9VKboq9fzq8s67uBtltASgH5tcgFP0MoNlrZtfXz1lQPT\nzN/u6GiTq6/2hakqETlSGbjRYH7XP16TIckfcCrbAGjEmryf32hyqXjYAy0O9wRP0ZFvoJBrf/+m\nDt9x6GxOr+Eg+467S0UwAyQlwTvv5LJ0aRbLlmWxYUMWmzdn0/dWG4e3OZsEEkkl6eD1zDZ8NONH\n3o4bgvu1N/BceXWYWnCSnE6clROZQD9+41xm0oGm/ERhf+9DTKJdPubTjnm0xzBMWrf2MWxY6Qtm\ngJ49ffTo4cXpPNTumBiTW2/1cNFFpa9HRKSsKnO37wRwzfia3PuH83VKc87nZxrzW5H7RwEO/ESb\n2KjOVnZxdHdOFG7m05bT2IybGGrVjSZt/g9F9xOWAqYJ558fy/bt+c+Ut5NDP8bTjS+5pPE+9s9e\nGJ4CS1DsyBHEvjwy77MQwKAj3zKfdvgo6DisSfv2PkaPzmHCBBdpadC4sZ+rrvKX6vtnmybMn29n\n3jw7djt06eKjSZP8wRzxx1zV/ohuP5ThY87h5OnUGXv9c+n5/lhsKefin70NR9rRe8xebMzmCr7h\nSn6gGU/zBM1Yyld0PWraZizhdP4mmRTsBGAdRH32yYGb4pdihgEzZrgZckMaP/yWSA7RNOA3bucd\nbmcMANlNB4a5ypKR/cAjkJVJ9PRp2Hdsh/Ll+arNWHY905CPvkpi1iwHf/1lx+u1U6OGj06d/Nx1\nlwebDe65p3TuKRfEMOCyy/xcdpkubhaxqjK553wk21+bSLjrVpwrlmOYZt6RRj82mrCcNZxX5Pw1\n2MxMrqAe6/INd1/fm8zXRget7pAyTTzdbiHn+5XU4p8DGyCA76yzSftsKmb10nfyU2GMjHTsv/1G\noFatAk960V6D1oHaH9ntB+05h0Tg9DrsnzEP25bNGCl7MFJTiR/+OI4/fmcAY3mAF3HnPbwgv1px\n//JLVv2Cb/8ZXfDJZaWSYeD65C0SR47Avnwp3qxsfOc2InvQfWUqmAHMhHL4mrcIdxkiIoWKiHD+\nT+C0mnBaTQDS2rTF9c1X9Nm9G4eRyufzXGzZYsPttuPz+UlKgiZN/DzWbBnxQ7xHnZgbiIkl59oT\nuyzHsqKjyX5iOHHJCaRF+FaziEg4RVQ45+Nw4OlyLQBdga4DcoD/ujIOv1tZW7LX30nMhHHYMg4E\nViAxieyBd+Jr2izERYuISCSI3HA+DtmPPUVO915ET5uCadjI7d6LQK3a4S5LRETKKIVzMQXOrkv2\nQ8PCXYaIiESAUny1poiISNmkcBYREbEYhbOIiIjFKJxFREQsRuEsIiJiMQpnERERi1E4i4iIWIzC\nWURExGIUziIiIhZjmUdGioiIyAHacxYREbEYhbOIiIjFKJxFREQsRuEsIiJiMQpnERERi1E4i4iI\nWIzC+TDPPvssPXr0oGfPnvzyyy/hLics1q9fT7t27fj444/DXUpYvPDCC/To0YNrr72W2bNnh7uc\nkHK73QwePJgbb7yR6667jgULFoS7pLDIycmhXbt2TJ06NdylhNxPP/1Es2bN6N27N71792b48OHh\nLikspk+fzlVXXUXXrl1ZuHBhWGpwhOVdLWjZsmVs3ryZSZMmsWnTJoYOHcqkSZPCXVZIZWdnM3z4\ncJo3bx7uUsJi6dKlbNiwgUmTJpGamso111zD5ZdfHu6yQmbBggU0aNCAAQMGsH37dvr160fbtm3D\nXVbIvf3225QvXz7cZYTNhRdeyBtvvBHuMsImNTWV0aNH88UXX5Cdnc2oUaO45JJLQl6HwvmgJUuW\n0K5dOwDq1KnD/v37yczMJD4+PsyVhY7L5WLs2LGMHTs23KWERdOmTWnYsCEA5cqVw+124/f7sdvt\nYa4sNDp16pT3/507d1KlSpUwVhMemzZtYuPGjWH5MRZrWLJkCc2bNyc+Pp74+Piw9R6oW/uglJQU\nkpKS8l5XqFCBPXv2hLGi0HM4HERHR4e7jLCx2+3ExsYCMGXKFFq3bh0xwXy4nj17cv/99zN06NBw\nlxJyI0eO5OGHHw53GWG1ceNGBg4cSK9evfjhhx/CXU7Ibdu2jZycHAYOHMj111/PkiVLwlKH9pwL\nobuaRq65c+cyZcoUxo8fH+5SwuKzzz5j7dq1PPDAA0yfPh3DMMJdUkhMmzaNxo0bU6NGjXCXEja1\natXirrvuomPHjmzdupU+ffowe/ZsXC5XuEsLqbS0NN5880127NhBnz59WLBgQci/BwrngypXrkxK\nSkre6927d5OcnBzGiiQcvv/+e9555x3GjRtHQkJCuMsJqd9++42K/9/e3YRCu8ZxHP+OBkUJI+8k\nK7LyukCRDQtLJKWUnSLKy8KE1CxYoCgJC80QSnYklGxQshrZKaFMXqaImDE8C8/Rec7pdDqnc2bu\nZjLoiGYAAAOMSURBVH6f5czV9L8XV7/r5Z7rslhISUkhNzcXn8/Hw8MDFosl0KX5xd7eHpeXl+zt\n7XFzc0NERATJycmUlpYGujS/SUpK+t7eyMzMJCEhAZfLFVIDFovFQn5+PmazmczMTKKjowPSD7Ss\n/VNZWRlbW1sAnJ6ekpiYGFL7zQJPT0+Mjo4yMzNDbGxsoMvxu+Pj4+/Vgru7O15eXn7Z6gl2ExMT\nrK2tsbq6Sn19PW1tbSEVzPD1lvL8/DwAt7e33N/fh9y7B+Xl5RweHvLx8YHb7Q5YP9DM+aeCggLy\n8vJobGzEZDIxODgY6JL8zul0MjIywvX1NWazma2tLSYnJ0MmqDY2NnC73XR2dn5/NjIyQmpqagCr\n8p/Gxkb6+/tpamri9fWVgYEBwsI0fg8lVVVVdHd3s7u7i9frZWhoKOSWtJOSkqiurqahoQEAq9Ua\nkH6gKyNFREQMRsNiERERg1E4i4iIGIzCWURExGAUziIiIgajcBYRETEY/ZVKJEhcXV1RU1NDfn4+\nAF6vl7S0NAYHB4mJifnXvzs5Ocn7+ztdXV3/Vaki8jc0cxYJIvHx8djtdux2O8vLyyQmJjI9PR3o\nskTkH9LMWSSIFRcXs7Kywvb2NnNzc0RERODz+RgdHSU9PZ3m5mZycnI4OztjYWGB/f19pqamiIyM\nJCsri+HhYQBcLhcdHR2cn59TUlLCwMBAgJ9MJLhp5iwSpHw+H9vb2xQWFvL4+Mj4+Dh2u52KigoW\nFxe/20VFReFwOPB4PFitVmZnZ1laWiIuLo6TkxMALi4uGBsbY21tjfX1ddxud6AeSyQkaOYsEkQe\nHh5obm4G4OPjg6KiIlpaWjg4OKCvr4/Pz09ub2+/96Xh6+ha+LoqMDk5mfj4eAB6enoAODo6orCw\nELPZjNlsJi4ujqenp5A6d1vE3xTOIkHktz3n3/N6vXR2drK+vk5WVhYOhwOn0/n9fXh4OAAmk+kv\nr0r9473WOvVX5P+lZW2RIPf8/ExYWBhpaWm8vb2xu7uLx+P5U7vs7GxcLhc3NzcA2Gw2dnZ2/F2u\niKCZs0jQi42Npba2lrq6OlJTU2ltbaW3t5fNzc1f2kVFRWGz2Whvbyc8PJyMjAwqKys5OzsLUOUi\noUu3UomIiBiMlrVFREQMRuEsIiJiMApnERERg1E4i4iIGIzCWURExGAUziIiIgajcBYRETEYhbOI\niIjB/ACNHRlKzua2kQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f15580ab630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d379e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colors = np.where(dataset.Survived == 1, 'blue', 'red')\n",
"scatter_plot(x=rand_jitter(dataset.Parch), y=rand_jitter(dataset.SibSp), x_label= 'Parch', \n",
" y_label = 'SibSp', colors=colors)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "b8d71444-8284-a85d-cb87-9f5752cc2010"
},
"outputs": [
{
"data": {
"image/png": 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FiBEjmDhxItHR0cydO5eyZcuyaNEiT368S23b2nigSpJTucGg0ratDUvb9jy/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\nPkKCthBCCOEj/g8EJhFwDfd44wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d3127b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Wk+oCNas+NakuULPqsy91KS/0E9Zs3rlzZ+bOnQvAihUryM7OJiUlBYAGDRqw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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d294e48>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d246588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"scatter_plot(x=rand_jitter(dataset.Pclass), y=rand_jitter(dataset.Age), x_label= 'Pclass', \n",
" y_label = 'Age', colors=colors)\n",
"\n",
"\n",
"scatter_plot(x=rand_jitter(dataset.Age), y=rand_jitter(dataset.Survived), x_label= 'Age', \n",
" y_label = 'Survived', colors=colors)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "cfe49095-c570-fc85-fb64-55d9de401987"
},
"outputs": [
{
"data": {
"image/png": 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naarOvEmKoqEIsyDDLG43F85zvfwQ9/+EMEAoFlH6ejhtyzzkin2Ju1TTf9ITMGehmxu3UzfjX5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\nvqnGybraI51ZxUtF0fZkbJ5RDjHUrhsTa9rRPKM8zyWifJpT0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iU9r6mc+1ze3xLxa8YJ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\nuiGcuh2JE3di8vAt6N3fC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smmsgBQHvFcPfPZG+G45gPw6/eC3/gW+PV74bjmAzzz2RtL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\nAr/Xmfdov1zar8eMfb7S+m4+3xlzmfG9zzBz2wFKMmbk2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"text/plain": [
"<matplotlib.figure.Figure at 0x7f15946a46d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
" \n",
"pair_grid(df=preprocessedDataset, target='Survived')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "d6b695ca-f168-49a1-8d31-5bee5e591d14"
},
"outputs": [
{
"data": {
"image/png": 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AlTDa0s7KysK+ffsQGhoKAEhKSoJWq7V4YURKGTnybxVuE9V2K1eGVbhN1sNoaM+dOxeN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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153bc20c50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153b8e5780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"\n",
"box_plot(data=preprocessedDataset.iloc[:,[0,1,2,3,4,5,6]])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "6e74d9a8-8f21-57a5-9e07-fe1ae6e5e2cc"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f153af79ac8>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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6xy/0m4c+etbHSGz5DQDeyAF4g7PGscdDq4sewdruf1Kd246R3kAm9gaB8oMB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RfxUsWbV4AACnk86j2bM4t7uTK3EQk8WtlsJbwSdW6ROq9EwisSrpQgHglZYfC92Oms87WScItc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\nJBKJRNJN+f/FOQAfKHNzlgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153b009390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas.tools.plotting import parallel_coordinates\n",
"parallel_coordinates(preprocessedDataset, 'Survived')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "b96a0060-8529-e94b-2b1f-ba5d786e1ea3"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f153af79438>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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iI7ARfD67gVftzD6PeXlg+B8P7uV3BdGXF/pBJGRgPZUlfYYvBMvcPPWqwSDV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2BM8D2f8FyH1TT/llhFj97IpbXDvKe0DR2fISVGk195wRR8PnBjwyWMC5Pvj99DNaykrLCHp4Ce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XWMxVw28PhJpdqy/VC0nTqh8e1SD82KftC8W9ESQIBX2lElWekNeOVujPh0FD1FzGqhu7ST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\n0geUKLEwIEMBCkLnnuhkYkYTc4x6H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4KkYmeW4WRWE6o6g0j5BIY5HQzOLOIjpZw5wXoa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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153af794a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas.tools.plotting import andrews_curves\n",
"andrews_curves(preprocessedDataset, 'Survived')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "a65f39d8-8ef7-eb5c-794b-019e7b18a2f6"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f153cb577f0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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FP+SJRKBo/1mWOr7hnMRvY928kuinq0R2+P5CBczu6EDmsYZZ++rHJSuKtQkAxeoFwAXM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\nFdns9UxyWEZvvEUbxKUnq+7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yx/PvhxkHYj5qkj1umAgw9Pb2smjRIu666y68Xi+TJ0/m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\ngsT/AgjVbZ+5y3IlAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153bf4be10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from pandas.tools.plotting import radviz\n",
"radviz(preprocessedDataset, 'Survived')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "4737c8b8-ba4f-3181-369c-af939d785c0e"
},
"outputs": [
{
"data": {
"image/png": 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RQfUFUd3piYiIZMIOkYiI5KHyDpEFkYiIZMFZpkRERAA7RCIiIgBcqYaIiAiAoh3ivHnz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\nkecOJ8rCUgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d3c4978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corr_heatmap(df=preprocessedDataset)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "f2d3df14-3fcb-7824-95c8-5c8bd3acbea2"
},
"outputs": [
{
"data": {
"image/png": 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FtohILRN6PXEj5Ni1ff++4LaVzuWZROz7JdjmveRSioonj3mSemM01I6LVqHQ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\n/y/dmjXWCoikAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153c9e8f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"prob_plot(column_index=5, data=X)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3753ff84-dad5-c000-23ed-e5e27f406e93"
},
"source": [
"\n",
"Some outliers on fare feature"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "c35adc42-ad63-5878-264c-d0e2d4953d2b"
},
"source": [
"Correlation between features is low\n",
"Correlation with target is higher for fare and then parch\n",
"\n",
"Time to build baseline classifier with Logistic Regression and simple split"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "89f19333-b22b-37e9-ac8b-0ffb7a0449c1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[109 16]\n",
" [ 22 68]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.83 0.87 0.85 125\n",
" Survived 0.81 0.76 0.78 90\n",
"\n",
" avg / total 0.82 0.82 0.82 215\n",
"\n"
]
},
{
"data": {
"text/plain": [
"\"def extract_different_predictions(X_test, y_test, y_pred):\\n diff_result = DataFrame(data=X_test)\\n diff_result['pred'] = y_pred\\n #diff_result['test'] = y_test\\n diff_rows = np.where(diff_result['pred'] != y_test)\\n return diff_result.loc[diff_rows[0],:] \\n\\nres = extract_different_predictions(X_test, y_test,y_pred)\""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wBgDgWIQgAMCxGIcCAIxgTxAA4FicDgUAOJaFGUgIAgDMsLET5GAMAMCxCEEAgGMxDgUA\nGGHhNJQQBACYYeOeICEIADDDwg02QhAAYISNnaCFuQ0AgBmEIADAsRiHAgCMsHAaSggCAMywcU+Q\nEAQAGGFhBhKCAABDLExBDsYAAByLThAAYIQrhE4QAABr0AkCAIywcEuQEAQAmMFHJAAAjmVhBrIn\nCABwLjpBAIAZFraChCAAwAg+IgEAgEXoBAEARlg4DSUEAQCGWJiCjEMBAI5FJwgAMMLCRpAQBACY\nYePpUEIQAGCEjbdNY08QAOBYdIIAADPsawTpBAEAzkUnCAAwwsY9QUIQAGAEIQgAcC4LN9gIQQCA\nETZ2ghbmNgAAZhCCAADHYhwKADDCxnEoIQgAMMO+DCQEAQBmcANtAIBzWTgO5WAMAMCxCEEAgGMx\nDrXMun9s0PyXlurkqVNq3aqVMsaN1sUdO2jGvD9qQ8Em+U6fVt9ePZU25n65Q0ODXS7wo8Jat1C/\nu25QS2+kTlWe0KfZa1W2r1h9hiUrulM7+U779PX23dry+oeSzxfsctEAgZ6GVlVV6eabb9YDDzyg\nhIQEpaWlqaamRjExMcrKypLH42n0mnSCFikuLdXjWTP0VEaa/vziAt308+s0ZeZzWv76Cu3eV6js\nBc/p1YXPa+fuPVqZ+16wywXq1e+uG/T19t16e+KL2vTaOl0yoKd+emNfhYSG6t0nXtZ7U5YrqmOs\nOiX+LNilooFcLpffj4aYN2+eWrduLUmaPXu2UlNTtXz5csXHxysnJ8evmglBi7hD3ZqSkaafxHeU\nJPXu3k279uxVnx7d9cjoUWrWrJmaNWum7l0v05e79wa5WuDHNY9sqciOsdq5ZrMkqWTHfm1Y9LZa\nt49WyY79kk86XV2j0i8PqFVc2yBXiwYLcfn/OINdu3Zp586dGjBggCQpPz9fKSkpkqTk5GTl5eX5\nV7Jfr5JUXl7u70vhp6jINkrse2Xtr9d/tFHdu3ZR965d1KljB0lSdU2NNnzyqbp3vSxYZQJn1KZ9\njI5/c0Q9bu2vmyaN0ICHf6U2F8Wo6PN9at+rs0Kahcod5lFs144q+mxPsMtFAwWyE5w6daomTJhQ\n++vKysra8Wd0dLRKSkr8qtnvEHzwwQf9fSkM+Khgk5a/vkLj77+39prP59Mzs+cqNqatrr8uKYjV\nAfVrFn6BWse1VekXhXp30sva89HnShx1s3at2yxXaIhumTZKv5j2Wx0rOaKD23YHu1wE2YoVK9Sr\nVy916NDhB3/ua8Kecb0HY5YtW/ajPysqKvL7TdE0a9bnKWvufM3MnFQ7Gq2uqdHk6TNVduSIsh7/\nX4VyKAbnsFOVJ1RVXqEDW76UJH21fpt63pakq+8ZrOPflOuDOSsUEhqiq+8ZpC7XX6F/vvdJkCtG\ngwToYMzatWu1b98+rV27VgcPHpTH41F4eLiqqqoUFhamoqIieb1ev9auNwSXLFmihISEH1y8urra\nrzdE0+QXfKrpzy/Q3Kcz1elfAShJT86YraoTJzRj8mNq5ubQL85tFYeOyh3m+fYPzX/9T7zP59OF\n3S7Wh/NWynf6tGpOn9aBLV+qfc9LCEGHmzlzZu3fz5kzR+3bt9enn36q3Nxc3XLLLVq1apWSkvyb\nftX7p+XcuXP15JNPauLEid87epqfn+/XG8J/lVVVemL6TD07aWKdAFz9wXp9uXevXvhDFgEIKxwp\nLFXVkWPq1L+7vvpwmy7qc6lOVpzQ4X37FNejk4o/3yu5XGr3s4t15MA3wS4XDXQ2b6A9ZswYpaen\nKzs7W3FxcRoyZIhf67h8ZximVlZW6oILLlBISN3tw+3bt6tbt25nfINje3f6VRi+793Va/XE9Jm6\nsF1snettoyL11Z59iohoWXut589+qsd/99DZLvG89c7TbwW7hPNORLso9bvrBnlaNteJoxUq+NMa\nnThaoT6//rkiYiMlSYd2F6ng/1aruupkkKs9f9w+L3B/Lux7622/X9vh5v8wWEnDnTEEm4oQxPmA\nEMT5IqAh+Nd3/H5th8GDDFbScMzOAABG2Ph9gnxYHgDgWHSCAAAz7GsE6QQBAM5FJwgAMIJvlgcA\nOJeFB2MIQQCAEZwOBQDAInSCAAAz2BMEADgV41AAACxCJwgAMMO+RpAQBACYwTgUAACL0AkCAMzg\ndCgAwKlsHIcSggAAMywMQfYEAQCORScIADDCxnEonSAAwLHoBAEAZnA6FADgVDaOQwlBAIAZhCAA\nwKlcFo5DORgDAHAsQhAA4FiMQwEAZrAnCABwKk6HAgCcixAEADgVp0MBALAIIQgAcCzGoQAAM9gT\nBAA4FiEIAHAqPiIBAHAuTocCAGAPOkEAgBEul319lX0VAwBgCJ0gAMAMDsYAAJyK06EAAOfidCgA\nAPagEwQAGME4FADgXBaGIONQAIBj0QkCAMyw8MPyhCAAwAi+WR4AAIvQCQIAzLDwYAwhCAAwgo9I\nAACcy8KDMfZVDACAIXSCAAAjbDwdSggCAM5506ZN0yeffKLq6mqNGjVKPXr0UFpammpqahQTE6Os\nrCx5PJ5Gr0sIAgDMCNDBmA0bNuiLL75Qdna2ysrKdOuttyohIUGpqakaNGiQZsyYoZycHKWmpjZ6\nbfYEAQBGuFwuvx/16du3r2bNmiVJatWqlSorK5Wfn6+UlBRJUnJysvLy8vyqmRAEAJjhCvH/UY/Q\n0FCFh4dLknJycnTttdeqsrKydvwZHR2tkpISv0omBAEAZoS4/H80wN/+9jfl5OToscceq3Pd5/P5\nX7LfrwQA4Cz54IMPNH/+fC1cuFAREREKDw9XVVWVJKmoqEher9evdQlBAMA57ejRo5o2bZoWLFig\nNm3aSJISExOVm5srSVq1apWSkpL8WpvToQAAIwJ127S3335bZWVleuihh2qvPfPMM5o4caKys7MV\nFxenIUOG+LW2y9eUYWoDHNu7M5DLA2fFO0+/FewSACNun/fQmZ/kp8qSQr9f2zymvcFKGo5OEABg\nBDfQBgA4FzfQBgDAHoQgAMCxGIcCAIzgWyQAAM7FwRgAgFO5LDwYQwgCAMywsBMM+IflAQA4V9nX\nuwIAYAghCABwLEIQAOBYhCAAwLEIQQCAYxGCAADHIgQtN2XKFA0dOlTDhg3Tli1bgl0O4LcdO3Zo\n4MCBWrp0abBLgYPwYXmLffTRR9qzZ4+ys7O1a9cuZWRkKDs7O9hlAY1WUVGhzMxMJSQkBLsUOAyd\noMXy8vI0cOBASVLnzp115MgRHTt2LMhVAY3n8Xi0cOFCeb3eYJcChyEELVZaWqrIyMjaX0dFRamk\npCSIFQH+cbvdCgsLC3YZcCBC8DzCHfAAoHEIQYt5vV6VlpbW/rq4uFgxMTFBrAgA7EIIWqx///7K\nzc2VJG3fvl1er1ctW7YMclUAYA++RcJy06dP18aNG+VyufT444+ra9euwS4JaLRt27Zp6tSpKiws\nlNvtVmxsrObMmaM2bdoEuzSc5whBAIBjMQ4FADgWIQgAcCxCEADgWIQgAMCxCEEAgGMRggAAxyIE\nAQCORQgCABzr/wEaR5w1kgT5wgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cf34a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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KYsnC60m1Nrmm19Uk2Js2bUpubi5nz54lPDycb7/9lpdeekmLUoQQQog/xlaEIeswxowE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\nxgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153bffa630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cbb44e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
" test_size = 0.30, random_state = 0)\n",
"\n",
"y_train = y_train.astype(int)\n",
"y_test = y_test.astype(int)\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"classifier = LogisticRegression(random_state = 0) # basic setting\n",
"classifier.fit(X_train, y_train)\n",
"y_test_pred = classifier.predict(X_test)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred, target_names = ['Not Survived', 'Survived'])\n",
" \n",
"'''def extract_different_predictions(X_test, y_test, y_pred):\n",
" diff_result = DataFrame(data=X_test)\n",
" diff_result['pred'] = y_pred\n",
" #diff_result['test'] = y_test\n",
" diff_rows = np.where(diff_result['pred'] != y_test)\n",
" return diff_result.loc[diff_rows[0],:] \n",
"\n",
"res = extract_different_predictions(X_test, y_test,y_pred)'''"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "681a035d-245e-096d-3c1a-8c8f3edcf559"
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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64oEHFuKzz/4PaWlnwWTSY/ass85GdvYBAMCZZ+rTy+7d+x327PkG3333LQDA7XbD4/EE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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cb18080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"plot_learning_curve(classifier, \"Training\", X, y.astype(int), (0.7, 1.01), cv=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "62772682-d953-b69f-124c-94c9fbe11579"
},
"source": [
"AUC is ok, f1 score is not bad\n",
"looking at learning curve, it seems variance is ok but bias cannot be lowered woth more data"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "ae2f72ea-b25e-16ea-8382-be6f55698e6a"
},
"outputs": [
{
"data": {
"image/png": 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Zqtvp6emws6uaozkuLg4tW7aEUln1hvLx8cGVK1ceG+w8UUNcPBlGM9hn8bHH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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153b96b400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153ccf8048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_precision_recall_curve(y_test, y_test_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e375ee57-d2f2-f170-54af-3e8596ec2f7a"
},
"source": [
"mmm I'm sure can be better...\n",
"\n",
"Let's check the accuracy doing k-fold cross validation"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "3e9b8dc2-7959-0d00-d7cd-5aed266209d2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.79 (+/- 0.06)\n",
"Precision: 0.77 (+/- 0.11)\n",
"Recall: 0.71 (+/- 0.19)\n",
"Roc_AUC: 0.86 (+/- 0.06)\n"
]
}
],
"source": [
"estimator_scores(estimator=classifier, X=X, y=y, cv=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6de744f6-1e0d-1bd1-f66b-c72b81273d96"
},
"source": [
"\n",
"Before changing algorithm, let's try to work on features\n",
"\n",
"*Feature selection* using RFE (recursive feature elimination)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "10b7ffa3-6a72-e217-b871-40ebf4a3fc3b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ True True True True False False]\n",
"[1 1 1 1 3 2]\n",
"Optimal number of features : 4\n"
]
},
{
"data": {
"image/png": 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k7GyHpWXdHiJ41DKL4vHJPNaeuc3hyl2plFdU88fnu+Ht6VInY5rTHLq5OdKlgwe/vVXK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\nIsvFiYT/yXsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153ccd54a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#['Sex','Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
"evaluate_rfe(classifier=classifier, X=X, y=y.astype(int), step=1, cv=10) \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ed47ba2e-f460-780b-edf6-5942a4778883"
},
"source": [
"It seems 4 feature could be the best solution\n",
"\n",
"Feature engineering using PCA"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "7ef15033-db00-54bb-830c-94db860453f1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[106 19]\n",
" [ 24 66]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.82 0.85 0.83 125\n",
" Survived 0.78 0.73 0.75 90\n",
"\n",
" avg / total 0.80 0.80 0.80 215\n",
"\n"
]
},
{
"data": {
"text/plain": [
"\"\\nplt.figure()\\ncolors = ['red', 'blue']\\ntarget_names = ['Not Survived', 'Survived']\\nlw = 2\\nfor color, i, target_name in zip(colors, [0, 1], target_names):\\n plt.scatter(X_test_pca[y_test == i, 0], X_test_pca[y_test == i, 1], color=color, alpha=.8, lw=lw,\\n label=target_name)\\nplt.legend(loc='best', shadow=False, scatterpoints=1)\\nplt.title('PCA')\\n\""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f153c95fa20>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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3al2WwI3BnpiYSPv27V2PLRYLCQkJ+Pv74+PjwxNPPEFYWBg+Pj4MHTqUpk2b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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cce57f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cd4df60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.decomposition import PCA\n",
"pca = PCA(n_components = 4)\n",
"X_train_pca = pca.fit_transform(X_train)\n",
"X_test_pca = pca.transform(X_test)\n",
"explained_variance = pca.explained_variance_ratio_\n",
"\n",
"\n",
"# Fitting Logistic Regression to the Training set\n",
"from sklearn.linear_model import LogisticRegression\n",
"classifier = LogisticRegression(random_state = 0)\n",
"classifier.fit(X_train_pca, y_train)\n",
"\n",
"# Predicting the Test set results\n",
"y_test_pred_pca = classifier.predict(X_test_pca)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred_pca, target_names = ['Not Survived', 'Survived'])\n",
"\n",
"'''\n",
"plt.figure()\n",
"colors = ['red', 'blue']\n",
"target_names = ['Not Survived', 'Survived']\n",
"lw = 2\n",
"for color, i, target_name in zip(colors, [0, 1], target_names):\n",
" plt.scatter(X_test_pca[y_test == i, 0], X_test_pca[y_test == i, 1], color=color, alpha=.8, lw=lw,\n",
" label=target_name)\n",
"plt.legend(loc='best', shadow=False, scatterpoints=1)\n",
"plt.title('PCA')\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "31e1a6d2-acb6-b8e1-a710-f1308a9172b5"
},
"source": [
"Yep, with 4 features, the results are close to 6...but the problem here is underfitting, so there is no need to reduce features, but add instead "
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "be2aa38a-4de0-4b82-a8ed-bd8439c59888"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect\n",
"because the backend has already been chosen;\n",
"matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
"or matplotlib.backends is imported for the first time.\n",
"\n",
" warnings.warn(_use_error_msg)\n"
]
},
{
"data": {
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AAHC+a17SZrFYFBsbq9jYWGfUAwAArpND16mvW7dOr732moqLi2UYhn3+lZ47AACoew6F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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153bd29630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import hypertools as hyp\n",
"\n",
"data_matrix,labels=hyp.tools.df2mat(preprocessedDataset,return_labels=True)\n",
"\n",
"loadings = sorted(zip(pca.components_[0],labels),key =lambda x: x[0])\n",
"top = loadings[-5:]\n",
"\n",
"df = pd.DataFrame(top,columns=['loading','features'])\n",
"sns.barplot(data=df,x='features',y='loading')\n",
"plt.xticks(rotation=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "6639314a-48bc-732c-eeac-c42c206ec6d6"
},
"outputs": [
{
"data": {
"text/plain": [
"\"\\nfrom matplotlib.colors import ListedColormap\\npca = PCA(n_components = 2)\\nX_train_pca = pca.fit_transform(X_train)\\nX_test_pca = pca.transform(X_test)\\nX_set, y_set = X_test_pca, y_test\\nX1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\\n np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\\nplt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\\n alpha = 0.75, cmap = ListedColormap(('red', 'blue')))\\nplt.xlim(X1.min(), X1.max())\\nplt.ylim(X2.min(), X2.max())\\nfor i, j in enumerate(np.unique(y_set)):\\n plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\\n c = ListedColormap(('red', 'blue'))(i), label = j)\\nplt.title('Logistic Regression (Test set)')\\nplt.xlabel('PC1')\\nplt.ylabel('PC2')\\nplt.legend()\\nplt.show()\\n\""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"\n",
"'''\n",
"from matplotlib.colors import ListedColormap\n",
"pca = PCA(n_components = 2)\n",
"X_train_pca = pca.fit_transform(X_train)\n",
"X_test_pca = pca.transform(X_test)\n",
"X_set, y_set = X_test_pca, y_test\n",
"X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\n",
" np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\n",
"plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\n",
" alpha = 0.75, cmap = ListedColormap(('red', 'blue')))\n",
"plt.xlim(X1.min(), X1.max())\n",
"plt.ylim(X2.min(), X2.max())\n",
"for i, j in enumerate(np.unique(y_set)):\n",
" plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\n",
" c = ListedColormap(('red', 'blue'))(i), label = j)\n",
"plt.title('Logistic Regression (Test set)')\n",
"plt.xlabel('PC1')\n",
"plt.ylabel('PC2')\n",
"plt.legend()\n",
"plt.show()\n",
"'''"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7fb89830-ab86-f738-fdb2-01c031347210"
},
"source": [
"Let's try LDA"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "6af3d63c-22de-1611-f275-f9b711273d3c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[107 18]\n",
" [ 22 68]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.83 0.86 0.84 125\n",
" Survived 0.79 0.76 0.77 90\n",
"\n",
" avg / total 0.81 0.81 0.81 215\n",
"\n"
]
},
{
"data": {
"image/png": 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DWrRokfbs2aNRo0bJ7/cHvvbzj+uLEAQAGBGq06GxsbHq2bOnPB6P2rRpoyZNmigiIkLV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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153ce20400>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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mobTktMS+a9cuoqKiAGjRogUZGRlkZ2fj5+eHh4cHHh4e5Obm4uPjQ15eHgEB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\nhV0IIYSwIFLYhRBCCAsihV0IIYSwIP8P8+ydypbbv8AAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f155c4e1da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cb60470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
"lda = LDA(n_components = 4, solver='svd', store_covariance=True)\n",
"X_train_lda = lda.fit_transform(X_train, y_train)\n",
"X_test_lda = lda.transform(X_test)\n",
"\n",
"# Fitting Logistic Regression to the Training set\n",
"from sklearn.linear_model import LogisticRegression\n",
"classifier = LogisticRegression(random_state = 0)\n",
"classifier.fit(X_train_lda, y_train)\n",
"\n",
"# Predicting the Test set results\n",
"y_test_pred_lda = classifier.predict(X_test_lda)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred_lda, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "174e6cb2-e7d6-d7e3-7b63-5b98536c0974"
},
"source": [
"let's finish with kernel-pca using not linear approach"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "5f31ac3c-81c2-b2a5-3d29-f506e0dd184d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[106 19]\n",
" [ 19 71]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.85 0.85 0.85 125\n",
" Survived 0.79 0.79 0.79 90\n",
"\n",
" avg / total 0.82 0.82 0.82 215\n",
"\n"
]
},
{
"data": {
"image/png": 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IUO4O/frrr7VixQodP35caWlp8vv9gdd+/HNtEYIAACNClYExMTHq3r273G632rZtq8aN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\nwLH+HwQLQKpO4bZHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d046d30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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s9FpFRFz5SIfLeuzbtm3j+PHjLFu2jMOHDzNx4kSWLVsGQF5eHv/3f//HmjVr\nMBqNPPDAA/z666/ccMMNrgpHXC1r3oWdx3JKDaHnHKm4+ppv7VKbllzYwMQRGOWsviaE8Hx5eRZe\nfHEz7777K0rB669vZ+nSQQQFSZlkLbkssW/dupW4uDgAmjZtSnZ2Nnl5eQQFBeHj44OPjw8FBQUE\nBARQWFhIaKhseKEZpdAVpUDyDvyO7yxbQrXgVPmHOKuvNb+wgUnJJDapviaE9/vf/w7y8MNfc/p0\nHkajnscf78zo0TdqHZbAhYk9LS2N1q1bO5+Hh4eTmppKUFAQvr6+PP7448TFxeHr68stt9xC48aN\nXRWKOM9hR593rHjjkqyDpTYxOYjekgXAnwd9lN4Xe0izskvHnNXXAtx/DUKIamHr1pOcPp1Hhw51\nmTfvZlq3jtA6JFHCbZPnSt/Kz8vL46233mL16tUEBQUxcuRI9u/fT4sWLS55jqu511AjWQsh8wBk\n7If0fZCxr/hx5kGwm8s/xrcWhLeE8BZQu6XzsS60MUa9QWZZVjH5LruetHHVUkqxZMmvNGoUQnx8\nUyZN+hvR0bW4//4bMBjkFlt14rKf15GRkaSlpTmfp6SkEBFR/Bvd4cOHadSoEeHh4QB07tyZPXv2\nVJrYZTJMWbqi9JL13ue3Dj2AMfsQ+rzj6Ch/TqQ9oGEF1dciiYgMKdvGNiC9wD0XU4PIxC7Xkzau\nWkeOZDJ27Do2b06mUaMQNm0ayfXXhzNoUHMyMsrfqlhUjWo1ea579+4sXLiQ4cOHs3fvXiIjIwkK\nKq781bBhQw4fPkxRURF+fn7s2bOHXr16uSoUrxP48/P4Hf4IvTm93NeVzogtpEmpe98xzolsykd6\nMUKIy2O12lm8eAevvLIVs9lO7dr+TJzYHX9/GcOrzlz2t9OxY0dat27N8OHD0el0TJkyhRUrVhAc\nHEx8fDwPPvgg9957LwaDgQ4dOtC5c2dXheJV9AVnCPh9IQAOY1DJeu8/V19rLNXXhBDX7NNPf+fF\nFzcDcPvtrZg6tRe1a8sSturOpevYq5oMrYHfwSUE//gU5oYJ5PT5tEqrr8nwpXtIO7uetPHVy8uz\ncPhwJu3b18Vmc/Dww//j3nvbcdNN15d5n7Sxe1SroXjhGqbkVQBYrrtVSqoKIarUd98dZdy4dRQV\n2dmyZSRhYf68++4ArcMSV0imMnoSWwGms0kAWBomaByMEMJbpKYW8Mgj/+POO7/g5Mlc6tcPIjOz\nSOuwxFWBXl66AAAgAElEQVSSHrsHMZ3ZgM5ehLV2RxwB9bQORwjhBY4cyaRfv4/JzCzC39/Is8/+\nlX/+syNGo/T7PJUkdg9iSv4GAEuj/hpHIoTwdEVFNvz8jDRuXItWrepgMOh55ZU4oqNl4yVPJ4nd\nUygHvieLE7s5qp/GwQghPJXN5uCtt37hzTd3sHbt3dSrF8T77w8iONgku7B5CRlr8RDG9F/QF6Vg\nD4jCHtZG63CEEB7ot9/OkZj4EVOnbuTcuXy+/vogACEhvpLUvYj02D3EhWH4fjIbXghxRex2BzNm\nbObNN3dgtysaNQphzpy+9O0re3R4I0nsHkKG4YUQV0uv13H4cCZKwT//2ZHx4/8qW6t6MRmK9wD6\nvBMYM/fgMAZhrddT63CEEB4gI6OQZ55Zw5Ejmeh0OmbP7sM339zJ9Ok3SVL3ctJj9wCmk6sBsDbo\nCwZfjaMRQlRnSilWrNjPpEkbSE8v5OzZPD7+eAj16wdTv77sFVETSGL3AL4l1ebMjRI1jkQIUZ2d\nOJHNs89+x/r1xwDo3j2KF1/srW1Qwu0ksVdzOmsuPuc2o9BJtTkhxCW99to21q8/RmioLy+88Dfu\nuquNzHavgSSxV3M+p9ejc1iwRnRF+dXROhwhRDWzZ08qRqOOFi3q8PzzPVBKMX58d+rWDdQ6NKER\nmTxXzV0YhpfZ8EKICwoLrcyYsYn4+A8YNepb7HYH4eH+zJt3syT1Gk567NWZw47p1BoALLLMTQhR\nYtOmE4wdu46jR7PQ6aBTp/pYLHb8/aWvJiSxV2vGtO3ozenYgxtjD43VOhwhRDWwYsV+HnmkeCSv\nRYvazJ0bz1/+0kDjqER1Iom9GnMOw0dJtTkhajKlFJmZRYSH+3PzzU1o0qQWt9/eiiee+Asmk0Hr\n8EQ1I4m9GjOVVJuTYXghaq5Tp3IZP/47jh7NYv36ewgKMrFx40hJ6KJCckOmmtLnHMaYfQCHTyjW\nun/VOhwhhJvZ7Q7+7/920qPHe6xZc4SzZ/P4/fc0AEnq4pKkx15Nna8Nb2kYB3ofjaMRQrjT2bN5\nPPDA1/z88xkAbrmlGTNn9qFevSCNIxOeQBJ7NXW+jKwMwwtR84SF+ZGTY6ZevUBmzepL//7NtA5J\neBBJ7NWQzpyJz7ktKJ0BS8N4rcMRQrjBjz+eZO7cn1iyZABBQSaWLBlI3bqBhITI/hDiysg99mrI\ndHodOmXHGvlXlG+Y1uEIIVwoO7uIMWPWMnDgpyQlHeett34BoHnzcEnq4qpIj70aMiWX3F+XanNC\neLWVKw/x3HPrOXcuHx8fPaNGdeGJJzprHZbwcJLYqxuHFdPpdQBYomQ3NyG8lcOhWLhwG+fO5dO5\nc33mzYunRQvZD0JcO0ns1YxPylb0lixsoTHYQ2TCjBDexOFQfPTRHvr1a0bt2v7MnXsz27ad4r77\n2qPXSxEqUTUksVczzmF4mQ0vhFc5dCiD0aPX8tNPp/jxx1O8/noibdpE0KZNhNahCS8jib06UQrf\nk8VlZCWxC+EdLBY7CxZsY/78bVgsdiIiArj55iZahyW8mCT2asSQfRBD7lEcvuFYI7poHY4Qogo8\n99x6/vOf3QDcc08bJk/+G7Vq+WkclfBml7XcLTMzk927i7+YDofDpQHVZM7a8A1vBr38ziWEp8rN\nNZOaWgDA4493plWrOnzxxTDmzbtZkrpwuUoT+8qVK7njjjt47rnnAJg+fTqfffaZywOric6XkTXL\nMLwQHmv16sP06PE+o0evQSlFkyZhfP/9CLp3b6R1aKKGqDSxL1myhK+++oqwsOJCKePHj+fTTz91\neWA1ja4oHWPqTyi9D9YGfbUORwhxhc6dy+cf/1jJvfd+xZkzeaSk5JObawFAJ9suCzeqdLw3ODgY\nf39/53M/Pz98fGRTkqpmOvUtOuXAUq8XyhSidThCiCuQlHScf/xjJdnZZgICfJg4sTsPPngDBoMU\n9xTuV2liDwsL44svvsBsNrN3715WrVpFeHi4O2KrUXxLNn2RYXghPIdSCp1OR2xsbZSCvn2jmTMn\njkaN5JdzoZ1Kf52cOnUqu3fvJj8/n0mTJmE2m3nxxRfdEVvNYTfjc6qk2pyUkRWi2rNa7cyf/xN3\n3LECh0NRr14Q69bdzUcfDZakLjRXaY9906ZNTJ48ucyfffzxx9x5550uC6qm8Tm3Gb0tD1ut1jiC\nrtc6HCHEJezYcYbRo9eyb18aAFu3nqR790ZER9fSODIhilWY2H///Xf27t3Lu+++S2FhofPPbTYb\nixYtksRehXxLqs2ZpbcuRLWVl2dh1qwtvPPOTpSC668P5ZVX4mS2u6h2Kkzsvr6+pKenk5uby44d\nO5x/rtPpePbZZ90SXI2g1IX163J/XYhqy2q1s2LFAfR6HY880olx47oRECATiUX1U2Fib9q0KU2b\nNqVr167ccMMNZV779ttvXR5YTWHI2oshPxmHXyS2Op20DkcIUUpqagHvvPMLzz77V8LC/Fm0KJHa\ntf1p166u1qEJUaFK77FHRkYyZ84cMjMzAbBYLPz0008kJCS4PLiawDkMH5UAOlkaI0R1oJRi2bLf\nmTIliczMIkJD/Xj88c707h2tdWhCVKrSTPLss89Sq1Ytfv31V9q0aUNmZiZz5sxxR2w1gsm56Ut/\njSMRQgAcPZrFsGGfM2rUt2RmFtGr1/XccotsoSw8R6WJ3WAw8PDDD1OnTh3uvvtu3njjDT788EN3\nxOb1dIXn8EnbgdL7Yql/k9bhCFHjKaW4996v2LjxBOHhfrz+eiKffjpEZrwLj1JpYjebzZw9exad\nTkdycjJGo5FTp065Izav53uyeK6CpX4v8AnUOBohaq7du1MoLLSi0+mYOrUXt93Wgs2b7+P221tJ\nOVjhcSpN7P/4xz/YunUrDz74IIMGDaJr16506NDBHbF5PVNyyTB8IxmGF0IL+flWpkxJIj7+Q159\n9ScA+vSJ5o03+lOnToDG0QlxdSqdPBcXF+d8vG3bNvLz8wkNDXVpUDWCrRDTme8BsEQlahyMEDXP\n998fY9y47zhxIhu9XofNJltSC+9QYY/d4XDwySefMH36dFauXAmA0WjEZDIxdepUtwXorUxnk9DZ\nC7GG34AjoIHW4QhRo8yZ8wN33LGCEyeyad06gm++uZPJk/+mdVhCVIkKE/v06dPZtm0b119/PZ98\n8gn/+c9/2Lp1KwMHDsTPz8+dMXolU8kyN6kNL4R7KKWwWOwA9O4dTUCAkUmTerBmzV106FBP2+CE\nqEIVDsXv27ePTz75BIChQ4fSu3dvGjZsyKuvvkqbNm3cFqBXUgpTyW5uUm1OCNc7fjybZ59dR3R0\nLWbP7stf/tKAX355iPBw/8oPFsLDVJjYS++5HhAQQOPGjfnwww8xGAyXffKXXnqJXbt2odPpmDhx\nIu3atXO+dubMGUaPHo3VaqVVq1ZMmzbtKi/B8xgzfsVQeAZ7QANs4e21DkcIr2WzOXjnnZ3Mnr2F\nggIb4eHnmDChuIqcJHXhrSociv/zEg+TyXRFSX3btm0cP36cZcuW8eKLL1601eusWbN44IEHWL58\nOQaDgdOnT19h6J7LOQwf1Q9kKY0QLrF/fxr9+3/MlClJFBTY+PvfY9m4cSRhYZLQhXersMeekpLC\n8uXLnc9TU1PLPB86dOglT7x161bnjPqmTZuSnZ1NXl4eQUFBOBwOduzYwbx58wCYMmXKNV2Ep7mw\n6YvMhhfCVZSCPXtSadAgiDlz4rj55iZahySEW1SY2Dt06FBmV7cbbrihzPPKEntaWhqtW7d2Pg8P\nDyc1NZWgoCAyMjIIDAxk5syZ7N27l86dOzNmzJhruQ6Poc8/hU/GLpQxoLgwjRCiymzadIKkpOO8\n9lp/Wrasw9KlA+naNYqgIJPWoQnhNhUm9pkzZ1bpBymlyjw+d+4c9957Lw0bNuThhx9mw4YN3HTT\nTZc8R0REcJXGpInTGwDQRd9MRL0ILSMpl1e0sQeQdq5aGRmFjBu3hnff/RWAQYNa0qdPY+68U+aw\nuJJ8j6unSgvUXK3IyEjS0tKcz1NSUoiIKE5kYWFhNGjQgOuuuw6Abt26cejQoUoTe2pqrqvCdZuQ\nfSvwBXIj4imqZtcTERHsFW1c3Uk7Vx2lFF99dZCJE78nLa0Ak8nA6NE30qPHddLGLibfY/e4ml+e\nXLZPaPfu3Z37tu/du5fIyEiCgoKA4kI3jRo14tixY87XGzdu7KpQqg9rPqYzG1HoirdpFUJck5SU\nfJ5++lvS0gro1q0hGzaMYPTorphMlz/RVwhv47Iee8eOHWndujXDhw9Hp9MxZcoUVqxYQXBwMPHx\n8UycOJEJEyaglCImJoY+ffq4KpRqw3Tme3QOM9Y6f0H5R2odjhAeyW538O23R+jXryl16wbxwgu9\nMBh03H13W/R6WWUihE6Vvvldjv379zNx4kQKCgpYvXo1ixYtokePHrRv7/57V54+7BO05TH8D39A\nfofJFLQdq3U4F5GhNfeQdr56+/alMXr0GnbsOMtbb/Vn8OAW5b5P2tj1pI3dwyVD8dOmTeOll15y\n3h/v379/lU+sqxGUA99TxbcmzFJtTogrUlRkY+bMLfTt+wE7dpylXr1AgoNlprsQ5al0KN5oNNKi\nxYXfihs3bozR6LIRfK9lTPsZfVEq9qDrsddqpXU4QngMpRSDB3/Gjh1nALjvvvZMmtSDkBBfjSMT\nonq6rMSenJzsrESXlJREJaP3ohy+JdXmzFGJUm1OiMuQk2MmKMiEXq/j7rvbkJNjZu7ceLp2bah1\naEJUa5Um9vHjx/PYY49x9OhROnXqRMOGDZkzZ447YvMqF6rNyTC8EJeilGLlykM899z3jB3blfvu\na8/dd7dh2LCW+PrKaKEQlan0X4mPjw9ff/01GRkZmEwm55I1cfn0uccwZv2OwycYa90eWocjRLV1\n5kwu48evZ/XqwwB8++1hRo5sh06nk6QuxGWq9F/Ko48+SnBwMAMHDuTWW291R0xex/d8b71BHBhk\nwo8Q5fnss98ZP349eXkWgoJMTJrUg/vua3/RhlRCiEurNLF/++237Nmzh2+++Ybhw4fTuHFjBg0a\nRP/+/d0Rn1e4sPe6bPoiREX8/Izk5VlITGzKrFl9aNBAypUKcTUqXcdeWmpqKosXL+azzz5jz549\nroyrgs/3vDWTOks2tT9tAspO+rDDKL/aWodUIVmX6h7SzsXMZhsLFmwnKMjEo492QinFTz+d5sYb\nG1xzL13a2PWkjd3jataxV9pjT0lJYc2aNaxevZqMjAz69+/P//73v6sKsCYynf4OncOKJfKv1Tqp\nC+FO27adZvToNRw8mIG/v5Fhw1pSp06AzHgXogpUmthvu+02+vfvz/jx42nbtq07YvIqMhteiAty\nc83MmLGZ997bhVLQpEkt5s6Np06dAK1DE8JrVJjYU1JSiIyMZOnSpc6CNMnJyc7XGzVq5ProPJ3D\nhunkGgAsjWROghC//nqOJUt2YTTqefLJv/DMMzfi5yez3YWoShX+i5o9ezZz587lwQcfRKfTlSlK\no9Pp+O6779wSoCfzSf0JvSUTW3BT7KHNtQ5HCE2cO5fHli0nGTKkBT17XsekST2Ii2tMq1YRWocm\nhFeqMLHPnTsXgHfeeYemTZuWeW3nzp2ujcpLmEqqzVkayTC8qHkcDsWHH+5m6tRN5OdbaN48nLZt\nIxk1qovWoQnh1SrcBCYnJ4cTJ04wceJEkpOTnf8dOXKECRMmuDNGj3Xh/roMw4ua5Y8/Mhg8+FPG\njFlHTo6Z3r2jCQvz0zosIWqECnvsO3fu5P3332ffvn2MHDnS+ed6vZ4ePaR6WmUMOYcw5hzCYaqF\nNbKr1uEI4TapqQXExX1AQYGNOnX8efHF3vz977FSaEYIN6kwsffq1YtevXrx8ccfc+edd7ozJq9g\nSi4pStMwHvQyOUh4v+TkHBo1CiEiIoARI9qRnW3mhRf+Rni4v9ahCVGjVJhxPv/8c2677TbOnTvH\na6+9dtHrTz31lEsD83QyDC9qirw8CzNnbuHdd3/liy+G0bVrFFOn9kKvlx66EFqo8B67Xl/8ktFo\nxGAwXPSfqJjOnIFPylaUzoilYZzW4QjhMmvXHqFnz/d5553iCbW7d6cASFIXQkMV9tgHDx4MwBNP\nPEFeXh5BQUGkpaVx7NgxOnbs6LYAPZHp1Fp0yo6lXi+UKVTrcISockopRo36lmXLfgegffu6zJsX\nT9u2kRpHJoSosMd+3vTp0/nmm2/Iyspi+PDhfPDBB7zwwgtuCM1zOYfhZZmb8DLn61nodDoaNQoh\nIMDI1Km9+OabOyWpC1FNVJrYf//9d4YNG8Y333zD4MGDmT9/PsePH3dHbJ7JbsF0ah0AZikjK7zI\nkSOZDB36OWvWHAHgqae6sHHjSB59tBNGY6U/SoQQblLpdO3zv6Fv2LCBp59+GgCLxeLaqDyYT8oP\n6K052EJb4AhurHU4Qlwzq9XOG2/s4JVXtlJUZCcrq4j4+Mb4+hq57jq51SREdVNpYm/cuDH9+/cn\nPDycli1b8uWXXxIaKv+YK3JhGF5mwwvPt2vXOZ55Zg179qQCMHRoS6ZN6yVr0oWoxipN7DNmzODg\nwYPOsrLNmjVjzpw5Lg/MIymFb0kZWRmGF97gxx9PsWdPKtddF8KcOXH06ROtdUhCiEpUmtiLiopY\nv349r732GjqdjhtuuIFmzZq5IzaPY8jejyHvGA7f2tjqdNY6HCGuyvffH6Ow0Eb//s34xz9uwOFQ\n3HtvOwIDfbQOTQhxGSqd8fKvf/2LvLw8hg8fzu23305aWhqTJk1yR2wex7npS1Qi6GWtv/AsaWkF\nPPbYN9xxxwrGjFlLenohBoOeRx/tJEldCA9SaY89LS2NefPmOZ/37t2bESNGuDQoT+V7chUgw/DC\nsyil+OyzfUyevIGMjCL8/Aw89lhnQkJMWocmhLgKlSb2wsJCCgsL8fcvrvdcUFCA2Wx2eWCeRleY\nijF1O0pvwtKgj9bhCHHZ1q8/xhNPFO9t0LNnI15+OY4mTcI0jkoIcbUqTex33HEH/fr1o02bNgDs\n3btX6sSXw3RqDToUlnp/A58grcMR4pJsNgf79qXRtm0kffpEM3BgDHFxjbnjjlYy410ID1dpYh86\ndCjdu3dn79696HQ6/vWvf1G3bl13xOZRZBheeIrdu1MYPXothw9nsmnTSBo2DObf/75V67CEEFXk\nkok9KSmJI0eO0KlTJ+LiZDOTCtmLMJ1eD5RMnBOiGioosPLKK1t5440d2O2KqKhgzp7No2HDYK1D\nE0JUoQpnxS9cuJA33niDlJQUJk2axH//+193xuVRfM5uQmfLxxrWDkdQI63DEeIiGRmF3HTTUl5/\n/WccDsXDD3dg48aRdOpUX+vQhBBVrMIe++bNm/nwww8xGo3k5uby5JNPMnDgQHfG5jF8k4uH4S2N\npLcuqher1Y6Pj4HwcH/atInE39+HefPiJaEL4cUq7LGbTCaMxuK8HxwcjN1ud1tQHkUpTCe/BcAi\n99dFNaGU4osv9nPjje/yxx8ZALz6ajxr194tSV0IL1dhYv/zzFiZKVs+Q+ZuDAUnsfvXxVa7g9bh\nCMHJkzncffeX/POfqzh5MpelS3cDEBrqh8kkhZOE8HYVDsUfPnyYZ599tsLnUi++mO/5TV+i+oFO\ntq4U2vr3v3cyY8ZmCgqshIT4MmVKT+6+u63WYQkh3KjCxD527Ngyz7t16+byYDyR6fz9dRmGF9XA\nnj0pFBRYufXW5syc2Zu6daWmghA1TYWJffDgwe6MwyPpC87gk74TZfDDUr+X1uGIGqioyMarr/5E\n//7NaN++Li+80IvExGYkJjbVOjQhhEYqLVAjKuacNFe/NxgDNI5G1DQ//JDM6NFrOXIki/Xrj7Fm\nzV3UquUnSV2IGk4S+zUwnZRheOF+WVlFTJu2kQ8+2ANATEw4L77YWya4CiGAy9i2FSAzM5Pdu4tn\n1jocDpcG5DFsBZjObACk2pxwrzff3MEHH+zBx0fPuHHd+O67e+jSpYHWYQkhqolKe+wrV65kwYIF\nmEwmVq5cyfTp02nVqhXDhg1zR3zVlulMEjp7EdbaHXEE1NM6HOHlzpzJJS2tkLZtI3nyyS4cPZrF\n6NFdiY2trXVoQohqptIe+5IlS/jqq68ICyvexnH8+PF8+umnLg+sunPOhm8kw/DCdRwOxZIlu+je\n/X0eemglhYVWAgN9eOutWySpCyHKVWmPPTg42LkXO4Cfnx8+Pj4uDaraUw5Mp4r3r5bd3ISrHDiQ\nzpgxa9m27TRQvFd6YaENf/8a/u9PCHFJlSb2sLAwvvjiC8xmM3v37mXVqlWEh4e7I7Zqy5i+E0Ph\nOewBUdjDpPiHqHpbt55k6NDlWK0OIiMDmTWrD7fe2lzrsIQQHqDSofipU6eye/du8vPzmTRpEmaz\nmRkzZrgjtmrLVHrTF5mJLKpQbq4ZgE6d6tOsWTgjRrRly5aRktSFEJet0h57SEgIkydPdkcsHsP3\npAzDi6qVk2NmxozNrFlzmE2bRhIc7Ms339xJQIAMuwshrkylib1Xr17lro/dsGGDK+Kp9vR5yRgz\nd+MwBmGt9zetwxFeYNWqP5gw4TvOns3HaNTzww8nSUhoKkldCHFVKk3sH330kfOx1Wpl69atmM3m\nyzr5Sy+9xK5du9DpdEycOJF27dpd9J65c+fy66+/8p///OcKwtaOqWTTF2uDPmDw1Tga4clyc808\n9dQaVq48BECnTvWYOzeeVq0iNI5MCOHJKk3sDRs2LPM8OjqaBx98kPvuu++Sx23bto3jx4+zbNky\nDh8+zMSJE1m2bFmZ9/zxxx9s377do2bZn9/NTYbhxbUKCPDhzJk8AgN9eP75Htx/f3sMBtkhUAhx\nbSpN7Fu3bi3z/OzZs5w4caLSE2/dupW4uDgAmjZtSnZ2Nnl5eQQFXdhtatasWTzzzDO8/vrrVxq3\nJnTWXHzObkKhwxKVoHU4wgP98UcGDz+8ihdfvImIiAAWLUrEZDIQFRWidWhCCC9RaWJfvHix87FO\npyMoKIipU6dWeuK0tDRat27tfB4eHk5qaqozsa9YsYIuXbpcNCJQnfmcXo/OYcEacSPKr47W4QgP\nYrHYef317bz66k+YzXZCQ028/HIcTZqEaR2aEMLLVJrYJ0yYUCZBXy2llPNxVlYWK1asYMmSJZw7\nd+6yzxEREXzNcVyTHesA8In9u/axuIi3XpeWfvzxJA899DV79qQA8MADN/DyyzcTHu5fyZHiWsh3\n2fWkjaunShP77NmzWbp06RWfODIykrS0NOfzlJQUIiKKJwX9+OOPZGRkcPfdd2OxWDhx4gQvvfQS\nEydOvOQ5U1NzrziOKuOwU/uPleiBjPC+2LWMxUUiIoK1bWMvNWnSd+zZk0J0dChz58YzZEhrUlNz\npa1dSL7Lridt7B5X88tTpYm9QYMGjBgxgvbt25eZ5PbUU09d8rju3buzcOFChg8fzt69e4mMjHQO\nwycmJpKYWLwj2smTJ3nuuecqTepaM6ZtR29Oxx4UjT00VutwRDW3du0RWrSoQ6NGIcya1ZcPP9zN\nM8/cKOVghRAuV2lij4qKIioq6opP3LFjR1q3bs3w4cPR6XRMmTKFFStWEBwcTHx8/FUFqyXf5JLZ\n8I36SbU5UaGUlHwmTdrAl18eIC6uMR9++Heuvz6UiRN7aB2aEKKGqDCx//e//2XgwIE88cQTV33y\nsWPHlnneokWLi94TFRXlEWvYz69ft0T11zgSUR0ppfjkk71MmZJEVpaZgAAjPXteh1Lye6AQwr0q\nXDS7fPlyd8ZRrelzj2DM3o/DJxRr3b9qHY6ohubP38ZTT60hK8tM797Xk5Q0kkcf7YReL1ldCOFe\nUg3jMpwfhrc07At6uUcqilmtdlJS8gG46642NG0axuLF/fjkkyFcf32oxtEJIWqqCofid+7cyU03\n3XTRnyul0Ol0NapWvKlk0xcZhhfn/frrWZ55Zi0BAT58/fUd1K0byObNI6VynBBCcxUm9latWjFv\n3jx3xlIt6SxZ+JzbgtIZsDT0vEl/omrl51uZNWsL77yzE4dDcd11IZw+nUtUVIgkdSFEtVBhYjeZ\nTB5VFc5VTKfWoVM2LHV7oHylSlhNtndvKiNHfsWJEzno9ToefbQTzz77VwID5faMEKL6qDCxl7cT\nW03knA3fSIbha6rzt5+iooIxm+20bRvJvHnxtG9fV+vQhBDiIhUm9nHjxrkzjurJYcV0ai0AlqhE\njYMR7qaU4rPP9rFs2e988slgQkP9+OKLYURH18JolGF3IUT1VGmBmprMJ+VH9JYsbCHNsYc00zoc\n4UbHj2czbtw6Nmw4DsCXXx5g2LBWNGsWrnFkQghxaZLYL0GG4Wsem83B22//wpw5P1BQYCMszI+p\nU3sxdGhLrUMTQojLIom9IkphSl4FgCWqn8bBCHexWOwsWbKLggIbQ4bEMn16byIiArQOSwghLpsk\n9goYcg5hzD2CwxSGNaKL1uEIFyoosPL227/w8MMdCQjwYcGCBPLzLcTFNdE6NCGEuGKS2CtgOl9t\nLioB9NJM3iop6Thjx67j+PFscnLMTJ78N7p1u/JNj4QQorqQjFUB35PFw/BmGYb3ShkZhUyZksSy\nZb8D0LJlHW65pbnGUQkhxLWTxF4OXVE6xtSfUHofrA36ah2OcIGHH/4fGzeewNfXwJgxXXn88c74\n+Bi0DksIIa6ZJPZymE6tQaccWOr1QplCtA5HVJHk5BxCQkyEhvoxcWJ3lFLMmRNH06ZSUVAI4T2k\nykY5fEuWuckwvHew24uXsPXs+T7Tpm0CoGPH+nz++TBJ6kIIryM99j+zW/A5/R0AlkaS2D3d3r2p\njBmzll9+OQtATo4Zu90hG7YIIbyWJPY/8Tm3Gb01F1ut1jiCrtc6HHENPvpoD2PHrsNmc1C/fhCz\nZ2rI1SQAACAASURBVPclMbGp1mEJIYRLSWL/E9+SojRm6a17rPM98k6d6mMw6Lj33vY8/3wPgoN9\ntQ5NCCFcThJ7aUphOrkakE1fPFFWVhHTpm0kP9/KW2/dQmxsbX7++UHq1g3SOjQhhHAbSeylGLJ+\nx5B/AodfBLY6nbUOR1wmpRRff32I555bT2pqASaTgaNHs2jcuJYkdSFEjSOJvRTnMHxUIuhkcpUn\nOHs2j2ef/Y7Vqw8DcOONDZk3L57GjWtpHJkQQmhDEnspzt3cZJmbx7BaHWzceILgYBOTJ/+NESPa\notfrtA5LCCE0I4m9hK4wBWPaDpTeF0v93lqHIy5h//40li37ncmTe9KoUQjvvHMLbdpEUL9+sNah\nCSGE5iSxl/A9+S06FOb6vcAnUOtwRDnMZhvz529jwYJtWK0O2raNZMiQFsTHyy5sQghxniT2EqaT\nsvd6dfbjj6cYM2Ythw5lADBiRFv69o3WNighhKiGJLED2IswnfkekGVu1VFBgZX77/8v6emFNGsW\nxty58bK1qhBCVEASO2A6k4TOVoA1/AYcgQ21DkeUSEo6To8ejQgI8GHatF4cPpzJ00/fiJ+ffG2F\nEKIi8hOS0rPhpbdeHZw9m8eECetZteoPZs7szYMPdmDYsFZahyWEEB5BEnvpanON+mscTM3mcCiW\nLv2N6dM3kZtrITDQB19f+YoKIcSVqPE/NY0ZuzAUnMYe0ABbeHutw6nRHn10FV98cQCAhIQmzJrV\nl4YNZQmbEEJciRqf2E3J52fDJ4JOCpu4m8ViR6cDHx8DgwbFsnlzMjNn9mHAgObo5O9DCCGuWI2v\nm3ph0xdZ5uZu27efJi7uAxYu3A5A//7N+OmnBxg4MEaSuhBCXKUandj1+afwyfgVZQzAUr+X1uHU\nGHl5Fp57bj233voJ+/en89VXB7DZHAAEBZk0jk4IITxbjR6Kd/bW6/cGg5/G0dQMGzeeYNSo1Zw+\nnYfRqOfxxzszevSNGI01+ndMIYSoMjU8sZ9f5iaz4d1Fr4fTp/Po0KEuc+feTJs2EVqHJIQQXqXm\nJnZrPqYzSSh0mKMStI7Gayml+OijPZw5k8fYsd3o0eM6Pv30Nnr2bITBIL10IYSoajU2sZvOfI/O\nYcZapzPKP1LrcLzSkSOZjBmzli1bTqLX6xg0KJbmzcO56abrtQ5NCCG8Vs1N7OeH4aUoTZWzWu0s\nXryDV17Zitlsp3Ztf2bMuIlmzcK0Dk0IIbxezUzsyoFvycQ5syxzq3IHD2Ywc+YWHP/f3p3Hx3i1\nDRz/zUwyCRLZF5KgFClK7VtEVYKqtvRRkYr94bFVqdqXUKKqCSrValWrSivqTfu2XlsFtSulFG0R\nKgvZI5LIMsv9/pGK5lGx1JjM5Pp+Pv18ZO6573PN1XDNOfe5zzEq9OvXiHnzOuPmVsXcYQkhRKVQ\nKQu7TcYx1IXpGKrVwuAsa5A/DHl5xezceYnevRvSuLEHs2d3onFjDxl2F0KIR6xSFvabj7kV+T0r\nq809BLt2XWLy5DgSE6/j7l6FgIBajB3bytxhCSFEpVQpC7td4s3H3GQY/p9IT7/B7Nl7iI39DYAn\nn/TEyUnWAxBCCHOqdIVdnXcZm2tnMNo6ovMKMHc4FquwUE/Xrp+TkpJPlSo2TJ7cnlGjWspCM0II\nYWaVrrBr/+yt62p2BY0sX3q/0tLy8fSshr29DUOHPsWBA4lERgZRp46zuUMTQghBJVwr3u7Px9xk\nNvz90euNrFhxjNatV7NlywUAxo9vzVdf/UuKuhBCVCCVqrCriq9jm7ofRaWm2KebucOxGKdOpdKj\nxxfMm7eXggI9hw4lAaDRqGUXNiGEqGAq1VC87ZU4VEYdOs/2KPZu5g7HIkRGHiIq6jAGg4KvryOL\nF3clKKiuucMSQghxByYt7AsXLuTkyZOoVCpmzJhB06ZNS48dPnyYJUuWoFareeyxx4iIiECtNu0A\nwq1heFlt7l65uNhjNCr85z8tmDq1g2yrKoQQFZzJKumPP/7I5cuXiYmJISIigoiIiDLH58yZw/Ll\ny9mwYQP5+fns27fPVKGUMOrRJm0HoNhP7q/fSVZWAePGbWPDhjMADBnSjLi4gcyf/7QUdSGEsAAm\n67EfOnSIoKAgAOrVq0dOTg55eXk4ODgAEBsbW/pnV1dXsrOzTRUKALbpP6IuzkbvWBdD9fombcsS\nlezC9gvjx28lM7OAvXsv89JL/mi1GtlaVQghLIjJeuwZGRm4uNza9MPV1ZX09PTSn28W9bS0NA4c\nOEDnzp1NFQrwX5u+yISvMhIScggN/ZoBA2LJzCygY0dfvvmmH1qtxtyhCSGEuE+PbPKcoii3vZaZ\nmcmoUaMIDw8v8yXgTjw8HB88gCsly8hWbfIvqv6T61ih7dsvsWvXHzg72xMZGcywYc1ltruJ/aPf\nZXFPJMemJzmumExW2D09PcnIyCj9OS0tDQ+PW0O6eXl5jBgxggkTJhAQcG8rwKWn5z5QLJrrF3DN\n/h2j1plMbVN4wOtYkzNn0rlwIYsXX2xIt251mDUrgLFj26LRKGRk5Jk7PKvm4eH4wL/L4t5Ijk1P\ncvxoPMiXJ5MNxXfs2JHt20smq505cwZPT8/S4XeARYsWMXjwYAIDA00VQqmbm74U+wSD2tbk7VVk\nBQU6IiL2Exy8ntde20Fi4nVUKhXjx7fB29vh7hcQQghRoZmsx96iRQsaN25M//79UalUhIeHExsb\ni6OjIwEBAXzzzTdcvnyZTZs2AdCrVy9CQkJMEos2cQsgm77s35/ApEk7uXTpGioVhIY2xdnZztxh\nCSGEeIhMeo/9jTfeKPOzv79/6Z9Pnz5tyqZLqYqysU07hKKyodgn6JG0WRGdOZPOSy+VfIny93cj\nKiqY1q1rmjkqIYQQD5vVrzynTf4elWKg2LszirZyrWmuKArnz2fRoIEbjRt70K9fIx57zJlXX20t\nM96FEMJKWX9hT7o5DN/DzJE8WsnJuUydGsfu3X+wa9dAGjZ0Izq6u8x2F0IIK2fdm8AYdWiT4wAo\nqiSrzRkMRlavPkFAwBp27LiIvb0NFy+WLP4jRV0IIayfVffYbVMPotbloHfyx+ho/RuXFBcb6NPn\nK44evQJAz56P89ZbXahRQ541FUKIysKqC/utYXjr7q0bjQpqtQqtVoO/vxsJCTm89dYz9OolS+cK\nIURlY71D8YqCXeKfu7lZ8TD84cNJdO68luPHrwIwd24g+/cPlqIuhBCVlNUWdk3O72jy/sBo54be\nvbW5w3nocnIKmTTpe154YSO//57JihXHAHB0tMPJyd7M0QkhhDAXqx2Kv7UoTXdQW9ejXVu2XGDq\n1DhSU/OxtVUzfnwbJkxoY+6whBBCVABWW9jt/tzNrcgK768fO3aF1NR8WrWqwZIlwfj7u5s7JCGE\nEBWEVRZ2VWEGNuk/oqi16Go+Y+5w/jGjUeHzz3+hbl1nOnWqxRtvtOfxx13p378xarU8wiaEEOIW\nqyzs2uTtqFAo9u6EYmvZj3qdP5/F669/z5EjydSu7cS+fYOpWtWWV15pYu7QhBBCVEBWWdhLZ8Nb\n8DB8cbGB5ct/ZNmyHykuNuDpWY05czphZ2dd8wWEEEI8XNZX2A1F2F7ZBVj28+vr159m8eJDAISF\nNWHOnECcnWW2uxBCiPJZXWG3TdmHWp+H3uVJjA5+5g7nvuTmFnHp0jWaNvUiLKwJ+/YlMHz4U3Ts\naFmfQwghhPlYXWG3+3O1uSIL2/Rl27Z4pk6Nw2BQOHBgME5O9nzyyfPmDksIIYSFsa7CrihoE7cB\nUOzX08zB3JvU1HxmztzNt9+eA6B5cy+yswtlkRkhhBAPxKoKuyb7NJobSRiqeKF3a27ucO7q/Pks\nevb8kpycIqpWtWX69I78+99PodFY7YKAQgghTMyqCvvNYfhinx6gqrjFsaBAR5UqttSr54K/vzsO\nDrYsXhyEn191c4cmhBDCwllVYdf++ZhbRR2G1+kMrFhxjI8//pm4uDC8vKqxfn1vHB21sle6EEKI\nh6Lidmvvk/pGCraZx1E09hTX6GzucG5z/PhVgoLWs3DhAdLS8tm69QIA1avbSVEXQgjx0FhNj12b\n9OekuRpPg01V8wbzFzqdgXnz9rJq1QkUBWrXdiIyMojOnWubOzQhhBBWyIoK+5/D8L4VaxjexkbN\nxYvXUKtVjBrVksmT21O1qq25wxJCCGGlrKOw62+gvboHgOIK8Px6evoN5s/fx6RJ7ahd24nFi7uS\nnV3Ik096mjs0IYQQVs4qCrv26g+oDAXo3JpjrOpttjgURSEm5izh4T+QnV3ItWuFrF37Ir6+1fH1\nlRnvQgghTM86CnuS+WfDX7p0jcmTd7J3bwIAnTvX5s03K94kPiGEENbN8gu7YiydOGfO3dyWLj3C\n3r0JuLra8+abT/Pyy0/IbHchhBCPnMUXdpvME2gKUjBU9cXg8uQjbfvkyVSqVLGhQQM35szphFar\nYdq0Dri7V5xZ+UIIISoXi3+O/dYwfA94RD3k/Hwd4eE/0L37F7z22nYMBiPu7lWJjAySoi6EEMKs\nLL7HfnPTl0c1DL979x9MnhxHQkIOarWKVq1qotMZZX13IYQQFYJFF3Z1XiK22adQbKqh8+5k8vZi\nYs7y6qslXyQaNXJn6dJuNG9uvln4QgghxH+z6G5m6WpzNbuCxjTbnCqKQmZmAQA9e9bjscecmTUr\ngO+/HyBFXQghRIVj0T12uz/vr5tqGD4hIYcpU+K4ciWXnTvDcHS0Y//+wdjaakzSnhBCCPFPWWyP\nXaXLxTZlLwoqin26PdRrGwxGVq78icDAz9i16w9SUvL4/fdMACnqQgghKjSL7bHbXtmNyliMzqMt\nShWPh3bd5ORchg37lhMnUgHo3bshCxY8jadntYfWhhBCPExXr15h0KD+NGzoD4BOp6Nu3cd5441p\naDQaCgsLiY5ewtmzp7GxscHFxY1Jk6bi5VVyOzExMYHly6O4di0bg8HIk082ZezYCWi1WrN9JoPB\nwNSpE5k4cQo+Pr5miyMvL49582aSl5dHlSpVmTt3AdWrO5WJ8513FpKYmIBOp+Oll16mR4/nyMvL\nY8GCOeTl5WE0GpkyZSZFRUWsW7eG+fMXmTRmi+2xm2oY3tXVnpycImrWdGDdut589NFzUtSFEBVe\nrVq1ee+9j3jvvY/48MNP0et1fP99yTyk6OgluLt78OmnX7Bq1VrCwgYzadJ49Ho9BoOBWbOm8Mor\ng1i1ai2rV38OwKefrjLnx+GbbzbRrFlzsxZ1gI0bv6B585Z88MFqOnfuwrp1n5U5fvjwQQoKClix\nYhXR0Sv54INojEYjMTHrefLJZrz33keEhQ1h9eoPadjQHzc3d3bv3mnSmC2zx2403Jo45/fPC/u+\nfQksX36UNWteoFo1W9aufZGaNR1xcDDft1UhhPgnGjVqQlJSIjdu5HP48EFiYr4pPda06VM0atSY\nffv2UKVKVWrVqkPz5i0BUKlUjBkzHpWqbL9Pr9ezYEE4qalX0WrtWLo0im3b4rh4MZ5x4yZw48YN\nBg0KYdOm7+jfvw/t2nXExcWFrVv/jw0bYgHYunUzFy6cIzR0IG+9NR+9XodarWbq1Nl4e5edjLxp\nUwwffvgpADt2bGXTphg0GjV16tRj6tSZbNnyHYcPHyQjI5158xayd+8edu7chkqlplOnpwkNDSMt\nLZX58+eUxj9r1rwyXxQOHtzPF1+sLdPuCy+8RLdutzYT++mno0yfXnKNjh0DmTJlQpn3Ozk5l/bK\nb9wooGrVqqjVasLChqBWl+TQ2dmZ69dzAOjbN4SIiLl06RJ0P/8774tFFnabjGOoizIxONTB4OT/\nwNfJzi5g7ty9fPnlGQBWrz7B+PFtaNDA7WGFKoSoZKrH9cUuecdDvWaRTzeud910z+/X6/Xs2/cD\nvXv/i+TkJGrXroONTdl/7uvXb0hCwmWqVKlC/foNyhyzs7v9KaOtWzfj5ubG3LkR7Ny5nbi4uHLb\nb9euA+3adeD48WNcvBhP3br12LfvB0JDw1i16gP69x9A69ZtOXRoP5999jFTp84qPT8lJQWtVls6\n5F1QUEBUVDSOjo6MHTuC+PgLAKSmprBy5SdcvXqFPXvieP/91QCMHj2cLl2CyM7OZOjQEbRo0YrN\nm/+X2NivePXViaXtdOgQQIcOAeXmMjMzE2dnFwBcXFzIzMwoc7xJkyfx8vLi5ZdfID8/v/RLgJ2d\nXel7vvpqA8HB3QHw9fUjNTWFwsJC7O1N8zSXRRb20mF4v2cfaLU5RVH43/89x4wZu8nIuIFWq+H1\n19syalTLhx2qEEI8EgkJlxk3biQA8fEXGDBgEIGBT3P+/DkMBuNt71cUBbVaA6gwGm8//t9+//03\nWrVqDUBQUHc8PBz57LMv7vj+Ro0aAxAY2IUDB/bh4+PLpUvxNGnSlEWL5pOQcJnPPluN0WgsLZw3\nZWSk4+Fxa5vr6tWrM336JAAuX75ETs41AJ54ohEqlYpffz1DUlIir776HwBu3MgnJeUKNWrUZNmy\nSFav/pDc3Os0bPjEXT9neRRFue21kydPkJaWSkzMN2RnZzF+/Cg6dAjA1tYWgPffX46trS29evUu\nPcfNzY3MzAyT3WawyMKuTdwCQPED3l83GBSio4+SkXGDdu18iIoKpn5914cZohCikrqfnvXDdPMe\nO8CsWVPw86sNgI+PD4mJl9HpdKXFBuDChXMEBj6Nra2W//mfjWWuVVxcTFJSAnXrPl76mkajxmgs\nW9j+utGVXq8vc8zGpqStzp27MHv2NOrWrUfbtu1RqVTY2Ngyf/7buLu73/Hz3Ly2TqdjyZLFrFnz\nBW5u7mWGwm+2YWNjS/v2HZkyZWaZayxcOI+2bdvRu3dfdu/eycGD+8scv5eheHd3d7KyMnBwcCAj\nIx1397KTtX/55SQtW7bBxsYGDw9Pqld3Ii0tFR8fXz7+eCXXrmUzbdrsO35OU7C4yXPq3EvY5PyG\n0dYJnVfHez7PYDCyZs1Jrl0rxMZGzdKlwbzzThDffNNPiroQwqqMGfMaK1dGU1hYSNWq1ejQoROf\nfPJR6fFffjnJuXO/0759AK1btyU19Sr79+8FwGg08sEH0cTFfV/mmv7+jTh+/CgABw7sY+XKlVSt\nWq10aPrUqZ//NhZ3dw9UKhU7d27n6ae7AiX3//ft2wOU3MPesWPbbeekpaUBJb1vjUaDm5s7qakp\n/Pbbr7d9iWjY8AmOH/+JwsJCFEVh2bJIiooKuXbtGj4+viiKwv79P6DT6cqc16FDQOmEw5v//bWo\nA7Rp045du0omu+3ZE0fbtu3LHPf19ePXX0tu5+bn55Genoa7uzsnT/7M2bNnmDZtdum99puysrJw\nc7vzl5p/yuIK+81h+GKfrqC2vcu7S/z6awa9em1gypQ45s0r+eVt2tSLwYObolbL1qpCCOtSs6YP\nTz/dlc8+K7nn/NprkyguLmLw4FBGjBjE2rWfMH/+IjQaDWq1mqio9/j2268ZPnwgY8b8GwcHB4YP\n/0+ZawYFdaegoIBx40ayceOX9OnTh1atWpfeAkhI+OO2CXc3BQQE8vPPx2na9CkAhg8fyb59exg7\ndgSffrqKJk3K7szp7e1NUVER169fx8nJmdat2/Lvfw/i009X8corA1m+fEmZ4u7t7U2/fqGMHTuC\nkSOH4Obmhp2dPS+++BJLl77DpEnj6dq1Oz//fJwffzx8X7ns27c/v//+K2PG/Jvjx3/ilVcGAfDu\nu1FcuZJMYGAXHBwcGD16OK+//ipjxozHzs6er7/+irS0FMaPH8W4cSOZMWMyAMnJSXh6eprs/jqA\nSvm7mwYVVHp6Lk47nkeb8gPXA1ZRVDek3PcXFupZtuwIy5cfRa834u1djUWLutKz5+PlnldZeXg4\nkp6ea+4wrJ7k2fQkx6Zn6hx/9dUGiooKCQsbYrI2zGH58igaN25K167B9/R+Dw/H+27DonrsquIc\nbFMPoKg0FPvcPSlTpsSxZMkR9HojQ4Y0Y//+IVLUhRDCAvTp05effz5OcnKSuUN5aM6f/520tLR7\nLuoPyqImz2mTd6JS9BR7BaDY/f198ZycQnS6kv3Rx49vzenTaSxc+Azt2vk84miFEEI8KBsbGyIj\nl5s7jIeqfv2GLFjwtsnbsageuzap/NnwmzefJyDgMyZPLpno8PjjrsTFhUlRF0IIUWlYTo/dqEeb\nXDJL879Xm7t6NZdp03axdWs8AGlpN8jLK8bBQVvmcQwhhBDC2llOYU8+gLr4Gvrq9TFUv3WffNeu\nS4wY8X/k5pYU8lmzAhgypJnMdhdCCFEpWU5hj/8WuDUMrygKKpUKf393FAV69KjHokXPULPm/c8g\nFEIIIayFSQv7woULOXnyJCqVihkzZtC0adPSYwcPHmTJkiVoNBoCAwMZO3Zs+Re7+B0AuV49iHrn\nECdOpLB+fW9q1nRk9+6B1KpVXYbdhRBCVHomK+w//vgjly9fJiYmhvj4eGbMmEFMTEzp8QULFrB6\n9Wq8vLwICwuje/fuPP54OY+iZZ9nf1Ijhvc/z7nz2X+2cYW2bX2oXdvpzucJIYQQlYjJZsUfOnSI\noKCSbenq1atHTk4OeXl5ACQmJuLk5ESNGjVQq9V07tyZQ4cOlXu9cbE9CXz3Zc6dz6ZuXWe+/vpl\n2raV2e5CCCHEX5mssGdkZODicmvHHldXV9LT0wFIT0/H1dX1b4/dSczJxmjUKiZMaMOePYPo2NHP\nNIELIYQQFuyRTZ77pyvXpuctfkiRiPI8yPKF4v5Jnk1Pcmx6kuOKyWQ9dk9PTzIybm1In5aWhoeH\nx98eS01NxdPT87ZrCCGEEOL+mKywd+zYke3btwNw5swZPD09cXBwAMDX15e8vDySkpLQ6/Xs3r2b\njh3vfQtWIYQQQvw9k+7uFhkZybFjx1CpVISHh3P27FkcHR0JDg7m6NGjREZGAtCtWzeGDx9uqjCE\nEEKISsOitm0VQgghRPksahMYIYQQQpRPCrsQQghhRSpkYV+4cCEhISH079+fU6dOlTl28OBB+vbt\nS0hICCtWrDBThJavvBwfPnyYfv360b9/f6ZPn47RaDRTlJatvBzfFBUVxcCBAx9xZNajvBxfvXqV\n0NBQ+vbty5w5c8wUoXUoL8/r168nJCSE0NBQIiIizBSh5Tt37hxBQUGsW7futmP3XfeUCubIkSPK\nyJEjFUVRlAsXLij9+vUrc/zZZ59Vrly5ohgMBiU0NFQ5f/68OcK0aHfLcXBwsHL16lVFURTl1Vdf\nVfbs2fPIY7R0d8uxoijK+fPnlZCQECUsLOxRh2cV7pbj8ePHKzt27FAURVHmzp2rJCcnP/IYrUF5\nec7NzVW6dOmi6HQ6RVEUZejQocqJEyfMEqcly8/PV8LCwpRZs2Ypn3/++W3H77fuVbge+8Neilbc\nrrwcA8TGxuLt7Q2UrAqYnZ1tljgt2d1yDLBo0SImTpxojvCsQnk5NhqN/PTTTzzzzDMAhIeHU7Nm\nTbPFasnKy7OtrS22trbcuHEDvV5PQUEBTk6yd8f90mq1rFq16m/Xc3mQulfhCvvDXopW3K68HAOl\n6w2kpaVx4MABOnfu/MhjtHR3y3FsbCxt2rTBx0f2O3hQ5eU4KyuLatWq8dZbbxEaGkpUVJS5wrR4\n5eXZzs6OsWPHEhQURJcuXWjWrBmPPfaYuUK1WDY2Ntjb2//tsQepexWusP83RZ7GM7m/y3FmZiaj\nRo0iPDy8zF9q8WD+muNr164RGxvL0KFDzRiR9flrjhVFITU1lUGDBrFu3TrOnj3Lnj17zBecFflr\nnvPy8vjwww/Ztm0bcXFxnDx5kt9++82M0QmogIVdlqI1vfJyDCV/WUeMGMGECRMICAgwR4gWr7wc\nHz58mKysLAYMGMC4ceM4c+YMCxcuNFeoFqu8HLu4uFCzZk1q1aqFRqOhffv2nD9/3lyhWrTy8hwf\nH4+fnx+urq5otVpatWrF6dOnzRWqVXqQulfhCrssRWt65eUYSu79Dh48mMDAQHOFaPHKy3GPHj3Y\nsmULGzdu5L333qNx48bMmDHDnOFapPJybGNjg5+fH3/88UfpcRkifjDl5dnHx4f4+HgKCwsBOH36\nNHXq1DFXqFbpQepehVx5TpaiNb075TggIIDWrVvTvHnz0vf26tWLkJAQM0Zrmcr7Pb4pKSmJ6dOn\n8/nnn5sxUstVXo4vX77MtGnTUBSFBg0aMHfuXNTqCteXsQjl5XnDhg3Exsai0Who3rw5U6ZMMXe4\nFuf06dO8/fbbJCcnY2Njg5eXF8888wy+vr4PVPcqZGEXQgghxIORr69CCCGEFZHCLoQQQlgRKexC\nCCGEFZHCLoQQQlgRKexCCCGEFbExdwBCVAZJSUn06NGjzGOEADNmzOCJJ57423Oio6PR6/X/aD35\nI0eOMGbMGBo1agRAUVERjRo1YubMmdja2t7Xtfbu3cuZM2cYPXo0x48fx8PDAz8/PyIiInjxxRdp\n0qTJA8cZHR1NbGwsvr6+AOj1ery9vXnzzTdxdHS843mpqalcvHiR9u3bP3DbQlgbKexCPCKurq5m\neV69QYMGpe0qisLEiROJiYkhLCzsvq4TGBhYumhRbGwsPXv2xM/Pj5kzZz6UOF944YUyX2Leeecd\nVq5cyeTJk+94zpEjR4iPj5fCLsRfSGEXwszi4+MJDw9Ho9GQl5fHhAkT6NSpU+lxvV7PrFmzuHTp\nEiqViieeeILw8HCKi4t58803uXz5Mvn5+fTq1Ythw4aV25ZKpaJly5ZcvHgRgD179rBixQrs7e2p\nUqUK8+fPx8vLi8jISA4fPoxWq8XLy4u3336bzZs3c/DgQbp37862bds4deoU06dP5/3332f06NFE\nRUUxc+ZMWrRoAcCQIUMYOnQo9evXZ968eRQUFHDjxg1ef/11OnTocNe8NG/enI0bNwJw7NgxyN2+\nCgAABFlJREFUIiMj0Wq1FBYWEh4eTvXq1Vm2bBmKouDs7MyAAQPuOx9CWCMp7EKYWUZGBq+99hqt\nW7fmxIkTzJ8/v0xhP3fuHCdPnmTr1q0AbNy4kdzcXGJiYvD09GTBggUYDAb69etHhw4d8Pf3v2Nb\nRUVF7N69m759+1JQUMCsWbPYtGkT3t7erFu3jmXLljFt2jTWr1/PsWPH0Gg0bNmypcxa1cHBwaxd\nu5bRo0fTvn173n//fQCef/55tm/fTosWLcjMzCQ+Pp6AgABGjx7NsGHDaNeuHenp6YSEhLBjxw5s\nbO78z49er2fz5s089dRTQMnGOXPnzsXf35/Nmzfz4Ycfsnz5cvr06YNer2fo0KF8/PHH950PIayR\nFHYhHpGsrCwGDhxY5rV3330XDw8PFi9ezNKlS9HpdFy7dq3Me+rVq4eLiwsjRoygS5cuPPvsszg6\nOnLkyBFSUlI4evQoAMXFxSQkJNxWyM6dO1em3S5dutCzZ09+/fVX3Nzc8Pb2BqBNmzZs2LABJycn\nOnXqRFhYGMHBwfTs2bP0PeV57rnnCA0NZfr06Wzbto0ePXqg0Wg4cuQI+fn5rFixAihZxz0zMxMv\nL68y53/77bccP34cRVE4e/YsgwYNYuTIkQC4u7uzePFiioqKyM3N/ds9v+81H0JYOynsQjwid7rH\nPmnSJJ577jn69u3LuXPnGDVqVJnjdnZ2fPHFF5w5c6a0t/3ll1+i1WoZO3YsPXr0KLfdv95j/yuV\nSlXmZ0VRSl9bvnw58fHx/PDDD4SFhREdHX3Xz3dzMt2pU6fYunUr06ZNA0Cr1RIdHV1mT+m/89d7\n7KNGjcLHx6e0Vz9lyhTmzZtH+/bt2b17N5988slt599rPoSwdvK4mxBmlpGRQf369QHYsmULxcXF\nZY7/8ssvfP311zRu3Jhx48bRuHFj/vjjD1q2bFk6PG80Gnnrrbdu6+2Xp06dOmRmZnLlyhUADh06\nRLNmzUhMTGTNmjXUq1ePYcOGERwcfNse2yqVCp1Od9s1n3/+eTZt2kROTk7pLPm/xpmVlUVERMRd\nYwsPDyc6OpqUlJQyOTIYDGzbtq00RyqVCr1ef1s7D5IPIayFFHYhzGzYsGFMmTKF4cOH07JlS5yc\nnFi0aFHp8Vq1arF9+3b69+/PoEGDqF69Oi1atGDAgAFUrVqVkJAQ+vXrh6OjI87Ozvfcrr29PRER\nEUycOJGBAwdy6NAhJkyYgJeXF2fPnqVv374MHjyY5ORkunXrVubcjh07Eh4ezo4dO8q83q1bN777\n7juee+650tdmzpzJzp07eeWVVxg5ciTt2rW7a2w1atRgxIgRzJ49G4ARI0YwePBgRo0aRZ8+fbh6\n9Spr1qyhVatWxMbGsmzZsn+cDyGshezuJoQQQlgR6bELIYQQVkQKuxBCCGFFpLALIYQQVkQKuxBC\nCGFFpLALIYQQVkQKuxBCCGFFpLALIYQQVkQKuxBCCGFF/h/wl6TwUNVYkQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153c7dbac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153c9992b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.decomposition import KernelPCA\n",
"kpca = KernelPCA(n_components = 4, kernel = 'rbf')\n",
"X_train_kpca = kpca.fit_transform(X_train)\n",
"X_test_kpca = kpca.transform(X_test)\n",
"\n",
"# Fitting Logistic Regression to the Training set\n",
"from sklearn.linear_model import LogisticRegression\n",
"classifier = LogisticRegression(random_state = 0)\n",
"classifier.fit(X_train_kpca, y_train)\n",
"\n",
"# Predicting the Test set results\n",
"y_test_pred_kpca = classifier.predict(X_test_kpca)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred_kpca, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f18783bc-d19a-44c2-e861-09895af6ddb6"
},
"source": [
"slightly better on survived prediction, but same overall measures\n",
"\n",
"time to change algorithm\n",
"\n",
"Kernel-SVM maybe?"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"_cell_guid": "499a6d30-13d7-14ef-991c-4a6f524c1008"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[113 12]\n",
" [ 31 59]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.78 0.90 0.84 125\n",
" Survived 0.83 0.66 0.73 90\n",
"\n",
" avg / total 0.80 0.80 0.80 215\n",
"\n"
]
},
{
"data": {
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MQwEARlg4DSUEAQBm2LgnSAgCAMywcIONEAQAGGFjJ2hhbgMAYAYhCABwLMahAAAjLJyG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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153ce25c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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4i+zsQmbP3sabb/6AUvDKK3tYuXIogYEWvUOr1jyW2Hfs2EFMTAwAjRo1IiMj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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f15946a8128>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f154207efd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Fitting Kernel SVM to the Training set\n",
"from sklearn.svm import SVC\n",
"classifier = SVC(kernel = 'rbf', random_state = 2)\n",
"\n",
"classifier.fit(X_train, y_train)\n",
"\n",
"# Predicting the Test set results\n",
"y_test_pred_svm = classifier.predict(X_test)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred_svm, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e06d354a-489e-8145-64c3-5b3ee23ae12e"
},
"source": [
"Decision Tree?"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"_cell_guid": "702383d8-842e-cd10-7856-34ba9396fa27"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[94 31]\n",
" [22 68]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.81 0.75 0.78 125\n",
" Survived 0.69 0.76 0.72 90\n",
"\n",
" avg / total 0.76 0.75 0.75 215\n",
"\n"
]
},
{
"data": {
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GOn2WwAMAGGH7E8/Z0gQAOIHAAwA4gZEmAMAI20eaBB4AwAy7847AAwCYEeqbR18qAg8A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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1542069940>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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smzdv5tixYyxbtoxDhw6RkJDAsmXLAMjNzeVf//oXq1evxmg08uCDD7Jjxw7a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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d022a58>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d13a080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"classifier = DecisionTreeClassifier(criterion = 'gini', random_state = 0, max_features = 4)\n",
"classifier.fit(X_train, y_train)\n",
"\n",
"# Predicting the Test set results\n",
"y_test_pred_dt = classifier.predict(X_test)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred_dt, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"_cell_guid": "2462bfc0-260f-614c-51a5-72eb67ddbaeb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[107 18]\n",
" [ 30 60]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.78 0.86 0.82 125\n",
" Survived 0.77 0.67 0.71 90\n",
"\n",
" avg / total 0.78 0.78 0.77 215\n",
"\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d12b5f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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xI4l3392LUvDGG7tYtmwIgYFGT5dWo7ks2Ldt20ZcXBwAzZs359y5cxQUFBAY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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cde57b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cca9898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"classifier = KNeighborsClassifier(n_neighbors = 5, metric = 'euclidean', p = 2, algorithm = 'auto')\n",
"classifier.fit(X_train, y_train)\n",
"\n",
"# Predicting the Test set results\n",
"y_test_pred_kn = classifier.predict(X_test)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred_kn, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1ddd93ee-d3ee-ea08-480e-b202090897b2"
},
"source": [
"time to take a break and start over, maybe with new assumptions on dataset\n",
"in the meantime, let's try the new tool Hypertools that use PCA to render dataset graph in 2 and 3 dimensions"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"_cell_guid": "a9a96a51-949f-762f-b1f5-f022ce95cffb"
},
"outputs": [
{
"data": {
"image/png": 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0NTo6SjVyscP71Uw1L8/zSCQSyoxNo+dE2yCAdtFPPB5HJpNBOBzuaq5mNxgV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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153ca94fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import hypertools as hyp\n",
"\n",
"class_labels = preprocessedDataset.Survived \n",
"hyp.plot(preprocessedDataset, 'o', group=class_labels, legend=list(set(class_labels)))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"_cell_guid": "ff5114be-ddb0-c646-7a80-a400d21ecc7c"
},
"outputs": [
{
"data": {
"text/plain": [
"\"reduced_data = hyp.reduce(hyp.tools.df2mat(preprocessedDataset), ndims=3)\\nprint(reduced_data[:,0])\\nclassifier = LogisticRegression(random_state = 0)\\nclassifier.fit(reduced_data[0], y_train)\\n\\nsns.regplot(x=reduced_data[0][:,0],y=classifier.predict(reduced_data[0]), label='PC1',x_bins=10)\\nsns.regplot(x=reduced_data[0][:,1],y=classifier.predict(reduced_data[0]), label='PC2',x_bins=10)\""
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'''reduced_data = hyp.reduce(hyp.tools.df2mat(preprocessedDataset), ndims=3)\n",
"print(reduced_data[:,0])\n",
"classifier = LogisticRegression(random_state = 0)\n",
"classifier.fit(reduced_data[0], y_train)\n",
"\n",
"sns.regplot(x=reduced_data[0][:,0],y=classifier.predict(reduced_data[0]), label='PC1',x_bins=10)\n",
"sns.regplot(x=reduced_data[0][:,1],y=classifier.predict(reduced_data[0]), label='PC2',x_bins=10)'''"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "5da26938-85c8-1729-1896-eb10b260407a"
},
"source": [
"As there is no great improvement, let's try to work on dataset from start\n",
"\n",
"Age seems not to be much relevant, so instead of removing the missing data, let's replace with mean value"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"_cell_guid": "eca96baa-1691-de99-f7df-e192c5278cce"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 7 columns):\n",
"Sex 891 non-null float64\n",
"Pclass 891 non-null float64\n",
"Age 891 non-null float64\n",
"SibSp 891 non-null float64\n",
"Parch 891 non-null float64\n",
"Fare 891 non-null float64\n",
"Survived 891 non-null int64\n",
"dtypes: float64(6), int64(1)\n",
"memory usage: 48.8 KB\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:7: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
]
}
],
"source": [
"from sklearn.preprocessing import Imputer\n",
"\n",
"#let's confirm the same columns\n",
"workingDataset = dataset.iloc[:, [1,2,4,5,6,7,9]]\n",
"\n",
"imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
"workingDataset[\"Age\"]=imp.fit_transform(workingDataset[[\"Age\"]]).ravel()\n",
"\n",
"\n",
"# feature/target selection\n",
"workingData = workingDataset.values\n",
"X = workingData[:, 1:]\n",
"y = workingData[:, 0]\n",
"\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"labelencoder_X = LabelEncoder()\n",
"X[:,1] = labelencoder_X.fit_transform(X[:, 1])\n",
"onehotencoder = OneHotEncoder(categorical_features = [1])\n",
"X = onehotencoder.fit_transform(X).toarray()\n",
"\n",
"# avoid dummy trap\n",
"X = X[:, 1:]\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"from pandas import DataFrame\n",
"sc = StandardScaler()\n",
"X = sc.fit_transform(X)\n",
"# rebuild feature's dataframe with normalized data for graphs purpose\n",
"preprocessedDataset = DataFrame(data=X)\n",
"\n",
"preprocessedDataset.columns = ['Sex','Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
"preprocessedDataset['Survived'] = y.astype(int)\n",
"preprocessedDataset.info()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"_cell_guid": "227015a6-cfc3-9947-5649-8730e8f7abc5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[142 26]\n",
" [ 27 73]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.84 0.85 0.84 168\n",
" Survived 0.74 0.73 0.73 100\n",
"\n",
" avg / total 0.80 0.80 0.80 268\n",
"\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cc90588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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CQ2+oXaK4w2TB7unpSXR0tOF+ZGQk5cuXB+DSpUtUqlQJd3d3AJo0acKpU6cK\nDfaAADnKMaXQ0NDSu48zkyDiH+P3xeMv5t/Oxgk8GxudZmbtVp0yGise9npUpXo/FxPZx0XrwoUY\nhg/fwu7dV6lSxZXTpztw5swJpk9/Ue3SLN7j/IJqsmBv2bIlS5YsITg4mLCwMDw9PXF2zjk9x8fH\nh0uXLpGeno69vT2nTp2iTZs2pipFCGNZqRB5zPhypLFnuf/o1YbGA19k9KooRbKydHz66V9MnbqX\njAwdHh6OzJrVAQcHOVO6JDPZv07jxo2pW7cuwcHBaDQapkyZwoYNG3BxcaFTp04MHjyYAQMGYG1t\nTaNGjWjSpImpShGlWXYGRJ8wHvhS4OjV+sbvi5erA9Y26tQtRAmwatVxQkJ2ATBgQAPmz38eDw9H\nlasShTHpr11jxowxup+31R4cHExwcLApX16UNrqsnNDOO/Al+iTos4y301jnhLhX07stdY96MnpV\nCCA5OZNz56IJCKjAwIEN+fXXiwwfHkCnTr5qlyYekvRThHnS6yD2jPF54pHHChi9Wsd4dXr5BmAj\nRx1C3OvXXy8wYsQvpKdnc+bMW7i7O7B+/StqlyUekQS7KPlyR68aDXz5p4DRqzWMQ9yzEdi6FH/N\nQpiRyMgU3n9/G2vWnAKgUSNvYmJSZXKcmZJgFyWLokDClXtCPBQyE/NvW6aq8dQ2z8ZgX7bYSxbC\nnF28GMszz3xBbGwaDg5apk1rx/vvN0ertVK7NPGYJNiFehQFkm4YX440z+hVI84+dxe2eTcBzwBw\n9Cj+moWwEOnp2djba/H1daN+fS+0Wis+/7wbTz/tpnZp4glJsIvik3I7J7zzvi+eGpl/uzujVw0t\nda8AcH6q+OsVwgJlZ+tZuPAgCxYc4MiRYVSo4MLPP/ehTBk7uQqbhZBgF6aRGm18nnjEEUi+mX87\ne3fjy5F6NQGXinentgkhisw///zL0KGb+eefnAmK69ef5t13n8HV1V7lykRRkmAXTy49Pud98LxB\nnng1/3a2ZXKOvnOD3LtpzvvkEuJCmJROp2fixJ0sWHAAnU6hShVXli/vSufONdQuTZiABLt4NJlJ\nEHn07lH47cP3H72qdQSvxnkuR9oE3KqDRhbkCFHcrKw0nD8fg6LAqFHNmTatHc7OtmqXJUxEgl0U\nLCsVoo4bX470fqNXre3As6HxwBf3WjJ6VQgVxcSkMn78DsaPb0mNGuVYurQLH374HE2b+qhdmjAx\nCXaRIzviwNDsAAAgAElEQVQDx8QwOHbobks9OgwUnfF2uaNX854rXq6ujF4VooRQFIU1a07x3nvb\niI5O5datJLZufQ0fnzL4+DzspYqEOZNgL40Mo1fzrE6POkHtAkev5glxj3o5F0cRQpQ4V6/G8+ab\nv7BtW87bY23bVmXRoiCVqxLFTYLd0ul1Oe3zvKvTo45Bdvo9G2pIc6yGQ9VWd98Xl9GrQpiVWbP2\nsW3bRcqWtefTTzsxaFAjOYWtFJJgtySKHuIuGg98iTwKWSn5ty1b3fhypJ6NOH3yvFzDWggzc/z4\nbbRaK+rW9WT27A4oisL06e3x9nZWuzShEgl2c5ZwxfhypAWOXq1yz8CXxmAv06WEMGdpaVlMm7aX\nTz75i0aNnuLgwcGUK+fIypU91C5NqEyC3VztHQdHPsn/cWefewa+BIBj+eKvTwhhMrt2XWH48C1c\nvBiLRgPNm/uQmanDwUFOJxUS7OYpOwNOrsi5XTUIvJvdCfIAcK6gbm1CCJNas+Ykr766AYC6dcuz\ncmV3WrSopHJVoiSRYDdH13dARkLOivWXflW7GiGEiSmKQmxsGuXKOdK9ux81argzYEADxo1ria2t\nzIsQxiTYzdH5/+X8XfNldesQQphceHgCI0du5eLFWI4dG46zsy2nTo2UQBcFkjdkzI0uEy5uzLkt\nwS6ExdLp9PznP4eoU2cZW7ac59atJE6ciACQUBcPJEfs5ubaDsiIzxkU4+6ndjVCCBO4dSuJ3r1/\n4MCBGwD06lWbJUs6U6GCi8qVCXMgwW5upA0vhMUrV86B+Ph0KlRwYenSLrzwQi21SxJmRFrx5kSX\nCRd/zrktwS6ERdm37xrPP/8dSUkZ2Nlp2bChD6dPj5RQF49Mgt2cXN95pw3vD+Xkh10ISxAfn87w\n4Ztp3fobfv/9MgsXHgSgVi0PXF3lugzi0Ukr3pyckza8EJZkw4YzvP32Vv79NxkbGysmTmzFuHEt\n1S5LmDkJdnOhy4RL0oYXwlLo9Qpz5uzn33+TadGiIitXdqduXU+1yxIWQILdXFzfCelxOdc+L1db\n7WqEEI9Br1f46qujvPBCLTw8HFm5sjt//hnOiBFNsLKSq7CJoiHBbi6kDS+EWTt7NpqhQzezf/91\n9u27zrffvkCDBt40aOCtdmnCwkiwmwNd1t02vJ8EuxDmJDNTx5w5+5k5cx+ZmTq8vJzo1q2G2mUJ\nCybBbg6M2vB11K5GCPEI3nlnKytW/APAkCGNmDevE25uDipXJSzZQ53uFhcXx8mTJwHQ6/UmLUjc\nhwylEcKsJCZmEBmZAsDYsS2pX9+L3bsHsnJlDwl1YXKFBvuWLVvo06cPEydOBGD69On873//M3lh\n4g5d1t2hNNKGF6LE27TpHHXqLGXIkE0oikL16u4cOzactm2rql2aKCUKDfavv/6ajRs34ubmBsD4\n8eP54YcfTF6YuCN8F6TH5rTgpQ0vRIl1+3Yyr7zyP3r2XMvNm0ncvp1MYmIGABqNrHgXxafQ99hd\nXFxwcLjbOrK3t8fGxsakRYk8ZDW8ECXejh2Xefnl/xEfn46Tkw0zZ7bn7bebYW0twz1F8Ss02N3c\n3Pjpp5/IyMggLCyMrVu34u7uXhy1CV0WXPwp57YEuxAljqIoaDQa6tQpj6IodO5cneXLu1KlSlm1\nSxOlWKG/Tk6dOpWTJ0+SkpLCpEmTyMjIYObMmcVRmwjfndOGd68NHnXVrkYIcUdWlo5Zs/YRFPQ9\ner1ChQou/PPPcH755VUJdaG6Qo/Y9+3bx+TJk40+tmbNGvr27WuyosQdshpeiBLn779vMHToZk6e\njATgjz+u0bZtVZ5+2k3lyoTIUWCwnz59mrCwML766ivS0tIMH8/Ozmbp0qUS7Kamy4ILd9rwshpe\nCNUlJ2cyadIuFi/+G0WBp5924/PPu8lqd1HiFBjsdnZ2xMTEkJSURGhoqOHjGo2GcePGFUtxpVr4\nbkiPAfdaOYNphBCqyszUsWbNKaysNIwe3YKPP26Lo6MsJBYlT4HB7uvri6+vL82bN6dhw4ZGj/32\n228mL6zUy9uGl1NlhFBFZGQKixYdZOrUdri7O/Dddy/i4eFI48ZPqV2aEAUq9D12T09P5s2bR1xc\nHACZmZn8/fffBAYGmry4UitvG17eXxei2CmKwqpVxxk9ejuxsWm4uTkwZsyzPP+8r9qlCVGoQlfF\njxs3jrJly3Ls2DH8/f2Ji4tj3rx5xVFb6RW+524b3sNf7WqEKFUuXYrl+edX8/rrG4mNTaNTp6fp\n1UsulSzMR6HBbm1tzbBhw/Dw8OC1115j+fLlfP/998VRW+klbXghVKEoCj16rGXHjsuUK+fAqlUv\n8Ntv/WTFuzArhQZ7RkYGt2/fRqPREB4ejlar5ebNm8VRW+mkz5ahNEIUs2PHbpOWloVGo2HBgud5\n7bV6nDnzFv37N5BxsMLsFBrsQ4YM4cCBAwwePJiePXvSvHlzGjVqVBy1lU7heyAtGtz8pA0vhIml\npGQyZsx2AgJWMGPGHwAEBlZn9epelC/vpHJ1QjyeQhfPdezY0XD70KFDpKSk4OrqatKiSrXcNryf\ntOGFMKXt2y8xYsQWrlyJx8pKQ3a2XJJaWIYCj9j1ej1r165l+vTpbNmyBQCtVoutrS1Tp04ttgJL\nFX02XNiQc1va8EKYzMcf7yEwcDVXrsTToIEXBw8OZu7cTmqXJUSRKDDYp0+fzqFDh6hSpQpr167l\nu+++48CBA/To0QN7e/virLH0CN97pw1fEzzqqV2NEBZFURQyM3UABAb64uhow+zZHTh8eChNm/qo\nXJ0QRafAVvyZM2dYu3YtAL1796Zdu3b4+Pjw2Wef4e8v7/2ahKyGF8IkrlyJ4803f8HX142lS7vS\nokUlrl9/n3LlHNUuTYgiV2Cw573muqOjI9WqVeP777/H2tr6oZ981qxZHD9+HI1GQ0hICPXr1zc8\n9u+//zJ69GiysrKoU6cO06ZNe8wvwUJIG16IIpedrWfx4r/56KPdpKZmUa6cA9Ont8fd3UFCXVis\nAlvx957iYWtr+0ihfujQIa5du8a6deuYOXNmvku9zpkzh0GDBrF+/Xqsra25devWI5ZuYcL3QloU\nuNWA8vUL314I8UBhYZG0aPElH3ywndTULPr0qUtY2Ejc3R3ULk0IkyrwiD0yMpL169cb7kdFRRnd\n79279wOf+MCBA4YV9b6+viQkJJCcnIyzszN6vZ7Q0FAWLFgAwJQpU57oi7AI0oYXokgpSs756RUr\nlmH58q5061ZT7ZKEKBYFBnujRo2MrurWsGFDo/uFBXt0dDR16969Kpm7uztRUVE4OzsTGxuLk5MT\ns2fPJiwsjCZNmvDBBx88yddh3qQNL0SR2LXrCr//fonevd3w9/dk48ZgnnuuMi4udmqXJkSxKTDY\nZ8+eXaQvpCiK0e2IiAgGDBiAj48Pw4YNY8+ePbRt2/aBz5H3FwtL4hJ3mJppUaQ7VCLsejaEq/d1\nWuo+LmlkPxethIRMFi06w6ZN4QBUqdIcCMXLC86fP6VucRZMvo9LpkIH1DwuT09PoqOjDfcjIyMp\nX748AG5ublSoUIHKlSsD0KJFCy5cuFBosAcEBJiqXHXt+AIA+/r9CWjSRLUyQkNDLXcflyCyn4uO\noij88EMY7767m8jIFGxtrfnoo9Y0bOgg+9jE5Pu4eDzOL0+FjpR9XC1btjRctz0sLAxPT0+cnZ2B\nnEE3lSpV4urVq4bHq1WrZqpSSja9TtrwQjym27eTGTRoE5GRKbRuXYUTJ0YwaVJrbGxM9l+bECWe\nyY7YGzduTN26dQkODkaj0TBlyhQ2bNiAi4sLnTp1IiQkhAkTJqAoCjVr1qR9+/amKqVku/EHpEZC\n2epQvoHa1QhR4ul0ejZvPk/Pnn489ZQL8+c/j7W1hsGDG2NlJQtPhSg02M+ePUtISAipqals27aN\npUuX0qpVKxo0KDyExowZY3S/Vq1ahttVqlRhzZo1j1GyhZHV8EI8tFOnIhkyZBN//32TNWteIjjY\nnxEj1Hv7SoiSqNB+1bRp05g1a5bh/fEuXboU+cK6Ukva8EI8lPT0bCZN2kWjRp/z9983qVDBhTJl\nZKW7EPdT6BG7Vqs1OtKuVq0aWq3JOvily819kBoBZX3Bs6Ha1QhRIimKQrt233Lw4A0A3nyzCbNn\nd8DVVa5ZIcT9PFSwh4eHGybR7d271+jUNfEEzkkbXoiCJCSk4+Jih5WVhsGDGxEfn87Kld1p1aqy\n2qUJUaIVGuzjx49n5MiRXLlyhYCAAHx8fJg3b15x1GbZ9Dq48GPObWnDC2GgKAo//niGd975lSlT\n2jBiRBMGD25E//71sbOTbqEQhSn0p8TGxobNmzcTGxuLra2t4ZQ18YRy2/CuT4NnI7WrEaJEuHkz\nkbfe2srGjecA2LTpHMOHB6DRaCTUhXhIhf6kvPnmm7i4uNCjRw+6detWHDWVDtKGF8LI6tUnGDny\nF5KSMnFxsWXOnI6MGNEk3wWphBAPVmiw//bbb5w6dYpff/2V4OBgqlWrRs+ePenSpUtx1GeZ8rbh\n/aQNLwSAg4OWpKRMevb04z//6ULFimXULkkIs/RQ45n8/f0ZO3Ys33//PRUqVGDcuHGmrsuy3dx/\npw1fDTwbq12NEKrIyMhm6tQ9LFhwAIBevWqzb98b/PRTHwl1IZ5AoUfskZGRbN++nW3bthEbG0uX\nLl345ZdfiqM2y2UYSvOKtOFFqfTXX+EMGbKJM2eicXDQ0r9/fcqXd5IV70IUgUKD/aWXXqJLly6M\nHz+eevXqFUdNlk3a8KIUS0zMYOLEHSxffgRFgRo13FmxojvlyzupXZoQFqPAYI+MjMTT05NVq1YZ\nBtKEh4cbHq9UqZLpq7NEt/6ElNvShhel0pEjt1i27AharRXjx7dk0qTW2NvLanchilKBP1Fz585l\n/vz5DB48GI1GYzSURqPRsHPnzmIp0OLIanhRyvz7bxJ79lylb996tG9fjdmzO9C1aw3q1fNSuzQh\nLFKBwT5//nwAVq5cia+vr9FjR48eNW1VlkrRy1AaUWro9QpffvkPY8f+TnJyJrVqedCo0VNMmNBK\n7dKEsGgFropPTEzk+vXrhISEEB4ebvhz+fJlJkyYUJw1Wo6bf0LKv1CmKngFqF2NECZz7lw07dp9\ny7BhW0hIyCAwsDrlyjmqXZYQpUKBR+xHjx7l22+/5cyZMwwcONDwcSsrK1q1kt+4H4tcolWUApGR\nKTRuvILU1CzKl3dk8eLO9OlTVwbNCFFMCgz2Nm3a0KZNG9asWUPfvn2LsybLpOjh/Pqc27IaXlig\na9fiqVKlLJ6eTgwb1pj4+Aw+/bSTHKkLUcwKDPYff/yRl156iYiICBYtWpTv8ffee8+khVkcozZ8\nE7WrEaLIJCVlMGnSLpYuPczu3QN57rkqzJ8fiJWVHKELoYYC32O3ssp5SKvVYm1tne+PeESGNnxv\nacMLi/HLL+epW3cZixcfAuDo0dsAEupCqKjAI/YXX3wRgLfffpvk5GScnZ2Jjo7m6tWrNG4s518/\nElkNLyyMoii88cZGvv32OAABAU/xxRc9aNjQW+XKhBCFzoqfPn06v/76K/Hx8QQHB7N69Wo+/vjj\nYijNgtz8C5JvQZkq4N1U7WqEeGy58yw0Gg1Vq5bF0dGG+fOf5+DBIRLqQpQQhQb76dOnefnll/n1\n11958cUXWbhwIdeuXSuO2iyHrIYXFuDixVg6dfqOLVvOAzBxYivCwkYyenQLtNqHup6UEKIYFPrT\nmPsb+p49e2jfvj0AmZmZpq3Kkih6uHBnNby04YUZysrSMXfufurVW87OnVeYPHk3iqJgZ6elatWy\napcnhLhHoUOaq1WrRpcuXXB3d6d27dr8/PPPuLq6FkdtluHWAWnDC7MVGnqLIUM2c+xYzqK4fv3q\ns2DB83JOuhAlWKHBPmPGDM6fP28YK1u9enXmzZtn8sIsRm4bvoashhfmZ9++6xw7dpuqVcvy3/92\nJTCwutolCSEKUWiwp6ens2vXLhYtWoRGo6Fhw4ZUry4/3A9FhtIIM7R9+yVSU7N44YVavPNOM/R6\nheHDA3ByslW7NCHEQyj0PfaPPvqI5ORkgoODeeWVV4iOjmbSpEnFUZv5u3UQkm+CS2XwbqZ2NUI8\nUFRUCv37/0Rg4GqGDt1MdHQq1tZWjB7dQkJdCDNS6BF7dHQ0CxYsMNxv164d/fv3N2lRFkOG0ggz\noCgKq1efYNSo34iJScPeXsvYsc/i6mqndmlCiMdQaLCnpaWRlpaGg4MDAKmpqWRkZJi8MLOn6I1P\ncxOihNq27SIDBvwMQPv21fj8825Ur+6uclVCiMdVaLD36dOHzp074+/vD0BYWJjMiX8YhjZ8JXjq\nGbWrEcJIdraeU6ciadjQm6Cg6rz8ch26dKnBwIENZMW7EGau0GDv3bs3LVu2JCwsDI1Gw0cffYSX\nl1dx1GbepA0vSqhjx24zZMgmzp+PISxsJJUqufLDD9JVEsJSPDDY9+7dy+XLlwkICKBjx47FVZP5\ny7saXtrwooRITc1i6tQ9zJ9/AJ1OoXJlV27dSqJSJZlLIYQlKXBV/JIlS1i+fDmRkZFMmjSJTZs2\nFWdd5u3fvyH5BjhXlDa8KBFiYlKpX3858+b9hV6v8N57zxAWNpJnnqmodmlCiCJW4BH7/v37+f77\n79FqtSQlJfHOO+/Qo0eP4qzNfOW24f1eBo3M0BbqycrSYWNjTblyjjRs6I2jow0rV3aXQBfCghWY\nOra2tmi1Obnv4uKCTqcrtqLMmrThRQmgKApr156ievUlnDsXDcAXX/TgyJFhEupCWLgCg/3elbGy\nUvYh/XsIksKlDS9Uc/16At26raFv3x+5fj2BFStCAShb1h5bW2uVqxNCmFqBrfhLly4xbty4Au/L\nvPgCGK2Glza8KF5LlvzNxIk7SUnJwtXVjk8+6cTgwY3VLksIUYwKDPYxY8YY3W/RooXJizF7iiJt\neKGqY8duk5KSxUsv1WbJks489ZSL2iUJIYpZgcH+4osvFmcdluH2IUi6Ds4+UKG52tWIUiA9PZsZ\nM/7gxRdrERBQgU8/fZ6ePWvRo4ef2qUJIVRS6IAa8QjOSRteFJ+9e68ydOhmLlyIZdu2ixw+PBQ3\nNwcJdSFKOQn2oqIoMhteFIu4uDTGjfudL744CkDt2h4sXtxZFrgKIYCHuGwrQFxcHCdPngRAr9eb\ntCCzZdSGl/UIwnQ+++wgX3xxFBsbKz7+uA1Hjw7n2WcrqV2WEKKEKPSIfcuWLSxevBhbW1u2bNnC\n9OnTqVOnDi+/LEelRnLb8DVekja8KHI3byYSGZlCo0ZPMX58Sy5ciOWjj1pTp055tUsTQpQwhSbQ\n119/zcaNG3FzcwNg/Pjx/PDDDyYvzKwoClyQ1fCi6On1CsuXH6Z27aX06bOetLQsnJxsWbPmJQl1\nIcR9FXrE7uLiYrgWO4C9vT02NjYmLcrs3D4MidfAuQL4PKt2NcJCnD4dxbBhm/nzz3AAOnTwJDU1\nCwcH+fkTQhSs0GB3c3Pjp59+IiMjg7CwMLZu3Yq7u3tx1GY+chfN1ZDV8KJo/PHHNTp2XEVWlh5v\nb2eWLu1Cr1611S5LCGEGCk2hqVOncvLkSVJSUpg0aRIZGRnMmDGjOGozD7IaXhShxMQMAJo3r0it\nWh4MG9aYM2feklAXQjy0Qo/Yy5Qpw+TJk4ujFvMUcUTa8OKJJSSkM3HiTjZvPk9Y2EjKlLHj4MEh\nODpK210I8WgKDfY2bdrc9/zYPXv2mKIe8yOr4cUT+vnns7z11lZu3UpCq7Vi796rdO/uJ6EuhHgs\nhQb7//3f/xluZ2VlceDAATIyMh7qyWfNmsXx48fRaDSEhIRQv379fNvMnz+fY8eO8d133z1C2SWE\ntOHFE0hMzGDQoI38+OMZAJ55xoeVK7tTr56XypUJIcxZocHu4+NjdL9q1aoMHjyY119//YGfd+jQ\nIa5du8a6deu4dOkSISEhrFu3zmibixcvcvjwYfNdZR8RColXwekp8GmpdjXCzDg52XDzZhLOzrbM\nmtWekSObYm0tXR8hxJMpNNgPHDhgdP/27dtcv3690Cc+cOAAHTt2BMDX15eEhASSk5NxdnY2bDNn\nzhxGjRrFf/7zn0etu2Q4L2148WjOnYtmzJjDrFtXC09PJ1ategE7Oy2VK7uqXZoQwkIUGuzLli0z\n3NZoNDg7OzN16tRCnzg6Opq6desa7ru7uxMVFWUI9g0bNtCsWbN8HQGzoShw7s6gHj9pw4sHy8zU\nMW/en8yY8QcZGTqmTNnN8uXdqFGjnNqlCSEsTKHBPmHCBKOAflyKohhux8fHs2HDBr7++msiIiIe\n+jlCQ0OfuI6i4ph4mtqJV8myLceJ2/Y5bXkLUJL2saU4eTKOGTNOcOlSEgA9elTi5ZfLyb42Mdm/\npif7uGQqNNjnzp3LqlWrHvmJPT09iY6ONtyPjIykfPmcEZgHDx4kNjaW1157jczMTK5fv86sWbMI\nCQl54HMGBAQ8ch0m80fO0bpNnWACmjRTuZiiERoaWrL2sYX46KPvuXQpCV9fN1as6I6ra6zsZxOT\n72XTk31cPB7nl6dCg71ChQr079+fBg0aGC1ye++99x74eS1btmTJkiUEBwcTFhaGp6enoQ0fFBRE\nUFAQADdu3GDixImFhnqJIqvhRSF++eU8/v6eVKlSlqVLu/DFF/8waVJrHBxsCA2NVbs8IYQFKzTY\nK1asSMWKFR/5iRs3bkzdunUJDg5Go9EwZcoUNmzYgIuLC506dXqsYkuMyH8g4Qo4eoFPK7WrESVI\nREQy7723jXXrwujSpQZbtvSlWjU3Zs7soHZpQohSosBg37RpEz169ODtt99+7CcfM2aM0f1atWrl\n26ZixYrmdw577lCamr3BylrdWkSJoCgK33xzjA8+2E5cXDqOjjZ06FANRYH7zHcSQgiTKfAcrfXr\n1xdnHeZD2vDiPmbN2segQZuIi0snMNCXU6feZPToFlhZSaoLIYqXnHz9qCKPQsJlacMLsrJ0REQk\nAzB4cGNq1izH6tUv8uuvr1GtmpvK1QkhSqsCW/FHjx6lbdu2+T6uKAoajab0zorPO5RG2vCl1pEj\ntxgyZBNOTrbs2/cG3t7OnD49UibHCSFUV2Cw16lThwULFhRnLSVf3ja8DKUplVJSMvnoo90sWvQ3\ner1C1apluXEjkcqVXSXUhRAlQoHBbmtra75T4Uwl8hjEX7rThn9O7WpEMTtxIoKePddy9Wo8VlYa\nPvigBVOntsXJyVbt0oQQwqDAYL/fldhKPUMbvpe04UuR3LefKld2JSMjm0aNvFm5sjsBARXULk0I\nIfIpMNjHjh1bnHWUfIoC5+/MhpfV8KWCoiisXn2Cb789zq+/vkbZsvbs3j0QX193tFppuwshSib5\n3+lhGdrwnlCxtdrVCBO7ciWOoKDvGTDgZ3buvMK6dWEA+Pl5SKgLIUq0QifPiTukDV8qZGfrWbTo\nIJMn7yE1NQt3dwcWLHie116rp3ZpQgjxUCTYH4YMpSk1MjN1LFt2hNTULPr29WfhwiA8PZ3ULksI\nIR6aBPvDiDoO8RfBoby04S1QamoWCxce5P33m+PoaMPXX/ckOTmTLl1qqF2aEEI8Mgn2h2E4Wn8J\nrGSXWZIdOy4zfPgWLl+OIyEhnblzO9G6dRW1yxJCiMcmKVUYacNbpJiYVD74YDvffnscgHr1POnV\nq7bKVQkhxJOTYC9M1AmIuyBteAsTHPwjO3Zcxs7OmsmT2zB27LPY2MiiSCGE+ZNgL4zRanjZXebs\n2rV4XF3tKVvWnpkz26PXKyxf3pWaNcupXZoQQhQZOSH3QaQNbxF0upxT2OrWXcb48b8D0KyZDzt3\nDpBQF0JYHDkEfZDokxB3Hhw8oFIbtasRj+HEiQiGDt3MoUM3AYiPz0Cn08sFW4QQFkuC/UGkDW/W\nvvrqKMOHbyE7W4+PjwvLlnWlRw8/tcsSQgiTkrQqiKLAOZkNb45yj8ibN6+ItbWG4cObMmtWB8qU\nsVO7NCGEMDkJ9oIYteHbql2NeAhxcWmMG/c7yclZrFnzEnXqlOfKlfd46ikXtUsTQohiI8FekNw2\nfPUXpQ1fwimKwvr1p3nnnV+JiEjB1taaS5di8fV1l1AXQpQ6soLofhQFzslqeHNw61YSL7ywjlde\nWU9ERAqtWlXm+PER+Pq6q12aEEKoQg5F7yf6FMSdA/tyULmd2tWIB8jK0rFz52XKlLFj3ryODB0a\ngJWVRu2yhBBCNRLs92NYDS9t+JIoLCySb789zty5HalSpSzr1vWmYUNvfHzKqF2aEEKoTlLrXkZD\naV5RtxZhJCMjm1mz9jF79n6ysvQ0auRN37716Nq1ptqlCSFEiSHBfq+YMIg9K234Emb//usMHbqZ\ns2ejARg2rDGdO8tlVYUQ4l4S7Pc6J234kiY1NYsXX1xHdHQqfn7lWLGiu1xaVQghCiDJdS+ZDV9i\n7NhxmXbtquLoaMNnnwVy7lw0H37YGnt7+bYVQoiCyP+QeUWHQewZsHeHStKGV8utW0m8/fZWfvrp\nLEuWdObtt5vRr199tcsSQgizIMGeV96hNNY26tZSCun1CitWhDJ+/A4SEzNwdraVo3MhhHhE8r9m\nXrmz4f2kDa+G117bwNq1pwDo3r0mS5d2oVIlV5WrEkII8yKT53IZteHbq11NqZGZqSMrSwdAnz51\n8fJy4ocferNxY7CEuhBCPAYJ9lyGNvwL0oYvJgcOhNO48efMnfsnAC+8UIuLF9/l5ZfrotHI9Dgh\nhHgcEuy5ZDV8sUlKyuCdd7bSsuVXhIVFsW5dGNnZegCcnW1Vrk4IIcybvMcOEHM654+9G1TuoHY1\nFm3nzsu8/vpGbtxIRKu1YuzYZ/noo9ZotfI7phBCFAUJdrg7lMZX2vCmZmWl4caNRJo2rcDKld1p\n0KdtmkMAAB/ZSURBVMBb7ZKEEMKiSLDD3Ta8n8yGL2qKovDVV0e5eTOJyZPb0K5dNbZv70f79tWw\ntpajdCGEKGoS7DFncubDSxu+yF24EMOwYVvYs+cqVlYaXnmlLrVqedCpk6/apQkhhMWSYD8vbfii\nlpWl49NP/2Lq1L1kZOjw8HBk0aIg/PzKqV2aEEJYPAl2QxteVsMXlTNnopk0aTd6vcKAAQ2YP/95\nPDwc1S5LCCFKhdId7DFnIfoU2JWVNvwTSk7O5JdfztOnjz/163sxd25HGjTwkra7EEIUs9Id7EZD\naeT86ce1bdtFRozYwrVrCXh6OtGuXTXGjHlW7bKEEKJUKuXBfmc2vAyleSyRkSmMGvUb//d/JwFo\n1MgbNzcHlasSQojSrfQGe942fJWOaldjdtLTs2nU6HNu3UrCwUHL1KltGTWqhQyaEUIIlZXeYDe0\n4XtKG/4RREQk4+XljL29lpEjm7BnzzU+/7wbTz/tpnZpQgghKM2z4mU2/CPJztbz6ad/Ua3aIn7+\n+SwAEya0Yvv2fhLqQghRgpTOYI89B9Enwc4VqnRSu5oS759//uWZZ75g7NjfSUvL5o8/rgFgbW0l\nV2ETQogSpnS24g1DaaQNX5hp0/YybdpedDqFypVdWb68K1261FC7LCGEEAUwabDPmjWL48ePo9Fo\nCAkJoX79+obHDh48yIIFC7CysqJatWrMnDkTK6tiaiDIbPiH5u7ugF6v8P77zzB9enu5rKoQQpRw\nJkvSQ4cOce3aNdatW8fMmTOZOXOm0eOTJ09m8eLFrF27lpSUFPbt22eqUozFnoeoE9KGL0BMTCoD\nB/7MN98cA+DNN5tw9OhwPvssSEJdCCHMgMmO2A8cOEDHjjmnkfn6+pKQkEBycjLOzs4AbNiwwXDb\n3d2duLg4U5ViTNrw96UoCtu23WThwl1ER6eyY8dlXn21Hra21nJpVSGEMCMmO2KPjo7Gze3uaml3\nd3eioqIM93NDPTIykj///JM2bdqYqhRjsho+n6tX4+nS5f+YNOko0dGptG1blT17BmJra612aUII\nIR5RsS2eUxQl38diYmIYMWIEU6ZMMfoloCChoaFPVINd6nX8o46js3bieGw5lPgnez5LsXXrDbZt\nu4iLiw3vvVebnj0rkZh4ldDQq2qXZrGe9HtZFE72senJPi6ZTBbsnp6eREdHG+5HRkZSvnx5w/3k\n5GSGDh3K+++/T6tWrR7qOQMCAp6sqL9/A8DarxeNm7Z4sucycydORHD2bDSvvFKXxo0bY2NTjkaN\nNAQGtlS7NIsXGhr65N/L4oFkH5ue7OPi8Ti/PJmsFd+yZUt++y0nSMPCwvD09DS03wHmzJnDwIED\nad26talKyO+czIZPS8siJGQnAQErGDRoI9euxaPRaJgwoRUeHvZqlyeEEOIJmeyIvXHjxtStW5fg\n4GA0Gg1Tpkxhw4YN/9/e3cfVfP+PH3+cTuWyXHWFai7GJGMu5iallSWU7MqtRJgWCvNhmwnbJyPX\nzMjl8PluLubi16e1CfFh2mzI1caUSNKSkkrISlfv3x/N+Tgf5GLldI7n/XZzu+30Puf9fp7n6Hle\nz/frvF6YmZnh4uJCdHQ0aWlpREZGAjBgwAD8/PyqKxy4ngzXToGpObzgWX3XqcEOHEhl9OgYLlzI\nQ6WCkSO7yqYtQghhYKr1HvtHH32k9bhdu3aa/z5z5kx1Xvp+mtnwA8G41rO9dg1w+vRVevfeAICj\noyVr1/rg5GSn46iEEEJUtedn5blzz99seEVRSErKwcHBko4drRk+vBMvvtiIKVNcZMa7EEIYqOej\nsF+/ANd+A1MzaPF8tOHT028wduwu9uy5wG+/BdO+vSVfffWGrO0uhBAG7vnYBEarDW/YE8TKyspZ\nvvwo7duvJCbmPHXqmJCcnAsgRV0IIZ4Dz8eIXbMojWGvDV9cXIa7+9ccOpQOwFtvtSMioj/Nm5vr\nODIhhBDPiuEX9vwUyP7VoNvw5eUKRkYqTE3VdOhgSWrqdZYv9+Lttx10HZoQQohnzPBb8ecMuw1/\n8GAaL7+8iqNHMwBYuNCTxMRxUtSFEOI5ZfiF3UDXhs/PL2LMmB24un5FYuI1Fi48BIC5eS0aNjS8\nDzBCCCEej2G34vMvQvbJv9rwfXUdTZWJjk5i7NidZGYWYGJixNSpLkyb1kvXYQkhhKgBDLuw3x2t\nt/IxqDb84cPpZGYW4ORky9q1Pjg6Wuk6JCGEEDWEYRd2A1kbvrxcYe3aE7Rp04TevVsSFubGSy9Z\n8O67r2BkJF9hE0II8V+GW9jvtuFN6ut1Gz4pKYdRo3bw889/0KpVIxISxlK3rgmBgZ11HZoQQoga\nyHALu2ZRGh8w0b+NToqLy5g372dmzz5IcXEZNjb1mT/fg1q1ZClYIYQQD2f4hV1P2/Dr158kLCwO\ngKCgzixY0Ed2YhNCCPFIhlnY8y/C1RN/teH76Tqax3bz5h0uXMijS5emBAV1Yf/+VMaP746bWwtd\nhyaEEEJPGGZhP1+xxzutBuhNG/77788xduxOysoUzp4dR8OGtYmMNOwlcIUQQlQ9w1yg5m4b/qWa\nXxizsgrw9f1/vPHGVjIybmFnZ05eXqGuwxJCCKGnDG/EfiMVrh7XizZ8UlIOTk7ryc8vol49E8LD\ne/P++91Rqw3z85YQQojqZ3iFXQ/a8IWFJdSpY0Lbtk3o0MEKMzNTVq3y5oUXGuo6NCGEEHrOAAv7\n3TZ8zZsNX1JSxsKFh4iIOMqvv47BxqY+MTH+mJvXkr3ShRBCVAnD6vneuARZx8CkHrTor+totBw9\nmkHXrl8yffoPZGUVEB2dBECDBrWlqAshhKgyhjVir4Ft+JKSMiZP/g/LlsWjKNCqVSPWrBmAh0cr\nXYcmhBDCABlYYa95a8MbGxuRnJyHkZGKDz5wYsYMN+rWNdF1WEIIIQyU4RT2u21447rQUrdt+Ozs\n20yZso9//tOVli0bsWqVN7m5f9K5c1OdxiWEEMLwGU5h12rD19VJCIqisGHDKT74YC95eYVcv15I\ndPRg7O0bYG/fQCcxCSGEeL4YUGHX7Wz4lJQ8goN3sm/fRQD69GnF55/r765yQggh9JNhFPabaZB1\n9K82vJdOQggPP8i+fRdp0qQOS5b0JSCgo8x2F0II8cwZRmHXtOG9n2kb/sSJK9Sta4KDgyULFlRs\nqTprljuWlvWeWQxCCCHEvQzje+zPeG3427eL+eijvXTvvo6RI7+jrKwcS8t6rF49QIq6EEIIndL/\nEfvNPyAz/pm14ffuTSE4OIbU1HyMjFT07GlHSUm5rO8uhBCiRtD/wv4M2/AbNpxixIhoADp2tGbd\nOh9efbV5tV5TCCGEeBIGUNj/asNX06I0iqKQm1uIhUVd3nyzHS++2Jj33uvMhx86YWKirpZrCiGE\nEE9Lvwv7zXTIPALGdaBV1bfhL13KJyRkJ+npNzh5cgzm5rVITBwrBV0IIUSNpd83hpPvbcNX3aS1\nsrJyliw5jKPjSmJjL3Dlyi0SErIBpKgLIYSo0fR7xH6u6teGT0+/wTvvbOfYsSsA+Pk5snRpP6yt\n61fZNYQQoipdvnwZHx8fOnToAEBxcTFt27ZlxowZqNVqCgsLmTt3LqdPn8bY2BgLCwvCwsJo2rRi\nmetLly4xZ84c8vLyKC8vp3PnzkyZMgVTU1OdvaeysjKCg4P59NNPsbe311kct27d4sMPP+TWrVvU\nrVuXxYsX07BhQ83xuLg41q9fr3mckJDA7t27MTIyIjQ0lKKiIpo0acLcuXO5dOkSX375JUuXLq3W\nmPV3xK7VhveustNaWNQlP78IW1tzduzwZ+vWQVLUhRA1XsuWLdm4cSMbN25k27ZtlJSUsGPHDgDm\nzp2LlZUV0dHRREZGMmrUKIKCgigpKaGsrIz333+foKAgIiMj+fe//w3AihUrdPl22LJlC926ddNp\nUQf4+uuv6d69O1u2bMHT05O1a9dqHXdzc9PkPTw8nB49emBtbc2aNWt4/fXX2bx5M71792bjxo04\nOjpiaWlJbGxstcasvyP2u234ll5/uw3/ww+pzJv3M99+60e9eqZ8991gbG3NMTOrVQWBCiHEs9ex\nY0fS0tIoKCjg4MGD/Oc//9Ec69q1Kx07dmT//v3UrVuXVq1a0b17dwBUKhWTJ0/GyEh73FdSUkJo\naCgZGRnUqlWLgIAAoqKiSE5OZsqUKdy+fRsfHx9++OEHPD09cXV1pUmTJkRHR7Nnzx4Avv32W5KS\nkggMDGT69OmUlJSgVqsJDw+nWbNmWte7+wEF4Pvvv2fTpk0YGRnRpk0bZs2aRVRUFD/99BPZ2dks\nWbKEffv2sWPHDoyMjPDw8CAwMJCsrCwmT54MQGlpKfPnz9f6oPC/o20AX19ffHx8NI8PHz7MnDlz\nAHB3dyc4OPihOY+IiGD8+PEApKWl8eabbwLQq1cvJk6cSHBwMMOGDSM0NJR+/fo96n/hU9Pfwn7u\n78+Gz8sr5KOP9vJ///cbAMuXH2XKFBccHCyrIkIhxPMoyhtSd1XtOVt6wds7H/vpJSUl7N+/H39/\nf9LT02nVqhXGxtq/7h0cHEhNTaVOnTo4ODhoHatdu/Z954yOjsbCwoLFixezc+dOTpw4Qdu2bR94\n/dLSUlxdXXF1deXIkSMkJyfTpk0b9u/fT2BgIEuXLiUwMJCePXvy448/snLlSsLDwzWvv3LlCqam\nppqWd2FhIevWrcPc3JyhQ4dy7tw5ADIzM9m6dSuXL18mNjaWLVu2AODv70+/fv3Iyclh3Lhx9OjR\ng8jISL755htCQ0M113Fzc8PNza3SXObk5NC4cWMAmjRpQnZ29gOfd/XqVXJycmjfvj0Abdu2JS4u\njg4dOnDw4EFyc3MBeOGFF8jMzKSwsJA6depUeu2npZ+F/WY6ZB4G49pP1YZXFIXt2xOYMCGW7Ozb\nmJqq+fRTVyZNcqqGYIUQovqlpqYybNgwAM6dO0dQUBAeHh4kJSVRVlZ23/MVRUGtVqNSqR54/H8l\nJCTg5FTxO9Lb2xsbGxvS0tIe+vyOHTsC4OnpyYEDB7C3tyc5OZnOnTszffp0UlNTWbVqFWVlZZrC\neVd2djY2Njaaxw0aNGDs2LEApKSkkJ+fD8DLL7+MSqXi999/Jy0tjeHDhwNw+/ZtMjIysLW1JTw8\nnIiICG7evImjo+Mj32dlFEV56LHo6GgGDhyoeTxmzBhmzJhBQEAAr732mtZrLSwsyMnJwc7O7m/F\n8zD6WdiTK+4B0dILTJ/8/ndZmcL8+b+QnX2bXr3s+fJLH9q1s6jiIIUQz6UnGFlXpbv32AEmTJhA\ny5YtAbC1tSU1NZXi4mKtyXBJSUl4eHhgamrK5s2btc5VXFzMpUuXtEbkarWa8vJyrefdu9FVaWmp\n1jETExMAPDw8mDhxIm3atKFXr16oVCpMTExYunQpVlZWD30/d89dXFzMzJkz+e6777C0tGTMmDH3\nXcPExAQ3NzdmzpypdY6pU6fi4uKCv78/sbGxxMXFaR1/nFa8lZUV165dw8zMjKtXrz405ri4OJYs\nWaJ5bG5uzueffw7AxYsXOXLkyEPfa1XTz8lzmkVpHn9t+LKyclavPs7164UYGxuxbt1AVq/2Ji7u\nXSnqQgiDMnnyZBYtWkRhYSH169fH3d2d5cuXa46fPHmSxMRE3NzccHZ2JiMjgx9++AGA8vJyFi5c\nyK5d2rcTXn75ZU1xOnDgANHR0dSvX1/Tmj5x4sQDY7G2tkalUhETE0PfvhVbWXfq1Il9+/YBFfew\n707yu8vKyoqsrCygYvStVquxtLQkMzOTM2fOUFJSovV8R0dH4uPjKSwsRFEUwsPDKSoq4vr169jb\n26MoCvv377/vdfdOfLv7596iDuDs7KyZ7LZ371569er1wPeZnp6u1WXYvn275tZAVFQUvXv31hzL\nzc3FwqL66o7+FfZbl+HKoSdqw585k42z878ICdnJ5MkVE0i6dGnKmDHdMDKSrVWFEIbFzs6Ovn37\nsmrVKgCmTZvGnTt3GDhwIIMGDWL16tUsXboUtVqNkZER69evZ/v27bz99tsMGTIEMzMzJkyYoHVO\nLy8vCgsLCQgI4Ouvv8bV1RUnJyfNLYCLFy8+dKvq3r17c+zYMbp27QrA+PHj2b9/P0OHDmXFihW8\n8sorWs9v1qwZd+7c4caNGzRq1AhnZ2feeecdli9fTlBQEHPnztXqEDRr1ozhw4czdOhQfH19sbS0\npHbt2vj5+TFr1iyCgoLw9vbm6NGj/Pzzz0+Uy2HDhnHmzBmGDBlCfHw8QUFBAMyePZv09HQArl+/\njpmZmdbrXn/9dWJiYvD19SUzMxNf34qB6B9//IG1tXW13V8HUCmV3TSoQU6cOFHxl+LkUjgwEdq8\nDQP/XelriopKmT37J+bN+4XS0nKaNTNjxQov3nyz3TOKWr9ociyqleS5+kmOq19153jDhg0UFRUx\nevToaruGLsyZM4dXXnkFL6/HWy31afKsfyP2J5gNHxKyk/Dwg5SWlhMS0o3ExLFS1IUQQg8MGTKE\nY8eOaUbFhuDs2bNkZWU9dlF/Wvo1ee5WBlz55a82/IAHPiU/v4iSkjIsLesRGurMb79lERHRHxcX\n3S5yIIQQ4vEZGxvftxiMvnNwcGDZsmXVfh39GrHfnQ3fov8DZ8NHRZ2lffsVBAdXzEp96SULTp4c\nLUVdCCHEc0O/RuwPWRs+I+Mm48fvJjo6CYCsrAIKCoqpX9/0oZM5hBBCCEOkX4X9yi+grgWt/9uG\nj429gJ9fJDdv3sHMzJR58zwIDpbZ7kIIIZ5P+lXYAVr2B1MzFEVBpVLRoYMViqIwcOBLrFjhha2t\nua4jFEIIIXSmWgv7nDlzOHXqFCqVimnTpmmWGAQ4dOgQn3/+OWq1GldXV8aNG/dY57zT4h3mfRbH\n0aNXiInxx9bWnFOngmnRoqG03YUQQjz3qq2wHz16lLS0NLZt20ZKSgrTpk3T7NQDEB4ezvr167G2\ntiYgIIC+ffvy4osvVnrOQ3+0Imjwdc4mpVQ8PpSOs7M9LVs2qq63IYQQQuiVapsVf/jwYTw8PABo\n3bo1N27coKCgAKhYeq9BgwY0bdoUIyMjXnvtNQ4fPvzIc7pEDONsUh5t2jTmwIERODvLbHchhBDi\nXtVW2HNycmjU6L8j6caNG3Pt2jUArl27prWbz73HKqNWq5g2zYXTp0Nwc2tR5TELIYQQ+u6ZTZ6r\nipVrjxypmA2fkHDqb59LPNjDNnIQVUvyXP0kx9VPclwzVVtht7KyIicnR/M4OzsbS0vLBx6rbCu8\nu2TdZyGEEOLRqq0V7+zszJ49ewBISEjAysqK+vUrVouztbWloKCAy5cvU1payoEDB3B2dq6uUIQQ\nQojnRrXu7rZo0SKOHz+OSqUiLCyMxMREzMzM6NOnD8eOHWPRokUAeHp68t5771VXGEIIIcRzQ2+2\nbRVCCCHEo+nXJjBCCCGEqJQUdiGEEMKA1MjCPmfOHPz8/Bg8eDCnT5/WOnbo0CEGDRqEn58fK1as\n0FGE+q+yHB85cgRfX18GDx7M1KlTKS8v11GU+q2yHN+1ePFihg0b9owjMxyV5TgzMxN/f38GDRrE\nP//5Tx1FaBgqy/PmzZvx8/PD39+f2bNn6yhC/Xf+/Hk8PDzYtGnTfceeuO4pNUx8fLwyevRoRVEU\n5cKFC4qvr6/W8f79+ytXrlxRysrKFH9/fyU5OVkXYeq1R+W4T58+SmZmpqIoivL+++8rcXFxzzxG\nffeoHCuKoiQnJyt+fn5KQEDAsw7PIDwqxxMmTFD27t2rKIqizJgxQ8nIyHjmMRqCyvJ869Ytxd3d\nXSkpKVEURVFGjhyp/PrrrzqJU5/dvn1bCQgIUD755BNl48aN9x1/0rpX40bs1bEUrdBWWY4BoqKi\nsLGxASpWBbx+/bpO4tRnj8oxwLx585g0aZIuwjMIleW4vLycEydO0Lt3bwDCwsJo1qyZzmLVZ5Xl\n2cTEBBMTE/78809KS0spLCykQYMGugxXL5mamrJ27doHrufyNHWvxhX26liKVmirLMeAZr2B7Oxs\nfvnlF1577bVnHqO+e1SOo6Ki6N69O82bN9dFeAahshzn5eVRr1495s6di7+/P4sXL9ZVmHqvsjzX\nqlWLcePG4eHhgbu7O506daJly5a6ClVvGRsbU7t27Qcee5q6V+MK+/9S5Nt41e5BOc7NzSU4OJiw\nsDCtf9Ti6dyb4/z8fKKiohg5cqQOIzI89+ZYURSuXr3K8OHD2bRpE4mJicTFxekuOANyb54LCgpY\ns2YNsbGx7N+/n1OnTpGUlKTD6ATUwMJe1UvRivtVlmOo+Mc6atQoJk6ciIuLiy5C1HuV5fjIkSPk\n5eUxdOhQxo8fT0JCAnPmzNFVqHqrshw3atSIZs2aYW9vj1qtxsnJieTkZF2Fqtcqy3NKSgp2dnY0\nbtwYU1NTunXrxpkzZ3QVqkF6mrpX4wq7LEVb/SrLMVTc+x0xYgSurq66ClHvVZbjfv36sWvXLrZv\n387y5ctxdHRk2rRpugxXL1WWY2NjY+zs7Lh06ZLmuLSIn05leW7evDkpKSkUFRUBcObMGVq0aKGr\nUA3S09S9GrnynCxFW/0elmMXFxdeffVVOnfurHnugAED8PPz02G0+qmyv8d3Xb58malTp7Jx40Yd\nRqq/KstxWloaoaGhKIpC27ZtmTFjBkZGNW4soxcqy/PWrVuJiopCrVbTuXNnPv74Y12Hq3fOnDnD\n/PnzycjIwNjYGGtra3r37o2tre1T1b0aWdiFEEII8XTk46sQQghhQKSwCyGEEAZECrsQQghhQKSw\nCyGEEAZECrsQQghhQIx1HYAQz4PLly/Tr18/ra8RAkybNg0HB4cHviYiIoLS0tK/tZ58fHw8Y8eO\npX379gDcuXOH9u3bM336dExMTJ7oXD/99BMJCQmEhIRw8uRJLC0tsbOzY/bs2bzxxht06NDhqeOM\niIggKioKW1tbAEpLS7GxsWHmzJmYmZk99HVXr17l4sWLODk5PfW1hTA0UtiFeEYaN26sk++rt23b\nVnNdRVGYNGkS27ZtIyAg4InO4+rqqlm0KCoqCi8vL+zs7Jg+fXqVxDlw4ECtDzELFy5k9erVTJ48\n+aGviY+PJyUlRQq7EPeQwi6EjqWkpBAWFoZaraagoICJEyfSq1cvzfHS0lI++eQTUlNTUalUODg4\nEBYWRnFxMTNnziQtLY3bt28zYMAAAgMDK72WSqWia9euXLx4EYC4uDhWrFhB7dq1qVOnDrNmzcLa\n2ppFixZx5MgRTE1Nsba2Zv78+cTExHDo0CH69u1LbGwsp0+fZurUqaxcuZKQkBAWL17M9OnT6dKl\nCwDvvvsuI0eOpE2bNnz22WcUFhby559/8sEHH9CzZ89H5qVz585s374dgOPHj7No0SJMTU0pKioi\nLCwMc3NzvvjiCxRFoWHDhgwdOvSJ8yGEIZLCLoSO5eTk8I9//INXX32VX3/9lVmzZmkV9vPnz3Pq\n1Cl2794NwPbt27l16xbbtm3DysqK8PBwysrK8PX1pWfPnrRr1+6h17pz5w4HDhxg0KBBFBYW8skn\nnxAZGYmNjQ2bNm3iiy++IDQ0lM2bN3P8+HHUajW7du3SWqu6T58+bNiwgZCQEJycnFi5ciUAPj4+\n7Nmzhy5dupCbm0tKSgouLi6EhIQQGBhIjx49uHbtGn5+fuzduxdj44f/+iktLSUmJoZXXnkFqNg4\nZ8aMGbRr146YmBjWrFnDsmXLeOuttygtLWXkyJGsW7fuifMhhCGSwi7EM5KXl8ewYcO0frZ06VIs\nLS1ZsGABS5YsoaSkhPz8fK3ntG7dmkaNGjFq1Cjc3d3p378/ZmZmxMfHk5WVxbFjxwAoLi7mjz/+\nuK+QnT9/Xuu67u7ueHl5cfbsWZo0aYKNjQ0A3bt3Z+vWrTRo0IBevXoREBBAnz598PLy0jynMt7e\n3vj7+zN16lRiY2Pp168farWa+Ph4bt++zYoVK4CKddxzc3OxtrbWev3333/PyZMnURSFxMREhg8f\nzujRowGwsLBgwYIF3Llzh1u3bj1wz+/HzYcQhk4KuxDPyMPusX/44Yd4e3szaNAgzp8/T3BwsNbx\nWrVq8c0335CQkKAZbW/ZsgVTU1PGjRtHv379Kr3uvffY76VSqbQeK4qi+dmyZctISUnhxx9/JCAg\ngIiIiEe+v7uT6U6fPs3u3bsJDQ0FwNTUlIiICK09pR/k3nvswcHBNG/eXDOq//jjj/nss89wcnLi\nwIED/Otf/7rv9Y+bDyEMnXzdTQgdy8nJoU2bNgDs2rWL4uJireO///473377LY6OjowfPx5HR0cu\nXbpE165dNe358vJy5s6de99ovzItWrQgNzeXK1euAHD48GE6depEeno6X331Fa1btyYwMJA+ffrc\nt8e2SqWipKTkvnP6+PgQGRnJjRs3NLPk740zLy+P2bNnPzK2sLAwIiIiyMrK0spRWVkZsbGxmhyp\nVCpKS0vvu87T5EMIQyGFXQgdCwwM5OOPP+a9996ja9euNGjQgHnz5mmO29vbs2fPHgYPHszw4cMx\nNzenS5cuDB06lLp16+Ln54evry9mZmY0bNjwsa9bu3ZtZs+ezaRJkxg2bBiHDx9m4sSJWFtbk5iY\nyKBBgxgxYgQZGRl4enpqvdbZ2ZmwsDD27t2r9XNPT0927NiBt7e35mfTp09n3759DBkyhNGjR9Oj\nR49Hxta0aVNGjRrFp59+CsCoUaMYMWIEwcHBvPXWW2RmZvLVV1/RrVs3oqKi+OKLL/52PoQwFLK7\nmxBCCGFAZMQuhBBCGBAp7EIIIYQBkcIuhBBCGBAp7EIIIYQBkcIuhBBCGBAp7EIIIYQBkcIuhBBC\nGBAp7EIIIYQB+f8DdcXwITiAOwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cc902b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153c994358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
" test_size = 0.30, random_state = 0)\n",
"\n",
"y_train = y_train.astype(int)\n",
"y_test = y_test.astype(int)\n",
"\n",
"classifier = LogisticRegression(random_state = 0)\n",
"classifier.fit(X_train, y_train)\n",
"\n",
"y_test_pred = classifier.predict(X_test)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "49ae9048-4fd2-7a5d-aa75-8dd3d43bf51b"
},
"source": [
"Ouch!\n",
"AUC is worse with more age data"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"_cell_guid": "e7fccafb-2875-fbbb-c2b7-7e575ecaa267"
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Da/7o1QEDBuDKlSv4z3/+A8D0yNPXX3/dck7YLCAgAIIg4OpV0ymAI0eOICwsDN9//z3O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\nSrrVHuUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cd2f0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_learning_curve(classifier, \"Test\", X, y.astype(int), (0.7, 1.01), cv=10)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"_cell_guid": "4d7d5aba-fc78-6279-fe5f-87bb3a1d03b9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Sex Pclass Age SibSp Parch Fare Survived\n",
"Sex 1.000000 0.131900 0.084153 -0.114631 -0.245489 -0.182333 -0.543351\n",
"Pclass 0.131900 1.000000 -0.331339 0.083081 0.018443 -0.549500 -0.338481\n",
"Age 0.084153 -0.331339 1.000000 -0.232625 -0.179191 0.091566 -0.069809\n",
"SibSp -0.114631 0.083081 -0.232625 1.000000 0.414838 0.159651 -0.035322\n",
"Parch -0.245489 0.018443 -0.179191 0.414838 1.000000 0.216225 0.081629\n",
"Fare -0.182333 -0.549500 0.091566 0.159651 0.216225 1.000000 0.257307\n",
"Survived -0.543351 -0.338481 -0.069809 -0.035322 0.081629 0.257307 1.000000\n"
]
}
],
"source": [
"corMat = DataFrame(preprocessedDataset.corr())\n",
"print(corMat)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f266f746-85e2-427c-1d11-c8e742aa0fe3"
},
"source": [
"SibSp and Parch have high correlation even between themself\n",
"Let's try to use them as unique value (sibSp+Parch)/2"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"_cell_guid": "2e757bb9-5543-f95f-90fd-48dddc7a8fdd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Sex Pclass Age Fare Survived Relative\n",
"0 0.737695 0.827377 -0.592481 -0.502445 0 -0.020440\n",
"1 -1.355574 -1.566107 0.638789 0.786845 1 -0.020440\n",
"2 -1.355574 0.827377 -0.284663 -0.488854 1 -0.474109\n",
"3 -1.355574 -1.566107 0.407926 0.420730 1 -0.020440\n",
"4 0.737695 0.827377 0.407926 -0.486337 0 -0.474109\n",
" Sex Pclass Age Fare Survived Relative\n",
"Sex 1.000000 0.131900 0.084153 -0.182333 -0.543351 -0.214081\n",
"Pclass 0.131900 1.000000 -0.331339 -0.549500 -0.338481 0.060353\n",
"Age 0.084153 -0.331339 1.000000 0.091566 -0.069809 -0.244813\n",
"Fare -0.182333 -0.549500 0.091566 1.000000 0.257307 0.223448\n",
"Survived -0.543351 -0.338481 -0.069809 0.257307 1.000000 0.027528\n",
"Relative -0.214081 0.060353 -0.244813 0.223448 0.027528 1.000000\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1542037f28>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+SFRUFGvXrqVz584mRxMREfEelxfmHzt2jO3bt7Njxw4Mw2DoUN+vGREREf9h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\nKogiIiIA/D+sTesilRrG+QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1542069ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"engineeredDataset = preprocessedDataset \n",
"\n",
"engineeredDataset['Relative'] = (engineeredDataset['SibSp'] + engineeredDataset['Parch']) / 2\n",
"\n",
"engineeredDataset.drop(labels=['SibSp', 'Parch'], axis=1,inplace = True)\n",
"\n",
"engineeredDataset.columns = ['Sex','Pclass', 'Age', 'Fare', 'Survived', 'Relative']\n",
"print(engineeredDataset.head())\n",
"\n",
"workingData = engineeredDataset.values\n",
"X = workingData[:, [0,1,2,3,5]]\n",
"y = workingData[:, 4].astype(int)\n",
"\n",
"import seaborn as sns\n",
"corMat = DataFrame(preprocessedDataset.corr())\n",
"print(corMat)\n",
"\n",
"#visualize correlations using heatmap\n",
"sns.heatmap(corMat)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"_cell_guid": "aac7eea1-1468-5af3-e2a5-d72592e6febc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[144 24]\n",
" [ 29 71]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.83 0.86 0.84 168\n",
" Survived 0.75 0.71 0.73 100\n",
"\n",
" avg / total 0.80 0.80 0.80 268\n",
"\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cd9a3c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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7p12aUwWSS/ib/6S61UTR/rsMawoQFgfEFXD1RZ98lvOXoihs3HidsmWdadmy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\nIYQQwohIYRdCCCGMyP8Bi6rTwH0TCCsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cd9ab00>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d030be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
" test_size = 0.30, random_state = 0)\n",
"\n",
"y_train = y_train.astype(int)\n",
"y_test = y_test.astype(int)\n",
"\n",
"classifier = LogisticRegression(random_state = 0)\n",
"classifier.fit(X_train, y_train)\n",
"\n",
"y_test_pred = classifier.predict(X_test)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"_cell_guid": "f533b3e2-fd1c-3f31-eb23-27c1a9d21e55"
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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OiKiVSfyj40evLh18449eHTRoEC5duoT/9//+HwDTI09fffVV6Zqwmb+/PwRBwOXLpksA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\nIiKZYKgTERHJBEOdiIhIJv4/pKpVCyulxGYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cdc0cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_learning_curve(classifier, \"Test\", X, y, (0.7, 1.01), cv=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "45c3c433-0a43-5ef4-0394-17eb86332242"
},
"source": [
"Features space seems not large enough, must find something else"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d8b120f0-99de-fb45-3717-8bd8e604bc9c"
},
"source": [
"**Round 2**\n",
"\n",
"From what I actually know, there are several availables options from here\n",
"- play with classifiers hyper parameters, trying to get the best combination\n",
"- do feature engineering, trying to squeeze more data form original dataset\n",
"- try other classifiers and ensemble methods \n",
"- a combination of all :)\n",
"\n",
"Before change data or classifier, let's try to tune the LogisticRegression \n",
"As I'm for sure not able to tune it by know how, I'm gonna use GridSearch to find the best combinations (hoping at leatst to pick the right candidate values)\n",
"\n",
"I''ll will use the first preprocessed dataset, with the nan ages rows discarderd\n",
"\n",
"\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"_cell_guid": "e0ba37c3-66e8-e2d9-4486-90358650833f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" <th>Survived</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.530377</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.518978</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>0.571831</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>0.691897</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.254825</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.506214</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>0.365167</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>0.348049</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.365167</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.503850</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>1.674039</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>0.324648</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-1.908136</td>\n",
" <td>2.677117</td>\n",
" <td>0.666862</td>\n",
" <td>-0.257546</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.185937</td>\n",
" <td>-0.551703</td>\n",
" <td>1.839619</td>\n",
" <td>-0.445544</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>-1.081480</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.087435</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.770360</td>\n",
" <td>0.524570</td>\n",
" <td>0.666862</td>\n",
" <td>-0.340278</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.949591</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.154013</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.668153</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.503850</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.640719</td>\n",
" <td>0.524570</td>\n",
" <td>5.357890</td>\n",
" <td>-0.064663</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.081480</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.507552</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>1.742927</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.353515</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-1.908136</td>\n",
" <td>3.753390</td>\n",
" <td>0.666862</td>\n",
" <td>-0.105320</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>0.089615</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.315695</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>0.365167</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.164414</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>0.296279</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.410245</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.012592</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.504243</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.117049</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>0.015232</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.494808</td>\n",
" <td>2.677117</td>\n",
" <td>0.666862</td>\n",
" <td>-0.257546</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>0.571831</td>\n",
" <td>0.524570</td>\n",
" <td>5.357890</td>\n",
" <td>-0.062536</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.737041</td>\n",
" <td>2.677117</td>\n",
" <td>1.839619</td>\n",
" <td>4.317274</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>0.709607</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.131873</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>2.500694</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.457520</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.117049</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>0.897780</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>0.847383</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>0.327248</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.599265</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.503850</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.805929</td>\n",
" <td>1.600843</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.315695</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>684</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.054047</td>\n",
" <td>0.524570</td>\n",
" <td>0.666862</td>\n",
" <td>2.461566</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>685</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>1.467375</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.154013</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>686</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.392601</td>\n",
" <td>-0.551703</td>\n",
" <td>3.012376</td>\n",
" <td>-0.291900</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>687</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.778495</td>\n",
" <td>1.600843</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.389287</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>688</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.599265</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.438610</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>689</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.260711</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.165753</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>690</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.392601</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.410245</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>691</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>0.847383</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.410245</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>692</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.185937</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.394014</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>693</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>0.089615</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>0.298804</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>694</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-1.770360</td>\n",
" <td>0.524570</td>\n",
" <td>0.666862</td>\n",
" <td>-0.445544</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>695</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.254825</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.506766</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>696</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.191823</td>\n",
" <td>0.524570</td>\n",
" <td>0.666862</td>\n",
" <td>0.337728</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>697</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>0.227391</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.561526</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>698</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>1.191823</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.485885</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>699</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.117049</td>\n",
" <td>0.524570</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.202234</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>700</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-1.012592</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.519451</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>701</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.668153</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.469891</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>702</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.737041</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.506766</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>703</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>1.811815</td>\n",
" <td>-0.551703</td>\n",
" <td>0.666862</td>\n",
" <td>0.916454</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>704</th>\n",
" <td>-1.317434</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.323713</td>\n",
" <td>-0.551703</td>\n",
" <td>0.666862</td>\n",
" <td>-0.164414</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>705</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.227391</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.506766</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>706</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>-0.530377</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.457204</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>707</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.117049</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.457520</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>708</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>-0.323713</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.522760</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>709</th>\n",
" <td>-1.317434</td>\n",
" <td>0.911232</td>\n",
" <td>0.640719</td>\n",
" <td>-0.551703</td>\n",
" <td>5.357890</td>\n",
" <td>-0.105320</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>710</th>\n",
" <td>0.759051</td>\n",
" <td>-0.282566</td>\n",
" <td>-0.185937</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.410245</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>711</th>\n",
" <td>-1.317434</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.737041</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.088774</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>712</th>\n",
" <td>0.759051</td>\n",
" <td>-1.476364</td>\n",
" <td>-0.254825</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.088774</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>713</th>\n",
" <td>0.759051</td>\n",
" <td>0.911232</td>\n",
" <td>0.158503</td>\n",
" <td>-0.551703</td>\n",
" <td>-0.505895</td>\n",
" <td>-0.509523</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>714 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" Sex Pclass Age SibSp Parch Fare Survived\n",
"0 0.759051 0.911232 -0.530377 0.524570 -0.505895 -0.518978 0\n",
"1 -1.317434 -1.476364 0.571831 0.524570 -0.505895 0.691897 1\n",
"2 -1.317434 0.911232 -0.254825 -0.551703 -0.505895 -0.506214 1\n",
"3 -1.317434 -1.476364 0.365167 0.524570 -0.505895 0.348049 1\n",
"4 0.759051 0.911232 0.365167 -0.551703 -0.505895 -0.503850 0\n",
"5 0.759051 -1.476364 1.674039 -0.551703 -0.505895 0.324648 0\n",
"6 0.759051 0.911232 -1.908136 2.677117 0.666862 -0.257546 0\n",
"7 -1.317434 0.911232 -0.185937 -0.551703 1.839619 -0.445544 1\n",
"8 -1.317434 -0.282566 -1.081480 0.524570 -0.505895 -0.087435 1\n",
"9 -1.317434 0.911232 -1.770360 0.524570 0.666862 -0.340278 1\n",
"10 -1.317434 -1.476364 1.949591 -0.551703 -0.505895 -0.154013 1\n",
"11 0.759051 0.911232 -0.668153 -0.551703 -0.505895 -0.503850 0\n",
"12 0.759051 0.911232 0.640719 0.524570 5.357890 -0.064663 0\n",
"13 -1.317434 0.911232 -1.081480 -0.551703 -0.505895 -0.507552 0\n",
"14 -1.317434 -0.282566 1.742927 -0.551703 -0.505895 -0.353515 1\n",
"15 0.759051 0.911232 -1.908136 3.753390 0.666862 -0.105320 0\n",
"16 -1.317434 0.911232 0.089615 0.524570 -0.505895 -0.315695 0\n",
"17 0.759051 -0.282566 0.365167 -0.551703 -0.505895 -0.164414 0\n",
"18 0.759051 -0.282566 0.296279 -0.551703 -0.505895 -0.410245 1\n",
"19 -1.317434 0.911232 -1.012592 -0.551703 -0.505895 -0.504243 1\n",
"20 0.759051 -1.476364 -0.117049 -0.551703 -0.505895 0.015232 1\n",
"21 -1.317434 0.911232 -1.494808 2.677117 0.666862 -0.257546 0\n",
"22 -1.317434 0.911232 0.571831 0.524570 5.357890 -0.062536 1\n",
"23 0.759051 -1.476364 -0.737041 2.677117 1.839619 4.317274 0\n",
"24 0.759051 -1.476364 0.709607 -0.551703 -0.505895 -0.131873 0\n",
"25 0.759051 -0.282566 2.500694 -0.551703 -0.505895 -0.457520 0\n",
"26 0.759051 -1.476364 -0.117049 0.524570 -0.505895 0.897780 0\n",
"27 0.759051 -1.476364 0.847383 0.524570 -0.505895 0.327248 0\n",
"28 0.759051 0.911232 -0.599265 -0.551703 -0.505895 -0.503850 0\n",
"29 -1.317434 0.911232 -0.805929 1.600843 -0.505895 -0.315695 0\n",
".. ... ... ... ... ... ... ...\n",
"684 -1.317434 -1.476364 1.054047 0.524570 0.666862 2.461566 1\n",
"685 0.759051 -1.476364 1.467375 -0.551703 -0.505895 -0.154013 1\n",
"686 -1.317434 0.911232 -0.392601 -0.551703 3.012376 -0.291900 1\n",
"687 0.759051 0.911232 0.778495 1.600843 -0.505895 -0.389287 0\n",
"688 0.759051 -0.282566 -0.599265 0.524570 -0.505895 -0.438610 0\n",
"689 -1.317434 -1.476364 1.260711 -0.551703 -0.505895 -0.165753 1\n",
"690 0.759051 -0.282566 -0.392601 -0.551703 -0.505895 -0.410245 0\n",
"691 -1.317434 -0.282566 0.847383 -0.551703 -0.505895 -0.410245 1\n",
"692 -1.317434 -0.282566 -0.185937 0.524570 -0.505895 -0.394014 1\n",
"693 0.759051 -1.476364 0.089615 -0.551703 -0.505895 0.298804 0\n",
"694 0.759051 0.911232 -1.770360 0.524570 0.666862 -0.445544 1\n",
"695 0.759051 0.911232 -0.254825 -0.551703 -0.505895 -0.506766 0\n",
"696 -1.317434 -1.476364 1.191823 0.524570 0.666862 0.337728 1\n",
"697 0.759051 -1.476364 0.227391 -0.551703 -0.505895 -0.561526 0\n",
"698 0.759051 0.911232 1.191823 -0.551703 -0.505895 -0.485885 0\n",
"699 -1.317434 -0.282566 -0.117049 0.524570 -0.505895 -0.202234 1\n",
"700 -1.317434 0.911232 -1.012592 -0.551703 -0.505895 -0.519451 1\n",
"701 0.759051 0.911232 -0.668153 -0.551703 -0.505895 -0.469891 0\n",
"702 0.759051 0.911232 -0.737041 -0.551703 -0.505895 -0.506766 0\n",
"703 -1.317434 -1.476364 1.811815 -0.551703 0.666862 0.916454 1\n",
"704 -1.317434 -0.282566 -0.323713 -0.551703 0.666862 -0.164414 1\n",
"705 0.759051 0.911232 0.227391 -0.551703 -0.505895 -0.506766 0\n",
"706 -1.317434 0.911232 -0.530377 -0.551703 -0.505895 -0.457204 0\n",
"707 0.759051 -0.282566 -0.117049 -0.551703 -0.505895 -0.457520 0\n",
"708 0.759051 0.911232 -0.323713 -0.551703 -0.505895 -0.522760 0\n",
"709 -1.317434 0.911232 0.640719 -0.551703 5.357890 -0.105320 0\n",
"710 0.759051 -0.282566 -0.185937 -0.551703 -0.505895 -0.410245 0\n",
"711 -1.317434 -1.476364 -0.737041 -0.551703 -0.505895 -0.088774 1\n",
"712 0.759051 -1.476364 -0.254825 -0.551703 -0.505895 -0.088774 1\n",
"713 0.759051 0.911232 0.158503 -0.551703 -0.505895 -0.509523 0\n",
"\n",
"[714 rows x 7 columns]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#get all relevant columns\n",
"workingDataset = dataset.iloc[:, [1,2,4,5,6,7,9]]\n",
"\n",
"# get rid of age nan rows (first approach)\n",
"workingDataset = workingDataset[np.isfinite(workingDataset['Age'])]\n",
"\n",
"# feature/target selection\n",
"\n",
"workingData = workingDataset.values\n",
"X = workingData[:, 1:]\n",
"y = workingData[:, 0]\n",
"\n",
"# encoding feature (sex)\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
"labelencoder_X = LabelEncoder()\n",
"X[:,1] = labelencoder_X.fit_transform(X[:, 1])\n",
"onehotencoder = OneHotEncoder(categorical_features = [1])\n",
"X = onehotencoder.fit_transform(X).toarray()\n",
"\n",
"# avoid dummy trap\n",
"X = X[:, 1:]\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"from pandas import DataFrame\n",
"sc = StandardScaler()\n",
"X = sc.fit_transform(X)\n",
"#y = sc.fit_transform(y.reshape(-1,1))\n",
"# rebuild feature's dataframe with normalized data for graphs purpose\n",
"preprocessedDataset = DataFrame(data=X)\n",
"\n",
"preprocessedDataset.columns = ['Sex','Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\n",
"preprocessedDataset['Survived'] = y.astype(int)\n",
"preprocessedDataset"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"_cell_guid": "e0d3931f-1819-13fb-df2a-c77fe325297f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.789579158317\n",
"{'C': 1, 'max_iter': 100, 'penalty': 'l2', 'solver': 'sag'}\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_fit_time</th>\n",
" <th>mean_score_time</th>\n",
" <th>mean_test_score</th>\n",
" <th>mean_train_score</th>\n",
" <th>param_C</th>\n",
" <th>param_max_iter</th>\n",
" <th>param_penalty</th>\n",
" <th>param_solver</th>\n",
" <th>params</th>\n",
" <th>rank_test_score</th>\n",
" <th>...</th>\n",
" <th>split7_test_score</th>\n",
" <th>split7_train_score</th>\n",
" <th>split8_test_score</th>\n",
" <th>split8_train_score</th>\n",
" <th>split9_test_score</th>\n",
" <th>split9_train_score</th>\n",
" <th>std_fit_time</th>\n",
" <th>std_score_time</th>\n",
" <th>std_test_score</th>\n",
" <th>std_train_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.005770</td>\n",
" <td>0.000248</td>\n",
" <td>0.789579</td>\n",
" <td>0.794032</td>\n",
" <td>10</td>\n",
" <td>1000</td>\n",
" <td>l2</td>\n",
" <td>lbfgs</td>\n",
" <td>{'C': 10, 'max_iter': 1000, 'penalty': 'l2', '...</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.72</td>\n",
" <td>0.799555</td>\n",
" <td>0.88</td>\n",
" <td>0.77951</td>\n",
" <td>0.795918</td>\n",
" <td>0.797778</td>\n",
" <td>0.003616</td>\n",
" <td>0.000014</td>\n",
" <td>0.046665</td>\n",
" <td>0.008308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.021272</td>\n",
" <td>0.000245</td>\n",
" <td>0.789579</td>\n",
" <td>0.794254</td>\n",
" <td>100</td>\n",
" <td>100</td>\n",
" <td>l2</td>\n",
" <td>lbfgs</td>\n",
" <td>{'C': 100, 'max_iter': 100, 'penalty': 'l2', '...</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.72</td>\n",
" <td>0.801782</td>\n",
" <td>0.88</td>\n",
" <td>0.77951</td>\n",
" <td>0.795918</td>\n",
" <td>0.797778</td>\n",
" <td>0.027640</td>\n",
" <td>0.000014</td>\n",
" <td>0.046665</td>\n",
" <td>0.008481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.009703</td>\n",
" <td>0.006791</td>\n",
" <td>0.789579</td>\n",
" <td>0.794254</td>\n",
" <td>100</td>\n",
" <td>500</td>\n",
" <td>l2</td>\n",
" <td>liblinear</td>\n",
" <td>{'C': 100, 'max_iter': 500, 'penalty': 'l2', '...</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.72</td>\n",
" <td>0.801782</td>\n",
" <td>0.88</td>\n",
" <td>0.77951</td>\n",
" <td>0.795918</td>\n",
" <td>0.797778</td>\n",
" <td>0.020177</td>\n",
" <td>0.019546</td>\n",
" <td>0.046665</td>\n",
" <td>0.008481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.019516</td>\n",
" <td>0.000273</td>\n",
" <td>0.789579</td>\n",
" <td>0.794254</td>\n",
" <td>100</td>\n",
" <td>500</td>\n",
" <td>l2</td>\n",
" <td>sag</td>\n",
" <td>{'C': 100, 'max_iter': 500, 'penalty': 'l2', '...</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.72</td>\n",
" <td>0.801782</td>\n",
" <td>0.88</td>\n",
" <td>0.77951</td>\n",
" <td>0.795918</td>\n",
" <td>0.797778</td>\n",
" <td>0.025664</td>\n",
" <td>0.000023</td>\n",
" <td>0.046665</td>\n",
" <td>0.008481</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.021123</td>\n",
" <td>0.000268</td>\n",
" <td>0.789579</td>\n",
" <td>0.794254</td>\n",
" <td>100</td>\n",
" <td>500</td>\n",
" <td>l2</td>\n",
" <td>lbfgs</td>\n",
" <td>{'C': 100, 'max_iter': 500, 'penalty': 'l2', '...</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.72</td>\n",
" <td>0.801782</td>\n",
" <td>0.88</td>\n",
" <td>0.77951</td>\n",
" <td>0.795918</td>\n",
" <td>0.797778</td>\n",
" <td>0.030535</td>\n",
" <td>0.000056</td>\n",
" <td>0.046665</td>\n",
" <td>0.008481</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 34 columns</p>\n",
"</div>"
],
"text/plain": [
" mean_fit_time mean_score_time mean_test_score mean_train_score param_C \\\n",
"17 0.005770 0.000248 0.789579 0.794032 10 \n",
"20 0.021272 0.000245 0.789579 0.794254 100 \n",
"21 0.009703 0.006791 0.789579 0.794254 100 \n",
"22 0.019516 0.000273 0.789579 0.794254 100 \n",
"23 0.021123 0.000268 0.789579 0.794254 100 \n",
"\n",
" param_max_iter param_penalty param_solver \\\n",
"17 1000 l2 lbfgs \n",
"20 100 l2 lbfgs \n",
"21 500 l2 liblinear \n",
"22 500 l2 sag \n",
"23 500 l2 lbfgs \n",
"\n",
" params rank_test_score \\\n",
"17 {'C': 10, 'max_iter': 1000, 'penalty': 'l2', '... 1 \n",
"20 {'C': 100, 'max_iter': 100, 'penalty': 'l2', '... 1 \n",
"21 {'C': 100, 'max_iter': 500, 'penalty': 'l2', '... 1 \n",
"22 {'C': 100, 'max_iter': 500, 'penalty': 'l2', '... 1 \n",
"23 {'C': 100, 'max_iter': 500, 'penalty': 'l2', '... 1 \n",
"\n",
" ... split7_test_score split7_train_score split8_test_score \\\n",
"17 ... 0.72 0.799555 0.88 \n",
"20 ... 0.72 0.801782 0.88 \n",
"21 ... 0.72 0.801782 0.88 \n",
"22 ... 0.72 0.801782 0.88 \n",
"23 ... 0.72 0.801782 0.88 \n",
"\n",
" split8_train_score split9_test_score split9_train_score std_fit_time \\\n",
"17 0.77951 0.795918 0.797778 0.003616 \n",
"20 0.77951 0.795918 0.797778 0.027640 \n",
"21 0.77951 0.795918 0.797778 0.020177 \n",
"22 0.77951 0.795918 0.797778 0.025664 \n",
"23 0.77951 0.795918 0.797778 0.030535 \n",
"\n",
" std_score_time std_test_score std_train_score \n",
"17 0.000014 0.046665 0.008308 \n",
"20 0.000014 0.046665 0.008481 \n",
"21 0.019546 0.046665 0.008481 \n",
"22 0.000023 0.046665 0.008481 \n",
"23 0.000056 0.046665 0.008481 \n",
"\n",
"[5 rows x 34 columns]"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"parameters = [{'C': [1, 10, 100, 1000], 'solver': ['liblinear', 'sag', 'lbfgs'], \n",
" 'penalty' : ['l2'], 'max_iter' : [100,500,1000]}\n",
" ]\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
" test_size = 0.30, random_state = 0)\n",
"\n",
"y_train = y_train.astype(int)\n",
"y_test = y_test.astype(int)\n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"classifier = LogisticRegression(random_state = 0) # basic setting\n",
"classifier.fit(X_train, y_train)\n",
"\n",
"grid_search = GridSearchCV(estimator = classifier,\n",
" param_grid = parameters,\n",
" cv = 10,\n",
" n_jobs = -1)\n",
"\n",
"grid_search = grid_search.fit(X_train, y_train)\n",
"print(grid_search.best_score_)\n",
"print(grid_search.best_params_)\n",
"result_df = DataFrame(data=grid_search.cv_results_)\n",
"result_df.sort_values(by=['rank_test_score'],ascending=[1]).head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "59aba3e2-5578-929d-1aa1-a1d13cdbb795"
},
"source": [
"No improvements, let's get back to data"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"_cell_guid": "c1396200-01ae-4e82-b3d8-caaa3ce7950b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1664843e-3213-3213-76e2-4e01c4fb089a"
},
"source": [
"in round 1 I discarded Name, Ticket and Embarked \n",
"\n",
"Maybe they could be useful combined in some way"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"_cell_guid": "ee7fce19-7b19-cc59-12ff-7811044ce32d"
},
"outputs": [
{
"data": {
"image/png": 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m5+crJibGV5sHgNrJYvH1BOfjvKFquVT3uFIXAAAGIMgAABiAIAMAYACCDACAAQgyAAAG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\n+H8Rh8XWGtf7OAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cca1a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cross_tab(feature=dataset.Embarked, target=dataset.Survived.astype(bool), \n",
" kind='bar', colors=['red','blue'], stacked=False, grid=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7e6ae6de-0503-7f32-b61c-780e6561d67c"
},
"source": [
"It seems \"S\" has major contribution but could just be because is a larger town...\n",
"I can't find a valid relation to the fact someone can survive or not based on that, so I continue to skip it\n",
"\n",
"Cabin has only 204 filled rows but maybe this data could be useful in relation with Pclass and maybe the position of certain cabins could be relevant\n",
"\n",
"As there are more cabins per row too, this can be related to Parch and Sibsp\n",
"\n",
"Let' analyze this"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2d0da565-6179-52d1-eedf-e9980ee6c0e7"
},
"source": [
"Previously I discarded tickets, but I noticed looking at the data that persons sharing the same cabin often shared the same fate\n",
"So let's use them both (ticket and cabin), encoding"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"_cell_guid": "70e7cc5a-1ed0-cec4-50d1-37f6f5efc49e"
},
"outputs": [
{
"data": {
"text/plain": [
"(891, 835)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"workingDataset = dataset.loc[:, ['Survived','Pclass','Sex','Age', 'SibSp', \n",
" 'Parch', 'Ticket', 'Fare','Cabin']]\n",
"\n",
"#enconding ticket\n",
"workingDataset = pd.get_dummies(workingDataset)\n",
"\n",
"#dummy trap\n",
"workingDataset = workingDataset.drop(workingDataset.loc[:,['Sex_male']], axis=1) \n",
"\n",
"workingDataset.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a1695e75-66dc-f984-bfa7-4ecfc9c66590"
},
"source": [
"Now we have tons of new features, with sparse data but I hope that can be useful...\n",
"\n",
"Let's find out"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"_cell_guid": "f986ea4e-ceba-a01a-f83b-b70071f4a9a4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[107 18]\n",
" [ 19 71]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.85 0.86 0.85 125\n",
" Survived 0.80 0.79 0.79 90\n",
"\n",
" avg / total 0.83 0.83 0.83 215\n",
"\n"
]
},
{
"data": {
"image/png": 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ffafFixfr6NGjGjJkiHw+X8X3fv51dRGCAAAjAnU6NCIiQh07dpTb7VbTpk1Vt25dhYaG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\nWP8Pk0JLs9d50PkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cd2fe80>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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8/bsEos9g6Dx2T+23dB4TQpS+tWtPEBS0B4ChQ5uzaFEPKld2VDkqURSjfu2a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\nW2AQwaEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cd4c860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1541dfd208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# get rid of age nan rows (first approach)\n",
"workingDataset = workingDataset[np.isfinite(workingDataset['Age'])]\n",
"\n",
"# feature/target selection\n",
"workingData = workingDataset.values\n",
"X = workingData[:, 1:]\n",
"y = workingData[:, 0]\n",
"\n",
"workingDataset.describe()\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"from pandas import DataFrame\n",
"sc = StandardScaler()\n",
"X = sc.fit_transform(X)\n",
"#y = sc.fit_transform(y.reshape(-1,1))\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
" test_size = 0.30, random_state = 0)\n",
"\n",
"y_train = y_train.astype(int)\n",
"y_test = y_test.astype(int)\n",
"\n",
"classifier = LogisticRegression(random_state = 0)\n",
"classifier.fit(X_train, y_train)\n",
"\n",
"y_test_pred = classifier.predict(X_test)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"_cell_guid": "3c435723-73da-75ab-4a1d-6110735114ed"
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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GYtiwYRg/fjwef/xxCIKA6dOnQ6fTYciQIZgzZw727dsHo9GIpUuXsumdiIjISYJkf7G7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\nCyGEEHniB6WAch+lHISNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153c8b1160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_learning_curve(classifier, \"Test\", X, y, (0.7, 1.01), cv=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4ab61e7e-6dac-d6da-cd16-0a574cbd225c"
},
"source": [
"wow, that's interesting...the score increased a bit and the learning curve is totally different, probably because the are more features than rows ...\n",
"\n",
"let's fill the missing ages with mean instead of delete the rows"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"_cell_guid": "81a7a443-73ea-4475-aee7-67fe7ae44995"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion Matrix\n",
"[[107 18]\n",
" [ 19 71]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.85 0.86 0.85 125\n",
" Survived 0.80 0.79 0.79 90\n",
"\n",
" avg / total 0.83 0.83 0.83 215\n",
"\n"
]
},
{
"data": {
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ffafFixfr6NGjGjJkiHw+X8X3fv51dRGCAAAjAnU6NCIiQh07dpTb7VbTpk1Vt25dhYaG\nqrS0VGFhYSooKJDX6/VrbfYEAQBGuFz+PyrTrVs3bd++XRcuXNDp06dVXFysuLg4ZWZmSpI2bdqk\n7t27+1UznSAAwIhAdYJRUVHq06ePHn30UUnS1KlT1bZtW02cOFHp6emKjo5W//79/VqbEAQAXPUS\nExOVmJh40bUVK1bUeF1CEABghIU3jGFPEADgXHSCAAAzLGwFCUEAgBHcQBsA4FgWZiAhCAAw43L3\nAL0acTAGAOBYhCAAwLEYhwIAjGBPEADgWJwOBQA4loUZSAgCAMywsRPkYAwAwLEIQQCAYzEOBQAY\nYeE0lBAEAJhh454gIQgAMMPCDTZCEABghI2doIW5DQCAGYQgAMCxGIcCAIywcBpKCAIAzLBxT5AQ\nBAAYYWEGEoIAAEMsTEEOxgAAHItOEABghCuEThAAAGvQCQIAjLBwS5AQBACYwVskAACOZWEGsicI\nAHAuOkEAgBkWtoKEIADACN4iAQCARegEAQBGWDgNJQQBAIZYmIKMQwEAjkUnCAAwwsJGkBAEAJhh\n4+lQQhAAYISNt01jTxAA4Fh0ggAAMwLcCJaWluqhhx5ScnKyduzYoZycHDVs2FCSNHz4cPXs2bPa\naxKCAAArvPHGG7ruuusqno8dO1bx8fE1WpMQBAAYEcg9wQMHDmj//v1+dXuVYU8QAGCEy+Xy+3E5\nc+fO1aRJky66tnr1ag0ZMkRjxozRqVOn/KqZEAQAmBFSg0cl3n//fXXo0EFNmjSpuNavXz+NHz9e\nq1atUkxMjBYvXuxXyYxDAQBGBGocum3bNh0+fFjbtm3T8ePH5fF4NHPmTMXExEiSevXqpRkzZvi1\nNiEIALiqvfbaaxVfL1q0SDfddJPeeecdNWnSRE2aNFF2drZatGjh19qEIADAOoMGDdLo0aNVp04d\nhYeHa86cOX6tQwgCAIy4EneMGTlyZMXXGzZsqPF6hCAAwAz77ppGCAIAzOAG2gAA5+IG2gAA2IMQ\nBAA4FuNQC50vK9PrS5Zq1dp0bdq4QTdEeVVcXKw5817TF3v3qaysTM8/PVwP9e0T7FKBf6lp5xbq\n8JtuF1277sbGeid5ocLqh+ve5F/r3E+l+q95GUGqEP6wcBpKCNpo1PjJuuP21hdde3P5n1RSWqoP\n0lfrRGGRBg17Wh3at9XN0dFBqhL4ZYf+9rUO/e3riufN7mqpZne1UnjDeur5Qj8V5B5RfW/DIFYI\nf/Churginhn+Oz3/9PCLrmXt2Kl+D/ZVSEiIbojyKr5Hd2395LMgVQhUXYg7VB0e6ard6z9V+fky\nfZy2XoUHjgW7LPgjxOX/I0j87gTPnDmjBg0amKwFVdS+bZtLrrnkUvmFCxXPw+vU0eEj+VeyLMAv\nt93bVif2H9WPhd8HuxTUkKM6wRdeeMFkHaih2C53aV3Gezp79qyOHS/Qlm1/1dlzZ4NdFlA5l3R7\nn8763492BbsSOFSlneCaNWt+8XsFBQXGi4H/nh72O82d/7oGDHpSTW6+Wd3iusjtrhXssoBKRTaP\nVlnpeX1/9GSwS4EJ9jWClYfgypUrFRsbK6/Xe8n3ysrKAlYUqi+8Th39fuo/PnByWuocde7UKogV\nAZd3c/tblb/3m2CXAQerNASXLFmiV155RVOnTpXH47noe9nZ2QEtDNWzfNUanTp9WuNHvaADB7/R\n9p27NH40I2tc3Ro1idS3O/4v2GXAEBv3BCsNwZYtW+rNN9+U233py/75Y+5xZZw8eUpDn/vHXdSH\nJ7+o0NBQLX19vl6eOVt9H3lUYbVra9b0qWpQv34QKwUuL7xRfZV8/1PF8xY92ynmvs6qVccjT53a\nenjWUBV9c0z/veyjIFaJqrLx3qEun8/nC+QvOPvdiUAuD1wR7459O9glAEY8sXxcwNY+/JcP/f7Z\nJg/2NVhJ1fFmeQCAETaOQ3mzPADAsegEAQBm2NcI0gkCAJyLThAAYISNp0MJQQCAGRYejCEEAQBG\ncDoUAACL0AkCAMxgTxAA4FSMQwEAsAidIADADPsaQUIQAGAG41AAACxCJwgAMIPToQAAp7JxHEoI\nAgDMsDAE2RMEADgWnSAAwAgbx6F0ggAAx6ITBACYwelQAIBT2TgOJQQBAGYQggAAp3JZOA7lYAwA\nwLEIQQCAYzEOBQCYwZ4gAMCpAnU6tKSkRJMmTdLJkyd19uxZJScnq3Xr1kpJSVF5ebkiIyOVlpYm\nj8dT7bUJQQCAGQEKwa1bt6pNmzYaMWKE8vPzNWzYMHXq1ElJSUnq27evFixYoIyMDCUlJVV7bfYE\nAQBGuEJcfj8q88ADD2jEiBGSpGPHjikqKkrZ2dlKSEiQJMXHxysrK8uvmukEAQBWSExM1PHjx7V0\n6VINHTq0YvwZERGhwsJCv9YkBAEAVli3bp2+/PJLTZgwQT6fr+L6z7+uLsahAAAzXC7/H5XYt2+f\njh07JkmKiYlReXm56tatq9LSUklSQUGBvF6vXyUTggAAMwIUgrt27dLy5cslSUVFRSouLlZcXJwy\nMzMlSZs2bVL37t39KplxKADAiEC9RSIxMVEvvfSSkpKSVFpaqmnTpqlNmzaaOHGi0tPTFR0drf79\n+/u1NiEIADAjQPcODQsL0/z58y+5vmLFihqvzTgUAOBYdIIAACNcLvv6KvsqBgDAEDpBAIAZ3EAb\nAOBUgTodGkiEIADADD5ZHgAAe9AJAgCMYBwKAHAuC0OQcSgAwLHoBAEAZlj4ZnlCEABgxOU+If5q\nZF9sAwBgCJ0gAMAMCw/GEIIAACN4iwQAwLksPBhjX8UAABhCJwgAMILToQAAWIROEABgBgdjAABO\nxelQAIBzWXg6lBAEAJjBwRgAAOxBCAIAHItxKADACA7GAACci4MxAACnohMEADiXhZ2gfRUDAGAI\nIQgAcCzGoQAAI2z8FAlCEABgBgdjAABO5bLwYAwhCAAww8JO0OXz+XzBLgIAgGCwr3cFAMAQQhAA\n4FiEIADAsQhBAIBjEYIAAMciBAEAjkUIWm727Nl67LHHlJiYqD179gS7HMBvubm56t27t1avXh3s\nUuAgvFneYjt27FBeXp7S09N14MABTZkyRenp6cEuC6i24uJipaamKjY2NtilwGHoBC2WlZWl3r17\nS5KaN2+u77//Xj/++GOQqwKqz+Px6K233pLX6w12KXAYQtBiRUVFatSoUcXzxo0bq7CwMIgVAf5x\nu90KCwsLdhlwIELwGsId8ACgeghBi3m9XhUVFVU8P3HihCIjI4NYEQDYhRC0WNeuXZWZmSlJysnJ\nkdfrVb169YJcFQDYg0+RsNy8efO0a9cuuVwuTZ8+Xa1btw52SUC17du3T3PnzlV+fr7cbreioqK0\naNEiNWzYMNil4RpHCAIAHItxKADAsQhBAIBjEYIAAMciBAEAjkUIAgAcixAEADgWIQgAcCxCEADg\nWP8Pk0JLs9d50PkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153ca44390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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8/bsEos9g6Dx2T+23dB4TQpS+tWtPEBS0B4ChQ5uzaFEPKld2VDkqURSjfu2a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\nW2AQwaEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cfd91d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cabf470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"workingDataset =dataset.loc[:, ['Survived', 'Pclass','Sex','Age', \n",
" 'SibSp', 'Parch', 'Ticket', 'Fare','Cabin']]\n",
"\n",
"imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
"workingDataset[\"Age\"]=imp.fit_transform(workingDataset[[\"Age\"]]).ravel()\n",
"\n",
"#enconding ticket and cabin\n",
"workingDataset = pd.get_dummies(workingDataset)\n",
"\n",
"#dummy trap\n",
"workingDataset = workingDataset.drop(workingDataset.loc[:,['Sex_male']], axis=1) \n",
"\n",
"workingData = workingDataset.values\n",
"X = workingData[:, 1:]\n",
"y = workingData[:, 0]\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"from pandas import DataFrame\n",
"sc = StandardScaler()\n",
"X = sc.fit_transform(X)\n",
"#y = sc.fit_transform(y.reshape(-1,1))\n",
"# rebuild feature's dataframe with normalized data for graphs purpose\n",
"preprocessedDataset = DataFrame(data=X)\n",
"\n",
"preprocessedDataset['Survived'] = y.astype(int)\n",
"#preprocessedDataset\n",
"\n",
"def split_and_train(X,y, test_size, classifier):\n",
" X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
" test_size = test_size, random_state = 0)\n",
" y_train = y_train.astype(int)\n",
" y_test = y_test.astype(int)\n",
" classifier.fit(X_train, y_train)\n",
" y_test_pred = classifier.predict(X_test)\n",
" return y_test_pred\n",
"\n",
"classifier = LogisticRegression(random_state = 0)\n",
"\n",
"split_and_train(X = X,y = y, test_size = 0.30, classifier = classifier)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred, target_names = ['Not Survived', 'Survived'])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"_cell_guid": "33865304-e50c-1dc5-f255-08a6592ef10b"
},
"outputs": [
{
"data": {
"text/plain": [
"<module 'matplotlib.pyplot' from '/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py'>"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8VKQW/hXefz1OK0hz2BVelgABk7tNLncM2xLaWo3WyTUiV2GoUzn5xebT0kSI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\nJ/4CYWsFXOfF+eEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153c062278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_learning_curve(classifier, \"Test\", X, y, (0.7, 1.01), cv=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "9709dce4-d7aa-0ec4-79b9-942dca203f9d"
},
"source": [
"let's replace Fare (having some outliers) with mean of SibSp and Parch"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"_cell_guid": "b989c732-845c-a208-ac99-813bcf36e444"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Survived', 'Pclass', 'Age', 'Relatives', 'Sex_female', 'Ticket_110152', 'Ticket_110413', 'Ticket_110465', 'Ticket_110564', 'Ticket_110813', 'Ticket_111240', 'Ticket_111320', 'Ticket_111361', 'Ticket_111369', 'Ticket_111426', 'Ticket_111428', 'Ticket_112050', 'Ticket_112053', 'Ticket_112059', 'Ticket_112277', 'Ticket_113043', 'Ticket_113050', 'Ticket_113051', 'Ticket_113055', 'Ticket_113059', 'Ticket_113501', 'Ticket_113503', 'Ticket_113505', 'Ticket_113509', 'Ticket_113514', 'Ticket_113572', 'Ticket_113760', 'Ticket_113773', 'Ticket_113776', 'Ticket_113781', 'Ticket_113783', 'Ticket_113784', 'Ticket_113786', 'Ticket_113787', 'Ticket_113788', 'Ticket_113789', 'Ticket_113792', 'Ticket_113794', 'Ticket_113798', 'Ticket_113800', 'Ticket_113803', 'Ticket_113804', 'Ticket_113806', 'Ticket_113807', 'Ticket_11668', 'Ticket_11751', 'Ticket_11752', 'Ticket_11753', 'Ticket_11755', 'Ticket_11765', 'Ticket_11767', 'Ticket_11769', 'Ticket_11771', 'Ticket_11813', 'Ticket_11967', 'Ticket_12233', 'Ticket_12749', 'Ticket_13049', 'Ticket_13213', 'Ticket_13214', 'Ticket_13502', 'Ticket_13507', 'Ticket_13509', 'Ticket_13567', 'Ticket_13568', 'Ticket_14973', 'Ticket_1601', 'Ticket_16966', 'Ticket_17421', 'Ticket_17453', 'Ticket_17463', 'Ticket_17465', 'Ticket_17466', 'Ticket_17474', 'Ticket_17764', 'Ticket_19877', 'Ticket_19928', 'Ticket_19943', 'Ticket_19950', 'Ticket_19952', 'Ticket_19972', 'Ticket_19996', 'Ticket_2003', 'Ticket_211536', 'Ticket_21440', 'Ticket_218629', 'Ticket_219533', 'Ticket_220367', 'Ticket_220845', 'Ticket_2223', 'Ticket_223596', 'Ticket_226875', 'Ticket_228414', 'Ticket_229236', 'Ticket_230080', 'Ticket_230136', 'Ticket_230433', 'Ticket_230434', 'Ticket_231919', 'Ticket_231945', 'Ticket_233639', 'Ticket_233866', 'Ticket_234360', 'Ticket_234604', 'Ticket_234686', 'Ticket_234818', 'Ticket_236171', 'Ticket_236852', 'Ticket_236853', 'Ticket_237442', 'Ticket_237565', 'Ticket_237668', 'Ticket_237671', 'Ticket_237736', 'Ticket_237789', 'Ticket_237798', 'Ticket_239865', 'Ticket_240929', 'Ticket_24160', 'Ticket_243847', 'Ticket_243880', 'Ticket_244252', 'Ticket_244270', 'Ticket_244278', 'Ticket_244310', 'Ticket_244358', 'Ticket_244361', 'Ticket_244367', 'Ticket_248698', 'Ticket_248706', 'Ticket_248723', 'Ticket_248727', 'Ticket_248731', 'Ticket_248733', 'Ticket_248738', 'Ticket_248740', 'Ticket_248747', 'Ticket_250643', 'Ticket_250644', 'Ticket_250646', 'Ticket_250647', 'Ticket_250648', 'Ticket_250649', 'Ticket_250651', 'Ticket_250652', 'Ticket_250653', 'Ticket_250655', 'Ticket_2620', 'Ticket_2623', 'Ticket_2625', 'Ticket_2627', 'Ticket_2628', 'Ticket_26360', 'Ticket_2648', 'Ticket_2650', 'Ticket_2651', 'Ticket_2653', 'Ticket_2659', 'Ticket_2663', 'Ticket_2665', 'Ticket_2666', 'Ticket_2667', 'Ticket_2669', 'Ticket_26707', 'Ticket_2672', 'Ticket_2678', 'Ticket_2680', 'Ticket_2683', 'Ticket_2685', 'Ticket_2687', 'Ticket_2690', 'Ticket_2691', 'Ticket_2693', 'Ticket_2694', 'Ticket_2695', 'Ticket_2697', 'Ticket_2699', 'Ticket_27042', 'Ticket_27267', 'Ticket_27849', 'Ticket_28134', 'Ticket_28206', 'Ticket_28213', 'Ticket_28220', 'Ticket_28228', 'Ticket_28403', 'Ticket_28424', 'Ticket_28425', 'Ticket_28551', 'Ticket_28664', 'Ticket_28665', 'Ticket_29011', 'Ticket_2908', 'Ticket_29103', 'Ticket_29104', 'Ticket_29105', 'Ticket_29106', 'Ticket_29108', 'Ticket_2926', 'Ticket_29750', 'Ticket_29751', 'Ticket_3101264', 'Ticket_3101265', 'Ticket_3101267', 'Ticket_3101276', 'Ticket_3101277', 'Ticket_3101278', 'Ticket_3101281', 'Ticket_3101295', 'Ticket_3101296', 'Ticket_3101298', 'Ticket_31027', 'Ticket_31028', 'Ticket_312992', 'Ticket_31418', 'Ticket_315082', 'Ticket_315084', 'Ticket_315086', 'Ticket_315088', 'Ticket_315089', 'Ticket_315090', 'Ticket_315093', 'Ticket_315094', 'Ticket_315096', 'Ticket_315097', 'Ticket_315098', 'Ticket_315151', 'Ticket_315153', 'Ticket_323951', 'Ticket_324669', 'Ticket_330923', 'Ticket_330958', 'Ticket_335097', 'Ticket_33638', 'Ticket_336439', 'Ticket_341826', 'Ticket_34218', 'Ticket_342826', 'Ticket_343120', 'Ticket_343275', 'Ticket_343276', 'Ticket_345364', 'Ticket_345572', 'Ticket_345763', 'Ticket_345764', 'Ticket_345765', 'Ticket_345767', 'Ticket_345769', 'Ticket_345770', 'Ticket_345773', 'Ticket_345774', 'Ticket_345778', 'Ticket_345779', 'Ticket_345780', 'Ticket_345781', 'Ticket_345783', 'Ticket_3460', 'Ticket_347054', 'Ticket_347060', 'Ticket_347061', 'Ticket_347062', 'Ticket_347063', 'Ticket_347064', 'Ticket_347067', 'Ticket_347068', 'Ticket_347069', 'Ticket_347071', 'Ticket_347073', 'Ticket_347074', 'Ticket_347076', 'Ticket_347077', 'Ticket_347078', 'Ticket_347080', 'Ticket_347081', 'Ticket_347082', 'Ticket_347083', 'Ticket_347085', 'Ticket_347087', 'Ticket_347088', 'Ticket_347089', 'Ticket_3474', 'Ticket_347464', 'Ticket_347466', 'Ticket_347468', 'Ticket_347470', 'Ticket_347742', 'Ticket_347743', 'Ticket_348121', 'Ticket_348123', 'Ticket_348124', 'Ticket_349203', 'Ticket_349204', 'Ticket_349205', 'Ticket_349206', 'Ticket_349207', 'Ticket_349209', 'Ticket_349210', 'Ticket_349212', 'Ticket_349213', 'Ticket_349219', 'Ticket_349224', 'Ticket_349228', 'Ticket_349231', 'Ticket_349233', 'Ticket_349236', 'Ticket_349237', 'Ticket_349239', 'Ticket_349240', 'Ticket_349241', 'Ticket_349242', 'Ticket_349243', 'Ticket_349244', 'Ticket_349245', 'Ticket_349246', 'Ticket_349247', 'Ticket_349248', 'Ticket_349249', 'Ticket_349251', 'Ticket_349252', 'Ticket_349256', 'Ticket_349257', 'Ticket_349909', 'Ticket_349910', 'Ticket_349912', 'Ticket_350025', 'Ticket_350026', 'Ticket_350029', 'Ticket_350034', 'Ticket_350035', 'Ticket_350036', 'Ticket_350042', 'Ticket_350043', 'Ticket_350046', 'Ticket_350047', 'Ticket_350048', 'Ticket_350050', 'Ticket_350052', 'Ticket_350060', 'Ticket_350404', 'Ticket_350406', 'Ticket_350407', 'Ticket_350417', 'Ticket_35273', 'Ticket_35281', 'Ticket_35851', 'Ticket_358585', 'Ticket_363291', 'Ticket_363294', 'Ticket_363592', 'Ticket_364499', 'Ticket_364500', 'Ticket_364506', 'Ticket_364511', 'Ticket_364512', 'Ticket_364516', 'Ticket_364846', 'Ticket_364849', 'Ticket_364850', 'Ticket_365222', 'Ticket_365226', 'Ticket_367231', 'Ticket_367232', 'Ticket_36864', 'Ticket_36928', 'Ticket_36947', 'Ticket_36963', 'Ticket_36967', 'Ticket_36973', 'Ticket_370129', 'Ticket_370369', 'Ticket_370373', 'Ticket_370376', 'Ticket_371362', 'Ticket_373450', 'Ticket_374887', 'Ticket_376566', 'Ticket_382651', 'Ticket_382652', 'Ticket_392091', 'Ticket_392096', 'Ticket_4134', 'Ticket_4135', 'Ticket_4136', 'Ticket_4137', 'Ticket_4138', 'Ticket_4579', 'Ticket_54636', 'Ticket_5727', 'Ticket_6563', 'Ticket_693', 'Ticket_695', 'Ticket_7267', 'Ticket_7534', 'Ticket_7540', 'Ticket_7545', 'Ticket_7546', 'Ticket_7552', 'Ticket_7553', 'Ticket_7598', 'Ticket_8471', 'Ticket_8475', 'Ticket_A./5. 2152', 'Ticket_A.5. 11206', 'Ticket_A.5. 18509', 'Ticket_A/4 45380', 'Ticket_A/4 48871', 'Ticket_A/4. 20589', 'Ticket_A/4. 39886', 'Ticket_A/5 21171', 'Ticket_A/5 21172', 'Ticket_A/5 21173', 'Ticket_A/5 21174', 'Ticket_A/5 3536', 'Ticket_A/5 3540', 'Ticket_A/5 3594', 'Ticket_A/5 3902', 'Ticket_A/5. 10482', 'Ticket_A/5. 13032', 'Ticket_A/5. 2151', 'Ticket_A/5. 3336', 'Ticket_A/5. 3337', 'Ticket_A/5. 851', 'Ticket_C 17369', 'Ticket_C 4001', 'Ticket_C 7075', 'Ticket_C 7076', 'Ticket_C 7077', 'Ticket_C.A. 17248', 'Ticket_C.A. 18723', 'Ticket_C.A. 2315', 'Ticket_C.A. 24579', 'Ticket_C.A. 24580', 'Ticket_C.A. 2673', 'Ticket_C.A. 29178', 'Ticket_C.A. 29395', 'Ticket_C.A. 29566', 'Ticket_C.A. 31026', 'Ticket_C.A. 31921', 'Ticket_C.A. 33111', 'Ticket_C.A. 33112', 'Ticket_C.A. 33595', 'Ticket_C.A. 34260', 'Ticket_C.A. 34651', 'Ticket_C.A. 37671', 'Ticket_C.A. 5547', 'Ticket_C.A./SOTON 34068', 'Ticket_CA 2144', 'Ticket_CA. 2314', 'Ticket_F.C. 12750', 'Ticket_F.C.C. 13528', 'Ticket_F.C.C. 13529', 'Ticket_F.C.C. 13531', 'Ticket_LINE', 'Ticket_P/PP 3381', 'Ticket_PC 17473', 'Ticket_PC 17474', 'Ticket_PC 17475', 'Ticket_PC 17476', 'Ticket_PC 17477', 'Ticket_PC 17482', 'Ticket_PC 17485', 'Ticket_PC 17558', 'Ticket_PC 17569', 'Ticket_PC 17572', 'Ticket_PC 17582', 'Ticket_PC 17590', 'Ticket_PC 17592', 'Ticket_PC 17593', 'Ticket_PC 17595', 'Ticket_PC 17596', 'Ticket_PC 17597', 'Ticket_PC 17599', 'Ticket_PC 17601', 'Ticket_PC 17603', 'Ticket_PC 17604', 'Ticket_PC 17608', 'Ticket_PC 17609', 'Ticket_PC 17610', 'Ticket_PC 17611', 'Ticket_PC 17754', 'Ticket_PC 17755', 'Ticket_PC 17756', 'Ticket_PC 17757', 'Ticket_PC 17758', 'Ticket_PC 17759', 'Ticket_PC 17760', 'Ticket_PC 17761', 'Ticket_PP 4348', 'Ticket_PP 9549', 'Ticket_S.C./PARIS 2079', 'Ticket_S.O./P.P. 3', 'Ticket_S.O.C. 14879', 'Ticket_S.O.P. 1166', 'Ticket_S.P. 3464', 'Ticket_S.W./PP 752', 'Ticket_SC 1748', 'Ticket_SC/AH 29037', 'Ticket_SC/AH 3085', 'Ticket_SC/AH Basle 541', 'Ticket_SC/PARIS 2133', 'Ticket_SC/PARIS 2149', 'Ticket_SC/PARIS 2167', 'Ticket_SC/Paris 2123', 'Ticket_SC/Paris 2163', 'Ticket_SCO/W 1585', 'Ticket_SO/C 14885', 'Ticket_SOTON/O.Q. 3101306', 'Ticket_SOTON/O.Q. 3101307', 'Ticket_SOTON/O.Q. 3101310', 'Ticket_SOTON/O.Q. 3101311', 'Ticket_SOTON/O.Q. 3101312', 'Ticket_SOTON/O.Q. 392078', 'Ticket_SOTON/O2 3101272', 'Ticket_SOTON/O2 3101287', 'Ticket_SOTON/OQ 3101317', 'Ticket_SOTON/OQ 392076', 'Ticket_SOTON/OQ 392089', 'Ticket_SOTON/OQ 392090', 'Ticket_STON/O 2. 3101269', 'Ticket_STON/O 2. 3101273', 'Ticket_STON/O 2. 3101274', 'Ticket_STON/O 2. 3101275', 'Ticket_STON/O 2. 3101280', 'Ticket_STON/O 2. 3101285', 'Ticket_STON/O 2. 3101286', 'Ticket_STON/O 2. 3101288', 'Ticket_STON/O 2. 3101289', 'Ticket_STON/O 2. 3101292', 'Ticket_STON/O 2. 3101293', 'Ticket_STON/O 2. 3101294', 'Ticket_STON/O2. 3101271', 'Ticket_STON/O2. 3101279', 'Ticket_STON/O2. 3101282', 'Ticket_STON/O2. 3101283', 'Ticket_STON/O2. 3101290', 'Ticket_SW/PP 751', 'Ticket_W./C. 14258', 'Ticket_W./C. 14263', 'Ticket_W./C. 6608', 'Ticket_W.E.P. 5734', 'Ticket_W/C 14208', 'Ticket_WE/P 5735', 'Cabin_A10', 'Cabin_A16', 'Cabin_A20', 'Cabin_A23', 'Cabin_A24', 'Cabin_A26', 'Cabin_A31', 'Cabin_A34', 'Cabin_A36', 'Cabin_A5', 'Cabin_A6', 'Cabin_A7', 'Cabin_B101', 'Cabin_B18', 'Cabin_B19', 'Cabin_B20', 'Cabin_B22', 'Cabin_B28', 'Cabin_B3', 'Cabin_B30', 'Cabin_B35', 'Cabin_B37', 'Cabin_B38', 'Cabin_B39', 'Cabin_B4', 'Cabin_B41', 'Cabin_B42', 'Cabin_B49', 'Cabin_B5', 'Cabin_B50', 'Cabin_B51 B53 B55', 'Cabin_B57 B59 B63 B66', 'Cabin_B58 B60', 'Cabin_B69', 'Cabin_B71', 'Cabin_B73', 'Cabin_B77', 'Cabin_B79', 'Cabin_B80', 'Cabin_B82 B84', 'Cabin_B86', 'Cabin_B94', 'Cabin_B96 B98', 'Cabin_C101', 'Cabin_C103', 'Cabin_C104', 'Cabin_C110', 'Cabin_C111', 'Cabin_C118', 'Cabin_C123', 'Cabin_C124', 'Cabin_C125', 'Cabin_C126', 'Cabin_C148', 'Cabin_C2', 'Cabin_C22 C26', 'Cabin_C23 C25 C27', 'Cabin_C30', 'Cabin_C32', 'Cabin_C45', 'Cabin_C46', 'Cabin_C49', 'Cabin_C50', 'Cabin_C52', 'Cabin_C54', 'Cabin_C62 C64', 'Cabin_C65', 'Cabin_C68', 'Cabin_C7', 'Cabin_C70', 'Cabin_C78', 'Cabin_C82', 'Cabin_C83', 'Cabin_C85', 'Cabin_C86', 'Cabin_C87', 'Cabin_C90', 'Cabin_C91', 'Cabin_C92', 'Cabin_C93', 'Cabin_C99', 'Cabin_D', 'Cabin_D10 D12', 'Cabin_D11', 'Cabin_D15', 'Cabin_D17', 'Cabin_D19', 'Cabin_D20', 'Cabin_D26', 'Cabin_D28', 'Cabin_D30', 'Cabin_D33', 'Cabin_D35', 'Cabin_D36', 'Cabin_D37', 'Cabin_D46', 'Cabin_D47', 'Cabin_D48', 'Cabin_D49', 'Cabin_D50', 'Cabin_D56', 'Cabin_D6', 'Cabin_D7', 'Cabin_D9', 'Cabin_E10', 'Cabin_E101', 'Cabin_E12', 'Cabin_E121', 'Cabin_E17', 'Cabin_E24', 'Cabin_E25', 'Cabin_E31', 'Cabin_E33', 'Cabin_E34', 'Cabin_E36', 'Cabin_E38', 'Cabin_E40', 'Cabin_E44', 'Cabin_E46', 'Cabin_E49', 'Cabin_E50', 'Cabin_E58', 'Cabin_E63', 'Cabin_E67', 'Cabin_E68', 'Cabin_E77', 'Cabin_E8', 'Cabin_F G63', 'Cabin_F G73', 'Cabin_F2', 'Cabin_F33', 'Cabin_F4', 'Cabin_G6', 'Cabin_T']\n",
"Confusion Matrix\n",
"[[107 18]\n",
" [ 19 71]]\n",
"\n",
"\n",
"Report\n",
" precision recall f1-score support\n",
"\n",
"Not Survived 0.85 0.86 0.85 125\n",
" Survived 0.80 0.79 0.79 90\n",
"\n",
" avg / total 0.83 0.83 0.83 215\n",
"\n"
]
},
{
"data": {
"image/png": 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ffafFixfr6NGjGjJkiHw+X8X3fv51dRGCAAAjAnU6NCIiQh07dpTb7VbTpk1Vt25dhYaG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\nWP8Pk0JLs9d50PkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d05a208>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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8/bsEos9g6Dx2T+23dB4TQpS+tWtPEBS0B4ChQ5uzaFEPKld2VDkqURSjfu2a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\nW2AQwaEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153d05a5c0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f153cb94588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"workingDataset = dataset.loc[:, ['Survived','Pclass','Sex','Age', \n",
" 'SibSp', 'Parch', 'Ticket', 'Fare','Cabin']]\n",
"\n",
"# get rid of age nan rows (first approach)\n",
"workingDataset = workingDataset[np.isfinite(workingDataset['Age'])]\n",
"\n",
"\n",
"workingDataset['Relatives'] = (workingDataset['SibSp'] + workingDataset['Parch']) / 2\n",
"\n",
"workingDataset = workingDataset.drop(workingDataset.loc[:, ['SibSp', 'Parch', 'Fare']], axis=1)\n",
"\n",
"workingDataset = pd.get_dummies(workingDataset)\n",
"\n",
"workingDataset = workingDataset.drop(workingDataset.loc[:, ['Sex_male']], axis=1)\n",
"\n",
"\n",
"print(list(workingDataset.columns.values))\n",
"\n",
"workingData = workingDataset.values\n",
"X = workingData[:, 1:]\n",
"y = workingData[:, 0]\n",
"\n",
"\n",
"classifier = LogisticRegression(random_state = 0)\n",
"\n",
"split_and_train(X = X,y = y, test_size = 0.30, classifier = classifier)\n",
"\n",
"evaluate_classifier(y_test, y_test_pred, target_names = ['Not Survived', 'Survived'])\n",
"\n",
"\n",
"#cabins = dataset[dataset.Cabin.notnull()]\n",
"#cabins[['Pclass','Cabin','Parch', 'SibSp']]\n",
"\n",
"#corr_heatmap(cabins[['Pclass','Cabin','Parch', 'SibSp']])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2274e6e5-e126-2b66-64c7-4d861d051497"
},
"source": [
"Let's upload result and see what happens\n",
"\n",
"Oh wait, I cannot do this because using "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"_cell_guid": "cc87d3bf-b1a4-574c-3d61-a53edecba926"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 418 entries, 0 to 417\n",
"Data columns (total 11 columns):\n",
"PassengerId 418 non-null int64\n",
"Pclass 418 non-null int64\n",
"Name 418 non-null object\n",
"Sex 418 non-null object\n",
"Age 332 non-null float64\n",
"SibSp 418 non-null int64\n",
"Parch 418 non-null int64\n",
"Ticket 418 non-null object\n",
"Fare 417 non-null float64\n",
"Cabin 91 non-null object\n",
"Embarked 418 non-null object\n",
"dtypes: float64(2), int64(4), object(5)\n",
"memory usage: 36.0+ KB\n",
"['Pclass', 'Age', 'Relatives', 'Sex_female', 'Sex_male', 'Ticket_110469', 'Ticket_110489', 'Ticket_110813', 'Ticket_111163', 'Ticket_112051', 'Ticket_112058', 'Ticket_112377', 'Ticket_112378', 'Ticket_112901', 'Ticket_113038', 'Ticket_113044', 'Ticket_113054', 'Ticket_113059', 'Ticket_113503', 'Ticket_113509', 'Ticket_113773', 'Ticket_113778', 'Ticket_113780', 'Ticket_113781', 'Ticket_113790', 'Ticket_113791', 'Ticket_113795', 'Ticket_113796', 'Ticket_113801', 'Ticket_11753', 'Ticket_11765', 'Ticket_11767', 'Ticket_11769', 'Ticket_11770', 'Ticket_11778', 'Ticket_11813', 'Ticket_1222', 'Ticket_12749', 'Ticket_13050', 'Ticket_13236', 'Ticket_13508', 'Ticket_13567', 'Ticket_13695', 'Ticket_13905', 'Ticket_1601', 'Ticket_16966', 'Ticket_17463', 'Ticket_17464', 'Ticket_17475', 'Ticket_17765', 'Ticket_17770', 'Ticket_19877', 'Ticket_19924', 'Ticket_19928', 'Ticket_19950', 'Ticket_2003', 'Ticket_211535', 'Ticket_21228', 'Ticket_21332', 'Ticket_220844', 'Ticket_220845', 'Ticket_226875', 'Ticket_228414', 'Ticket_230136', 'Ticket_233478', 'Ticket_233734', 'Ticket_235509', 'Ticket_236853', 'Ticket_236854', 'Ticket_237216', 'Ticket_237249', 'Ticket_237393', 'Ticket_237670', 'Ticket_237734', 'Ticket_237735', 'Ticket_237789', 'Ticket_239059', 'Ticket_240261', 'Ticket_240276', 'Ticket_24065', 'Ticket_24160', 'Ticket_242963', 'Ticket_244346', 'Ticket_244358', 'Ticket_244360', 'Ticket_244368', 'Ticket_248659', 'Ticket_248726', 'Ticket_248734', 'Ticket_248738', 'Ticket_248744', 'Ticket_248746', 'Ticket_250650', 'Ticket_250651', 'Ticket_2543', 'Ticket_2621', 'Ticket_2622', 'Ticket_2625', 'Ticket_26360', 'Ticket_2650', 'Ticket_2652', 'Ticket_2653', 'Ticket_2654', 'Ticket_2655', 'Ticket_2656', 'Ticket_2657', 'Ticket_2658', 'Ticket_2660', 'Ticket_2661', 'Ticket_2662', 'Ticket_2668', 'Ticket_2670', 'Ticket_26707', 'Ticket_2673', 'Ticket_2675', 'Ticket_2676', 'Ticket_2678', 'Ticket_2679', 'Ticket_2680', 'Ticket_2681', 'Ticket_2682', 'Ticket_2684', 'Ticket_2688', 'Ticket_2689', 'Ticket_2692', 'Ticket_2696', 'Ticket_2698', 'Ticket_28004', 'Ticket_28034', 'Ticket_28133', 'Ticket_28220', 'Ticket_28221', 'Ticket_28404', 'Ticket_28664', 'Ticket_28666', 'Ticket_29103', 'Ticket_29105', 'Ticket_29107', 'Ticket_2926', 'Ticket_29750', 'Ticket_3101266', 'Ticket_3101295', 'Ticket_3101297', 'Ticket_3101298', 'Ticket_315083', 'Ticket_315085', 'Ticket_315087', 'Ticket_315091', 'Ticket_315092', 'Ticket_315095', 'Ticket_315152', 'Ticket_315153', 'Ticket_315154', 'Ticket_32302', 'Ticket_329944', 'Ticket_330844', 'Ticket_330910', 'Ticket_330911', 'Ticket_330920', 'Ticket_330924', 'Ticket_330963', 'Ticket_330968', 'Ticket_330971', 'Ticket_330972', 'Ticket_334914', 'Ticket_334915', 'Ticket_335432', 'Ticket_33638', 'Ticket_3410', 'Ticket_342441', 'Ticket_342684', 'Ticket_342712', 'Ticket_343271', 'Ticket_345498', 'Ticket_345501', 'Ticket_345572', 'Ticket_345763', 'Ticket_345768', 'Ticket_345771', 'Ticket_345775', 'Ticket_3470', 'Ticket_347065', 'Ticket_347066', 'Ticket_347070', 'Ticket_347072', 'Ticket_347075', 'Ticket_347077', 'Ticket_347079', 'Ticket_347080', 'Ticket_347086', 'Ticket_347090', 'Ticket_347091', 'Ticket_347465', 'Ticket_347467', 'Ticket_347469', 'Ticket_347471', 'Ticket_348122', 'Ticket_348125', 'Ticket_349202', 'Ticket_349211', 'Ticket_349220', 'Ticket_349226', 'Ticket_349229', 'Ticket_349230', 'Ticket_349232', 'Ticket_349235', 'Ticket_349238', 'Ticket_349250', 'Ticket_349255', 'Ticket_349256', 'Ticket_349909', 'Ticket_349910', 'Ticket_349911', 'Ticket_350026', 'Ticket_350033', 'Ticket_350045', 'Ticket_350053', 'Ticket_350054', 'Ticket_350403', 'Ticket_350405', 'Ticket_350408', 'Ticket_350409', 'Ticket_350410', 'Ticket_350416', 'Ticket_359306', 'Ticket_359309', 'Ticket_363272', 'Ticket_363611', 'Ticket_364498', 'Ticket_364856', 'Ticket_364858', 'Ticket_364859', 'Ticket_365235', 'Ticket_365237', 'Ticket_36568', 'Ticket_366713', 'Ticket_367226', 'Ticket_367227', 'Ticket_368364', 'Ticket_368402', 'Ticket_368573', 'Ticket_368702', 'Ticket_368783', 'Ticket_36928', 'Ticket_3701', 'Ticket_370129', 'Ticket_370368', 'Ticket_370371', 'Ticket_370374', 'Ticket_371109', 'Ticket_371362', 'Ticket_376563', 'Ticket_376566', 'Ticket_382650', 'Ticket_382652', 'Ticket_382653', 'Ticket_383123', 'Ticket_383162', 'Ticket_386525', 'Ticket_392091', 'Ticket_392095', 'Ticket_4133', 'Ticket_65305', 'Ticket_680', 'Ticket_694', 'Ticket_7266', 'Ticket_7538', 'Ticket_7548', 'Ticket_7935', 'Ticket_9232', 'Ticket_A. 2. 39186', 'Ticket_A./5. 3338', 'Ticket_A.5. 3236', 'Ticket_A/4 31416', 'Ticket_A/4 48871', 'Ticket_A/4 48873', 'Ticket_A/5 1478', 'Ticket_A/5 21175', 'Ticket_A/5. 3337', 'Ticket_A/5. 851', 'Ticket_AQ/3. 30631', 'Ticket_AQ/4 3130', 'Ticket_C 17368', 'Ticket_C 4001', 'Ticket_C.A. 15185', 'Ticket_C.A. 2315', 'Ticket_C.A. 2673', 'Ticket_C.A. 30769', 'Ticket_C.A. 31029', 'Ticket_C.A. 31030', 'Ticket_C.A. 33112', 'Ticket_C.A. 33595', 'Ticket_C.A. 34050', 'Ticket_C.A. 34644', 'Ticket_C.A. 34651', 'Ticket_C.A. 37671', 'Ticket_C.A. 42795', 'Ticket_C.A. 49867', 'Ticket_C.A. 6212', 'Ticket_CA 2144', 'Ticket_CA 31352', 'Ticket_CA. 2343', 'Ticket_F.C. 12750', 'Ticket_F.C. 12998', 'Ticket_F.C.C. 13528', 'Ticket_F.C.C. 13534', 'Ticket_F.C.C. 13540', 'Ticket_LP 1588', 'Ticket_PC 17483', 'Ticket_PC 17531', 'Ticket_PC 17558', 'Ticket_PC 17562', 'Ticket_PC 17569', 'Ticket_PC 17580', 'Ticket_PC 17585', 'Ticket_PC 17591', 'Ticket_PC 17592', 'Ticket_PC 17594', 'Ticket_PC 17597', 'Ticket_PC 17598', 'Ticket_PC 17599', 'Ticket_PC 17603', 'Ticket_PC 17606', 'Ticket_PC 17607', 'Ticket_PC 17608', 'Ticket_PC 17613', 'Ticket_PC 17755', 'Ticket_PC 17756', 'Ticket_PC 17757', 'Ticket_PC 17758', 'Ticket_PC 17759', 'Ticket_PC 17760', 'Ticket_PC 17761', 'Ticket_PP 9549', 'Ticket_S.C./PARIS 2079', 'Ticket_S.O./P.P. 2', 'Ticket_S.O./P.P. 251', 'Ticket_S.O./P.P. 752', 'Ticket_S.O.C. 14879', 'Ticket_SC 14888', 'Ticket_SC/A.3 2861', 'Ticket_SC/A4 23568', 'Ticket_SC/AH 29037', 'Ticket_SC/AH 3085', 'Ticket_SC/PARIS 2147', 'Ticket_SC/PARIS 2148', 'Ticket_SC/PARIS 2159', 'Ticket_SC/PARIS 2166', 'Ticket_SC/PARIS 2167', 'Ticket_SC/PARIS 2168', 'Ticket_SC/Paris 2123', 'Ticket_SOTON/O.Q. 3101262', 'Ticket_SOTON/O.Q. 3101263', 'Ticket_SOTON/O.Q. 3101308', 'Ticket_SOTON/O.Q. 3101309', 'Ticket_SOTON/O.Q. 3101314', 'Ticket_SOTON/O.Q. 3101315', 'Ticket_SOTON/O2 3101284', 'Ticket_SOTON/OQ 392083', 'Ticket_STON/O 2. 3101268', 'Ticket_STON/O 2. 3101291', 'Ticket_STON/O2. 3101270', 'Ticket_STON/OQ. 369943', 'Ticket_W./C. 14260', 'Ticket_W./C. 14266', 'Ticket_W./C. 6607', 'Ticket_W./C. 6608', 'Ticket_W.E.P. 5734', 'Cabin_A11', 'Cabin_A18', 'Cabin_A21', 'Cabin_A29', 'Cabin_A34', 'Cabin_A9', 'Cabin_B10', 'Cabin_B11', 'Cabin_B24', 'Cabin_B26', 'Cabin_B36', 'Cabin_B41', 'Cabin_B45', 'Cabin_B51 B53 B55', 'Cabin_B52 B54 B56', 'Cabin_B57 B59 B63 B66', 'Cabin_B58 B60', 'Cabin_B61', 'Cabin_B69', 'Cabin_B71', 'Cabin_B78', 'Cabin_C101', 'Cabin_C105', 'Cabin_C106', 'Cabin_C116', 'Cabin_C130', 'Cabin_C132', 'Cabin_C22 C26', 'Cabin_C23 C25 C27', 'Cabin_C28', 'Cabin_C31', 'Cabin_C32', 'Cabin_C39', 'Cabin_C46', 'Cabin_C51', 'Cabin_C53', 'Cabin_C54', 'Cabin_C55 C57', 'Cabin_C6', 'Cabin_C62 C64', 'Cabin_C7', 'Cabin_C78', 'Cabin_C80', 'Cabin_C85', 'Cabin_C86', 'Cabin_C89', 'Cabin_C97', 'Cabin_D', 'Cabin_D10 D12', 'Cabin_D15', 'Cabin_D19', 'Cabin_D21', 'Cabin_D22', 'Cabin_D28', 'Cabin_D30', 'Cabin_D34', 'Cabin_D37', 'Cabin_D38', 'Cabin_D40', 'Cabin_D43', 'Cabin_E31', 'Cabin_E34', 'Cabin_E39 E41', 'Cabin_E45', 'Cabin_E46', 'Cabin_E50', 'Cabin_E52', 'Cabin_E60', 'Cabin_F', 'Cabin_F E46', 'Cabin_F E57', 'Cabin_F G63', 'Cabin_F2', 'Cabin_F33', 'Cabin_F4', 'Cabin_G6']\n"
]
},
{
"ename": "ValueError",
"evalue": "X has 443 features per sample; expecting 680",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-48-96d8a949e0f1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 33\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworkingDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclassifier\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m \u001b[0;34m'PassengerId'\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Survived'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 318\u001b[0m \u001b[0mPredicted\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0mper\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 319\u001b[0m \"\"\"\n\u001b[0;32m--> 320\u001b[0;31m \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecision_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 321\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mdecision_function\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 300\u001b[0m raise ValueError(\"X has %d features per sample; expecting %d\"\n\u001b[0;32m--> 301\u001b[0;31m % (X.shape[1], n_features))\n\u001b[0m\u001b[1;32m 302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 303\u001b[0m scores = safe_sparse_dot(X, self.coef_.T,\n",
"\u001b[0;31mValueError\u001b[0m: X has 443 features per sample; expecting 680"
]
}
],
"source": [
"\n",
"data_test = pd.read_csv('../input/test.csv')\n",
"\n",
"data_test.info()\n",
"data_test.describe()\n",
"data_test.isnull().sum()\n",
"\n",
"#workingDataset = data_test.loc[:, [1,4,5,6,7,8,9]]\n",
"\n",
"workingDataset = data_test.loc[:, ['Pclass','Sex','Age', \n",
" 'SibSp', 'Parch', 'Ticket', 'Fare','Cabin']]\n",
"\n",
"\n",
"\n",
"imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
"workingDataset[\"Age\"]=imp.fit_transform(workingDataset[[\"Age\"]]).ravel()\n",
"\n",
"workingDataset['Relatives'] = (workingDataset['SibSp'] + workingDataset['Parch']) / 2\n",
"\n",
"\n",
"\n",
"workingDataset = workingDataset.drop(workingDataset.loc[:, ['SibSp', 'Parch', 'Fare']], axis=1)\n",
"\n",
"\n",
"workingDataset = pd.get_dummies(workingDataset)\n",
"\n",
"print(list(workingDataset.columns.values))\n",
"\n",
"workingDataset = workingDataset.drop(workingDataset.loc[:, ['Sex_male']], axis=1)\n",
"\n",
"\n",
"\n",
"X = workingDataset.values\n",
"\n",
"predictions = classifier.predict(X)\n",
"\n",
"output = pd.DataFrame({ 'PassengerId' : ids, 'Survived': predictions })\n",
"# output.to_csv('titanic-predictions.csv', index = False)\n",
"output.head()"
]
}
],
"metadata": {
"_change_revision": 504,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| 0001/159/1159967.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6cdba187-46f1-62c6-2abf-5cf110f33762"
},
"source": [
"Word2Vec Skipgram model with Tensorflow"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "febb73e0-2f29-d376-6c45-c9b37c890764"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"%matplotlib inline\n",
"from __future__ import print_function\n",
"import collections\n",
"import math\n",
"import numpy as np\n",
"import os\n",
"import random\n",
"import tensorflow as tf\n",
"import zipfile\n",
"from matplotlib import pylab\n",
"from six.moves import range\n",
"from six.moves.urllib.request import urlretrieve\n",
"from sklearn.manifold import TSNE\n",
"import pandas as pd \n",
"import six\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "41612e08-a414-f5b2-4fad-da7e41617319"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data size 404290\n",
"Data headers ['id' 'qid1' 'qid2' 'question1' 'question2' 'is_duplicate']\n"
]
}
],
"source": [
"def read_train_data():\n",
" df_train = pd.read_csv('../input/train.csv')\n",
" print('Data size %d' % len(df_train))\n",
" print('Data headers %s' % df_train.columns.values)\n",
" return df_train\n",
"\n",
"df_train = read_train_data()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "61fe4f81-a6be-cee7-76ba-e51f99f32bce"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of words: 8944578\n"
]
}
],
"source": [
"def extract_words(df_train):\n",
" words = list()\n",
" for index, row in df_train.iterrows():\n",
" q1 = row['question1']\n",
" q2 = row['question2']\n",
" if not q1 or not q2 or not isinstance(q1, six.string_types) \\\n",
" or not isinstance(q2, six.string_types):\n",
" continue\n",
" q_words = q1.split()\n",
" for word in q_words:\n",
" words.append(word)\n",
" q_words = q2.split()\n",
" for word in q_words:\n",
" words.append(word)\n",
" #words.append(\" \")\n",
" return words\n",
"vocabulary_size = 50000\n",
"words = extract_words(df_train)\n",
"print('Number of words: %d' % len(words))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "3db5ac91-8baf-5e09-1460-d2c5c6b31fdb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"size of count\n",
"50000\n",
"Most common words (+UNK) [['UNK', 287442], ('the', 371820), ('What', 292717), ('is', 216145), ('I', 211497)]\n",
"Sample data [2, 3, 1, 1412, 55, 1412, 3314, 6, 514, 8]\n"
]
}
],
"source": [
"def build_dataset(words):\n",
" count = [['UNK', -1]]\n",
" count.extend(collections.Counter(words).most_common(vocabulary_size - 1))\n",
" print(\"size of count\")\n",
" print(len(count))\n",
" dictionary = dict()\n",
" for word, _ in count:\n",
" dictionary[word] = len(dictionary)\n",
" data = list()\n",
" unk_count = 0\n",
" for word in words:\n",
" if word in dictionary:\n",
" index = dictionary[word]\n",
" else:\n",
" index = 0 # dictionary['UNK']\n",
" unk_count = unk_count + 1\n",
" data.append(index)\n",
" count[0][1] = unk_count\n",
" reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))\n",
" return data, count, dictionary, reverse_dictionary\n",
"\n",
"data, count, dictionary, reverse_dictionary = build_dataset(words)\n",
"print('Most common words (+UNK)', count[:5])\n",
"print('Sample data', data[:10])\n",
"del words # Hint to reduce memory.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "46ba1d80-6aa0-826b-ac65-6845d79ca25a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data: ['What', 'is', 'the', 'step', 'by', 'step', 'guide', 'to']\n",
"\n",
"with num_skips = 2 and skip_window = 1:\n",
" batch: ['is', 'is', 'the', 'the', 'step', 'step', 'by', 'by']\n",
" labels: ['the', 'What', 'step', 'is', 'by', 'the', 'step', 'step']\n",
"\n",
"with num_skips = 4 and skip_window = 2:\n",
" batch: ['the', 'the', 'the', 'the', 'step', 'step', 'step', 'step']\n",
" labels: ['is', 'step', 'by', 'What', 'the', 'step', 'by', 'is']\n"
]
}
],
"source": [
"data_index = 0\n",
"\n",
"def generate_batch(batch_size, num_skips, skip_window):\n",
" global data_index\n",
" assert batch_size % num_skips == 0\n",
" assert num_skips <= 2 * skip_window\n",
" batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
" labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
" span = 2 * skip_window + 1 # [ skip_window target skip_window ]\n",
" buffer = collections.deque(maxlen=span)\n",
" for _ in range(span):\n",
" buffer.append(data[data_index])\n",
" data_index = (data_index + 1) % len(data)\n",
" for i in range(batch_size // num_skips):\n",
" target = skip_window # target label at the center of the buffer\n",
" targets_to_avoid = [ skip_window ]\n",
" for j in range(num_skips):\n",
" while target in targets_to_avoid:\n",
" target = random.randint(0, span - 1)\n",
" targets_to_avoid.append(target)\n",
" batch[i * num_skips + j] = buffer[skip_window]\n",
" labels[i * num_skips + j, 0] = buffer[target]\n",
" buffer.append(data[data_index])\n",
" data_index = (data_index + 1) % len(data)\n",
" return batch, labels\n",
"\n",
"print('data:', [reverse_dictionary[di] for di in data[:8]])\n",
"\n",
"for num_skips, skip_window in [(2, 1), (4, 2)]:\n",
" data_index = 0\n",
" batch, labels = generate_batch(batch_size=8, num_skips=num_skips, skip_window=skip_window)\n",
" print('\\nwith num_skips = %d and skip_window = %d:' % (num_skips, skip_window))\n",
" print(' batch:', [reverse_dictionary[bi] for bi in batch])\n",
" print(' labels:', [reverse_dictionary[li] for li in labels.reshape(8)])\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "0b6746d3-b023-ebde-11dc-0ff3587b06d7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shapes of tensors similarity, embeddings, norm, normalized_embeddings, valid_embeddings\n",
"Tensor(\"MatMul:0\", shape=(16, 50000), dtype=float32, device=/device:CPU:0)\n",
"<tf.Variable 'Variable:0' shape=(50000, 128) dtype=float32_ref>\n",
"Tensor(\"Sqrt:0\", shape=(50000, 1), dtype=float32, device=/device:CPU:0)\n",
"Tensor(\"truediv:0\", shape=(50000, 128), dtype=float32, device=/device:CPU:0)\n",
"Tensor(\"embedding_lookup_1:0\", shape=(16, 128), dtype=float32, device=/device:CPU:0)\n"
]
}
],
"source": [
"batch_size = 128\n",
"embedding_size = 128 # Dimension of the embedding vector.\n",
"skip_window = 1 # How many words to consider left and right.\n",
"num_skips = 2 # How many times to reuse an input to generate a label.\n",
"# We pick a random validation set to sample nearest neighbors. here we limit the\n",
"# validation samples to the words that have a low numeric ID, which by\n",
"# construction are also the most frequent.\n",
"valid_size = 16 # Random set of words to evaluate similarity on.\n",
"valid_window = 100 # Only pick dev samples in the head of the distribution.\n",
"valid_examples = np.array(random.sample(range(valid_window), valid_size))\n",
"num_sampled = 64 # Number of negative examples to sample.\n",
"\n",
"graph = tf.Graph()\n",
"\n",
"with graph.as_default(), tf.device('/cpu:0'):\n",
" # Input data.\n",
" train_dataset = tf.placeholder(tf.int32, shape=[batch_size])\n",
" train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
" valid_dataset = tf.constant(valid_examples, dtype=tf.int32)\n",
"\n",
" # Variables.\n",
" embeddings = tf.Variable(\n",
" tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
" softmax_weights = tf.Variable(\n",
" tf.truncated_normal([vocabulary_size, embedding_size],\n",
" stddev=1.0 / math.sqrt(embedding_size)))\n",
" softmax_biases = tf.Variable(tf.zeros([vocabulary_size]))\n",
"\n",
" # Model.\n",
" # Look up embeddings for inputs.\n",
" embed = tf.nn.embedding_lookup(embeddings, train_dataset)\n",
" # Compute the softmax loss, using a sample of the negative labels each time.\n",
" loss = tf.reduce_mean(\n",
" tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=embed,\n",
" labels=train_labels, num_sampled=num_sampled, num_classes=vocabulary_size))\n",
"\n",
" # Optimizer.\n",
" # Note: The optimizer will optimize the softmax_weights AND the embeddings.\n",
" # This is because the embeddings are defined as a variable quantity and the\n",
" # optimizer's `minimize` method will by default modify all variable quantities\n",
" # that contribute to the tensor it is passed.\n",
" # See docs on `tf.train.Optimizer.minimize()` for more details.\n",
" optimizer = tf.train.AdagradOptimizer(1.0).minimize(loss)\n",
"\n",
" # Compute the similarity between minibatch examples and all embeddings.\n",
" # We use the cosine distance:\n",
" norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
" normalized_embeddings = embeddings / norm\n",
" valid_embeddings = tf.nn.embedding_lookup(\n",
" normalized_embeddings, valid_dataset)\n",
" similarity = tf.matmul(valid_embeddings, tf.transpose(normalized_embeddings))\n",
" print(\"Shapes of tensors similarity, embeddings, norm, normalized_embeddings, valid_embeddings\")\n",
" print(similarity)\n",
" print(embeddings)\n",
" print(norm)\n",
" print(normalized_embeddings)\n",
" print(valid_embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "f8e99437-1a2a-2c3a-a0fd-e9667a27eaab"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Average loss at step 0: 7.598386\n",
"[45966 14259 33952 9600 13569 15047 23395 22229]\n",
"50000\n",
"45966\n",
"shoul\n",
"14259\n",
"IN?\n",
"33952\n",
"reasonable?\n",
"9600\n",
"wood?\n",
"13569\n",
"treaty\n",
"15047\n",
"Micro\n",
"23395\n",
"IIM's\n",
"22229\n",
"\"In\n",
"Nearest to any: shoul, IN?, reasonable?, wood?, treaty, Micro, IIM's, \"In,\n",
"[49322 30464 49980 41461 47917 5145 4947 9538]\n",
"50000\n",
"49322\n",
"150k\n",
"30464\n",
"usability\n",
"49980\n",
"shows/films\n",
"41461\n",
"bastards\n",
"47917\n",
"integrated?\n",
"5145\n",
"transplant\n",
"4947\n",
"theoretical\n",
"9538\n",
"powered\n",
"Nearest to to: 150k, usability, shows/films, bastards, integrated?, transplant, theoretical, powered,\n",
"[16924 20472 21019 43528 9458 23698 40828 9712]\n",
"50000\n",
"16924\n",
"Honor\n",
"20472\n",
"Shanghai?\n",
"21019\n",
"Regina?\n",
"43528\n",
"D5300\n",
"9458\n",
"smile?\n",
"23698\n",
"evolved?\n",
"40828\n",
"Yodlee\n",
"9712\n",
"stores?\n",
"Nearest to become: Honor, Shanghai?, Regina?, D5300, smile?, evolved?, Yodlee, stores?,\n",
"[ 7465 12996 25925 37674 40864 4106 41697 45300]\n",
"50000\n",
"7465\n",
"compiler\n",
"12996\n",
"persuade\n",
"25925\n",
"lg\n",
"37674\n",
"Leica\n",
"40864\n",
"monitored?\n",
"4106\n",
"iPad?\n",
"41697\n",
"$1000-$2000\n",
"45300\n",
"separator\n",
"Nearest to Who: compiler, persuade, lg, Leica, monitored?, iPad?, $1000-$2000, separator,\n",
"[18805 45515 8013 44182 42270 28038 16820 49342]\n",
"50000\n",
"18805\n",
"\"Don't\n",
"45515\n",
"shit\"\n",
"8013\n",
"tummy?\n",
"44182\n",
"15,000\n",
"42270\n",
"Bank.\n",
"28038\n",
"Marin\n",
"16820\n",
"prompt?\n",
"49342\n",
"Amendments\n",
"Nearest to at: \"Don't, shit\", tummy?, 15,000, Bank., Marin, prompt?, Amendments,\n",
"[29434 11809 2559 23524 32142 6941 5985 2554]\n",
"50000\n",
"29434\n",
"hypertension\n",
"11809\n",
"insect\n",
"2559\n",
"hacks?\n",
"23524\n",
"Luthor\n",
"32142\n",
"neglect\n",
"6941\n",
"signature\n",
"5985\n",
"PSU\n",
"2554\n",
"atheists\n",
"Nearest to way: hypertension, insect, hacks?, Luthor, neglect, signature, PSU, atheists,\n",
"[36775 13433 46669 33977 46412 31621 29840 764]\n",
"50000\n",
"36775\n",
"bunker\n",
"13433\n",
"trips\n",
"46669\n",
"Claude\n",
"33977\n",
"parents’\n",
"46412\n",
"(C)\n",
"31621\n",
"ligament\n",
"29840\n",
"sony\n",
"764\n",
"file\n",
"Nearest to money: bunker, trips, Claude, parents’, (C), ligament, sony, file,\n",
"[31417 43422 8201 44039 30038 5015 43406 30630]\n",
"50000\n",
"31417\n",
"internalized\n",
"43422\n",
"(IN)?\n",
"8201\n",
"configure\n",
"44039\n",
"\"perfume\",\n",
"30038\n",
"exam(\n",
"5015\n",
"violence\n",
"43406\n",
"\"Come\n",
"30630\n",
"Hate\n",
"Nearest to ever: internalized, (IN)?, configure, \"perfume\",, exam(, violence, \"Come, Hate,\n",
"[24956 48581 46539 10109 38006 34057 25408 4244]\n",
"50000\n",
"24956\n",
"Kernel?\n",
"48581\n",
"JBIMS?\n",
"46539\n",
"surcharge\n",
"10109\n",
"esteem\n",
"38006\n",
"deterministic\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"25408\n",
"associate?\n",
"4244\n",
"options?\n",
"Nearest to best: Kernel?, JBIMS?, surcharge, esteem, deterministic, 1800</v\\>251<’-‘>4919, associate?, options?,\n",
"[42547 15245 23265 24159 28188 2821 19632 2741]\n",
"50000\n",
"42547\n",
"bihar\n",
"15245\n",
"LLC?\n",
"23265\n",
"Genesis\n",
"24159\n",
"b.com\n",
"28188\n",
"Manga?\n",
"2821\n",
"criteria\n",
"19632\n",
"Mars,\n",
"2741\n",
"Mac?\n",
"Nearest to on: bihar, LLC?, Genesis, b.com, Manga?, criteria, Mars,, Mac?,\n",
"[20261 22852 1294 13356 26264 34963 6740 638]\n",
"50000\n",
"20261\n",
"threshold\n",
"22852\n",
"gloves\n",
"1294\n",
"third\n",
"13356\n",
"mitochondria?\n",
"26264\n",
"crunches\n",
"34963\n",
"overloaded\n",
"6740\n",
"confident?\n",
"638\n",
"deleted\n",
"Nearest to you: threshold, gloves, third, mitochondria?, crunches, overloaded, confident?, deleted,\n",
"[31945 30300 41176 4575 30182 43386 22189 27521]\n",
"50000\n",
"31945\n",
"Iraq,\n",
"30300\n",
"uTorrent?\n",
"41176\n",
"tartar\n",
"4575\n",
"logo?\n",
"30182\n",
"snoring?\n",
"43386\n",
"Malays\n",
"22189\n",
"masala\n",
"27521\n",
"kettle\n",
"Nearest to Indian: Iraq,, uTorrent?, tartar, logo?, snoring?, Malays, masala, kettle,\n",
"[ 6137 15110 21356 9822 19329 36309 41384 3302]\n",
"50000\n",
"6137\n",
"25?\n",
"15110\n",
"(besides\n",
"21356\n",
"subatomic\n",
"9822\n",
"rude?\n",
"19329\n",
"unavailability?\n",
"36309\n",
"belts?\n",
"41384\n",
"Monsters\n",
"3302\n",
"notice\n",
"Nearest to with: 25?, (besides, subatomic, rude?, unavailability?, belts?, Monsters, notice,\n",
"[36677 23095 31581 15955 4723 48534 25672 6550]\n",
"50000\n",
"36677\n",
"Library?\n",
"23095\n",
"exes\n",
"31581\n",
"cubes?\n",
"15955\n",
"nurse\n",
"4723\n",
"orgasm\n",
"48534\n",
"skin)\n",
"25672\n",
"Toronto,\n",
"6550\n",
"language,\n",
"Nearest to when: Library?, exes, cubes?, nurse, orgasm, skin), Toronto,, language,,\n",
"[ 3374 45061 36419 11822 36798 4083 3513 30654]\n",
"50000\n",
"3374\n",
"Windows?\n",
"45061\n",
"multiplying\n",
"36419\n",
"I’ll\n",
"11822\n",
"Program?\n",
"36798\n",
"advance.\n",
"4083\n",
"powder\n",
"3513\n",
"7th\n",
"30654\n",
"accounting,\n",
"Nearest to good: Windows?, multiplying, I’ll, Program?, advance., powder, 7th, accounting,,\n",
"[38213 33603 32082 32590 6709 5650 39685 19020]\n",
"50000\n",
"38213\n",
"free\"?\n",
"33603\n",
"Way's\n",
"32082\n",
"re-access\n",
"32590\n",
"analogue\n",
"6709\n",
"manufacturers\n",
"5650\n",
"Indonesia\n",
"39685\n",
"(President\n",
"19020\n",
"collaborate\n",
"Nearest to what: free\"?, Way's, re-access, analogue, manufacturers, Indonesia, (President, collaborate,\n",
"Average loss at step 2000: 4.290053\n",
"Average loss at step 4000: 3.668409\n",
"Average loss at step 6000: 3.489848\n",
"Average loss at step 8000: 3.405310\n",
"Average loss at step 10000: 3.329534\n",
"[45966 33952 7751 778 9396 40611 9894 5]\n",
"50000\n",
"45966\n",
"shoul\n",
"33952\n",
"reasonable?\n",
"7751\n",
"refrigerator\n",
"778\n",
"eating\n",
"9396\n",
"industries?\n",
"40611\n",
"malpractice\n",
"9894\n",
"Norway\n",
"5\n",
"a\n",
"Nearest to any: shoul, reasonable?, refrigerator, eating, industries?, malpractice, Norway, a,\n",
"[ 4 13 33273 14 15814 43 35 18559]\n",
"50000\n",
"4\n",
"I\n",
"13\n",
"for\n",
"33273\n",
"invested?\n",
"14\n",
"can\n",
"15814\n",
"bought?\n",
"43\n",
"would\n",
"35\n",
"if\n",
"18559\n",
"eyebrows?\n",
"Nearest to to: I, for, invested?, can, bought?, would, if, eyebrows?,\n",
"[ 29 16924 305 23698 667 50 42029 43528]\n",
"50000\n",
"29\n",
"have\n",
"16924\n",
"Honor\n",
"305\n",
"create\n",
"23698\n",
"evolved?\n",
"667\n",
"develop\n",
"50\n",
"make\n",
"42029\n",
"'a'\n",
"43528\n",
"D5300\n",
"Nearest to become: have, Honor, create, evolved?, develop, make, 'a', D5300,\n",
"[ 2 16 7 31 66 115 106 47326]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"7\n",
"How\n",
"31\n",
"Which\n",
"66\n",
"Where\n",
"115\n",
"When\n",
"106\n",
"Should\n",
"47326\n",
"meaningless.\n",
"Nearest to Who: What, Why, How, Which, Where, When, Should, meaningless.,\n",
"[42270 28 14764 33 8 8577 31475 44182]\n",
"50000\n",
"42270\n",
"Bank.\n",
"28\n",
"with\n",
"14764\n",
"monarchy\n",
"33\n",
"from\n",
"8\n",
"in\n",
"8577\n",
"lied\n",
"31475\n",
"supplies?\n",
"44182\n",
"15,000\n",
"Nearest to at: Bank., with, monarchy, from, in, lied, supplies?, 15,000,\n",
"[11809 6941 117 10222 19782 29434 22557 322]\n",
"50000\n",
"11809\n",
"insect\n",
"6941\n",
"signature\n",
"117\n",
"ways\n",
"10222\n",
"clever\n",
"19782\n",
"Mongolia\n",
"29434\n",
"hypertension\n",
"22557\n",
"profile,\n",
"322\n",
"places\n",
"Nearest to way: insect, signature, ways, clever, Mongolia, hypertension, profile,, places,\n",
"[36775 33977 908 22154 43277 10673 46143 35666]\n",
"50000\n",
"36775\n",
"bunker\n",
"33977\n",
"parents’\n",
"908\n",
"us\n",
"22154\n",
"Sudan\n",
"43277\n",
"hyperinflation\n",
"10673\n",
"Labrador\n",
"46143\n",
"Ottomans\n",
"35666\n",
"photography.\n",
"Nearest to money: bunker, parents’, us, Sudan, hyperinflation, Labrador, Ottomans, photography.,\n",
"[43422 8201 48004 44039 45026 30038 43406 18681]\n",
"50000\n",
"43422\n",
"(IN)?\n",
"8201\n",
"configure\n",
"48004\n",
"816?\n",
"44039\n",
"\"perfume\",\n",
"45026\n",
"adjective?\n",
"30038\n",
"exam(\n",
"43406\n",
"\"Come\n",
"18681\n",
"expires\n",
"Nearest to ever: (IN)?, configure, 816?, \"perfume\",, adjective?, exam(, \"Come, expires,\n",
"[ 1663 68 34057 38 1428 521 619 6077]\n",
"50000\n",
"1663\n",
"various\n",
"68\n",
"difference\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"38\n",
"good\n",
"1428\n",
"maximum\n",
"521\n",
"easiest\n",
"619\n",
"safety\n",
"6077\n",
"block?\n",
"Nearest to best: various, difference, 1800</v\\>251<’-‘>4919, good, maximum, easiest, safety, block?,\n",
"[ 33 9 28 48260 42547 32612 40842 8]\n",
"50000\n",
"33\n",
"from\n",
"9\n",
"of\n",
"28\n",
"with\n",
"48260\n",
"untucked?\n",
"42547\n",
"bihar\n",
"32612\n",
"addictions\n",
"40842\n",
"Internships:\n",
"8\n",
"in\n",
"Nearest to on: from, of, with, untucked?, bihar, addictions, Internships:, in,\n",
"[ 51 4 64 39 30752 17002 225 238]\n",
"50000\n",
"51\n",
"we\n",
"4\n",
"I\n",
"64\n",
"they\n",
"39\n",
"people\n",
"30752\n",
"Dream?\n",
"17002\n",
"surrounded\n",
"225\n",
"women\n",
"238\n",
"anyone\n",
"Nearest to you: we, I, they, people, Dream?, surrounded, women, anyone,\n",
"[31945 43386 30300 27521 44262 16643 6240 37216]\n",
"50000\n",
"31945\n",
"Iraq,\n",
"43386\n",
"Malays\n",
"30300\n",
"uTorrent?\n",
"27521\n",
"kettle\n",
"44262\n",
"pick:\n",
"16643\n",
"Kalam?\n",
"6240\n",
"repeat\n",
"37216\n",
"Movers\n",
"Nearest to Indian: Iraq,, Malays, uTorrent?, kettle, pick:, Kalam?, repeat, Movers,\n",
"[ 8 13 9 19 22384 23265 33 37]\n",
"50000\n",
"8\n",
"in\n",
"13\n",
"for\n",
"9\n",
"of\n",
"19\n",
"on\n",
"22384\n",
"Group)\n",
"23265\n",
"Genesis\n",
"33\n",
"from\n",
"37\n",
"at\n",
"Nearest to with: in, for, of, on, Group), Genesis, from, at,\n",
"[ 35 23095 65 36677 189 28220 25365 47618]\n",
"50000\n",
"35\n",
"if\n",
"23095\n",
"exes\n",
"65\n",
"after\n",
"36677\n",
"Library?\n",
"189\n",
"while\n",
"28220\n",
"pistol?\n",
"25365\n",
"Jonathan\n",
"47618\n",
"hex\n",
"Nearest to when: if, exes, after, Library?, while, pistol?, Jonathan, hex,\n",
"[ 8075 9310 381 17 3513 48493 27893 10321]\n",
"50000\n",
"8075\n",
"Lady\n",
"9310\n",
"incidents\n",
"381\n",
"common\n",
"17\n",
"best\n",
"3513\n",
"7th\n",
"48493\n",
"cliché\n",
"27893\n",
"rhyme?\n",
"10321\n",
"traditions\n",
"Nearest to good: Lady, incidents, common, best, 7th, cliché, rhyme?, traditions,\n",
"[ 52 32590 32082 48710 4210 11800 5650 37559]\n",
"50000\n",
"52\n",
"how\n",
"32590\n",
"analogue\n",
"32082\n",
"re-access\n",
"48710\n",
"hertz?\n",
"4210\n",
"evolutionary\n",
"11800\n",
"interactive\n",
"5650\n",
"Indonesia\n",
"37559\n",
"poisons\n",
"Nearest to what: how, analogue, re-access, hertz?, evolutionary, interactive, Indonesia, poisons,\n",
"Average loss at step 12000: 3.285912\n",
"Average loss at step 14000: 3.241752\n",
"Average loss at step 16000: 3.198114\n",
"Average loss at step 18000: 3.173786\n",
"Average loss at step 20000: 3.140364\n",
"[ 7751 45966 5 178 9396 33952 9894 871]\n",
"50000\n",
"7751\n",
"refrigerator\n",
"45966\n",
"shoul\n",
"5\n",
"a\n",
"178\n",
"no\n",
"9396\n",
"industries?\n",
"33952\n",
"reasonable?\n",
"9894\n",
"Norway\n",
"871\n",
"temperatures\n",
"Nearest to any: refrigerator, shoul, a, no, industries?, reasonable?, Norway, temperatures,\n",
"[ 4 43 13 33273 15 135 14 15814]\n",
"50000\n",
"4\n",
"I\n",
"43\n",
"would\n",
"13\n",
"for\n",
"33273\n",
"invested?\n",
"15\n",
"you\n",
"135\n",
"could\n",
"14\n",
"can\n",
"15814\n",
"bought?\n",
"Nearest to to: I, would, for, invested?, you, could, can, bought?,\n",
"[ 29 24 305 27 50 667 16924 183]\n",
"50000\n",
"29\n",
"have\n",
"24\n",
"be\n",
"305\n",
"create\n",
"27\n",
"get\n",
"50\n",
"make\n",
"667\n",
"develop\n",
"16924\n",
"Honor\n",
"183\n",
"had\n",
"Nearest to become: have, be, create, get, make, develop, Honor, had,\n",
"[ 2 16 7 31 66 115 57 106]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"7\n",
"How\n",
"31\n",
"Which\n",
"66\n",
"Where\n",
"115\n",
"When\n",
"57\n",
"If\n",
"106\n",
"Should\n",
"Nearest to Who: What, Why, How, Which, Where, When, If, Should,\n",
"[ 8 42270 31475 309 44182 17753 18805 6196]\n",
"50000\n",
"8\n",
"in\n",
"42270\n",
"Bank.\n",
"31475\n",
"supplies?\n",
"309\n",
"against\n",
"44182\n",
"15,000\n",
"17753\n",
"telecast\n",
"18805\n",
"\"Don't\n",
"6196\n",
"Then\n",
"Nearest to at: in, Bank., supplies?, against, 15,000, telecast, \"Don't, Then,\n",
"[ 117 322 6941 11809 166 19782 11932 10222]\n",
"50000\n",
"117\n",
"ways\n",
"322\n",
"places\n",
"6941\n",
"signature\n",
"11809\n",
"insect\n",
"166\n",
"books\n",
"19782\n",
"Mongolia\n",
"11932\n",
"diffusion\n",
"10222\n",
"clever\n",
"Nearest to way: ways, places, signature, insect, books, Mongolia, diffusion, clever,\n",
"[36775 10673 43277 33977 24598 908 47440 436]\n",
"50000\n",
"36775\n",
"bunker\n",
"10673\n",
"Labrador\n",
"43277\n",
"hyperinflation\n",
"33977\n",
"parents’\n",
"24598\n",
"sacred\n",
"908\n",
"us\n",
"47440\n",
"scheduling?\n",
"436\n",
"friends\n",
"Nearest to money: bunker, Labrador, hyperinflation, parents’, sacred, us, scheduling?, friends,\n",
"[43422 329 44039 48004 8201 43406 45026 30038]\n",
"50000\n",
"43422\n",
"(IN)?\n",
"329\n",
"never\n",
"44039\n",
"\"perfume\",\n",
"48004\n",
"816?\n",
"8201\n",
"configure\n",
"43406\n",
"\"Come\n",
"45026\n",
"adjective?\n",
"30038\n",
"exam(\n",
"Nearest to ever: (IN)?, never, \"perfume\",, 816?, configure, \"Come, adjective?, exam(,\n",
"[ 1663 521 38 399 31 34057 619 1428]\n",
"50000\n",
"1663\n",
"various\n",
"521\n",
"easiest\n",
"38\n",
"good\n",
"399\n",
"worst\n",
"31\n",
"Which\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"619\n",
"safety\n",
"1428\n",
"maximum\n",
"Nearest to best: various, easiest, good, worst, Which, 1800</v\\>251<’-‘>4919, safety, maximum,\n",
"[ 192 8 33 38561 38826 34306 23844 28]\n",
"50000\n",
"192\n",
"through\n",
"8\n",
"in\n",
"33\n",
"from\n",
"38561\n",
"parody\n",
"38826\n",
"hack/spy\n",
"34306\n",
"Inbox\n",
"23844\n",
"undergraduates?\n",
"28\n",
"with\n",
"Nearest to on: through, in, from, parody, hack/spy, Inbox, undergraduates?, with,\n",
"[ 51 4 64 39 238 368 30752 95]\n",
"50000\n",
"51\n",
"we\n",
"4\n",
"I\n",
"64\n",
"they\n",
"39\n",
"people\n",
"238\n",
"anyone\n",
"368\n",
"you've\n",
"30752\n",
"Dream?\n",
"95\n",
"someone\n",
"Nearest to you: we, I, they, people, anyone, you've, Dream?, someone,\n",
"[31945 44262 30300 43386 27521 31736 2567 37216]\n",
"50000\n",
"31945\n",
"Iraq,\n",
"44262\n",
"pick:\n",
"30300\n",
"uTorrent?\n",
"43386\n",
"Malays\n",
"27521\n",
"kettle\n",
"31736\n",
"tumor\n",
"2567\n",
"refer\n",
"37216\n",
"Movers\n",
"Nearest to Indian: Iraq,, pick:, uTorrent?, Malays, kettle, tumor, refer, Movers,\n",
"[22384 33 8 19 2789 34306 20921 23265]\n",
"50000\n",
"22384\n",
"Group)\n",
"33\n",
"from\n",
"8\n",
"in\n",
"19\n",
"on\n",
"2789\n",
"vs.\n",
"34306\n",
"Inbox\n",
"20921\n",
"lobster?\n",
"23265\n",
"Genesis\n",
"Nearest to with: Group), from, in, on, vs., Inbox, lobster?, Genesis,\n",
"[ 35 189 65 26 23095 56 41111 36677]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"65\n",
"after\n",
"26\n",
"that\n",
"23095\n",
"exes\n",
"56\n",
"not\n",
"41111\n",
"Motion\n",
"36677\n",
"Library?\n",
"Nearest to when: if, while, after, that, exes, not, Motion, Library?,\n",
"[ 400 17 9310 114 25998 5774 8075 1023]\n",
"50000\n",
"400\n",
"great\n",
"17\n",
"best\n",
"9310\n",
"incidents\n",
"114\n",
"better\n",
"25998\n",
"hub?\n",
"5774\n",
"houses\n",
"8075\n",
"Lady\n",
"1023\n",
"marked\n",
"Nearest to good: great, best, incidents, better, hub?, houses, Lady, marked,\n",
"[ 52 112 141 32082 32590 253 37559 4210]\n",
"50000\n",
"52\n",
"how\n",
"112\n",
"which\n",
"141\n",
"why\n",
"32082\n",
"re-access\n",
"32590\n",
"analogue\n",
"253\n",
"where\n",
"37559\n",
"poisons\n",
"4210\n",
"evolutionary\n",
"Nearest to what: how, which, why, re-access, analogue, where, poisons, evolutionary,\n",
"Average loss at step 22000: 3.131945\n",
"Average loss at step 24000: 3.100567\n",
"Average loss at step 26000: 3.089211\n",
"Average loss at step 28000: 3.050137\n",
"Average loss at step 30000: 3.048801\n",
"[ 178 25 45966 7751 33952 40967 33866 9894]\n",
"50000\n",
"178\n",
"no\n",
"25\n",
"some\n",
"45966\n",
"shoul\n",
"7751\n",
"refrigerator\n",
"33952\n",
"reasonable?\n",
"40967\n",
"ghosts/scientifically\n",
"33866\n",
"terminology?\n",
"9894\n",
"Norway\n",
"Nearest to any: no, some, shoul, refrigerator, reasonable?, ghosts/scientifically, terminology?, Norway,\n",
"[ 4 43 31050 15814 956 14 33273 44]\n",
"50000\n",
"4\n",
"I\n",
"43\n",
"would\n",
"31050\n",
"Cheney\n",
"15814\n",
"bought?\n",
"956\n",
"back?\n",
"14\n",
"can\n",
"33273\n",
"invested?\n",
"44\n",
"will\n",
"Nearest to to: I, would, Cheney, bought?, back?, can, invested?, will,\n",
"[ 24 27 29 305 183 50 260 667]\n",
"50000\n",
"24\n",
"be\n",
"27\n",
"get\n",
"29\n",
"have\n",
"305\n",
"create\n",
"183\n",
"had\n",
"50\n",
"make\n",
"260\n",
"write\n",
"667\n",
"develop\n",
"Nearest to become: be, get, have, create, had, make, write, develop,\n",
"[ 2 16 31 66 7 115 57 29239]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"31\n",
"Which\n",
"66\n",
"Where\n",
"7\n",
"How\n",
"115\n",
"When\n",
"57\n",
"If\n",
"29239\n",
"trust,\n",
"Nearest to Who: What, Why, Which, Where, How, When, If, trust,,\n",
"[ 8 13 8577 7610 7239 10756 7785 49540]\n",
"50000\n",
"8\n",
"in\n",
"13\n",
"for\n",
"8577\n",
"lied\n",
"7610\n",
"libraries\n",
"7239\n",
"Colorado\n",
"10756\n",
"Prime?\n",
"7785\n",
"Doesn't\n",
"49540\n",
"(Most\n",
"Nearest to at: in, for, lied, libraries, Colorado, Prime?, Doesn't, (Most,\n",
"[ 117 322 6941 166 11809 24453 10222 2265]\n",
"50000\n",
"117\n",
"ways\n",
"322\n",
"places\n",
"6941\n",
"signature\n",
"166\n",
"books\n",
"11809\n",
"insect\n",
"24453\n",
"sabha\n",
"10222\n",
"clever\n",
"2265\n",
"smoke\n",
"Nearest to way: ways, places, signature, books, insect, sabha, clever, smoke,\n",
"[10673 230 43277 33977 21047 24598 47440 13323]\n",
"50000\n",
"10673\n",
"Labrador\n",
"230\n",
"money?\n",
"43277\n",
"hyperinflation\n",
"33977\n",
"parents’\n",
"21047\n",
"todays\n",
"24598\n",
"sacred\n",
"47440\n",
"scheduling?\n",
"13323\n",
"entrepreneurial\n",
"Nearest to money: Labrador, money?, hyperinflation, parents’, todays, sacred, scheduling?, entrepreneurial,\n",
"[ 329 43422 44039 148 30038 18681 43406 8801]\n",
"50000\n",
"329\n",
"never\n",
"43422\n",
"(IN)?\n",
"44039\n",
"\"perfume\",\n",
"148\n",
"been\n",
"30038\n",
"exam(\n",
"18681\n",
"expires\n",
"43406\n",
"\"Come\n",
"8801\n",
"dimension\n",
"Nearest to ever: never, (IN)?, \"perfume\",, been, exam(, expires, \"Come, dimension,\n",
"[ 1663 521 38 68 399 1428 619 34057]\n",
"50000\n",
"1663\n",
"various\n",
"521\n",
"easiest\n",
"38\n",
"good\n",
"68\n",
"difference\n",
"399\n",
"worst\n",
"1428\n",
"maximum\n",
"619\n",
"safety\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"Nearest to best: various, easiest, good, difference, worst, maximum, safety, 1800</v\\>251<’-‘>4919,\n",
"[ 33 192 23844 8 38826 31118 48260 38561]\n",
"50000\n",
"33\n",
"from\n",
"192\n",
"through\n",
"23844\n",
"undergraduates?\n",
"8\n",
"in\n",
"38826\n",
"hack/spy\n",
"31118\n",
"underarm\n",
"48260\n",
"untucked?\n",
"38561\n",
"parody\n",
"Nearest to on: from, through, undergraduates?, in, hack/spy, underarm, untucked?, parody,\n",
"[ 51 4 64 368 39 30752 362 225]\n",
"50000\n",
"51\n",
"we\n",
"4\n",
"I\n",
"64\n",
"they\n",
"368\n",
"you've\n",
"39\n",
"people\n",
"30752\n",
"Dream?\n",
"362\n",
"girls\n",
"225\n",
"women\n",
"Nearest to you: we, I, they, you've, people, Dream?, girls, women,\n",
"[31945 30300 31736 27521 44262 43386 2567 37216]\n",
"50000\n",
"31945\n",
"Iraq,\n",
"30300\n",
"uTorrent?\n",
"31736\n",
"tumor\n",
"27521\n",
"kettle\n",
"44262\n",
"pick:\n",
"43386\n",
"Malays\n",
"2567\n",
"refer\n",
"37216\n",
"Movers\n",
"Nearest to Indian: Iraq,, uTorrent?, tumor, kettle, pick:, Malays, refer, Movers,\n",
"[22384 33 34306 8 29025 309 45 369]\n",
"50000\n",
"22384\n",
"Group)\n",
"33\n",
"from\n",
"34306\n",
"Inbox\n",
"8\n",
"in\n",
"29025\n",
"contraceptive\n",
"309\n",
"against\n",
"45\n",
"between\n",
"369\n",
"around\n",
"Nearest to with: Group), from, Inbox, in, contraceptive, against, between, around,\n",
"[ 35 189 65 173 96 23095 47618 45964]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"65\n",
"after\n",
"173\n",
"before\n",
"96\n",
"but\n",
"23095\n",
"exes\n",
"47618\n",
"hex\n",
"45964\n",
"awa\n",
"Nearest to when: if, while, after, before, but, exes, hex, awa,\n",
"[ 400 234 114 17 9310 5774 281 594]\n",
"50000\n",
"400\n",
"great\n",
"234\n",
"bad\n",
"114\n",
"better\n",
"17\n",
"best\n",
"9310\n",
"incidents\n",
"5774\n",
"houses\n",
"281\n",
"safe\n",
"594\n",
"suitable\n",
"Nearest to good: great, bad, better, best, incidents, houses, safe, suitable,\n",
"[ 52 112 253 141 32590 30583 32082 94]\n",
"50000\n",
"52\n",
"how\n",
"112\n",
"which\n",
"253\n",
"where\n",
"141\n",
"why\n",
"32590\n",
"analogue\n",
"30583\n",
"girlfriend/wife\n",
"32082\n",
"re-access\n",
"94\n",
"who\n",
"Nearest to what: how, which, where, why, analogue, girlfriend/wife, re-access, who,\n",
"Average loss at step 32000: 3.032011\n",
"Average loss at step 34000: 3.016267\n",
"Average loss at step 36000: 2.997701\n",
"Average loss at step 38000: 2.996201\n",
"Average loss at step 40000: 2.991678\n",
"[ 178 45966 25 33952 7751 40611 5 42239]\n",
"50000\n",
"178\n",
"no\n",
"45966\n",
"shoul\n",
"25\n",
"some\n",
"33952\n",
"reasonable?\n",
"7751\n",
"refrigerator\n",
"40611\n",
"malpractice\n",
"5\n",
"a\n",
"42239\n",
"Sakura?\n",
"Nearest to any: no, shoul, some, reasonable?, refrigerator, malpractice, a, Sakura?,\n",
"[ 13 33273 4 43 28 14856 44 17132]\n",
"50000\n",
"13\n",
"for\n",
"33273\n",
"invested?\n",
"4\n",
"I\n",
"43\n",
"would\n",
"28\n",
"with\n",
"14856\n",
"alumni\n",
"44\n",
"will\n",
"17132\n",
"Kuan\n",
"Nearest to to: for, invested?, I, would, with, alumni, will, Kuan,\n",
"[ 24 27 29 305 19333 667 183 260]\n",
"50000\n",
"24\n",
"be\n",
"27\n",
"get\n",
"29\n",
"have\n",
"305\n",
"create\n",
"19333\n",
"naruto\n",
"667\n",
"develop\n",
"183\n",
"had\n",
"260\n",
"write\n",
"Nearest to become: be, get, have, create, naruto, develop, had, write,\n",
"[ 2 16 31 7 66 115 47800 35116]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"31\n",
"Which\n",
"7\n",
"How\n",
"66\n",
"Where\n",
"115\n",
"When\n",
"47800\n",
"Columbus,\n",
"35116\n",
"AK-47\n",
"Nearest to Who: What, Why, Which, How, Where, When, Columbus,, AK-47,\n",
"[35841 8 7239 7610 14764 200 8577 31475]\n",
"50000\n",
"35841\n",
"fade?\n",
"8\n",
"in\n",
"7239\n",
"Colorado\n",
"7610\n",
"libraries\n",
"14764\n",
"monarchy\n",
"200\n",
"during\n",
"8577\n",
"lied\n",
"31475\n",
"supplies?\n",
"Nearest to at: fade?, in, Colorado, libraries, monarchy, during, lied, supplies?,\n",
"[ 117 322 6941 1111 166 533 19782 2265]\n",
"50000\n",
"117\n",
"ways\n",
"322\n",
"places\n",
"6941\n",
"signature\n",
"1111\n",
"method\n",
"166\n",
"books\n",
"533\n",
"websites\n",
"19782\n",
"Mongolia\n",
"2265\n",
"smoke\n",
"Nearest to way: ways, places, signature, method, books, websites, Mongolia, smoke,\n",
"[ 230 266 10673 33977 43277 24598 47440 1389]\n",
"50000\n",
"230\n",
"money?\n",
"266\n",
"them\n",
"10673\n",
"Labrador\n",
"33977\n",
"parents’\n",
"43277\n",
"hyperinflation\n",
"24598\n",
"sacred\n",
"47440\n",
"scheduling?\n",
"1389\n",
"files\n",
"Nearest to money: money?, them, Labrador, parents’, hyperinflation, sacred, scheduling?, files,\n",
"[ 329 43422 18681 2761 148 8801 8201 30038]\n",
"50000\n",
"329\n",
"never\n",
"43422\n",
"(IN)?\n",
"18681\n",
"expires\n",
"2761\n",
"don’t\n",
"148\n",
"been\n",
"8801\n",
"dimension\n",
"8201\n",
"configure\n",
"30038\n",
"exam(\n",
"Nearest to ever: never, (IN)?, expires, don’t, been, dimension, configure, exam(,\n",
"[ 521 1663 399 38 31 1049 619 1593]\n",
"50000\n",
"521\n",
"easiest\n",
"1663\n",
"various\n",
"399\n",
"worst\n",
"38\n",
"good\n",
"31\n",
"Which\n",
"1049\n",
"latest\n",
"619\n",
"safety\n",
"1593\n",
"cheapest\n",
"Nearest to best: easiest, various, worst, good, Which, latest, safety, cheapest,\n",
"[ 192 38561 23844 33 11821 8 48260 42547]\n",
"50000\n",
"192\n",
"through\n",
"38561\n",
"parody\n",
"23844\n",
"undergraduates?\n",
"33\n",
"from\n",
"11821\n",
"away.\n",
"8\n",
"in\n",
"48260\n",
"untucked?\n",
"42547\n",
"bihar\n",
"Nearest to on: through, parody, undergraduates?, from, away., in, untucked?, bihar,\n",
"[ 51 4 64 368 39 472 225 630]\n",
"50000\n",
"51\n",
"we\n",
"4\n",
"I\n",
"64\n",
"they\n",
"368\n",
"you've\n",
"39\n",
"people\n",
"472\n",
"i\n",
"225\n",
"women\n",
"630\n",
"humans\n",
"Nearest to you: we, I, they, you've, people, i, women, humans,\n",
"[31945 31736 30300 2567 27521 44262 36772 40199]\n",
"50000\n",
"31945\n",
"Iraq,\n",
"31736\n",
"tumor\n",
"30300\n",
"uTorrent?\n",
"2567\n",
"refer\n",
"27521\n",
"kettle\n",
"44262\n",
"pick:\n",
"36772\n",
"22,000/month,\n",
"40199\n",
"sashimi,\n",
"Nearest to Indian: Iraq,, tumor, uTorrent?, refer, kettle, pick:, 22,000/month,, sashimi,,\n",
"[ 6 22384 14696 30011 5538 33 45 49788]\n",
"50000\n",
"6\n",
"to\n",
"22384\n",
"Group)\n",
"14696\n",
"Player\n",
"30011\n",
"Carroll\n",
"5538\n",
"hates\n",
"33\n",
"from\n",
"45\n",
"between\n",
"49788\n",
"aspirations\n",
"Nearest to with: to, Group), Player, Carroll, hates, from, between, aspirations,\n",
"[ 35 189 65 173 23095 1012 200 46405]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"65\n",
"after\n",
"173\n",
"before\n",
"23095\n",
"exes\n",
"1012\n",
"though\n",
"200\n",
"during\n",
"46405\n",
"melatonin?\n",
"Nearest to when: if, while, after, before, exes, though, during, melatonin?,\n",
"[ 400 234 114 281 17 594 8075 37857]\n",
"50000\n",
"400\n",
"great\n",
"234\n",
"bad\n",
"114\n",
"better\n",
"281\n",
"safe\n",
"17\n",
"best\n",
"594\n",
"suitable\n",
"8075\n",
"Lady\n",
"37857\n",
"bussiness?\n",
"Nearest to good: great, bad, better, safe, best, suitable, Lady, bussiness?,\n",
"[ 52 253 112 141 32590 159 30583 94]\n",
"50000\n",
"52\n",
"how\n",
"253\n",
"where\n",
"112\n",
"which\n",
"141\n",
"why\n",
"32590\n",
"analogue\n",
"159\n",
"why?\n",
"30583\n",
"girlfriend/wife\n",
"94\n",
"who\n",
"Nearest to what: how, where, which, why, analogue, why?, girlfriend/wife, who,\n",
"Average loss at step 42000: 2.967443\n",
"Average loss at step 44000: 2.962680\n",
"Average loss at step 46000: 2.965515\n",
"Average loss at step 48000: 2.942364\n",
"Average loss at step 50000: 2.942756\n",
"[ 178 25 45966 33866 7751 5 6175 45981]\n",
"50000\n",
"178\n",
"no\n",
"25\n",
"some\n",
"45966\n",
"shoul\n",
"33866\n",
"terminology?\n",
"7751\n",
"refrigerator\n",
"5\n",
"a\n",
"6175\n",
"Wars?\n",
"45981\n",
"\"did\n",
"Nearest to any: no, some, shoul, terminology?, refrigerator, a, Wars?, \"did,\n",
"[33273 4 13 44 43 8 49322 15814]\n",
"50000\n",
"33273\n",
"invested?\n",
"4\n",
"I\n",
"13\n",
"for\n",
"44\n",
"will\n",
"43\n",
"would\n",
"8\n",
"in\n",
"49322\n",
"150k\n",
"15814\n",
"bought?\n",
"Nearest to to: invested?, I, for, will, would, in, 150k, bought?,\n",
"[ 24 27 183 305 667 19333 260 29]\n",
"50000\n",
"24\n",
"be\n",
"27\n",
"get\n",
"183\n",
"had\n",
"305\n",
"create\n",
"667\n",
"develop\n",
"19333\n",
"naruto\n",
"260\n",
"write\n",
"29\n",
"have\n",
"Nearest to become: be, get, had, create, develop, naruto, write, have,\n",
"[ 2 31 115 16 66 7 1693 42623]\n",
"50000\n",
"2\n",
"What\n",
"31\n",
"Which\n",
"115\n",
"When\n",
"16\n",
"Why\n",
"66\n",
"Where\n",
"7\n",
"How\n",
"1693\n",
"She\n",
"42623\n",
"undergrad.\n",
"Nearest to Who: What, Which, When, Why, Where, How, She, undergrad.,\n",
"[35841 8 7610 33 12002 49540 7239 8577]\n",
"50000\n",
"35841\n",
"fade?\n",
"8\n",
"in\n",
"7610\n",
"libraries\n",
"33\n",
"from\n",
"12002\n",
"fun,\n",
"49540\n",
"(Most\n",
"7239\n",
"Colorado\n",
"8577\n",
"lied\n",
"Nearest to at: fade?, in, libraries, from, fun,, (Most, Colorado, lied,\n",
"[ 117 322 6941 1111 1126 313 166 831]\n",
"50000\n",
"117\n",
"ways\n",
"322\n",
"places\n",
"6941\n",
"signature\n",
"1111\n",
"method\n",
"1126\n",
"way?\n",
"313\n",
"website\n",
"166\n",
"books\n",
"831\n",
"chance\n",
"Nearest to way: ways, places, signature, method, way?, website, books, chance,\n",
"[ 230 33977 24598 43277 266 10673 1389 1003]\n",
"50000\n",
"230\n",
"money?\n",
"33977\n",
"parents’\n",
"24598\n",
"sacred\n",
"43277\n",
"hyperinflation\n",
"266\n",
"them\n",
"10673\n",
"Labrador\n",
"1389\n",
"files\n",
"1003\n",
"away\n",
"Nearest to money: money?, parents’, sacred, hyperinflation, them, Labrador, files, away,\n",
"[ 329 2761 8201 43422 29 861 44039 148]\n",
"50000\n",
"329\n",
"never\n",
"2761\n",
"don’t\n",
"8201\n",
"configure\n",
"43422\n",
"(IN)?\n",
"29\n",
"have\n",
"861\n",
"already\n",
"44039\n",
"\"perfume\",\n",
"148\n",
"been\n",
"Nearest to ever: never, don’t, configure, (IN)?, have, already, \"perfume\",, been,\n",
"[ 1663 399 521 38 1593 619 34057 3221]\n",
"50000\n",
"1663\n",
"various\n",
"399\n",
"worst\n",
"521\n",
"easiest\n",
"38\n",
"good\n",
"1593\n",
"cheapest\n",
"619\n",
"safety\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"3221\n",
"innovative\n",
"Nearest to best: various, worst, easiest, good, cheapest, safety, 1800</v\\>251<’-‘>4919, innovative,\n",
"[ 192 33 32612 9863 8 38561 11821 2741]\n",
"50000\n",
"192\n",
"through\n",
"33\n",
"from\n",
"32612\n",
"addictions\n",
"9863\n",
"aged\n",
"8\n",
"in\n",
"38561\n",
"parody\n",
"11821\n",
"away.\n",
"2741\n",
"Mac?\n",
"Nearest to on: through, from, addictions, aged, in, parody, away., Mac?,\n",
"[ 51 64 4 368 630 39 362 472]\n",
"50000\n",
"51\n",
"we\n",
"64\n",
"they\n",
"4\n",
"I\n",
"368\n",
"you've\n",
"630\n",
"humans\n",
"39\n",
"people\n",
"362\n",
"girls\n",
"472\n",
"i\n",
"Nearest to you: we, they, I, you've, humans, people, girls, i,\n",
"[31945 1844 44262 31736 162 358 2567 8865]\n",
"50000\n",
"31945\n",
"Iraq,\n",
"1844\n",
"indian\n",
"44262\n",
"pick:\n",
"31736\n",
"tumor\n",
"162\n",
"US\n",
"358\n",
"American\n",
"2567\n",
"refer\n",
"8865\n",
"ISS?\n",
"Nearest to Indian: Iraq,, indian, pick:, tumor, US, American, refer, ISS?,\n",
"[22384 45 8539 30011 34306 29025 23265 6645]\n",
"50000\n",
"22384\n",
"Group)\n",
"45\n",
"between\n",
"8539\n",
"Line\n",
"30011\n",
"Carroll\n",
"34306\n",
"Inbox\n",
"29025\n",
"contraceptive\n",
"23265\n",
"Genesis\n",
"6645\n",
"dragon\n",
"Nearest to with: Group), between, Line, Carroll, Inbox, contraceptive, Genesis, dragon,\n",
"[ 35 189 173 65 200 48 46405 1012]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"173\n",
"before\n",
"65\n",
"after\n",
"200\n",
"during\n",
"48\n",
"what\n",
"46405\n",
"melatonin?\n",
"1012\n",
"though\n",
"Nearest to when: if, while, before, after, during, what, melatonin?, though,\n",
"[234 400 114 594 281 17 381 648]\n",
"50000\n",
"234\n",
"bad\n",
"400\n",
"great\n",
"114\n",
"better\n",
"594\n",
"suitable\n",
"281\n",
"safe\n",
"17\n",
"best\n",
"381\n",
"common\n",
"648\n",
"sentence?\n",
"Nearest to good: bad, great, better, suitable, safe, best, common, sentence?,\n",
"[ 52 112 253 141 96 159 32590 94]\n",
"50000\n",
"52\n",
"how\n",
"112\n",
"which\n",
"253\n",
"where\n",
"141\n",
"why\n",
"96\n",
"but\n",
"159\n",
"why?\n",
"32590\n",
"analogue\n",
"94\n",
"who\n",
"Nearest to what: how, which, where, why, but, why?, analogue, who,\n",
"Average loss at step 52000: 2.918905\n",
"Average loss at step 54000: 2.919753\n",
"Average loss at step 56000: 2.918291\n",
"Average loss at step 58000: 2.921430\n",
"Average loss at step 60000: 2.904043\n",
"[ 178 25 45966 6914 33866 7751 40967 38304]\n",
"50000\n",
"178\n",
"no\n",
"25\n",
"some\n",
"45966\n",
"shoul\n",
"6914\n",
"lenses\n",
"33866\n",
"terminology?\n",
"7751\n",
"refrigerator\n",
"40967\n",
"ghosts/scientifically\n",
"38304\n",
"cow,\n",
"Nearest to any: no, some, shoul, lenses, terminology?, refrigerator, ghosts/scientifically, cow,,\n",
"[33273 8 43 13 44 14856 4 34]\n",
"50000\n",
"33273\n",
"invested?\n",
"8\n",
"in\n",
"43\n",
"would\n",
"13\n",
"for\n",
"44\n",
"will\n",
"14856\n",
"alumni\n",
"4\n",
"I\n",
"34\n",
"should\n",
"Nearest to to: invested?, in, would, for, will, alumni, I, should,\n",
"[ 27 24 183 305 19333 260 29 667]\n",
"50000\n",
"27\n",
"get\n",
"24\n",
"be\n",
"183\n",
"had\n",
"305\n",
"create\n",
"19333\n",
"naruto\n",
"260\n",
"write\n",
"29\n",
"have\n",
"667\n",
"develop\n",
"Nearest to become: get, be, had, create, naruto, write, have, develop,\n",
"[ 2 16 31 7 115 66 94 1693]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"31\n",
"Which\n",
"7\n",
"How\n",
"115\n",
"When\n",
"66\n",
"Where\n",
"94\n",
"who\n",
"1693\n",
"She\n",
"Nearest to Who: What, Why, Which, How, When, Where, who, She,\n",
"[ 8 7610 49540 35841 7239 16098 12573 19636]\n",
"50000\n",
"8\n",
"in\n",
"7610\n",
"libraries\n",
"49540\n",
"(Most\n",
"35841\n",
"fade?\n",
"7239\n",
"Colorado\n",
"16098\n",
"Bill?\n",
"12573\n",
"turkey\n",
"19636\n",
"perhaps\n",
"Nearest to at: in, libraries, (Most, fade?, Colorado, Bill?, turkey, perhaps,\n",
"[ 117 987 1111 322 6941 1126 831 124]\n",
"50000\n",
"117\n",
"ways\n",
"987\n",
"solution\n",
"1111\n",
"method\n",
"322\n",
"places\n",
"6941\n",
"signature\n",
"1126\n",
"way?\n",
"831\n",
"chance\n",
"124\n",
"possible\n",
"Nearest to way: ways, solution, method, places, signature, way?, chance, possible,\n",
"[ 230 33977 266 1389 24598 8061 6247 10673]\n",
"50000\n",
"230\n",
"money?\n",
"33977\n",
"parents’\n",
"266\n",
"them\n",
"1389\n",
"files\n",
"24598\n",
"sacred\n",
"8061\n",
"bills\n",
"6247\n",
"Venezuela's\n",
"10673\n",
"Labrador\n",
"Nearest to money: money?, parents’, them, files, sacred, bills, Venezuela's, Labrador,\n",
"[ 329 29 8201 861 44039 368 43422 2761]\n",
"50000\n",
"329\n",
"never\n",
"29\n",
"have\n",
"8201\n",
"configure\n",
"861\n",
"already\n",
"44039\n",
"\"perfume\",\n",
"368\n",
"you've\n",
"43422\n",
"(IN)?\n",
"2761\n",
"don’t\n",
"Nearest to ever: never, have, configure, already, \"perfume\",, you've, (IN)?, don’t,\n",
"[ 1663 521 399 38 1087 619 68 34057]\n",
"50000\n",
"1663\n",
"various\n",
"521\n",
"easiest\n",
"399\n",
"worst\n",
"38\n",
"good\n",
"1087\n",
"funniest\n",
"619\n",
"safety\n",
"68\n",
"difference\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"Nearest to best: various, easiest, worst, good, funniest, safety, difference, 1800</v\\>251<’-‘>4919,\n",
"[ 192 38561 32612 8 137 43537 23844 11922]\n",
"50000\n",
"192\n",
"through\n",
"38561\n",
"parody\n",
"32612\n",
"addictions\n",
"8\n",
"in\n",
"137\n",
"using\n",
"43537\n",
"ABC,\n",
"23844\n",
"undergraduates?\n",
"11922\n",
"demonetized\n",
"Nearest to on: through, parody, addictions, in, using, ABC,, undergraduates?, demonetized,\n",
"[ 51 4 64 368 630 39 362 472]\n",
"50000\n",
"51\n",
"we\n",
"4\n",
"I\n",
"64\n",
"they\n",
"368\n",
"you've\n",
"630\n",
"humans\n",
"39\n",
"people\n",
"362\n",
"girls\n",
"472\n",
"i\n",
"Nearest to you: we, I, they, you've, humans, people, girls, i,\n",
"[31945 1844 358 31736 578 30300 2567 162]\n",
"50000\n",
"31945\n",
"Iraq,\n",
"1844\n",
"indian\n",
"358\n",
"American\n",
"31736\n",
"tumor\n",
"578\n",
"international\n",
"30300\n",
"uTorrent?\n",
"2567\n",
"refer\n",
"162\n",
"US\n",
"Nearest to Indian: Iraq,, indian, American, tumor, international, uTorrent?, refer, US,\n",
"[30011 22384 45 23514 34306 23265 38924 7393]\n",
"50000\n",
"30011\n",
"Carroll\n",
"22384\n",
"Group)\n",
"45\n",
"between\n",
"23514\n",
"intriguing\n",
"34306\n",
"Inbox\n",
"23265\n",
"Genesis\n",
"38924\n",
"call.\n",
"7393\n",
"Grade\n",
"Nearest to with: Carroll, Group), between, intriguing, Inbox, Genesis, call., Grade,\n",
"[ 35 189 173 65 1012 363 23095 47618]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"173\n",
"before\n",
"65\n",
"after\n",
"1012\n",
"though\n",
"363\n",
"always\n",
"23095\n",
"exes\n",
"47618\n",
"hex\n",
"Nearest to when: if, while, before, after, though, always, exes, hex,\n",
"[ 234 400 594 281 114 17 381 18495]\n",
"50000\n",
"234\n",
"bad\n",
"400\n",
"great\n",
"594\n",
"suitable\n",
"281\n",
"safe\n",
"114\n",
"better\n",
"17\n",
"best\n",
"381\n",
"common\n",
"18495\n",
"wires?\n",
"Nearest to good: bad, great, suitable, safe, better, best, common, wires?,\n",
"[ 52 112 141 253 96 94 30583 39861]\n",
"50000\n",
"52\n",
"how\n",
"112\n",
"which\n",
"141\n",
"why\n",
"253\n",
"where\n",
"96\n",
"but\n",
"94\n",
"who\n",
"30583\n",
"girlfriend/wife\n",
"39861\n",
"dude\n",
"Nearest to what: how, which, why, where, but, who, girlfriend/wife, dude,\n",
"Average loss at step 62000: 2.893795\n",
"Average loss at step 64000: 2.879786\n",
"Average loss at step 66000: 2.867269\n",
"Average loss at step 68000: 2.873041\n",
"Average loss at step 70000: 2.878690\n",
"[ 178 7751 25 33866 40967 6914 45966 3774]\n",
"50000\n",
"178\n",
"no\n",
"7751\n",
"refrigerator\n",
"25\n",
"some\n",
"33866\n",
"terminology?\n",
"40967\n",
"ghosts/scientifically\n",
"6914\n",
"lenses\n",
"45966\n",
"shoul\n",
"3774\n",
"BITS\n",
"Nearest to any: no, refrigerator, some, terminology?, ghosts/scientifically, lenses, shoul, BITS,\n",
"[33273 43 44 14856 13 4 45104 15814]\n",
"50000\n",
"33273\n",
"invested?\n",
"43\n",
"would\n",
"44\n",
"will\n",
"14856\n",
"alumni\n",
"13\n",
"for\n",
"4\n",
"I\n",
"45104\n",
"sales.\n",
"15814\n",
"bought?\n",
"Nearest to to: invested?, would, will, alumni, for, I, sales., bought?,\n",
"[ 27 24 183 19333 305 260 667 1074]\n",
"50000\n",
"27\n",
"get\n",
"24\n",
"be\n",
"183\n",
"had\n",
"19333\n",
"naruto\n",
"305\n",
"create\n",
"260\n",
"write\n",
"667\n",
"develop\n",
"1074\n",
"becoming\n",
"Nearest to become: get, be, had, naruto, create, write, develop, becoming,\n",
"[ 2 16 31 7 66 115 42623 1693]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"31\n",
"Which\n",
"7\n",
"How\n",
"66\n",
"Where\n",
"115\n",
"When\n",
"42623\n",
"undergrad.\n",
"1693\n",
"She\n",
"Nearest to Who: What, Why, Which, How, Where, When, undergrad., She,\n",
"[ 7610 7239 49540 8 200 10922 23364 10191]\n",
"50000\n",
"7610\n",
"libraries\n",
"7239\n",
"Colorado\n",
"49540\n",
"(Most\n",
"8\n",
"in\n",
"200\n",
"during\n",
"10922\n",
"personnel\n",
"23364\n",
"born,\n",
"10191\n",
"progressive\n",
"Nearest to at: libraries, Colorado, (Most, in, during, personnel, born,, progressive,\n",
"[ 117 1111 1126 987 6941 322 831 124]\n",
"50000\n",
"117\n",
"ways\n",
"1111\n",
"method\n",
"1126\n",
"way?\n",
"987\n",
"solution\n",
"6941\n",
"signature\n",
"322\n",
"places\n",
"831\n",
"chance\n",
"124\n",
"possible\n",
"Nearest to way: ways, method, way?, solution, signature, places, chance, possible,\n",
"[ 230 1389 100 33977 24598 8061 266 10673]\n",
"50000\n",
"230\n",
"money?\n",
"1389\n",
"files\n",
"100\n",
"time\n",
"33977\n",
"parents’\n",
"24598\n",
"sacred\n",
"8061\n",
"bills\n",
"266\n",
"them\n",
"10673\n",
"Labrador\n",
"Nearest to money: money?, files, time, parents’, sacred, bills, them, Labrador,\n",
"[ 329 861 368 2761 43422 8201 821 183]\n",
"50000\n",
"329\n",
"never\n",
"861\n",
"already\n",
"368\n",
"you've\n",
"2761\n",
"don’t\n",
"43422\n",
"(IN)?\n",
"8201\n",
"configure\n",
"821\n",
"I've\n",
"183\n",
"had\n",
"Nearest to ever: never, already, you've, don’t, (IN)?, configure, I've, had,\n",
"[ 399 521 1663 1087 38 1593 34057 1049]\n",
"50000\n",
"399\n",
"worst\n",
"521\n",
"easiest\n",
"1663\n",
"various\n",
"1087\n",
"funniest\n",
"38\n",
"good\n",
"1593\n",
"cheapest\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"1049\n",
"latest\n",
"Nearest to best: worst, easiest, various, funniest, good, cheapest, 1800</v\\>251<’-‘>4919, latest,\n",
"[ 192 38561 23844 8 43537 137 33 1728]\n",
"50000\n",
"192\n",
"through\n",
"38561\n",
"parody\n",
"23844\n",
"undergraduates?\n",
"8\n",
"in\n",
"43537\n",
"ABC,\n",
"137\n",
"using\n",
"33\n",
"from\n",
"1728\n",
"regarding\n",
"Nearest to on: through, parody, undergraduates?, in, ABC,, using, from, regarding,\n",
"[ 51 4 64 368 39 630 225 362]\n",
"50000\n",
"51\n",
"we\n",
"4\n",
"I\n",
"64\n",
"they\n",
"368\n",
"you've\n",
"39\n",
"people\n",
"630\n",
"humans\n",
"225\n",
"women\n",
"362\n",
"girls\n",
"Nearest to you: we, I, they, you've, people, humans, women, girls,\n",
"[ 1844 31945 358 578 162 2567 30300 17181]\n",
"50000\n",
"1844\n",
"indian\n",
"31945\n",
"Iraq,\n",
"358\n",
"American\n",
"578\n",
"international\n",
"162\n",
"US\n",
"2567\n",
"refer\n",
"30300\n",
"uTorrent?\n",
"17181\n",
"toothpaste?\n",
"Nearest to Indian: indian, Iraq,, American, international, US, refer, uTorrent?, toothpaste?,\n",
"[22384 45 1314 8539 36075 30011 23514 5538]\n",
"50000\n",
"22384\n",
"Group)\n",
"45\n",
"between\n",
"1314\n",
"with?\n",
"8539\n",
"Line\n",
"36075\n",
"fading\n",
"30011\n",
"Carroll\n",
"23514\n",
"intriguing\n",
"5538\n",
"hates\n",
"Nearest to with: Group), between, with?, Line, fading, Carroll, intriguing, hates,\n",
"[ 35 189 65 173 1012 200 363 4480]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"65\n",
"after\n",
"173\n",
"before\n",
"1012\n",
"though\n",
"200\n",
"during\n",
"363\n",
"always\n",
"4480\n",
"(as\n",
"Nearest to when: if, while, after, before, though, during, always, (as,\n",
"[ 400 234 281 114 381 594 17 18495]\n",
"50000\n",
"400\n",
"great\n",
"234\n",
"bad\n",
"281\n",
"safe\n",
"114\n",
"better\n",
"381\n",
"common\n",
"594\n",
"suitable\n",
"17\n",
"best\n",
"18495\n",
"wires?\n",
"Nearest to good: great, bad, safe, better, common, suitable, best, wires?,\n",
"[ 52 112 141 253 96 159 30583 37559]\n",
"50000\n",
"52\n",
"how\n",
"112\n",
"which\n",
"141\n",
"why\n",
"253\n",
"where\n",
"96\n",
"but\n",
"159\n",
"why?\n",
"30583\n",
"girlfriend/wife\n",
"37559\n",
"poisons\n",
"Nearest to what: how, which, why, where, but, why?, girlfriend/wife, poisons,\n",
"Average loss at step 72000: 2.868727\n",
"Average loss at step 74000: 2.861959\n",
"Average loss at step 76000: 2.866524\n",
"Average loss at step 78000: 2.844037\n",
"Average loss at step 80000: 2.840935\n",
"[ 178 40967 25 45966 25048 33866 6914 31172]\n",
"50000\n",
"178\n",
"no\n",
"40967\n",
"ghosts/scientifically\n",
"25\n",
"some\n",
"45966\n",
"shoul\n",
"25048\n",
"stairs\n",
"33866\n",
"terminology?\n",
"6914\n",
"lenses\n",
"31172\n",
"Comparing\n",
"Nearest to any: no, ghosts/scientifically, some, shoul, stairs, terminology?, lenses, Comparing,\n",
"[ 13 33273 49322 14856 906 30464 4 31050]\n",
"50000\n",
"13\n",
"for\n",
"33273\n",
"invested?\n",
"49322\n",
"150k\n",
"14856\n",
"alumni\n",
"906\n",
"to?\n",
"30464\n",
"usability\n",
"4\n",
"I\n",
"31050\n",
"Cheney\n",
"Nearest to to: for, invested?, 150k, alumni, to?, usability, I, Cheney,\n",
"[ 27 24 183 19333 667 305 29 260]\n",
"50000\n",
"27\n",
"get\n",
"24\n",
"be\n",
"183\n",
"had\n",
"19333\n",
"naruto\n",
"667\n",
"develop\n",
"305\n",
"create\n",
"29\n",
"have\n",
"260\n",
"write\n",
"Nearest to become: get, be, had, naruto, develop, create, have, write,\n",
"[ 2 16 31 66 7 115 1693 42623]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"31\n",
"Which\n",
"66\n",
"Where\n",
"7\n",
"How\n",
"115\n",
"When\n",
"1693\n",
"She\n",
"42623\n",
"undergrad.\n",
"Nearest to Who: What, Why, Which, Where, How, When, She, undergrad.,\n",
"[ 7610 7239 8 49540 200 45645 35841 7898]\n",
"50000\n",
"7610\n",
"libraries\n",
"7239\n",
"Colorado\n",
"8\n",
"in\n",
"49540\n",
"(Most\n",
"200\n",
"during\n",
"45645\n",
"214\n",
"35841\n",
"fade?\n",
"7898\n",
"admin\n",
"Nearest to at: libraries, Colorado, in, (Most, during, 214, fade?, admin,\n",
"[ 117 1111 1126 987 6941 831 322 316]\n",
"50000\n",
"117\n",
"ways\n",
"1111\n",
"method\n",
"1126\n",
"way?\n",
"987\n",
"solution\n",
"6941\n",
"signature\n",
"831\n",
"chance\n",
"322\n",
"places\n",
"316\n",
"place\n",
"Nearest to way: ways, method, way?, solution, signature, chance, places, place,\n",
"[ 230 1389 100 24598 8061 23017 33977 2825]\n",
"50000\n",
"230\n",
"money?\n",
"1389\n",
"files\n",
"100\n",
"time\n",
"24598\n",
"sacred\n",
"8061\n",
"bills\n",
"23017\n",
"2015:\n",
"33977\n",
"parents’\n",
"2825\n",
"holes\n",
"Nearest to money: money?, files, time, sacred, bills, 2015:, parents’, holes,\n",
"[ 329 861 368 43422 183 2761 148 30038]\n",
"50000\n",
"329\n",
"never\n",
"861\n",
"already\n",
"368\n",
"you've\n",
"43422\n",
"(IN)?\n",
"183\n",
"had\n",
"2761\n",
"don’t\n",
"148\n",
"been\n",
"30038\n",
"exam(\n",
"Nearest to ever: never, already, you've, (IN)?, had, don’t, been, exam(,\n",
"[ 521 38 399 1593 1663 1049 34057 2528]\n",
"50000\n",
"521\n",
"easiest\n",
"38\n",
"good\n",
"399\n",
"worst\n",
"1593\n",
"cheapest\n",
"1663\n",
"various\n",
"1049\n",
"latest\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"2528\n",
"recommended\n",
"Nearest to best: easiest, good, worst, cheapest, various, latest, 1800</v\\>251<’-‘>4919, recommended,\n",
"[ 8 31693 192 32612 38561 48260 9863 23844]\n",
"50000\n",
"8\n",
"in\n",
"31693\n",
"Jr\n",
"192\n",
"through\n",
"32612\n",
"addictions\n",
"38561\n",
"parody\n",
"48260\n",
"untucked?\n",
"9863\n",
"aged\n",
"23844\n",
"undergraduates?\n",
"Nearest to on: in, Jr, through, addictions, parody, untucked?, aged, undergraduates?,\n",
"[ 51 64 4 368 630 362 39 472]\n",
"50000\n",
"51\n",
"we\n",
"64\n",
"they\n",
"4\n",
"I\n",
"368\n",
"you've\n",
"630\n",
"humans\n",
"362\n",
"girls\n",
"39\n",
"people\n",
"472\n",
"i\n",
"Nearest to you: we, they, I, you've, humans, girls, people, i,\n",
"[ 1844 31945 358 162 578 31736 2567 30300]\n",
"50000\n",
"1844\n",
"indian\n",
"31945\n",
"Iraq,\n",
"358\n",
"American\n",
"162\n",
"US\n",
"578\n",
"international\n",
"31736\n",
"tumor\n",
"2567\n",
"refer\n",
"30300\n",
"uTorrent?\n",
"Nearest to Indian: indian, Iraq,, American, US, international, tumor, refer, uTorrent?,\n",
"[22384 181 45 30011 36075 8539 1314 23265]\n",
"50000\n",
"22384\n",
"Group)\n",
"181\n",
"over\n",
"45\n",
"between\n",
"30011\n",
"Carroll\n",
"36075\n",
"fading\n",
"8539\n",
"Line\n",
"1314\n",
"with?\n",
"23265\n",
"Genesis\n",
"Nearest to with: Group), over, between, Carroll, fading, Line, with?, Genesis,\n",
"[ 35 189 173 65 363 200 26 1012]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"173\n",
"before\n",
"65\n",
"after\n",
"363\n",
"always\n",
"200\n",
"during\n",
"26\n",
"that\n",
"1012\n",
"though\n",
"Nearest to when: if, while, before, after, always, during, that, though,\n",
"[ 400 234 114 281 594 17 18495 838]\n",
"50000\n",
"400\n",
"great\n",
"234\n",
"bad\n",
"114\n",
"better\n",
"281\n",
"safe\n",
"594\n",
"suitable\n",
"17\n",
"best\n",
"18495\n",
"wires?\n",
"838\n",
"healthy\n",
"Nearest to good: great, bad, better, safe, suitable, best, wires?, healthy,\n",
"[ 52 112 253 141 96 159 94 32590]\n",
"50000\n",
"52\n",
"how\n",
"112\n",
"which\n",
"253\n",
"where\n",
"141\n",
"why\n",
"96\n",
"but\n",
"159\n",
"why?\n",
"94\n",
"who\n",
"32590\n",
"analogue\n",
"Nearest to what: how, which, where, why, but, why?, who, analogue,\n",
"Average loss at step 82000: 2.835201\n",
"Average loss at step 84000: 2.833764\n",
"Average loss at step 86000: 2.827922\n",
"Average loss at step 88000: 2.814393\n",
"Average loss at step 90000: 2.833458\n",
"[ 178 25 40967 45966 5 6914 33866 9894]\n",
"50000\n",
"178\n",
"no\n",
"25\n",
"some\n",
"40967\n",
"ghosts/scientifically\n",
"45966\n",
"shoul\n",
"5\n",
"a\n",
"6914\n",
"lenses\n",
"33866\n",
"terminology?\n",
"9894\n",
"Norway\n",
"Nearest to any: no, some, ghosts/scientifically, shoul, a, lenses, terminology?, Norway,\n",
"[33273 13 906 14856 4 43 8 44]\n",
"50000\n",
"33273\n",
"invested?\n",
"13\n",
"for\n",
"906\n",
"to?\n",
"14856\n",
"alumni\n",
"4\n",
"I\n",
"43\n",
"would\n",
"8\n",
"in\n",
"44\n",
"will\n",
"Nearest to to: invested?, for, to?, alumni, I, would, in, will,\n",
"[ 24 27 667 183 260 305 19333 1074]\n",
"50000\n",
"24\n",
"be\n",
"27\n",
"get\n",
"667\n",
"develop\n",
"183\n",
"had\n",
"260\n",
"write\n",
"305\n",
"create\n",
"19333\n",
"naruto\n",
"1074\n",
"becoming\n",
"Nearest to become: be, get, develop, had, write, create, naruto, becoming,\n",
"[ 2 16 31 66 115 7 1693 1350]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"31\n",
"Which\n",
"66\n",
"Where\n",
"115\n",
"When\n",
"7\n",
"How\n",
"1693\n",
"She\n",
"1350\n",
"He\n",
"Nearest to Who: What, Why, Which, Where, When, How, She, He,\n",
"[23364 8 200 7898 7610 49540 8385 35841]\n",
"50000\n",
"23364\n",
"born,\n",
"8\n",
"in\n",
"200\n",
"during\n",
"7898\n",
"admin\n",
"7610\n",
"libraries\n",
"49540\n",
"(Most\n",
"8385\n",
"instances\n",
"35841\n",
"fade?\n",
"Nearest to at: born,, in, during, admin, libraries, (Most, instances, fade?,\n",
"[ 117 1126 987 1111 831 322 6941 2128]\n",
"50000\n",
"117\n",
"ways\n",
"1126\n",
"way?\n",
"987\n",
"solution\n",
"1111\n",
"method\n",
"831\n",
"chance\n",
"322\n",
"places\n",
"6941\n",
"signature\n",
"2128\n",
"possibility\n",
"Nearest to way: ways, way?, solution, method, chance, places, signature, possibility,\n",
"[ 230 100 1389 33977 24598 23017 2825 34147]\n",
"50000\n",
"230\n",
"money?\n",
"100\n",
"time\n",
"1389\n",
"files\n",
"33977\n",
"parents’\n",
"24598\n",
"sacred\n",
"23017\n",
"2015:\n",
"2825\n",
"holes\n",
"34147\n",
"shotgun\n",
"Nearest to money: money?, time, files, parents’, sacred, 2015:, holes, shotgun,\n",
"[ 329 368 861 183 43422 2761 148 18681]\n",
"50000\n",
"329\n",
"never\n",
"368\n",
"you've\n",
"861\n",
"already\n",
"183\n",
"had\n",
"43422\n",
"(IN)?\n",
"2761\n",
"don’t\n",
"148\n",
"been\n",
"18681\n",
"expires\n",
"Nearest to ever: never, you've, already, had, (IN)?, don’t, been, expires,\n",
"[ 399 521 1376 1663 34057 38 2528 619]\n",
"50000\n",
"399\n",
"worst\n",
"521\n",
"easiest\n",
"1376\n",
"Best\n",
"1663\n",
"various\n",
"34057\n",
"1800</v\\>251<’-‘>4919\n",
"38\n",
"good\n",
"2528\n",
"recommended\n",
"619\n",
"safety\n",
"Nearest to best: worst, easiest, Best, various, 1800</v\\>251<’-‘>4919, good, recommended, safety,\n",
"[38561 13 192 1728 9863 8 32612 48260]\n",
"50000\n",
"38561\n",
"parody\n",
"13\n",
"for\n",
"192\n",
"through\n",
"1728\n",
"regarding\n",
"9863\n",
"aged\n",
"8\n",
"in\n",
"32612\n",
"addictions\n",
"48260\n",
"untucked?\n",
"Nearest to on: parody, for, through, regarding, aged, in, addictions, untucked?,\n",
"[ 51 4 64 368 630 472 39 362]\n",
"50000\n",
"51\n",
"we\n",
"4\n",
"I\n",
"64\n",
"they\n",
"368\n",
"you've\n",
"630\n",
"humans\n",
"472\n",
"i\n",
"39\n",
"people\n",
"362\n",
"girls\n",
"Nearest to you: we, I, they, you've, humans, i, people, girls,\n",
"[ 1844 358 31945 162 578 361 41361 31736]\n",
"50000\n",
"1844\n",
"indian\n",
"358\n",
"American\n",
"31945\n",
"Iraq,\n",
"162\n",
"US\n",
"578\n",
"international\n",
"361\n",
"Chinese\n",
"41361\n",
"Mythology\n",
"31736\n",
"tumor\n",
"Nearest to Indian: indian, American, Iraq,, US, international, Chinese, Mythology, tumor,\n",
"[22384 1314 34306 7393 30011 32433 45 16936]\n",
"50000\n",
"22384\n",
"Group)\n",
"1314\n",
"with?\n",
"34306\n",
"Inbox\n",
"7393\n",
"Grade\n",
"30011\n",
"Carroll\n",
"32433\n",
"70s\n",
"45\n",
"between\n",
"16936\n",
"Notes\n",
"Nearest to with: Group), with?, Inbox, Grade, Carroll, 70s, between, Notes,\n",
"[ 35 189 173 65 200 1012 26 46405]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"173\n",
"before\n",
"65\n",
"after\n",
"200\n",
"during\n",
"1012\n",
"though\n",
"26\n",
"that\n",
"46405\n",
"melatonin?\n",
"Nearest to when: if, while, before, after, during, though, that, melatonin?,\n",
"[ 400 234 114 594 281 17 838 23547]\n",
"50000\n",
"400\n",
"great\n",
"234\n",
"bad\n",
"114\n",
"better\n",
"594\n",
"suitable\n",
"281\n",
"safe\n",
"17\n",
"best\n",
"838\n",
"healthy\n",
"23547\n",
"Hub\n",
"Nearest to good: great, bad, better, suitable, safe, best, healthy, Hub,\n",
"[ 52 112 253 141 96 94 159 254]\n",
"50000\n",
"52\n",
"how\n",
"112\n",
"which\n",
"253\n",
"where\n",
"141\n",
"why\n",
"96\n",
"but\n",
"94\n",
"who\n",
"159\n",
"why?\n",
"254\n",
"then\n",
"Nearest to what: how, which, where, why, but, who, why?, then,\n",
"Average loss at step 92000: 2.821405\n",
"Average loss at step 94000: 2.813942\n",
"Average loss at step 96000: 2.821814\n",
"Average loss at step 98000: 2.791144\n",
"Average loss at step 100000: 2.790211\n",
"[ 178 25 40967 6914 238 45966 33866 9894]\n",
"50000\n",
"178\n",
"no\n",
"25\n",
"some\n",
"40967\n",
"ghosts/scientifically\n",
"6914\n",
"lenses\n",
"238\n",
"anyone\n",
"45966\n",
"shoul\n",
"33866\n",
"terminology?\n",
"9894\n",
"Norway\n",
"Nearest to any: no, some, ghosts/scientifically, lenses, anyone, shoul, terminology?, Norway,\n",
"[33273 13 43 906 14 31050 135 14856]\n",
"50000\n",
"33273\n",
"invested?\n",
"13\n",
"for\n",
"43\n",
"would\n",
"906\n",
"to?\n",
"14\n",
"can\n",
"31050\n",
"Cheney\n",
"135\n",
"could\n",
"14856\n",
"alumni\n",
"Nearest to to: invested?, for, would, to?, can, Cheney, could, alumni,\n",
"[ 27 24 667 305 260 1766 1074 183]\n",
"50000\n",
"27\n",
"get\n",
"24\n",
"be\n",
"667\n",
"develop\n",
"305\n",
"create\n",
"260\n",
"write\n",
"1766\n",
"became\n",
"1074\n",
"becoming\n",
"183\n",
"had\n",
"Nearest to become: get, be, develop, create, write, became, becoming, had,\n",
"[ 2 16 7 115 31 66 1693 1383]\n",
"50000\n",
"2\n",
"What\n",
"16\n",
"Why\n",
"7\n",
"How\n",
"115\n",
"When\n",
"31\n",
"Which\n",
"66\n",
"Where\n",
"1693\n",
"She\n",
"1383\n",
"We\n",
"Nearest to Who: What, Why, How, When, Which, Where, She, We,\n",
"[23364 49540 7610 45645 17147 48812 8 45999]\n",
"50000\n",
"23364\n",
"born,\n",
"49540\n",
"(Most\n",
"7610\n",
"libraries\n",
"45645\n",
"214\n",
"17147\n",
"equilateral\n",
"48812\n",
"Fusion?\n",
"8\n",
"in\n",
"45999\n",
"rash?\n",
"Nearest to at: born,, (Most, libraries, 214, equilateral, Fusion?, in, rash?,\n",
"[ 117 1126 1111 831 987 316 533 124]\n",
"50000\n",
"117\n",
"ways\n",
"1126\n",
"way?\n",
"1111\n",
"method\n",
"831\n",
"chance\n",
"987\n",
"solution\n",
"316\n",
"place\n",
"533\n",
"websites\n",
"124\n",
"possible\n",
"Nearest to way: ways, way?, method, chance, solution, place, websites, possible,\n",
"[ 230 2825 436 100 33977 1389 1003 11317]\n",
"50000\n",
"230\n",
"money?\n",
"2825\n",
"holes\n",
"436\n",
"friends\n",
"100\n",
"time\n",
"33977\n",
"parents’\n",
"1389\n",
"files\n",
"1003\n",
"away\n",
"11317\n",
"Hamilton\n",
"Nearest to money: money?, holes, friends, time, parents’, files, away, Hamilton,\n",
"[ 329 861 148 368 2761 183 43422 30038]\n",
"50000\n",
"329\n",
"never\n",
"861\n",
"already\n",
"148\n",
"been\n",
"368\n",
"you've\n",
"2761\n",
"don’t\n",
"183\n",
"had\n",
"43422\n",
"(IN)?\n",
"30038\n",
"exam(\n",
"Nearest to ever: never, already, been, you've, don’t, had, (IN)?, exam(,\n",
"[ 399 38 521 1376 1087 1593 1049 2528]\n",
"50000\n",
"399\n",
"worst\n",
"38\n",
"good\n",
"521\n",
"easiest\n",
"1376\n",
"Best\n",
"1087\n",
"funniest\n",
"1593\n",
"cheapest\n",
"1049\n",
"latest\n",
"2528\n",
"recommended\n",
"Nearest to best: worst, good, easiest, Best, funniest, cheapest, latest, recommended,\n",
"[ 1728 38561 8 32612 48260 192 13 11922]\n",
"50000\n",
"1728\n",
"regarding\n",
"38561\n",
"parody\n",
"8\n",
"in\n",
"32612\n",
"addictions\n",
"48260\n",
"untucked?\n",
"192\n",
"through\n",
"13\n",
"for\n",
"11922\n",
"demonetized\n",
"Nearest to on: regarding, parody, in, addictions, untucked?, through, for, demonetized,\n",
"[ 51 4 64 368 630 362 472 39]\n",
"50000\n",
"51\n",
"we\n",
"4\n",
"I\n",
"64\n",
"they\n",
"368\n",
"you've\n",
"630\n",
"humans\n",
"362\n",
"girls\n",
"472\n",
"i\n",
"39\n",
"people\n",
"Nearest to you: we, I, they, you've, humans, girls, i, people,\n",
"[ 1844 358 31945 578 162 361 31840 760]\n",
"50000\n",
"1844\n",
"indian\n",
"358\n",
"American\n",
"31945\n",
"Iraq,\n",
"578\n",
"international\n",
"162\n",
"US\n",
"361\n",
"Chinese\n",
"31840\n",
"prerequisites?\n",
"760\n",
"British\n",
"Nearest to Indian: indian, American, Iraq,, international, US, Chinese, prerequisites?, British,\n",
"[22384 1314 45 30011 34306 19 1316 45881]\n",
"50000\n",
"22384\n",
"Group)\n",
"1314\n",
"with?\n",
"45\n",
"between\n",
"30011\n",
"Carroll\n",
"34306\n",
"Inbox\n",
"19\n",
"on\n",
"1316\n",
"via\n",
"45881\n",
"Terms\n",
"Nearest to with: Group), with?, between, Carroll, Inbox, on, via, Terms,\n",
"[ 35 189 173 65 601 46405 200 1012]\n",
"50000\n",
"35\n",
"if\n",
"189\n",
"while\n",
"173\n",
"before\n",
"65\n",
"after\n",
"601\n",
"also\n",
"46405\n",
"melatonin?\n",
"200\n",
"during\n",
"1012\n",
"though\n",
"Nearest to when: if, while, before, after, also, melatonin?, during, though,\n",
"[ 400 234 17 594 114 281 1440 838]\n",
"50000\n",
"400\n",
"great\n",
"234\n",
"bad\n",
"17\n",
"best\n",
"594\n",
"suitable\n",
"114\n",
"better\n",
"281\n",
"safe\n",
"1440\n",
"cool\n",
"838\n",
"healthy\n",
"Nearest to good: great, bad, best, suitable, better, safe, cool, healthy,\n",
"[ 52 112 253 141 94 96 159 43490]\n",
"50000\n",
"52\n",
"how\n",
"112\n",
"which\n",
"253\n",
"where\n",
"141\n",
"why\n",
"94\n",
"who\n",
"96\n",
"but\n",
"159\n",
"why?\n",
"43490\n",
"Naik?\n",
"Nearest to what: how, which, where, why, who, but, why?, Naik?,\n"
]
}
],
"source": [
"num_steps = 100001\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.global_variables_initializer().run()\n",
" print('Initialized')\n",
" average_loss = 0\n",
" for step in range(num_steps):\n",
" batch_data, batch_labels = generate_batch(\n",
" batch_size, num_skips, skip_window)\n",
" feed_dict = {train_dataset : batch_data, train_labels : batch_labels}\n",
" _, l = session.run([optimizer, loss], feed_dict=feed_dict)\n",
" average_loss += l\n",
" if step % 2000 == 0:\n",
" if step > 0:\n",
" average_loss = average_loss / 2000\n",
" # The average loss is an estimate of the loss over the last 2000 batches.\n",
" print('Average loss at step %d: %f' % (step, average_loss))\n",
" average_loss = 0\n",
" # note that this is expensive (~20% slowdown if computed every 500 steps)\n",
" if step % 10000 == 0:\n",
" sim = similarity.eval()\n",
" for i in range(valid_size):\n",
" valid_word = reverse_dictionary[valid_examples[i]]\n",
" top_k = 8 # number of nearest neighbors\n",
" nearest = (-sim[i, :]).argsort()[1:top_k+1]\n",
" log = 'Nearest to %s:' % valid_word\n",
" print(nearest)\n",
" print(len(reverse_dictionary))\n",
" for k in range(top_k):\n",
" a = nearest[k]\n",
" print(a)\n",
" close_word = reverse_dictionary[nearest[k]]\n",
" print(close_word)\n",
" log = '%s %s,' % (log, close_word)\n",
" print(log)\n",
" final_embeddings = normalized_embeddings.eval()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "8e45232a-ceee-678d-b24e-560019bd522b"
},
"outputs": [],
"source": [
"num_points = 400\n",
"\n",
"tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)\n",
"two_d_embeddings = tsne.fit_transform(final_embeddings[1:num_points+1, :])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "7e53e4b1-a23b-a41e-ba83-4c6948b78b8b"
},
"outputs": [
{
"data": {
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fGEa8+gXZ2dmcP38eDw+Pq4qjOvyQlMAXSxdTdL4AgDPHj/HF0sUAeAXfbM3QRERELLQy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t133+WxGfDx8UFDo5CPUjVaRTTq35/Wp4OwvhFN69NBbxQ+ef/999myZUueMtzcFOZPBWBgYFC6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SkJBAkyZNGDhwIHPnzmX48OGoqKiwd+9ewajc2dmZwMBA2rRpQ69evUhISEBPT49+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mDGjBmYmpqW2Y8s+c4ujh515WDouxw96krynV0vcinUlAMmJia0NTFBU0ODd3V0cNSvgYaG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\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5kZgbXyxDUag8/VFRWMiNL5bV6OjcDPsZBB0OUppqqaelxwz7GTV2jseVn5/PmDFjuHz5\nMqWlpfj4+HD16lU8PT0xNjYmIiKCTZs2sXjxYhQKBYMGDeKzzz4Dyr9PpkyZQlhYGCYmJmzevJlW\nrVpp+IoEQaiPZI8TDrq2ODo6KuLj4zVdDaGeqeixr3yjpy+XEdK1fa3d5AnCo6qJxtzjqovpbLXN\nNCIZVX+NZECWp21dV4fg4GB0dXXx9/dn5syZHD9+nPDwcMLDw/n+++8ZPHiw2pvw119/nf379zNq\n1CgWLlxI165dpZt3AwMDZsyYwe7du9HX12fnzp01kiOvvjndvQeour+Qyeh++lSNnmtPxh6+TPyS\na/nXMGliwgz7GQzqOEjt9qWlpWhpadVoHVTZunUrv//+O6tXrwbKE6Hb2NgQHx+PsbExV69exdnZ\nmYSEBJo3b46Xlxf+/v4MHz4cmUzGTz/9xPjx41mwYAE3btxQSrkhCMLTTSaTJSgUiodKMvtE0yxl\nMlmwTCZLk8lkKTKZbLtMJmtW6bk5MpnsnEwmOyOTybyf5DzCs+1+Pfa1aUfSFVyXhGMeuAfXJeHs\nSLpSq+cTHt9PP/1Ez549sbW15fXXX+fixYt07tyZ7OxsysrKcHNzIywsDChPVO3g4ICFhQWrVq2S\njmFgYEBAQAAWFha88MILxMbG4uHhQceOHfn111+B8qAdw4YNw8PDg86dOzN//nyV9QkODsbJyQlr\na2s+/vhjoLyXftCgQdjY2GBpaUloaGiNXHtdTWerbW11dR6pvLa5ubkRHR0NQHx8PHfv3qW4uJjo\n6Gi6dOnC7NmzCQ8PJzk5mbi4OCmfY35+Pr169eL48eN89NFHmJmZERERQUREhPS8s7Mzx48fx93d\nXbrRf9pom6ped6iu/HFU/N7PHTkX8z/MSZqQRKcDnfh4zMdYWFhIv3tQPuo9e/Zs7O3t2bJlCx4e\nHsyePZuePXvSpUsX6b2uSVZWVvzxxx/Mnj2b6OjoalFZ4+Li8PDwoFWrVmhrazN+/HgpIJdcLpeW\nGUyYMEFtQCRBEIQnXTP3B2CpUCisgXRgDoBMJusBjAMsgIHANzKZrPa7wYSn0pWi6lEv71deE3Yk\nXWHOthNcyS1AAVzJLWDOthOiQVcPnT59mtDQUA4dOkRycjJaWlocPHiQ2bNn8+abb/Lf//6XHj16\n4OXlBcCaNWtISEggPj6e5cuXc+vWLaD8Jrtfv36cPHmSpk2b8uGHH/LHH3+wfft2PvroI+l8sbGx\nbN26lZSUFLZs2ULV2QRhYWGcPXuW2NhYkpOTSUhIICoqit9//x0zMzOOHz9OamoqAwcOrJHrv990\ntoZkTkdT9OUypTJ9uYw5HTUTjMTBwYGEhATy8vLQ1dXFxcWF+Ph4oqOjadasmdqbcC0tLUaNGqX2\nuI0aNWLw4MHSOarmy3xatJ75DjI9PaUymZ4erWe+UyPHV/V7v2HDBhYtWkR8fDwpKSkcPHiQlJQU\naZ+WLVuSmJjIuHHjgPL0NrGxsSxbtkxtx8yT6NKlC4mJiVhZWfHhhx+yYMGCxz6WTCZ78EaCIDyT\nnqgxp1AowhQKRUWIwKNAu39+HgZsVigURQqF4gJwDrh/fHlBUEMTPfbB+85QUFyqVFZQXErwvjO1\ndk7h8Rw4cICEhAScnJywtbXlwIEDZGRkMHnyZPLy8li5cqWUMxBg+fLl2NjY4OzszJ9//snZs2eB\n8pvsigaWlZUVffv2RUdHBysrK6Ub7gEDBtCyZUv09fUZOXJktR7zsLAwwsLCsLOzw97enrS0NM6e\nPfvAXvrHVZKleoRaXXl9NcqkBSFd29NOVwcZ0E5XR6NTqXV0dDA3N2fdunX07t0bNzc3IiIiOHfu\nHB06dFC7n56e3n2n8Ono6Eg35rWdWkKTjIYMwfSTBWibmYFMhraZGaafLKix6b/qfu9//vln7O3t\nsbOz4+TJk5w69e+UzoqRrgqqUtHUpKtXr9K4cWMmTJhAQEAAiYmJNG3alDt37gDl6XMOHjxIdnY2\npaWlbNq0SQrIVVZWJqX/2bhxI3369Knx+gmC8HSoyQAok4CKeUNtKW/cVbj8T5kgPLI5LDRI4gAA\nIABJREFUHU1VrpmrzR77q7kFj1QuaI5CoWDixIl8+umnSuX37t3j8uXLQHm01KZNmxIZGcn+/fs5\ncuQIjRs3lvIGgvJNtlwul9JUyOVyCgoKWL58OYaGhsTHx9OvXz/Cw8O5cOECe/bs4cyZMxQUFGBh\nYYGhoSFz5syhc+fOLF++XJp+98cff2BhYYGFhQU+Pj6UlZXRpk0bJk2axMyZMx/7+rVNTcunWKoo\nb2hGmbSoV+tg3dzcCAkJYc2aNVhZWfHuu+/i4OBAz5498ff3Jzs7m+bNm7Np0ybefvttlceouHk3\nNjau49prntGQIbW2dlPV7/2FCxcYMGAAcXFxNG/eHD8/P+n3G8qjxFamKhVNTTpx4gQBAQHI5XJ0\ndHT49ttvOXLkCAMHDpSm3y5ZsgRPT09p7eWwYcOkusbGxrJw4UJat279wGnZQUFBGBgYMGuW6kis\nD3oeYMeOHXTp0oUePXo8/kULglDnHjgyJ5PJ9stkslQV/4ZV2mYuUAJsUH8ktcefKpPJ4mUyWfzN\nmzcfdXfhGaCJHnuzZvqPVC5oTv/+/fnll1+4ceMGADk5OVy8eJHZs2dLwQOmTJkClAcgaN68OY0b\nNyYtLY2jR4/e79ASLS0taU3N+fPnyc3NJS8vj7CwMLy9vVm0aBH6+vqkpKSQn5/PihUrcHJyIi0t\njZSUFG7cuMHXX3/NpEmTsLCwwMTEhN69e3PixAlee+21J7r+2p7O9ixzc3MjKysLFxcX2rRpg56e\nHm5ubpiamko34TY2Njg4OEg34VVNnTqVgQMH4unpWce1f7qp+r2/dOkSTZo0wcjIiOvXr7N3716N\n1tHb25uUlBRpXaWjoyNvv/02Z86ckdZQ+vr6Sil9KoLoVFi6dCmpqamEh4fXSSTLHTt2KI1kCoLQ\nMDxwZE6hULxwv+dlMpkfMBjor/g3NOYVoH2lzdr9U6bq+KuAVVAezfLBVRaeRXXdYx/g3ZU5204o\nTbXU19EiwPvZSxxe3/Xo0YOFCxfi5eVFWVkZOjo6LF26lLi4OA4dOoSWlhZbt25l7dq1vPzyy6xc\nuZLu3bvTtWtXnJ2dH+occrmchIQE+vXrh6GhITdu3MDCwoImTZowYcIEfv75ZwoKCrCzsyMrK4uX\nXnqJ3r17k5uby9ChQ9m+fTtHjx7l/PnzAJw9e5YuXbrw+++/S2v5HlfFyEdDj2ZZH/Xv35/i4n/X\n5qanp0s/+/r64uvrW22fqhFN3377baVRu7i9u1g1/TXu3MqmaUtjZv9nYi3U/Omn6vf+66+/xs7O\njm7dutG+fXtcXV01Xc3Hcjo6gpKiIv47bghNWxrjNu5VurtV7wxYtGgRP/zwA61bt6Z9+/Y4ODhw\n/vx5pk+fzs2bN2ncuDGrV6+mW7duSvup2iYnJ4dff/2VgwcPsnDhQrZu3QrwwGMJgqB5T5SaQCaT\nDQSWAn0VCsXNSuUWwEbK18mZAQeAzgqFolTlgf4hUhMI9cmOpCsE7zvD1dwCzJrpE+DdleF2Yrbw\ns6p///6YmJiQnp7O7NmzSU9PZ9WqVURERFSb2uXh4YGfnx9Xr15lyJAhTJ48mQsXLvD5558D5Tf8\n+/bt48cff6RFixasWbNGw1dXPzxueofIyEhCQkLqff7H09ERhK1aQcnfRVKZdiNdvKa+pfJmXagd\n9fm7/WE/IwkJCfj5+XHs2DFKSkqwt7fnjTfeYO/evaxcuZLOnTtz7Ngx5syZQ3h4uNI0y/79+6vc\nxs/Pj8GDBzN69GgAtdsJglD7HiU1wZOumVsB6AJ//LPW5KhCoXhDoVCclMlkPwOnKJ9+Of1BDTlB\nqG+G27WtN3/gBc1zc3Pjq6++ok+fPri5uUnrp/Ly8qpN7fLw8ADAzMwMWctWzPgoCKPgbwk/fJLp\nRo0Y1b4No0aNomvXrkyYMEGzFybUmejN65Vu0gFK/i4ievN60ZirIxWRiitmXVREKgbqxff9w35G\noqOjGTFihJSMfujQoRQWFnL48GF8fHyk7YqKlI919+7dB27zKNsJgqB5TxrN8v8UCkV7hUJh+8+/\nNyo9t0ihUHRSKBRdFQqFZieuP6MWL16s9Lh3794aqokgNHxubm7k5eWxceNGpfVTNjY20tSul19+\nWWlq19ZrOVx09oRWbdB6viOXi4r58GgStq5u2NraMmHChGqBW4Ty4BYBAQFYWlpiZWUlBX9QV15Z\nXFwcdnZ20pTW+uTOrexHKhdqXn2PVPwkn5GysjKaNWtGcnKy9O/06dOPvM2jbCcIguY9aZ45oR6r\n2pg7fPiwhmoiCA1fxfqpioh46enpvPvuu0B5MvH09HQOHDjAtm3b8PPzA8oT3uenJKE/aIR0nNKO\nXWjx3UbpBunFF1+s82up77Zt20ZycjLHjx9n//79BAQEkJWVpba8wuHDh3njjTfYuXMnnTp10uAV\nqNa0peqIlurKhZpX3yMVP+xnxN3dnR07dlBQUMCdO3fYtWsXjRs3xtzcnC1btgDlnR/Hjx9X2s/Q\n0FDtNpXTJtxvO0EQ6hfRmNOwRYsW0aVLF/r06YOvry8hISF4eHhIiYizs7OlnEalpaUEBATg5OSE\ntbU13333HQBZWVm4u7tja2uLpaUl0dHRBAYGUlBQgK2tLePHjwfK16OA+t7tyMhIPDw8GD16NN26\ndWP8+PFUrKkMDAykR48eWFtb3ze0sSAI/zru50NJRjp6LwxSKr9S+DdZS2LJT7qhoZrVbzExMfj6\n+qKlpUWbNm3o27cvcXFxasuhPIn01KlT2bVrF88999xDn6tDhw5kZ9fNyJjbuFfRbqSrVKbdSBe3\nca/WyfmF+h+p+GE/I/b29owdOxYbGxtefPFFnJycANiwYQPff/89NjY2WFhYsHPnzmrnULfNuHHj\nCA4Olka2H+ZYgiBoXk3mmRMeUUJCAps3byY5OVlawOzg4KB2+++//x4jIyPi4uIoKirC1dUVLy8v\ntm3bhre3N3PnzqW0tJR79+7h5ubGihUrSE5Ornacyr3b2dnZODk54e7uDkBSUhInT57EzMwMV1dX\nDh06RPfu3dm+fTtpaWnIZDJyc3Nr7TURhCeRm5vLxo0bmTZtGlevXsXf319KvKsJNuu2cLmouFp5\nm0IFpblF5G4rT1jexK51XVftqWNqakphYSFJSUmYmZlpujoqVax5it68XopmqS5SoVA76nuk4kf5\njMydO5e5c+dWK//999+rlQUFBUk/m5ubq9zG1dVVSk2QfuwaMTvPM6TD+xjY6+IyrBNdepk87mUJ\nglCLRGNOg1QtYL6fsLAwUlJSpJvT27dvc/bsWZycnJg0aRLFxcUMHz4cW1vb+x5HXe+2oaEhPXv2\npF27dgDY2tqSmZmJs7Mzenp6/Oc//2Hw4MEMHjy4Bq5eEGpebm4u33zzDdOmTcPMzEyjDTlQnfBe\nr1TB9PTyQAKK4jLy9mWKxlwVbm5ufPfdd0ycOJGcnByioqLw9/dny5YtbNmyhZCQEFq3bk1GRgbT\np0/H39+fM2fO0K1bN959912aNGmChYUFb7zxBpcuXQJg2bJluLq6cuvWLXx9fbly5QouLi48SUTn\nx9HdzVM03jSoIshJfY1mCZr/jKQfu0bEhjRK/i4D4G5OEREb0gBEg04Q6iExzbIe0tbWpqys/Eu0\nsLBQKlcoFHz11VfSWpsLFy7g5eWFu7s7UVFRtG3bFj8/P9avX//Y59bV/Xd6h5aWFiUlJWhraxMb\nG8vo0aPZvXs3AwcOfPyLE4RaFBgYyPnz57G1tcXHxwdLS0ugfE3bsGHD8PDwoHPnzsyfPx+A/Px8\nBg0ahI2NDZaWlioDajwJpYT3CgUmBWXMTS3kxWsl0jaluSJCXFUjRozA2toaGxsb+vXrx+eff06r\nVq3Iysqif//+yOVykpKSGDp0KHPnzsXf3x83NzeWL19Oy5YtmT59OuPHj2fmzJnExcWxdetWJk+e\nDMD8+fPp06cPJ0+eZMSIEVJjT3h2DLdry6HAflxYMohDgf3qVUOuPjiy87zUkKtQ8ncZR3bWv6BC\ngiCIxpxGqVrADOVrOBISEgCURha8vb359ttvpSS26enp5Ofnc/HiRdq0acOUKVOYPHkyiYmJAOjo\n6CglvK3g5uZGaGgopaWl3Lx5k6ioKHr27Km2nnfv3uX27du89NJLfPHFF2IRdAP3MFFNly1bxr17\n92rsnJMnT5am79SmJUuW0KlTJ5KTkwkODlZ6LjY2lq1bt5KSksKWLVuIj4/n999/x8zMjOPHj5Oa\nmlorHRWjTFoQ39uCxNhidkflKzXkALSa6arZ89lTkWNOJpMRHBxMamoqJ06cYOzYsQB07NiRH374\ngdTUVD744ANMTEw4fPgwISEhXL58mddff52//vqLkydPkpyczFtvvYWtrS1Dhw4lLy+Pu3fvEhUV\nJaWDGDRoEM2bN9fY9QpCfXQ3R3UHk7pyQRA0S0yz1KDKC5hbt24tLWCeNWsWY8aMYdWqVQwa9G/g\nhMmTJ5OZmYm9vT0KhYJWrVqxY8cOIiMjCQ4ORkdHBwMDA2lkburUqVhbW2Nvb8+GDRuk44wYMYIj\nR45gY2ODTCbj888/x8TEhLS0NJX1vHPnDsOGDaOwsBCFQsHSpUtr8VURatvDRDVdtmwZEyZMkKYA\nP4zS0lK0tLRUPve///3voY9TWwYMGEDLli0BGDlyJDExMbz00ku89957zJ49m8GDB+Pm5lZr5zf0\n7kDutrMoiv/t8ZbpyDH07lBr53zaVJ05cP36dQwaN2aaq53S+iIoD61+9OhR9PT0NFVdQWiQDFro\nqmy4GbQQHU+CUB+JkTkNmzt3Lunp6cTExNClSxcAunXrRkpKCklJSSxcuJDMzEwA5HI5ixcv5sSJ\nE6SmphIREYGRkRETJ04kNTWVpKQkoqOjMTc3B+Czzz7j9OnTUkPuQb3eHh4e7N69W6rbihUr8PPz\nw9TUlNjYWFJSUjhx4gQTJ06sq5dHqAUVUU3VRS9dvnw5V69exdPTE0/P8nUbYWFhuLi4YG9vj4+P\nj/RZ6tChA7Nnz8be3p7g4GClEd7MzEysrKwAlCK0qjpWXFwcI0eOBGDnzp3o6+vz999/U1hYSMeO\nHWvkumUyWbXHXbp0ITExESsrKz788EMWLFhQI+dSpYlda5qN7CyNxGk106XZyM5ivdwTKPrrFgZy\niElKAYWCvJs3WPvZQk5HR+Dl5cVXX30lbVsRDMrd3Z2NGzcCsHfvXv766y+N1F3QvHXr1vHWW29p\nuhr1jsuwTmg3Ur491G4kx2VY/Uv3IQiCGJkTHmDrtRw+zcjiSlExbXV1mNPRlFEmLTRdrWo6dOhA\nfHw8xsYiX9OjUBW91N/fn6VLlxIREYGxsTHZ2dksXLiQ/fv306RJEz777DOWLl3KRx99BEDLli2l\nqb2bN2/mwoULmJubExoaKnUUVFB3rA8++EC62Y6OjsbS0pK4uDhKSkro1avXQ19P5TxJVf3xxx/k\n5OSgr6/Pjh07WLNmDVevXqVFixZMmDCBZs2a1foIYhO71qLxVoMupR7Ht6cN2xJT2X/qLKUKBbbt\nzYjevJ7ly5czffp0rK2tKSkpwd3dnZUrV/Lxxx/j6+uLhYUFvXv3fqQ0BoLwLKgIcnJk53nu5hRh\n0EJEsxSE+kw05uqRyqGD64Ot13KUIvFdLipm1pk/Aeplg054dKqil/bp00dpm6NHj3Lq1ClcXV0B\n+Pvvv3FxcZGer9xgGzNmDKGhoQQGBhIaGlotoIi6Y2lra9OpUydOnz5NbGws7777LlFRUZSWlj7S\n1MeWLVvi6uqKpaUl3bt3r3ato0aN4vLly0yYMAFHR0f27dtHQEAAcrkcHR0dvv3224c+l1C3OnTo\nQGpqqvR41qxZyOIPgkLBFHflNb93bmVjbGxc7fOXfuwaR3aeZ1jHQAwcy29QV69eXSf1F2pHZmYm\nAwcOxNnZmcOHD+Pk5MRrr73Gxx9/zI0bN6SZKTNmzKCwsBB9fX3Wrl1L167KqQj27NnDwoUL2bVr\nFwqFQmUk1GdJl14movEmCA2EaMwJan2akaUUUh2goEzBpxlZGmvMVfzhdnBwIDExEQsLC2mN4Fdf\nfcWuXbsoLi5my5YtdOvWjZycHCZNmkRGRgaNGzdm1apVWFtbExQUxKVLl8jIyODSpUu88847+Pv7\nA/DTTz+xfPly/v77b3r16sU333yjdi1YQ6cqemlVCoWCAQMGsGnTJpXHaNKkifTz2LFj8fHxYeTI\nkchkMjp37vzQx3J3d2fv3r3o6Ojwwgsv4OfnR2lpabVAJg9SMYWuqnbt2rFjxw7p8dZrOXzatB03\nv1xPW10dAjua4ig6KRqUpi2NuZN9U2V5VSLc+r+WLVvG1KlTH2lNbH127tw5tmzZwpo1a3BycmLj\nxo3ExMTw66+/snjxYtavX090dDTa2trs37+fDz74gK1bt0r7b9++naVLl/Lbb7/RvHlzXn75ZWbO\nnEmfPn24dOkS3t7enD59WoNXKAiCoJ5YMyeodUVFsuP7ldeVM2fOMG3aNE6fPo2hoSHffPMNAMbG\nxiQmJvLmm28SEhICwMcff4ydnR0pKSksXryYV199VTpOWloa+/btIzY2lvnz51NcXMzp06cJDQ3l\n0KFDJCcno6WlpRQ85llRebqis7Mzhw4d4ty5c0B5OP/09HSV+3Xq1AktLS0++eSTalMsH3QsNzc3\nli1bhouLC61ateLWrVucOXNGSi9QkypGnS8XFaPg31HnrddyavxcQu1xG/cq2o2UgzJoN9KVgqBU\nJsKtlystLa3xaLWaZm5ujpWVFXK5HAsLC/r3749MJsPKyorMzExu374tpSqZOXMmJ0+elPYNDw/n\ns88+Y8+ePVJk0/3796uMhCoIglAficacoFZbXZ1HKq8r7du3l6a8TJgwgZiYGAApgIaDg4MUNCYm\nJoZXXnkFgH79+nHr1i3y8vKA8rDkurq6GBsb07p1a65fv86BAwdISEjAyckJW1tbDhw4QEZGRh1f\noeZNnTqVgQMH4unpSatWrVi3bh2+vr5YW1vj4uKiNvIplI/O/fTTT4wZM6bac/c7Vq9evbh+/Tru\n7u4AWFtbY2VlVS1wyePw8/NjxYoV0uP7jTrXB5mZmY/UiPXz85PSmNRVGoj6oLubJ15T36KpcSuQ\nyWhq3AqvqW+pTLhcH8Ktq8pr2KFDB7KzswGIj4/Hw8MDKJ92/8orr+Di4kLnzp2l6aCRkZG4u7sz\naNAgunbtyhtvvCHlJd20aRNWVlZYWloye/Zs6bwGBga899572NjYsGjRomoBjhq6yjMM5HK59Fgu\nl1NSUsK8efPw9PQkNTWVXbt2KeVv7dSpE3fu3FHqoKqIhFqR0/XKlStS4ChBEIT6RkyzFNSa09FU\nac0cgL5cxpyOphqsleqohPDvH3R10wWrUjXFUKFQMHHiRD799NMarHH9UtHD7OHhId04AkqNnbff\nfpu3335betyvXz/i4uKqHaui0VzZrFmzmDVrllJZZGTkA4+lr69PUdG/N9arVq164LU8rvo66lwT\n6kMaiLrU3c1TZeOtqvoQbr0ir+GePXsAuH37tlKjq6qUlBSOHj1Kfn4+dnZ2Uqqa2NhYTp06xfPP\nP8/AgQPZtm0bvXv3Zvbs2SQkJNC8eXO8vLzYsWMHw4cPJz8/n169evHf//4XgDVr1kgBjp4Ft2/f\npm3b8sTg69atU3ru+eefJzg4mJEjR7JlyxYsLCykSKgBAQFAeSRUW1vbuq62IAjCQxEjc4Jao0xa\nENK1Pe10dZAB7XR1COnaXuPBTy5dusSRI0eA8vVRVQN2VObm5iZNk4yMjMTY2BhDQ0O12/fv359f\nfvmFGzduAJCTk8PFixdrsPaCOluv5eB4+CSmEck4Hj5Zq1Me6+uoc2UlJSWMHz+e7t27M3r0aO7d\nu0dCQgJ9+/bFwcEBb29vsrKqjyQ+KA3E77//jo+Pj7R9ZGQkgwcPVrv906I+hFu3srLijz/+YPbs\n2URHR2NkZHTf7YcNG4a+vj7GxsZ4enoSGxsLlAfz6dixI1paWvj6+hITE0NcXBweHh60atUKbW1t\nxo8fT1RUFFDeWTVq1Khav7766v3332fOnDnY2dmp7Ojr1q0bGzZswMfHh/Pnz7N8+XLi4+Oxtram\nR48erFy58oHneNTR9KCgIGk5gCAIwpMQjTnhvkaZtCC+twVZnrbE97bQeEMOoGvXrnz99dd0796d\nv/76izfffFPttkFBQSQkJGBtbU1gYCA//PDDfY/do0cPFi5ciJeXF9bW1gwYMEDlDbNQs+p6Dduc\njqboy5VHeOvDqHNlVdeGfv3117z99tv88ssvJCQkMGnSJObOnat2/8ppIBITE3F0dGTp0qW88MIL\nHDt2jPz8fABCQ0MZN26c2u2fFl16meA5vps0EmfQQhfP8d3qNPiJqryG2tra0jTJytP/QP0sBHXl\n6ujp6T21QZyqRjldt24do0ePVnrOxcWF9PT0arlbK0+/trOz49SpUyQ3ac7A9OtEvTGHRis38Ul4\nzEM15gRBEDRFTLMUGhxtbW1++uknpbLK0/0cHR2laX0tWrRQimBYoWoaiMo3A2PHjlUZvEOoPXUd\nObXimPU5h2LVtaGLFy8mNTWVAQMGAOWBLExN1Tc+75cGYuDAgezatYvRo0ezZ88ePv/8cw4ePHjf\nFBRPA02HW1eV17BDhw4kJCTw4osvKkVYBNi5cydz5swhPz+fyMhIlixZQnp6OrGxsVy4cIHnn3+e\n0NBQpk6dSs+ePfH39yc7O5vmzZuzadMmpanSlVUEOHpWplk+rCdNx1NaWsqUKVM4fPgwbdu2ZefO\nnVy9epXp06dz8+ZNGjduzOrVq+nWrZvSfh4eHtjY2HDw4EFKSkpYs2YNPXv2VHMWQRAEZaIxJwj/\n2JF0heB9Z7iaW4BZM30CvLsy3K6tpqv1TNDEGrZRJi3qVeOtqqqjLU2bNsXCwkKaYvwg90sDMW7c\nOFasWEGLFi1wdHSkadOmD0xBITy5EydOVMtrWFBQwH/+8x/mzZuntIYVyoMAeXp6kp2dzbx58zAz\nMyM9PR0nJyfeeustzp07h6enJyNGjEAul7NkyRI8PT1RKBQMGjSIYcOGqaxHRYAjMzMzIiIi6uDK\nG4Yn7VQ6e/YsmzZtYvXq1YwZM4atW7eydu1aVq5cSefOnTl27BjTpk0jPDy82r737t0jOTmZqKgo\nJk2apNTBKAiCcD+iMSc0KFWn1NSUHUlXmLPtBAXFpQBcyS1gzrYTAKJBVwfa6upwWUXDrT6tYatr\nFWtDXVxc2LhxI87OzqxevVoqKy4uJj09HQsLC5X7Ozs7M336dM6dO8f//d//kZ+fz5UrV+jSpQt9\n+/Zl0qRJrF69mnHjxj1we6FmeHt74+3tXa1cXaoPa2trKY9mZYaGhuzevbtaua+vL76+vtXK7969\nS37SDfL2ZVKaW8ToZr2YtHksTexaP8ZVPL2etFPJ3NxcCpRSEVX58OHDSmtUKwd5qqzifXN3dycv\nL4/c3FyaNWv2KNUXBOEZJdbMCQIQvO+M1JCrUFBcSvC+Mxqq0bOlIaxhq2tV14ZWrJebPXs2NjY2\n2NracvjwYbX73y8NhJaWFoMHD2bv3r1S8JNHTUEhNBz5STfI3XaW0tzyhkRpbhG5286Sn3RDwzWr\nX540MFLVCMk5OTk0a9ZMSnGQnJysNvn4o66DFARBqCBG5gQBuJpb8EjlQs1qCGvY6lKHDh1UNqRs\nbW2lCIWVVQ63/jBpIKA8FUXldBQP2l6oW1XX9VaomlLkYeTty0RRrJwwXVFcRt6+TDE6V0lNp+Mx\nNDTE3NycLVu24OPjg0KhICUlBRsbm2rbhoaG4unpSUxMDEZGRg+MdCoIglBBNOYEATBrps8VFQ03\ns2b6GqjNs6m+r2F76qX8DAcWwO3LYNQO+n8E1tUTv9em/Px8xowZw+XLlyktLWXevHmcOXOGXbt2\nUVBQQO/evfnuu+/IyMjAx8eHxMREoHyt0tixY6XHgrKKEbmHLX9W1Uan0oYNG3jzzTdZuHAhxcXF\njBs3TmVjTk9PDzs7O4qLi1mzZs1jn08QhGePTKFQPHirOuLo6KioyI8kCHWp6po5AH0dLT4daSXW\nzAlPv5SfYZc/FFfq0NDRhyHL67RBt3XrVn7//XdWr14NlCd7Li0tpUWL8pvpV155hTFjxjBkyBA8\nPT354osvsLW15YMPPsDU1FRt9Man0bp164iPj682uqpK1pJYlQ03rWa6mAaKqIma5uHhQUhICI6O\njpquiiAI9YRMJktQKBQP9aUg1swJAuVBTj4daUXbZvrIgLbN9EVDTnh2HFig3JCD8scHFtRpNVQl\n1Y6IiKBXr15YWVkRHh7OyZMnAZg8eTJr166ltLSU0NBQXn755Tqta0Ni6N0BmY7yn3uZjhxD7w6a\nqZDwr5Sf4XIcrPKALyzLHwuCIDwCMc1SEP4x3K6taLwJz6bblx+tvJZUJNX+7bff+PDDD+nfvz9f\nf/018fHxtG/fnqCgICmx9qhRo5g/fz79+vXDwcGBli1b1mldn8Tw4cP5888/KSwsZMaMGUydOhUD\nAwOmTJlCWFgYJiYmbN68mVatWj0wB9mdO3ewtrYmPT0dHR0d8vLysLGxkR4D0rq4imiWWs10MfTu\nINbLado/I+KRE/65Fbv9Z/kIOdT5FGdBEBouMTInCILwrDNq92jlteTq1as0btyYCRMmEBAQIK2B\nMzY25u7du/zyyy/Stnp6enh7e/Pmm2/y2muv1Wk9n9SaNWtISEggPj6e5cuXc+vWLfLz83F0dOTk\nyZP07duX+fPnS9tX5CD75ptvmDRpktKxmjZtioeHB3v27AFg8+bNjBw5UmrIVWhi1xrTwJ60W+KG\naWDPJ27IZWZm0q1bN8aPH0/37t0ZPXo09+7dY8GCBTg5OWFpacnUqVNRKBScP3832wk8AAAgAElE\nQVQee3t7ad+zZ88qPX5m1ZMRcUEQGjbRmBMEQXjW9f+ofI1cZTr65eV16MSJE/Ts2RNbW1vmz5/P\nhx9+yJQpU7C0tMTb2xsnJyel7cePH49cLsfLy6tO6/mkli9fjo2NDc7Ozvz555+cPXsWuVzO2LFj\nAZgwYQIxMTHS9qpykFVWMeUUYO3atXXWuD1z5gzTpk3j9OnTGBoa8s033/DWW28RFxdHamoqBQUF\n7N69m06dOmFkZERycnKd17Feqycj4oIgNGximqUgCMKzrmJKl4ajWapKqu3o6MjChQtVbh8TE8Nr\nr72GlpZWXVSvRkRGRrJ//36OHDlC48aN8fDwkKaOVlY5z9iDcpC5urqSmZlJZGQkpaWlWFpa1k7l\nq2jfvj2urq5AeQN0+fLlmJub8/nnn3Pv3j1ycnKwsLBgyJAhUoNz6dKlhIaGEhsbWyd1rNeM2pVP\nrVRVLgiC8JDEyJwgCIJQ3nCbmQpBueX/19M1O+nHrvHDB4ewMe/Dl5+vZFCfcZqu0iO5ffs2zZs3\np3HjxqSlpXH06FEAysrKpGmkGzdupE+fPtI+oaGhAPfNQfbqq6/y8ssv1+mIl6pG5rRp0/jll184\nceIEU6ZMUVrjuHfvXnbv3t3g1jjWmnoyIi4IQsMmGnOCIAhCg5B+7BoRG9K4m1PEVO8FBI5cxfE9\nN0k/dk3TVXtoAwcOpKSkhO7duxMYGIizszMATZo0ITY2FktLS8LDw/noo39v6CtykL3xxht8//33\nKo87fvx4/vrrL2lKZl249P/snXtczff/wJ+ni0qRuVaYu67ndJfKoTQx16EModgXw2TmMraZbGxG\nm/tmzDR3zWVmNtcyIVNRIUJpbplcSkV0ef/+6Ndnjspc0oXP8/E4jzrv835/Pq/355zzOe/X+3W7\neJHIyEhAUwF92WIcXxiqfoXlP4wbA4rCv+VcDkRGRqbqI7tZvqQEBQVhZGTExIkTCQgIoHv37vj4\n+FS0WBVGXl4eOjryx/1VYO7cuejp6REYGMj48eOJi4sjLCyMsLAwVqxYQc2aNYmKiuLevXv4+Pgw\nY8YMwsLCWLhwIb/88gsAe/bs4dtvv2Xr1q0VPBuZh4nclkTegwKNtrwHBURuS6K1i0kFSfV4rl69\nSmBgoKTY6Onp8ccff2j0cXNzAyAwMBAnJ6diZRYGDRrE/PnzNdoCAgIICAiQnh88eBAfHx9q1ar1\nAmZRMubm5ixZsoRhw4ZhZWXFqFGjuH37NjY2NpiYmJQY47h169YqF+P4QlH1k5U3GRmZ50K2zMlU\nOVatWoVKpcLW1pbBgwezfft2XFxcsLe354033uCff/4BChXawYMH4+7uzuDBg5/5fE2bNuXGjRtP\n1Dc6OprAwMLU0vv37+fw4cPPfF6ZZ0OtVhMREQEUvh9ZWVnk5uYSERFB+/btmTVrFtHR0cTHx/Pn\nn38SHx+Pp6cnZ86cIS0tDShM0PBo1sCnJSUl5alil0JCQrh69ep/9gsICNCweLxKZN0qXvj6ce2V\nATMzs/98v4ruEykpKaxbt+6pjn86IhxPlSXvDvWndUE2pyPCn1nWp0VHR4c1a9Zw+vRpNm/eTPXq\n1Zk5cyZJSUkcOnSIlStXEhQUJPWvijGOT8uzvIcyMjIyz4OszFUxHlVkUlJS6NixIyqVCi8vLy5e\nvCj1jYqK4r333tMYHxMTQ4cOHXB0dKRz586kpqZKfVUqFXZ2dkyaNElahObn5zNp0iT09PSwsrLi\n+++/L7/JlsCpU6eYOXMmYWFhxMXFsWDBAtq1a8eRI0c4fvw4/fv3Z86cOVL/hIQE9u7dy/r168tF\nPicnJxYuXAiUrTJXtHNfEvv376d79+5lcp6XAUdHR2JiYrhz5w56enq4uroSHR1NREQEarWa0NBQ\nHBwcsLe359SpUyQkJKBQKBg8eDBr1qwhPT2dyMhI3nzzzXKV+0mVuVeB0r47RrX1SuxfWnt5M2XK\nFJYsWSI9DwoKIjg4WLqfnjp1SsrWqVKpOHfuHABGRkZkZWUxZcoUIiIisLOzY968eUDhtXBycirx\nfKcjwtm9bDHdLVswtasn1fMesHvZ4nJV6J6Ezddu8VoHL6Z+t4ztrp3ZfO1WRYv0ROTl5T31GFmZ\nk5GRKW9kZa4KUZIiM3bsWPz9/YmPj8fPz0+yCpVEbm4uY8eOZdOmTcTExDBs2DA+/vhjAIYOHcr3\n339PbGysxq7pihUrMDY2xtTUlD179rB8+XIuXLjwwudaGmFhYfj6+lK3bl0AateuzeXLl/H29kap\nVDJ37lxOnTol9e/ZsycGBoUB5nPnzpUUrfHjx9OxY0fpmH5+fuzevRtXV1ccHBzw9fUlKytLOs6c\nOXNQKpW0adOG8+fPA/Dzzz9jY2ODra0t7du3B/5VrFJSUli6dCnz5s3Dzs6OiIgI0tLS6Nu3L87O\nzjg7O3Po0KEnnrds4XtydHV1adasGSEhIbi5uaFWqwkPD+f8+fMYGBgQHBzMvn37iI+Pp1u3blKC\nhqFDh7JmzRrWr1+Pr69vmbjl5ufnM3z4cKytrfH29ubevXvExsbStm1bVCoVvXv35vbt22zatIno\n6Gj8/Pyws7Pj3r17pW68vAqUpsy59mqBTjXNny2dalq49mpRXqI9lrfffpvQ0FDpeWhoKC4uLtLz\npUuXMm7cOGJjY4mOjqZRI82shbNnz0atVhMbG8v48eP/83wRG1aR90DTKpn34D4RG1Y950z+m6ZN\nm3Ly5Mn/7Lf52i0mJl5CL+hr6vwQyjUDIyYmXqo0Ct2jG6QBAQG8++67uLi4MHnyZLKzsxk2bBht\n2rTB3t6ebdu2AYVKm1qtxsHBAQcHB+nz+qhCXrQh6uzsjEqlqvANURkZmZcQIUSleTg6OgqZ0lm4\ncKH46KOPRFZWlujatatQqVRCW1tbrFmzRhw9elS0bdtWaGtrC2dnZzFlyhTRr18/0bt3b2FmZiZM\nTEzE0KFDRY0aNYStra14/fXXhZ6enjA0NBSBgYHi9ddfF0IIsW7dOtGyZUuhp6cnJk+eLPr27Sta\ntWoldHV1hbW1tWjatKnYtWtXuc77woULwtzcXAwcOFA0aNBAmJubi+zsbNGkSRMxefJkYWRkJCZM\nmCCOHz8uLC0thaGhoXjrrbfE5MmTxdy5c8W5c+eEl5eXaNmypahVq5Y4f/68aNeunWjUqJFwdHQU\n9evXF15eXkKtVot//vlHdO3aVZiYmIj69euLDRs2iCZNmgi1Wi0sLS1Fo0aNRLNmzYQQQtjY2IjL\nly8LIYS4ffu2EEKI8PBw0a1bNyGEENOnTxdz586V5jFgwAAREREhhBDi77//FhYWFk98DQwNDUVB\nQYGYOHGisLa2FjY2NmLDhg3SOdVqtejatato3bq1GDlypMjPz3/+C1+FmT59umjcuLHYs2ePuHbt\nmmjcuLF46623RGxsrFCpVCI/P19cu3ZN1K9fX6xcuVIa1717d2FmZiYSEhKeW4YLFy4IbW1tcfz4\ncSGEEL6+vmL16tVCqVSK/fv3CyGEmDZtmhg3bpwQQogOHTqIqKgoIYQQDx48EK6uruL69etCCCE2\nbNgghg4dKoQQwt/fX/z888/PLV9ZMmfOHLFgwQIhhBDvv/++8PT0FEIIsW/fPjFw4EDx7rvvCkdH\nR2FlZSU+/fRTaVyTJk3Ep59+Kuzt7YWNjY04ffq0uHDhgmjQoIEwMzMTtra24sCBAxrnSjySKkKm\nHhSLR+4TIVMPisQjqeU30SfAwsJCXLlyRcTGxgo3Nzdx4cIFYW1tLYQQYu3atcLKykrMnj1bnD17\nVhpjaGgohNC8fzwJwW93F8H9uhV/vN29bCf1HDgeOikahB0v9nA8dLKiRRMnT54UrVq1EmlpaUII\nIW7evCn8/f1Ft27dRF5enhBCiKlTp4rVq1cLIQrv861atRJZWVkiOztb3Lt3TwghxNmzZ0XR+uXR\n9/D7778Xn3/+uQgNDRWtW7cWRkZGIjk5WfTv318olUrxzTffPJXMt2/fFkuWLHnuucvIyFRugGjx\nhPqTnBGiCrJz507MzMzYsWMHdevWpXPnzrRp04a1a9fSo0cP9u7dS3BwMACxsbF06NCB7t27M3Hi\nRFq2bMlvv/0mFat97bXX6NixI3fv3uXq1at8+OGHrFq1ijFjxkhJIhYtWsTIkSPZv3+/ZBErbxIT\nE1mxYgUfffQRbm5uzJ07FwADAwNatmzJgAEDGDJkCE2aNKF+/foolUp2796Nj48Pfn5+TJkyhe7d\nu9OqVSsMDQ3JysrC2NiYhQsXMm3aNFJTU7l8+TJt2rQhMzOTBg0a4OrqSpcuXZg4cSKXL18mKSmJ\nvLw86tevDxTWdgoICKBfv3706dPnP+ewd+9eEhISpOd37twhKysLIyOjJ7oGW7ZsITY2lri4OG7c\nuIGzs7NkETx69CgJCQk0adKELl26sGXLllc64Y1arWbWrFm4urpiaGiIvr4+arUaW1tb7O3tsbCw\n0KiRVYSfnx9paWlYWlqWiRzNmjXDzs4OKHT/TEpKIj09nQ4dOgDg7++Pr69vsXGJiYmcPHmSTp06\nAYUWPlNT0zKR6UWgVqv5+uuvCQwMJDo6mvv372vEKfr6+lK7dm3y8/Px8vIiPj4elUoFFGY+PHbs\nGN9++y3BwcH88MMPvPvuu1ICp0dp7WJSaZOdAPj6+rJp0yauXbsmFQEvYuDAgbi4uLBjxw66du3K\n999/L3kIPAs16tQl80Zaie2VhSv3c5+qvTwpydMDCt/DIg+V3bt38+uvv0q/qTk5OVy8eBEzMzPe\ne+89yZvl7NmzJZ5j9+7dxMfHc+3aNRo0aEDdunX566+/iIqKkrw8nob09HS+/fZbRo8e/SxTlpGR\neQmRlbkqRMeOHenduzc9e/Zkz549BAYGYm5uzrJlyzA1NSUxMRG1Wk3NmjXR0ip0RfLy8iI3N5dq\n1aqhVCo5efIkq1evxsPDg1q1apGYmIi/vz8ffvgha9euxcPDg927d6NQKPDz82P9+vV89913kgxn\nz56lYcOGGBoaluvcH154v/vuu3z99dfk5ORw8uRJgoKC6Nu3L1evXqVz585ERUXh7+/P8uXLycnJ\n4cqVK/Tu3RuAFi1aEBoair6+PleuXKFv377cvHmTOnXqYG5uzpo1a/D29qZHjx50794dY2NjtLS0\n0NPT45133qFLly7StV26dCl//fUXO3bskOK0HkdBQQFHjhxBX1//ma7BwYMHGTBgANra2jRo0IAO\nHToQFRVFzZo1adOmDc2bNwdgwIABUma7V5Wiz30RDy+0QkJCSh138OBBhg8fXmZy6On9G8ulra1N\nenr6E40TQmBtbS2lfa/sPBqn6ODgIMUpLly4kNDQUJYtW0ZeXh6pqakkJCRIylzRRoijoyNbtmyp\nyGmUCW+//TbDhw/nxo0b/Pnnn9y//68bZHJyMs2bNycwMJCLFy8SHx+voczVqFGDzMzMJz6Xuv8Q\ndi9brOFqqVNND3X/IWUzmTKgoZ4ul0tQ3Brq6VaANE/Gw79vQgg2b96Mubm5Rp+goCAaNGhAXFwc\nBQUFGvf16OhoHB0dycnJQU9Pj7Zt27Jlyxa0tLTw8fHhiy++4MqVK9jZ2bFo0SLMzMwYM2YMaWlp\nVK9eneXLl2NhYcE///zDu+++S3JyMgDfffcdCxcuJCkpCTs7Ozp16iRtbMrIyLy6yDFzVQhra2s+\n/vhj3nnnHQwNDTl+/Dh5eXksX76c2NhYVq9ezYIFCzTGPLyY1NXVZcaMGaxatYrt27djZ2cn+fl3\n6tSJJUuW8Ntvv5GdnS0VpVUqlVhZWXH16lXatWvHyJEjnyko/Hl5uDht586d8fLywszMjKVLl9Kr\nVy/i4uIwMTFh7ty57N+/HwBTU1PGjRuncRy1Wk1wcDANGzZk0qRJ6Orq0q1bN2nnVEtLi2PHjtG6\ndWsmTJjAZ599Jim2Pj4+LFmyRJIlKSkJFxcXPvvsM+rVq8elS5c0zvXowszb25tFixZJz2NjY1/I\n9SnpuUzpZGzfzrmOXljrG/DXmjX0eO21F3YuY2NjXnvtNSnb5urVqyUr3cOfF3Nzc9LS0iRlLjc3\nVyMWtLLxrHGKUHiPMjIyQltbm+zs7Cq/CWFtbU1mZiYNGzYsZk0NDQ3FxsYGOzs7Tp48yZAhmkqX\nSqVCW1sbW1tbKQHK47BUe+I94j1q1K0HCgU16tbDe8R7WKo9y3ROz8PU5qYYaGnejwy0FExtXvGW\n5o4dO/Lzzz9z8+ZNAG7dKh7H17lzZxYtWkSh1xMcP34cKCz+bmpqipaWFqtXryY/Px8o/B63aNGC\nmJgYoqOjuXbtGjdu3MDBwYG1a9cyfPhwNmzYQIsWLYiNjUWtVjNixAgWLVpETEwMwcHBktUtMDCQ\nDh06EBcXx7Fjx7C2tmb27NnSWFmRk5GRAeSYuarIlStXJF/97du3izfffFM0a9ZMHD16VAghxJ07\nd0Rubq5YuXKlGDNmjDSuW7duIjw8XFy9elW8/vrrIi0tTeTl5QkvLy+xfv16qf2TTz4R7733nvDy\n8hK//PKLEKIwtqUorqC8uXDhggDE4cOHhRBCvPPOOyI4OLiYTCqVSqxdulWETD0o3nQcIryd+4nE\nI6nCxcVFbN26VQghxO+//y60tbXFtm3bRJs2bUSLFi3E119/LS5fviw2bdokVCqVsLa2FkqlUnzy\nySeiV69eonHjxmLMmDFCqVQKOzs7UatWLSGEEL179xY2NjbC2tpaBAYGioKCAo14icTERKFUKqW4\nn7S0NNGvXz+hVCqFpaWlGDly5BNfA0NDQ7F582bh7e0t8vLyxPXr18Xrr78uUlNTRXh4uNDX1xfJ\nyckiPz9feHt7i02bNpXV5X+pSf/1V3Ha1k4kmFtIj9O2diL911+f+9gPx0oJIcTcuXPF9OnTxfHj\nx4WLi4tQKpWiV69e4tatW0IIITZt2iRat24tbG1txd27d8Xx48eFWq0WKpVKWFlZiWXLlgkhKmfM\nnBDPFqdY9B02NDQUUVFRokOHDkIIIYKDgzVi615mrqb+Ig4ebCf27mshDh5sJ66m/lLuMjwcr/ki\n2JR6UzgeOilM/j9WblPqzRd2rqclJCREWFtbC5VKJfz9/Yt9v+7evStGjBghbGxshJWVlXR/P3v2\nrFAqlUKlUonJkydLcY8PHjwQTZs2Ffr6+sLU1FTUqFFDDBkyRBgaGormzZsLDw8PERcXJ90bMjMz\nhb6+vrC1tZUeRfHUdevWFTk5ORryPnpfkZGReTnhKWLmKlyBe/ghK3NPxs6dOyUlwcnJSURFRYmj\nR48KFxcXoVKphIuLi8jMzCxVmROiMNFJkSIyefJksWHDBmFraysaNWokjIyMhIWFheg1Zqz0A1zN\nxEz8ePJsKRK9WIoSoPj5+QkLCwvRp08fKQHKw8rcL6v2iGYNLIVZ7WZC1dRNzAn4RSwdGy52hR4S\nnp6eQqlUCgcHB5GUlCSEEGL+/PnCxsZG2NjYiLZt24rz58+XeG2vXr0qnJ2dhVKpFDY2NiIkJKTc\nr4GRkZGcAOUFcNazo4YiV/Q469mxokWrcuzdu1fo6OiIrKwsIYQQrVq1El9//bUQolABbdWqlejY\nsaPo3bt3qcqci4uLsLa2FomJicLAwECYm5tLCVCKFI6srCwxdOhQ4ezsLOzs7KQNp6rI1dRfRFi4\ntdi7r7n0CAu3LneFriyVuWnTpok9e/aUybGqIuHh4cLd3V1kZ2cLIQqvbXh4uMY1flghy8jIECYm\nJiUeS1bmZGReXZ5GmVOI/3cdqAw4OTmJ6OjoihZDhn/TSd8r+PfzYaClINi8MX1NaperLCkpKXTv\n3v0/02D/9NGhEosHG9XWw/8L9xJGlB87knew4NgCrmVfw8TQhHEO4+jWvNsTjb158yYODg78/fff\nL1jKV4/TllZQ0j1QocDydELx9vImPhT2fQYZl8G4EXh9Cqp+FS1VmWNkZMQ/Ecmc2nCYwT9+wP4J\nG1iVtpO7RvnMmDGD1NRUPDw8SExM5KOPPsLKyopBgwaRnp5OmzZtOH78eLnH8ZYFhw6pyblfvLag\nvp4Z7u4RZX6+lJQUunTpgqOjo+S2t2rVKrp27UpwcDBOTk6MGjVKSn7l4+PDjBkzCAsLY+HChfzy\nyy8A7Nmzh2+//ZatW7eWuYxVnW3btvHDDz+wfft2/tgYQU+/Nxjd9Ut2xa1m1owv6RvQpdhvmpub\nG+PHj8fX1xchBPHx8dja2tK/f3/atm3L+++/T35+PllZWeTl5cm/BzIyrwAKhSJGCFFykdFHkGPm\nZIpx9q9rnJ8bz4T1Nxm7PR3rvwsVpHsFgi+TK2+tq5IUuce1Pwmbr93C6fApTMNjcTp86plqI+1I\n3kHQ4SBSs1MRCFKzUwk6HMSO5B3/Ofbq1au4urqWmNUPCosGLxszlK/792DZmKGVrlhwZUenlAyR\npbWXK/GhsD0QMi4BovDv9sDC9peNAkH6lnPk33kAQH76fbzyVISu2QgUxpoVxdLt3r2b2bNnY2dn\nh4eHh5RdsCqSc7/k+2lp7WVBYmIio0eP5vTp09SsWZNvv/1W4/VZs2YRHR1NfHw8f/75J/Hx8Xh6\nenLmzBnS0gozZ65cuZJhw4bx+eefY25uTrt27RgwYADBwcEEBASwadMmdu7cqZGptagGJ/DYmp5V\nnS5dupCXl0eLpq2YOnUqTesXZsbNzxXE/PE3Z/+6VmzM2rVrWbFiBba2tlhbW0u17BYsWEB4eDhK\npRJHR0cSEhKoU6cO7u7u2NjYMGnSpHKdm4yMTOVEzmYpo8HZv64RvvYMRg8KAKh1t4DuUdkAnGqi\nVyHppJ+0OK1Rbb1SLXPPwqPWycv3c5mYWJjk5GmskwuOLSAnP0ejLSc/hwXHFvyndc7MzKzUlNen\nI8I1Mtll3khj97LFAJUqAUJlpv7490md9inioYQcCn196o9/vwKl+n/2fQa59zTbcu8Vtr9k1jmR\nLxC5BRptJgZ1MC4wID4+no0bN7J06dLCvqVkF6yK6OuZlmKZe3GbCQ9nBh40aBALFy7UeL20zKOD\nBw9mzZo1DB06lMjISMaOHcvmzZuJi4sjNzcXBwcHHB0dpeO88cYbjBgxguzsbAwNDdm4cSP9+/fn\nxo0bzJw5k71792JoaMhXX33FN998w6effvrC5lye6Onp8ccffxTzFGnds7BESeS2JPy/cNf4TWvW\nrBk7d+4sdqwGDRpIit3DrFu37gVILiMjU1WRLXMyGkRuSyLvgeaiqlo+dIwvXFRW5nTSrr1aoFNN\n8yOtU00L114tnul4XyanariZwrNZJ69lF9+JfVz7kxKxYZVGSnKAvAf3idiw6rmO+yph3KMHpp9/\nho6ZGSgU6JiZYfr5Zxj36FFm51AoFEyYMEF6HhwcTFBQ0H8PzLj8dO1VmVK8/bu38mDOnDlkZGRI\npQxKyy5YFWneYiJaWgYabVpaBjRvUbIlvix4XObbCxculJp5dOjQoaxZs4b169fj6+vLX3/9Ra9e\nvdDX16dGjRr0eOQ7o6OjQ5cuXdi+fTt5eXns2LGDXr16ceTIERISEnB3d8fOzo6ffvrppXQZLEtP\nEdkDQ0ZG5nHIypyMBqX90BjfLag06aRLo7WLCZ5+FpIlzqi2Hp5+Fs9cYLisit2aGJZ8/tLan5TM\nmzeeql2mZIx79KBV2D4sTyfQKmxfmSpyULhTv2XLFm7ceMr3xbjR07VXZUqppNHT2ZsNGzbQr9+/\nlshp06aRm5uLSqXC2tqaadOmlZOQZY+pSS8sLGahr2cGKNDXM8PCYhamJr1e2DkvXrwolbxYt24d\n7dq1k167c+cOhoaGGBsb888///DHH39Ir5mZmWFmZsbMmTMZOnToE52rf//+hIaGEhYWhpOTEzVq\n1EAIQadOnYiNjSU2NpaEhARWrFhRtpOsBJTmEfK0niJFHhiZN9JACMkDQ1boZGRkipCVORkNSvuh\nyTbUqpDkJ09LaxcT/L9wZ8zSjvh/4f7MihyUboV8WuvkOIdx6GtrFgrX19ZnnMO4UkY8GTXq1H2q\n9srAzZs3sbOzw87ODhMTExo2bIidnR0KhYJdu3Zp9J0/fz6jRo2qIEnLDh0dHUaMGFFi3bC0tDT6\n9u2Ls7Mzzs7OHDp0CCis75juPAGho0+dOZmsiiuMJRuy7QF7qr9VrvKXB9ePpKDQ1aKxsSn73vkJ\nAIWuFi18HMnLy2P69OlSXwMDA77//ntOnDjBqVOn+O233ypK7DLB1KQX7u4ReHU8j7t7xAtV5KCw\nhuGSJUuwtLTk9u3bGt8xW1tb7O3tsbCwYODAgZI7ZhF+fn40btwYS0tL3N3d2b59Ozk5OWRlZZX4\nPnTo0IFjx46xfPly+vfvD0Dbtm05dOgQ58+fByA7O7tUV/KqTFl5isgeGDIyMv+FHDMno4FrrxaE\nrz2j4WqpU02L3v0saF3JFbmyZmpz0xIzej6tdbIoLu5Zs1mWhrr/EI2YOQCdanqo+w95zKiKpU6d\nOlKx9KCgIIyMjJg4cSLLli1jw4YNdO7cWeq7YcMG5syZU1GililjxoxBpVIxefJkjfZx48Yxfvx4\n2rVrx8WLF+ncuTOnT5/G3d2dQxn1aWI5ieZ1pxHxdz5D2jcm8tZNvhs0tYJm8eIwtK8PwJ1dKeSn\n30e7lh41OzeV2qEwnjdyWxJZt+5jVFsP114tnmuz5lVFR0eHNWvWaLTt379f+j8kJKTUsQcPHmT4\n8OEAODs707NnT1QqFQ0aNECpVGJsbKzRX1tbm+7duxMSEsJPPxUq6fXq1SMkJIQBAwZw/37hvWvm\nzJm0bt26DGZXeSj6bD7vZ1b2wJCRkfkv5NIEMsUoz0XT/PnzGTFiBNWrV38hx39eNl+7xZfJqVy5\nn0tDPV2mNjetVNbJ0xHhRGxYRebNG9SoUxd1/yFVJvnJw8rcrVu3sLCw4MmDQIAAACAASURBVPLl\ny1SrVo2UlBTat2/P33//XSzGp6phZGREVlYWn376Kbq6uhgYGJCVlUVQUBD169fHzMxM6puWlkZi\nYiLbtm0jPj6eJk2aoK+vz7Jly9i8eTO9e/fm6NGjFTibiqEoMdOjm0zP40b9KvKkZV4eZfO1Wwzx\nUJNbTQ+bhSv42KIJfU1qk5WVhZGREXfv3qV9+/YsW7YMBweHFyT9q8myMUMLXSwfoUbdeoxYsrIC\nJJKRkSkPnqY0gWyZkylGaxcTaYG0atUqfIaPQqFQoFKp+Pzzzxk2bBg3btygXr16rFy5ktdff52A\ngAAMDAw4fvw4169f58cff2TVqlVERkbi4uIi7fYaGRkxfPhwdu/ejYmJCefOnWPQoEGsXbuWZcuW\n8eDBA1q2bMnq1avR09PjnXfeoWbNmkRHR3Pt2jXmzJmDj48PQ4YMoU+fPrz1VqHLmZ+fH/369aNX\nr7J1UeprUrtSKW+PYqn2rDLK2+OoXbs2bdq04Y8//qBXr15SnFRVV+Qe5v3338fBwUEj3qigoIAj\nR46gr6/phtu+fXuWLFnCxYsXmTVrFlu3bmXTpk2o1eryFrtSUFJiprwHBURuS5KVuf/nSRS1hzMD\n//rrryQkJDBlyhSNjZVHj9m+y5tof7+BGt+tBeCqQMrqu/mD90hISCAnJwd/f/8nUuSq8gZURVAV\nPTBkZGTKFzlmTqZUTp06xcyZMxk5ciRCCI4fP067du3o2rUrdevW5ezZszg6Oko1nnbu3MmECROY\nN28ePXv2ZMOGDZw6dYrDhw/j5OSEj48P2dnZHD16lJMnT6Knp8eVK1fw9PTkp59+IioqiqSkJK5c\nuUKrVq2YNWsWYWFhpKamcvDgQT766CMCAgIAeOeddyQFMSMjg8OHD9Ot2/O5LcpULAMGDGDDhg1A\noYvlgAEDKliisqV27dr069dPI9mDt7c3ixYtkp4XuaA2btyYGzducO7cOZo3b067du0IDg6mffv2\n5S53ZeBF1JB81enZsydTpkz5z35pD/JKzeq7bt06YmNjOXPmDFOn/rf7r5zM4+mxVHviPeI9atSt\nBwoFNerWw3vEe7ICLCMjIyErczKlEhYWhoeHBwsWLCAsLIyTJ09y9+5dwsPD8ff3Jzk5mXv37hEY\nGAgULkAVCgVKpZIGDRqgpaWFlpYWTZs25fTp08yfPx+FQkF+fj6HDh1i8eLFaGtrEx4ezqxZs1Cr\n1WRnZ5OcnEyPHj2YNm0aGRkZdOzYES0tLcLCwsjPzwcKA+vPnTtHWloa69evp2/fvujoyIbmqkyv\nXr3Yt28fx44d4+7duxo1q14WJkyYoJHVcuHChURHR6NSqbCyspJqqQG4uLhIcURqtZorV65oZB58\nlSirzIAvO3l5efj5+WFpaYmPjw93796ladOm0mcuOjoaDw8PoDA27r333it2jJiYGGxtbbG1tWXJ\nkiXklRKK8Sw1R+VkHs+GpdqTEUtWMmHDdkYsWSkrcjIyMhrIypzMY0lJScHX15e6dQszJGppafHX\nX38xcOBAoDDt+sGDB6XXiv7q6f27yNLS0qJFixY0atQIhUKBra0tKSkpGucJCAiQlLvZs2eTk5OD\nQqGgRYsW/PXXX6SnpxMZGSmdA2DIkCGsWbOGlStXMmzYsBd5GWTKASMjIzw9PRk2bNhLZZXLysqS\n/m/QoAF3796V6szVrVuXjRs3Eh8fT0JCgoYyt3r1aqk4sJubGwUFBdSpU6dcZa8slHUNyZeVxMRE\nRo8ezenTp6lZsybffvvtUx9j6NChLFq0iLi4OAB0SnF1fpaao3IyDxkZGZmyR1bmZEqlY8eOxMbG\ncu9eYcHwW7du4ebmJmUgW7t2rYalQFtbm4KCwrgWIQQPHjyQXtPVLfzhLygoICUlhby8PNatWyfF\nCmVmZmJqaoq+vj7r16+XxrVs2ZIDBw5IhWofjqEKCAhg/vz5AFhZWb2ISyBTzgwYMIC4uLiXSpl7\nVjZfu4XT4VOYhsfidPgUm6/dqmiRKoyyriH5stK4cWOpnMCgQYOkjbYnJT09nfT0dMmdd/DgwdSr\npoOBlqZC96w1R6tiORUZGRmZyo7slyZTKtbW1rz//vvMmDGDXbt24ezszOeff46XlxfNmzendevW\ndO3aVbKW1a9fn5iYGNq0aUNmZia5ucXdcAwNDfnnn3/4+OOPMTc3p1GjRmRmZvL555/j4uLCvXv3\nsLCwIDMzE4Dq1atTu3ZtZs6cyd69ezV2mhs0aIClpaWUBEWmalFknQLYkbxDKt3Q6edOJFVLwgKL\nihOugtl87ZZGWYzL93OlpBOVOSHPi+ThxEwyJfNowiCFQoGOjo60yZaTk/PUx6ypo80M88ZlktVX\nTuYhIyMjU/bIljmZx/LRRx+xbNkyFAoFx48fZ968ecTExNCqVSvS0tL4/fffWbBgASEhIcyfP58/\n//yTXr16MWDAAAwNDQGYMmUKpqb/7uK2a9dOSm4yatQounTpQmhoKBcuXMDAwIBFixZJyU1CQkKY\nOHGiVKj2YZe1u3fvcu7cOdmK84Q8HDtTmdiRvIOgw0GkZqciEKRmpxJ0OIgdyTsqWrQK48vk1FKT\nTsjIlMbFixeJjIwEYN26dbRr146mTZsSExMDwObNmx87vlatWtSqVUuy6K1dW5jBsq9JbaLdrEn1\ntCPazfqZNxTkZB4yMjIyZY9cZ06mXCmqufUkZGzfzvV58wk6fhzrBg0YM3cOxj168MvxK3y8ZB2J\nP8+lsdqXeZ9/xFv2DV+w5FWfpk2bEh0dLcU/Vha8N3mTml1cSTE1NGW3z+4KkKjiMQ2PpaQ7swJI\n9bQrb3FkqgApKSl06dIFJycnYmJisLKyYvXq1cTExEglXjw8PIiOjmb//v2EhIQQHR3N4sWLNUoT\nxMTEMGzYMBQKBd7e3vz+++9PXZdORkZGRub5eJo6c7IyJ1Mpydi+ndRpn9L3zGkMtLRY0agxetWr\nc+Wd8bx3vT73cvOlvga62nzZR/nKKHRz585FT0+PwMBAxo8fT1xcHGFhYYSFhbFixQr8/f2ZPn06\n9+/fp0WLFqxcuRIjIyOaNm1Kv379+OOPPzAwMGDdunW0bNmyoqeD6icVogTVRYGCeP/4CpCo4nE6\nfIrLJWQLbKSnS7SbdQVIJPPSEx8K+z6DjMtg3Ai8PgVVv4qWSkZGRuaV5GmUOdnNUqZScn3efERO\nDpuaNmP1602opqWFyMmhWshSDUUO4F5uPnN3JZbJeaOjo6VSC6Wxf/9+unfvXibnexbUajURERFA\nobxZWVnk5uYSERGBSqWS4guPHTuGk5MT33zzjTTW2NiYEydO8N577/H+++9X1BQ0MDEsOQ6qtPZX\nganNTcss6YRMxVBkBasSxIfC9kDIuASIwr/bAwvbZWRkZGQqNbIyJ1MpyUstOTaodvbtEtuvpt8r\nk/M6OTmxcOHCMjnWi8LR0ZGYmBju3LmDnp4erq6uREdHExERgYGBAQkJCbi7u2NnZ8dPP/3E33//\nLY0tii8cMGCAFFtT0YxzGIe+tr5Gm762PuMcxlWQRBVPX5PaBJs3ppGeLgoKLXLB5o1f2eQnMi+Y\nfZ9B7iP30Nx7he0yMjIyMpUaWZmTqZTomJZsgbhl+FqJ7Wa1DADIzs6mW7du2NraYmNjw8aNG9m3\nbx/29vYolUqGDRsmlVaIiorCzc0NW1tbKQPnw1a3o0eP4urqir29PW5ubiQmlo3173nR1dWlWbNm\nhISE4ObmhlqtJjw8nPPnz9OsWTM6depEbGwssbGxJCQksGLFCmnsw9nuHs18V1F0a96NILcgTA1N\nUaDA1NCUILcgujXvVtGiVShllXRCpmx56623cHR0xNrammXLlpGfn09AQAA2NjYolUrmzZun0b+g\noICAgAA++eSTCpL4Cci4/HTtMjIyMjKVBrk0gUylpP7490md9inioVTaCn19HgS8i8F17WIxc5M6\nmwOwc+dOzMzM2LGjMBNiRkYGNjY27Nu3j9atWzNkyBC+++47Ro8ezdtvv83GjRtxdnbmzp07GBgY\naMhgYWFBREQEOjo67N27l48++ug/s8GVF2q1muDgYH788UeUSiUffPABjo6OtG3bljFjxnD+/Hla\ntmxJdnY2V65coXXr1gBs3LiRKVOmsHHjRlxdXSt4Fv/SrXm3V155k6ka/Pjjj9SuXZt79+7h7OyM\no6MjV65ckZKEpKenS33z8vLw8/PDxsaGjz/+uKJELhE3NzcOHz5c+MS40f+7WD6CcaPyFUpGRkZG\n5qmRLXMylRLjHj0w/fwzdMzMQKFAx8wM088/w2vsEL7so6RhLQMUQMNaBhrJT5RKJXv27OHDDz8k\nIiKClJQUmjVrJikz/v7+HDhwgMTERExNTXF2dgagZs2a6Oho7m1kZGTg6+uLjY0N48eP59SpU+V6\nDR6HWq0mNTUVV1dXGjRogL6+Pmq1mnr16hESEsKAAQNQqVS4urpy5swZadzt27dRqVQsWLCgmAVB\nRpP//e9/JCQkVLQYVZ709HSN+pAvivIqvbFw4UJsbW1p27Ytly5d4sGDByQnJzN27Fh27txJzZo1\npb4jR46slIoc8K8iB4XJTnQ1N7PQNShsfwx5eXkvQDIZGRkZmadBtszJVFqMe/TAuEePYu1v2Tcs\nNXNl69atOXbsGL///juffPIJHTt2fObzT5s2DU9PT7Zu3UpKSgoeHh7PfKyyxsvLS6Mo+9mzZ6X/\nO3bsSFRUVLExKSkpAHz11VfPdM6UlBS6d+/+XGnK8/Pz0dbWfubxeXl5xZTuF8UPP/xQLud52SlS\n5kaPHq3RXp7vZVmxf/9+9u7dS2RkJNWrV8fDw4P79+8TFxfHrl27WLp0KaGhofz4449AofUrPDyc\nCRMmoK+v/x9HL1+KysTs37+foKBvqavblJPxx3FsIFgT0ArFG9OJut+McW5uZGdno6enx759+9i8\neTNbtmwhKyuL/Px8/vzzT+bOnUtoaCj379+nd+/ezJgxAyh0Sb106RI5OTmMGzeOESNGkJ+fzzvv\nvEN0dDQKhYJhw4Yxfvx4kpKSGDNmDGlpaVSvXp3ly5djYWFRwVdJRkZGpvIjW+ZkXiquXr1K9erV\nGTRoEJMmTSIyMpKUlBTOnz8PwOrVq+nQoQPm5uakpqZKSk9mZmaxXeaMjAwaNixUGouKmFdVso9f\nJ3X2US5PiSB19lGyj18v83OkpKRgYWGBn58flpaW+Pj4cPfuXZo2bcqHH36Ig4MDP//8M7GxsbRt\n2xaVSkXv3r25fbswqU1UVBQqlQo7OzsmTZqEjY0NUHjte/bsSceOHfHy8iIrKwsvLy8cHBxQKpVs\n27ZN4/wBAQG0bt0aPz8/9u7di7u7O61ateLo0aMABAUF4e/vj1qtpkmTJmzZsoXJkyejVCrp0qWL\npCQ/nI3QyMiIjz/+WLLI/PPPPwAkJSXRtm1blEoln3zyCUZGRmV+Xas6U6ZMISkpCTs7O5ydnVGr\n1fTs2RMrKyugeAwawNKlS5k0aZJ0jJCQEN577z0A1qxZQ5s2bbCzs2PkyJHk5+cXP+kLIiMjg9de\ne43q1atz5swZjhw5wo0bNygoKKBv377MnDmTY8eOSf3feecdunbtSr9+/Sq1Fev48ePMX7mJhNR7\nJBs6cqjN9zyweIu3336bBQsWEBcXx969eyVX9GPHjrFp0yb+/PNPdu/ezblz5zh69CixsbHExMRw\n4MABoNAlNSYmhujoaBYuXMjNmzeJjY2V3FJPnDjB0KFDARgxYgSLFi0iJiaG4ODgYsq/jIyMjEzJ\nyMqczEvFiRMnpIXejBkzmDlzJitXrsTX1xelUomWlhbvvvsu1apVY+PGjYwdOxZbW1s6depEzkPx\neQCTJ09m6tSp2NvbV+qF2H+Rffw66VvOkZ9emPglP/0+6VvOPZNCl5+fz/Dhw7G2tsbb25t79+6x\nfPlynJ2defPNN0lMTGTYsGGcPn2auLg4vLy8SE1N5ccff+Szzz6jf//+9OjRAx0dHWrXrs2BAwd4\n8803ARg6dCi9e/emWrVqrFq1iqtXr0oL9d9++w07OzvS09Px8vLi+++/59ixY7z//vv4+vpia2tL\nv379OH/+PBMmTODMmTOcOXOGdevWcfDgQYKDg/niiy+keSQlJREWFsavv/7KoEGD8PT05MSJExgY\nGEjxlhrXMDubtm3bEhcXR/v27Vm+fDkA48aNY9y4cZw4cYJGjV5MfFGVSnFfArNnz6ZFixbExsYy\nd+5cjh07xoIFCyRrckkL/r59+7J161bpGBs3bqR///6cPn2ajRs3cujQIWJjY9HW1mbt2rXlNpcu\nXbqQl5eHpaUlU6ZMoW3btly5cgUPDw/s7OwYNGgQX375pcaYDz74AHt7ewYPHkxBQUG5yfo0tGnT\nhkaNGqGlpYWdnR0pKSmPdUXv1KkTtWsXJuTZvXs3u3fvxt7eHgcHB86cOcO5c+eA4i6p586do3nz\n5sXcUrOysjh8+DC+vr6Skp5aSkZjGRkZGRlNqpaPi4zMf9C5c2c6d+5crP348ePF2pydnTly5IhG\nm4eHh+RO6erqquG+OHPmzGJ9qgJ3dqUgcjUXkSK3gDu7UjC0r/9Uxzp37hzr169n+fLl9OvXj82b\nN9OnTx+GDx9OSkoKtra2JCQk4OXlRfPmzYmNjcXExISffvqJQYMGERMTw927d0lKSuLkyZNcu3YN\nJycnwsPDuXXrFseOHePQoUOcPn0aDw8PaaEuhMDT05NvvvmGCRMmMGjQIO7evcu5c+fQ0tJi165d\nXL9+nb59+6JUKgGwtrbGy8sLhUKBUqmU3EwB3nzzTXR1dVEqleTn59OlSxeAYv2KqFatmpTl1NHR\nkT179gAQGRnJL7/8AsDAgQOZOHHiE11HDw8PgoODcXJ6onqgLxVt2rShWbNm0vOFCxdKilvRgr9t\n27Y0b96cI0eO0KpVK86cOYO7uztLliwhJiZGUjDu3btH/fpP9xl+HvT09Pjjjz+KtY8bV7yMxv79\n+6X/i9wOKyt6enrS/9ra2v+5eWVoaCj9L4Rg6tSpjBw5UqNPSS6pOTk5vPbaa8XcUufPn0+tWrWI\njY0t24nJyMjIvALIljkZmf9gR/IOvDd5o/pJhfcmb3YkF7fcVGaKLHJP2v44mjVrhp2dHVCo1KSk\npHDy5EnUajVdunQhOztbI1FMw4YNUSgU2NjY0Lx5c0k57tSpE3Xq1EFfX59atWpx5MgRcnJypIV6\nv379yM7OJjk5GQAtLS1Jmbp79y7Xr18nJiYGf39/FAoFK1euJD8/X2NRqqWlJT3X0tLSWKA+3K6r\nqyuVaXi0XxEP93mSxe6zUJqb6sOMGjUKJycnrK2tmT59utReUpmN/Px8Jk2ahLOzMyqViu+//77M\nZX4WHlYEHl7wx8XFYW9vL1nI+/fvT2hoKJs3b6Z3794oFAqEEPj7+0ulNxITEwkKCqqgmZTO2b+u\n8dNHh1jybhg/fXSIs39dq2iRnponcUWHwg20H3/8kaysLACuXLnC9evXS3RJBUp0S61ZsybNmjXj\n559/BgoVxLi4uHKaqYyMjEzVRlbmZGQew47kHQQdDiI1OxWBIDU7laDDQVVKodOupfdU7Y+jpB38\ngIAAFi9ezM6dO8nPz+fSpcIU5xcuXKBVq1Ya42vWrEn16tW5fr3QxXP16tU0adKE6tWro6enJ9XI\n69OnD61atZIW6lpaWpIylZOTg56eHrq6urz99tvk5OSQmppKjx49yt0dtm3btlK5ig0bNhR7/WkU\nNG9vbxITExk9ejSnT5/m/v37WFlZER0djb+/P5mZmXz22Wd4enpSvXp1vv76a6ZNm8aDBw/o27cv\n9+7dQ6FQkJWVRUxMDCtWrMDY2JioqCiioqJYvnw5Fy5cKJfr8jA1atQgMzOzxNdKW/AD9O7dm23b\ntrF+/Xr69+8PFCb+2bRpk/T5uXXrFn///feLn8RTcPava4SvPUPWrcLNkqxb9wlfe6bKKXRP4ooO\n4O3tzcCBA3F1dUWpVOLj40NmZmaJLqlAqW6pa9euZcWKFdja2mJtbS3FwsrIyMjIPB7ZzVJG5jEs\nOLaAnHzNBUxOfg4Lji2oMnXRanZuSvqWcxqulgpdLWp2blomx8/MzMTU1JSMjAxpUW5pacn9+/dJ\nT09HCMGFCxdITk7G3Nyc4cOHM3PmTKysrGjRogWZmZm4u7szb948/P39iY6O5o033qB69eolLtTV\najW//fYbSqWS1q1bY2FhwQcffMCff/4pWQfKi/nz5zNo0CBmzZpFly5dMDY2LtYnMTGRFStW4O7u\nzrBhw4ql6Z81axa1a9cmKSkJa2tratSowYMHD4iJiaFVq1Y0bdqUoKAgDAwMePfddwkPD6du3bro\n6+uzdu1a3Nzc0NLSwsfHh48//pj8/Hzu3r3L4sWLiY+PZ9OmTUCh4nTu3DkNF8fyoE6dOri7u2Nj\nY4OBgQENGjSQXuvSpQtLly7F0tISc3NzacEP8Nprr2FpaUlCQgJt2rQBwMrKipkzZ+Lt7U1BQQG6\nurosWbKEJk2alOucHkfktiTyHmi6Nec9KCByWxKtXUwqSKriFH1XHnUbX7x4sfR/Sa7oAQEBBAQE\naLQVxY4+SkkuqYBGkhgo3DRbcGwB1wZcw8TQhHEO46rM/VVGRkamopGVORmZx3Atu+Td9NLaKyNF\ncXF3dqWQn34f7Vp61Ozc9Knj5Urj888/x8XFhZo1a0r17kJCQggICEBfX5+bN2/i5+fH0qVL0dfX\n5/XXX6dr165kZGRw5swZBg0ahJOTExYWFqxatYovv/yS0NBQ8vPzSU1NJSAgQMpkCIXWvW7duhES\nEkKfPn3Q0dGhe/fueHl5MX/+fKnfwxlImzZtKpVUeNQt72EF8OHXHo55eriPj48PPj4+QKEb6ZEj\nR1AoFGzYsIHExMRi16dx48a4u7sDMGjQIBYuXKjxemhoKMuWLePu3bvk5uaSkJCAQqGgdu3a1K5d\nm9u3b2NkZMSlS5fYtm0bJiYmaGlpUVBQQGZmJhcvXsTIyIiVK1eSm5vLW2+9hZ2dHUIIFi1aVGIM\naXmzbt26EttLi0Er4rfffivW9vbbb/P2228Xay8p1rEiKLLIPWn7q06R90PRplmR9wMgK3QyMjIy\nT4CszMnIPAYTQxNSs4tnVTMxrDw77E+CoX3951beHlaIAI1kH6NGjZLq0D2sRL3xxhssXbq02LEa\nNWokJQ4pYseOHXz55Zfk5eXh4OBASEgI9erVA0pXprZs2fJcc3oefjl+hWlLfyZx6wJ0tRU0Na3P\nttA1xfoVuYeW9PzChQsEBwcTFRVFRkYGzZo14+TJk1hbW5OWlsbQoUPZvn07AHfu3EFbW5tFixbh\n4OCASqXiq6++YuDAgXz11VcsXryYK1euMGTIEMaPH0/nzp357rvv6NixI7q6upw9e5aGDRtqxKxV\nZSRrTnblsuYY1dYrUXEzqv30bs2vAi+D94OMjIxMRSIrczIyj2GcwziNXWMAfW19xjkUdyl61XlU\n2XtaSrO4PEp8fDz79u0jIyMDY2NjvLy8UKlUz3zeZ+GX41eYuuUE915rhdmwQre0Al1tTmYa0PKR\nvhcvXiQyMhJXV1fWrVtHu3btNBQ0Q0NDjI2NOX/+PNra2uzbt49NmzaRlZVFmzZt2L59O9nZ2bi7\nu2NtbY2vry/Ozs64u7tz7do1cnNzmT9/PjNmzCAnJ4c7d+4QHR3NokWLSElJwcHBASEE9erVK6ZA\nV1UqszXHtVcLwtee0XC11KmmhWuvFtLzoo2P5/m+vCy8DN4PMjIyMhWJnABFRuYxdGvejSC3IEwN\nTVGgwNTQlCC3oApfMFYFQkJCJAvawxQlTHkW4uPj2b59OxkZGUBhHNj27duJj49/Llmflrm7ErmX\nq1ms+l5uPnN3FXezNDc3Z8mSJVhaWnL79m1GjRolvWZra4u9vT0WFha8//77VK9enZEjR3LmzBn2\n79/PpEmTuH37Nh9++CE5OTkcPHiQwMBAbty4wdmzZ9m1axd5eXncvn2b7OxstLS0aNy4MRMnTkRL\nS4svvviCEydOcPLkScLDw0uM6auKPM6aU9G0djHB089CssQZ1dbD08+iUsXLVSZK83Koat4PMjIy\nMhWFQghR0TJIODk5iapcHFem8nP16lUCAwOlpBAyVYt58+ZJitzDGBsbM378+HKTo9mUHZR051QA\nF2b/q+g/jQXmmaw18aGw7zPIuAzGjcDrU1D143REOBEbVpF58wY16tRF3X8IlmrPJz9uJUf1kwpR\nwjugQEG8f/kq9s/Cw+91cnIyffv2ZeDAgURGRkp1GHv37s2cOXMAWL9+PV988QVCCLp168ZXX33F\nzz//TGRkJN988w0LFixgwYIFJCcnk5yczODBgzl06FAFz/LJeNTKCoXeD4/bNEtJSeHw4cMMHDjw\nqc4lW0RlZGSqCgqFIkYI8UTFaGXLnMwrhZmZmazIVWFKUuQe1/6iMKtl8FTtT8JTu6nGh8L2QMi4\nBIjCv9sDuRIaxO5li8m8kQZCkHkjjd3LFnM6IvyZZatsvCzWnMTERPr27SvFh8bGxrJx40ZOnDjB\nxo0buXTpElevXuXDDz8kLCyM2NhYoqKi+OWXX1Cr1URERAAQERFBnTp1uHLlChEREbRv3/655EpP\nTy+WdbUkjIyMgEIlycbG5pnO9SzeDykpKaUm1Snv8iQyMjIyFY2szMm8EFatWoVKpcLW1pbBgweT\nkpJCx44dUalUeHl5cfHiRaDQ5W7UqFG0bduW5s2bs3//foYNG4alpaVG+msjIyPGjx+PtbU1Xl5e\npKWlAbB8+XKcnZ2xtbWlb9++Ug2vgIAAAgMDcXNzo3nz5pIC9/Cio7SiyqmpqbRv3x47OztsbGyk\nBdPz8ryLDCEEBQUF/93xJaY0N8Hydh+c1NkcA11tjTYDXW0mdTbX60h+ZAAAIABJREFUaHveOMLH\nsu8zyL2n2ZZ7j5onlpL3QDMBR96D+0RsWPVi5KgAxjmMQ19bX6OtqsWypqWl0atXL9auXYutrS1Q\nWEfP2NgYfX19rKys+Pvvv4mKisLDw4N69eqho6ODn58fBw4cwMTEhKysLDIzM7l06RIDBw7kwIED\nREREoFarn0u2J1XmnpVHfx+stazJ+zYPvob87/JR6iiB0u/jU6ZMISIiAjs7O+bNm0dISAg9e/ak\nY8eOeHl5IYRg0qRJ2NjYoFQq2bhx4wubi4yMjExFIytzryDz588vVrj4Yf73v/+RkJDwzMc/deoU\nM2fOJCwsjLi4OBYsWMDYsWPx9/cnPj4ePz8/AgMDpf63b98mMjKSefPm0bNnT8aPH8+pU6c4ceIE\nsbGxAGRnZ+Pk5MSpU6fo0KEDM2bMAKBPnz5ERUURFxeHpaUlK1askI6bmprKwYMH+e2335gyZUox\nOUsrqrxu3To6d+5MbGwscXFx2NnZFRv76GJk+/btuLi4YG9vzxtvvME///wDFKa6Hzx4MO7u7gwe\nPLhUBRJg7ty5Uvv06dOBQuXT3NycIUOGYGNjIxXkflXx8vJCV1dXo01XVxcvL69yleMt+4Z82UdJ\nw1oGKICGtQz4so+St+wblp8QGZdLbDbSuldie+bNG6Ue6uHF+9WrV0uMdaxMvAyxrMbGxrz++usc\nPHhQatPT+zfjpba29n9uALm5ubFy5UrMzc0lS11kZKRUCuNZmTJlCklJSdjZ2TFp0qQS703PytP+\nPpR0H589ezZqtZrY2FjJvfrYsWNs2rSJP//8ky1btkj377179zJp0iRSU4tnJZaRkZF5GZCzWb6C\nFBU6rl69erHX8vPz+eGHH57r+GFhYfj6+lK3bl0AateuTWRkpJRGfvDgwUyePFnq36NHDxQKBUql\nkgYNGqBUFu7KWltbk5KSgp2dHVpaWlKmw0GDBtGnTx8ATp48ySeffEJ6ejpZWVkaNbXeeusttLS0\nsLKykpSrh9m9e3eJRZWdnZ0ZNmyYRs2uhylajBw+fJi6dety69YtFAqFVG/shx9+YM6cOXz99dcA\nJCQkcPDgQQwMDFi2bJmkQN6/fx93d3e8vb05d+4c586d4+jRowgh6NmzJwcOHOD111/n3Llz/PTT\nTxoFlV9VirJWPmk2y/T0dNatW8fo0aPZv38/wcHBJdYu+9///scHH3yAlZXVE8vyln3D8lXeHsW4\n0f+7WGqSVVCyq2eNOnVLPVSRMjd69Ogq44rcrXm3KqW8PUq1atXYunUrnTt3ltwVS6JNmzZS0pvX\nXnuN9evXM3bsWADUajWffvopn376Kfb29oSHh2NgYPBUluqAgAC6d++uocDPnj2bkydPEhsby+7d\nu9m0aVOxe9OzunKW9vvQvn17bGxsEELw999/M3fuXE6fPk1gYCATJkwgLi6Of/75h7CwML744gv0\n9fUxMjJi3LhxrFq1ivv375ObmwvAwYMHGTBgANra2jRo0IAOHToQFRVV7llvZWRkZMoD2TJXSXka\nN8WHF15Fi4L9+/fj4eGBj48PFhYW+Pn5IYRg4cKFXL16FU9PTzw9PaUxEyZMwNbWlsjISDw8PChK\nRLN7925cXV1xcHDA19dXqvc1ZcoUrKysUKlUGvXGnoWi3WgtLS2NnWktLa1Sd6aLanUVZUY8ceIE\n06dPJyfn3yD6h49VUqKfoqLKsbGxxMbGcuHCBby9vWnfvj0HDvwfe/cel/P5P3D8VUmlohHKsRhJ\n3XcHpZJy+hLfcj5kYzRz2Jjj2Bw2YrbZMmdjbIQxZ81hiNSUYwcdsJxzSL5OK5VKh8/vj/vXZ90q\niii6no/HHnVf9+dwfe7d7u7357qu9/so9evXx8fHh/Xr1aenFfVl5NatW3h4eKBQKPDz8+PcuXPy\n9j169EBPT/UFOzAwkPXr12Nra4uTkxMPHjzg0qVLBAYGEhgYiJ2dHfb29sTHx3Pp0iUAGjduXK6B\n3MushynIzMyM+/eLHx0qKaVSycSJE/H19WXixInP/IJW0uliv/zyS6kCuQqh00zQfipw09bjkeJj\nqlRVr2lWpaoObgOHFHuogiMx/fv3l/9/+/v706tXLzp37oyZmRnLli1jwYIF2NnZ4ezszMOHDwG4\ncuUKXbt2pVWrVri5uREfH1+21/qW0tfXZ+/evSxcuJBHjx4VuY2pqSnz5s2jQ4cO2NjY0KpVK3r2\n7AmogrmbN2/i7u6OlpYWDRs2pG3btmXax2d9NpWVnJwc1q1bx6lTpwgLCyMzM5O2bdvyv//9Dx0d\nHSIiIkhLS0OSJEJDQ+V/8+np6Tg7O/P111/TsGFDVq9eXab9EgRBeBOIYK4CKu00lOKcOXOGRYsW\ncf78ea5evcqxY8cYN24c9erVIzg4mOBgVUKE9PR0nJyciImJUfsicP/+febOncvhw4eJiorCwcGB\nBQsW8ODBA3bt2sW5c+eIjY3lyy+/VDtvx44d2bZtGw8ePADg4cOHtGnThs2bNwOwcePGUq/pyMvL\nk4PW/FpdAKmpqZiampKdnc3GjRtLdcz8osr5d3MvXrxIeno6169fp27duowYMYLhw4cTFRX13GON\nHTuWTz/9lLi4OH7++We1oLJgkebiAkhJkpg2bZrcfvnyZT766KNC+78NnjUKUdaeni6WlpZW6AYH\noHYDw8DAgBkzZmBjY4Ozs7M8qnvlyhWcnZ1RKBR8+eWXr/U6iqQcAN2XQI2GgIbqZ/cl1B/gS5eR\nn2JoXBs0NDA0rk2XkZ8+M5vlvHnzaNq0KdHR0fj5+ak9d/bsWXbu3El4eDgzZsygWrVqnDlzBhcX\nF/lGx8iRI1m6dCmRkZHMnz+f0aNHv8orf+MVXEtpZGREeHg448aNUyvZsXfvXtq3bw/Ae++9J5eY\n+P777+VtmjZtyu2kAO7d+4igI+8ya1YG06Y/e8rx0zcKAY4ePVpoXVpeXh6dOnVi/fr1ZGdnM3v2\nbKKjozl8+DDz589nxIgRPH78mC5dusifd/mjX/n/3opan7x06VJ++eUXtb8PDRo04N1330VfX58/\n/viDd999l9OnT/PgwQMeP36Mjo4OLi4u5OXlERoaiouLC6mpqVStWhUvLy8AateuTUJCAqAKcrds\n2UJubi737t3j6NGjtG7d+mX+lwmCIFRYIpirgIqbhpKfhvmDDz5QW2dRnNatW9OgQQM0NTWxtbWV\n/9A9TUtLi759+xZqP3nyJOfPn8fV1RVbW1vWrVvH9evX5QX6H330ETt37iw0XdPKyooZM2bQrl07\nbGxsmDRpEkuXLmXt2rUolUo2bNjA4sWlqwelr6/P6dOnsba25siRI8ycOROAr7/+GicnJ1xdXWnR\nokWpjjl8+HBatmyJvb091tbWjBo1ipycHEJCQuT6X1u2bGH8ePWkCkUFqykpKdSvr5pyt27dumLP\nWVwA6eHhwZo1a+SRz8TERO7evVuq63mVcnJyGDRoEJaWlvTr14/Hjx8TFBSEnZ0dCoWCYcOGkZWl\nSrqxceNG9PT0CrUX1K1bt+feRS+Lkbyng5SibnA8Lf9uf0xMDO7u7nI/x48fz/jx44mLi6NBgwYv\n1a8yoxwAE8+Cb7Lqp3IAAJZuHRi5fC2fbd7DyOVrX6osQYcOHTA0NKR27drUqFGD7t27A6BQKEhI\nSCAtLY3jx4/Tv39/bG1tGTVqlFif9Jok3fmD+PgZZGbdBiQys24THz+DpDt/FLl9UTcKofC6NEND\nQ9LT09m1axe//fYbJiYmTJw4EUmSuHPnDhcvXmTMmDFUq1YNIyMj9u/fD8CHH37Izz//THR0NFpa\n/yYIKrg+OS4ujmrVqtGmTRv570Pfvn2JiYmR/z506dIFDQ0NDAwMCAkJoU2bNri5uZGbm8vly5fp\n2bOnvKZw0aJFgGq2Rv5Mjt69e8sBa8eOHfnhhx8wMXmzMp0KgiCUmCRJFea/Vq1aSYIkLVmyRJo+\nfbpaW61ataQnT55IkiRJT548kWrVqiVJkiR99NFH0pYtWyRJkqTc3FxJW1tbkiRJCg4Oljw9PeX9\nx4wZI61du1aSJElq3LixdO/ePfk5fX19tXO1a9dOCg8Pl3bv3i0NHDiwyD5mZmZK+/btkz788EOp\nQ4cOL3G1JfN0H1+FvVf2Sp23dZYU/gqp87bO0t4re4vd1t/fX7KyspKUSqU0dOhQKSAgQDI3N5fs\n7e2lyZMnS+3atZMkSZJmzZol+fn5yfvl5uZK06ZNk6ytrSUrKyupffv2UnJysiRJkrRo0SLJ2tpa\nsra2lpydnaXLly9L165dk6ysrF7pdT/PtWvXJEAKCwuTJEmSPvzwQ+nrr7+WGjRoIF24cEGSJEn6\n4IMPpIULF0oZGRmSqamp9O6776q1S5LqfXft2jVJS0tLWrdunZSamip17NhRsrOzk6ytraWAgAD5\nfC1atJAMDAwkCwsLqXPnztLjx48lSZKk06dPSwqFQrKxsZEmT54svzZr166VxowZI/fZ09NTCg4O\nlq5duya98847UqtWraTGjRtL5ubm8jbdunWTTE1NJXt7e6l+/fpS27ZtJUmSJG1tbcnHx0dydHSU\nzMzMpP/85z+SJElSzZo1pezsbEmSJCklJeW1vCdfl4Lvs4K/P/26FvzsyH8uJSVFMjExef2dFqSw\nsLbS4aAmhf4LC2tb5PZF/W0ZOnSo9Ntvv8mPDQwMJEmSJG9vb6lmzZqSsbGxVK9ePUlDQ0Nq0aKF\nZGdnJzVu3FiSJNXn8rx586RJkyZJLVq0kBo1aiQfJyYmRn4f9e3bV2rWrJlkY2Mj2djYSGZmZtLB\ngwflbSMjIyWFQiGlp6dLaWlpkpWVlRQVFSXNmjVLatiwoXTo0CHpzp07UsOGDaVevXrJ+xX8N7ht\n2zZp6NChateWvHu3dLFDR+l8C0vpYoeOUvLu3aV4dQVBEMoPECGVMH4SI3MVUGmmKZqZmREZGQnA\n7t275RGfZzE0NCQ1NfW52zk7O3Ps2DEuX74MqEYsLl68SFpaGikpKfz3v/9l4cKFxMTEvNB1ViT5\nhWuT0pOQkEhKT8L3uC/7ru4rcvuhQ4dy9uxZYmJi8Pf3p2fPnly9epXIyEj8/PwICQkBVNksC64p\n1NTU5Ntvv5WnTAUHB8vJCvJHfTZu3MiAAQPYsGEDu3btKrae0uvUsGFDOUPe4MGDCQoKwtzcnObN\nmwOq1+Po0aNcuHCBBg0aoKWlxaBBgwgJCcHPz4/Hjx+TmZmJpaUleXl5hISEoKGhwa5du+RpfQMG\nDODDDz8kKyuLS5cuYWhoSFhYGIaGhjg7O7N69WqGDh2KgYEBkiTh7+9fovpyderUISIigl9//ZWU\nlBRiY2PJzMwkNDSUiRMnEhkZyZMnT9T26dSpE6dPn8bX15fTp0+Tnp5exq9oxVLSz4SiVK9eHXNz\nc7Zt2waobhC+DZ8Jb4LMrKJHQItrL05R64u7du3Kf/7zH27fvk1iYiKNGjVi//797Ny5U55inJaW\nhpaWFoaGhpw4caLY40vFTC/PZ29vj4+PD61bt8bJyYnhw4djZ2eHm5sbSUlJuLi4ULduXXR1dUs8\nRT9lzx6SvppJzu3bIEnk3L5N0lczSdmzp1SvjSAIQkUngrkKqDTTFEeMGMFff/0lJy8pyfqqkSNH\n0rVrVzkBSnFq166Nv78/7733HkqlEhcXF+Lj40lNTcXLywulUknbtm1ZsGBBmVz3s+RPP3xVFkct\nJjM3U60tMzeTxVGlmw76IkJDQ7GyssLW1paAgADmzp0rBykpKSns2bOH2NjYV96PZ8lPOJPPyMjo\nmdtfuHCB0aNHs3btWqpUqcKCBQu4f/8+vXv3RktLi+zsbFauXMnnn39Ot27dyMnJQVNTk7S0NDZu\n3Ii5uTlVq1YlLS2NmJgY3n33Xfr378+9e/ewsrIiJiaGoKAgDA0Nn9kPQ0ND7t69i729PSNHjiQt\nLY3z588THx9P9erVqV27NqAK+PLl5uYyb948bG1tmTVrFrm5udy4cQNnZ2d27NgBIN9YeVvUqlUL\nV1dXrK2tmTJlSqn337hxI7/++is2NjZYWVnxxx9FT/MTypaujmmp2ou6UViclJQU6tSpg7a2NsHB\nwVy/fl3t+R13HuJw/BxzLt9m1c27BGXmYWhoyKlTpwD1fyPFTS8vaNKkSZw9e5azZ88yYcIEQHVT\nJTs7W/67dvHiRSZNmiTvU/DvQr9+/fD395cf3124CClT/TNdyszk7sJFxV6zIAjCm0iUJqighg4d\nytChQ9Xajhw5Umi7unXrcvLkSflx/uL49u3by4vnAbWF9WPHjpVTW0PhQCl/VAlUf/zDw8MLnff0\n6dMlu5A3xJ30O6VqL0sbN25k2rRpDB48mDFjxhAdHa2WWTE7O5ugoKAXTqstD8Nrvvi9mxs3bnDi\nxAlcXFzYtGkTDg4O/Pzzz1y+fJl3332XDRs20K5dOywsLEhMTMTExARXV1c57XlQUBBVqlRhyZIl\nbN++nZSUFDZt2oSxsTGtW7cmLCwMMzMzevXqxfr169HR0SE7O5uePXvSunVreQSwatWqHDp0iC++\n+AJLS0v5mqpUqaJWUD0/IcOjR4/Izs4mIyODmjVr8uTJE7XkNMXZsWMHFhYWbN++nb1792JpaSmX\n9Pjmm2/o2rXray9U/qoVNQLs4+ODj4+P/LjgutuCz5mbm3PgwIFX3EPhaU2aTiY+fgZ5ef/WFtTU\n1KNJ06IzDBe8UailpYWdnV2xxx40aBDdu3dHoVDg4OCgtib5UU4uky/cJCNPQgIe5eQx+cJNRv6w\ngBEjRqCpqUm7du3kfyPDhw8nISEBe3t7JEmidu3aBAQEvNS1p5+5y6ODCeQmZ6FlpEN1DzP07f69\nKZNTzLrN4toFQRDeVCKYE0olNja2xDW+3iQm+iYkpRf+I2+i/2KL5tPT0xkwYAC3bt0iNzeXr776\nCmNjYyZPnkxOTg6Ojo6sWLGCDRs2sHXrVg4ePMj+/fv566+/uH//PitXrsTGxoZr167JBbHt7Ozo\n3bu3XFeqYcOGvPfee/Ts2ZN//vmH7Oxs5s6dS8+ePUlISMDDwwMnJyciIyP5888/uXDhArNmzSIr\nK4umTZuydu3aEmdktLCwYPny5QwbNoyWLVuyZMkSnJ2d6d+/v3w9H3/8MTo6Ovzwww98+OGHKBQK\nHB0d6d69O7dv35aPVbVqVZ48ecKNGzdo3rw5GRkZRd75B3B1deXUqVM0a9YMIyMj3nnnHRYsWMDd\nu3eZPn26nFzFzMyMn376iby8PBITE+WbDY8ePaJJkyacOXOGe/fuye9VCwsLNDU15RseNjY28mjo\nF198wdKlS1m6dCn9+vWjadOmANSvX1+uJbh582YuXLjwAu+Mt8+OOw/57moSiVnZ1NfRZloTU/qa\n1CzvblUKpiaqEgVXr8wnMysJXR1TmjSdLLcXpagbhQXl39wzNjYudupknTXbuZWlGmXT91aVvMjI\nk9ha9R15FsG8efNwcHAA/p1e/u2335byCouWfuYuyTsvIWWrbuDkJmeRvFNVLiE/oKtiaqqaYvmU\nKqZFj1oKgiC8qUQwJ5RYbGwse/bskafK5E8BBN74gG68/Xh8j/uqTbXU1dJlvP34Z+xVvAMHDlCv\nXj327VOtuUtJScHa2pqgoCCaN2/OkCFDWLFiBRMmTCAsLEwu2jtmzBgOHTokZy7Nzc3l+vXrNGzY\nkCpVqsjZF0NDQ1m5ciW6urrs2rWL6tWrc//+fZydnenRoweAWrHxgmUm9PX1+f7771mwYIGcFfRZ\nzMzMiqwb1qlTJ86cOVOo3dXVlezsbFatWoWLiwvDhw/HwcGByMhIkpOTATAxMWHChAns3r2bM2fO\nsGzZMlq0aMGuXbtwcnKSC8zPmTOHXr168eeffzJ79mzmzZvHZ599hpaWFra2tvLr4erqirm5OS1b\ntsTS0hJ7e3sAOStpixYt1Nb96enp8dNPP9G1a1f09fVxdHSU+//VV18xYcIElEoleXl5mJubs3fv\nXrZu3cr06dPJzc3FwMCg1BlZ30Y77jyUR2gAbmVlM/mCqpC5COheD1OTns8M3l6FxKyi12ZfO3oE\n28kjyMnJoXHjxvj7+7+SYP/RwQQ5kMsnZefx6GCCHMzVmTiBpK9mqk211NDVpc7ECS91bkEQhIpG\nBHNCiQUFBRVKsPKyUwArCs8mnoBq7dyd9DuY6Jsw3n683F5aCoWCzz77jC+++AIvLy85SUTBhCHL\nly+X14bks7e3JygoSH7cqFEjwsPDqVGjBp6enhw6dIjHjx9z7do1LCwsyM7OZvr06Rw9ehRNTU0S\nExPlumgFi40XLDMB8OTJE1xcXF7o2kqi4Ehe83pNmdZoCE3b6NPL3ZPGpg3R1NRk8uTJzJgxg6Cg\nICZPnkyVKlWoXr06M2bMYM6cOZiZmQGqwHXYsGF8/vnntGnTRl6/FxUVRefOnQHVmr7i6gwWXEdT\nUIcOHYiPj0eSJMaMGYODg4M8deurdwajNegjeepWbGwsiYmJjBw5Ut4/JiaGRo0avfHv/Zfx3dUk\nOZDLl5En8d3VJBHMvcXq62jLI3MFvdvVi4g5X8iPX1Wwn5tcuNzJ0+01/r+Ext2Fi8hJSqKKqSl1\nJk6Q2wVBEN4WIpgTSqy4zIElySiYLyEhAS8vL7lg7ou4ffs248aNk4vblhXPJp4vHLw9rXnz5kRF\nRfHnn3/y5Zdf0rFjxxLt17RpU+rUqUONGjVISUnB0tKSQ4cOcevWLTp37sz9+/dZvXo1rVq1AlTr\n7e7du0dkZCTa2tqYmZnJa8KeLlbeuXNnfv/99zK5vmcpOJInT4d6nEdbs1YcMPsFDW1NjPo0kzPo\nFTfCV3B91tq1awHYsmULmpqa5OTkYGNjw9KlS1+4n6tXr2bdunU8efIEOzs7Bjv1LnbqVlDI23sj\n42UUN0JTXLvwdpjWxFQtSAPQ09RgWhP1KYyvKtjXMtIpMqDTMtJRe1yje3cRvAmC8NYTwZxQYvkB\nRlHtr1O9evXKPJAra7dv36ZmzZoMHjwYIyMjli1bRkJCQqGEIU8zNDREQ0ODiRMnym379u1j27Zt\nzJw5k3v37jF58mS53MHzMs7lc3Z2ZsyYMfL509PTSUxMlEcKX5WSTIcqib9DgwndvJ7UB/cZ7WqH\n28AhL1UIG2DixIlqr3PSvNPF9jUl8+VvZLyNihuhqa+jXQ69KV8GBgavPOtuRZEfiD1v+uSrCvar\ne5ip3XgB0NDWpLqH2UsdVxAE4U0kShMIJdapUye0tdW/pGlra8sJOkoqJyeHQYMGYWlpSb9+/Xj8\n+DFmZmbcv38fgIiICDkxxV9//YWtrS22trbY2dmRmppKQkIC1tbWgGoKXZ8+fejatSvNmjXj888/\nl88TGBiIi4sL9vb29O/fX/6iNXXqVFq2bIlSqZSDom3btmFtbY2NjQ3u7u4v9PoUFBcXR+vWrbG1\ntWX27NnMnTuXtWvX0r9/fxQKBZqamnz88ceF9lMqlWhpaWFjY8PChQsBcHNzo06dOujp6eHm5sat\nW7fkWkuDBg0iIiIChULB+vXr1TLOFVRcmYlXrSTToZ7n79BgAlctI/X+PZAkUu/fI3DVMv4ODS6r\nbj6zT7nJWcXesHjbMlqW1rQmpuhpqpetKGqERii53Nzc8u5CifQ1qUlEGyuSOtgS0caqyJG24oL6\nlw329e3qYNSnmTwSp2Wkg1GfZqW6QSQIgvC20MgvEFoRODg4SBEREeXdDeEZXjabZUJCAubm5oSF\nheHq6ipnR1y2bBkREREYGxsTERHB5MmTCQkJoXv37kydOhVXV1fS0tLQ1dXl1q1b8lRNf39/5syZ\nw5kzZ9DR0cHCwoKwsDD09PTo06cP+/fvlxN+ZGVlMWbMGNq0aUN8fDwaGhokJydjZGSEQqHgwIED\n1K9fX257WzwvhferlDTvdLHToUynti7RMVaN+VAVyD3F0Lg2I5evfek+5ntWX+/9V1ct+Q+obmR0\n7969Uk+zBJHNMl/BkTk/Pz+2bt1KVlYWvXv3Zvbs2QD06tWLmzdvkpmZyfjx4+U1mAYGBowaNYrD\nhw+zfPlyBg8ezNChQ+X33LZt24q9UVORPb1mDlTBftXJI7gQ/naVtxEEQShLGhoakZIkOZRkWzHN\nUigVpVL50l9eC2YVHDx4MEuWLCl2W1dXVyZNmsSgQYPo06cPDRo0KLRNp06d5BGSli1bcv36dZKT\nk4tM+FGjRg10dXX56KOP8PLywsvLSz6Pj48PAwYMoE+fPi91fRVJSVJ4v0plMR0q9cH9UrW/qGf1\n1VSpeq3exrIcL6uvSc1KGbwVJzAwkEuXLnH69GkkSaJHjx4cPXoUd3d31qxZQ82aNcnIyMDR0ZG+\nfftSq1Yt0tPTcXJy4scff5SPY2xsTFRUFD/99BPz58/nl19+KcerejHFTsd8BYFcTk4OVaqIrzSC\nIFQ+4pNPeO3ysxEWfFyw6HPBos5Tp07F09OTP//8E1dXVw4ePIiurq7a/vmJNAC0tLTIycl5ZsKP\n06dPExQUxPbt21m2bBlHjhxh5cqVnDp1in379tGqVSsiIyOpVatWWV52uSirNWsvKv8cLzMyaFjL\nuOiRuVrGZdZPeH5fy+JGhvD2CwwMJDAwUC7InZaWxqVLl3B3d2fJkiXs2rULgJs3b3Lp0iVq1aqF\nlpYWffv2VTtO/k2lVq1ayaU63kRFBfv5o5ghISHMmjULIyMj4uLiGDBgAAqFgsWLF5ORkUFAQABN\nmzbFx8cHXV1dIiIiePToEQsWLMDLywt/f3927txJWloaubm5hISE8Pnnn7N//340NDT48ssv8fb2\nZuDAgXzwwQd4eqoSXPn4+ODl5UXv3r2ZOnUqISEh8syNUaNGlcfLJAiC8MJEMCe8djdu3ODEiRO4\nuLiwadMm2rZtS2pqKpGRkXTr1o0dO3bI2165cgWFQoFCoSBtZ2lgAAAgAElEQVQ8PJz4+HhsbW2f\ne47iEn7Uq1ePx48f89///hdXV1eaNGkin8fJyQknJyf279/PzZs334pgrizWrL0sfbs6LxU4ug0c\nQuCqZeQ8+bfPVarq4DZwSFl0T83L9lUQJEli2rRphYKCkJAQDh8+zIkTJ6hWrRrt27eXb1zp6uqi\npaWltn3+Tar8G1Rvq5iYGP7++29q1qxJkyZNGD58OKdPn2bx4sUsXbqURYsWAaop+qdPn+bKlSt0\n6NCBy5cvA6oSJbGxsdSsWZMdO3YQHR1NTEwM9+/fx9HREXd3d7y9vdm6dSuenp48efKEoKAgVqxY\nwa+//kqNGjUIDw8nKysLV1dXunTpgrm5eXm+JIIgCKUiEqAIr11+DTJLS0v++ecfPvnkE2bNmsX4\n8eNxcHBQ+1KzaNEirK2tUSqVaGtr061btxKdo7iEH6mpqXh5eaFUKmnbti0LFiwAYMqUKSgUCqyt\nrWnTpg02Njav5Npft6dTdT+vvSKydOtAl5GfYmhcGzQ0MDSuTZeRn750NktBeBU8PDxYs2aNvH4u\nMTGRu3fvkpKSwjvvvEO1atWIj4/n5MmT5dzTisHR0RFTU1N0dHRo2rQpXbp0AVS1OguWJxkwYACa\nmpo0a9aMJk2ayAmcOnfuTM2aqpG/sLAw3nvvPbS0tKhbty7t2rUjPDycbt26ERwcTFZWFvv378fd\n3R09PT0CAwNZv349tra2ODk58eDBAy5duvTaXwNBEISXIUbmhNeqYA2ygtzc3Lh48WKh9qLqiJmZ\nmcl16nx8fPDx8ZGf27t3r/x7x44dCQ8PL7T/6dOF12u8ydOYnuVtSeFt6dZBBG/CG6FLly78/fff\nuLi4AKophb/99htdu3Zl5cqVWFpaYmFhgbOzczn3tGIoOE1eU1NTfpxfTzJfUdPzQb2eZnF0dXVp\n3749Bw8eZMuWLQwcOBBQjaIuXboUDw+Pl74OQRCE8iKCOaFSu3jqDif+uELawywMaurg0rMpzZ1M\nyrtbZaYs1qwJgvB8BWvMjR8/nvHjxxfaZv/+/c/dF1AbkXJwcCAkJKRM+vgm27ZtG0OHDuXatWtc\nvXoVCwsLzpw5o7aNm5sbP//8M0OHDuXhw4ccPXoUPz8/ALy9vfnll1+IiIjA398fUI2irlixgo4d\nO6Ktrc3FixepX79+iQJEQRCEikIEc0KldfHUHYI3xpPzRDVqlfYwi+CNqlHDty2gE8GbIFR8AWcS\n8Tt4gdvJGdQz0mOKhwW97OqXd7cqhEaNGtG6dWsePXrEypUrCyXCAujduzcnTpzAxsYGDQ0Nfvjh\nB0xMVJ/lXbp04YMPPqBnz55UrVoVgOHDh5OQkIC9vT2SJFG7dm0CAgJe63UJgiC8LFFnTqi01k0/\nRtrDwolADGrqMPRb13LokSAIlVXAmUSm7YwjI/vfouF62lp810fx2gK6hIQEuYZnRZKffbJfv35l\nd9DYrRA0B1JuQY0G0GkmKAeU3fEFQJX4Z/78+WpLIARBeL7S1JkTCVCESquoQO5Z7YIgVDyLFi3i\n8ePHpd7PwMDgFfTmxfkdvKAWyAFkZOfid/BCiY8hSZJc4kV4htitsGccpNwEJNXPPeNU7UKJiPea\nIFQcIpgTKi2DmkVndCyuXRCEiudFg7mK5nZyRqna8yUkJGBhYcGQIUOwtrZmw4YNuLi4YG9vT//+\n/eX1eHPmzMHR0RFra2tGjhxJ/qycyMhIbGxssLGxYfny5WV7UWXE39+/bEflguZA9lOva3aGql0o\nVknfawcOHKBFixbY29urJRfz9fVl/vz58mNra2t5fej69etRKpXY2NjwwQcfAHDv3j369u2Lo6Mj\njo6OHDt27PVdrCC8QUQwJ1RaLj2bUqWq+j+BKlU1cenZtJx6JAjCs6Snp+Pp6YmNjQ3W1tbMnj2b\n27dv06FDBzp0UGU7LTjitn37djnb7bVr13BxcUGhUPDll1/K2wwZMkRtndSgQYP4448/Xs8FFVDP\nSK9U7QVdunSJ0aNH89dff/Hrr79y+PBhoqKicHBwkMuvfPrpp4SHh3P27FkyMjLkaW8ffvghS5cu\nJSYmpuwupqJLuVW6dkH2vPdaZmYmI0aMYM+ePURGRnLnzp3nHvPcuXPMnTuXI0eOEBMTw+LFiwFV\nIqGJEycSHh7Ojh07GD58+Ku+PEF4I4lgTqi0mjuZ0GFQC3kkzqCmDh0GtXirkp8IwtvkwIED1KtX\nj5iYGM6ePcuECROoV68ewcHBBAcHP3Pf8ePH88knnxAXF4epqanc/tFHH8nZDVNSUjh+/Dienp6v\n8jKKNMXDAj1t9cLhetpaTPGweO6+jRs3xtnZmZMnT3L+/HlcXV2xtbVl3bp1XL9+HYDg4GCcnJxQ\nKBQcOXKEc+fOkZycTHJyMu7u7gDyiMhbr0aD0rULsue91+Lj4zE3N6dZs2ZoaGgwePDg5x7zyJEj\n9O/fH2NjYwC5buDhw4f59NNPsbW1pUePHjx69KhQ5ldBEEQ2S6GSa+5kIoI3QXhDKBQKPvvsM774\n4gu8vLxwc3Mr8b7Hjh1jx44dgCpo+eKLLwBo164do0eP5t69e+zYsYO+fftSpcrr/9OYn+TkRbJZ\n5qfSlySJzp078/vvv6s9n5mZyejRo4mIiKBhw4b4+vqSmZlZ9hfxpug0U7VGruBUS209VbvwTM97\nr0VHRxe7b5UqVdTW2T3vPZiXl8fJkyeLzFwqCMK/xMicIAhCJbNkyRIsLS0ZNGhQeXelVJo3b05U\nVJQ8VXLOnMJrnAoWl376y+LThafzDRkyhN9++421a9cybNiwsu10KfSyq8+xqR25Ns+TY1M7ljqL\npbOzM8eOHePy5cuAalrqxYsX5dfB2NiYtLQ0tm/fDoCRkRFGRkaEhYUBsHHjxjK8mgpMOQC6L4Ea\nDQEN1c/uS0Q2y1Io7r3WokULEhISuHLlCoBasGdmZkZUVBQAUVFRXLt2DYCOHTuybds2Hjx4AMDD\nhw8BVTmJpUuXyvs/K1AUhMpMjMwJgiBUMj/99BOHDx+mQYN/p5Xl5OSUy4hUady+fZuaNWsyePBg\njIyM+OWXXzA0NCQ1NVWeolW3bl3+/vtvLCws2LVrF4aGhgC4urqyefNmBg8eXCho8fHxoXXr1piY\nmNCyZcvXfl1lpXbt2vj7+/Pee++RlaXKyjt37lyaN2/OiBEjsLa2xsTEBEdHR3mf/ABWQ0ODLl26\nlFfXXz/lABG8vYRnvddWrVqFp6cn1apVw83NjdTUVAD69u3L+vXrsbKywsnJiebNmwNgZWXFjBkz\naNeuHVpaWtjZ2eHv78+SJUsYM2YMSqWSnJwc3N3dWblyZbldsyBUVKLOnPDWmjlzJu7u7vznP/8p\n764IQoXx8ccfs2bNGiwsLLhx4wY9evTg6tWrNGrUiN9++42pU6cSEhJCVlYWY8aMYdSoUQD4+fmx\ndetWsrKy6N27N7Nnz37tfT948CBTpkxBU1MTbW1tVqxYwYkTJ1i2bJm8dm779u188cUX1K5dGwcH\nB9LS0vD39+fatWu8//77pKWl0bNnTxYtWqS2/qZr16706tWLjz/++LVflyAIgiAUVJo6cyKYE95o\nb8JoglAxJCcns2nTJkaPHv3MQrbDhw9n0qRJb/QIzfOYmZkRERHBsmXL2LNnD2FhYejp6bFq1Sru\n3r3Ll19+SVZWFq6urmzbto1Lly6xfft2fv75ZyRJokePHnz++edy4ow33ePHj1EoFERFRVGjRo3y\n7s5rEXAm8YXW5wnC6xAbG0tQUBApKSnUqFGDTp06oVQqy7tbgvDaiKLhwhvn6ZTjW7ZsITIyknbt\n2tGqVSs8PDxISkoCoH379kyYMAEHBwe++eYbGjduLC+qTk9Pp2HDhmRnZ+Pj4yOvDQkPD6dNmzbY\n2NjQunVrUlNTyc3NZcqUKTg6OqJUKvn555/L7fqFVy85OZmffvrpudv98ssvb3Ug97QePXqgp6dK\nfx8YGMj69euxtbXFycmJBw8ecOnSJQIDAwkMDMTOzg57e3vi4+O5dOlSOfe8DMRu5fAnZljWM2Cs\n4jE1rh8s7x69FgFnEpm2M47E5AwkIDE5g2k74wg4k1jeXRMEYmNj2bNnDykpKYAqy+yePXuIjY0t\n554JQsUkgjmhQng65XjXrl0ZO3Ys27dvJzIykmHDhjFjxgx5+ydPnhAREcGsWbOwtbXlr7/+AmDv\n3r14eHigra2ttq23tzeLFy8mJiaGw4cPo6enx6+//kqNGjUIDw8nPDyc1atXywuyhbfP1KlTuXLl\nCra2tkyZMoW0tDT69etHixYtGDRokFxEuX379uTPEDAwMGDGjBnY2Njg7OzM//73PwCuXLmCs7Oz\nnIgjv7ZZUlIS7u7u2NraYm1tTWhoaPlcbCnkZ6cDVYa6pUuXEh0dTXR0NNeuXaNLly5IksS0adPk\n9suXL/PRRx+VY6+LN3z4cM6fPw/At99+W/yGsVthzzj+U/cfrk8wZILtY1WGw9itr6mn5cfv4AUy\nsnPV2jKyc/E7eKGceiQI/woKCiI7O1utLTs7m6CgoHLqkSBUbCKYEyoEhULBoUOH+OKLLwgNDeXm\nzZucPXuWzp07Y2try9y5c7l169+Crt7e3mq/b9myBYDNmzerPQdw4cIFTE1N5UX/1atXp0qVKsWO\nQghvp3nz5tG0aVOio6Px8/PjzJkzLFq0iPPnz3P16lWOHTtWaJ/09HScnZ2JiYnB3d2d1atXA6qa\nZePHjycuLk4ticimTZvw8PAgOjqamJgYbG1tX9v1lQUPDw9WrFghf5G6ePEi6enpeHh4sGbNGnmN\nWWJiInfv3i3PrhYpNzdXbWT1mcFc0Bz11PSgehxUOEPm2+Z2ckap2gXhdcofkStpuyBUdiKYEyqE\np1OO79ixAysrK3kkIC4ujsDAQHn7gqMJPXr04MCBAzx8+JDIyEg6duxYonMWNwohVA6tW7emQYMG\naGpqYmtrS0JCQqFtqlatipeXFwCtWrWStzlx4gT9+/cH4P3335e3d3R0ZO3atfj6+hIXFydnUnxT\nDB8+nJYtW2Jvb4+1tTWjRo0iJyeHLl268P777+Pi4oJCoaBfv35yhrpXzc/PjyVLlgAwceJE+d/3\nkSNHGDRoEAYGBnz22WfY2Nhw4sQJeWR16tSpZGRkYGtrK5dg+O2332jdujW2traM+u0SuXlFrBlP\nuVW47S1Tz0ivVO2C8DoVt261sqxnFYTSEsGcUCHcvn2batWqMXjwYKZMmcKpU6e4d+8eJ06cAFRT\nLM6dO1fkvgYGBjg6OjJ+/Hi8vLzQ0tJSe97CwoKkpCTCw8MBSE1NJScnp9hRCKFy0NHRkX/X0tIi\nJyen0Dba2tpybbLitinI3d2do0ePUr9+fXx8fFi/fn3ZdrqMJCQkYGxsjK+vL5MnT5bbNTU1+fbb\nb4mLi+Ps2bMEBwfLX6DyRyLj4uI4ceIETZs2fS19dXNzk6erRkREkJaWRnZ2NqGhobi7u5Oeno6T\nkxMxMTG0bdtW3m/evHno6ekRHR3Nxo0b+fvvv9myZQvHjh0jOjoaLV19NsZlFz5hjQaF294yUzws\n0NNW/5zU09ZiiodFOfVIEP7VqVMntaUSoPos7tSpUzn1SBAqNpEGUKgQ4uLiCqUcr1KlCuPGjSMl\nJYWcnBwmTJiAlZVVkft7e3vTv39/QkJCCj1XtWpVtmzZwtixY8nIyEBPT4/Dhw8zfPhwEhISsLe3\nR5IkateuTUBAwCu+UqG85NcjKwvOzs7s2LEDb29vNm/eLLdfv36dBg0aMGLECLKysoiKimLIkCFl\ncs5yE7tVNfUw5ZYq0Ok087XW52rVqhWRkZE8evQIHR0d7O3tiYiIIDQ0lCVLlqClpUXfvn2fe5yg\noCAiIyPl6dYZybrU0XmivpG2nur63nL5WSsLZrNseG0vl4POgt3k5+xdOgkJCXh5eXH27NmXPpaP\njw9eXl7069evDHomVFT5WStFNktBKBkRzAkVgoeHBx4eHoXajx49WqitqICtX79+PF1mw9/fX/7d\n0dGRkydPyo/3Xd3H4qjF3LG4g6m9KePtx+PZxPPFL0Co8GrVqoWrqyvW1tbo6elRt27dFz7WokWL\nGDx4MN988w1du3aVR69CQkLw8/NDW1sbAwODCjsyV2L/nyREXluWclP1GF5bQKetrY25uTn+/v60\nadMGpVJJcHAwly9fxtLSEl1d3UKj8UWRJImhQ4fy3Xff/dtYzoFqeeplV1+tFIGvb+HPWkEoL0ql\nUgRvglBCIpgTKp19V/fhe9yXzNxMAJLSk/A97gsgArq33KZNm4psX7Zsmfx7wZsFBYtK9+vXTx4R\nqF+/PidPnkRDQ4PNmzdz4YIqC+DQoUMZOnToK+h5OXlWkpDXGPS4ubkxf/581qxZg0KhYNKkSbRq\n1UqeAlscbW1tsrOz5SlaPXv2ZOLEidSpU4eHDx+SWsOJxhNffsToTfXNN9+wbt066tSpQ8OGDWnV\nqhXR0dF8/PHHPH78mKZNm7JmzRqys7Pp1q0bkZGRcmKf69ev06hRI5o2bUpcXByjR4+mevXqRERE\ncOfOHX744YdCI2iZmZl88sknREREUKVKFRYsWECHDh1ISEjggw8+kKe5L1u2jDZt2iBJEmPHjuXQ\noUM0bNiQqlWrlsfLJAiCUKGJNXNCpbM4arEcyOXLzM1kcdTicuqR8KaJjIzE1tYWpVLJTz/9xI8/\n/kjSnT84dsyNoCPvcuyYG0l3/ijvbr684pKBvOYkIW5ubiQlJeHi4kLdunXR1dXFzc3tufuNHDkS\npVLJoEGDaNmyJXPnzqVLly4olUo6d+4s166sjCIjI9m8eTPR0dH8+eef8priIUOG8P333xMbG4tC\noWD27NnUqVOHzMxMHj16RGhoKA4ODoSGhnL9+nXq1KlDtWrVAFVpjrCwMPbu3cvUqVMLnXP58uVo\naGgQFxfH77//ztChQ8nMzKROnTocOnSIqKgotmzZwrhxqtHfXbt2ceHCBc6fP8/69es5fvz4K3s9\n8suLvEq7d+9m3rx5r/w8giBULmJkTqh07qTfKVW7IDzNzc2NmJgY+XHSnT+Ij59BXp5qFCsz6zbx\n8TP45Ze9bNoYhr29PRs3bnzh85mZmREREYGxsfFL971UajRQTa0sqv016tSpk1rdqYsXL8q/Fxw9\nBfWR1e+//57vv/9efuzt7V2odEllFRoaSu/eveVArEePHqSnp5OcnEy7du0A1UhzftbWNm3acOzY\nMY4ePcr06dM5cOAAkiSpBdW9evVCU1OTli1byjUZCwoLC2Ps2LEAtGjRgsaNG3Px4kUaN27Mp59+\nqkpMo6Ul//89evQo7733HlpaWtSrV6/EmYrLU25ubrHTfnv06EGPHj1ec48EQXjbiZE5odIx0Tcp\nVbsgPM/VK/PlQC5fXl4Gv6zeyKFDh14qkCtXnWaqkoIU9IYmCdlx5yEOx89hGhyNw/Fz7LjzsLy7\n9EZxd3eXR+N69uxJTEwMYWFhasFcwQyxT69hfpaFCxdSt25dYmJiiIiI4MmTJ8/f6RXy8/PD0dER\npVLJrFmz5PZevXrRqlUrrKysWLVqldz+dHkMMzMzZs2ahb29PQqFgvj4eEC1jvvTTz8FVMlcxo0b\nR5s2bWjSpAnbt28HIC8vj9GjR9OiRQs6d+7Mf//7X/k5QRCEoohgTqh0xtuPR1dLV61NV0uX8fbj\ny6lHwpsuM6vwdL1FC+9x+3YG3bp148cff6RXr14olUqcnZ2JjY0F4OHDh0W2P3jwgC5dumBlZcXw\n4cNL9cW4TCkHQPclUKMhoKH62X3JG5ckZMedh0y+cJNbWdlIwK2sbCZfuFmpAzp3d3cCAgLIyMgg\nNTWVPXv2oK+vzzvvvCOXgtiwYYM8Sufm5sZvv/1Gs2bN0NTUpGbNmvz5559q5SCex83NTb6xcfHi\nRW7cuIGFhQUpKSmYmpqiqanJhg0byM3Nlfu4ZcsWcnNzSUpKIjg4uIxfhcICAwO5dOkSp0+fJjo6\nmsjISDkR15o1a4iMjCQiIoIlS5bw4MEDgCLLYxgbGxMVFcUnn3zC/PnzizxXUdNSd+7cSUJCAufP\nn2fDhg1yeR5BEITiiGBOqHQ8m3ji28YXU31TNNDAVN8U3za+z0x+4uvrW+wfZICAgADOnz//Kror\nvAF0dUwLtU2YWBtjYx2Cg4NJSEjAzs6O2NhYvv32W7lcwaxZs4psnz17Nm3btuXcuXP07t2bGzdu\nvNbrUaMcABPPgm+y6ucbFsgBfHc1iYynCoRn5El8d7Xyrpmzt7fH29sbGxsbunXrJpdsWLduHVOm\nTEGpVBIdHc3MmapRWDMzMyRJwt3dHYC2bdtiZGTEO++8U+Jzjh49mry8PBQKBd7e3vj7+6Ojo8Po\n0aNZt24dNjY2xMfHo6+vD0Dv3r1p1qwZLVu2ZMiQIbi4uJTxq1BYYGAggYGB2NnZYW9vT3x8PJcu\nXQJgyZIl2NjY4OzszM2bN+X2ospj9OnTB1CV1khISCjyXEVNSw0LC6N///5oampiYmJChw4dXtGV\nCoLwthBr5oRKybOJZ5lmrgwICMDLy4uWLVuW2TGFN0eTppPV1swBaGrqUaVKdUD1BW3Hjh0AdOzY\nkQcPHvDo0aNi248ePcrOnTsB8PT0LNUXZqGwxKwiioM/o72ymDFjBjNmzCjUXrCMS0E3b/67fnL6\n9OlMnz5dflywFAz8u5bRzMxMrjGnq6vL2rVrCx23WbNm8qg0IK9z1NDQUMs0+zpIksS0adMYNWqU\nWntISAiHDx/mxIkTVKtWjfbt25OZqUqkVVR5jPwpp1paWuTk5BR5rhedlioIglCQGJkThGJ88803\nNG/enLZt28qp51evXo2joyM2Njb07duXx48fc/z4cXbv3s2UKVOwtbXlypUrRW4nvL1MTXrSosU3\n6OrUAzTQ1alHixbfoKVVrby7JgD1dbRL1S6oW7Ro0XM/w543e+FFlEeGWA8PD9asWSMHo4mJidy9\ne5eUlBTeeecdqlWrRnx8fLEB78tydXVlx44d5OXl8b///a/IuqqCIAgFiWBOEIpQXNruPn36EB4e\nTkxMDJaWlvz666+0adOGHj164OfnR3R0NE2bNi1yO+HNt3LlymILgZua9MTVNZROHS/j6hqKqUlP\n+bmCa4VCQkIwNjamevXqxba7u7vLNfH279/PP//884qv7O02rYkpeprqNen0NDWY1qTw9FihsJIE\nc2UtP0NsZtZtQJIzxL7qgK5Lly68//77uLi4oFAo6NevH6mpqXTt2pWcnBwsLS2ZOnUqzs7Or+T8\nffv2pUGDBrRs2ZLBgwdjb29PjRo1Xsm5BEF4O2hUpKF9BwcHKSIiory7IQgsWrSIhw8fMmfOHAAm\nTZpEvXr1cHR05MsvvyQ5OZm0tDQ8PDxYuXIlPj4+eHl5yUVy//rrryK3EyqX/JICmpqaDBs2jKtX\nr1KtWjVWrVqFUqnk4cOHRbY/ePCA9957j8TERNq0aUNgYCCRkZGvvzTBW2THnYd8dzWJxKxs6uto\nM62JKX1NapZ3t14rPz8/dHR0GDduHBMnTiQmJoYjR45w5MgRfv31V6pXr054eDgZGRn069eP2bNn\ns2TJEiZPnoyFhQXGxsYEBwdz4MABpk+fTm5uLsbGxgQFBeHr68uNGze4evUqN27cYMKECXK9uBdx\n7Jjb/wdy6nR16uHqGvoyL0OFl5aWhoGBAQ8ePKB169YcO3YMExORbVkQKhMNDY1ISZIcSrKtWDMn\nCKXg4+NDQEAANjY2+Pv7FzsFpqTbCa9OQkICXbt2xdnZmePHj+Po6MiHH37IrFmzuHv3Lhs3buTd\nd98tFExZW1vTpEkToqOjMTIyAlRresLCwlixYgUGBgZMnjyZK1euMGbMGO7du0e1atVYvXo1LVq0\nYNu2bcyePRstLS0aNWokB2ABAQGF+lizZs0i22vVqkVgYOCrfYEqmb4mNStd8PY0Nzc3fvzxR8aN\nG0dERARZWVlkZ2cTGhqKu7s7/fv3p2bNmuTm5tKpUydiY2MZN24cCxYsIDg4GGNjY+7du8eIESM4\nevQo5ubmPHz4b0bQ+Ph4goODSU1NxcLCgk8++QRt7RebylpUhthntb8VYrdC0By8FseTnK3NE91a\nfPXVXBHICYLwTGKapSAUoai03QCpqamYmpqSnZ2tVjvM0NCQ1NRU+XFx270u+dNDK7vLly/z2Wef\nER8fT3x8PJs2bSIsLIz58+fz7bffFplNUlNTk549e7Jr1y4ATp06RePGjalbt67asUeOHMnSpUuJ\njIxk/vz5jB49GoA5c+Zw8OBBYmJi2L179wv1O+BMIq7zjmA+dR+u844QcCbx5V4IQUCVWTEyMpJH\njx6ho6ODi4sLERERhIaG4ubmxtatW7G3t8fOzo5z584VmaH35MmTuLu7Y25uDqhuSOTz9PRER0cH\nY2Nj6tSpU2Th8JIqKkPss9rfeLFbYc84SLlJiI8+0SOqcn5YDj72Yt2tIAjPJoI5QShCcWm7v/76\na5ycnHB1daVFixby9gMHDsTPzw87OzuuXLlS7Havy4sEc8VlXHuTmZubo1Ao0NTUxMrKik6dOqGh\noYFCoSAhIYGwsDA++OADQD2bpLe3N1u2bAFg8+bNeHt7qx03LS2N48eP079/f2xtbRk1ahRJSaoR\nA1dXV3x8fFi9erVcL6s0As4kMm1nHInJGUhAYnIG03bGiYBOeGna2tqYm5vj7+9PmzZtcHNzIzg4\nmMuXL6Onp8f8+fMJCgoiNjYWT09POVtjSRXMzvisLI4l0aTpZDQ11QvWa2rq0aTp5Bc+ZoUWNAey\nM9TbsjNU7YIgCM8gplkKQjGKS9v9ySefFGpzdXVVu4utr6+PoaEhWVlZJCcn8/XXX9OxY0fu379P\n7dq1Wbt2LY0aNcLHxwc9PT3OnDnD3bt3WbNmDevXr2tf6tIAACAASURBVOfEiRM4OTnJ6b4NDAwY\nMWIEgYGBmJiYsHnzZmrXrk379u2ZP38+Dg4O3L9/HwcHBy5evMjMmTPJyMggLCyMadOm4eXlxdix\nYzl79izZ2dn4+vrSs2dP/P392blzJ2lpaeTm5vLXX3+9stezPBT8cqmpqSk/1tTUJCcnp9gpYC4u\nLly+fJl79+4REBDAl19+qfZ8Xl4eRkZGREdHF9p35cqVnDp1in379skjIbVq1Spxn/0OXiAjWz0I\nzMjOxe/gBXrZ1S/xcQShKG5ubsyfP581a9agUCiYNGkSrVq14tGjR+jr61OjRg3+97//sX//ftq3\nbw/8O/PA2NgYZ2dnRo8ezbVr1+RplgVH58pKfgKhq1fmk5mVhK6OKU2aTlZLLPRWSblVunZBEIT/\nJ0bmBKGM/XTgJz6Z+gk5I3Oo+1VdPCd6MnbsWIYOHUpsbCyDBg1SSwzwzz//cOLECRYuXEiPHj2Y\nOHEi586dIy4uTg4W0tPTcXBw4Ny5c7Rr147Zs2cXe/6qVasyZ84cvL29iY6Oxtvbm2+++YaOHTty\n+vRpgoODmTJlCunp6QBERUWxffv2ty6QK4nisklqaGjQu3dvJk2ahKWlZaFgrHr16pibm7Nt2zZA\nVSMqJiYGgCtXruDk5MScOXOoXbu2Wm2ukridnFGqdkEoDTc3N5KSknBxcaFu3bro6uri5uaGjY0N\ndnZ2tGjRgvfffx9XV1d5n5EjR9K1a1c6dOhA7dq1WbVqFX369MHGxqbQqHVZelaG2LdOjQalaxcE\nQfh/YmROEMrQvqv7mLdpHvqt9NEy1CIpPYkfz//IxWMX5SLQH3zwAZ9//rm8T/fu3eWpf3Xr1kWh\nUABgZWVFQkICtra2aGpqyl+aBg8eTJ8+fUrVr8DAQHbv3i3XgcrMzOTGjRsAdO7c+ZXcWX8T+Pr6\nMmzYMJRKJdWqVWPdunXyc97e3jg6OhYqhpxv48aNfPLJJ8ydO5fs7GwGDhyIjY0NU6ZM4dKlS0iS\nRKdOnbCxsSlVn+oZ6ZFYROBWz0iviK0FoXQ6depEdva/xdIvXrwo/17ce33s2LGMHTtWftytWze6\ndeumto2vr6/a4/xC4UIJdZqpWjNXcKqltp6qXRAE4RlEMCcIZWhx1GJy8tTXiWTmZpKRU/yoSsGp\nf09PCyxuzYmGhqpmVpUqVcjLy1Od5xnrWyRJYseOHVhYWKi1nzp1Cn19/Wdc0ZvLzMxM7QtlwS+q\nBZ8rKpskgIODA0+Xbin4hdXc3JwDBw78+2TsVlhozU7lLXBroPoSphxQ6n5P8bBg2s44tamWetpa\nTPGweMZeglC+/g4NJnTzelIf3MewljFuA4dg6dahvLv15sj/rAiao5paWePFP0MEQahcxDRLQShD\nd9LvoN9Sn5TwFHLSVIFYTloOuk112bx5M6Aa0XFzcyvVcfPy8ti+fTsAmzZtom3btoAqKImMjASQ\nn4fC2TU9PDxYunSpHJycOXPmBa9QKFKBTHQgqX7uGadqL6VedvX5ro+C+kZ6aAD1jfT4ro9CrJcT\nKqy/Q4MJXLWM1Pv3QJJIvX+PwFXL+Ds0uLy79mZRDoCJZ8E3WfVTBHKCIJSACOYEoQyZ6JugW1+X\n2t1rc+27a1z+6jJ3fr+DzUgb1q5di1KpZMOGDSxevLhUx9XX1+f06dNYW1tz5MgRZs5UTb2ZPHky\nK1aswM7Ojvv378vbd+jQgfPnz2Nra8uWLVv46quvyM7ORqlUYmVlxVdffVWm113plXEmul529Tk2\ntSPX5nlybGpHEcgJFVro5vXkPMlSa8t5kkXo5vXl1CNBEITKQ+PpaUTlycHBQYqIiCjvbgjCC9t3\ndR++x33JzP13yqOuli6+bXzxbOL5wsc1MDAgLS2tLLpIwJlE/A5e4HZyBvWM9JjiYSGChZflawQU\n9VmqobrLLghvsR8HdoeivktoaPDZ5j2vv0OCIAhvOA0NjUhJkhxKsq1YMycIZSg/YFsctZg76Xcw\n0TdhvP34lwrkylJ+DbP89Vj5NcwAEdC9jBoN/n+KZRHtgvCWM6xlrJpiWUS7IAiC8GqJYE4Qyphn\nE88yD97KalRO1DB7RUQmOqEScxs4hMBVy9SmWlapqoPbwCHl2CtBEITKQayZE4RKRNQwK1u3b9+m\nX79+qkQF3ZdAjYaABtRoSPuAWkQ8aVLeXRSEV87SrQNdRn6KoXFt0NDA0Lg2XUZ+KrJZCoIgvAZi\nZE4QKhFRw6xs1atX798sosoB6tnn/mhfLn0ShPJg6dZBBG+CIAjlQIzMCUIlMsXDAj1tLbU2UcOs\nZKZOncry5cvlx76+vsyfPx9ra2sAMjIyGDhwIJaWlvTu3ZuMjH+D5sDAQFxcXLC3t6d///7ytNmg\noCDs7OxQKBQMGzaMrCz1jICCIAiCIAjPIoI5QahERA2zF+ft7c3Wrf/Wjdu6dStOTk7y4xUrVlCt\nWjX+/vtvZs+eLdf/u3//PnPnzuXw4cNERUXh4ODAggULyMzMxMfHhy1bthAXF0dOTg4rVqx47dcl\nCIIgCMKbS0yzFIRKppdd/VIHb/7+/nTp0oV69eq9ol5VfHZ2dty9e5fbt29z79493nnnHRo2bCg/\nf/ToUcaNGweAUqlEqVQCcPLkSc6fP4+rqysAT548wcXFhQsXLmBubk7z5s0BGDp0KMuXL2fChAmv\n+coEQRAEQXhTiWBOEITn8vf3x9raulIHcwD9+/dn+/bt3LlzB29v7xLtI0kSnTt35vfff1drj4mJ\neRVdFARBEAShEimTaZYaGhqfaWhoSBoaGsYF2qZpaGhc1tDQuKChoeFRFucRBOHFpKen4+npiY2N\nDdbW1mzZsoVevXrJzx86dIjevXuTm5uLj48P1tbWKBQKFi5cyPbt24n4v/buPa6qKv//+GsJCt51\nMkuyb9h8EbmLIGEO3lDhN2o2pFnpaPJtypwMG2+Vl6zs8vgO41iW09XI0cTExst0I0u/XjIVFAUV\n8xJqSomapCjKZf3+AE8yat7Qw4H38/Hwwdlr7732Zx+Xcj5nrb1WWhoDBw6kbdu2nDx5kvT0dDp3\n7kxYWBgxMTHk5uYC0KVLF5544gnCw8Px8/Nj/fr1xMXF4ePjw4QJE5x1+5VmwIABJCcnk5KSQv/+\n/Svs69SpEx988AEAWVlZbN68GYDIyEhWr17Nzp07gbK/i2+//RZfX19ycnIc5f/85z/p3Lnzdbwb\nERERcXVXncwZY24FegJ7zyrzB+4DAoBYYIYxxu38NYjItfbZZ5/h5eXFpk2byMrKIjY2luzsbPLy\nyhb6fe+994iPjycjI4P9+/eTlZVFZmYmQ4cOpV+/foSHhzNnzhwyMjJwd3dnxIgRpKSkkJ6eTnx8\nPOPHj3dcq06dOqSlpTFs2DD69u3L66+/TlZWFklJSRw+fNhZb0GlCAgI4NixY9xyyy20aNGiwr5H\nH32U48eP4+fnx6RJkwgLCwPgxhtvJCkpifvvv5/g4GA6dOhAdnY2np6evPfee/Tv35+goCBq1arF\nsGHDnHFbIiIi4qIqY5jl34GxwKKzyvoCydbaU8B3xpidQASwphKuJ1LpvL29SUtLo1mzZhc/+CJ+\n//vf88EHH9CkSZPLOi8pKYm0tDRee+21q47hPwUFBTFq1CjGjRtH7969iYqK4o9//COzZ89m6NCh\nrFmzhlmzZnHs2DF2797NiBEj6NWrFz179jynru3bt5OVlUWPHj0AKCkpqZDY3HXXXY5rBgQEOPbd\nfvvt7Nu3jxtuuKHS7+96yszMdLz29vYmKysLgLp165KcnHzec7p168b69evPKY+Ojmbjxo3XJlAR\nERGp9q4qmTPG9AX2W2s3GWPO3nUL8M1Z29+Xl4lUe5988omzQzhH69at2bBhA5988gkTJkwgOjqa\nhx56iD59+uDp6Un//v1xd3enadOmbNq0ic8//5w33niDDz/8kJkzZ1aoy1pLQEAAa9ac/7sZDw8P\nAGrVquV4fWa7uLj42t2kq9n8IXz5HOR/D41bQvSkiuvUiYiIiFzERYdZGmOWGmOyzvOnL/A0MOlq\nAjDGPGyMSTPGpJ0Z8iXya3JycmjTpg0PPvggrVu3ZuDAgSxdupSOHTvi4+PDunXrKCgoID4+noiI\nCEJDQ1m0qKzjuKSkhNGjRxMYGEhwcDDTp0931Dt9+nTatWtHUFAQ2dnZAKxbt44OHToQGhrKnXfe\nyfbt24GyXrS4uDhiY2Px8fFh7Nixjnq8vb05dOgQALNmzSI4OJiQkBD++Mc/ArBkyRLuuOMOQkND\n6d69Oz/++OM1f88OHDhAvXr1GDRoEGPGjGHDhg14eXnh5eXFlClTGDp0KFA2jX5paSn33HMPU6ZM\nYcOGDQA0bNiQY8eOAeDr60teXp4jmSsqKmLLli3X/B6qlc0fwpLHIX8fYMt+Lnm8rFxERETkEl20\nZ85a2/185caYIKAVcKZXriWwwRgTAewHbj3r8JblZeer/y3gLYDw8HB7OcFLzbVz507mz5/PzJkz\nad++PR988AGrVq1i8eLFvPjii/j7+9OtWzdmzpzJ0aNHiYiIoHv37syaNYucnBzHs19Hjhxx1Nms\nWTM2bNjAjBkzSExM5J133qFNmzasXLkSd3d3li5dytNPP82CBQsAyMjIYOPGjXh4eODr68uIESMq\nTFW/ZcsWpkyZwtdff02zZs0c1/rd737HN998gzGGd955h//93//lb3/72zV9vzIzMxkzZgy1atWi\ndu3ajvXMBg4cSF5eHn5+fgDs37+foUOHUlpaCsBLL70EwIMPPsiwYcOoW7cua9asISUlhccff5z8\n/HyKi4sZOXIkAQEB1/QeqpUvn4OikxXLik6Wlat3TkRERC7RFQ+ztNZmAs3PbBtjcoBwa+0hY8xi\n4ANjzFTAC/AB1l1lrCIOrVq1IigoCCiblCI6OhpjDEFBQeTk5PD999+zePFiEhMTASgsLGTv3r0s\nXbqUYcOG4e5e1vR/85vfOOqMi4sDICwsjI8++giA/Px8hgwZwo4dOzDGUFRU5Dg+Ojqaxo0bA+Dv\n78+ePXsqJHNfffUV/fv3dzyHd+Za33//PQMGDCA3N5fTp0/TqlWra/IenS0mJoaYmHMnlV21ahV/\n+tOfHNshISGO3riz3XPPPdxzzz2O7bZt27JixYpzjlu+fDkAH+/+mFcOvcIP/X+gZ0pPEtolOPYJ\nZUMrL6dcRERE5DwqZWmC/2St3QJ8CGwFPgP+bK0tuRbXkprpP5/FOvs5reLiYqy1LFiwgIyMDDIy\nMti7d6+j9+lidbq5uTme7Zo4cSJdu3YlKyuLJUuWUFhYeN4Yzj7nYkaMGMFjjz1GZmYmb775ZoU6\nr6ewsDA2b97MoEGDKrXej3d/zOSvJ5NbkIvFkluQy+SvJ/Px7o8r9TourXHLyyuXaiEnJ4fAwEBn\nhyEiItVIpSVz1lpva+2hs7ZfsNb+1lrra639tLKuI3IpYmJimD59OtaWjdw9M2Ngjx49ePPNNx2J\n19nDLM8nPz+fW24pm7snKSnpsmLo1q0b8+fPd0zHf+ZaZ9f5/vvvX1adlSk9PZ0VK1ZUSEorwysb\nXqGwpGKCWlhSyCsbXqnU67i06ElQu27Fstp1y8pFRERELtE16ZkTcbaJEydSVFREcHAwAQEBTJw4\nEYCHHnqI//qv/3JMSnJmkecLGTt2LE899RShoaGXPRNjQEAA48ePp3PnzoSEhPCXv/wFgMmTJ9O/\nf3/CwsIqZSmEquaHgh8uq7xGCr4X+rwKjW8FTNnPPq/qebkaoLi4mIEDB+Ln50e/fv04ceJEhUmT\n0tLS6NKlC6Wlpfj4+DjWgiwtLeW///u/0URhIiJyNnOm56IqCA8Pt2lpac4OQ+S6KNh4kJ8/z6Hk\n6CncmnjQKMab+qHNL35iFdczpSe5BbnnlLeo34LUfqlOiEikasjJyaFVq1asWrWKjh07Eh8fj7+/\nP6+99ppjncu0tDRGjx7N8uXLefbZZ2ncuDEjR44kNTWVN9980zEBk4iIVF/GmHRrbfilHKueOREn\nKNh4kKMf7aDk6CkASo6e4uhHOyjYeNDJkV29hHYJeLp5VijzdPMkoV2CkyKqGt544w1mzZrl7DDE\nyW699VY6duwIwKBBg1i1atUFj42Pj3e0mZkzZzqWEBERETnjqhYNF5Er8/PnOdii0gpltqiUnz/P\ncfneuV639wLKnp37oeAHbq5/MwntEhzlNdWwYcOcHYJUAeVL+VTYdnd3dywHcvaESLfeeis33XQT\nX331FevWrWPOnDnXNVYREan61DMn4gRneuQutdzV9Lq9F6n9Utk8ZDOp/VJdLpG7lIXpjxw5wt13\n301wcDCRkZFs3ryZ0tJSvL29OXr0qKMuHx8ffvzxRyZPnuxYKmPXrl3ExsYSFhZGVFSUY5F6qf72\n7t3LmjVrAPjggw/43e9+h7e3N+np6QDnDKN86KGHGDRoEP3798fNze26xysiIlWbkjkRJ3Brcv4Z\nJC9ULtffzp07GTVqFNnZ2WRnZzsWpk9MTOTFF1/kmWeeITQ0lM2bN/Piiy8yePBgatWqRd++ffnX\nv/4FwNq1a7ntttu46aabKtT98MMPM336dNLT00lMTGT48OHOuEVxAl9fX15//XX8/Pz46aefePTR\nR3nmmWdISEggPDz8nITtrrvu4vjx4xpiKSIi56VhlpWsQYMGHD9+/IrOnTZtGg8//DD16tWr5Kik\nqmkU483Rj3ZUGGppateiUYy384KSCi62MP2ePXscvSjdunXj8OHD/PzzzwwYMIDnnnuOoUOHkpyc\nzIABAyrUe/z4cb7++mv69+/vKDt1qnr0yMqv8/b2Pm8vbFRUFN9++22FstwfFrF7VyKbNn9Hq1aG\nxk22A22uU6QiIuIqlMxVIdOmTWPQoEFK5mqAM8/FVcfZLKuLiy1MX7t27fOe16FDB3bu3EleXh4L\nFy5kwoQJFfaXlpbSpEkTMjIyrl3w4tJyf1hEdvZ45szJZcnin3nq6eZkZ48HoMXNfZ0cnYiIVCUa\nZnmNHD9+nOjoaNq1a0dQUBCLFi0CfnkW5z/XGXr11Vc5cOAAXbt2pWvXrgDMnTuXoKAgAgMDGTdu\nnKPuBg0a8MQTTzh6C7TukGuqH9qcFk9G0PLlKFo8GaFEzsVERUU5JqRYvnw5zZo1o1GjRhhj+MMf\n/sBf/vIX/Pz8uOGGGyqc16hRI1q1asX8+fMBsNayadOm6x6/VF27dyVSWnqS++9vwgdz/4ugIE9K\nS0+ye1eis0MTEZEqRsncNeLp6cm//vUvNmzYwLJlyxg1ahRn1vTbvn07w4cPZ9u2bTRq1IgZM2bw\n+OOP4+XlxbJly1i2bBkHDhxg3LhxfPXVV2RkZLB+/XoWLlwIQEFBAeHh4WzZsoXOnTvz7LPPOvNW\nRWqkyZMnk56eTnBwME8++STvv/++Y9+AAQOYPXv2OUMsz5gzZw7vvvsuISEhBAQEOL7sEQEoPHXu\nOo2/Vi4iIjWXFg2vZGeemSsqKuKJJ55gxYoV1KpVi+3bt/Pdd99RWFhIp06d2Lt3LwBfffUVr776\nKgsXLsTb29uxcOyiRYtYsGCBY42hd999ly1btjB16lTc3Nw4deoU7u7u7N69m7i4OA3ZEhGpJlav\njqLw1IFzyj09vOjYcaUTIhIRketJi4ZXAXPmzCEvL4/09HQyMjK46aabHOsHnW+doatxteeLyPWT\nv2QJO7pFs83Pnx3doslfssTZIUkVc/tvR1OrVt0KZbVq1eX23452UkQiIlJVKZm7RvLz82nevDm1\na9dm2bJl7Nmzx7HvfOsMATRs2JBjx44BEBERwf/93/9x6NAhSkpKmDt3Lp07dwbKJlBISUk553wR\nqdrylywhd+Ikig8cAGspPnCA3ImTlNBJBS1u7kubNi/g6eEFGDw9vGjT5gVNfiIiIufQbJbXyMCB\nA+nTpw9BQUGEh4fTps0vU0qfWWcoPj4ef39/Hn30UaBs7anY2FjHs3Mvv/wyXbt2xVpLr1696Nu3\n7Bd5/fr1WbduHVOmTKF58+bMmzfPKfcoIpfn4N+nYct76M+whYUc/Ps0Gvfp46SopCpqcXNfJW8i\nInJRembuOsvJyaF3795kZWVdcR1Xs5adiDjPNj9/ON//ucbgt23r9Q9IREREqhw9M1eNbVu5jOJT\np/jbfX14689D2bZymbNDEpFL5N6ixWWVi4iIiPwaJXPXmbe39xX3ym1buYzUt17jhbgYsJZjh/JI\nfes1JXQiLqL5EyMxnp4VyoynJ82fGOmkiERERMSVKZlzISuTZ1F8+lSFsuLTp1iZPMtJEYnI5Wjc\npw8tnn8Ody8vMAZ3Ly9aPP+cnpcTERGRK6IJUFzIscOHLqtcRKqexn36KHkTERGRSqGeORfS8IZm\nl1UuIiIiIiLVl5I5FxJ132Dc63hUKHOv40HUfYOdFJGIiIiIiDiLhlm6EL+orkDZs3PHDh+i4Q3N\niLpvsKNcRERERERqDiVzLsYvqquSNxERERER0TBLERERERERV6RkTkRERERExAUpmRMREREREXFB\nSuZERERERERckJI5ERERERERF6RkTkQASEtL4/HHH3d2GCIiIiJyibQ0gYgAEB4eTnh4uLPDEBER\nEZFLpJ45kWquoKCAXr16ERISQmBgIPPmzWP9+vXceeedhISEEBERwbFjx1i+fDm9e/d2nBMfH09E\nRAShoaEsWrQIgKSkJOLi4oiNjcXHx4exY8c6rvPZZ5/Rrl07QkJCiI6O/tV6REREROTqqWdOpJr7\n7LPP8PLy4uOPPwYgPz+f0NBQ5s2bR/v27fn555+pW7duhXNeeOEFunXrxsyZMzl69CgRERF0794d\ngIyMDDZu3IiHhwe+vr6MGDECT09P/vSnP7FixQpatWrFkSNHfrWe+vXrX983QURERKQaUjInUs0F\nBQUxatQoxo0bR+/evWnSpAktWrSgffv2ADRq1Oicc1JTU1m8eDGJiYkAFBYWsnfvXgCio6Np3Lgx\nAP7+/uzZs4effvqJTp060apVKwB+85vf/Go9fn5+1/amRURERGoAJXMi1Vzr1q3ZsGEDn3zyCRMm\nTKBbt24XPcday4IFC/D19a1QvnbtWjw8PBzbbm5uFBcXX3Y9IiIiInL19MycSDV34MAB6tWrx6BB\ngxgzZgxr164lNzeX9evXA3Ds2LFzErKYmBimT5+OtRaAjRs3/uo1IiMjWbFiBd999x2AY5jl5dYj\nIiIiIpdOPXMi1VxmZiZjxoyhVq1a1K5dm3/84x9YaxkxYgQnT56kbt26LF26tMI5EydOZOTIkQQH\nB1NaWkqrVq3497//fcFr3Hjjjbz11lvExcVRWlpK8+bN+eKLLy67HhERERG5dObMN+ZVQXh4uE1L\nS3N2GCIiIiIiIk5hjEm31l7SelEaZiki10z+kiXs6BbNNj9/dnSLJn/JEmeHJCIiIlJtaJiliFwT\n+UuWkDtxErawEIDiAwfInTgJgMZ9+jgzNBEREZFqQT1zInJNHPz7NEcid4YtLOTg36c5KSIRERGR\n6kXJnEgNdeDAAfr163fN6i/OzQUg+ehPLMrPP6dcRERERK6OhlmK1FBeXl6kpKRcs/rdW7SgcP9+\n7mvS9JxyEREREbl66pkTcUGzZ88mIiKCtm3b8sgjj1BSUkKDBg0YP348ISEhREZG8uOPPwKwa9cu\nIiMjCQoKYsKECTRo0ACAnJwcAgMDAUhKSiIuLo7Y2Fh8fHwYO3as41qpqal06NCBdu3a0b9/f44f\nPw5Aeno6nTt3JiwsjJiYGHLLe9y6dOnCyJEjuXffXmYfO8Zrh/KYeeQwAEO+38frTZsQERFB69at\nWblyJQAnTpzg3nvvxd/fnz/84Q/ccccdaGZbERERkV+nZE7ExWzbto158+axevVqMjIycHNzY86c\nORQUFBAZGcmmTZvo1KkTb7/9NgAJCQkkJCSQmZlJy5YtL1hvRkYG8+bNIzMzk3nz5rFv3z4OHTrE\nlClTWLp0KRs2bCA8PJypU6dSVFTEiBEjSElJIT09nfj4eMaPH++o6/Tp02zcsYOn33yDWg0bAgZ3\nLy/qtGqFm7c369atY9q0aTz77LMAzJgxg6ZNm7J161aef/550tPTr+l7KCIiIlIdaJiliIv58ssv\nSU9Pp3379gCcPHmS5s2bU6dOHXr37g1AWFgYX3zxBQBr1qxh4cKFADzwwAOMHj36vPVGR0fTuHFj\nAPz9/dmzZw9Hjx5l69atdOzYEShL0jp06MD27dvJysqiR48eAJSUlNDirOGTAwYMAMpmrbwhPZ0G\nDRrgM3o07l26EBcX54gxJycHgFWrVpGQkABAYGAgwcHBlfNmiYiIiFRjSuZEXIy1liFDhvDSSy9V\nKE9MTMQYA4CbmxvFxcWXVa+Hh4fj9ZnzrbX06NGDuXPnVjg2MzOTgIAA1qxZc9666tevf9HrXEmM\nIiIiIvILDbMUcTHR0dGkpKRw8OBBAI4cOcKePXsueHxkZCQLFiwAIDk5+bKuFRkZyerVq9m5cycA\nBQUFfPvtt/j6+pKXl+dI5oqKitiyZcuV3A4AHTt25MMPPwRg69atZGZmXnFdIiIiIjWFkjkRF+Pv\n78+UKVPo2bMnwcHB9OjRwzH5yPlMmzaNqVOnEhwczM6dOx1DKS/FjTfeSFJSEvfffz/BwcF06NCB\n7Oxs6tSpQ0pKCuPGjSMkJIS2bdvy9ddfX/E9DR8+nLy8PPz9/ZkwYQIBAQGXFaeIiIhITWSstc6O\nwSE8PNxqBjuRynXixAnq1q2LMYbk5GTmzp3LokWLnB1WBSUlJRQVFeHp6cmuXbvo3r0727dvp06d\nOs4OTUREROS6MsakW2vDL+VYPTMnUs2lp6fz2GOPYa2lSZMmzJw509khnSNvzR5i7utF0ekicDNM\nHf+iEjkRERGRi1DPnIg4VcHGgxz9aAe2qNRRKZzMIgAADipJREFUZmrXokmcD/VDmzsxMhEREZHr\n73J65vTMnIg41c+f51RI5ABsUSk/f57jnIBEREREXISSORFxqpKjpy6rXERERETKKJkTqSEOHDhA\nv379rvl13njjDWbNmnXJx7s1+WV9u0MFP3F/8hN0f3cIvWY/7FgSQa4Pay2lpaUXP1BERESqBCVz\nIjWEl5cXKSkp1/QaxcXFDBs2jMGDB1/yOY1ivDG1y/4rKi4tYULX4Xw57J/8z5D/4eWXX75WoVY5\nd999N2FhYQQEBPDWW28B0KBBA8aPH09ISAiRkZH8+OOPAMyfP5/AwEBCQkLo1KkTAL169WLz5s0A\nhIaG8txzzwEwadIk3n77bQD++te/0r59e4KDg3nmmWcAyMnJwdfXl8GDBxMYGMi+ffuu632LiIjI\nlVMyJ+ICZs+eTUREBG3btuWRRx6hpKTkgh/0d+3aRWRkJEFBQUyYMIEGDRoAZR/aAwMDAUhKSiIu\nLo7Y2Fh8fHwYO3as41qpqal06NCBdu3a0b9/f44fPw6UzYrZuXNnwsLCiImJcaxt16VLF0aOHEl4\neDivvPIKkydPJjEx0bFv3LhxRERE0Lp1a1auXAmULZdw77334u/vz6DnHuHuhSPIOr6bmxs2I9g3\nkCZxPnBTHTw9Pa/PG1wFzJw5k/T0dNLS0nj11Vc5fPgwBQUFREZGsmnTJjp16uRIyp577jk+//xz\nNm3axOLFiwGIiopi5cqV5Ofn4+7uzurVqwFYuXIlnTp1IjU1lR07drBu3ToyMjJIT09nxYoVAOzY\nsYPhw4ezZcsWbrvtNue8ASIiInLZlMyJVHHbtm1j3rx5rF69moyMDNzc3JgzZ84FP+gnJCSQkJBA\nZmYmLVu2vGC9GRkZzJs3j8zMTObNm8e+ffs4dOgQU6ZMYenSpWzYsIHw8HCmTp1KUVERI0aMICUl\nhfT0dOLj4xk/fryjrtOnT5OWlsaoUaPOuU5xcTHr1q1j2rRpPPvsswDMmDGDpk2bsnXrVp5//nk2\nbttMswcDaPlyFC2ejGCHOcC0adMYPXp0Jb+bVderr77qSMz37dvHjh07qFOnDr179wYgLCyMnJwc\nADp27MiDDz7I22+/TUlJCVCWzK1YsYLVq1fTq1cvjh8/zokTJ/juu+/w9fUlNTWV1NRUQkNDadeu\nHdnZ2ezYsQOA2267jcjISKfct4iIiFw5rTMnUsV9+eWXpKen0759ewBOnjxJ8+bNz/mg/8UXXwCw\nZs0aFi5cCMADDzxwwYQoOjqaxo0bA+Dv78+ePXs4evQoW7dupWPHjkBZktahQwe2b99OVlYWPXr0\nAMoW+W7RooWjrgEDBlww/ri4OEeMZ5KRVatWkZCQAEBgYCDBwcEVzomPjycpKQlvb+9Le5Nc3PLl\ny1m6dClr1qyhXr16dOnShcLCQmrXro0xBgA3NzeKi4uBsucS165dy8cff0xYWJijfaSlpXH77bfT\no0cPDh06xNtvv01YWBhQ9jzcU089xSOPPFLh2jk5OdSvX//63rCIiIhUCiVzIlWctZYhQ4bw0ksv\nVShPTEw87wf9S+Xh8cvEI2fOt9bSo0cP5s6dW+HYzMxMAgICWLNmzXnr+rVk4Mx1LifGnTt3Op4F\nqwny8/Np2rQp9erVIzs7m2+++eZXj9+1axd33HEHd9xxB59++in79u2jbdu23HrrrcyfP59JkyaR\nl5fH6NGjHcl8TEwMEydOZODAgTRo0ID9+/dTu3bt63F7IiIico1omKVIFRcdHU1KSgoHDx4E4MiR\nI+zZs+eCx0dGRrJgwQIAkpOTL+takZGRrF692jGLZEFBAd9++y2+vr7k5eU5krmioiK2bNlyJbcD\nlA0T/PDDDwHYunUrmZmZFfa/9957V1y3K4qNjaW4uBg/Pz+efPLJiw55HDNmDEFBQQQGBnLnnXcS\nEhIClA21bN68OXXr1iUqKorvv/+eqKgoAHr27MkDDzxAhw4dCAoKol+/fhw7duya35uIiIhcO+qZ\nE6ni/P39mTJlCj179qS0tJTatWvz+uuvX/D4adOmMWjQIF544QViY2MdQykvxY033khSUhL3338/\np06VrfM2ZcoUWrduTUpKCo8//jj5+fkUFxczcuRIAgICruiehg8fzpAhQ/D396dNmzYEBARUiPNv\nf/sb99xzzxXV7Yo8PDz49NNPzyk/M/kMQL9+/RxLS3z00Ufnref555/n+eefB8pmL7XWVth/5nnK\nbSuXsTJ5FgvHj6ThDc2Y/4/plXUrIiIich2Z//xl70zh4eE2LS3N2WGIuLQTJ05Qt25djDEkJycz\nd+5cFi1a5OywKigpKaGoqAhPT0927dpF9+7d2b59O3Xq1HF2aNXetpXLSH3rNYpP/7Iou3sdD3o+\n/Bh+UV2dGJmIiIgAGGPSrbXhl3KseuZEqpn09HQee+wxrLU0adKEmTNnOjukc5w4cYKuXbtSVFSE\ntZYZM2Zw+Min7N6VSOGpXDw9WnD7b0fT4ua+zg612lmZPKtCIgdQfPoUK5NnKZkTERFxMUrmRKqZ\nqKgoNm3a5OwwflXDhg05uxc+94dFZGePp7T0JACFpw6QnV229IESusp17PChyyoXERGRqksToIiI\n0+3elehI5M4oLT3J7l2JToqo+mp4Q7PLKhcREZGqS8mciDhd4ancyyqXKxd132Dc63hUKHOv40HU\nfYOdFJGIiIhcKQ2zFBGn8/RoQeGpA+ctl8p15rm4lcmzOHb4EA1vaEbUfYP1vJyIiIgLUjInIk53\n+29HV3hmDqBWrbrc/tvRToyq+vKL6qrkTUREpBpQMiciTndmkhPNZikiIiJy6ZTMiUiV0OLmvkre\nRERERC6DJkARERERERFxQUrmREREREREXJCSORERERERERekZE5ERERERMQFKZkTERERERFxQUrm\nREREREREXJCSORERERERERekZE5ERERERMQFKZkTERERERFxQUrmREREREREXJCSORERERERERek\nZE5ERERERMQFKZkTERERERFxQUrmREREREREXJCSORERERERERekZE5ERERERMQFKZkTERERERFx\nQUrmREREREREXJCSORERERERERekZE5ERERERMQFKZkTERERERFxQUrmREREREREXJCSORERERER\nERekZE5ERERERMQFKZkTERERERFxQUrmREREREREXJCSORERERERERekZE5ERERERMQFKZkTERER\nERFxQUrmREREREREXJCSORERERERERekZE5ERERERMQFKZkTERERERFxQUrmREREREREXJCSORER\nERERERdkrLXOjsHBGJMH7HF2HNVYM+CQs4OQKkltQy5EbUMuRG1DLkRtQy5EbePS3GatvfFSDqxS\nyZxcW8aYNGttuLPjkKpHbUMuRG1DLkRtQy5EbUMuRG2j8mmYpYiIiIiIiAtSMiciIiIiIuKClMzV\nLG85OwCpstQ25ELUNuRC1DbkQtQ25ELUNiqZnpkTERERERFxQeqZExERERERcUFK5moIY8woY4w1\nxjQ7q+wpY8xOY8x2Y0yMM+OT688Y81djTLYxZrMx5l/GmCZn7VPbqOGMMbHlf/87jTFPOjsecR5j\nzK3GmGXGmK3GmC3GmITy8t8YY74wxuwo/9nU2bGKcxhj3IwxG40x/y7fVtsQjDFNjDEp5Z81thlj\nOqhtVD4lczWAMeZWoCew96wyf+A+IACIBWYYY9ycE6E4yRdAoLU2GPgWeArUNqTsgxnwOvD/AH/g\n/vJ2ITVTMTDKWusPRAJ/Lm8PTwJfWmt9gC/Lt6VmSgC2nbWttiEArwCfWWvbACGUtRG1jUqmZK5m\n+DswFjj7Acm+QLK19pS19jtgJxDhjODEOay1qdba4vLNb4CW5a/VNiQC2Gmt3W2tPQ0kU9YupAay\n1uZaazeUvz5G2QeyWyhrE++XH/Y+cLdzIhRnMsa0BHoB75xVrLZRwxljGgOdgHcBrLWnrbVHUduo\ndErmqjljTF9gv7V203/sugXYd9b29+VlUjPFA5+Wv1bbELUBOS9jjDcQCqwFbrLW5pbv+gG4yUlh\niXNNo+wL49KzytQ2pBWQB7xXPgT3HWNMfdQ2Kp27swOQq2eMWQrcfJ5d44GnKRtiKTXQr7UNa+2i\n8mPGUzaMas71jE1EXIsxpgGwABhprf3ZGOPYZ621xhhNj13DGGN6AwettenGmC7nO0Zto8ZyB9oB\nI6y1a40xr/AfQyrVNiqHkrlqwFrb/Xzlxpggyr4Z2VT+S7clsMEYEwHsB2496/CW5WVSjVyobZxh\njHkQ6A1E21/WKVHbELUBqcAYU5uyRG6Otfaj8uIfjTEtrLW5xpgWwEHnRShO0hG4yxjze8ATaGSM\nmY3ahpSN6PjeWru2fDuFsmRObaOSaZhlNWatzbTWNrfWeltrvSn7h9XOWvsDsBi4zxjjYYxpBfgA\n65wYrlxnxphYyobG3GWtPXHWLrUNWQ/4GGNaGWPqUDYhzmInxyROYsq+DXwX2GatnXrWrsXAkPLX\nQ4BF1zs2cS5r7VPW2pblnzHuA76y1g5CbaPGK/+suc8Y41teFA1sRW2j0qlnroay1m4xxnxI2T+s\nYuDP1toSJ4cl19drgAfwRXnP7TfW2mFqG2KtLTbGPAZ8DrgBM621W5wcljhPR+CPQKYxJqO87Gng\nZeBDY8z/AHuAe50Un1Q9ahsCMAKYU/6l4G5gKGUdSWoblcj8MrJKREREREREXIWGWYqIiIiIiLgg\nJXMiIiIiIiIuSMmciIiIiIiIC1IyJyIiIiIi4oKUzImIiIiIiLggJXMiIiIiIiIuSMmciIiIiIiI\nC1IyJyIiIiIi4oL+PxpeFkRku5G3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f889a5f0c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot(embeddings, labels):\n",
" assert embeddings.shape[0] >= len(labels), 'More labels than embeddings'\n",
" pylab.figure(figsize=(15,15)) # in inches\n",
" for i, label in enumerate(labels):\n",
" x, y = embeddings[i,:]\n",
" pylab.scatter(x, y)\n",
" pylab.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points',\n",
" ha='right', va='bottom')\n",
" pylab.show()\n",
"\n",
"words = [reverse_dictionary[i] for i in range(1, num_points+1)]\n",
"plot(two_d_embeddings, words)"
]
}
],
"metadata": {
"_change_revision": 267,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160034.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "27a2ae16-7812-a8e4-21a0-501f101eed24"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"../input/Iris.csv\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>SepalLengthCm</th>\n",
" <th>SepalWidthCm</th>\n",
" <th>PetalLengthCm</th>\n",
" <th>PetalWidthCm</th>\n",
" <th>Species</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>5.1</td>\n",
" <td>3.5</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>4.9</td>\n",
" <td>3.0</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>4.7</td>\n",
" <td>3.2</td>\n",
" <td>1.3</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>4.6</td>\n",
" <td>3.1</td>\n",
" <td>1.5</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>5.0</td>\n",
" <td>3.6</td>\n",
" <td>1.4</td>\n",
" <td>0.2</td>\n",
" <td>Iris-setosa</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"0 1 5.1 3.5 1.4 0.2 Iris-setosa\n",
"1 2 4.9 3.0 1.4 0.2 Iris-setosa\n",
"2 3 4.7 3.2 1.3 0.2 Iris-setosa\n",
"3 4 4.6 3.1 1.5 0.2 Iris-setosa\n",
"4 5 5.0 3.6 1.4 0.2 Iris-setosa"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[]# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input/Iris.csv\"]).decode(\"utf8\"))\n",
"\n",
"iris = pd.read_csv(\"../input/Iris.csv\")\n",
"iris.head()\n",
"\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "37afcad0-030d-f5e2-8b5a-788fa2768e80"
},
"outputs": [
{
"data": {
"text/plain": [
"Iris-virginica 50\n",
"Iris-setosa 50\n",
"Iris-versicolor 50\n",
"Name: Species, dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris[\"Species\"].value_counts()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "7d3e0a8d-ff7f-4546-fa07-4083bbaefa26"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f4ba7dee358>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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mcT/he8C7qgiwLqrKvz76wIWTvneq5dYbqsjNL+LY8vFZf60uHw0DHwSeBM4GFkfEyoj4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\ngpmZNblTMDOzJncKZmbW5E7BzMya/j/YVHJr4v4CKgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4be06a0160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"iris.plot(kind=\"scatter\", x=\"SepalLengthCm\", y=\"SepalWidthCm\")\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "547cb452-10d9-f61d-fc30-3e99ce5cbaac"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "slice indices must be integers or None or have an __index__ method",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-46afce49c3b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"SepalWidthCm\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"SepalLengthCm\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0miris\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"kde\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"g\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mjointplot\u001b[0;34m(x, y, data, kind, stat_func, color, size, ratio, space, dropna, xlim, ylim, joint_kws, marginal_kws, annot_kws, **kwargs)\u001b[0m\n\u001b[1;32m 830\u001b[0m \u001b[0mmarginal_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"shade\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 831\u001b[0m \u001b[0mmarginal_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"color\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 832\u001b[0;31m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_marginals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmarginal_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 833\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 834\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"reg\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mplot_marginals\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m 1750\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"vertical\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1751\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max_marg_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1752\u001b[0;31m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1753\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1754\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"vertical\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mTypeError\u001b[0m: slice indices must be integers or None or have an __index__ method"
]
},
{
"data": {
"image/png": 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m03QaN18DvPOUFk1NTfrDH/6g/v37q02bNvrwww+1bt06LVpkevI+N0QRFFGGTeyfu7ER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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4ba4092f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"sns.jointplot(x=\"SepalWidthCm\", y=\"SepalLengthCm\", data=iris, size=6, kind=\"kde\", space=0, color=\"g\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "fb64ec43-29aa-82fe-5441-d7722170e58c"
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f4b8a1311d0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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+PFDjJ598gvXr1+Ott95CRUUF3G43evToAW0MvsgtIV6z4nPV5UY4WQ/sjBus\n2wNTejJGDc7B9PH5UCp+Of1KS9N26ldsu1GDe+DbigYwLW748MsZ25J7hoY1qzHYeGPl4uMklf7G\nfDg9LBxuB1weFpnJRozIGYZp+ZOhVPxyfKMVDwD0vOZ6lNd+D6/DAbXbi2adGk2D+2LEfYugGzQE\nXpcTHnsTvKwLalMm0keNaptdGMb3IXXAQMH9CrX3zyzwH0PWw8IU4BiG0m8kRPOzC0Yw8aSlUWKX\no6BuBaisrMSBAwewf/9+HD16FLm5uSguLsacOXOiESMAacpvid2vFmgacCzvcwtUfiuWoj19W+w+\nt1hMJxe7zy1d5UGTzO5zU+m84Bh53edGtwKEp6qqCvPnz+90m9bXX3+NqqoqzJo1K6J9ffnll7j5\n5psjus9ICiq5tausrMShQ4fw6aef4vjx41Fd9oZqS7ahmMTJLR6AYgqW3GKSOrm53B7YmlgY07VI\n1oR+L2w7vuQmlWnTpkX1XufuEjyadrsd+/fvx759+3D48GGYTCZcd911ePjhh3HNNddEK0ZCCEko\nHOfFW5+dwIHjNbA0OmHOSMF1g3Ix+zcDoVKFP8S7ePFiJCUlobGxEePGjcPp06fxX//1X1iwYAEs\nFgvcbjcefvhhFBcXd9ru008/xXvvvYekpCT0798fTz31FCoqKrBy5UooFAqkpaXhL3/5Cz788EOc\nOnUK8+bNw8svv4xnn30WpaWl4DjOX46Lb1/79u3DCy+8gKSkJKSnp/tXIpCCYHK77rrr0LNnT9x1\n111YvHixvyQLIYSQ0L312Qls/+eP/sd1Nqf/8f23Do5IHwaDAatWrfKfXZWXl8Nms2HTpk1oamry\n3+rV0YYNG/D6668jNzcXH3/8MVwuF1atWoWVK1fi8ssvx6ZNm7Bp0yY89NBDeOONN/Dyyy/j8OHD\nOH36NDZv3oyWlhZMnToVN954I+++7HY7SkpK0Lt3byxcuBDffPMNxo8fH5H3ezHB5PbZZ59h3759\n2LdvHz744AMMGjQI119/Pa677jr07t1bkoDiQTjLy4ht22B34tRPjSi8LAOZhpRIhUwkJtV1qCbG\nipqaCuTm5iNdxz8VP5AWewMafzyNjCuuRKohM2IxkfC43B4cOF7D23bgeA3unjQgIkOUQ4YM6fT4\niiuuQHNzMxYsWIAJEyZg8uTJXbaZMmUK5s6di6lTp2LKlClITk7GsWPH/KsCuN1uDB7cOfkeP34c\n1157LQAi2ytAAAAgAElEQVQgNTUV+fn5OHfuHO++TCYTli5dCo7jUFlZieuuuy7s9xmI4BHMz89H\nfn4+7rnnHnAch2+//RYHDhzAokWLUFdXh127dkkWmBwFW5MylG2d7lYsWr8fjPOXm4J1KWqsfeh6\npGiSpH5rJERS1VtkWSd2rl+GHmet0Dd7cSpNiQt9TZjw0CpotcI/elpdTpxYuQhaSxOUPsCuAFhz\nOgb+eS2SkukHU6zZmlhYGp28bfWNTtiaWORmhZ/ckpI6/91ISUnBhx9+iNLSUnzyySfYs2cPfve7\n32HdunUAgJKSEjzwwAP4zW9+gx07duDee+/Fe++9h5SUFGzcuNG/dM7FLn6+tbUVSqWSd19PPvkk\nXn/9dfTr1w8rV64M+z0KCWpwt7m5GV9//TW++OILfPnll6itrZU048pVe43HhiYWPvxS43HL7oqw\nt704sQEA4/Rg0fr9ErwTEint9RY9DQ2Az+evt2jZujms/e5cvwwFx+thaPZCCcDQ7EXB8XrsXL9M\ndNsTKxchta4JKh+gAKDyAal1TTixMj5L5iUaY7oW5gz+HxlZGSkwBijaEK4TJ07gs88+w7Bhw7B8\n+XKcOXMGRUVFePfdd/Huu+/CbDbjueeeg9lsxh/+8Adcc801OH/+PPr374+vv/4aAPD5559j//62\nv0ntcxEHDRqEgwcPAmjLFT/99BP69OnDuy+GYZCbm4umpiYcPHgQra2tkrxXQOTM7cUXX8S+fftQ\nXl6OoqIijB49GjNmzEB+fr5kAcmVWI3H347pF3CIUmzbcdf07JLY2jFODxrsThqilCGxeotZt90e\n0hBlE2NFj7P8pa56nLWiibEGHKJssTdAa2nibdNamtBib6AhyhhL1qhx3aDcTtfc2l03KDciQ5J8\n8vLysG7dOmzZsgUqlQp//OMfO7UrlUqkpaVh+vTp0Ov16N27NwYMGIAlS5Zg2bJleOONN6DVavHf\n//3fANqW2bn99tvx0UcfYdCgQbjzzjvh8Xjw+OOPIzU1lXdfs2bNwsyZM3H55Zfjvvvuw0svvYRx\n48YhOzs74u9X8FaA1atXY/To0Rg+fHhMbtzuKNa3AtTZWvDEawfAd7CUCmD1f14XcCVssW1vH9MP\nH+7tWuWi3X2TB2Dk4NwuMcmF3GKKVjzuujr8e8kigO+fkFKJy5/+CzQ//6PtTkynTh+Cb+0rvMMq\nHADlojkovJJ/lYnzZQfg+Our4BtA8gHQz30QPYuu63ZM0SK3mKS6FaDjbMn6RieyIjxbkoicuaWm\npqK0tBSlpfy/Th955BFJgpKj9hqPDTxFjMVqPIpte3V+pmByK7xMfgsBkl/qLXoaGrq2hVFvMTc3\nH6fSlDA0e7u0MWlKFOYGHjnJuOJK2BVtQ5EX8yra2knsqVRK3H/rYNw9aUBE73MjvxD8iaBWq6FS\nqQL+dylpr/HIR6zGo9i2uVk66FL4v9i6FDUNScpUe71FPuHUW0zXmXChL/+w44W+JsFZk6mGTLDm\ndN421pxOQ5Iyk6xRIzcrjRKbBASP6Lx58wK2rV27NuLByN308W2/mMvK62FzuGDUJ6OoIMv/fDjb\nrn3o+oCzJYl8me+YAeDntdRsVqiNJuiKivzPh2rCQ6v8syV1zV4wHWZLihn457WdZkt6O8yWJORS\nEVT5rX/9619Yt24dGhvb1tNyu93IyMjAZ599JnmA7WJ9za2jWN7nJrdrEoD8YopFPGL3uYUak5T3\nucntcwPkF1O81ZYkvwjqXPj555/HsmXLsHr1ajzzzDP44osvMGzYMKljky1tkirg5JFwt800pGDk\nYBqGjDdKrdY/eSSS0nUmpAeYPCIm1ZCJ1CIahiSXpqCm5eh0OlxzzTVISkrClVdeiUceeQT/8z//\nI3VshBBCSEiCSm4ejwdHjhxBeno6PvnkExw7dgxVVV0Xz5QbtpVDna0FbCsXUrsUHC1unPy3FY4A\na0QJxcS2cqipb45qvG7ODUtLA9xc99fYYtwMTlkrwLi7LuAazn5dTgdqz52Cy9l1uMjLsnDW1MLL\ndp2ZKratVNwNDajb+xXcPLMqAeFjIfZe3XV1Ad9rqMT2K1W/ocYUi3jkqqqqCtOmTev03Ndff433\n338/6rE89thjcLlcQb+eL/ZwBDUsuWLFCtTX12PhwoVYtWoV6uvr8eCDDwpu43Q6sXjxYjQ0NIBl\nWcyZMwfjxo3zt+/btw/r1q2DSqVCcXEx5s6dG9476UCs1FU4ZbRC5fZ48MzGUlRbGHh9bfe39TK3\nLWSqUasFYwLwS5uDhUkvfbycl8O2is9xzHICNrYRRm0GhpgHYlr+ZKiUwtcZ3R43SkpfQQ1TCy+8\nUEKJXF0O/jR0DlRKVcj79bS6cfDNtUg+dQ5pjAfVOjVchX0w4r5FUClVgmWwhLZVJ0lTlZxzOnH2\niQXwMr8kd6VOh75r/h9UKSmCx9jHcSG/11CJlRKTqtRYqDEBiHo8kcZ63LC57DAmG6BVS/M9vLjy\nf7Q899xzMem3XbfWc2toaIBCoYDJJH5h+4svvkB1dTXuv/9+VFdXY/bs2dixY4e/fdKkSdiwYQN6\n9OiBu+66CytXrhSsfNKdi8zv7yrHriNdzyxvHJaHWTcWiLa3i+TF7afeOoTKuq5nML2zdVgxe7hg\nTACCijeStpZvx96qb7o8PzbvBtxRMLXTcxcfp9WHnkc1c77Ltr10PXFlxhVB7/di/1q/CuajXe8H\ntPyqH640XoHGXTu7tGXcOAHZM+4U3HbUQ+IlrUJR8ei8TomtnVKnQ/7zLwse457/+Dbk9xqsiz+3\nus2bBPcr1h4J3YkJQNTjCfSa7uK8HN799mMcrjqG+hYrslJNuDZvCO6++reiP/KEtK/nVlBQ0O0l\nb3744QesXr0aGzduBAC8/PLLSE9Px8iRI7ssedPU1IQFCxYgNTUVd911F06fPo2dO3dCqVRi3Lhx\nePDBBzF+/Hh89tlnaGxsxOLFi8FxHHr27Im1a9fCYrHgySefRGtrKxQKBZ555hkoFAr/WnQHDx7E\nc889B7VajR49emDNmjX429/+hq+//hp1dXV47rnn0KNHD8FjEdTP/s8//xyjRo3CLbfcgqlTp6K4\nuFi0aPKkSZNw//33AwBqamo6BVJZWQmDwYDc3FwolUqMGTPGX68sXGKlrhwtbsF2KYb8HC1uVFu6\n/pEDgGoLgwa7M2BMpacsUY/XzblxzHKCt+27+hOCQ4mMm0ENU8vbdp6pwbeW70Lar8vpQPKpc7xt\nyafOoan0KH88ZWVosTcIbivFEKW7oYE3sQGAl2HAWGoCHuMTNcdCfq+hDs2JlRLzOByC7VIMCQrF\n5CgtBROguIRU8UTSu99+jC/K98DS0gAffLC0NOCL8j1499uPI9aHwWDASy+95H/cccmbDRs2wG63\nd3p9//79UVdXh6amtvJtu3fvxsSJE/1L3rzzzjsYNWoUNm3aBAA4efIkSkpKMG7cOLz11lv44IMP\nsHnzZqSnd77P8rnnnsPvf/97vP/++8jOzsbx48fxwgsv4Pbbb8e7776LWbNm4eWXX+60zVNPPYXn\nnnsO7733HgwGg39mfk1NDTZ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IEqoz2FRWCktjDW9bDVOLJsYqSY1Cxs2ghqnlbbM01qCp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\n7khzE0IIYXekuQkhhLA7/wJeeJ0ITe5pUgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4b8a0c9ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"sns.FacetGrid(iris, hue=\"Species\", size=5)\\\n",
" .map(plt.scatter, \"SepalLengthCm\", \"SepalWidthCm\")\\\n",
" .add_legend()"
]
}
],
"metadata": {
"_change_revision": 199,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160148.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "26d3757c-4579-81ca-abaa-23c3259aecb9"
},
"source": [
"Understand NSE"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "58082d0a-b829-dc50-e664-8a9a5d621ae4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"banknifty.csv\n",
"nifty50.csv\n",
"\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "2299f6a4-9680-fbf5-ae76-c06cbd84928d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>09:16</td>\n",
" <td>5701.15</td>\n",
" <td>5704.65</td>\n",
" <td>5694.30</td>\n",
" <td>5697.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>09:17</td>\n",
" <td>5697.05</td>\n",
" <td>5698.35</td>\n",
" <td>5695.65</td>\n",
" <td>5697.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>09:18</td>\n",
" <td>5697.90</td>\n",
" <td>5697.90</td>\n",
" <td>5690.60</td>\n",
" <td>5692.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>09:19</td>\n",
" <td>5691.65</td>\n",
" <td>5694.70</td>\n",
" <td>5691.65</td>\n",
" <td>5693.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>09:20</td>\n",
" <td>5694.40</td>\n",
" <td>5695.05</td>\n",
" <td>5693.35</td>\n",
" <td>5694.55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index date time open high low close\n",
"0 NIFTY 2013-04-01 09:16 5701.15 5704.65 5694.30 5697.00\n",
"1 NIFTY 2013-04-01 09:17 5697.05 5698.35 5695.65 5697.50\n",
"2 NIFTY 2013-04-01 09:18 5697.90 5697.90 5690.60 5692.15\n",
"3 NIFTY 2013-04-01 09:19 5691.65 5694.70 5691.65 5693.90\n",
"4 NIFTY 2013-04-01 09:20 5694.40 5695.05 5693.35 5694.55"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty = pd.read_csv(\"../input/banknifty.csv\",parse_dates=['date'])\n",
"nifty50 = pd.read_csv(\"../input/nifty50.csv\",parse_dates=['date'])\n",
"nifty50.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c188b768-7647-b926-99a3-1da8bc504740"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>09:16</td>\n",
" <td>12125.70</td>\n",
" <td>12161.70</td>\n",
" <td>12125.70</td>\n",
" <td>12160.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>09:17</td>\n",
" <td>12161.75</td>\n",
" <td>12164.80</td>\n",
" <td>12130.40</td>\n",
" <td>12130.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>09:18</td>\n",
" <td>12126.85</td>\n",
" <td>12156.10</td>\n",
" <td>12126.85</td>\n",
" <td>12156.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>09:19</td>\n",
" <td>12157.25</td>\n",
" <td>12164.75</td>\n",
" <td>12151.60</td>\n",
" <td>12164.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>09:20</td>\n",
" <td>12162.80</td>\n",
" <td>12162.80</td>\n",
" <td>12148.20</td>\n",
" <td>12151.15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index date time open high low close\n",
"0 BANKNIFTY 2012-12-03 09:16 12125.70 12161.70 12125.70 12160.95\n",
"1 BANKNIFTY 2012-12-03 09:17 12161.75 12164.80 12130.40 12130.40\n",
"2 BANKNIFTY 2012-12-03 09:18 12126.85 12156.10 12126.85 12156.10\n",
"3 BANKNIFTY 2012-12-03 09:19 12157.25 12164.75 12151.60 12164.20\n",
"4 BANKNIFTY 2012-12-03 09:20 12162.80 12162.80 12148.20 12151.15"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "b886e768-7f13-8240-ec50-ab622477f3cb"
},
"outputs": [],
"source": [
"banknifty = banknifty.drop(['index'],axis=1)\n",
"nifty50 = nifty50.drop(['index'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "97b33c5f-adfd-9c1e-4156-a9e79ce6f739"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>352920.000000</td>\n",
" <td>352920.000000</td>\n",
" <td>352920.000000</td>\n",
" <td>352920.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>7374.515649</td>\n",
" <td>7376.077510</td>\n",
" <td>7372.936147</td>\n",
" <td>7374.498883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1070.893139</td>\n",
" <td>1071.032665</td>\n",
" <td>1070.756739</td>\n",
" <td>1070.885970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5126.150000</td>\n",
" <td>5127.250000</td>\n",
" <td>5118.850000</td>\n",
" <td>5126.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6167.650000</td>\n",
" <td>6168.900000</td>\n",
" <td>6166.550000</td>\n",
" <td>6167.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7738.800000</td>\n",
" <td>7740.150000</td>\n",
" <td>7737.450000</td>\n",
" <td>7738.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>8298.762500</td>\n",
" <td>8300.900000</td>\n",
" <td>8297.050000</td>\n",
" <td>8298.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>9115.400000</td>\n",
" <td>9119.200000</td>\n",
" <td>9105.650000</td>\n",
" <td>9106.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"count 352920.000000 352920.000000 352920.000000 352920.000000\n",
"mean 7374.515649 7376.077510 7372.936147 7374.498883\n",
"std 1070.893139 1071.032665 1070.756739 1070.885970\n",
"min 5126.150000 5127.250000 5118.850000 5126.300000\n",
"25% 6167.650000 6168.900000 6166.550000 6167.700000\n",
"50% 7738.800000 7740.150000 7737.450000 7738.800000\n",
"75% 8298.762500 8300.900000 8297.050000 8298.750000\n",
"max 9115.400000 9119.200000 9105.650000 9106.500000"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50.describe()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "54bda9c2-925c-b517-9053-477168dd7e3c"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>240137</th>\n",
" <td>2015-03-04</td>\n",
" <td>09:16</td>\n",
" <td>9115.4</td>\n",
" <td>9119.2</td>\n",
" <td>9105.65</td>\n",
" <td>9106.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"240137 2015-03-04 09:16 9115.4 9119.2 9105.65 9106.5"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50.loc[nifty50['high']==9119.2]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "8d2d1d0b-8750-0907-87a0-4f5c7eaf2d71"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13949</th>\n",
" <td>2013-08-28</td>\n",
" <td>10:40</td>\n",
" <td>5127.25</td>\n",
" <td>5127.25</td>\n",
" <td>5122.7</td>\n",
" <td>5126.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"13949 2013-08-28 10:40 5127.25 5127.25 5122.7 5126.3"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50.loc[nifty50['high']==5127.25]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "ec58aecb-3585-1dfb-6687-99352c6905d7"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>5950.678267</td>\n",
" <td>5951.491067</td>\n",
" <td>5949.986667</td>\n",
" <td>5950.689733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>5995.653733</td>\n",
" <td>5996.420267</td>\n",
" <td>5994.909200</td>\n",
" <td>5995.663733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td>6003.101600</td>\n",
" <td>6003.903733</td>\n",
" <td>6002.381333</td>\n",
" <td>6003.124267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td>5994.905733</td>\n",
" <td>5995.648667</td>\n",
" <td>5994.187467</td>\n",
" <td>5994.906800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-07</th>\n",
" <td>6011.653067</td>\n",
" <td>6012.265200</td>\n",
" <td>6010.813600</td>\n",
" <td>6011.506400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"date \n",
"2013-01-01 5950.678267 5951.491067 5949.986667 5950.689733\n",
"2013-01-02 5995.653733 5996.420267 5994.909200 5995.663733\n",
"2013-01-03 6003.101600 6003.903733 6002.381333 6003.124267\n",
"2013-01-04 5994.905733 5995.648667 5994.187467 5994.906800\n",
"2013-01-07 6011.653067 6012.265200 6010.813600 6011.506400"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50_mean = nifty50.groupby('date').mean()\n",
"nifty50_mean.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "7728742c-70a6-7eb6-5762-925f642edb34"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbab1493400>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SQHM3V1DxuswkNTWnq7z/fz8nrV5PU3Fm5WFxpwLQWFPWjxMXonPb3/w7o1cd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\n4f8D7D2pBSVbxvAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbab14d75f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nifty50_mean.plot(figsize=(12,8))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "22786826-f1bf-3b7e-1f72-f351987a7687"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>367575.000000</td>\n",
" <td>367575.000000</td>\n",
" <td>367575.000000</td>\n",
" <td>367575.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>15078.023296</td>\n",
" <td>15082.498465</td>\n",
" <td>15073.480983</td>\n",
" <td>15077.993028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3184.438089</td>\n",
" <td>3185.213591</td>\n",
" <td>3183.628315</td>\n",
" <td>3184.411825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1405.050000</td>\n",
" <td>1407.050000</td>\n",
" <td>1404.600000</td>\n",
" <td>1405.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>12092.200000</td>\n",
" <td>12095.000000</td>\n",
" <td>12089.150000</td>\n",
" <td>12092.175000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>15526.100000</td>\n",
" <td>15531.200000</td>\n",
" <td>15521.400000</td>\n",
" <td>15525.950000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>17956.050000</td>\n",
" <td>17960.550000</td>\n",
" <td>17951.100000</td>\n",
" <td>17955.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>20903.950000</td>\n",
" <td>20907.550000</td>\n",
" <td>20899.250000</td>\n",
" <td>20907.550000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"count 367575.000000 367575.000000 367575.000000 367575.000000\n",
"mean 15078.023296 15082.498465 15073.480983 15077.993028\n",
"std 3184.438089 3185.213591 3183.628315 3184.411825\n",
"min 1405.050000 1407.050000 1404.600000 1405.200000\n",
"25% 12092.200000 12095.000000 12089.150000 12092.175000\n",
"50% 15526.100000 15531.200000 15521.400000 15525.950000\n",
"75% 17956.050000 17960.550000 17951.100000 17955.800000\n",
"max 20903.950000 20907.550000 20899.250000 20907.550000"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.describe()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "5ce342a1-9275-85d7-6f82-0c9371726544"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>236193</th>\n",
" <td>2015-01-28</td>\n",
" <td>12:35</td>\n",
" <td>20902.15</td>\n",
" <td>20907.55</td>\n",
" <td>20894.35</td>\n",
" <td>20907.55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"236193 2015-01-28 12:35 20902.15 20907.55 20894.35 20907.55"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.loc[banknifty['high']==20907.550]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "89a6752a-c26c-892c-bb94-936f831858f2"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>252479</th>\n",
" <td>2015-06-24</td>\n",
" <td>15:19</td>\n",
" <td>1405.05</td>\n",
" <td>1407.05</td>\n",
" <td>1404.6</td>\n",
" <td>1406.25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"252479 2015-06-24 15:19 1405.05 1407.05 1404.6 1406.25"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.loc[banknifty['high']==1407.050]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "b5239a29-b4d5-55ba-155d-8872316fea86"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-11-01</th>\n",
" <td>11305.950533</td>\n",
" <td>11308.328400</td>\n",
" <td>11303.584000</td>\n",
" <td>11306.027600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-02</th>\n",
" <td>11429.929733</td>\n",
" <td>11432.193733</td>\n",
" <td>11427.817200</td>\n",
" <td>11429.955600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-05</th>\n",
" <td>11451.321867</td>\n",
" <td>11453.444667</td>\n",
" <td>11449.234667</td>\n",
" <td>11451.354533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-06</th>\n",
" <td>11494.561733</td>\n",
" <td>11496.849600</td>\n",
" <td>11492.694133</td>\n",
" <td>11494.892667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-07</th>\n",
" <td>11666.770800</td>\n",
" <td>11669.293467</td>\n",
" <td>11664.445067</td>\n",
" <td>11666.936533</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"date \n",
"2012-11-01 11305.950533 11308.328400 11303.584000 11306.027600\n",
"2012-11-02 11429.929733 11432.193733 11427.817200 11429.955600\n",
"2012-11-05 11451.321867 11453.444667 11449.234667 11451.354533\n",
"2012-11-06 11494.561733 11496.849600 11492.694133 11494.892667\n",
"2012-11-07 11666.770800 11669.293467 11664.445067 11666.936533"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty_mean = banknifty.groupby('date').mean()\n",
"banknifty_mean.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "7f0677b3-d5fb-8fe2-e8d4-a8555542b17c"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fbab087dfd0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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sMwjgjIrAe9cxAtIKyfMHk9lS7bjdpAOc5PmDn18gNqOCwY0630KcLd9CB0UB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\nIYQnFAdoqwWTuujYfHV/Jj/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U5Hb88D7r330cVBVncREAgZFxNfold9deX5G3tvSlNhaz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbab08c2048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"banknifty_mean.plot(figsize=(12,8))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "727dbe8b-eb29-aacb-06c3-fe8cd17003f6"
},
"source": [
"There is 2 huge **drop** in year 2015. We will later on try to find the details of incident."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "29bda076-b30a-6eca-d699-78a87d0a289b"
},
"outputs": [
{
"data": {
"text/plain": [
"137.55000000000018"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maximum_drop_nifty50 = max(nifty50['open'] - nifty50['low'])\n",
"maximum_drop_nifty50"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "0140ba7d-0f47-4db1-42a8-b8a5578ff078"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>162096</th>\n",
" <td>2014-05-30</td>\n",
" <td>09:16</td>\n",
" <td>7256.0</td>\n",
" <td>7258.15</td>\n",
" <td>7118.45</td>\n",
" <td>7254.55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"162096 2014-05-30 09:16 7256.0 7258.15 7118.45 7254.55"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50.loc[(nifty50['open'] - nifty50['low']) == maximum_drop_nifty50]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d71a58ec-7038-4e0c-2f91-eb77e3a99200"
},
"source": [
"The day nifty50 dipped huge"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "8b683cfb-8989-d67e-4367-2d1f9b320fe0"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013-04-01</td>\n",
" <td>09:19</td>\n",
" <td>5691.65</td>\n",
" <td>5694.70</td>\n",
" <td>5691.65</td>\n",
" <td>5693.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2013-04-01</td>\n",
" <td>09:21</td>\n",
" <td>5694.10</td>\n",
" <td>5700.05</td>\n",
" <td>5694.10</td>\n",
" <td>5697.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>2013-04-01</td>\n",
" <td>09:35</td>\n",
" <td>5694.85</td>\n",
" <td>5698.65</td>\n",
" <td>5694.85</td>\n",
" <td>5696.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2013-04-01</td>\n",
" <td>09:38</td>\n",
" <td>5694.80</td>\n",
" <td>5697.55</td>\n",
" <td>5694.80</td>\n",
" <td>5697.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>2013-04-01</td>\n",
" <td>09:49</td>\n",
" <td>5705.25</td>\n",
" <td>5716.00</td>\n",
" <td>5705.25</td>\n",
" <td>5715.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>2013-04-01</td>\n",
" <td>09:52</td>\n",
" <td>5715.20</td>\n",
" <td>5718.25</td>\n",
" <td>5715.20</td>\n",
" <td>5717.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>2013-04-01</td>\n",
" <td>10:06</td>\n",
" <td>5711.65</td>\n",
" <td>5715.00</td>\n",
" <td>5711.65</td>\n",
" <td>5713.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>2013-04-01</td>\n",
" <td>10:13</td>\n",
" <td>5710.25</td>\n",
" <td>5712.20</td>\n",
" <td>5710.25</td>\n",
" <td>5711.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>2013-04-01</td>\n",
" <td>10:38</td>\n",
" <td>5707.10</td>\n",
" <td>5711.40</td>\n",
" <td>5707.10</td>\n",
" <td>5711.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>2013-04-01</td>\n",
" <td>10:42</td>\n",
" <td>5708.05</td>\n",
" <td>5710.35</td>\n",
" <td>5708.05</td>\n",
" <td>5710.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2013-04-01</td>\n",
" <td>10:46</td>\n",
" <td>5707.50</td>\n",
" <td>5709.15</td>\n",
" <td>5707.50</td>\n",
" <td>5709.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>2013-04-01</td>\n",
" <td>11:06</td>\n",
" <td>5708.10</td>\n",
" <td>5709.50</td>\n",
" <td>5708.10</td>\n",
" <td>5709.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>2013-04-01</td>\n",
" <td>11:09</td>\n",
" <td>5707.35</td>\n",
" <td>5708.95</td>\n",
" <td>5707.35</td>\n",
" <td>5708.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td>2013-04-01</td>\n",
" <td>11:15</td>\n",
" <td>5709.20</td>\n",
" <td>5709.85</td>\n",
" <td>5709.20</td>\n",
" <td>5709.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>137</th>\n",
" <td>2013-04-01</td>\n",
" <td>11:33</td>\n",
" <td>5694.40</td>\n",
" <td>5698.25</td>\n",
" <td>5694.40</td>\n",
" <td>5698.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>2013-04-01</td>\n",
" <td>11:37</td>\n",
" <td>5700.95</td>\n",
" <td>5703.45</td>\n",
" <td>5700.95</td>\n",
" <td>5703.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <td>2013-04-01</td>\n",
" <td>11:47</td>\n",
" <td>5698.50</td>\n",
" <td>5699.60</td>\n",
" <td>5698.50</td>\n",
" <td>5699.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>152</th>\n",
" <td>2013-04-01</td>\n",
" <td>11:48</td>\n",
" <td>5699.20</td>\n",
" <td>5700.50</td>\n",
" <td>5699.20</td>\n",
" <td>5699.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>2013-04-01</td>\n",
" <td>12:07</td>\n",
" <td>5698.35</td>\n",
" <td>5700.10</td>\n",
" <td>5698.35</td>\n",
" <td>5700.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>2013-04-01</td>\n",
" <td>12:22</td>\n",
" <td>5693.00</td>\n",
" <td>5695.05</td>\n",
" <td>5693.00</td>\n",
" <td>5695.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>2013-04-01</td>\n",
" <td>12:29</td>\n",
" <td>5693.35</td>\n",
" <td>5694.80</td>\n",
" <td>5693.35</td>\n",
" <td>5694.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>198</th>\n",
" <td>2013-04-01</td>\n",
" <td>12:34</td>\n",
" <td>5692.30</td>\n",
" <td>5694.20</td>\n",
" <td>5692.30</td>\n",
" <td>5694.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>203</th>\n",
" <td>2013-04-01</td>\n",
" <td>12:39</td>\n",
" <td>5686.25</td>\n",
" <td>5689.10</td>\n",
" <td>5686.25</td>\n",
" <td>5688.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>215</th>\n",
" <td>2013-04-01</td>\n",
" <td>12:51</td>\n",
" <td>5682.75</td>\n",
" <td>5685.20</td>\n",
" <td>5682.75</td>\n",
" <td>5685.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td>2013-04-01</td>\n",
" <td>13:05</td>\n",
" <td>5676.15</td>\n",
" <td>5681.55</td>\n",
" <td>5676.15</td>\n",
" <td>5681.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>234</th>\n",
" <td>2013-04-01</td>\n",
" <td>13:10</td>\n",
" <td>5686.50</td>\n",
" <td>5687.25</td>\n",
" <td>5686.50</td>\n",
" <td>5686.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>239</th>\n",
" <td>2013-04-01</td>\n",
" <td>13:15</td>\n",
" <td>5681.25</td>\n",
" <td>5684.30</td>\n",
" <td>5681.25</td>\n",
" <td>5684.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>2013-04-01</td>\n",
" <td>13:16</td>\n",
" <td>5684.15</td>\n",
" <td>5685.95</td>\n",
" <td>5684.15</td>\n",
" <td>5685.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>241</th>\n",
" <td>2013-04-01</td>\n",
" <td>13:17</td>\n",
" <td>5685.85</td>\n",
" <td>5687.20</td>\n",
" <td>5685.85</td>\n",
" <td>5687.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td>2013-04-01</td>\n",
" <td>13:18</td>\n",
" <td>5687.10</td>\n",
" <td>5688.95</td>\n",
" <td>5687.10</td>\n",
" <td>5688.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352742</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:33</td>\n",
" <td>8574.60</td>\n",
" <td>8581.40</td>\n",
" <td>8574.60</td>\n",
" <td>8581.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352745</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:36</td>\n",
" <td>8575.90</td>\n",
" <td>8584.80</td>\n",
" <td>8575.90</td>\n",
" <td>8584.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352746</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:37</td>\n",
" <td>8583.95</td>\n",
" <td>8590.25</td>\n",
" <td>8583.95</td>\n",
" <td>8586.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352753</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:44</td>\n",
" <td>8579.40</td>\n",
" <td>8581.65</td>\n",
" <td>8579.40</td>\n",
" <td>8580.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352762</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:53</td>\n",
" <td>8564.95</td>\n",
" <td>8574.75</td>\n",
" <td>8564.95</td>\n",
" <td>8574.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352765</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:56</td>\n",
" <td>8567.30</td>\n",
" <td>8572.50</td>\n",
" <td>8567.30</td>\n",
" <td>8568.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352768</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:59</td>\n",
" <td>8574.35</td>\n",
" <td>8579.25</td>\n",
" <td>8574.35</td>\n",
" <td>8579.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352771</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:02</td>\n",
" <td>8581.40</td>\n",
" <td>8585.20</td>\n",
" <td>8581.40</td>\n",
" <td>8584.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352792</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:23</td>\n",
" <td>8584.05</td>\n",
" <td>8587.20</td>\n",
" <td>8584.05</td>\n",
" <td>8585.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352799</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:30</td>\n",
" <td>8579.55</td>\n",
" <td>8585.25</td>\n",
" <td>8579.55</td>\n",
" <td>8585.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352806</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:37</td>\n",
" <td>8584.15</td>\n",
" <td>8588.05</td>\n",
" <td>8584.15</td>\n",
" <td>8587.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352815</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:46</td>\n",
" <td>8585.10</td>\n",
" <td>8587.00</td>\n",
" <td>8585.10</td>\n",
" <td>8587.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352822</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:53</td>\n",
" <td>8594.20</td>\n",
" <td>8599.35</td>\n",
" <td>8594.20</td>\n",
" <td>8597.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352825</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:56</td>\n",
" <td>8600.95</td>\n",
" <td>8604.55</td>\n",
" <td>8600.95</td>\n",
" <td>8603.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352832</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:03</td>\n",
" <td>8593.90</td>\n",
" <td>8597.30</td>\n",
" <td>8593.90</td>\n",
" <td>8594.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352836</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:07</td>\n",
" <td>8593.70</td>\n",
" <td>8597.35</td>\n",
" <td>8593.70</td>\n",
" <td>8597.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352837</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:08</td>\n",
" <td>8597.25</td>\n",
" <td>8599.25</td>\n",
" <td>8597.25</td>\n",
" <td>8598.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352838</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:09</td>\n",
" <td>8598.65</td>\n",
" <td>8603.00</td>\n",
" <td>8598.65</td>\n",
" <td>8600.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352843</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:14</td>\n",
" <td>8598.35</td>\n",
" <td>8600.10</td>\n",
" <td>8598.35</td>\n",
" <td>8599.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352845</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:16</td>\n",
" <td>8599.60</td>\n",
" <td>8607.45</td>\n",
" <td>8599.60</td>\n",
" <td>8607.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352846</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:17</td>\n",
" <td>8607.40</td>\n",
" <td>8611.05</td>\n",
" <td>8607.40</td>\n",
" <td>8610.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352847</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:18</td>\n",
" <td>8610.75</td>\n",
" <td>8613.30</td>\n",
" <td>8610.75</td>\n",
" <td>8611.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352851</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:22</td>\n",
" <td>8619.25</td>\n",
" <td>8623.60</td>\n",
" <td>8619.25</td>\n",
" <td>8622.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352854</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:25</td>\n",
" <td>8619.15</td>\n",
" <td>8623.40</td>\n",
" <td>8619.15</td>\n",
" <td>8621.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352864</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:35</td>\n",
" <td>8629.45</td>\n",
" <td>8634.65</td>\n",
" <td>8629.45</td>\n",
" <td>8633.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352870</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:41</td>\n",
" <td>8620.75</td>\n",
" <td>8624.55</td>\n",
" <td>8620.75</td>\n",
" <td>8622.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352888</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:59</td>\n",
" <td>8614.60</td>\n",
" <td>8617.50</td>\n",
" <td>8614.60</td>\n",
" <td>8615.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352908</th>\n",
" <td>2016-09-30</td>\n",
" <td>15:19</td>\n",
" <td>8611.75</td>\n",
" <td>8614.55</td>\n",
" <td>8611.75</td>\n",
" <td>8614.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352912</th>\n",
" <td>2016-09-30</td>\n",
" <td>15:23</td>\n",
" <td>8614.00</td>\n",
" <td>8617.20</td>\n",
" <td>8614.00</td>\n",
" <td>8616.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>352919</th>\n",
" <td>2016-09-30</td>\n",
" <td>15:30</td>\n",
" <td>8615.30</td>\n",
" <td>8622.75</td>\n",
" <td>8615.30</td>\n",
" <td>8620.95</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>33738 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"3 2013-04-01 09:19 5691.65 5694.70 5691.65 5693.90\n",
"5 2013-04-01 09:21 5694.10 5700.05 5694.10 5697.20\n",
"19 2013-04-01 09:35 5694.85 5698.65 5694.85 5696.90\n",
"22 2013-04-01 09:38 5694.80 5697.55 5694.80 5697.55\n",
"33 2013-04-01 09:49 5705.25 5716.00 5705.25 5715.55\n",
"36 2013-04-01 09:52 5715.20 5718.25 5715.20 5717.20\n",
"50 2013-04-01 10:06 5711.65 5715.00 5711.65 5713.80\n",
"57 2013-04-01 10:13 5710.25 5712.20 5710.25 5711.45\n",
"82 2013-04-01 10:38 5707.10 5711.40 5707.10 5711.25\n",
"86 2013-04-01 10:42 5708.05 5710.35 5708.05 5710.25\n",
"90 2013-04-01 10:46 5707.50 5709.15 5707.50 5709.05\n",
"110 2013-04-01 11:06 5708.10 5709.50 5708.10 5709.40\n",
"113 2013-04-01 11:09 5707.35 5708.95 5707.35 5708.55\n",
"119 2013-04-01 11:15 5709.20 5709.85 5709.20 5709.40\n",
"137 2013-04-01 11:33 5694.40 5698.25 5694.40 5698.25\n",
"141 2013-04-01 11:37 5700.95 5703.45 5700.95 5703.30\n",
"151 2013-04-01 11:47 5698.50 5699.60 5698.50 5699.35\n",
"152 2013-04-01 11:48 5699.20 5700.50 5699.20 5699.65\n",
"171 2013-04-01 12:07 5698.35 5700.10 5698.35 5700.00\n",
"186 2013-04-01 12:22 5693.00 5695.05 5693.00 5695.05\n",
"193 2013-04-01 12:29 5693.35 5694.80 5693.35 5694.45\n",
"198 2013-04-01 12:34 5692.30 5694.20 5692.30 5694.00\n",
"203 2013-04-01 12:39 5686.25 5689.10 5686.25 5688.85\n",
"215 2013-04-01 12:51 5682.75 5685.20 5682.75 5685.20\n",
"229 2013-04-01 13:05 5676.15 5681.55 5676.15 5681.55\n",
"234 2013-04-01 13:10 5686.50 5687.25 5686.50 5686.65\n",
"239 2013-04-01 13:15 5681.25 5684.30 5681.25 5684.00\n",
"240 2013-04-01 13:16 5684.15 5685.95 5684.15 5685.70\n",
"241 2013-04-01 13:17 5685.85 5687.20 5685.85 5687.20\n",
"242 2013-04-01 13:18 5687.10 5688.95 5687.10 5688.50\n",
"... ... ... ... ... ... ...\n",
"352742 2016-09-30 12:33 8574.60 8581.40 8574.60 8581.40\n",
"352745 2016-09-30 12:36 8575.90 8584.80 8575.90 8584.45\n",
"352746 2016-09-30 12:37 8583.95 8590.25 8583.95 8586.10\n",
"352753 2016-09-30 12:44 8579.40 8581.65 8579.40 8580.25\n",
"352762 2016-09-30 12:53 8564.95 8574.75 8564.95 8574.35\n",
"352765 2016-09-30 12:56 8567.30 8572.50 8567.30 8568.30\n",
"352768 2016-09-30 12:59 8574.35 8579.25 8574.35 8579.25\n",
"352771 2016-09-30 13:02 8581.40 8585.20 8581.40 8584.20\n",
"352792 2016-09-30 13:23 8584.05 8587.20 8584.05 8585.80\n",
"352799 2016-09-30 13:30 8579.55 8585.25 8579.55 8585.10\n",
"352806 2016-09-30 13:37 8584.15 8588.05 8584.15 8587.30\n",
"352815 2016-09-30 13:46 8585.10 8587.00 8585.10 8587.00\n",
"352822 2016-09-30 13:53 8594.20 8599.35 8594.20 8597.15\n",
"352825 2016-09-30 13:56 8600.95 8604.55 8600.95 8603.05\n",
"352832 2016-09-30 14:03 8593.90 8597.30 8593.90 8594.95\n",
"352836 2016-09-30 14:07 8593.70 8597.35 8593.70 8597.35\n",
"352837 2016-09-30 14:08 8597.25 8599.25 8597.25 8598.60\n",
"352838 2016-09-30 14:09 8598.65 8603.00 8598.65 8600.75\n",
"352843 2016-09-30 14:14 8598.35 8600.10 8598.35 8599.25\n",
"352845 2016-09-30 14:16 8599.60 8607.45 8599.60 8607.45\n",
"352846 2016-09-30 14:17 8607.40 8611.05 8607.40 8610.40\n",
"352847 2016-09-30 14:18 8610.75 8613.30 8610.75 8611.60\n",
"352851 2016-09-30 14:22 8619.25 8623.60 8619.25 8622.70\n",
"352854 2016-09-30 14:25 8619.15 8623.40 8619.15 8621.45\n",
"352864 2016-09-30 14:35 8629.45 8634.65 8629.45 8633.95\n",
"352870 2016-09-30 14:41 8620.75 8624.55 8620.75 8622.10\n",
"352888 2016-09-30 14:59 8614.60 8617.50 8614.60 8615.40\n",
"352908 2016-09-30 15:19 8611.75 8614.55 8611.75 8614.20\n",
"352912 2016-09-30 15:23 8614.00 8617.20 8614.00 8616.20\n",
"352919 2016-09-30 15:30 8615.30 8622.75 8615.30 8620.95\n",
"\n",
"[33738 rows x 6 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maximum_up_nifty50 = max(nifty50['open'] - nifty50['high'])\n",
"nifty50.loc[(nifty50['open'] - nifty50['low']) == maximum_up_nifty50]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "fd76408c-7e95-a24e-53f5-ca9f9af2d78f"
},
"outputs": [
{
"data": {
"text/plain": [
"282.39999999999964"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maximum_drop_banknifty = max(banknifty['open'] - banknifty['low'])\n",
"maximum_drop_banknifty"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "832da1bc-ac56-b01d-d7e7-b1bb07bbb18e"
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'numpy.float64' object has no attribute 'loc'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-6dace0ed605e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmaximum_drop_banknifty\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbanknifty\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'open'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mbanknifty\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'low'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmaximum_drop_banknifty\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m: 'numpy.float64' object has no attribute 'loc'"
]
}
],
"source": [
"maximum_drop_banknifty.loc[(banknifty['open'] - banknifty['low']) == maximum_drop_banknifty]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "804c1bb4-6d60-28d9-ac0c-67a29ff0399b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
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" <th>low</th>\n",
" <th>close</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2012-12-03</td>\n",
" <td>09:16</td>\n",
" <td>12125.70</td>\n",
" <td>12161.70</td>\n",
" <td>12125.70</td>\n",
" <td>12160.95</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>2012-12-03</td>\n",
" <td>09:18</td>\n",
" <td>12126.85</td>\n",
" <td>12156.10</td>\n",
" <td>12126.85</td>\n",
" <td>12156.10</td>\n",
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" <tr>\n",
" <th>13</th>\n",
" <td>2012-12-03</td>\n",
" <td>09:29</td>\n",
" <td>12131.75</td>\n",
" <td>12154.10</td>\n",
" <td>12131.75</td>\n",
" <td>12154.10</td>\n",
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" <th>21</th>\n",
" <td>2012-12-03</td>\n",
" <td>09:37</td>\n",
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" <td>12158.80</td>\n",
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" <th>24</th>\n",
" <td>2012-12-03</td>\n",
" <td>09:40</td>\n",
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" <td>12150.90</td>\n",
" <td>12155.55</td>\n",
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" <tr>\n",
" <th>51</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:07</td>\n",
" <td>12094.75</td>\n",
" <td>12100.05</td>\n",
" <td>12094.75</td>\n",
" <td>12096.15</td>\n",
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" <tr>\n",
" <th>54</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:10</td>\n",
" <td>12109.50</td>\n",
" <td>12117.00</td>\n",
" <td>12109.50</td>\n",
" <td>12117.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:13</td>\n",
" <td>12112.80</td>\n",
" <td>12116.90</td>\n",
" <td>12112.80</td>\n",
" <td>12116.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:19</td>\n",
" <td>12109.25</td>\n",
" <td>12112.05</td>\n",
" <td>12109.25</td>\n",
" <td>12112.05</td>\n",
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" <tr>\n",
" <th>70</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:26</td>\n",
" <td>12090.10</td>\n",
" <td>12101.05</td>\n",
" <td>12090.10</td>\n",
" <td>12098.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:33</td>\n",
" <td>12105.80</td>\n",
" <td>12114.65</td>\n",
" <td>12105.80</td>\n",
" <td>12114.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:35</td>\n",
" <td>12113.25</td>\n",
" <td>12117.60</td>\n",
" <td>12113.25</td>\n",
" <td>12117.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:38</td>\n",
" <td>12117.20</td>\n",
" <td>12119.80</td>\n",
" <td>12117.20</td>\n",
" <td>12117.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>2012-12-03</td>\n",
" <td>10:46</td>\n",
" <td>12106.50</td>\n",
" <td>12110.70</td>\n",
" <td>12106.50</td>\n",
" <td>12110.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:07</td>\n",
" <td>12081.95</td>\n",
" <td>12088.60</td>\n",
" <td>12081.95</td>\n",
" <td>12085.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:08</td>\n",
" <td>12086.85</td>\n",
" <td>12094.45</td>\n",
" <td>12086.85</td>\n",
" <td>12094.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:09</td>\n",
" <td>12093.75</td>\n",
" <td>12098.80</td>\n",
" <td>12093.75</td>\n",
" <td>12098.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:17</td>\n",
" <td>12091.70</td>\n",
" <td>12095.95</td>\n",
" <td>12091.70</td>\n",
" <td>12094.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:18</td>\n",
" <td>12096.65</td>\n",
" <td>12099.45</td>\n",
" <td>12096.65</td>\n",
" <td>12097.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:22</td>\n",
" <td>12096.05</td>\n",
" <td>12100.95</td>\n",
" <td>12096.05</td>\n",
" <td>12099.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:23</td>\n",
" <td>12099.60</td>\n",
" <td>12102.95</td>\n",
" <td>12099.60</td>\n",
" <td>12102.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:36</td>\n",
" <td>12097.95</td>\n",
" <td>12101.20</td>\n",
" <td>12097.95</td>\n",
" <td>12099.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>141</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:37</td>\n",
" <td>12099.80</td>\n",
" <td>12103.15</td>\n",
" <td>12099.80</td>\n",
" <td>12102.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>148</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:44</td>\n",
" <td>12096.50</td>\n",
" <td>12098.30</td>\n",
" <td>12096.50</td>\n",
" <td>12098.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>157</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:53</td>\n",
" <td>12084.05</td>\n",
" <td>12086.05</td>\n",
" <td>12084.05</td>\n",
" <td>12084.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>2012-12-03</td>\n",
" <td>11:54</td>\n",
" <td>12083.60</td>\n",
" <td>12090.10</td>\n",
" <td>12083.60</td>\n",
" <td>12090.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164</th>\n",
" <td>2012-12-03</td>\n",
" <td>12:00</td>\n",
" <td>12095.30</td>\n",
" <td>12099.20</td>\n",
" <td>12095.30</td>\n",
" <td>12098.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>2012-12-03</td>\n",
" <td>12:06</td>\n",
" <td>12090.40</td>\n",
" <td>12092.45</td>\n",
" <td>12090.40</td>\n",
" <td>12090.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>2012-12-03</td>\n",
" <td>12:17</td>\n",
" <td>12083.15</td>\n",
" <td>12086.65</td>\n",
" <td>12083.15</td>\n",
" <td>12086.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>2012-12-03</td>\n",
" <td>12:19</td>\n",
" <td>12083.25</td>\n",
" <td>12086.20</td>\n",
" <td>12083.25</td>\n",
" <td>12086.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>367163</th>\n",
" <td>2016-09-29</td>\n",
" <td>14:54</td>\n",
" <td>19167.55</td>\n",
" <td>19198.70</td>\n",
" <td>19167.55</td>\n",
" <td>19189.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367212</th>\n",
" <td>2016-09-30</td>\n",
" <td>09:28</td>\n",
" <td>19168.35</td>\n",
" <td>19198.40</td>\n",
" <td>19168.35</td>\n",
" <td>19197.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367219</th>\n",
" <td>2016-09-30</td>\n",
" <td>09:35</td>\n",
" <td>19175.60</td>\n",
" <td>19198.60</td>\n",
" <td>19175.60</td>\n",
" <td>19195.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367232</th>\n",
" <td>2016-09-30</td>\n",
" <td>09:48</td>\n",
" <td>19235.55</td>\n",
" <td>19274.75</td>\n",
" <td>19235.55</td>\n",
" <td>19268.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367243</th>\n",
" <td>2016-09-30</td>\n",
" <td>09:59</td>\n",
" <td>19190.20</td>\n",
" <td>19205.40</td>\n",
" <td>19190.20</td>\n",
" <td>19198.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367302</th>\n",
" <td>2016-09-30</td>\n",
" <td>10:58</td>\n",
" <td>19239.30</td>\n",
" <td>19245.85</td>\n",
" <td>19239.30</td>\n",
" <td>19244.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367308</th>\n",
" <td>2016-09-30</td>\n",
" <td>11:04</td>\n",
" <td>19242.25</td>\n",
" <td>19251.35</td>\n",
" <td>19242.25</td>\n",
" <td>19246.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367333</th>\n",
" <td>2016-09-30</td>\n",
" <td>11:29</td>\n",
" <td>19195.10</td>\n",
" <td>19203.70</td>\n",
" <td>19195.10</td>\n",
" <td>19199.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367339</th>\n",
" <td>2016-09-30</td>\n",
" <td>11:35</td>\n",
" <td>19192.25</td>\n",
" <td>19202.80</td>\n",
" <td>19192.25</td>\n",
" <td>19199.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367367</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:03</td>\n",
" <td>19189.00</td>\n",
" <td>19197.65</td>\n",
" <td>19189.00</td>\n",
" <td>19194.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367380</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:16</td>\n",
" <td>19155.20</td>\n",
" <td>19167.10</td>\n",
" <td>19155.20</td>\n",
" <td>19163.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367383</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:19</td>\n",
" <td>19172.20</td>\n",
" <td>19183.40</td>\n",
" <td>19172.20</td>\n",
" <td>19181.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367392</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:28</td>\n",
" <td>19154.90</td>\n",
" <td>19160.15</td>\n",
" <td>19154.90</td>\n",
" <td>19157.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367396</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:32</td>\n",
" <td>19158.50</td>\n",
" <td>19188.80</td>\n",
" <td>19158.50</td>\n",
" <td>19185.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367417</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:53</td>\n",
" <td>19158.30</td>\n",
" <td>19187.40</td>\n",
" <td>19158.30</td>\n",
" <td>19186.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367420</th>\n",
" <td>2016-09-30</td>\n",
" <td>12:56</td>\n",
" <td>19162.80</td>\n",
" <td>19179.05</td>\n",
" <td>19162.80</td>\n",
" <td>19170.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367426</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:02</td>\n",
" <td>19196.45</td>\n",
" <td>19210.70</td>\n",
" <td>19196.45</td>\n",
" <td>19207.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367478</th>\n",
" <td>2016-09-30</td>\n",
" <td>13:54</td>\n",
" <td>19241.70</td>\n",
" <td>19266.75</td>\n",
" <td>19241.70</td>\n",
" <td>19252.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367488</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:04</td>\n",
" <td>19245.95</td>\n",
" <td>19257.80</td>\n",
" <td>19245.95</td>\n",
" <td>19248.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367489</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:05</td>\n",
" <td>19248.15</td>\n",
" <td>19259.15</td>\n",
" <td>19248.15</td>\n",
" <td>19249.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367492</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:08</td>\n",
" <td>19244.00</td>\n",
" <td>19250.15</td>\n",
" <td>19244.00</td>\n",
" <td>19249.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367505</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:21</td>\n",
" <td>19270.90</td>\n",
" <td>19285.80</td>\n",
" <td>19270.90</td>\n",
" <td>19285.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367506</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:22</td>\n",
" <td>19286.80</td>\n",
" <td>19303.30</td>\n",
" <td>19286.80</td>\n",
" <td>19297.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367509</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:25</td>\n",
" <td>19275.10</td>\n",
" <td>19292.60</td>\n",
" <td>19275.10</td>\n",
" <td>19283.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367514</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:30</td>\n",
" <td>19273.50</td>\n",
" <td>19285.75</td>\n",
" <td>19273.50</td>\n",
" <td>19284.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367516</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:32</td>\n",
" <td>19288.35</td>\n",
" <td>19294.65</td>\n",
" <td>19288.35</td>\n",
" <td>19292.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367517</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:33</td>\n",
" <td>19292.35</td>\n",
" <td>19305.75</td>\n",
" <td>19292.35</td>\n",
" <td>19304.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367519</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:35</td>\n",
" <td>19299.95</td>\n",
" <td>19309.75</td>\n",
" <td>19299.95</td>\n",
" <td>19307.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367543</th>\n",
" <td>2016-09-30</td>\n",
" <td>14:59</td>\n",
" <td>19243.10</td>\n",
" <td>19256.45</td>\n",
" <td>19243.10</td>\n",
" <td>19249.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367567</th>\n",
" <td>2016-09-30</td>\n",
" <td>15:23</td>\n",
" <td>19309.20</td>\n",
" <td>19323.40</td>\n",
" <td>19309.20</td>\n",
" <td>19315.80</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>62096 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"0 2012-12-03 09:16 12125.70 12161.70 12125.70 12160.95\n",
"2 2012-12-03 09:18 12126.85 12156.10 12126.85 12156.10\n",
"13 2012-12-03 09:29 12131.75 12154.10 12131.75 12154.10\n",
"21 2012-12-03 09:37 12158.80 12161.55 12158.80 12161.15\n",
"24 2012-12-03 09:40 12150.90 12157.15 12150.90 12155.55\n",
"51 2012-12-03 10:07 12094.75 12100.05 12094.75 12096.15\n",
"54 2012-12-03 10:10 12109.50 12117.00 12109.50 12117.00\n",
"57 2012-12-03 10:13 12112.80 12116.90 12112.80 12116.90\n",
"63 2012-12-03 10:19 12109.25 12112.05 12109.25 12112.05\n",
"70 2012-12-03 10:26 12090.10 12101.05 12090.10 12098.55\n",
"77 2012-12-03 10:33 12105.80 12114.65 12105.80 12114.65\n",
"79 2012-12-03 10:35 12113.25 12117.60 12113.25 12117.60\n",
"82 2012-12-03 10:38 12117.20 12119.80 12117.20 12117.90\n",
"90 2012-12-03 10:46 12106.50 12110.70 12106.50 12110.70\n",
"111 2012-12-03 11:07 12081.95 12088.60 12081.95 12085.90\n",
"112 2012-12-03 11:08 12086.85 12094.45 12086.85 12094.25\n",
"113 2012-12-03 11:09 12093.75 12098.80 12093.75 12098.35\n",
"121 2012-12-03 11:17 12091.70 12095.95 12091.70 12094.55\n",
"122 2012-12-03 11:18 12096.65 12099.45 12096.65 12097.15\n",
"126 2012-12-03 11:22 12096.05 12100.95 12096.05 12099.85\n",
"127 2012-12-03 11:23 12099.60 12102.95 12099.60 12102.10\n",
"140 2012-12-03 11:36 12097.95 12101.20 12097.95 12099.95\n",
"141 2012-12-03 11:37 12099.80 12103.15 12099.80 12102.75\n",
"148 2012-12-03 11:44 12096.50 12098.30 12096.50 12098.30\n",
"157 2012-12-03 11:53 12084.05 12086.05 12084.05 12084.55\n",
"158 2012-12-03 11:54 12083.60 12090.10 12083.60 12090.10\n",
"164 2012-12-03 12:00 12095.30 12099.20 12095.30 12098.90\n",
"170 2012-12-03 12:06 12090.40 12092.45 12090.40 12090.45\n",
"181 2012-12-03 12:17 12083.15 12086.65 12083.15 12086.50\n",
"183 2012-12-03 12:19 12083.25 12086.20 12083.25 12086.20\n",
"... ... ... ... ... ... ...\n",
"367163 2016-09-29 14:54 19167.55 19198.70 19167.55 19189.15\n",
"367212 2016-09-30 09:28 19168.35 19198.40 19168.35 19197.20\n",
"367219 2016-09-30 09:35 19175.60 19198.60 19175.60 19195.10\n",
"367232 2016-09-30 09:48 19235.55 19274.75 19235.55 19268.20\n",
"367243 2016-09-30 09:59 19190.20 19205.40 19190.20 19198.55\n",
"367302 2016-09-30 10:58 19239.30 19245.85 19239.30 19244.30\n",
"367308 2016-09-30 11:04 19242.25 19251.35 19242.25 19246.50\n",
"367333 2016-09-30 11:29 19195.10 19203.70 19195.10 19199.00\n",
"367339 2016-09-30 11:35 19192.25 19202.80 19192.25 19199.95\n",
"367367 2016-09-30 12:03 19189.00 19197.65 19189.00 19194.05\n",
"367380 2016-09-30 12:16 19155.20 19167.10 19155.20 19163.30\n",
"367383 2016-09-30 12:19 19172.20 19183.40 19172.20 19181.20\n",
"367392 2016-09-30 12:28 19154.90 19160.15 19154.90 19157.25\n",
"367396 2016-09-30 12:32 19158.50 19188.80 19158.50 19185.85\n",
"367417 2016-09-30 12:53 19158.30 19187.40 19158.30 19186.80\n",
"367420 2016-09-30 12:56 19162.80 19179.05 19162.80 19170.25\n",
"367426 2016-09-30 13:02 19196.45 19210.70 19196.45 19207.10\n",
"367478 2016-09-30 13:54 19241.70 19266.75 19241.70 19252.20\n",
"367488 2016-09-30 14:04 19245.95 19257.80 19245.95 19248.45\n",
"367489 2016-09-30 14:05 19248.15 19259.15 19248.15 19249.15\n",
"367492 2016-09-30 14:08 19244.00 19250.15 19244.00 19249.55\n",
"367505 2016-09-30 14:21 19270.90 19285.80 19270.90 19285.80\n",
"367506 2016-09-30 14:22 19286.80 19303.30 19286.80 19297.70\n",
"367509 2016-09-30 14:25 19275.10 19292.60 19275.10 19283.95\n",
"367514 2016-09-30 14:30 19273.50 19285.75 19273.50 19284.05\n",
"367516 2016-09-30 14:32 19288.35 19294.65 19288.35 19292.80\n",
"367517 2016-09-30 14:33 19292.35 19305.75 19292.35 19304.45\n",
"367519 2016-09-30 14:35 19299.95 19309.75 19299.95 19307.55\n",
"367543 2016-09-30 14:59 19243.10 19256.45 19243.10 19249.85\n",
"367567 2016-09-30 15:23 19309.20 19323.40 19309.20 19315.80\n",
"\n",
"[62096 rows x 6 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maximum_up_banknifty = max(banknifty['open'] - banknifty['high'])\n",
"banknifty.loc[(banknifty['open'] - banknifty['low']) == maximum_up_nifty50]"
]
}
],
"metadata": {
"_change_revision": 577,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160208.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a0736384-ca97-7518-b84b-e8382bb2ea85"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data.csv\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt \n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"from sklearn.linear_model import LogisticRegression # to apply the Logistic regression\n",
"from sklearn.model_selection import train_test_split # to split the data into two parts\n",
"from sklearn.cross_validation import KFold # use for cross validation\n",
"from sklearn.model_selection import GridSearchCV# for tuning parameter\n",
"from sklearn.ensemble import RandomForestClassifier # for random forest classifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import svm # for Support Vector Machine\n",
"from sklearn import metrics \n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.feature_selection import SelectFromModel\n",
"from sklearn.metrics import roc_curve\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "13eb0a82-2251-38b9-da98-f69ffac8ef10"
},
"source": [
"##Import data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "0bcbb970-634f-b24b-a93b-ee79b1d1a15d"
},
"outputs": [],
"source": [
"df = pd.read_csv('../input/data.csv',header=0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a9dddf8d-7d72-2c38-fcdd-9d6b564a0f13"
},
"source": [
"##Explore the data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "0d397e23-2c04-bd93-709a-eeb5f6cc9e75"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
"0 842302 M 17.99 10.38 122.80 1001.0 \n",
"1 842517 M 20.57 17.77 132.90 1326.0 \n",
"2 84300903 M 19.69 21.25 130.00 1203.0 \n",
"3 84348301 M 11.42 20.38 77.58 386.1 \n",
"4 84358402 M 20.29 14.34 135.10 1297.0 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"0 0.11840 0.27760 0.3001 0.14710 \n",
"1 0.08474 0.07864 0.0869 0.07017 \n",
"2 0.10960 0.15990 0.1974 0.12790 \n",
"3 0.14250 0.28390 0.2414 0.10520 \n",
"4 0.10030 0.13280 0.1980 0.10430 \n",
"\n",
" ... texture_worst perimeter_worst area_worst smoothness_worst \\\n",
"0 ... 17.33 184.60 2019.0 0.1622 \n",
"1 ... 23.41 158.80 1956.0 0.1238 \n",
"2 ... 25.53 152.50 1709.0 0.1444 \n",
"3 ... 26.50 98.87 567.7 0.2098 \n",
"4 ... 16.67 152.20 1575.0 0.1374 \n",
"\n",
" compactness_worst concavity_worst concave points_worst symmetry_worst \\\n",
"0 0.6656 0.7119 0.2654 0.4601 \n",
"1 0.1866 0.2416 0.1860 0.2750 \n",
"2 0.4245 0.4504 0.2430 0.3613 \n",
"3 0.8663 0.6869 0.2575 0.6638 \n",
"4 0.2050 0.4000 0.1625 0.2364 \n",
"\n",
" fractal_dimension_worst Unnamed: 32 \n",
"0 0.11890 NaN \n",
"1 0.08902 NaN \n",
"2 0.08758 NaN \n",
"3 0.17300 NaN \n",
"4 0.07678 NaN \n",
"\n",
"[5 rows x 33 columns]\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 569 entries, 0 to 568\n",
"Data columns (total 33 columns):\n",
"id 569 non-null int64\n",
"diagnosis 569 non-null object\n",
"radius_mean 569 non-null float64\n",
"texture_mean 569 non-null float64\n",
"perimeter_mean 569 non-null float64\n",
"area_mean 569 non-null float64\n",
"smoothness_mean 569 non-null float64\n",
"compactness_mean 569 non-null float64\n",
"concavity_mean 569 non-null float64\n",
"concave points_mean 569 non-null float64\n",
"symmetry_mean 569 non-null float64\n",
"fractal_dimension_mean 569 non-null float64\n",
"radius_se 569 non-null float64\n",
"texture_se 569 non-null float64\n",
"perimeter_se 569 non-null float64\n",
"area_se 569 non-null float64\n",
"smoothness_se 569 non-null float64\n",
"compactness_se 569 non-null float64\n",
"concavity_se 569 non-null float64\n",
"concave points_se 569 non-null float64\n",
"symmetry_se 569 non-null float64\n",
"fractal_dimension_se 569 non-null float64\n",
"radius_worst 569 non-null float64\n",
"texture_worst 569 non-null float64\n",
"perimeter_worst 569 non-null float64\n",
"area_worst 569 non-null float64\n",
"smoothness_worst 569 non-null float64\n",
"compactness_worst 569 non-null float64\n",
"concavity_worst 569 non-null float64\n",
"concave points_worst 569 non-null float64\n",
"symmetry_worst 569 non-null float64\n",
"fractal_dimension_worst 569 non-null float64\n",
"Unnamed: 32 0 non-null float64\n",
"dtypes: float64(31), int64(1), object(1)\n",
"memory usage: 146.8+ KB\n",
"None\n",
" id radius_mean texture_mean perimeter_mean area_mean \\\n",
"count 5.690000e+02 569.000000 569.000000 569.000000 569.000000 \n",
"mean 3.037183e+07 14.127292 19.289649 91.969033 654.889104 \n",
"std 1.250206e+08 3.524049 4.301036 24.298981 351.914129 \n",
"min 8.670000e+03 6.981000 9.710000 43.790000 143.500000 \n",
"25% 8.692180e+05 11.700000 16.170000 75.170000 420.300000 \n",
"50% 9.060240e+05 13.370000 18.840000 86.240000 551.100000 \n",
"75% 8.813129e+06 15.780000 21.800000 104.100000 782.700000 \n",
"max 9.113205e+08 28.110000 39.280000 188.500000 2501.000000 \n",
"\n",
" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.096360 0.104341 0.088799 0.048919 \n",
"std 0.014064 0.052813 0.079720 0.038803 \n",
"min 0.052630 0.019380 0.000000 0.000000 \n",
"25% 0.086370 0.064920 0.029560 0.020310 \n",
"50% 0.095870 0.092630 0.061540 0.033500 \n",
"75% 0.105300 0.130400 0.130700 0.074000 \n",
"max 0.163400 0.345400 0.426800 0.201200 \n",
"\n",
" symmetry_mean fractal_dimension_mean radius_se texture_se \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.181162 0.062798 0.405172 1.216853 \n",
"std 0.027414 0.007060 0.277313 0.551648 \n",
"min 0.106000 0.049960 0.111500 0.360200 \n",
"25% 0.161900 0.057700 0.232400 0.833900 \n",
"50% 0.179200 0.061540 0.324200 1.108000 \n",
"75% 0.195700 0.066120 0.478900 1.474000 \n",
"max 0.304000 0.097440 2.873000 4.885000 \n",
"\n",
" perimeter_se area_se smoothness_se compactness_se concavity_se \\\n",
"count 569.000000 569.000000 569.000000 569.000000 569.000000 \n",
"mean 2.866059 40.337079 0.007041 0.025478 0.031894 \n",
"std 2.021855 45.491006 0.003003 0.017908 0.030186 \n",
"min 0.757000 6.802000 0.001713 0.002252 0.000000 \n",
"25% 1.606000 17.850000 0.005169 0.013080 0.015090 \n",
"50% 2.287000 24.530000 0.006380 0.020450 0.025890 \n",
"75% 3.357000 45.190000 0.008146 0.032450 0.042050 \n",
"max 21.980000 542.200000 0.031130 0.135400 0.396000 \n",
"\n",
" concave points_se symmetry_se fractal_dimension_se radius_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.011796 0.020542 0.003795 16.269190 \n",
"std 0.006170 0.008266 0.002646 4.833242 \n",
"min 0.000000 0.007882 0.000895 7.930000 \n",
"25% 0.007638 0.015160 0.002248 13.010000 \n",
"50% 0.010930 0.018730 0.003187 14.970000 \n",
"75% 0.014710 0.023480 0.004558 18.790000 \n",
"max 0.052790 0.078950 0.029840 36.040000 \n",
"\n",
" texture_worst perimeter_worst area_worst smoothness_worst \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 25.677223 107.261213 880.583128 0.132369 \n",
"std 6.146258 33.602542 569.356993 0.022832 \n",
"min 12.020000 50.410000 185.200000 0.071170 \n",
"25% 21.080000 84.110000 515.300000 0.116600 \n",
"50% 25.410000 97.660000 686.500000 0.131300 \n",
"75% 29.720000 125.400000 1084.000000 0.146000 \n",
"max 49.540000 251.200000 4254.000000 0.222600 \n",
"\n",
" compactness_worst concavity_worst concave points_worst \\\n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.254265 0.272188 0.114606 \n",
"std 0.157336 0.208624 0.065732 \n",
"min 0.027290 0.000000 0.000000 \n",
"25% 0.147200 0.114500 0.064930 \n",
"50% 0.211900 0.226700 0.099930 \n",
"75% 0.339100 0.382900 0.161400 \n",
"max 1.058000 1.252000 0.291000 \n",
"\n",
" symmetry_worst fractal_dimension_worst Unnamed: 32 \n",
"count 569.000000 569.000000 0.0 \n",
"mean 0.290076 0.083946 NaN \n",
"std 0.061867 0.018061 NaN \n",
"min 0.156500 0.055040 NaN \n",
"25% 0.250400 0.071460 NaN \n",
"50% 0.282200 0.080040 NaN \n",
"75% 0.317900 0.092080 NaN \n",
"max 0.663800 0.207500 NaN \n"
]
}
],
"source": [
"print(df.head())\n",
"print(df.info())\n",
"pd.options.display.max_columns=80\n",
"print(df.describe())"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "1bd64466-67f9-bf27-c051-18cd20b9f825"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',\n",
" 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',\n",
" 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',\n",
" 'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',\n",
" 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',\n",
" 'fractal_dimension_se', 'radius_worst', 'texture_worst',\n",
" 'perimeter_worst', 'area_worst', 'smoothness_worst',\n",
" 'compactness_worst', 'concavity_worst', 'concave points_worst',\n",
" 'symmetry_worst', 'fractal_dimension_worst'],\n",
" dtype='object')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#看起來，id和最後一欄可以drop掉，弄完後再看一次欄位\n",
"df.drop(['id','Unnamed: 32'], axis = 1, inplace=True)\n",
"df.columns "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "d52cdcf0-e6e7-5e26-a769-920f048ae7e1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean', 'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean']\n",
"['radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se', 'fractal_dimension_se']\n",
"['radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst', 'smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst']\n"
]
}
],
"source": [
"#把欄位分類一下...\n",
"features_mean = list(df.columns[1:11])\n",
"features_se = list(df.columns[11:21])\n",
"features_worst =list(df.columns[21:31])\n",
"print(features_mean)\n",
"print(features_se)\n",
"print(features_worst)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "67d9594b-3a97-198d-2aeb-24cb1174401a"
},
"outputs": [],
"source": [
"#把診斷資料變成0和1方便分類\n",
"df['diagnosis'] = df['diagnosis'].map({'M':1,'B':0}) "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "d5b7c0f7-59bd-b4db-2c08-7b1e76d6a314"
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe61e0f0828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#看一下目標class的數量\n",
"sns.countplot(df['diagnosis']);"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f8e7a032-31d2-3464-2c34-1e31b3bc34e1"
},
"source": [
"##資料預處理"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "93edc279-a0f1-d5c0-a1ed-a5eaba5a29b1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"diagnosis 0\n",
"radius_mean 0\n",
"texture_mean 0\n",
"perimeter_mean 0\n",
"area_mean 0\n",
"smoothness_mean 0\n",
"compactness_mean 0\n",
"concavity_mean 0\n",
"concave points_mean 0\n",
"symmetry_mean 0\n",
"fractal_dimension_mean 0\n",
"radius_se 0\n",
"texture_se 0\n",
"perimeter_se 0\n",
"area_se 0\n",
"smoothness_se 0\n",
"compactness_se 0\n",
"concavity_se 0\n",
"concave points_se 0\n",
"symmetry_se 0\n",
"fractal_dimension_se 0\n",
"radius_worst 0\n",
"texture_worst 0\n",
"perimeter_worst 0\n",
"area_worst 0\n",
"smoothness_worst 0\n",
"compactness_worst 0\n",
"concavity_worst 0\n",
"concave points_worst 0\n",
"symmetry_worst 0\n",
"fractal_dimension_worst 0\n",
"dtype: int64\n"
]
}
],
"source": [
"#檢測有沒有NA\n",
"print(df.isnull().sum())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "6338d0f0-5ad9-4f5f-e6b9-f7813b4bfc13"
},
"outputs": [
{
"data": {
"image/png": 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iEx68mciwchw6mUjMsXhybPa7ekO6XYGvt5Xnv5zHieQ0woIC6NCwNkN7dARg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe5e27be0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#了解變相的相關性，會不會有共線性問題，把內部相關的變相移除\n",
"corr = df[features_mean].corr()\n",
"plt.figure(figsize=(14,14))\n",
"_ = sns.heatmap(corr, cbar = True, square = True, annot=True,fmt= '.2f',annot_kws={'size': 15},\n",
" xticklabels= features_mean, yticklabels= features_mean,\n",
" cmap= 'coolwarm') # for more on heatmap you can visit Link(http://seaborn.pydata.org/generated/seaborn.heatmap.html)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "dd137fe9-a0cb-4e04-89a7-e2c8106c1766"
},
"outputs": [],
"source": [
"#for i in features_mean:\n",
"# df.boxplot(i, 'diagnosis', rot=60);"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "dd92f4a9-dd68-e3bb-5c0c-7ebd7fa029aa"
},
"outputs": [],
"source": [
"#把共線性高的特徵移除，剩下這些來當成預測變項\n",
"pred_vars=['radius_mean',\n",
" 'texture_mean',\n",
" 'smoothness_mean',\n",
" 'compactness_mean',\n",
" 'concave points_mean',\n",
" 'symmetry_mean',\n",
" 'fractal_dimension_mean']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "769fd57b-8e52-916f-0c4a-fc6cb02a8f05"
},
"outputs": [],
"source": [
"#切分訓練和測試資料\n",
"X = df[pred_vars]\n",
"y = df['diagnosis']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "5f810995-1807-3168-2787-7563b4ad72c9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.921052631579\n",
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.94 0.94 72\n",
" 1 0.90 0.88 0.89 42\n",
"\n",
"avg / total 0.92 0.92 0.92 114\n",
"\n",
"concave points_mean 0.407214\n",
"radius_mean 0.234577\n",
"compactness_mean 0.129208\n",
"texture_mean 0.102585\n",
"smoothness_mean 0.048358\n",
"fractal_dimension_mean 0.042476\n",
"symmetry_mean 0.035582\n",
"dtype: float64\n"
]
}
],
"source": [
"#用RandomForestClassifier作變數選擇\n",
"model=RandomForestClassifier(n_estimators=100)\n",
"model.fit(X_train, y_train)\n",
"y_pred = model.predict(X_test)\n",
"print(metrics.accuracy_score(y_test, y_pred))\n",
"print(classification_report(y_test, y_pred))\n",
"\n",
"\n",
"featimp = pd.Series(model.feature_importances_, index=pred_vars).sort_values(ascending=False)\n",
"print(featimp) # this is the property of Random Forest classifier that it provide us the importance \n",
"# of the features used"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "f2ad9691-e7e0-d212-d449-4e7643ad3b2d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.921052631579\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.93 0.94 72\n",
" 1 0.88 0.90 0.89 42\n",
"\n",
"avg / total 0.92 0.92 0.92 114\n",
"\n",
"concave points_mean 0.494789\n",
"radius_mean 0.299266\n",
"compactness_mean 0.108722\n",
"texture_mean 0.097223\n",
"dtype: float64\n"
]
}
],
"source": [
"#變數縮減\n",
"pred_vars=['concave points_mean', 'radius_mean','compactness_mean','texture_mean']\n",
"\n",
"#再跑一次看看\n",
"X = df[pred_vars]\n",
"y = df['diagnosis']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
"\n",
"model=RandomForestClassifier(n_estimators=100)\n",
"model.fit(X_train, y_train)\n",
"y_pred = model.predict(X_test)\n",
"print(metrics.accuracy_score(y_test, y_pred))\n",
"print(classification_report(y_test, y_pred))\n",
"\n",
"\n",
"featimp = pd.Series(model.feature_importances_, index=pred_vars).sort_values(ascending=False)\n",
"print(featimp) # this is the property of Random Forest classifier that it provide us the importance \n",
"# of the features used"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "57823ec6-4910-d5cb-4444-9cf5aa0c220d"
},
"source": [
"發現，少掉變數之後並不會比較難預測，準確率掉了一些而已，我們再拿掉兩個看看"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "8ad7cf6a-c881-9902-520f-18829c463726"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.90350877193\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 0.90 0.92 72\n",
" 1 0.84 0.90 0.87 42\n",
"\n",
"avg / total 0.91 0.90 0.90 114\n",
"\n",
"concave points_mean 0.531786\n",
"radius_mean 0.468214\n",
"dtype: float64\n"
]
}
],
"source": [
"#變數縮減\n",
"pred_vars=['concave points_mean', 'radius_mean']\n",
"\n",
"#再跑一次看看\n",
"X = df[pred_vars]\n",
"y = df['diagnosis']\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
"\n",
"model=RandomForestClassifier(n_estimators=100)\n",
"model.fit(X_train, y_train)\n",
"y_pred = model.predict(X_test)\n",
"print(metrics.accuracy_score(y_test, y_pred))\n",
"print(classification_report(y_test, y_pred))\n",
"\n",
"\n",
"featimp = pd.Series(model.feature_importances_, index=pred_vars).sort_values(ascending=False)\n",
"print(featimp) # this is the property of Random Forest classifier that it provide us the importance \n",
"# of the features used"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2cbfe628-150b-e28b-83bf-05da044626fc"
},
"source": [
"平均來說都在90以上，但減少2%不太好，還是把變數還原成4個吧"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "106db1f7-9cbc-148c-9d0a-4ad7f763eb24"
},
"outputs": [],
"source": [
"#變數還原成四個\n",
"pred_vars=['concave points_mean', 'radius_mean','compactness_mean','texture_mean']"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "57db2905-e210-9c70-5a9d-30892a092b42"
},
"source": [
"#建模"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "5d55550e-90c3-d9e7-c6f4-78ee1776719f"
},
"outputs": [],
"source": [
"X = df[pred_vars]\n",
"y = df['diagnosis']\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "2a04fa89-6543-7607-7e69-c181d9ec2b85"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9122807017543859\n",
" precision recall f1-score support\n",
"\n",
" 0 0.96 0.90 0.93 72\n",
" 1 0.85 0.93 0.89 42\n",
"\n",
"avg / total 0.92 0.91 0.91 114\n",
"\n"
]
}
],
"source": [
"#設定step，用KNN\n",
"steps = [('scaler', StandardScaler()),\n",
" ('knn', KNeighborsClassifier())]\n",
"\n",
"#設定pipeline\n",
"pipeline = Pipeline(steps)\n",
"pipeline.fit(X_train, y_train)\n",
"\n",
"y_pred = pipeline.predict(X_test)\n",
"\n",
"print(\"Accuracy: {}\".format(pipeline.score(X_test, y_test)))\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "975e53fc-ae4a-a28f-996c-ff503ff4961f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9473684210526315\n",
" precision recall f1-score support\n",
"\n",
" 0 0.95 0.97 0.96 72\n",
" 1 0.95 0.90 0.93 42\n",
"\n",
"avg / total 0.95 0.95 0.95 114\n",
"\n"
]
}
],
"source": [
"#SVM看看\n",
"steps = [('scaler', StandardScaler()),\n",
" ('SVM', svm.SVC())]\n",
"pipeline = Pipeline(steps)\n",
"pipeline.fit(X_train, y_train)\n",
"y_pred = pipeline.predict(X_test)\n",
"\n",
"print(\"Accuracy: {}\".format(pipeline.score(X_test, y_test)))\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "6b65c74c-d84b-83fa-5a8c-3016cedbd460"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9035087719298246\n",
" precision recall f1-score support\n",
"\n",
" 0 0.92 0.93 0.92 72\n",
" 1 0.88 0.86 0.87 42\n",
"\n",
"avg / total 0.90 0.90 0.90 114\n",
"\n"
]
}
],
"source": [
"#RandomForest看看\n",
"steps = [('scaler', StandardScaler()),\n",
" ('RandomForest', RandomForestClassifier())]\n",
"pipeline = Pipeline(steps)\n",
"pipeline.fit(X_train, y_train)\n",
"y_pred = pipeline.predict(X_test)\n",
"\n",
"print(\"Accuracy: {}\".format(pipeline.score(X_test, y_test)))\n",
"print(classification_report(y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "b226ad88-2714-5548-ff38-85067259b7ef"
},
"source": [
"##參數優化"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "5296b2aa-b580-2c2e-e70a-f08d967162c1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.956140350877193\n",
" precision recall f1-score support\n",
"\n",
" 0 0.97 0.96 0.97 72\n",
" 1 0.93 0.95 0.94 42\n",
"\n",
"avg / total 0.96 0.96 0.96 114\n",
"\n",
"Tuned Model Parameters: {'SVM__C': 10, 'SVM__gamma': 0.1}\n"
]
}
],
"source": [
"#用SVM\n",
"steps = [('scaler', StandardScaler()),\n",
" ('SVM', svm.SVC())]\n",
"\n",
"pipeline = Pipeline(steps)\n",
"\n",
"parameters = {'SVM__C':[1, 10, 100],\n",
" 'SVM__gamma':[0.1, 0.01]}\n",
"\n",
"cv = GridSearchCV(pipeline, parameters, cv=3)\n",
"\n",
"cv.fit(X_train, y_train)\n",
"y_pred = cv.predict(X_test)\n",
"\n",
"# Compute and print metrics\n",
"print(\"Accuracy: {}\".format(cv.score(X_test, y_test)))\n",
"print(classification_report(y_test, y_pred))\n",
"print(\"Tuned Model Parameters: {}\".format(cv.best_params_))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "8685c763-f3de-cbee-b4ad-9c3cdec16ef5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.9122807017543859\n",
" precision recall f1-score support\n",
"\n",
" 0 0.91 0.96 0.93 72\n",
" 1 0.92 0.83 0.88 42\n",
"\n",
"avg / total 0.91 0.91 0.91 114\n",
"\n",
"Tuned Model Parameters: {'knn__n_neighbors': 4}\n"
]
}
],
"source": [
"#用knn\n",
"steps = [('scaler', StandardScaler()),\n",
" ('knn', KNeighborsClassifier())]\n",
"\n",
"pipeline = Pipeline(steps)\n",
"\n",
"parameters = {'knn__n_neighbors':range(3,10)} \n",
"\n",
"cv = GridSearchCV(pipeline, parameters, cv=3)\n",
"\n",
"cv.fit(X_train, y_train)\n",
"y_pred = cv.predict(X_test)\n",
"\n",
"# Compute and print metrics\n",
"print(\"Accuracy: {}\".format(cv.score(X_test, y_test)))\n",
"print(classification_report(y_test, y_pred))\n",
"print(\"Tuned Model Parameters: {}\".format(cv.best_params_))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "2c8399d6-d456-6245-69fa-03a626245ba9"
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe5d895ea58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pipeline.fit(X_train, y_train)\n",
"y_pred_prob = pipeline.predict_proba(X_test)[:,1]\n",
"fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
"plt.plot([0, 1], [0, 1], 'k--')\n",
"plt.plot(fpr, tpr, label='KNN ROC Curve')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('KNN ROC Curve')\n",
"plt.show();"
]
}
],
"metadata": {
"_change_revision": 171,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160387.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f435854e-82aa-adc2-e307-b0573d4c307b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ap-south-1.csv\n",
"ca-central-1.csv\n",
"us-east-1.csv\n",
"us-west-1.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "c0418000-5fc3-d06e-a109-d440ef04ee42"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "95a63637-7d74-acf2-4e83-f7f0301f41ad"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price</th>\n",
" <th>datetime</th>\n",
" <th>instance_type</th>\n",
" <th>os</th>\n",
" <th>region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.8865</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>c4.8xlarge</td>\n",
" <td>Windows</td>\n",
" <td>ap-south-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0366</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>m4.xlarge</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>ap-south-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0243</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>m4.large</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>ap-south-1a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0346</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>m4.large</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>ap-south-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.2895</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>c4.8xlarge</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>ap-south-1a</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price datetime instance_type os region\n",
"0 1.8865 2017-03-07 16:05:01 c4.8xlarge Windows ap-south-1b\n",
"1 0.0366 2017-03-07 16:05:01 m4.xlarge Linux/UNIX ap-south-1b\n",
"2 0.0243 2017-03-07 16:05:01 m4.large Linux/UNIX ap-south-1a\n",
"3 0.0346 2017-03-07 16:05:01 m4.large Linux/UNIX ap-south-1b\n",
"4 0.2895 2017-03-07 16:05:01 c4.8xlarge Linux/UNIX ap-south-1a"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_south = pd.read_csv(\"../input/ap-south-1.csv\",sep=\",\")\n",
"data_south.fillna(0)\n",
"data_south.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "125df842-744d-00e1-c6e9-afd7dedfd398"
},
"outputs": [
{
"data": {
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"text/plain": [
" datetime os instance_type price region\n",
"0 2017-05-06 17:29:01 Linux/UNIX c4.large 0.0139 ca-central-1a\n",
"1 2017-05-06 17:29:01 Windows m4.4xlarge 0.8328 ca-central-1b\n",
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]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_central = pd.read_csv(\"../input/ca-central-1.csv\",sep=\",\")\n",
"data_central.fillna(0)\n",
"data_central.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "c1601bf0-feab-0ca7-871a-97396234c6ef"
},
"outputs": [
{
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],
"text/plain": [
" datetime os instance_type price region\n",
"0 2017-05-06 17:29:01 Linux/UNIX c4.large 0.0139 ca-central-1a\n",
"1 2017-05-06 17:29:01 Windows m4.4xlarge 0.8328 ca-central-1b\n",
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]
},
"execution_count": 5,
"metadata": {},
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}
],
"source": [
"data_central = pd.read_csv(\"../input/ca-central-1.csv\",sep=\",\")\n",
"data_central.fillna(0)\n",
"data_central.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "926811ad-b315-694f-de47-e27ce06292b8"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,1,2,3,4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
},
{
"data": {
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" <th>datetime</th>\n",
" <th>instance_type</th>\n",
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" <tr>\n",
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" <td>2017-04-29 22:48:07</td>\n",
" <td>m4.10xlarge</td>\n",
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" <tr>\n",
" <th>4</th>\n",
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"</div>"
],
"text/plain": [
" datetime instance_type os price region\n",
"0 2017-04-29 22:48:06 r4.2xlarge SUSE_Linux 0.1917 us-east-1b\n",
"1 2017-04-29 22:48:07 m4.10xlarge Linux/UNIX 0.7258 us-east-1a\n",
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]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_east = pd.read_csv(\"../input/us-east-1.csv\",sep=\",\")\n",
"data_east.fillna(0)\n",
"data_east.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "d766d372-3948-c505-0afb-ac71b04bc52a"
},
"outputs": [
{
"data": {
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],
"text/plain": [
" datetime instance_type os price region\n",
"0 2017-04-29 22:48:06 r4.2xlarge SUSE_Linux 0.1917 us-east-1b\n",
"1 2017-04-29 22:48:07 m4.10xlarge Linux/UNIX 0.7258 us-east-1a\n",
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"4 2017-04-29 22:48:07 i3.8xlarge SUSE_Linux 0.4532 us-east-1c"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_west = pd.read_csv(\"../input/us-west-1.csv\",sep=\",\")\n",
"data_west.fillna(0)\n",
"data_west.head()\n",
"data_east.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "f3ab0b3c-64c1-c309-1544-8934fdc11c86"
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 0.5]), <a list of 1 Text xticklabel objects>)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa1f8c9c3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#co-relation analysis\n",
"corr= data_south.corr()\n",
"plt.figure(figsize=(12,10))\n",
"sns.heatmap(corr,annot=True,cbar=True,cmap=\"coolwarm\")\n",
"plt.xticks(rotation=90)"
]
}
],
"metadata": {
"_change_revision": 1,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160473.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "986d2f40-3c0c-e41f-bf0c-d21549f607e6"
},
"source": [
"No introduction needed,pal."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"_cell_guid": "82bdedd2-aaaf-fcae-a64a-e7cd8c2147d6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "sample_submission.csv\ntest.csv\ntrain.csv\n\n"
},
{
"name": "stdout",
"output_type": "stream",
"text": "sample_submission.csv\ntest.csv\ntrain.csv\n\n"
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"\n",
"\n",
"train = pd.read_csv(\"../input/train.csv\")\n",
"test = pd.read_csv(\"../input/test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"_cell_guid": "73c67009-1b3a-8f7b-b5a6-d4a6f084d1f4"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>cat1</th>\n <th>cat2</th>\n <th>cat3</th>\n <th>cat4</th>\n <th>cat5</th>\n <th>cat6</th>\n <th>cat7</th>\n <th>cat8</th>\n <th>cat9</th>\n <th>...</th>\n <th>cont6</th>\n <th>cont7</th>\n <th>cont8</th>\n <th>cont9</th>\n <th>cont10</th>\n <th>cont11</th>\n <th>cont12</th>\n <th>cont13</th>\n <th>cont14</th>\n <th>loss</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>...</td>\n <td>0.718367</td>\n <td>0.335060</td>\n <td>0.30260</td>\n <td>0.67135</td>\n <td>0.83510</td>\n <td>0.569745</td>\n <td>0.594646</td>\n <td>0.822493</td>\n <td>0.714843</td>\n <td>2213.18</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>...</td>\n <td>0.438917</td>\n <td>0.436585</td>\n <td>0.60087</td>\n <td>0.35127</td>\n <td>0.43919</td>\n <td>0.338312</td>\n <td>0.366307</td>\n <td>0.611431</td>\n <td>0.304496</td>\n <td>1283.60</td>\n </tr>\n <tr>\n <th>2</th>\n <td>5</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>...</td>\n <td>0.289648</td>\n <td>0.315545</td>\n <td>0.27320</td>\n <td>0.26076</td>\n <td>0.32446</td>\n <td>0.381398</td>\n <td>0.373424</td>\n <td>0.195709</td>\n <td>0.774425</td>\n <td>3005.09</td>\n </tr>\n <tr>\n <th>3</th>\n <td>10</td>\n <td>B</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>...</td>\n <td>0.440945</td>\n <td>0.391128</td>\n <td>0.31796</td>\n <td>0.32128</td>\n <td>0.44467</td>\n <td>0.327915</td>\n <td>0.321570</td>\n <td>0.605077</td>\n <td>0.602642</td>\n <td>939.85</td>\n </tr>\n <tr>\n <th>4</th>\n <td>11</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>...</td>\n <td>0.178193</td>\n <td>0.247408</td>\n <td>0.24564</td>\n <td>0.22089</td>\n <td>0.21230</td>\n <td>0.204687</td>\n <td>0.202213</td>\n <td>0.246011</td>\n <td>0.432606</td>\n <td>2763.85</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 132 columns</p>\n</div>",
"text/plain": " id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 ... cont6 \\\n0 1 A B A B A A A A B ... 0.718367 \n1 2 A B A A A A A A B ... 0.438917 \n2 5 A B A A B A A A B ... 0.289648 \n3 10 B B A B A A A A B ... 0.440945 \n4 11 A B A B A A A A B ... 0.178193 \n\n cont7 cont8 cont9 cont10 cont11 cont12 cont13 \\\n0 0.335060 0.30260 0.67135 0.83510 0.569745 0.594646 0.822493 \n1 0.436585 0.60087 0.35127 0.43919 0.338312 0.366307 0.611431 \n2 0.315545 0.27320 0.26076 0.32446 0.381398 0.373424 0.195709 \n3 0.391128 0.31796 0.32128 0.44467 0.327915 0.321570 0.605077 \n4 0.247408 0.24564 0.22089 0.21230 0.204687 0.202213 0.246011 \n\n cont14 loss \n0 0.714843 2213.18 \n1 0.304496 1283.60 \n2 0.774425 3005.09 \n3 0.602642 939.85 \n4 0.432606 2763.85 \n\n[5 rows x 132 columns]"
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"_cell_guid": "509105ff-8129-f6d2-c777-c1584bf66ea6"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>cat1</th>\n <th>cat2</th>\n <th>cat3</th>\n <th>cat4</th>\n <th>cat5</th>\n <th>cat6</th>\n <th>cat7</th>\n <th>cat8</th>\n <th>cat9</th>\n <th>...</th>\n <th>cont5</th>\n <th>cont6</th>\n <th>cont7</th>\n <th>cont8</th>\n <th>cont9</th>\n <th>cont10</th>\n <th>cont11</th>\n <th>cont12</th>\n <th>cont13</th>\n <th>cont14</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>4</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.466591</td>\n <td>0.317681</td>\n <td>0.61229</td>\n <td>0.34365</td>\n <td>0.38016</td>\n <td>0.377724</td>\n <td>0.369858</td>\n <td>0.704052</td>\n <td>0.392562</td>\n </tr>\n <tr>\n <th>1</th>\n <td>6</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>...</td>\n <td>0.836443</td>\n <td>0.482425</td>\n <td>0.443760</td>\n <td>0.71330</td>\n <td>0.51890</td>\n <td>0.60401</td>\n <td>0.689039</td>\n <td>0.675759</td>\n <td>0.453468</td>\n <td>0.208045</td>\n </tr>\n <tr>\n <th>2</th>\n <td>9</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>...</td>\n <td>0.718531</td>\n <td>0.212308</td>\n <td>0.325779</td>\n <td>0.29758</td>\n <td>0.34365</td>\n <td>0.30529</td>\n <td>0.245410</td>\n <td>0.241676</td>\n <td>0.258586</td>\n <td>0.297232</td>\n </tr>\n <tr>\n <th>3</th>\n <td>12</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>...</td>\n <td>0.397069</td>\n <td>0.369930</td>\n <td>0.342355</td>\n <td>0.40028</td>\n <td>0.33237</td>\n <td>0.31480</td>\n <td>0.348867</td>\n <td>0.341872</td>\n <td>0.592264</td>\n <td>0.555955</td>\n </tr>\n <tr>\n <th>4</th>\n <td>15</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>...</td>\n <td>0.302678</td>\n <td>0.398862</td>\n <td>0.391833</td>\n <td>0.23688</td>\n <td>0.43731</td>\n <td>0.50556</td>\n <td>0.359572</td>\n <td>0.352251</td>\n <td>0.301535</td>\n <td>0.825823</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 131 columns</p>\n</div>",
"text/plain": " id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 ... cont5 \\\n0 4 A B A A A A A A B ... 0.281143 \n1 6 A B A B A A A A B ... 0.836443 \n2 9 A B A B B A B A B ... 0.718531 \n3 12 A A A A B A A A A ... 0.397069 \n4 15 B A A A A B A A A ... 0.302678 \n\n cont6 cont7 cont8 cont9 cont10 cont11 cont12 \\\n0 0.466591 0.317681 0.61229 0.34365 0.38016 0.377724 0.369858 \n1 0.482425 0.443760 0.71330 0.51890 0.60401 0.689039 0.675759 \n2 0.212308 0.325779 0.29758 0.34365 0.30529 0.245410 0.241676 \n3 0.369930 0.342355 0.40028 0.33237 0.31480 0.348867 0.341872 \n4 0.398862 0.391833 0.23688 0.43731 0.50556 0.359572 0.352251 \n\n cont13 cont14 \n0 0.704052 0.392562 \n1 0.453468 0.208045 \n2 0.258586 0.297232 \n3 0.592264 0.555955 \n4 0.301535 0.825823 \n\n[5 rows x 131 columns]"
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test[\"loss\"] = 0"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"_cell_guid": "bb96f33a-bb3a-2a12-00e7-a47ff14149d0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "(188318, 132)\n(125546, 132)\n"
}
],
"source": [
"print(train.shape)\n",
"print(test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"_cell_guid": "a1e28901-361c-cdc1-a8a9-afa8e5b6799e"
},
"outputs": [],
"source": [
"full = train.append(test)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"_cell_guid": "2359f7df-9db7-6a8a-6b50-23d80930ce85"
},
"outputs": [
{
"data": {
"text/plain": "['id',\n 'cat1',\n 'cat2',\n 'cat3',\n 'cat4',\n 'cat5',\n 'cat6',\n 'cat7',\n 'cat8',\n 'cat9',\n 'cat10',\n 'cat11',\n 'cat12',\n 'cat13',\n 'cat14',\n 'cat15',\n 'cat16',\n 'cat17',\n 'cat18',\n 'cat19',\n 'cat20',\n 'cat21',\n 'cat22',\n 'cat23',\n 'cat24',\n 'cat25',\n 'cat26',\n 'cat27',\n 'cat28',\n 'cat29',\n 'cat30',\n 'cat31',\n 'cat32',\n 'cat33',\n 'cat34',\n 'cat35',\n 'cat36',\n 'cat37',\n 'cat38',\n 'cat39',\n 'cat40',\n 'cat41',\n 'cat42',\n 'cat43',\n 'cat44',\n 'cat45',\n 'cat46',\n 'cat47',\n 'cat48',\n 'cat49',\n 'cat50',\n 'cat51',\n 'cat52',\n 'cat53',\n 'cat54',\n 'cat55',\n 'cat56',\n 'cat57',\n 'cat58',\n 'cat59',\n 'cat60',\n 'cat61',\n 'cat62',\n 'cat63',\n 'cat64',\n 'cat65',\n 'cat66',\n 'cat67',\n 'cat68',\n 'cat69',\n 'cat70',\n 'cat71',\n 'cat72',\n 'cat73',\n 'cat74',\n 'cat75',\n 'cat76',\n 'cat77',\n 'cat78',\n 'cat79',\n 'cat80',\n 'cat81',\n 'cat82',\n 'cat83',\n 'cat84',\n 'cat85',\n 'cat86',\n 'cat87',\n 'cat88',\n 'cat89',\n 'cat90',\n 'cat91',\n 'cat92',\n 'cat93',\n 'cat94',\n 'cat95',\n 'cat96',\n 'cat97',\n 'cat98',\n 'cat99',\n 'cat100',\n 'cat101',\n 'cat102',\n 'cat103',\n 'cat104',\n 'cat105',\n 'cat106',\n 'cat107',\n 'cat108',\n 'cat109',\n 'cat110',\n 'cat111',\n 'cat112',\n 'cat113',\n 'cat114',\n 'cat115',\n 'cat116',\n 'cont1',\n 'cont2',\n 'cont3',\n 'cont4',\n 'cont5',\n 'cont6',\n 'cont7',\n 'cont8',\n 'cont9',\n 'cont10',\n 'cont11',\n 'cont12',\n 'cont13',\n 'cont14',\n 'loss']"
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(full.columns)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"_cell_guid": "3d1d470c-4558-00c7-6590-59fe6e9c6ede"
},
"outputs": [
{
"data": {
"text/plain": "['cat1',\n 'cat2',\n 'cat3',\n 'cat4',\n 'cat5',\n 'cat6',\n 'cat7',\n 'cat8',\n 'cat9',\n 'cat10',\n 'cat11',\n 'cat12',\n 'cat13',\n 'cat14',\n 'cat15',\n 'cat16',\n 'cat17',\n 'cat18',\n 'cat19',\n 'cat20',\n 'cat21',\n 'cat22',\n 'cat23',\n 'cat24',\n 'cat25',\n 'cat26',\n 'cat27',\n 'cat28',\n 'cat29',\n 'cat30',\n 'cat31',\n 'cat32',\n 'cat33',\n 'cat34',\n 'cat35',\n 'cat36',\n 'cat37',\n 'cat38',\n 'cat39',\n 'cat40',\n 'cat41',\n 'cat42',\n 'cat43',\n 'cat44',\n 'cat45',\n 'cat46',\n 'cat47',\n 'cat48',\n 'cat49',\n 'cat50',\n 'cat51',\n 'cat52',\n 'cat53',\n 'cat54',\n 'cat55',\n 'cat56',\n 'cat57',\n 'cat58',\n 'cat59',\n 'cat60',\n 'cat61',\n 'cat62',\n 'cat63',\n 'cat64',\n 'cat65',\n 'cat66',\n 'cat67',\n 'cat68',\n 'cat69',\n 'cat70',\n 'cat71',\n 'cat72',\n 'cat73',\n 'cat74',\n 'cat75',\n 'cat76',\n 'cat77',\n 'cat78',\n 'cat79',\n 'cat80',\n 'cat81',\n 'cat82',\n 'cat83',\n 'cat84',\n 'cat85',\n 'cat86',\n 'cat87',\n 'cat88',\n 'cat89',\n 'cat90',\n 'cat91',\n 'cat92',\n 'cat93',\n 'cat94',\n 'cat95',\n 'cat96',\n 'cat97',\n 'cat98',\n 'cat99',\n 'cat100',\n 'cat101',\n 'cat102',\n 'cat103',\n 'cat104',\n 'cat105',\n 'cat106',\n 'cat107',\n 'cat108',\n 'cat109',\n 'cat110',\n 'cat111',\n 'cat112',\n 'cat113',\n 'cat114',\n 'cat115',\n 'cat116']"
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cats_cols = [name for name in list(full.columns) if \"cat\" in name]\n",
"cats_cols"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"_cell_guid": "3a635099-0da7-53c0-3bcb-936ca9fe0726"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>cat1</th>\n <th>cat2</th>\n <th>cat3</th>\n <th>cat4</th>\n <th>cat5</th>\n <th>cat6</th>\n <th>cat7</th>\n <th>cat8</th>\n <th>cat9</th>\n <th>cat10</th>\n <th>...</th>\n <th>cat107</th>\n <th>cat108</th>\n <th>cat109</th>\n <th>cat110</th>\n <th>cat111</th>\n <th>cat112</th>\n <th>cat113</th>\n <th>cat114</th>\n <th>cat115</th>\n <th>cat116</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>...</td>\n <td>J</td>\n <td>G</td>\n <td>BU</td>\n <td>BC</td>\n <td>C</td>\n <td>AS</td>\n <td>S</td>\n <td>A</td>\n <td>O</td>\n <td>LB</td>\n </tr>\n <tr>\n <th>1</th>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>B</td>\n <td>...</td>\n <td>K</td>\n <td>K</td>\n <td>BI</td>\n <td>CQ</td>\n <td>A</td>\n <td>AV</td>\n <td>BM</td>\n <td>A</td>\n <td>O</td>\n <td>DP</td>\n </tr>\n <tr>\n <th>2</th>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>B</td>\n <td>...</td>\n <td>F</td>\n <td>A</td>\n <td>AB</td>\n <td>DK</td>\n <td>A</td>\n <td>C</td>\n <td>AF</td>\n <td>A</td>\n <td>I</td>\n <td>GK</td>\n </tr>\n <tr>\n <th>3</th>\n <td>B</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>...</td>\n <td>K</td>\n <td>K</td>\n <td>BI</td>\n <td>CS</td>\n <td>C</td>\n <td>N</td>\n <td>AE</td>\n <td>A</td>\n <td>O</td>\n <td>DJ</td>\n </tr>\n <tr>\n <th>4</th>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>B</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>A</td>\n <td>B</td>\n <td>B</td>\n <td>...</td>\n <td>G</td>\n <td>B</td>\n <td>H</td>\n <td>C</td>\n <td>C</td>\n <td>Y</td>\n <td>BM</td>\n <td>A</td>\n <td>K</td>\n <td>CK</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 116 columns</p>\n</div>",
"text/plain": " cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 cat10 ... cat107 cat108 \\\n0 A B A B A A A A B A ... J G \n1 A B A A A A A A B B ... K K \n2 A B A A B A A A B B ... F A \n3 B B A B A A A A B A ... K K \n4 A B A B A A A A B B ... G B \n\n cat109 cat110 cat111 cat112 cat113 cat114 cat115 cat116 \n0 BU BC C AS S A O LB \n1 BI CQ A AV BM A O DP \n2 AB DK A C AF A I GK \n3 BI CS C N AE A O DJ \n4 H C C Y BM A K CK \n\n[5 rows x 116 columns]"
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_cat = full[cats_cols]\n",
"data_cat.head()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"_cell_guid": "231d4b13-268c-0ea6-6c32-96234769f0ce"
},
"outputs": [
{
"data": {
"text/plain": "array([0, 0, 0, ..., 1, 0, 0])"
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"def encode_cats(cat_array):\n",
" encoding = LabelEncoder()\n",
" return(encoding.fit_transform(cat_array))\n",
" \n",
" \n",
"data_cat = data_cat.apply(encode_cats)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"_cell_guid": "0362d2ae-9662-3eb8-7bfb-f06caad035d9"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>cat1</th>\n <th>cat2</th>\n <th>cat3</th>\n <th>cat4</th>\n <th>cat5</th>\n <th>cat6</th>\n <th>cat7</th>\n <th>cat8</th>\n <th>cat9</th>\n <th>...</th>\n <th>cont6</th>\n <th>cont7</th>\n <th>cont8</th>\n <th>cont9</th>\n <th>cont10</th>\n <th>cont11</th>\n <th>cont12</th>\n <th>cont13</th>\n <th>cont14</th>\n <th>loss</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.718367</td>\n <td>0.335060</td>\n <td>0.30260</td>\n <td>0.67135</td>\n <td>0.83510</td>\n <td>0.569745</td>\n <td>0.594646</td>\n <td>0.822493</td>\n <td>0.714843</td>\n <td>2213.18</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.438917</td>\n <td>0.436585</td>\n <td>0.60087</td>\n <td>0.35127</td>\n <td>0.43919</td>\n <td>0.338312</td>\n <td>0.366307</td>\n <td>0.611431</td>\n <td>0.304496</td>\n <td>1283.60</td>\n </tr>\n <tr>\n <th>2</th>\n <td>5</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.289648</td>\n <td>0.315545</td>\n <td>0.27320</td>\n <td>0.26076</td>\n <td>0.32446</td>\n <td>0.381398</td>\n <td>0.373424</td>\n <td>0.195709</td>\n <td>0.774425</td>\n <td>3005.09</td>\n </tr>\n <tr>\n <th>3</th>\n <td>10</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.440945</td>\n <td>0.391128</td>\n <td>0.31796</td>\n <td>0.32128</td>\n <td>0.44467</td>\n <td>0.327915</td>\n <td>0.321570</td>\n <td>0.605077</td>\n <td>0.602642</td>\n <td>939.85</td>\n </tr>\n <tr>\n <th>4</th>\n <td>11</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.178193</td>\n <td>0.247408</td>\n <td>0.24564</td>\n <td>0.22089</td>\n <td>0.21230</td>\n <td>0.204687</td>\n <td>0.202213</td>\n <td>0.246011</td>\n <td>0.432606</td>\n <td>2763.85</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows \u00d7 132 columns</p>\n</div>",
"text/plain": " id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 ... \\\n0 1 0 1 0 1 0 0 0 0 1 ... \n1 2 0 1 0 0 0 0 0 0 1 ... \n2 5 0 1 0 0 1 0 0 0 1 ... \n3 10 1 1 0 1 0 0 0 0 1 ... \n4 11 0 1 0 1 0 0 0 0 1 ... \n\n cont6 cont7 cont8 cont9 cont10 cont11 cont12 \\\n0 0.718367 0.335060 0.30260 0.67135 0.83510 0.569745 0.594646 \n1 0.438917 0.436585 0.60087 0.35127 0.43919 0.338312 0.366307 \n2 0.289648 0.315545 0.27320 0.26076 0.32446 0.381398 0.373424 \n3 0.440945 0.391128 0.31796 0.32128 0.44467 0.327915 0.321570 \n4 0.178193 0.247408 0.24564 0.22089 0.21230 0.204687 0.202213 \n\n cont13 cont14 loss \n0 0.822493 0.714843 2213.18 \n1 0.611431 0.304496 1283.60 \n2 0.195709 0.774425 3005.09 \n3 0.605077 0.602642 939.85 \n4 0.246011 0.432606 2763.85 \n\n[5 rows x 132 columns]"
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full[cats_cols] = data_cat\n",
"full.head()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"_cell_guid": "6c716cbe-56b0-de70-54c0-ac6e017eea40"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>cont1</th>\n <th>cont2</th>\n <th>cont3</th>\n <th>cont4</th>\n <th>cont5</th>\n <th>cont6</th>\n <th>cont7</th>\n <th>cont8</th>\n <th>cont9</th>\n <th>cont10</th>\n <th>cont11</th>\n <th>cont12</th>\n <th>cont13</th>\n <th>cont14</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0.726300</td>\n <td>0.245921</td>\n <td>0.187583</td>\n <td>0.789639</td>\n <td>0.310061</td>\n <td>0.718367</td>\n <td>0.335060</td>\n <td>0.30260</td>\n <td>0.67135</td>\n <td>0.83510</td>\n <td>0.569745</td>\n <td>0.594646</td>\n <td>0.822493</td>\n <td>0.714843</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0.330514</td>\n <td>0.737068</td>\n <td>0.592681</td>\n <td>0.614134</td>\n <td>0.885834</td>\n <td>0.438917</td>\n <td>0.436585</td>\n <td>0.60087</td>\n <td>0.35127</td>\n <td>0.43919</td>\n <td>0.338312</td>\n <td>0.366307</td>\n <td>0.611431</td>\n <td>0.304496</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0.261841</td>\n <td>0.358319</td>\n <td>0.484196</td>\n <td>0.236924</td>\n <td>0.397069</td>\n <td>0.289648</td>\n <td>0.315545</td>\n <td>0.27320</td>\n <td>0.26076</td>\n <td>0.32446</td>\n <td>0.381398</td>\n <td>0.373424</td>\n <td>0.195709</td>\n <td>0.774425</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0.321594</td>\n <td>0.555782</td>\n <td>0.527991</td>\n <td>0.373816</td>\n <td>0.422268</td>\n <td>0.440945</td>\n <td>0.391128</td>\n <td>0.31796</td>\n <td>0.32128</td>\n <td>0.44467</td>\n <td>0.327915</td>\n <td>0.321570</td>\n <td>0.605077</td>\n <td>0.602642</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0.273204</td>\n <td>0.159990</td>\n <td>0.527991</td>\n <td>0.473202</td>\n <td>0.704268</td>\n <td>0.178193</td>\n <td>0.247408</td>\n <td>0.24564</td>\n <td>0.22089</td>\n <td>0.21230</td>\n <td>0.204687</td>\n <td>0.202213</td>\n <td>0.246011</td>\n <td>0.432606</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " cont1 cont2 cont3 cont4 cont5 cont6 cont7 \\\n0 0.726300 0.245921 0.187583 0.789639 0.310061 0.718367 0.335060 \n1 0.330514 0.737068 0.592681 0.614134 0.885834 0.438917 0.436585 \n2 0.261841 0.358319 0.484196 0.236924 0.397069 0.289648 0.315545 \n3 0.321594 0.555782 0.527991 0.373816 0.422268 0.440945 0.391128 \n4 0.273204 0.159990 0.527991 0.473202 0.704268 0.178193 0.247408 \n\n cont8 cont9 cont10 cont11 cont12 cont13 cont14 \n0 0.30260 0.67135 0.83510 0.569745 0.594646 0.822493 0.714843 \n1 0.60087 0.35127 0.43919 0.338312 0.366307 0.611431 0.304496 \n2 0.27320 0.26076 0.32446 0.381398 0.373424 0.195709 0.774425 \n3 0.31796 0.32128 0.44467 0.327915 0.321570 0.605077 0.602642 \n4 0.24564 0.22089 0.21230 0.204687 0.202213 0.246011 0.432606 "
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count_cols = [cont for cont in full.columns if \"cont\" in cont]\n",
"count_data = full[count_cols]\n",
"count_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"_cell_guid": "f38a9368-b117-5f48-d276-24dd9e89f1f9"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>cont1</th>\n <th>cont2</th>\n <th>cont3</th>\n <th>cont4</th>\n <th>cont5</th>\n <th>cont6</th>\n <th>cont7</th>\n <th>cont8</th>\n <th>cont9</th>\n <th>cont10</th>\n <th>cont11</th>\n <th>cont12</th>\n <th>cont13</th>\n <th>cont14</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>count</th>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.00000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n <td>313864.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>0.494096</td>\n <td>0.507089</td>\n <td>0.498653</td>\n <td>0.492021</td>\n <td>0.487513</td>\n <td>0.491442</td>\n <td>0.485360</td>\n <td>0.486823</td>\n <td>0.48571</td>\n <td>0.498403</td>\n <td>0.493850</td>\n <td>0.493503</td>\n <td>0.493917</td>\n <td>0.495665</td>\n </tr>\n <tr>\n <th>std</th>\n <td>0.187768</td>\n <td>0.207056</td>\n <td>0.201961</td>\n <td>0.211101</td>\n <td>0.209063</td>\n <td>0.205394</td>\n <td>0.178531</td>\n <td>0.199442</td>\n <td>0.18185</td>\n <td>0.185906</td>\n <td>0.210002</td>\n <td>0.209716</td>\n <td>0.212911</td>\n <td>0.222537</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000016</td>\n <td>0.001149</td>\n <td>0.002634</td>\n <td>0.176921</td>\n <td>0.281143</td>\n <td>0.012683</td>\n <td>0.069503</td>\n <td>0.236880</td>\n <td>0.00008</td>\n <td>0.000000</td>\n <td>0.035321</td>\n <td>0.036232</td>\n <td>0.000228</td>\n <td>0.178568</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>0.347403</td>\n <td>0.358319</td>\n <td>0.336963</td>\n <td>0.327354</td>\n <td>0.281143</td>\n <td>0.336105</td>\n <td>0.351299</td>\n <td>0.317960</td>\n <td>0.35897</td>\n <td>0.364580</td>\n <td>0.310961</td>\n <td>0.314945</td>\n <td>0.315758</td>\n <td>0.294657</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>0.475784</td>\n <td>0.555782</td>\n <td>0.527991</td>\n <td>0.452887</td>\n <td>0.422268</td>\n <td>0.440945</td>\n <td>0.438650</td>\n <td>0.441060</td>\n <td>0.44145</td>\n <td>0.461190</td>\n <td>0.457203</td>\n <td>0.462286</td>\n <td>0.363547</td>\n <td>0.407020</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>0.625272</td>\n <td>0.681761</td>\n <td>0.634224</td>\n <td>0.652072</td>\n <td>0.643315</td>\n <td>0.655818</td>\n <td>0.591165</td>\n <td>0.623580</td>\n <td>0.56889</td>\n <td>0.619840</td>\n <td>0.678924</td>\n <td>0.679096</td>\n <td>0.689974</td>\n <td>0.724707</td>\n </tr>\n <tr>\n <th>max</th>\n <td>0.984975</td>\n <td>0.862654</td>\n <td>0.944251</td>\n <td>0.956046</td>\n <td>0.983674</td>\n <td>0.997162</td>\n <td>1.000000</td>\n <td>0.982800</td>\n <td>0.99540</td>\n <td>0.994980</td>\n <td>0.998742</td>\n <td>0.998484</td>\n <td>0.988494</td>\n <td>0.844848</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " cont1 cont2 cont3 cont4 \\\ncount 313864.000000 313864.000000 313864.000000 313864.000000 \nmean 0.494096 0.507089 0.498653 0.492021 \nstd 0.187768 0.207056 0.201961 0.211101 \nmin 0.000016 0.001149 0.002634 0.176921 \n25% 0.347403 0.358319 0.336963 0.327354 \n50% 0.475784 0.555782 0.527991 0.452887 \n75% 0.625272 0.681761 0.634224 0.652072 \nmax 0.984975 0.862654 0.944251 0.956046 \n\n cont5 cont6 cont7 cont8 \\\ncount 313864.000000 313864.000000 313864.000000 313864.000000 \nmean 0.487513 0.491442 0.485360 0.486823 \nstd 0.209063 0.205394 0.178531 0.199442 \nmin 0.281143 0.012683 0.069503 0.236880 \n25% 0.281143 0.336105 0.351299 0.317960 \n50% 0.422268 0.440945 0.438650 0.441060 \n75% 0.643315 0.655818 0.591165 0.623580 \nmax 0.983674 0.997162 1.000000 0.982800 \n\n cont9 cont10 cont11 cont12 \\\ncount 313864.00000 313864.000000 313864.000000 313864.000000 \nmean 0.48571 0.498403 0.493850 0.493503 \nstd 0.18185 0.185906 0.210002 0.209716 \nmin 0.00008 0.000000 0.035321 0.036232 \n25% 0.35897 0.364580 0.310961 0.314945 \n50% 0.44145 0.461190 0.457203 0.462286 \n75% 0.56889 0.619840 0.678924 0.679096 \nmax 0.99540 0.994980 0.998742 0.998484 \n\n cont13 cont14 \ncount 313864.000000 313864.000000 \nmean 0.493917 0.495665 \nstd 0.212911 0.222537 \nmin 0.000228 0.178568 \n25% 0.315758 0.294657 \n50% 0.363547 0.407020 \n75% 0.689974 0.724707 \nmax 0.988494 0.844848 "
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count_data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "eea38c22-094d-3977-97e2-0174ec3671e8"
},
"source": [
"All the data is normalized already, so no need to modify it."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"_cell_guid": "1c88e771-3e7d-b238-8d00-9524f73326d6"
},
"outputs": [
{
"data": {
"text/plain": "count 188318.000000\nmean 3037.337686\nstd 2904.086186\nmin 0.670000\n25% 1204.460000\n50% 2115.570000\n75% 3864.045000\nmax 121012.250000\nName: loss, dtype: float64"
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.loss.describe()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"_cell_guid": "e9885b8d-1781-3173-0210-cd7f39bb396d"
},
"outputs": [
{
"data": {
"text/plain": "(188318, 132)"
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = full.iloc[:len(train)]\n",
"train.shape"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"_cell_guid": "33f56c2b-bf6d-aa24-37aa-234832ad2141"
},
"outputs": [
{
"data": {
"text/plain": "(125546, 132)"
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = full.iloc[len(train):len(full)]\n",
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"_cell_guid": "5d01666b-60f5-62c5-ce35-c21a7365483e"
},
"outputs": [
{
"data": {
"text/plain": "(125546, 131)"
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = test.drop(\"loss\",axis=1)\n",
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"_cell_guid": "9edabf88-0fb8-7cba-b158-7aad0b4ba819"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>cat1</th>\n <th>cat2</th>\n <th>cat3</th>\n <th>cat4</th>\n <th>cat5</th>\n <th>cat6</th>\n <th>cat7</th>\n <th>cat8</th>\n <th>cat9</th>\n <th>...</th>\n <th>cont5</th>\n <th>cont6</th>\n <th>cont7</th>\n <th>cont8</th>\n <th>cont9</th>\n <th>cont10</th>\n <th>cont11</th>\n <th>cont12</th>\n <th>cont13</th>\n <th>cont14</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.310061</td>\n <td>0.718367</td>\n <td>0.335060</td>\n <td>0.30260</td>\n <td>0.67135</td>\n <td>0.83510</td>\n <td>0.569745</td>\n <td>0.594646</td>\n <td>0.822493</td>\n <td>0.714843</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.885834</td>\n <td>0.438917</td>\n <td>0.436585</td>\n <td>0.60087</td>\n <td>0.35127</td>\n <td>0.43919</td>\n <td>0.338312</td>\n <td>0.366307</td>\n <td>0.611431</td>\n <td>0.304496</td>\n </tr>\n <tr>\n <th>2</th>\n <td>5</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.397069</td>\n <td>0.289648</td>\n <td>0.315545</td>\n <td>0.27320</td>\n <td>0.26076</td>\n <td>0.32446</td>\n <td>0.381398</td>\n <td>0.373424</td>\n <td>0.195709</td>\n <td>0.774425</td>\n </tr>\n <tr>\n <th>3</th>\n <td>10</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.422268</td>\n <td>0.440945</td>\n <td>0.391128</td>\n <td>0.31796</td>\n <td>0.32128</td>\n <td>0.44467</td>\n <td>0.327915</td>\n <td>0.321570</td>\n <td>0.605077</td>\n <td>0.602642</td>\n </tr>\n <tr>\n <th>4</th>\n <td>11</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.704268</td>\n <td>0.178193</td>\n <td>0.247408</td>\n <td>0.24564</td>\n <td>0.22089</td>\n <td>0.21230</td>\n <td>0.204687</td>\n <td>0.202213</td>\n <td>0.246011</td>\n <td>0.432606</td>\n </tr>\n <tr>\n <th>5</th>\n <td>13</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.302678</td>\n <td>0.364464</td>\n <td>0.401162</td>\n <td>0.26847</td>\n <td>0.46226</td>\n <td>0.50556</td>\n <td>0.366788</td>\n <td>0.359249</td>\n <td>0.345247</td>\n <td>0.726792</td>\n </tr>\n <tr>\n <th>6</th>\n <td>14</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.295397</td>\n <td>0.381515</td>\n <td>0.363768</td>\n <td>0.24564</td>\n <td>0.40455</td>\n <td>0.47225</td>\n <td>0.334828</td>\n <td>0.352251</td>\n <td>0.342239</td>\n <td>0.382931</td>\n </tr>\n <tr>\n <th>7</th>\n <td>20</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.473767</td>\n <td>0.867021</td>\n <td>0.583389</td>\n <td>0.90267</td>\n <td>0.84847</td>\n <td>0.80218</td>\n <td>0.644013</td>\n <td>0.785706</td>\n <td>0.859764</td>\n <td>0.242416</td>\n </tr>\n <tr>\n <th>8</th>\n <td>23</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.628534</td>\n <td>0.384099</td>\n <td>0.61229</td>\n <td>0.38249</td>\n <td>0.51111</td>\n <td>0.682315</td>\n <td>0.669033</td>\n <td>0.756454</td>\n <td>0.361191</td>\n </tr>\n <tr>\n <th>9</th>\n <td>24</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.310061</td>\n <td>0.713343</td>\n <td>0.469223</td>\n <td>0.30260</td>\n <td>0.67135</td>\n <td>0.83510</td>\n <td>0.863052</td>\n <td>0.879347</td>\n <td>0.822493</td>\n <td>0.294523</td>\n </tr>\n <tr>\n <th>10</th>\n <td>25</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.429383</td>\n <td>0.877905</td>\n <td>0.39455</td>\n <td>0.53565</td>\n <td>0.50556</td>\n <td>0.550529</td>\n <td>0.538473</td>\n <td>0.336261</td>\n <td>0.715009</td>\n </tr>\n <tr>\n <th>11</th>\n <td>33</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.508483</td>\n <td>0.314683</td>\n <td>0.370419</td>\n <td>0.58354</td>\n <td>0.46226</td>\n <td>0.38016</td>\n <td>0.644013</td>\n <td>0.665644</td>\n <td>0.339244</td>\n <td>0.799124</td>\n </tr>\n <tr>\n <th>12</th>\n <td>34</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.302678</td>\n <td>0.408772</td>\n <td>0.363312</td>\n <td>0.32843</td>\n <td>0.32128</td>\n <td>0.44467</td>\n <td>0.327915</td>\n <td>0.321570</td>\n <td>0.605077</td>\n <td>0.818358</td>\n </tr>\n <tr>\n <th>13</th>\n <td>41</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.811271</td>\n <td>0.241574</td>\n <td>0.255339</td>\n <td>0.58934</td>\n <td>0.32496</td>\n <td>0.26029</td>\n <td>0.257148</td>\n <td>0.253044</td>\n <td>0.276878</td>\n <td>0.477578</td>\n </tr>\n <tr>\n <th>14</th>\n <td>47</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.894903</td>\n <td>0.586433</td>\n <td>0.80058</td>\n <td>0.93383</td>\n <td>0.78770</td>\n <td>0.880469</td>\n <td>0.871011</td>\n <td>0.822493</td>\n <td>0.251278</td>\n </tr>\n <tr>\n <th>15</th>\n <td>48</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.570733</td>\n <td>0.547756</td>\n <td>0.80438</td>\n <td>0.44352</td>\n <td>0.63026</td>\n <td>0.385085</td>\n <td>0.377003</td>\n <td>0.516660</td>\n <td>0.340325</td>\n </tr>\n <tr>\n <th>16</th>\n <td>49</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.525831</td>\n <td>0.411902</td>\n <td>0.593548</td>\n <td>0.31796</td>\n <td>0.38846</td>\n <td>0.48889</td>\n <td>0.457203</td>\n <td>0.447145</td>\n <td>0.301535</td>\n <td>0.205651</td>\n </tr>\n <tr>\n <th>17</th>\n <td>51</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.551723</td>\n <td>0.688705</td>\n <td>0.437192</td>\n <td>0.67263</td>\n <td>0.83505</td>\n <td>0.59334</td>\n <td>0.678924</td>\n <td>0.665644</td>\n <td>0.684242</td>\n <td>0.407411</td>\n </tr>\n <tr>\n <th>18</th>\n <td>52</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.443265</td>\n <td>0.637086</td>\n <td>0.36636</td>\n <td>0.52938</td>\n <td>0.39068</td>\n <td>0.678924</td>\n <td>0.665644</td>\n <td>0.304350</td>\n <td>0.310796</td>\n </tr>\n <tr>\n <th>19</th>\n <td>55</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.602545</td>\n <td>0.436312</td>\n <td>0.544355</td>\n <td>0.48864</td>\n <td>0.36285</td>\n <td>0.20496</td>\n <td>0.388786</td>\n <td>0.406090</td>\n <td>0.648701</td>\n <td>0.830931</td>\n </tr>\n <tr>\n <th>20</th>\n <td>57</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.718531</td>\n <td>0.441525</td>\n <td>0.437192</td>\n <td>0.31796</td>\n <td>0.32128</td>\n <td>0.44467</td>\n <td>0.377724</td>\n <td>0.369858</td>\n <td>0.605077</td>\n <td>0.743810</td>\n </tr>\n <tr>\n <th>21</th>\n <td>60</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.534484</td>\n <td>0.349885</td>\n <td>0.381185</td>\n <td>0.81542</td>\n <td>0.32311</td>\n <td>0.36458</td>\n <td>0.453334</td>\n <td>0.454705</td>\n <td>0.651733</td>\n <td>0.354002</td>\n </tr>\n <tr>\n <th>22</th>\n <td>61</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.183243</td>\n <td>0.253560</td>\n <td>0.40028</td>\n <td>0.21374</td>\n <td>0.19431</td>\n <td>0.167024</td>\n <td>0.165648</td>\n <td>0.404520</td>\n <td>0.725941</td>\n </tr>\n <tr>\n <th>23</th>\n <td>66</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.783230</td>\n <td>0.373500</td>\n <td>0.381883</td>\n <td>0.36083</td>\n <td>0.44352</td>\n <td>0.45017</td>\n <td>0.338312</td>\n <td>0.366307</td>\n <td>0.339244</td>\n <td>0.793518</td>\n </tr>\n <tr>\n <th>24</th>\n <td>73</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.543117</td>\n <td>0.382070</td>\n <td>0.451203</td>\n <td>0.33906</td>\n <td>0.47900</td>\n <td>0.54433</td>\n <td>0.812519</td>\n <td>0.800726</td>\n <td>0.246011</td>\n <td>0.215055</td>\n </tr>\n <tr>\n <th>25</th>\n <td>76</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.592478</td>\n <td>0.496452</td>\n <td>0.29758</td>\n <td>0.46226</td>\n <td>0.51111</td>\n <td>0.434083</td>\n <td>0.424625</td>\n <td>0.357400</td>\n <td>0.311644</td>\n </tr>\n <tr>\n <th>26</th>\n <td>86</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.435733</td>\n <td>0.769905</td>\n <td>0.60087</td>\n <td>0.40252</td>\n <td>0.28677</td>\n <td>0.550529</td>\n <td>0.538473</td>\n <td>0.298734</td>\n <td>0.698006</td>\n </tr>\n <tr>\n <th>27</th>\n <td>89</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.889301</td>\n <td>0.373500</td>\n <td>0.356037</td>\n <td>0.36083</td>\n <td>0.44352</td>\n <td>0.45017</td>\n <td>0.291268</td>\n <td>0.295524</td>\n <td>0.339244</td>\n <td>0.804795</td>\n </tr>\n <tr>\n <th>28</th>\n <td>90</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.422268</td>\n <td>0.671307</td>\n <td>0.464924</td>\n <td>0.33906</td>\n <td>0.62542</td>\n <td>0.66076</td>\n <td>0.607500</td>\n <td>0.594646</td>\n <td>0.678452</td>\n <td>0.285224</td>\n </tr>\n <tr>\n <th>29</th>\n <td>93</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.557431</td>\n <td>0.402942</td>\n <td>0.34445</td>\n <td>0.52728</td>\n <td>0.79139</td>\n <td>0.377724</td>\n <td>0.369858</td>\n <td>0.687115</td>\n <td>0.297788</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>188288</th>\n <td>587563</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.836443</td>\n <td>0.482425</td>\n <td>0.414750</td>\n <td>0.67263</td>\n <td>0.51890</td>\n <td>0.60401</td>\n <td>0.464956</td>\n <td>0.454705</td>\n <td>0.407736</td>\n <td>0.675983</td>\n </tr>\n <tr>\n <th>188289</th>\n <td>587564</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.725503</td>\n <td>0.690216</td>\n <td>0.498919</td>\n <td>0.33906</td>\n <td>0.62542</td>\n <td>0.73106</td>\n <td>0.622276</td>\n <td>0.609277</td>\n <td>0.687115</td>\n <td>0.360712</td>\n </tr>\n <tr>\n <th>188290</th>\n <td>587566</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.422268</td>\n <td>0.688705</td>\n <td>0.490407</td>\n <td>0.33906</td>\n <td>0.62542</td>\n <td>0.73106</td>\n <td>0.622276</td>\n <td>0.609277</td>\n <td>0.687115</td>\n <td>0.342155</td>\n </tr>\n <tr>\n <th>188291</th>\n <td>587567</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.499798</td>\n <td>0.808048</td>\n <td>0.694312</td>\n <td>0.94145</td>\n <td>0.64103</td>\n <td>0.80218</td>\n <td>0.745820</td>\n <td>0.753252</td>\n <td>0.717751</td>\n <td>0.216113</td>\n </tr>\n <tr>\n <th>188292</th>\n <td>587569</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.484775</td>\n <td>0.480521</td>\n <td>0.28768</td>\n <td>0.42289</td>\n <td>0.46119</td>\n <td>0.430255</td>\n <td>0.420899</td>\n <td>0.282249</td>\n <td>0.238973</td>\n </tr>\n <tr>\n <th>188293</th>\n <td>587570</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.594196</td>\n <td>0.850938</td>\n <td>0.611159</td>\n <td>0.68823</td>\n <td>0.91644</td>\n <td>0.83510</td>\n <td>0.569745</td>\n <td>0.576121</td>\n <td>0.828258</td>\n <td>0.243950</td>\n </tr>\n <tr>\n <th>188294</th>\n <td>587572</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.197932</td>\n <td>0.314927</td>\n <td>0.41762</td>\n <td>0.26401</td>\n <td>0.23545</td>\n <td>0.207238</td>\n <td>0.204687</td>\n <td>0.271571</td>\n <td>0.813596</td>\n </tr>\n <tr>\n <th>188295</th>\n <td>587573</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.422268</td>\n <td>0.651024</td>\n <td>0.452181</td>\n <td>0.33906</td>\n <td>0.62542</td>\n <td>0.69471</td>\n <td>0.492200</td>\n <td>0.481306</td>\n <td>0.678452</td>\n <td>0.382540</td>\n </tr>\n <tr>\n <th>188296</th>\n <td>587574</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.878617</td>\n <td>0.625784</td>\n <td>0.606340</td>\n <td>0.51256</td>\n <td>0.42084</td>\n <td>0.57172</td>\n <td>0.665172</td>\n <td>0.651918</td>\n <td>0.614594</td>\n <td>0.836524</td>\n </tr>\n <tr>\n <th>188297</th>\n <td>587575</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.448496</td>\n <td>0.735978</td>\n <td>0.36083</td>\n <td>0.40657</td>\n <td>0.40666</td>\n <td>0.776962</td>\n <td>0.800726</td>\n <td>0.287682</td>\n <td>0.804795</td>\n </tr>\n <tr>\n <th>188298</th>\n <td>587578</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.415039</td>\n <td>0.395131</td>\n <td>0.24123</td>\n <td>0.32865</td>\n <td>0.40666</td>\n <td>0.352419</td>\n <td>0.345316</td>\n <td>0.624025</td>\n <td>0.290736</td>\n </tr>\n <tr>\n <th>188299</th>\n <td>587579</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.563226</td>\n <td>0.451570</td>\n <td>0.54829</td>\n <td>0.29618</td>\n <td>0.36974</td>\n <td>0.472726</td>\n <td>0.462286</td>\n <td>0.657761</td>\n <td>0.239309</td>\n </tr>\n <tr>\n <th>188300</th>\n <td>587580</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.850206</td>\n <td>0.835720</td>\n <td>0.794598</td>\n <td>0.53046</td>\n <td>0.50840</td>\n <td>0.67554</td>\n <td>0.742852</td>\n <td>0.729856</td>\n <td>0.663739</td>\n <td>0.804769</td>\n </tr>\n <tr>\n <th>188301</th>\n <td>587584</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.425928</td>\n <td>0.636286</td>\n <td>0.27797</td>\n <td>0.50420</td>\n <td>0.31003</td>\n <td>0.742852</td>\n <td>0.780521</td>\n <td>0.333292</td>\n <td>0.359434</td>\n </tr>\n <tr>\n <th>188302</th>\n <td>587592</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.349083</td>\n <td>0.368005</td>\n <td>0.41762</td>\n <td>0.41675</td>\n <td>0.39068</td>\n <td>0.275431</td>\n <td>0.270746</td>\n <td>0.256038</td>\n <td>0.313505</td>\n </tr>\n <tr>\n <th>188303</th>\n <td>587595</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.806951</td>\n <td>0.555567</td>\n <td>0.74629</td>\n <td>0.93383</td>\n <td>0.78770</td>\n <td>0.757468</td>\n <td>0.772574</td>\n <td>0.812550</td>\n <td>0.843080</td>\n </tr>\n <tr>\n <th>188304</th>\n <td>587601</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>...</td>\n <td>0.895958</td>\n <td>0.437758</td>\n <td>0.535749</td>\n <td>0.54236</td>\n <td>0.47900</td>\n <td>0.51111</td>\n <td>0.705501</td>\n <td>0.692256</td>\n <td>0.357400</td>\n <td>0.283936</td>\n </tr>\n <tr>\n <th>188305</th>\n <td>587602</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.372405</td>\n <td>0.674671</td>\n <td>0.699628</td>\n <td>0.30768</td>\n <td>0.38249</td>\n <td>0.69471</td>\n <td>0.607500</td>\n <td>0.594646</td>\n <td>0.684242</td>\n <td>0.383437</td>\n </tr>\n <tr>\n <th>188306</th>\n <td>587603</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.310061</td>\n <td>0.728484</td>\n <td>0.414750</td>\n <td>0.30260</td>\n <td>0.67135</td>\n <td>0.83510</td>\n <td>0.872013</td>\n <td>0.879347</td>\n <td>0.833874</td>\n <td>0.708475</td>\n </tr>\n <tr>\n <th>188307</th>\n <td>587605</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.850206</td>\n <td>0.599275</td>\n <td>0.548122</td>\n <td>0.48864</td>\n <td>0.45391</td>\n <td>0.64056</td>\n <td>0.592525</td>\n <td>0.590961</td>\n <td>0.701266</td>\n <td>0.362479</td>\n </tr>\n <tr>\n <th>188308</th>\n <td>587606</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.758711</td>\n <td>0.201125</td>\n <td>0.259395</td>\n <td>0.24564</td>\n <td>0.30859</td>\n <td>0.21983</td>\n <td>0.207238</td>\n <td>0.204687</td>\n <td>0.357400</td>\n <td>0.348217</td>\n </tr>\n <tr>\n <th>188309</th>\n <td>587607</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.397069</td>\n <td>0.269520</td>\n <td>0.338963</td>\n <td>0.33906</td>\n <td>0.28066</td>\n <td>0.30529</td>\n <td>0.245410</td>\n <td>0.261799</td>\n <td>0.181433</td>\n <td>0.398571</td>\n </tr>\n <tr>\n <th>188310</th>\n <td>587611</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.302678</td>\n <td>0.186254</td>\n <td>0.317274</td>\n <td>0.27797</td>\n <td>0.32128</td>\n <td>0.24355</td>\n <td>0.180456</td>\n <td>0.178698</td>\n <td>0.304350</td>\n <td>0.381660</td>\n </tr>\n <tr>\n <th>188311</th>\n <td>587612</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.502705</td>\n <td>0.473897</td>\n <td>0.43518</td>\n <td>0.66201</td>\n <td>0.58257</td>\n <td>0.415029</td>\n <td>0.406090</td>\n <td>0.354344</td>\n <td>0.377315</td>\n </tr>\n <tr>\n <th>188312</th>\n <td>587619</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.422268</td>\n <td>0.445008</td>\n <td>0.377930</td>\n <td>0.36636</td>\n <td>0.29095</td>\n <td>0.44467</td>\n <td>0.327915</td>\n <td>0.321570</td>\n <td>0.731059</td>\n <td>0.721499</td>\n </tr>\n <tr>\n <th>188313</th>\n <td>587620</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.939556</td>\n <td>0.242437</td>\n <td>0.289949</td>\n <td>0.24564</td>\n <td>0.30859</td>\n <td>0.32935</td>\n <td>0.223038</td>\n <td>0.220003</td>\n <td>0.333292</td>\n <td>0.208216</td>\n </tr>\n <tr>\n <th>188314</th>\n <td>587624</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.704268</td>\n <td>0.334270</td>\n <td>0.382000</td>\n <td>0.63475</td>\n <td>0.40455</td>\n <td>0.47779</td>\n <td>0.307628</td>\n <td>0.301921</td>\n <td>0.318646</td>\n <td>0.305872</td>\n </tr>\n <tr>\n <th>188315</th>\n <td>587630</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>...</td>\n <td>0.482436</td>\n <td>0.345883</td>\n <td>0.370534</td>\n <td>0.24564</td>\n <td>0.45808</td>\n <td>0.47779</td>\n <td>0.445614</td>\n <td>0.443374</td>\n <td>0.339244</td>\n <td>0.503888</td>\n </tr>\n <tr>\n <th>188316</th>\n <td>587632</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>...</td>\n <td>0.340543</td>\n <td>0.704364</td>\n <td>0.562866</td>\n <td>0.34987</td>\n <td>0.44767</td>\n <td>0.53881</td>\n <td>0.863052</td>\n <td>0.852865</td>\n <td>0.654753</td>\n <td>0.721707</td>\n </tr>\n <tr>\n <th>188317</th>\n <td>587633</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0.281143</td>\n <td>0.844563</td>\n <td>0.533048</td>\n <td>0.97123</td>\n <td>0.93383</td>\n <td>0.83814</td>\n <td>0.932195</td>\n <td>0.946432</td>\n <td>0.810511</td>\n <td>0.721460</td>\n </tr>\n </tbody>\n</table>\n<p>188318 rows \u00d7 131 columns</p>\n</div>",
"text/plain": " id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 \\\n0 1 0 1 0 1 0 0 0 0 1 \n1 2 0 1 0 0 0 0 0 0 1 \n2 5 0 1 0 0 1 0 0 0 1 \n3 10 1 1 0 1 0 0 0 0 1 \n4 11 0 1 0 1 0 0 0 0 1 \n5 13 0 1 0 0 0 0 0 0 1 \n6 14 0 0 0 0 1 0 0 0 0 \n7 20 0 1 0 1 0 0 0 0 1 \n8 23 0 1 1 1 1 0 0 0 1 \n9 24 0 1 0 0 1 1 0 0 1 \n10 25 0 1 0 0 0 0 0 0 1 \n11 33 0 1 0 0 1 0 0 0 1 \n12 34 1 0 0 0 1 0 0 0 0 \n13 41 1 0 0 0 1 1 0 0 0 \n14 47 0 0 0 0 1 0 0 0 0 \n15 48 0 0 0 0 1 1 0 0 0 \n16 49 0 1 1 0 0 0 0 0 1 \n17 51 0 0 0 0 0 1 0 0 0 \n18 52 0 0 1 0 0 1 0 0 0 \n19 55 0 0 0 1 0 0 0 0 0 \n20 57 1 1 0 1 0 0 0 0 1 \n21 60 0 0 0 1 0 1 0 0 0 \n22 61 1 0 0 0 1 1 0 0 0 \n23 66 1 0 0 1 0 0 0 0 0 \n24 73 1 0 0 0 0 0 0 0 0 \n25 76 0 0 0 1 0 0 0 0 0 \n26 86 0 0 0 0 0 1 0 0 0 \n27 89 1 0 0 1 0 0 0 0 0 \n28 90 0 1 0 1 0 1 0 0 1 \n29 93 0 0 0 0 1 0 0 0 0 \n... ... ... ... ... ... ... ... ... ... ... \n188288 587563 0 0 0 1 0 0 0 0 0 \n188289 587564 0 0 0 0 0 1 0 0 0 \n188290 587566 0 0 0 1 0 0 0 0 0 \n188291 587567 1 0 0 0 1 0 0 0 0 \n188292 587569 1 0 0 0 0 1 0 0 0 \n188293 587570 0 0 0 1 0 0 0 0 0 \n188294 587572 0 0 0 1 0 1 0 0 0 \n188295 587573 0 1 0 0 0 0 0 0 1 \n188296 587574 0 0 0 0 1 0 1 0 0 \n188297 587575 0 1 0 0 0 0 0 0 1 \n188298 587578 0 0 1 0 0 1 0 0 0 \n188299 587579 0 0 0 0 1 0 0 0 0 \n188300 587580 0 0 0 1 0 0 0 0 0 \n188301 587584 0 0 0 0 1 0 0 0 0 \n188302 587592 0 0 0 1 1 0 0 0 0 \n188303 587595 0 1 0 1 0 0 0 0 1 \n188304 587601 0 0 0 0 1 0 0 1 0 \n188305 587602 0 0 0 0 0 1 0 0 0 \n188306 587603 0 1 0 0 1 0 0 0 1 \n188307 587605 1 0 0 0 0 1 0 0 0 \n188308 587606 0 0 0 0 1 0 0 0 0 \n188309 587607 0 1 0 1 1 1 0 0 1 \n188310 587611 0 1 0 0 1 0 0 0 1 \n188311 587612 0 0 0 0 1 0 0 0 0 \n188312 587619 0 0 0 0 0 1 0 0 0 \n188313 587620 0 1 0 0 0 0 0 0 1 \n188314 587624 0 0 0 0 0 1 0 0 0 \n188315 587630 0 1 0 0 0 0 0 1 1 \n188316 587632 0 1 0 0 0 0 0 0 1 \n188317 587633 1 0 0 1 0 0 0 0 0 \n\n ... cont5 cont6 cont7 cont8 cont9 cont10 \\\n0 ... 0.310061 0.718367 0.335060 0.30260 0.67135 0.83510 \n1 ... 0.885834 0.438917 0.436585 0.60087 0.35127 0.43919 \n2 ... 0.397069 0.289648 0.315545 0.27320 0.26076 0.32446 \n3 ... 0.422268 0.440945 0.391128 0.31796 0.32128 0.44467 \n4 ... 0.704268 0.178193 0.247408 0.24564 0.22089 0.21230 \n5 ... 0.302678 0.364464 0.401162 0.26847 0.46226 0.50556 \n6 ... 0.295397 0.381515 0.363768 0.24564 0.40455 0.47225 \n7 ... 0.473767 0.867021 0.583389 0.90267 0.84847 0.80218 \n8 ... 0.281143 0.628534 0.384099 0.61229 0.38249 0.51111 \n9 ... 0.310061 0.713343 0.469223 0.30260 0.67135 0.83510 \n10 ... 0.281143 0.429383 0.877905 0.39455 0.53565 0.50556 \n11 ... 0.508483 0.314683 0.370419 0.58354 0.46226 0.38016 \n12 ... 0.302678 0.408772 0.363312 0.32843 0.32128 0.44467 \n13 ... 0.811271 0.241574 0.255339 0.58934 0.32496 0.26029 \n14 ... 0.281143 0.894903 0.586433 0.80058 0.93383 0.78770 \n15 ... 0.281143 0.570733 0.547756 0.80438 0.44352 0.63026 \n16 ... 0.525831 0.411902 0.593548 0.31796 0.38846 0.48889 \n17 ... 0.551723 0.688705 0.437192 0.67263 0.83505 0.59334 \n18 ... 0.281143 0.443265 0.637086 0.36636 0.52938 0.39068 \n19 ... 0.602545 0.436312 0.544355 0.48864 0.36285 0.20496 \n20 ... 0.718531 0.441525 0.437192 0.31796 0.32128 0.44467 \n21 ... 0.534484 0.349885 0.381185 0.81542 0.32311 0.36458 \n22 ... 0.281143 0.183243 0.253560 0.40028 0.21374 0.19431 \n23 ... 0.783230 0.373500 0.381883 0.36083 0.44352 0.45017 \n24 ... 0.543117 0.382070 0.451203 0.33906 0.47900 0.54433 \n25 ... 0.281143 0.592478 0.496452 0.29758 0.46226 0.51111 \n26 ... 0.281143 0.435733 0.769905 0.60087 0.40252 0.28677 \n27 ... 0.889301 0.373500 0.356037 0.36083 0.44352 0.45017 \n28 ... 0.422268 0.671307 0.464924 0.33906 0.62542 0.66076 \n29 ... 0.281143 0.557431 0.402942 0.34445 0.52728 0.79139 \n... ... ... ... ... ... ... ... \n188288 ... 0.836443 0.482425 0.414750 0.67263 0.51890 0.60401 \n188289 ... 0.725503 0.690216 0.498919 0.33906 0.62542 0.73106 \n188290 ... 0.422268 0.688705 0.490407 0.33906 0.62542 0.73106 \n188291 ... 0.499798 0.808048 0.694312 0.94145 0.64103 0.80218 \n188292 ... 0.281143 0.484775 0.480521 0.28768 0.42289 0.46119 \n188293 ... 0.594196 0.850938 0.611159 0.68823 0.91644 0.83510 \n188294 ... 0.281143 0.197932 0.314927 0.41762 0.26401 0.23545 \n188295 ... 0.422268 0.651024 0.452181 0.33906 0.62542 0.69471 \n188296 ... 0.878617 0.625784 0.606340 0.51256 0.42084 0.57172 \n188297 ... 0.281143 0.448496 0.735978 0.36083 0.40657 0.40666 \n188298 ... 0.281143 0.415039 0.395131 0.24123 0.32865 0.40666 \n188299 ... 0.281143 0.563226 0.451570 0.54829 0.29618 0.36974 \n188300 ... 0.850206 0.835720 0.794598 0.53046 0.50840 0.67554 \n188301 ... 0.281143 0.425928 0.636286 0.27797 0.50420 0.31003 \n188302 ... 0.281143 0.349083 0.368005 0.41762 0.41675 0.39068 \n188303 ... 0.281143 0.806951 0.555567 0.74629 0.93383 0.78770 \n188304 ... 0.895958 0.437758 0.535749 0.54236 0.47900 0.51111 \n188305 ... 0.372405 0.674671 0.699628 0.30768 0.38249 0.69471 \n188306 ... 0.310061 0.728484 0.414750 0.30260 0.67135 0.83510 \n188307 ... 0.850206 0.599275 0.548122 0.48864 0.45391 0.64056 \n188308 ... 0.758711 0.201125 0.259395 0.24564 0.30859 0.21983 \n188309 ... 0.397069 0.269520 0.338963 0.33906 0.28066 0.30529 \n188310 ... 0.302678 0.186254 0.317274 0.27797 0.32128 0.24355 \n188311 ... 0.281143 0.502705 0.473897 0.43518 0.66201 0.58257 \n188312 ... 0.422268 0.445008 0.377930 0.36636 0.29095 0.44467 \n188313 ... 0.939556 0.242437 0.289949 0.24564 0.30859 0.32935 \n188314 ... 0.704268 0.334270 0.382000 0.63475 0.40455 0.47779 \n188315 ... 0.482436 0.345883 0.370534 0.24564 0.45808 0.47779 \n188316 ... 0.340543 0.704364 0.562866 0.34987 0.44767 0.53881 \n188317 ... 0.281143 0.844563 0.533048 0.97123 0.93383 0.83814 \n\n cont11 cont12 cont13 cont14 \n0 0.569745 0.594646 0.822493 0.714843 \n1 0.338312 0.366307 0.611431 0.304496 \n2 0.381398 0.373424 0.195709 0.774425 \n3 0.327915 0.321570 0.605077 0.602642 \n4 0.204687 0.202213 0.246011 0.432606 \n5 0.366788 0.359249 0.345247 0.726792 \n6 0.334828 0.352251 0.342239 0.382931 \n7 0.644013 0.785706 0.859764 0.242416 \n8 0.682315 0.669033 0.756454 0.361191 \n9 0.863052 0.879347 0.822493 0.294523 \n10 0.550529 0.538473 0.336261 0.715009 \n11 0.644013 0.665644 0.339244 0.799124 \n12 0.327915 0.321570 0.605077 0.818358 \n13 0.257148 0.253044 0.276878 0.477578 \n14 0.880469 0.871011 0.822493 0.251278 \n15 0.385085 0.377003 0.516660 0.340325 \n16 0.457203 0.447145 0.301535 0.205651 \n17 0.678924 0.665644 0.684242 0.407411 \n18 0.678924 0.665644 0.304350 0.310796 \n19 0.388786 0.406090 0.648701 0.830931 \n20 0.377724 0.369858 0.605077 0.743810 \n21 0.453334 0.454705 0.651733 0.354002 \n22 0.167024 0.165648 0.404520 0.725941 \n23 0.338312 0.366307 0.339244 0.793518 \n24 0.812519 0.800726 0.246011 0.215055 \n25 0.434083 0.424625 0.357400 0.311644 \n26 0.550529 0.538473 0.298734 0.698006 \n27 0.291268 0.295524 0.339244 0.804795 \n28 0.607500 0.594646 0.678452 0.285224 \n29 0.377724 0.369858 0.687115 0.297788 \n... ... ... ... ... \n188288 0.464956 0.454705 0.407736 0.675983 \n188289 0.622276 0.609277 0.687115 0.360712 \n188290 0.622276 0.609277 0.687115 0.342155 \n188291 0.745820 0.753252 0.717751 0.216113 \n188292 0.430255 0.420899 0.282249 0.238973 \n188293 0.569745 0.576121 0.828258 0.243950 \n188294 0.207238 0.204687 0.271571 0.813596 \n188295 0.492200 0.481306 0.678452 0.382540 \n188296 0.665172 0.651918 0.614594 0.836524 \n188297 0.776962 0.800726 0.287682 0.804795 \n188298 0.352419 0.345316 0.624025 0.290736 \n188299 0.472726 0.462286 0.657761 0.239309 \n188300 0.742852 0.729856 0.663739 0.804769 \n188301 0.742852 0.780521 0.333292 0.359434 \n188302 0.275431 0.270746 0.256038 0.313505 \n188303 0.757468 0.772574 0.812550 0.843080 \n188304 0.705501 0.692256 0.357400 0.283936 \n188305 0.607500 0.594646 0.684242 0.383437 \n188306 0.872013 0.879347 0.833874 0.708475 \n188307 0.592525 0.590961 0.701266 0.362479 \n188308 0.207238 0.204687 0.357400 0.348217 \n188309 0.245410 0.261799 0.181433 0.398571 \n188310 0.180456 0.178698 0.304350 0.381660 \n188311 0.415029 0.406090 0.354344 0.377315 \n188312 0.327915 0.321570 0.731059 0.721499 \n188313 0.223038 0.220003 0.333292 0.208216 \n188314 0.307628 0.301921 0.318646 0.305872 \n188315 0.445614 0.443374 0.339244 0.503888 \n188316 0.863052 0.852865 0.654753 0.721707 \n188317 0.932195 0.946432 0.810511 0.721460 \n\n[188318 rows x 131 columns]"
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_loss = train.loss\n",
"train.drop(\"loss\",axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"_cell_guid": "dc63e318-63c9-113d-8f98-3e58d9c6da67"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(train,train_loss, test_size=0.3, random_state=42)\n"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"_cell_guid": "382e6324-369c-aacb-a251-a84c2219739d"
},
"outputs": [],
"source": [
"from xgboost import XGBClassifier\n",
"xgb = XGBClassifier()\n",
"xgb.fit(X_train, y_train)\n",
"scores = cross_val_score(xgb,X_test,y_test)\n",
"accuracies.append(scores.mean())\n",
"scores.mean()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "2ed67445-02ad-1e1f-41ec-b4a6451d39ca"
},
"outputs": [],
"source": ""
}
],
"metadata": {
"_change_revision": 421,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | 0001/160/1160509.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "3ede82eb-e79a-3ca3-bc36-25fc3ed2af65"
},
"outputs": [],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"\n",
"import tensorflow as tf\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "662ee0fb-96a2-2c2f-2684-f6450228880f"
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>pixel0</th>\n",
" <th>pixel1</th>\n",
" <th>pixel2</th>\n",
" <th>pixel3</th>\n",
" <th>pixel4</th>\n",
" <th>pixel5</th>\n",
" <th>pixel6</th>\n",
" <th>pixel7</th>\n",
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" <tr>\n",
" <th>count</th>\n",
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" <td>42000.0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.456643</td>\n",
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" <th>std</th>\n",
" <td>2.887730</td>\n",
" <td>0.0</td>\n",
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" <td>3.274488</td>\n",
" <td>1.75987</td>\n",
" <td>1.894498</td>\n",
" <td>0.414264</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>4.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.0</td>\n",
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" <td>254.000000</td>\n",
" <td>254.000000</td>\n",
" <td>253.000000</td>\n",
" <td>253.00000</td>\n",
" <td>254.000000</td>\n",
" <td>62.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 785 columns</p>\n",
"</div>"
],
"text/plain": [
" label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 \\\n",
"count 42000.000000 42000.0 42000.0 42000.0 42000.0 42000.0 42000.0 \n",
"mean 4.456643 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"std 2.887730 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"min 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"25% 2.000000 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"50% 4.000000 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"75% 7.000000 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"max 9.000000 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
" pixel6 pixel7 pixel8 ... pixel774 pixel775 \\\n",
"count 42000.0 42000.0 42000.0 ... 42000.000000 42000.000000 \n",
"mean 0.0 0.0 0.0 ... 0.219286 0.117095 \n",
"std 0.0 0.0 0.0 ... 6.312890 4.633819 \n",
"min 0.0 0.0 0.0 ... 0.000000 0.000000 \n",
"25% 0.0 0.0 0.0 ... 0.000000 0.000000 \n",
"50% 0.0 0.0 0.0 ... 0.000000 0.000000 \n",
"75% 0.0 0.0 0.0 ... 0.000000 0.000000 \n",
"max 0.0 0.0 0.0 ... 254.000000 254.000000 \n",
"\n",
" pixel776 pixel777 pixel778 pixel779 pixel780 \\\n",
"count 42000.000000 42000.00000 42000.000000 42000.000000 42000.0 \n",
"mean 0.059024 0.02019 0.017238 0.002857 0.0 \n",
"std 3.274488 1.75987 1.894498 0.414264 0.0 \n",
"min 0.000000 0.00000 0.000000 0.000000 0.0 \n",
"25% 0.000000 0.00000 0.000000 0.000000 0.0 \n",
"50% 0.000000 0.00000 0.000000 0.000000 0.0 \n",
"75% 0.000000 0.00000 0.000000 0.000000 0.0 \n",
"max 253.000000 253.00000 254.000000 62.000000 0.0 \n",
"\n",
" pixel781 pixel782 pixel783 \n",
"count 42000.0 42000.0 42000.0 \n",
"mean 0.0 0.0 0.0 \n",
"std 0.0 0.0 0.0 \n",
"min 0.0 0.0 0.0 \n",
"25% 0.0 0.0 0.0 \n",
"50% 0.0 0.0 0.0 \n",
"75% 0.0 0.0 0.0 \n",
"max 0.0 0.0 0.0 \n",
"\n",
"[8 rows x 785 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = pd.read_csv(\"../input/train.csv\")\n",
"test = pd.read_csv(\"../input/test.csv\")\n",
"train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "2700dfe7-6d20-8c70-c0fc-b851c3a72636"
},
"outputs": [],
"source": [
"LEARNING_RATE = 1e-3\n",
"TRAIN_ITERATION = 100\n",
"BATCH_SIZE = 50\n",
"VALIDATION_SIZE = 2000\n",
"IMAGE_TO_DISPLAY = 10"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "ad965425-f3f2-3101-3062-23ad1adf7746"
},
"outputs": [],
"source": [
"imgs = train.iloc[:, 1:].values\n",
"imgs = imgs.astype(np.float)\n",
"imgs = np.multiply(imgs, 1. / 255.)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "d76ee44c-fc54-778f-b098-4c929e83f6e6"
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f02275b1780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def display(img):\n",
" h = np.ceil(np.sqrt(img.shape[0])).astype(np.uint8)\n",
" w = np.ceil(img.shape[0]/h).astype(np.uint8)\n",
" tmp_img = img.reshape(w, h)\n",
" plt.axis(\"off\")\n",
" plt.imshow(tmp_img, cmap=cm.binary)\n",
"display(imgs[6])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "af62f261-5332-fa08-ea4c-2ba647144170"
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 1, ..., 7, 6, 9])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"labels = train.iloc[:, 0].values.ravel()\n",
"labels"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "3c6ccdbc-3c96-895b-57d6-f951fc8ad987"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 0 1 4 0]\n"
]
},
{
"ename": "NameError",
"evalue": "name 'onehot' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-3b4921e93139>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0monehot_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdense_to_onehot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0monehot\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'onehot' is not defined"
]
}
],
"source": [
"def dense_to_onehot(dense):\n",
" count = dense.shape[0]\n",
" onehot = np.zeros((count, 10))\n",
" index = np.arange(count) * 10\n",
" onehot.flat[index + dense] = 1\n",
" return onehot\n",
"\n",
"onehot_labels = dense_to_onehot(labels)\n",
"print(labels[:5])\n",
"print(onehot[:5])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "38ab5738-a32b-3c68-358e-b7ed93254012"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'onehot_label' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-1ccd64a9d852>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mvalid_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0monehot_labels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mVALIDATION_SIZE\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mtrain_imgs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mVALIDATION_SIZE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mtrain_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0monehot_label\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mVALIDATION_SIZE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_imgs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'onehot_label' is not defined"
]
}
],
"source": [
"valid_imgs = imgs[:VALIDATION_SIZE]\n",
"valid_labels = onehot_labels[:VALIDATION_SIZE]\n",
"train_imgs = imgs[VALIDATION_SIZE:]\n",
"train_labels = onehot_label[VALIDATION_SIZE:]\n",
"\n",
"print(train_imgs.shape)\n",
"print(valid_imgs.shape)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "073327a4-2bf8-a266-e457-951e7f03fbc8"
},
"outputs": [],
"source": [
"def weight_variable(shape):\n",
" init = tf.truncated_normal(shape, stddev=.1)\n",
" return tf.Variable(init)\n",
"def bias_variable(shape):\n",
" init = tf.constant(0.1, shape=shape)\n",
" return tf.Variable(init)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "fec96198-8ec6-3fd7-d966-3dc5f15ec688"
},
"outputs": [],
"source": [
"def conv2d(x, W):\n",
" return tf.nn.conv2d(x, W, [1,1,1,1], padding='SAME')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "1db6b942-1b3d-d277-86c2-454e2ac0bf7e"
},
"outputs": [],
"source": [
"def max_pool_2x2(x):\n",
" return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "66cf1b44-56a5-dd5b-3a8e-4d51de001283"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 16,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160513.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "0df751aa-916f-2e65-d6b8-261a658b04a7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loan payments data.csv\n",
"\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "849117ba-7194-4ee1-7e12-04dba2786cf4"
},
"outputs": [
{
"data": {
"text/plain": [
"(500, 11)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"df = pd.read_csv('../input/Loan payments data.csv')\n",
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "f37ef311-037a-2792-92e6-bd75d985357f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Principal</th>\n",
" <th>terms</th>\n",
" <th>past_due_days</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>500.000000</td>\n",
" <td>500.000000</td>\n",
" <td>200.00000</td>\n",
" <td>500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>943.200000</td>\n",
" <td>22.824000</td>\n",
" <td>36.01000</td>\n",
" <td>31.116000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>115.240274</td>\n",
" <td>8.000064</td>\n",
" <td>29.38088</td>\n",
" <td>6.084784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>300.000000</td>\n",
" <td>7.000000</td>\n",
" <td>1.00000</td>\n",
" <td>18.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1000.000000</td>\n",
" <td>15.000000</td>\n",
" <td>3.00000</td>\n",
" <td>27.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1000.000000</td>\n",
" <td>30.000000</td>\n",
" <td>37.00000</td>\n",
" <td>30.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1000.000000</td>\n",
" <td>30.000000</td>\n",
" <td>60.00000</td>\n",
" <td>35.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1000.000000</td>\n",
" <td>30.000000</td>\n",
" <td>76.00000</td>\n",
" <td>51.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Principal terms past_due_days age\n",
"count 500.000000 500.000000 200.00000 500.000000\n",
"mean 943.200000 22.824000 36.01000 31.116000\n",
"std 115.240274 8.000064 29.38088 6.084784\n",
"min 300.000000 7.000000 1.00000 18.000000\n",
"25% 1000.000000 15.000000 3.00000 27.000000\n",
"50% 1000.000000 30.000000 37.00000 30.000000\n",
"75% 1000.000000 30.000000 60.00000 35.000000\n",
"max 1000.000000 30.000000 76.00000 51.000000"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "9ca7e1ae-fd68-06f2-701a-981e6381c192"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Loan_ID</th>\n",
" <td>xqd20166231</td>\n",
" <td>xqd20168902</td>\n",
" <td>xqd20160003</td>\n",
" <td>xqd20160004</td>\n",
" <td>xqd20160005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>loan_status</th>\n",
" <td>PAIDOFF</td>\n",
" <td>PAIDOFF</td>\n",
" <td>PAIDOFF</td>\n",
" <td>PAIDOFF</td>\n",
" <td>PAIDOFF</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Principal</th>\n",
" <td>1000</td>\n",
" <td>1000</td>\n",
" <td>1000</td>\n",
" <td>1000</td>\n",
" <td>1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>terms</th>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>30</td>\n",
" <td>15</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>effective_date</th>\n",
" <td>9/8/2016</td>\n",
" <td>9/8/2016</td>\n",
" <td>9/8/2016</td>\n",
" <td>9/8/2016</td>\n",
" <td>9/9/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>due_date</th>\n",
" <td>10/7/2016</td>\n",
" <td>10/7/2016</td>\n",
" <td>10/7/2016</td>\n",
" <td>9/22/2016</td>\n",
" <td>10/8/2016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>paid_off_time</th>\n",
" <td>9/14/2016 19:31</td>\n",
" <td>10/7/2016 9:00</td>\n",
" <td>9/25/2016 16:58</td>\n",
" <td>9/22/2016 20:00</td>\n",
" <td>9/23/2016 21:36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>past_due_days</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>age</th>\n",
" <td>45</td>\n",
" <td>50</td>\n",
" <td>33</td>\n",
" <td>27</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>education</th>\n",
" <td>High School or Below</td>\n",
" <td>Bechalor</td>\n",
" <td>Bechalor</td>\n",
" <td>college</td>\n",
" <td>college</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gender</th>\n",
" <td>male</td>\n",
" <td>female</td>\n",
" <td>female</td>\n",
" <td>male</td>\n",
" <td>female</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 1 2 \\\n",
"Loan_ID xqd20166231 xqd20168902 xqd20160003 \n",
"loan_status PAIDOFF PAIDOFF PAIDOFF \n",
"Principal 1000 1000 1000 \n",
"terms 30 30 30 \n",
"effective_date 9/8/2016 9/8/2016 9/8/2016 \n",
"due_date 10/7/2016 10/7/2016 10/7/2016 \n",
"paid_off_time 9/14/2016 19:31 10/7/2016 9:00 9/25/2016 16:58 \n",
"past_due_days NaN NaN NaN \n",
"age 45 50 33 \n",
"education High School or Below Bechalor Bechalor \n",
"Gender male female female \n",
"\n",
" 3 4 \n",
"Loan_ID xqd20160004 xqd20160005 \n",
"loan_status PAIDOFF PAIDOFF \n",
"Principal 1000 1000 \n",
"terms 15 30 \n",
"effective_date 9/8/2016 9/9/2016 \n",
"due_date 9/22/2016 10/8/2016 \n",
"paid_off_time 9/22/2016 20:00 9/23/2016 21:36 \n",
"past_due_days NaN NaN \n",
"age 27 28 \n",
"education college college \n",
"Gender male female "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(5).T"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "26ec25b1-ea3c-cfd0-0f07-2f5d8179543a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 500 entries, 0 to 499\n",
"Data columns (total 11 columns):\n",
"Loan_ID 500 non-null object\n",
"loan_status 500 non-null object\n",
"Principal 500 non-null int64\n",
"terms 500 non-null int64\n",
"effective_date 500 non-null object\n",
"due_date 500 non-null object\n",
"paid_off_time 400 non-null object\n",
"past_due_days 200 non-null float64\n",
"age 500 non-null int64\n",
"education 500 non-null object\n",
"Gender 500 non-null object\n",
"dtypes: float64(1), int64(3), object(7)\n",
"memory usage: 43.0+ KB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "e9dfb030-0685-14ee-45aa-cf437687e5e0"
},
"outputs": [
{
"data": {
"text/plain": [
"Loan_ID 0\n",
"loan_status 0\n",
"Principal 0\n",
"terms 0\n",
"effective_date 0\n",
"due_date 0\n",
"paid_off_time 100\n",
"past_due_days 300\n",
"age 0\n",
"education 0\n",
"Gender 0\n",
"dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "6963b9c6-eebd-f15c-113f-c3eda28e7e2b"
},
"outputs": [
{
"data": {
"text/plain": [
"PAIDOFF 300\n",
"COLLECTION_PAIDOFF 100\n",
"COLLECTION 100\n",
"Name: loan_status, dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['loan_status'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "f36340a5-c99b-b414-c18d-baa34679996b"
},
"outputs": [
{
"data": {
"text/plain": [
"male 423\n",
"female 77\n",
"Name: Gender, dtype: int64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Gender'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "f13fb61d-1816-4477-6734-7449397cec6e"
},
"outputs": [
{
"data": {
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CyRWnn71n5ekfRHfo2x1NewQiIgWnRCAiUnBKBCIiBadEICJScEoEIiIFp6uGurH2rqyA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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff15eb7cac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x=\"past_due_days\", data=df, palette=\"muted\");"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "63a51666-ed04-4f4b-28d2-ab5bc3262306"
},
"outputs": [
{
"data": {
"image/png": 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24N7MnAN8Dnj9EOs7ndLtMRe4Bjg6InajdFXMAfYAjq9v1oNZZ/k6/SXAvoMEwWZ1X9wA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\nG8XwyN5jsBEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7ff15ea847b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x=\"education\", data=df, palette=\"dark\");"
]
}
],
"metadata": {
"_change_revision": 224,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160611.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "26d3757c-4579-81ca-abaa-23c3259aecb9"
},
"source": [
"Understand NSE data. ***Upvote if you find it useful***"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "58082d0a-b829-dc50-e664-8a9a5d621ae4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"banknifty.csv\n",
"nifty50.csv\n",
"\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "2299f6a4-9680-fbf5-ae76-c06cbd84928d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>2017-05-15 09:16:00</td>\n",
" <td>5701.15</td>\n",
" <td>5704.65</td>\n",
" <td>5694.30</td>\n",
" <td>5697.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>2017-05-15 09:17:00</td>\n",
" <td>5697.05</td>\n",
" <td>5698.35</td>\n",
" <td>5695.65</td>\n",
" <td>5697.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>2017-05-15 09:18:00</td>\n",
" <td>5697.90</td>\n",
" <td>5697.90</td>\n",
" <td>5690.60</td>\n",
" <td>5692.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>2017-05-15 09:19:00</td>\n",
" <td>5691.65</td>\n",
" <td>5694.70</td>\n",
" <td>5691.65</td>\n",
" <td>5693.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NIFTY</td>\n",
" <td>2013-04-01</td>\n",
" <td>2017-05-15 09:20:00</td>\n",
" <td>5694.40</td>\n",
" <td>5695.05</td>\n",
" <td>5693.35</td>\n",
" <td>5694.55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index date time open high low close\n",
"0 NIFTY 2013-04-01 2017-05-15 09:16:00 5701.15 5704.65 5694.30 5697.00\n",
"1 NIFTY 2013-04-01 2017-05-15 09:17:00 5697.05 5698.35 5695.65 5697.50\n",
"2 NIFTY 2013-04-01 2017-05-15 09:18:00 5697.90 5697.90 5690.60 5692.15\n",
"3 NIFTY 2013-04-01 2017-05-15 09:19:00 5691.65 5694.70 5691.65 5693.90\n",
"4 NIFTY 2013-04-01 2017-05-15 09:20:00 5694.40 5695.05 5693.35 5694.55"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty = pd.read_csv(\"../input/banknifty.csv\",parse_dates=['date','time'])\n",
"nifty50 = pd.read_csv(\"../input/nifty50.csv\",parse_dates=['date','time'])\n",
"nifty50.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c188b768-7647-b926-99a3-1da8bc504740"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>2017-05-15 09:16:00</td>\n",
" <td>12125.70</td>\n",
" <td>12161.70</td>\n",
" <td>12125.70</td>\n",
" <td>12160.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>2017-05-15 09:17:00</td>\n",
" <td>12161.75</td>\n",
" <td>12164.80</td>\n",
" <td>12130.40</td>\n",
" <td>12130.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>2017-05-15 09:18:00</td>\n",
" <td>12126.85</td>\n",
" <td>12156.10</td>\n",
" <td>12126.85</td>\n",
" <td>12156.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>2017-05-15 09:19:00</td>\n",
" <td>12157.25</td>\n",
" <td>12164.75</td>\n",
" <td>12151.60</td>\n",
" <td>12164.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>BANKNIFTY</td>\n",
" <td>2012-12-03</td>\n",
" <td>2017-05-15 09:20:00</td>\n",
" <td>12162.80</td>\n",
" <td>12162.80</td>\n",
" <td>12148.20</td>\n",
" <td>12151.15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index date time open high low \\\n",
"0 BANKNIFTY 2012-12-03 2017-05-15 09:16:00 12125.70 12161.70 12125.70 \n",
"1 BANKNIFTY 2012-12-03 2017-05-15 09:17:00 12161.75 12164.80 12130.40 \n",
"2 BANKNIFTY 2012-12-03 2017-05-15 09:18:00 12126.85 12156.10 12126.85 \n",
"3 BANKNIFTY 2012-12-03 2017-05-15 09:19:00 12157.25 12164.75 12151.60 \n",
"4 BANKNIFTY 2012-12-03 2017-05-15 09:20:00 12162.80 12162.80 12148.20 \n",
"\n",
" close \n",
"0 12160.95 \n",
"1 12130.40 \n",
"2 12156.10 \n",
"3 12164.20 \n",
"4 12151.15 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "b886e768-7f13-8240-ec50-ab622477f3cb"
},
"outputs": [],
"source": [
"banknifty = banknifty.drop(['index'],axis=1)\n",
"nifty50 = nifty50.drop(['index'],axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "97b33c5f-adfd-9c1e-4156-a9e79ce6f739"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>352920.000000</td>\n",
" <td>352920.000000</td>\n",
" <td>352920.000000</td>\n",
" <td>352920.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>7374.515649</td>\n",
" <td>7376.077510</td>\n",
" <td>7372.936147</td>\n",
" <td>7374.498883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1070.893139</td>\n",
" <td>1071.032665</td>\n",
" <td>1070.756739</td>\n",
" <td>1070.885970</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>5126.150000</td>\n",
" <td>5127.250000</td>\n",
" <td>5118.850000</td>\n",
" <td>5126.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6167.650000</td>\n",
" <td>6168.900000</td>\n",
" <td>6166.550000</td>\n",
" <td>6167.700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7738.800000</td>\n",
" <td>7740.150000</td>\n",
" <td>7737.450000</td>\n",
" <td>7738.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>8298.762500</td>\n",
" <td>8300.900000</td>\n",
" <td>8297.050000</td>\n",
" <td>8298.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>9115.400000</td>\n",
" <td>9119.200000</td>\n",
" <td>9105.650000</td>\n",
" <td>9106.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"count 352920.000000 352920.000000 352920.000000 352920.000000\n",
"mean 7374.515649 7376.077510 7372.936147 7374.498883\n",
"std 1070.893139 1071.032665 1070.756739 1070.885970\n",
"min 5126.150000 5127.250000 5118.850000 5126.300000\n",
"25% 6167.650000 6168.900000 6166.550000 6167.700000\n",
"50% 7738.800000 7740.150000 7737.450000 7738.800000\n",
"75% 8298.762500 8300.900000 8297.050000 8298.750000\n",
"max 9115.400000 9119.200000 9105.650000 9106.500000"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50.describe()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "54bda9c2-925c-b517-9053-477168dd7e3c"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>240137</th>\n",
" <td>2015-03-04</td>\n",
" <td>2017-05-15 09:16:00</td>\n",
" <td>9115.4</td>\n",
" <td>9119.2</td>\n",
" <td>9105.65</td>\n",
" <td>9106.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"240137 2015-03-04 2017-05-15 09:16:00 9115.4 9119.2 9105.65 9106.5"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50.loc[nifty50['high']==9119.2]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "8d2d1d0b-8750-0907-87a0-4f5c7eaf2d71"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>13949</th>\n",
" <td>2013-08-28</td>\n",
" <td>2017-05-15 10:40:00</td>\n",
" <td>5127.25</td>\n",
" <td>5127.25</td>\n",
" <td>5122.7</td>\n",
" <td>5126.3</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"13949 2013-08-28 2017-05-15 10:40:00 5127.25 5127.25 5122.7 5126.3"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50.loc[nifty50['high']==5127.25]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "ec58aecb-3585-1dfb-6687-99352c6905d7"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-01-01</th>\n",
" <td>5950.678267</td>\n",
" <td>5951.491067</td>\n",
" <td>5949.986667</td>\n",
" <td>5950.689733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-02</th>\n",
" <td>5995.653733</td>\n",
" <td>5996.420267</td>\n",
" <td>5994.909200</td>\n",
" <td>5995.663733</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-03</th>\n",
" <td>6003.101600</td>\n",
" <td>6003.903733</td>\n",
" <td>6002.381333</td>\n",
" <td>6003.124267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-04</th>\n",
" <td>5994.905733</td>\n",
" <td>5995.648667</td>\n",
" <td>5994.187467</td>\n",
" <td>5994.906800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-01-07</th>\n",
" <td>6011.653067</td>\n",
" <td>6012.265200</td>\n",
" <td>6010.813600</td>\n",
" <td>6011.506400</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"date \n",
"2013-01-01 5950.678267 5951.491067 5949.986667 5950.689733\n",
"2013-01-02 5995.653733 5996.420267 5994.909200 5995.663733\n",
"2013-01-03 6003.101600 6003.903733 6002.381333 6003.124267\n",
"2013-01-04 5994.905733 5995.648667 5994.187467 5994.906800\n",
"2013-01-07 6011.653067 6012.265200 6010.813600 6011.506400"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50_mean = nifty50.groupby('date').mean()\n",
"nifty50_mean.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "7728742c-70a6-7eb6-5762-925f642edb34"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe44737bd30>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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u+UbPUXd7xth7OaJOYfoUd46BFuqfv1O9lTFIDofDrFy5koaGBgzD4JZbbuH5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\ni4iIiIh0oCJZRERERKSD/x/tz9/osUZF8AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe44736e7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nifty50_mean.plot(figsize=(12,8))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "22786826-f1bf-3b7e-1f72-f351987a7687"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>367575.000000</td>\n",
" <td>367575.000000</td>\n",
" <td>367575.000000</td>\n",
" <td>367575.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>15078.023296</td>\n",
" <td>15082.498465</td>\n",
" <td>15073.480983</td>\n",
" <td>15077.993028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3184.438089</td>\n",
" <td>3185.213591</td>\n",
" <td>3183.628315</td>\n",
" <td>3184.411825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1405.050000</td>\n",
" <td>1407.050000</td>\n",
" <td>1404.600000</td>\n",
" <td>1405.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>12092.200000</td>\n",
" <td>12095.000000</td>\n",
" <td>12089.150000</td>\n",
" <td>12092.175000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>15526.100000</td>\n",
" <td>15531.200000</td>\n",
" <td>15521.400000</td>\n",
" <td>15525.950000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>17956.050000</td>\n",
" <td>17960.550000</td>\n",
" <td>17951.100000</td>\n",
" <td>17955.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>20903.950000</td>\n",
" <td>20907.550000</td>\n",
" <td>20899.250000</td>\n",
" <td>20907.550000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"count 367575.000000 367575.000000 367575.000000 367575.000000\n",
"mean 15078.023296 15082.498465 15073.480983 15077.993028\n",
"std 3184.438089 3185.213591 3183.628315 3184.411825\n",
"min 1405.050000 1407.050000 1404.600000 1405.200000\n",
"25% 12092.200000 12095.000000 12089.150000 12092.175000\n",
"50% 15526.100000 15531.200000 15521.400000 15525.950000\n",
"75% 17956.050000 17960.550000 17951.100000 17955.800000\n",
"max 20903.950000 20907.550000 20899.250000 20907.550000"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.describe()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "5ce342a1-9275-85d7-6f82-0c9371726544"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>236193</th>\n",
" <td>2015-01-28</td>\n",
" <td>2017-05-15 12:35:00</td>\n",
" <td>20902.15</td>\n",
" <td>20907.55</td>\n",
" <td>20894.35</td>\n",
" <td>20907.55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"236193 2015-01-28 2017-05-15 12:35:00 20902.15 20907.55 20894.35 20907.55"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.loc[banknifty['high']==20907.550]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "89a6752a-c26c-892c-bb94-936f831858f2"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>252479</th>\n",
" <td>2015-06-24</td>\n",
" <td>2017-05-15 15:19:00</td>\n",
" <td>1405.05</td>\n",
" <td>1407.05</td>\n",
" <td>1404.6</td>\n",
" <td>1406.25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"252479 2015-06-24 2017-05-15 15:19:00 1405.05 1407.05 1404.6 1406.25"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.loc[banknifty['high']==1407.050]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "b5239a29-b4d5-55ba-155d-8872316fea86"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2012-11-01</th>\n",
" <td>11305.950533</td>\n",
" <td>11308.328400</td>\n",
" <td>11303.584000</td>\n",
" <td>11306.027600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-02</th>\n",
" <td>11429.929733</td>\n",
" <td>11432.193733</td>\n",
" <td>11427.817200</td>\n",
" <td>11429.955600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-05</th>\n",
" <td>11451.321867</td>\n",
" <td>11453.444667</td>\n",
" <td>11449.234667</td>\n",
" <td>11451.354533</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-06</th>\n",
" <td>11494.561733</td>\n",
" <td>11496.849600</td>\n",
" <td>11492.694133</td>\n",
" <td>11494.892667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-11-07</th>\n",
" <td>11666.770800</td>\n",
" <td>11669.293467</td>\n",
" <td>11664.445067</td>\n",
" <td>11666.936533</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" open high low close\n",
"date \n",
"2012-11-01 11305.950533 11308.328400 11303.584000 11306.027600\n",
"2012-11-02 11429.929733 11432.193733 11427.817200 11429.955600\n",
"2012-11-05 11451.321867 11453.444667 11449.234667 11451.354533\n",
"2012-11-06 11494.561733 11496.849600 11492.694133 11494.892667\n",
"2012-11-07 11666.770800 11669.293467 11664.445067 11666.936533"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty_mean = banknifty.groupby('date').mean()\n",
"banknifty_mean.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "7f0677b3-d5fb-8fe2-e8d4-a8555542b17c"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe44736eef0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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NGzczY6YbEL/6+AogPmKeketOVckelE31bqgrMTAtE12Lf4w6jkPZ5noczU/Y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\nAAAmUZ4BAAAAkyjPAAAAgEn/B2ot657lxrjKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe446d26da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"banknifty_mean.plot(figsize=(12,8))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "727dbe8b-eb29-aacb-06c3-fe8cd17003f6"
},
"source": [
"There is 2 huge **drop** in year 2015. We will later on try to find the details of incident."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "29bda076-b30a-6eca-d699-78a87d0a289b"
},
"outputs": [
{
"data": {
"text/plain": [
"137.55000000000018"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maximum_drop_nifty50 = max(nifty50['open'] - nifty50['low'])\n",
"maximum_drop_nifty50"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "0140ba7d-0f47-4db1-42a8-b8a5578ff078"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>162096</th>\n",
" <td>2014-05-30</td>\n",
" <td>2017-05-15 09:16:00</td>\n",
" <td>7256.0</td>\n",
" <td>7258.15</td>\n",
" <td>7118.45</td>\n",
" <td>7254.55</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"162096 2014-05-30 2017-05-15 09:16:00 7256.0 7258.15 7118.45 7254.55"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nifty50.loc[(nifty50['open'] - nifty50['low']) == maximum_drop_nifty50]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d71a58ec-7038-4e0c-2f91-eb77e3a99200"
},
"source": [
"The day nifty50 **dipped** huge is 2014-05-30\t"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "8b683cfb-8989-d67e-4367-2d1f9b320fe0"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>158346</th>\n",
" <td>2014-05-16</td>\n",
" <td>2017-05-15 09:16:00</td>\n",
" <td>7273.55</td>\n",
" <td>7433.2</td>\n",
" <td>7264.4</td>\n",
" <td>7417.65</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"158346 2014-05-16 2017-05-15 09:16:00 7273.55 7433.2 7264.4 7417.65"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maximum_up_nifty50 = max(nifty50['high'] - nifty50['open'])\n",
"nifty50.loc[(nifty50['high'] - nifty50['open']) == maximum_up_nifty50]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "df80eea0-d966-e9d3-217d-4d85956a9c80"
},
"source": [
"The day nifty50 **raised** huge is 2014-05-16"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "fd76408c-7e95-a24e-53f5-ca9f9af2d78f"
},
"outputs": [
{
"data": {
"text/plain": [
"282.39999999999964"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maximum_drop_banknifty = max(banknifty['open'] - banknifty['low'])\n",
"maximum_drop_banknifty"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "832da1bc-ac56-b01d-d7e7-b1bb07bbb18e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>105107</th>\n",
" <td>2013-09-20</td>\n",
" <td>2017-05-15 11:02:00</td>\n",
" <td>11132.8</td>\n",
" <td>11132.8</td>\n",
" <td>10850.4</td>\n",
" <td>10850.4</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"105107 2013-09-20 2017-05-15 11:02:00 11132.8 11132.8 10850.4 10850.4"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"banknifty.loc[(banknifty['open'] - banknifty['low']) == maximum_drop_banknifty]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "091f6b63-b777-cb7b-79d9-09fb2c5c4bca"
},
"source": [
"The day banknifty **dipped** huge is 2013-09-20"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "804c1bb4-6d60-28d9-ac0c-67a29ff0399b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>time</th>\n",
" <th>open</th>\n",
" <th>high</th>\n",
" <th>low</th>\n",
" <th>close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>318825</th>\n",
" <td>2016-01-20</td>\n",
" <td>2017-05-15 09:16:00</td>\n",
" <td>15049.35</td>\n",
" <td>15533.45</td>\n",
" <td>14978.1</td>\n",
" <td>14980.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" date time open high low close\n",
"318825 2016-01-20 2017-05-15 09:16:00 15049.35 15533.45 14978.1 14980.9"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"maximum_up_banknifty = max(banknifty['high'] - banknifty['open'])\n",
"banknifty.loc[(banknifty['high'] - banknifty['open']) == maximum_up_banknifty]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f822bb62-85ff-6352-a335-984fad12c3b2"
},
"source": [
"The day banknifty **raised** huge is 2016-01-20"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "c9e0e56e-a6c0-f7d3-7cae-49da0daa1dea"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe43acc09e8>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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6UFRUxCeffILVaiUmJobvvvvOrZrcngX97LPPEh4eTllZGRaLhWeffZauXbu69UNERMT/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\nIiIiJlAAi4iImEABLCIiYoL/B2/MW6+wuHuRAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe43acd7ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(banknifty.corr(),annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "cd1047d7-f31c-9a00-19e8-1f6471416298"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe43acf9780>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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kyZLU1zabDU6nE0ajEU6nEzabbdx9nZ2dcLvdE14TDAah0ST/4dfV1cHpdKYe\ns2vXLrz66qvYvn07DIbJtwNZrXqochwutNtNOV1XiiqlL6XYj7O9yZDb6DBl1b5sHtvoSD5WUClL\n8mdQiDa93+kBACyYbZX1+5Xaz3dmgxkAICoUWbet1PoyHZXSl2L3Q9Y1AGPlcob1RNdceds3v/lN\nbN26Fffffz9aW1sxa9astM/nHvnUkC273QSn05vTtaWmUvpSqv3o7k2+MQmJRMbty7Yv8Why6L/X\n6Su5n0Ghfi8fdQwCAAwahWzfrxT/jSnEBACgq2c4q7aVYl9yVSl9KVQ/JgsZsk0BOBwOuFyu1Nf9\n/f2w2+0T3tfX1weHw5H2Gr1ej1AoNO6xPT09ePfddwEAFosFra2tqa+JiqUwawB4ImDfyBbAemu1\nTQGMnAfg53kANH2yBYCVK1fiwIEDAIBTp07B4XCk5uibm5vh8/nQ1dWFWCyGQ4cOYeXKlWmvWbFi\nRer2gwcPYtWqVRgcHMTOnTsRi8UQj8dx6tQptLS0yNUdooxwDUBhOIeCUCoE1Jl1xW5KQXENAOWT\nbFMAra2tWLJkCTZt2gRBELBjxw7s378fJpMJ69atw86dO7Ft2zYAwPr169HS0oKWlparrgGArVu3\nYvv27Whra0NTUxM2bNgAtVqNO++8E/fdd19qG+DixYvl6g5RRrxB+c4BkBi5CwAuTwg2sxYKhVDs\nphSUVA6Y1QApHzIKAN/97nexceNGzJ07N6snf+yxx8Z9vWjRotT/L1++HG1tbVNeAySnDPbu3XvV\n7V/+8pfx5S9/Oas2EcmpEFMAOq0KglC9IwCRaBwefwSL51iL3ZSCU6sUMOhUPA+A8iKjAGCxWLBt\n2zbo9Xp8/vOfx9133w2tVit324jKjjcQgUatgFbG42kVggC9VgV/lZ4IODCcXA80w1Jdw/8Ss0HD\nKQDKi4wCwEMPPYSHHnoInZ2d+PWvf42/+qu/wqJFi3D//fdj/vz5creRqGx4A1GYC1ChzVCjrtpF\ngC5PdQcAi0GDnoEAYvEEVEpWc6fcZfWvp7e3Fx0dHfD7/TAYDHjiiSfw4osvytU2orKSEEUM+yOw\nGAsQAHTJEwFz2V5b7lxDQQDAjNrqLIMsrQOQppuIcpXRCMBzzz2HV155BXPnzsW9996Lv//7v4dS\nqUQkEsHGjRuxefNmudtJVPJ8wSjiCTG1VUtOhhoVYnERkWgCWk311MIHOAIwdiug1cSpWMpdRgHA\n5XJh797UZMtDAAAgAElEQVS9mDlzZuq2zs5OzJo1a8JFe0TVSFqZXYgRAKNudCtg9QaA6hwBkP59\ncScATdeUUwCJRALnzp1DU1MTEokEEokEIpFI6uS92267TfZGEpUDqTiLtFdbTlIxIF8VrgNweUJQ\nKYWCBK1SxFoAlC+TjgD893//N5599ll0dHSM22OvUChw6623yt44onIifSKrNRZmCgAAAlW4E8Dl\nCaLOrINCqK4aABIGAMqXSQPAZz7zGXzmM5/Bs88+i61btxaqTURlSfqDbOYIgGzCkTi8gShmO6r3\n5E/p39cwpwBomiYNAP/7v/+L1atXo6GhAfv27bvq/o0bN8rWMKJyMzoCIH8A0I9UAwyEq2sEwDVS\nA6CuSuf/AcBi5HkAlB+TBoAPP/wQq1evxvHjxye8nwGAaNToGoACTAHoqvM8gAHPyBbAKt0BAACm\nGjUEgVMANH2TBoAvfelLAIBvf/vbEEURgiAgEolgYGAAjY2NBWkgUbnw+CIQAJgN8p0DIEmNAFTZ\nGoBBbzJk2czVu/1NoRBg1rMaIE1fRtsAf/CDH0Cv1+MLX/gCPve5z8FgMODWW2/Fo48+Knf7iMrG\nkD8Ck14NpUL+6mwG6UCgKgsA7uFkALAWYKFlKbMYNOgfKYhElKuM/lIdOnQIW7Zswa9//WusWbMG\nL7/8Mt555x2520ZUVob9YZgLMPwPAPqRKYBAlU0BuH0jAaDKjgG+ktmoQSgSRzgSL3ZTqIxlFABU\nKhUEQcAf/vAHrF27FkCyPgARJYWjcQTD8YIsAASqdwRgyMsRAACwjJw34QlwGoByl1EAMJlM+NKX\nvoRz587h4x//OA4dOgShSvfgEk1kaOSTaaGK02jUSqiUiuobAfCGUaNVVV31wyuZjdwKSNOX0RqA\n733vezhy5AhaW1sBABqNBt/5zndkbRhRORkcmZu2mQo3NG2oqb4jgd3eMGysfz/uPACiXGUUAJTK\nZNo+dOhQ6vSxnp4ebgMkGuH2JvenF3J1ukGnhsdXPW8A4UgcgXAMLU3mYjel6FgNkPIhowDw4IMP\nQqFQjDsMCGAdACKJNAJgLeAIgF6nQs+AHwlRrIqyuKkFgFU+/w+MCQCcAqBpyCgAxGIx/OxnP5O7\nLURlqxj70w1aFUQRCIXjqboAlcw98jOu5RTA6ImAHAGgachoEeCCBQvgdrvlbgtR2XKPlKgt5Px0\ntW0FlHYAcA3A6AjAMAMATUNGHxt6e3tx5513Yv78+an1AADw05/+VLaGEZWTQW8YWrUSNdrCfRIf\nuxVwRsG+a/FIUwAcAQBqtCqolAouAqRpyeivlVQSmIgm5vaGYTNrC7o9drQccHWMALAK4ChBEGAx\nsBwwTU9GUwA333wzAoEAzpw5g5tvvhkNDQ1Yvny53G0jKgvhaBy+YBTWAn8yHT0QqDq2Ao5WAWQA\nAJLrAIb9kdTOLKJsZRQAnn76aezbtw/79+8HAPzqV7/Crl27ZG0YUbkYnZsubHlaQ011HQns9oah\nUgow1ch/2FI5sBg0iMXFqvn9U/5lFADeeustPPfcczAYDACARx55BKdOnZK1YUTlYnBkAWChRwCk\nRYD+YJVMAXhDqDUWdpqllJm5FZCmKaMAoNWO/8MWj8cRj/MQCiIAcI0EgLoCn1FfTecBxBMJePwR\nLgAcg8WAaLoyWgTY2tqKJ554Ak6nE3v37sWBAwdw8803y902orLgHEoGAHuBA0A1bQMc9kchitwC\nONZoAOBOAMpNRgHgs5/9LD788EO8++67OH78OB588EGsW7dO7rYRlQWXJ3kuu722pqDft5pGAAZH\nSi3XcgdAinT0NA8EolxNGgBCoRC2bduG06dP47rrrkN9fT3efvttaLVarF69GhpNYU4+IyplzqEg\nlAqh4KvTDVW0DTB1DDBHAFJYDZCma9I1AN///vfR2NiIAwcO4J//+Z/x4x//GL///e+h0+nwT//0\nT4VqI1FJcw2FYDNroVRktKQmb9QqJdQqRVWMALgZAK7CNQA0XZP+xXr77bfx+OOPQ6UaHSioqanB\njh078MYbb8jeOKJSF47G4fFHCj78L9HrVAhUQwDwMQBcycwAQNM0aQBQKpUTDvOr1WqYzTySk8jl\nSc5Nz7AUJwAYdWr4q2AKIDUCwDUAKcnS00puA6ScTRoAJttvO/ZMAKJq5RySFgAWdgeARK9TIRCO\nIVHh1eCGeBLghMwGLYa5C4ByNOkiwD/+8Y+4/fbbr7pdFEWeDkgEwDVUnB0AEoNOPXIkcCy1LbAS\nub1hmPVqqJSFXWdR6iwGDfrdASQSIhQKFkii7EwaAH7zm98Uqh1EZSlVA6CIawCA5FbASg0AoijC\n7Q2joU5f7KaUHItBA1EEvIEILJweoSxNGgBmzpxZqHYQlSWpBsCMAhcBkoyeCFi5CwED4RgisQTn\n/ycwdiEgAwBli+NpRNPgHApCp1HCWKQDakZPBKzchYDcApietBVwiAsBKQcMAEQ5EkURzqEQ7LU1\nRTugphpGAFgEKD1pBMAbYACg7DEAEOXI7Q0jHI2j3la8uWljFYwADHIHQFpmvRQAKvf3T/JhACDK\nUe9gAADQUMQAwBGA6mYyJAPgMIsBUQ4YAIhyJAWAxiIGgNE1AJUbAFJVALnI7SrSCMAwpwAoBwwA\nRDnqHRgZASji9rTRbYCVOwTMRYDpMQDQdDAAEOWoFKYAquFI4CFvGBq1AjXajE4vrypajRIatQJe\nf+UGQJKPrK+op556CidOnIAgCHjyySexbNmy1H1HjhzBM888A6VSidtuuw2PPPJI2mt6enrw+OOP\nIx6Pw2634+mnn4ZGo8Grr76KH//4x1AoFLjlllvwta99Tc7uEI3TOxiAxaAp6huTVPynko8EdvvC\nsJp0RdtpUerMeg1HACgnso0AHDt2DB0dHWhra8Pu3buxe/fucffv2rULzz77LF566SUcPnwYZ8+e\nTXvNnj17sHnzZrz44ouYM2cO9u3bh2AwiO9+97v4yU9+gra2Nhw5cgRnz56VqztE40SicQx4QkX9\n9A8AapUCmgo+EjgaS8AbiMJqvPpQMkoyGzTwBiIQK/w8CMo/2QJAe3s71q5dCwCYP38+PB4PfD4f\nAKCzsxMWiwWNjY1QKBRYvXo12tvb015z9OhR3HHHHQCANWvWoL29HTU1NXjllVdgNBohCAJqa2sx\nNDQkV3eIxul3ByGiuPP/kuSRwJU5AjDEY4CnZNZrEIuLCIYrMwSSfGQLAC6XC1arNfW1zWaD0+kE\nADidTthstqvuS3dNMBhMHUtcV1eXeh6j0QgA+PDDD9Hd3Y3rr79eru4QjVMK8/8SQ426YrcBulkD\nYEom/chWQNYCoCwVbPIyl+Gpia658raLFy/isccew/e+9z2o1ZOXY7Va9VCpcjvG2G435XRdKaqU\nvhSzH8MnLgMArm2py0s7pvMctSYdLrv8qKszlsSJcPn8vZzuGgYAzGqwFOX3XQ6vlQZ78oOQQq2a\ntL3l0JdMVUpfit0P2QKAw+GAy+VKfd3f3w+73T7hfX19fXA4HFCr1RNeo9frEQqFoNPpUo8FgN7e\nXjzyyCP4x3/8RyxevHjKNrndgZz6Yreb4HR6c7q21FRKX4rdj3OXktNNNSph2u2Ybl/UCgGiCHR2\nu4t+ImC+fy8dl5M/Z7WAgv++i/1vLFPSR5rOy0NwmCZeK1EufclEpfSlUP2YLGTINgWwcuVKHDhw\nAABw6tQpOByO1JB9c3MzfD4furq6EIvFcOjQIaxcuTLtNStWrEjdfvDgQaxatQoA8Hd/93fYuXMn\nlixZIlc3iCbUOxiAUiEU7RTAsSp5KyBrAEzNzCkAypFsIwCtra1YsmQJNm3aBEEQsGPHDuzfvx8m\nkwnr1q3Dzp07sW3bNgDA+vXr0dLSgpaWlquuAYCtW7di+/btaGtrQ1NTEzZs2IALFy7g7bffxp49\ne1Lf84EHHkgtFiSSiyiK6B0MwGGtgVJR/FIa+jHnAdhRU+TW5BcXAU4tdSAQywFTlmRdA/DYY4+N\n+3rRokWp/1++fDna2tqmvAZIThns3bt33G0tLS04ceJEnlpKlLlhfwTBcAyLZtcWuykAKn8EQBAA\ns6G4UxuljNUAKVfF//hCVGZSOwBKYAsgUNkHArm9YVgMmpIYaSlVppERAB4IRNniq4ooSz0ltAUQ\nGHsgUGXNAYuiiCFfmMP/UzDWqCCAawAoewwARFmSDgFqtBmK3JIkQ01ljgB4g1HE4iKspuIvtCxl\nSoUChho1vJwCoCwxABBlqfSmACpzBMA9zGOAM2U2aDgFQFljACDKUu9AAMYaNYw1pbEwzVChawAG\nhkMAAJuFAWAqZr0a/lAMsXii2E2hMsIAQJSFaCwOpyeIphL59A+MHQGosADgSQaAOjOnAKYibQX0\nBStrFIjkxQBAlIW+wSBEEWioK435f2DMNsAK++MvjQDUlUCxpVJn0nMnAGWPAYAoC9IOgFIaAVAp\nFdCoFZU3BTAyAjCDIwBTGq0GyABAmWMAIMpCj8sPoLRGAIDkVsBKWwToGg5BpVSk9rlTeqZUNcDK\n+jdA8mIAIMpCKY4AAMliQJU4AlBn1kIhFP+Ew1LHaoCUCwYAoiz0uPzQqBSwldi8tEGnRjAcQyKH\nY7dLUTgShy8Y5fx/hhgAKBcMAEQZSowcAtRg05fcp1KDTgURQDBcGaMAqQWAnP/PiGnkrAROAVA2\nGACIMjToCSESS6BxRmnN/wOj5wFUylZA7gDIDkcAKBcMAEQZupwqAVxa8//A6HkAgQpZCMgaANnR\naZRQqxQsB0xZYQAgylDvQHIHAEcA5NfvDgIA6q2lF7ZKkSAIMOvVrANAWWEAIMpQagSgxHYAAGNO\nBKyQYkB97uTP2mGrKXJLyodJr8FwIAqxQhaCkvwYAIgy1DPghyCU5qdSfYWdB9DvDqJGq4SpRM5b\nKAdmgwbRWAKhSLzYTaEywQBAlKGegQDstTVQq0rvZZMqB1wBawASooj+oSAcVj2EEtttUcpMI9UA\nuQ6AMlV6f8mISpA3EIEvGC3JBYDA2EWA5T8CMOQNIxpLoN7K4f9sjO4EKP8QSIXBAECUgR5p/r8E\nFwAClbUIsG+k2qKjBKdaSpk5VQ6YIwCUGQYAogz0SDsASnABIFBZ2wD7hqQdABwByAZrAVC2GACI\nMpAaASixQ4AklTQC0D/ILYC5kKoBcisgZYoBgCgDPSW8BRBIHgmsVSsrYg3A5QHpxMXS/FmXKq4B\noGwxABBloGfAD7NBkxpqL0V6naoidgF0OX2wGDUwcgtgVkwjAYC7AChTDABEUwhH4xjwhEruCOAr\nGXSqsp8CCISiGBwOo9luLHZTyo60DZBTAJQpBgCiKfQNBiCidOf/JakjgRPlWwmuy5kc/m+2l/bP\nuhSplAoYdCp4OQVAGWIAIJqCNP9f6nPSqWqAZXwkcLfTBwAcAciR2aDhLgDKGAMA0RSkLYBNZTAC\nAJT3VsDREQAGgFyY9Br4AlHEE4liN4XKAAMA0RRK+RCgsYypUrDlGwA6nT4IQun/rEuVWa+GCMAX\nLN9RICocBgCiKfQO+KFVK2E1aYvdlEmltoGV6SKweCKBS31eNNUZoFEri92csmRiNUDKAgMA0SQS\nCRG9g0E01JX+wTSWkT/+njKdA+7q9yMSTWD+THOxm1K2WA2QssEAQDQJlyeIWDxR8lsAAcBsHPnj\n7yvPP/7nLnsAAPObLEVuSfkyS1sBGQAoAwwARJO4XOIlgMey6Mt7BOBc90gAmMkAkKtUMSB/+a4D\nocJhACCaRG+ZLAAERk+DK9sRgO5h6LWqkt9uWcpS/wbKNARSYTEAEE3icuoUwNIfATDWqKEQhGmN\nAHT2+9D2+49wusOdx5ZNzeMLo38oiHkzzVCU+FqLUpYKAFwESBlgACCaxGWXH0qFAEcZHE2rUAgw\n6dU5//F3DQXxf376Dg4c68R3f/YnXOgZznML03vvwiAA4GNzbAX7npXIXAFbQalwGACI0kiIIrpd\nfjTW6aFSlsdLxWzQwJNjAPjv9osIhuNYvsiBhCjil29cyG/jJnFqJABc18IAMB01WhWUCoFTAJSR\n8virRlQEg54QwpE4ZpZRVTqLQYNwJI5wJJ7VdcP+CA6/24t6mx5f/uwStDSa8d75wYKcLJcQRZy6\nOAiLUYOZPANgWgRBSJYD5hQAZYABgCiNLldy/n/mjPJ5UzLnWAvgT2ddiCdE3H5DExQKATdda0dC\nFPGnsy45mjnOpT4vvIEorptrK/laC+XApFdzCoAywgBAlIZ0ME05fSrNdRHYnz5KvtF/fOEMAEDr\nNXYAwImzA3ls3cTe+dAJALh+wQzZv1c1MOs1CEezHwWi6sMAQJRGtzQCUEZTALmUAw5H43j/4iCa\nZhjgsCa34DmsNbCatPioawiiKN/xwqIo4u3T/dCoFVg6v06271NNpBBYiOkbKm8MAERpdDv90KgV\nmGHRFbspGasdqQY45AtnfM0HF92IxBK4YcwncEEQsGCmBd5AFP1Dwby3U9LZ70OfO4hl82dAy/r/\neWEu84JQVDgMAEQTiCcS6Bnwo6nOUFb70m3mZFgZGA5lfM2fziaH4G9YOH4IfkFzsiLf2S5Pnlp3\ntbdO9wMAbl7kkO17VBuTYWQrIKsB0hRkDQBPPfUU7r33XmzatAknT54cd9+RI0ewceNG3HvvvXj+\n+ecnvaanpwf3338/Nm/ejEcffRSRSDLZejwePPjgg/jqV78qZzeoCvW7g4jFxbKa/weAupEAMDic\n2QhAcqHfAEx6NeY1jj+ER6rJf7HHm99GjhBFEW9x+D/veCAQZUq2AHDs2DF0dHSgra0Nu3fvxu7d\nu8fdv2vXLjz77LN46aWXcPjwYZw9ezbtNXv27MHmzZvx4osvYs6cOdi3bx8AYMeOHbjxxhvl6gJV\nsW6ntAOgfOb/AaDWpIFCEDDgyWwE4ELPMIb9EVw/fwYUivEjHTPtBggC0NkvTwD4qMuDfncQrQvt\nHP7Po9R5AAwANAXZAkB7ezvWrl0LAJg/fz48Hg98vuSq6s7OTlgsFjQ2NkKhUGD16tVob29Pe83R\no0dxxx13AADWrFmD9vZ2AMkQwQBAcuga2QHQXGYjAEqFAlaTJuMpAGn1/5XD/wCgVSvRYNOj0+mX\nZSHgGyd7AACrljXm/bmrmXlkCmCYUwA0BdkCgMvlgtVqTX1ts9ngdCbnGp1OJ2w221X3pbsmGAxC\no0mm2rq6utTzGI3l9emMykdqBKCMdgBI6sw6DPnCiMUTUz72T2ddUCkVWDJ34gp8sxxGBMOxjEcU\nMhUMx/DW6X7MsOhw7Rzr1BdQxswcAaAMqQr1jXL5BDHRNdP5JGK16qFS5TbUaLebcv6+paZS+iJn\nPzpdfliMGixsqStIcZp89qXJYcKZLg8UGjXstvQn6112+tDt9OOmxfVonlk74WMWtdTh2Af9GArF\nsTjDNmbSl4NHOxCOxrHxloWod5infHyxlONrpXZkK2comhjX/nLsSzqV0pdi90O2AOBwOOByjVYR\n6+/vh91un/C+vr4+OBwOqNXqCa/R6/UIhULQ6XSpx+bC7Q7kdJ3dboLTKc88aKFVSl/k7IcvGEX/\nYADXtdjgcvlk+R5j5bsvJl3yZf3BOScU8fS19X/7ZgcAYGmLNe33t40MJ58668SChqlHQzLty6uH\nz0MAcEOLrWT/PZbza6VGq4JrKJBqfzn35UqV0pdC9WOykCHbFMDKlStx4MABAMCpU6fgcDhSQ/bN\nzc3w+Xzo6upCLBbDoUOHsHLlyrTXrFixInX7wYMHsWrVKrmaTYRLfckX5ZyG8vyU0ViX/ATYM1LI\nKJ13PnRCIQj4+EJ72sfMciR/Bp39+QtC5y8P41z3MK6bV4e6MqqxUE7MejWGWQ6YpiDbCEBrayuW\nLFmCTZs2QRAE7NixA/v374fJZMK6deuwc+dObNu2DQCwfv16tLS0oKWl5aprAGDr1q3Yvn072tra\n0NTUhA0bNiAej+OBBx7A8PAw+vr6cP/99+Phhx/GLbfcIleXqEp0SAGgvlwDQHLhYs9A+hGvAU8I\nF3qGsXiOFcYaddrH1Ro1MOhUqbLI+fCbY5cAAHfdPCtvz0njmQ0a9A95EE8koFSw3AtNTNY1AI89\n9ti4rxctWpT6/+XLl6OtrW3Ka4DklMHevXuvuv2FF17IQyuJxuvoTQaA2WU6AtBQp4cAoGcg/QjA\nG+8mV+B/4mP1kz6XIAiYaTfio84hRKJxaKa5Xa9/KIh3PuzH7HojFnPxn2ysJi1EMbkTwGrSFrs5\nVKIYDYmu0NHng16rgr1Mh6e1aiXqLLq0IwCJhIg/nLgMnUaJmxdPvZ5mpt0AEZOPKGTqt291QhSB\nP7t5Nk/+k1GtMfmm7/ZmXhKaqg8DANEYwXAMfYMBzGkwlfUb1MwZBnj8kQnPBDh5bgBubxifXNIA\nnWbqQcDmka2QXdOcBvAFo3j95GXYzFrcxNK/spI+9bu9+d2+SZWFAYBojEtlPv8vSVfHXxRFvHL4\nAgDgUx+fmdFzzZyRXFMg1UbI1aE/diMSTeDOm2ZBpeSfHjmNBgCOAFB6fBUSjXG+ZxgAMLexvAPA\nwubkvv4zXUPjbv/TWRcu9npx0yIHmh2ZFTmSzkPomsaWyGgsjt+904UarQqrrm/K+XkoM6kAkMWp\nkFR9GACIxpA+MS+YaSlyS6anpdEElVLABxfdqeJZ4Ugcbb87C0EA/vzWloyfy6BTw2rSTmsEoP1U\nH4b9Edx+QxNqtAWrP1a1OAJAmWAAIBohiiLOdXtgM2tTx+qWK7VKievnz0C3y48LI6f57XvtHPqH\ngrjr5tmpYf1MzbQb4PaG4Q9lv7c8IYo4cOwSlAoBa2/i1r9CkBYBDjEA0CQYAIhGOIeCGA5Ey/7T\nv2T1Dcmh9l++cQG/eP08fne8C411emzI4tO/pHnkVMRcRgFOnhtAz0AAn/xYPbekFYhKqYBZr+YI\nAE2KAYBoxEcjw//zKyQAfKzFhmtn1eLd8wN45fBF1Bo1eHTjspz28kvrALqnqC44kQNHpcI/s7O+\nlnJnNeng9oZlOcmRKgMn44hGnOuujPl/iUIQ8NWNy/DbtzuRSIhYe9OsSav+TSYVALLcCnihZxgf\ndg7hunm2jBcdUn5YTVp09HkRCMeK3RQqUQwARCPOdnugUSkwq4LeqGq0Knx2ZfZD/ldqqjNAANCV\n5RTAb0Y+/f8ZP/0XXK20EHA4jLnFbQqVKE4BECFZpKbL6ce8JjP3qE9Ao1bCYa1Bt9OX8ZCycyiI\ntz/sx2wHy/4WQ505GQBcwywGRBPjXzoiAKc73ADAN6pJzLQb4Q/F4PFHMnp8quzvJ1j2txjstTUA\nANdQsMgtoVLFAEAE4INLUgCwFbklpSubioC+YBR/YNnfopKOWnZ5OAJAE2MAIEJyBECrVpZ9BUA5\nSYv4MjkT4LWRsr/rWPa3aGZYkiMAAwwAlAZfmVT13N4wegYCuGZWLd+sJtE8shOgY+S8hHSisTj+\n7ztdqNEqcRvL/haNWa+GRqWA08MpAJoY/9pR1Tt9ifP/mai36aHXqnD+8vCkjzvyXm+y7O/HZ7Ls\nbxEJgoA6i44jAJQWAwBVvfcvDAJgAJiKQhAwr8mMfncQ3sDECwETCRG/OdaZLPt7I8v+FtsMSw38\noRj8wexLOFPlYwCgqpYQRbx7YRBmgwaz6itn/79c5jWZASDtKMDRU73oGwzglusaWPa3BMyoTS4E\n7HcHitwSKkUMAFTVLvV5MeyPYOk8GxTcqjala2Yljxn+YGTb5FiiKOK/Dn0EgIV/SsWMkZ0AfYMM\nAHQ1BgCqau+eGwAALJ1XV+SWlIeFzRZoVAqcGpk2GeujLg8+7HDj+vl1aMrytEGSh31kJ0BPDmc4\nUOVjAKCqdvL8AAQBWNLC/f+ZUKuUuGZ2Lbpd/qsWl/3yjQsAgLs/OacYTaMJNNTpAWR/hgNVBwYA\nqlq+YBTnu4exYKYFBl1uh+RUo9aFdgDAm+/3pm57/+IgPuhw4+PX2FPTBFR89dYaCALQ1c8AQFdj\nAKCq9d6FAYjg8H+2bl5cD7VKgT+cuIxYPIFoLIGf/S459//F9R8rcutoLLVKiRkWHboZAGgCDABU\ntU6eTc7/L5vPAJANvU6FlUsb4RwK4ed/OI+f/Po0upx+rL6hCQv46b/kNNYZMOQLw8etgHQFBgCq\nStFYAifOuVBn1lXU8b+F8vnV82AxavDro5fQfqoXLY0m3LNmQbGbRRNosCXXAfRyJwBdgWW6qCp9\n0OFGMBzHqmVNPKkuBwadGn+35Ub89u0uGHQqrFs+i1X/SlTjyELAngE/Fsy0FLk1VEr4iqWqdPyM\nEwDQeo29yC0pXzNqa3Df2oXFbgZNobEuuSWTIwB0JU4BUNVJJET88SMnzHo1PxFRxZNqMnRyISBd\ngQGAqs5HXUPwBqL4+DV2KBQc/qfKZqxRw2GtQUevF6IoFrs5VEIYAKjqvPMhh/+puiyYVQtvIAq3\nN1zsplAJYQCgqhKLJ3Dsgz4Ya9Q8/Y+qxoLm5PbMCz3eIreESgkDAFWV9y4MYjgQxSc+Vg+Vkv/8\nqTpIAaCjb+JTHKk68S8gVZUj7yXL1664rqHILSEqHKlA00WOANAYDABUNfyhKP70kQuNdXrMbTAV\nuzlEBWPSa+Cw1uDc5WHEE4liN4dKBAMAVY3DJ3sQiydw69JGFv+hqrNothXBcAyX+rgdkJIYAKgq\nJBIi/u87XdCoFFh1fVOxm0NUcNKi1w863EVuCZUKBgCqCn8664LLE8It1zXAWMOjf6n6LJ5jhQDg\n3XMDxW4KlQgGAKp4CVHEL9+4AAHAuptmFbs5REVhNmgwf6YFZ7qGMByIFLs5VAIYAKjivX26H539\nPnxySX2qLCpRNWq9xg5RHD0Lg6obAwBVtGA4hv88dBZKhYA/v7Wl2M0hKqqbFzsgCMDrJ3qK3RQq\nAY9v2sQAAA0ISURBVAwAVNH+63/PYXA4jE/fMgcOq77YzSEqKptZh2Xz6nChZxgXelgUqNoxAFDF\nevP9Xvz+eDca6/T49C1zi90copKwdnlyHcwv37hQ5JZQsTEAUEU6dXEQe189DZ1GiUf+YinUKv5T\nJwKAj82x4tpZtTh5bgAnzrqK3RwqIv5VpIpz5L0e/PPLJyGKIv5mw3Vc+Ec0hiAI+Mt110CpELD3\n1Q/gGgoWu0lUJLIGgKeeegr33nsvNm3ahJMnT46778iRI9i4cSPuvfdePP/885Ne09PTg/vvvx+b\nN2/Go48+ikgkuYXllVdewec//3l84QtfwMsvvyxnV6gM9A0G8Nz+d/Fv//0B1CoBj268Hkvn1RW7\nWUQlp9lhxKY7FmI4EMX/efE4LvZyPUA1Usn1xMeOHUNHRwfa2tpw7tw5PPnkk2hra0vdv2vXLvzo\nRz9CfX09tmzZgrvuuguDg4MTXrNnzx5s3rwZd999N5555hns27cPGzZswPPPP499+/ZBrVZj48aN\nWLduHWpra+XqEpUYURTR5w7io64hvH3aifcuDEAUgQXNFvz1pxdz0R/RJO64sRmRaBz7XjuHXf/+\nDm65rh4rrmvEvEYztBplsZtHBSBbAGhvb8fatWsBAPPnz4fH44HP54PRaERnZycsFgsaGxsBAKtX\nr0Z7ezsGBwcnvObo0aP41re+BQBYs2YNfvzjH6OlpQVLly6FyZQ81KW1tRXHjx/Hpz71Kbm6NE7P\ngB+/OnIRsVjyYA3xygeIE/4vRPGqR465L/33G3vdJA8b9xzilY8UAY1GhUgkNv6eK76xmP6utG2a\nvB0Zfq90d2B8XxIJwB+OYdATRCw+entLoxl3f2I2brzWzlr/RBm4+5NzMKveiJ/97iwOv9uLw+/2\nQgBgM2thrNFAr1NBpVRAEACFIEChECAIQDFfXVqtGuFwtIgtyI90/Wi2G/HZAm1Zli0AuFwuLFmy\nJPW1zWaD0+mE0WiE0+mEzWYbd19nZyfcbveE1wSDQWg0GgBAXV0dnE4nXC7XVc/hdE5e3MJq1UOl\nyi3Z2u3jT497v9ODN0/15fRc1e7K92YhzZ1X/pEZvUuAxajBvJkWNNYZsbjFhmULZmBWffme8Hfl\nv69yxr6UpnR9WWM34bab5uDds0689X4fznV70DcYQK87gHAkXuBW0sVeLx7486VQKuSPWbIFgCtN\n9mkxm2vSPU8mz+92B7JuA5B84Tid48/R/tgsC577f1chlhj9vle/YU38C5z0DXDMV5N9iL36OYSx\nX6Rtk91ugsvlvereTL+XcOUzTvK95PwUPtHv5Mqvy8VEfSlX7EtpyqQvM601mLly7rjbYvEE4gkR\niYQIURSREJOltYtpRp0RroHyP9EwXT9qNCoM5rF/k4VY2QKAw+GAyzW6xaS/vx92u33C+/r6+uBw\nOKBWqye8Rq/XIxQKQafTpR470fPfcMMNcnVnQnpd+R0qo1Eroc5xFISIqotKqUCp/bmwGLWIBMv/\nLINS6IdsuwBWrlyJAwcOAABOnToFh8MBo9EIAGhubobP50NXVxdisRgOHTqElStXpr1mxYoVqdsP\nHjyIVatW4frrr8e7776L4eFh+P1+HD9+HDfddJNc3SEiIqooso0AtLa2YsmSJdi0aRMEQcCOHTuw\nf/9+mEwmrFu3Djt37sS2bdsAAOvXr0dLSwtaWlquugYAtm7diu3bt6OtrQ1NTU3YsGED1Go1tm3b\nhgcffBCCIOCRRx5JLQgkIiKiyQliLpPzZSrX+bxqmwssB5XSD4B9KVXsS2mqlL4Uqh+TrQFgJUAi\nIqIqxABARERUhRgAiIiIqhADABERURViACAiIqpCDABERERViAGAiIioCjEAEBERVaGqKgRERERE\nSRwBICIiqkIMAERERFWIAYCIiKgKMQAQERFVIQYAIiKiKsQAQEREVIVUxW5AsQSDQTzxxBMYGBhA\nOBzGww8/jEWLFuHxxx9HPB6H3W7H008/DY1Gg1deeQX//u//DoVCgXvuuQdf+MIXEI1G8cQTT+Dy\n5ctQKpX49re/jVmzZhW1T6FQCJ/5zGfw8MMP45ZbbinLvhw9ehSPPvooFi5cCAC45ppr8Nd//ddl\n2ZdXXnkF//Zv/waVSoWvfvWruPbaa8uyHy+//DJeeeWV1NfvvfceXn311bLsi9/vx/bt2+HxeBCN\nRvHII49gwYIFZdmXRCKBHTt24KOPPoJarcbOnTuh1+vLqi9nzpzBww8/jAceeABbtmxBT0/PtNt/\n+vRp7Ny5EwBw7bXX4lvf+lZR+gIA//Ef/4HvfOc7OHbsGAwGAwCUVl/EKvU///M/4r/+67+KoiiK\nXV1d4p133ik+8cQT4quvviqKoih+73vfE3/605+Kfr9fvPPOO8Xh4WExGAyKn/70p0W32y3u379f\n3LlzpyiKovj666+Ljz76aNH6InnmmWfEz33uc+J//dd/lW1f3nzzTXHr1q3jbivHvgwODop33nmn\n6PV6xb6+PvGb3/xmWfbjSkePHhV37txZtn154YUXxO9+97uiKIpib2+veNddd5VtXw4ePJj6/h0d\nHeKXvvSlsuqL3+8Xt2zZIn7zm98UX3jhBVEU8/Na37Jli3jixAlRFEXx61//uvjaa68VpS8///nP\nxWeeeUa8/fbbRZ/Pl3pcKfWlaqcA1q9fj4ceeggA0NPTg/r6ehw9ehR33HEHAGDNmjVob2/HiRMn\nsHTpUphMJuh0OrS2tuL48eNob2/HunXrAAArVqzA8ePHi9YXADh37hzOnj2L22+/HQDKui9XKse+\ntLe345ZbboHRaITD4cA//MM/lGU/rvT888/j4YcfLtu+WK1WDA0NAQCGh4dhtVrLti8XL17EsmXL\nAACzZ8/G5cuXy6ovGo0GP/zhD+FwOFK3Tbf9kUgE3d3dqZ+L9BzF6MvatWvxta99DYIgpG4rtb5U\nbQCQbNq0CY899hiefPJJBINBaDQaAEBdXR2cTidcLhdsNlvq8Tab7arbFQoFBEFAJBIpSh8A4Dvf\n+Q6eeOKJ1Nfl3JezZ8/iK1/5Cu677z4cPny4LPvS1dWFUCiEr3zlK9i8eTPa29vLsh9jnTx5Eo2N\njbDb7WXbl09/+tO4fPky1q1bhy1btmD79u1l25drrrkGb7zxBuLxOM6fP4/Ozk50d3eXTV9UKhV0\nOt2426b7u3C5XDCbzanHSs9RjL4YjcarHldqfanaNQCSn/3sZ/jggw/wjW98A+KYqshimgrJ2d5e\nCL/4xS9www03pJ2/K6e+zJ07F3/7t3+Lu+++G52dnfjiF7+IeDw+ZdtKsS9DQ0N47rnncPnyZXzx\ni18s239fkn379uEv/uIvrrq9nPryy1/+Ek1NTfjRj36E06dP48knnxx3fzn1ZfXq1Th+/Dj+8i//\nEtdeey3mzZuHM2fOTNm2UuzLRPLR/lLr05WK3ZeqHQF477330NPTAwBYvHgx4vE4DAYDQqEQAKCv\nrw8OhwMOhwMulyt1XX9/f+p2KY1Fo1GIophKroX22muv4Xe/+x3uuecevPzyy/j+978PvV5fln2p\nr6/H+vXrIQgCZs+e/f+3dz+v7McBHMefspE+R7RQFBdkzUqp7eNfcFBu/gFaScmPkXJC7bByNecl\nLk7johxErUUcHHZwWYqWfDJN+PQ9LPp+z+pr796vxx/wac/2bnvt8zmMtrY2np+fjWtpbW0lGo0S\nCATo7u7GcRxjz9eXi4sLotEogLHnq1Ao4LouAP39/Tw8PNDS0mJkC8Dc3BzZbJb19XU8zyMUChnb\nAj8/V+3t7d+PeP6+Rr2otxZrB0A+n2d3dxeo3ZZ5fX0lFotxdHQEwPHxMWNjY0QiEa6vr/E8j0ql\nQqFQYGRkhHg8Ti6XA+Dk5ITR0dFfa0mn0xwcHLC3t8fk5CQzMzPGthweHpLJZAB4fHykXC4zMTFh\nXIvrupyfn+P7Pk9PT0afL6h9+DiO8/0FYWpLT08PV1dXAJRKJRzHIR6PG9lye3vL8vIyAKenpwwO\nDhr7vnz56esPBoP09vaSz+f/uUa9qLcWa/8NsFqtsrKywv39PdVqlUQiwdDQEIuLi7y9vdHZ2cnG\nxgbBYJBcLkcmk6GhoYGpqSnGx8f5/PxkdXWVu7s7mpqa2NzcpKOj47ez2N7epqurC9d1jWx5eXlh\nfn4ez/N4f38nkUgwMDBgZEs2m2V/fx+A6elpwuGwkR1Qu2OWTqfZ2dkBar9cTGypVCokk0nK5TIf\nHx/Mzs7S19dnZIvv+ySTSYrFIs3NzaRSKRobG41pubm5YWtri1KpRCAQIBQKkUqlWFpa+tHrLxaL\nrK2t4fs+kUjkeyT975ZYLMbZ2RmXl5eEw2GGh4dZWFioqxZrB4CIiIjNrH0EICIiYjMNABEREQtp\nAIiIiFhIA0BERMRCGgAiIiIW0gAQERGxkAaAiIiIhTQARERELPQHWdMR6FZOgLEAAAAASUVORK5C\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe43ac230f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nifty50['high'].plot('kde')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "acf1fb86-e6e4-2a31-339f-dacd4f796e2f"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe43abad5c0>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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EGSUfIpUqs5BvEQEAHp+0XLLfK/3flicm/PrBxj71XySTZRaUY6rHWYb49Mse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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe438d23630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"banknifty['high'].plot('kde')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "b725dbb0-29f0-1dc1-afb2-7189ed6e7bca"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe438b7d278>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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kyZLU1zabDU6nE0ajEU6nEzabbdx9nZ2dcLvdE14TDAah0ST/4dfV1cHpdKYe\ns2vXLrz66qvYvn07DIbJtwNZrXqochwutNtNOV1XiiqlL6XYj7O9yZDb6DBl1b5sHtvoSD5WUClL\n8mdQiDa93+kBACyYbZX1+5Xaz3dmgxkAICoUWbet1PoyHZXSl2L3Q9Y1AGPlcob1RNdceds3v/lN\nbN26Fffffz9aW1sxa9astM/nHvnUkC273QSn05vTtaWmUvpSqv3o7k2+MQmJRMbty7Yv8Why6L/X\n6Su5n0Ghfi8fdQwCAAwahWzfrxT/jSnEBACgq2c4q7aVYl9yVSl9KVQ/JgsZsk0BOBwOuFyu1Nf9\n/f2w2+0T3tfX1weHw5H2Gr1ej1AoNO6xPT09ePfddwEAFosFra2tqa+JiqUwawB4ImDfyBbAemu1\nTQGMnAfg53kANH2yBYCVK1fiwIEDAIBTp07B4XCk5uibm5vh8/nQ1dWFWCyGQ4cOYeXKlWmvWbFi\nRer2gwcPYtWqVRgcHMTOnTsRi8UQj8dx6tQptLS0yNUdooxwDUBhOIeCUCoE1Jl1xW5KQXENAOWT\nbFMAra2tWLJkCTZt2gRBELBjxw7s378fJpMJ69atw86dO7Ft2zYAwPr169HS0oKWlparrgGArVu3\nYvv27Whra0NTUxM2bNgAtVqNO++8E/fdd19qG+DixYvl6g5RRrxB+c4BkBi5CwAuTwg2sxYKhVDs\nphSUVA6Y1QApHzIKAN/97nexceNGzJ07N6snf+yxx8Z9vWjRotT/L1++HG1tbVNeAySnDPbu3XvV\n7V/+8pfx5S9/Oas2EcmpEFMAOq0KglC9IwCRaBwefwSL51iL3ZSCU6sUMOhUPA+A8iKjAGCxWLBt\n2zbo9Xp8/vOfx9133w2tVit324jKjjcQgUatgFbG42kVggC9VgV/lZ4IODCcXA80w1Jdw/8Ss0HD\nKQDKi4wCwEMPPYSHHnoInZ2d+PWvf42/+qu/wqJFi3D//fdj/vz5creRqGx4A1GYC1ChzVCjrtpF\ngC5PdQcAi0GDnoEAYvEEVEpWc6fcZfWvp7e3Fx0dHfD7/TAYDHjiiSfw4osvytU2orKSEEUM+yOw\nGAsQAHTJEwFz2V5b7lxDQQDAjNrqLIMsrQOQppuIcpXRCMBzzz2HV155BXPnzsW9996Lv//7v4dS\nqUQkEsHGjRuxefNmudtJVPJ8wSjiCTG1VUtOhhoVYnERkWgCWk311MIHOAIwdiug1cSpWMpdRgHA\n5XJh797UZMtDAAAgAElEQVS9mDlzZuq2zs5OzJo1a8JFe0TVSFqZXYgRAKNudCtg9QaA6hwBkP59\ncScATdeUUwCJRALnzp1DU1MTEokEEokEIpFI6uS92267TfZGEpUDqTiLtFdbTlIxIF8VrgNweUJQ\nKYWCBK1SxFoAlC+TjgD893//N5599ll0dHSM22OvUChw6623yt44onIifSKrNRZmCgAAAlW4E8Dl\nCaLOrINCqK4aABIGAMqXSQPAZz7zGXzmM5/Bs88+i61btxaqTURlSfqDbOYIgGzCkTi8gShmO6r3\n5E/p39cwpwBomiYNAP/7v/+L1atXo6GhAfv27bvq/o0bN8rWMKJyMzoCIH8A0I9UAwyEq2sEwDVS\nA6CuSuf/AcBi5HkAlB+TBoAPP/wQq1evxvHjxye8nwGAaNToGoACTAHoqvM8gAHPyBbAKt0BAACm\nGjUEgVMANH2TBoAvfelLAIBvf/vbEEURgiAgEolgYGAAjY2NBWkgUbnw+CIQAJgN8p0DIEmNAFTZ\nGoBBbzJk2czVu/1NoRBg1rMaIE1fRtsAf/CDH0Cv1+MLX/gCPve5z8FgMODWW2/Fo48+Knf7iMrG\nkD8Ck14NpUL+6mwG6UCgKgsA7uFkALAWYKFlKbMYNOgfKYhElKuM/lIdOnQIW7Zswa9//WusWbMG\nL7/8Mt555x2520ZUVob9YZgLMPwPAPqRKYBAlU0BuH0jAaDKjgG+ktmoQSgSRzgSL3ZTqIxlFABU\nKhUEQcAf/vAHrF27FkCyPgARJYWjcQTD8YIsAASqdwRgyMsRAACwjJw34QlwGoByl1EAMJlM+NKX\nvoRz587h4x//OA4dOgShSvfgEk1kaOSTaaGK02jUSqiUiuobAfCGUaNVVV31wyuZjdwKSNOX0RqA\n733vezhy5AhaW1sBABqNBt/5zndkbRhRORkcmZu2mQo3NG2oqb4jgd3eMGysfz/uPACiXGUUAJTK\nZNo+dOhQ6vSxnp4ebgMkGuH2JvenF3J1ukGnhsdXPW8A4UgcgXAMLU3mYjel6FgNkPIhowDw4IMP\nQqFQjDsMCGAdACKJNAJgLeAIgF6nQs+AHwlRrIqyuKkFgFU+/w+MCQCcAqBpyCgAxGIx/OxnP5O7\nLURlqxj70w1aFUQRCIXjqboAlcw98jOu5RTA6ImAHAGgachoEeCCBQvgdrvlbgtR2XKPlKgt5Px0\ntW0FlHYAcA3A6AjAMAMATUNGHxt6e3tx5513Yv78+an1AADw05/+VLaGEZWTQW8YWrUSNdrCfRIf\nuxVwRsG+a/FIUwAcAQBqtCqolAouAqRpyeivlVQSmIgm5vaGYTNrC7o9drQccHWMALAK4ChBEGAx\nsBwwTU9GUwA333wzAoEAzpw5g5tvvhkNDQ1Yvny53G0jKgvhaBy+YBTWAn8yHT0QqDq2Ao5WAWQA\nAJLrAIb9kdTOLKJsZRQAnn76aezbtw/79+8HAPzqV7/Crl27ZG0YUbkYnZsubHlaQ011HQns9oah\nUgow1ch/2FI5sBg0iMXFqvn9U/5lFADeeustPPfcczAYDACARx55BKdOnZK1YUTlYnBkAWChRwCk\nRYD+YJVMAXhDqDUWdpqllJm5FZCmKaMAoNWO/8MWj8cRj/MQCiIAcI0EgLoCn1FfTecBxBMJePwR\nLgAcg8WAaLoyWgTY2tqKJ554Ak6nE3v37sWBAwdw8803y902orLgHEoGAHuBA0A1bQMc9kchitwC\nONZoAOBOAMpNRgHgs5/9LD788EO8++67OH78OB588EGsW7dO7rYRlQWXJ3kuu722pqDft5pGAAZH\nSi3XcgdAinT0NA8EolxNGgBCoRC2bduG06dP47rrrkN9fT3efvttaLVarF69GhpNYU4+IyplzqEg\nlAqh4KvTDVW0DTB1DDBHAFJYDZCma9I1AN///vfR2NiIAwcO4J//+Z/x4x//GL///e+h0+nwT//0\nT4VqI1FJcw2FYDNroVRktKQmb9QqJdQqRVWMALgZAK7CNQA0XZP+xXr77bfx+OOPQ6UaHSioqanB\njh078MYbb8jeOKJSF47G4fFHCj78L9HrVAhUQwDwMQBcycwAQNM0aQBQKpUTDvOr1WqYzTySk8jl\nSc5Nz7AUJwAYdWr4q2AKIDUCwDUAKcnS00puA6ScTRoAJttvO/ZMAKJq5RySFgAWdgeARK9TIRCO\nIVHh1eCGeBLghMwGLYa5C4ByNOkiwD/+8Y+4/fbbr7pdFEWeDkgEwDVUnB0AEoNOPXIkcCy1LbAS\nub1hmPVqqJSFXWdR6iwGDfrdASQSIhQKFkii7EwaAH7zm98Uqh1EZSlVA6CIawCA5FbASg0AoijC\n7Q2joU5f7KaUHItBA1EEvIEILJweoSxNGgBmzpxZqHYQlSWpBsCMAhcBkoyeCFi5CwED4RgisQTn\n/ycwdiEgAwBli+NpRNPgHApCp1HCWKQDakZPBKzchYDcApietBVwiAsBKQcMAEQ5EkURzqEQ7LU1\nRTugphpGAFgEKD1pBMAbYACg7DEAEOXI7Q0jHI2j3la8uWljFYwADHIHQFpmvRQAKvf3T/JhACDK\nUe9gAADQUMQAwBGA6mYyJAPgMIsBUQ4YAIhyJAWAxiIGgNE1AJUbAFJVALnI7SrSCMAwpwAoBwwA\nRDnqHRgZASji9rTRbYCVOwTMRYDpMQDQdDAAEOWoFKYAquFI4CFvGBq1AjXajE4vrypajRIatQJe\nf+UGQJKPrK+op556CidOnIAgCHjyySexbNmy1H1HjhzBM888A6VSidtuuw2PPPJI2mt6enrw+OOP\nIx6Pw2634+mnn4ZGo8Grr76KH//4x1AoFLjlllvwta99Tc7uEI3TOxiAxaAp6huTVPynko8EdvvC\nsJp0RdtpUerMeg1HACgnso0AHDt2DB0dHWhra8Pu3buxe/fucffv2rULzz77LF566SUcPnwYZ8+e\nTXvNnj17sHnzZrz44ouYM2cO9u3bh2AwiO9+97v4yU9+gra2Nhw5cgRnz56VqztE40SicQx4QkX9\n9A8AapUCmgo+EjgaS8AbiMJqvPpQMkoyGzTwBiIQK/w8CMo/2QJAe3s71q5dCwCYP38+PB4PfD4f\nAKCzsxMWiwWNjY1QKBRYvXo12tvb015z9OhR3HHHHQCANWvWoL29HTU1NXjllVdgNBohCAJqa2sx\nNDQkV3eIxul3ByGiuPP/kuSRwJU5AjDEY4CnZNZrEIuLCIYrMwSSfGQLAC6XC1arNfW1zWaD0+kE\nADidTthstqvuS3dNMBhMHUtcV1eXeh6j0QgA+PDDD9Hd3Y3rr79eru4QjVMK8/8SQ426YrcBulkD\nYEom/chWQNYCoCwVbPIyl+Gpia658raLFy/isccew/e+9z2o1ZOXY7Va9VCpcjvG2G435XRdKaqU\nvhSzH8MnLgMArm2py0s7pvMctSYdLrv8qKszlsSJcPn8vZzuGgYAzGqwFOX3XQ6vlQZ78oOQQq2a\ntL3l0JdMVUpfit0P2QKAw+GAy+VKfd3f3w+73T7hfX19fXA4HFCr1RNeo9frEQqFoNPpUo8FgN7e\nXjzyyCP4x3/8RyxevHjKNrndgZz6Yreb4HR6c7q21FRKX4rdj3OXktNNNSph2u2Ybl/UCgGiCHR2\nu4t+ImC+fy8dl5M/Z7WAgv++i/1vLFPSR5rOy0NwmCZeK1EufclEpfSlUP2YLGTINgWwcuVKHDhw\nAABw6tQpOByO1JB9c3MzfD4furq6EIvFcOjQIaxcuTLtNStWrEjdfvDgQaxatQoA8Hd/93fYuXMn\nlixZIlc3iCbUOxiAUiEU7RTAsSp5KyBrAEzNzCkAypFsIwCtra1YsmQJNm3aBEEQsGPHDuzfvx8m\nkwnr1q3Dzp07sW3bNgDA+vXr0dLSgpaWlquuAYCtW7di+/btaGtrQ1NTEzZs2IALFy7g7bffxp49\ne1Lf84EHHkgtFiSSiyiK6B0MwGGtgVJR/FIa+jHnAdhRU+TW5BcXAU4tdSAQywFTlmRdA/DYY4+N\n+3rRokWp/1++fDna2tqmvAZIThns3bt33G0tLS04ceJEnlpKlLlhfwTBcAyLZtcWuykAKn8EQBAA\ns6G4UxuljNUAKVfF//hCVGZSOwBKYAsgUNkHArm9YVgMmpIYaSlVppERAB4IRNniq4ooSz0ltAUQ\nGHsgUGXNAYuiiCFfmMP/UzDWqCCAawAoewwARFmSDgFqtBmK3JIkQ01ljgB4g1HE4iKspuIvtCxl\nSoUChho1vJwCoCwxABBlqfSmACpzBMA9zGOAM2U2aDgFQFljACDKUu9AAMYaNYw1pbEwzVChawAG\nhkMAAJuFAWAqZr0a/lAMsXii2E2hMsIAQJSFaCwOpyeIphL59A+MHQGosADgSQaAOjOnAKYibQX0\nBStrFIjkxQBAlIW+wSBEEWioK435f2DMNsAK++MvjQDUlUCxpVJn0nMnAGWPAYAoC9IOgFIaAVAp\nFdCoFZU3BTAyAjCDIwBTGq0GyABAmWMAIMpCj8sPoLRGAIDkVsBKWwToGg5BpVSk9rlTeqZUNcDK\n+jdA8mIAIMpCKY4AAMliQJU4AlBn1kIhFP+Ew1LHaoCUCwYAoiz0uPzQqBSwldi8tEGnRjAcQyKH\nY7dLUTgShy8Y5fx/hhgAKBcMAEQZSowcAtRg05fcp1KDTgURQDBcGaMAqQWAnP/PiGnkrAROAVA2\nGACIMjToCSESS6BxRmnN/wOj5wFUylZA7gDIDkcAKBcMAEQZupwqAVxa8//A6HkAgQpZCMgaANnR\naZRQqxQsB0xZYQAgylDvQHIHAEcA5NfvDgIA6q2lF7ZKkSAIMOvVrANAWWEAIMpQagSgxHYAAGNO\nBKyQYkB97uTP2mGrKXJLyodJr8FwIAqxQhaCkvwYAIgy1DPghyCU5qdSfYWdB9DvDqJGq4SpRM5b\nKAdmgwbRWAKhSLzYTaEywQBAlKGegQDstTVQq0rvZZMqB1wBawASooj+oSAcVj2EEtttUcpMI9UA\nuQ6AMlV6f8mISpA3EIEvGC3JBYDA2EWA5T8CMOQNIxpLoN7K4f9sjO4EKP8QSIXBAECUgR5p/r8E\nFwAClbUIsG+k2qKjBKdaSpk5VQ6YIwCUGQYAogz0SDsASnABIFBZ2wD7hqQdABwByAZrAVC2GACI\nMpAaASixQ4AklTQC0D/ILYC5kKoBcisgZYoBgCgDPSW8BRBIHgmsVSsrYg3A5QHpxMXS/FmXKq4B\noGwxABBloGfAD7NBkxpqL0V6naoidgF0OX2wGDUwcgtgVkwjAYC7AChTDABEUwhH4xjwhEruCOAr\nGXSqsp8CCISiGBwOo9luLHZTyo60DZBTAJQpBgCiKfQNBiCidOf/JakjgRPlWwmuy5kc/m+2l/bP\nuhSplAoYdCp4OQVAGWIAIJqCNP9f6nPSqWqAZXwkcLfTBwAcAciR2aDhLgDKGAMA0RSkLYBNZTAC\nAJT3VsDREQAGgFyY9Br4AlHEE4liN4XKAAMA0RRK+RCgsYypUrDlGwA6nT4IQun/rEuVWa+GCMAX\nLN9RICocBgCiKfQO+KFVK2E1aYvdlEmltoGV6SKweCKBS31eNNUZoFEri92csmRiNUDKAgMA0SQS\nCRG9g0E01JX+wTSWkT/+njKdA+7q9yMSTWD+THOxm1K2WA2QssEAQDQJlyeIWDxR8lsAAcBsHPnj\n7yvPP/7nLnsAAPObLEVuSfkyS1sBGQAoAwwARJO4XOIlgMey6Mt7BOBc90gAmMkAkKtUMSB/+a4D\nocJhACCaRG+ZLAAERk+DK9sRgO5h6LWqkt9uWcpS/wbKNARSYTEAEE3icuoUwNIfATDWqKEQhGmN\nAHT2+9D2+49wusOdx5ZNzeMLo38oiHkzzVCU+FqLUpYKAFwESBlgACCaxGWXH0qFAEcZHE2rUAgw\n6dU5//F3DQXxf376Dg4c68R3f/YnXOgZznML03vvwiAA4GNzbAX7npXIXAFbQalwGACI0kiIIrpd\nfjTW6aFSlsdLxWzQwJNjAPjv9osIhuNYvsiBhCjil29cyG/jJnFqJABc18IAMB01WhWUCoFTAJSR\n8virRlQEg54QwpE4ZpZRVTqLQYNwJI5wJJ7VdcP+CA6/24t6mx5f/uwStDSa8d75wYKcLJcQRZy6\nOAiLUYOZPANgWgRBSJYD5hQAZYABgCiNLldy/n/mjPJ5UzLnWAvgT2ddiCdE3H5DExQKATdda0dC\nFPGnsy45mjnOpT4vvIEorptrK/laC+XApFdzCoAywgBAlIZ0ME05fSrNdRHYnz5KvtF/fOEMAEDr\nNXYAwImzA3ls3cTe+dAJALh+wQzZv1c1MOs1CEezHwWi6sMAQJRGtzQCUEZTALmUAw5H43j/4iCa\nZhjgsCa34DmsNbCatPioawiiKN/xwqIo4u3T/dCoFVg6v06271NNpBBYiOkbKm8MAERpdDv90KgV\nmGHRFbspGasdqQY45AtnfM0HF92IxBK4YcwncEEQsGCmBd5AFP1Dwby3U9LZ70OfO4hl82dAy/r/\neWEu84JQVDgMAEQTiCcS6Bnwo6nOUFb70m3mZFgZGA5lfM2fziaH4G9YOH4IfkFzsiLf2S5Pnlp3\ntbdO9wMAbl7kkO17VBuTYWQrIKsB0hRkDQBPPfUU7r33XmzatAknT54cd9+RI0ewceNG3HvvvXj+\n+ecnvaanpwf3338/Nm/ejEcffRSRSDLZejwePPjgg/jqV78qZzeoCvW7g4jFxbKa/weAupEAMDic\n2QhAcqHfAEx6NeY1jj+ER6rJf7HHm99GjhBFEW9x+D/veCAQZUq2AHDs2DF0dHSgra0Nu3fvxu7d\nu8fdv2vXLjz77LN46aWXcPjwYZw9ezbtNXv27MHmzZvx4osvYs6cOdi3bx8AYMeOHbjxxhvl6gJV\nsW6ntAOgfOb/AaDWpIFCEDDgyWwE4ELPMIb9EVw/fwYUivEjHTPtBggC0NkvTwD4qMuDfncQrQvt\nHP7Po9R5AAwANAXZAkB7ezvWrl0LAJg/fz48Hg98vuSq6s7OTlgsFjQ2NkKhUGD16tVob29Pe83R\no0dxxx13AADWrFmD9vZ2AMkQwQBAcuga2QHQXGYjAEqFAlaTJuMpAGn1/5XD/wCgVSvRYNOj0+mX\nZSHgGyd7AACrljXm/bmrmXlkCmCYUwA0BdkCgMvlgtVqTX1ts9ngdCbnGp1OJ2w221X3pbsmGAxC\no0mm2rq6utTzGI3l9emMykdqBKCMdgBI6sw6DPnCiMUTUz72T2ddUCkVWDJ34gp8sxxGBMOxjEcU\nMhUMx/DW6X7MsOhw7Rzr1BdQxswcAaAMqQr1jXL5BDHRNdP5JGK16qFS5TbUaLebcv6+paZS+iJn\nPzpdfliMGixsqStIcZp89qXJYcKZLg8UGjXstvQn6112+tDt9OOmxfVonlk74WMWtdTh2Af9GArF\nsTjDNmbSl4NHOxCOxrHxloWod5infHyxlONrpXZkK2comhjX/nLsSzqV0pdi90O2AOBwOOByjVYR\n6+/vh91un/C+vr4+OBwOqNXqCa/R6/UIhULQ6XSpx+bC7Q7kdJ3dboLTKc88aKFVSl/k7IcvGEX/\nYADXtdjgcvlk+R5j5bsvJl3yZf3BOScU8fS19X/7ZgcAYGmLNe33t40MJ58668SChqlHQzLty6uH\nz0MAcEOLrWT/PZbza6VGq4JrKJBqfzn35UqV0pdC9WOykCHbFMDKlStx4MABAMCpU6fgcDhSQ/bN\nzc3w+Xzo6upCLBbDoUOHsHLlyrTXrFixInX7wYMHsWrVKrmaTYRLfckX5ZyG8vyU0ViX/ATYM1LI\nKJ13PnRCIQj4+EJ72sfMciR/Bp39+QtC5y8P41z3MK6bV4e6MqqxUE7MejWGWQ6YpiDbCEBrayuW\nLFmCTZs2QRAE7NixA/v374fJZMK6deuwc+dObNu2DQCwfv16tLS0oKWl5aprAGDr1q3Yvn072tra\n0NTUhA0bNiAej+OBBx7A8PAw+vr6cP/99+Phhx/GLbfcIleXqEp0SAGgvlwDQHLhYs9A+hGvAU8I\nF3qGsXiOFcYaddrH1Ro1MOhUqbLI+fCbY5cAAHfdPCtvz0njmQ0a9A95EE8koFSw3AtNTNY1AI89\n9ti4rxctWpT6/+XLl6OtrW3Ka4DklMHevXuvuv2FF17IQyuJxuvoTQaA2WU6AtBQp4cAoGcg/QjA\nG+8mV+B/4mP1kz6XIAiYaTfio84hRKJxaKa5Xa9/KIh3PuzH7HojFnPxn2ysJi1EMbkTwGrSFrs5\nVKIYDYmu0NHng16rgr1Mh6e1aiXqLLq0IwCJhIg/nLgMnUaJmxdPvZ5mpt0AEZOPKGTqt291QhSB\nP7t5Nk/+k1GtMfmm7/ZmXhKaqg8DANEYwXAMfYMBzGkwlfUb1MwZBnj8kQnPBDh5bgBubxifXNIA\nnWbqQcDmka2QXdOcBvAFo3j95GXYzFrcxNK/spI+9bu9+d2+SZWFAYBojEtlPv8vSVfHXxRFvHL4\nAgDgUx+fmdFzzZyRXFMg1UbI1aE/diMSTeDOm2ZBpeSfHjmNBgCOAFB6fBUSjXG+ZxgAMLexvAPA\nwubkvv4zXUPjbv/TWRcu9npx0yIHmh2ZFTmSzkPomsaWyGgsjt+904UarQqrrm/K+XkoM6kAkMWp\nkFR9GACIxpA+MS+YaSlyS6anpdEElVLABxfdqeJZ4Ugcbb87C0EA/vzWloyfy6BTw2rSTmsEoP1U\nH4b9Edx+QxNqtAWrP1a1OAJAmWAAIBohiiLOdXtgM2tTx+qWK7VKievnz0C3y48LI6f57XvtHPqH\ngrjr5tmpYf1MzbQb4PaG4Q9lv7c8IYo4cOwSlAoBa2/i1r9CkBYBDjEA0CQYAIhGOIeCGA5Ey/7T\nv2T1Dcmh9l++cQG/eP08fne8C411emzI4tO/pHnkVMRcRgFOnhtAz0AAn/xYPbekFYhKqYBZr+YI\nAE2KAYBoxEcjw//zKyQAfKzFhmtn1eLd8wN45fBF1Bo1eHTjspz28kvrALqnqC44kQNHpcI/s7O+\nlnJnNeng9oZlOcmRKgMn44hGnOuujPl/iUIQ8NWNy/DbtzuRSIhYe9OsSav+TSYVALLcCnihZxgf\ndg7hunm2jBcdUn5YTVp09HkRCMeK3RQqUQwARCPOdnugUSkwq4LeqGq0Knx2ZfZD/ldqqjNAANCV\n5RTAb0Y+/f8ZP/0XXK20EHA4jLnFbQqVKE4BECFZpKbL6ce8JjP3qE9Ao1bCYa1Bt9OX8ZCycyiI\ntz/sx2wHy/4WQ505GQBcwywGRBPjXzoiAKc73ADAN6pJzLQb4Q/F4PFHMnp8quzvJ1j2txjstTUA\nANdQsMgtoVLFAEAE4INLUgCwFbklpSubioC+YBR/YNnfopKOWnZ5OAJAE2MAIEJyBECrVpZ9BUA5\nSYv4MjkT4LWRsr/rWPa3aGZYkiMAAwwAlAZfmVT13N4wegYCuGZWLd+sJtE8shOgY+S8hHSisTj+\n7ztdqNEqcRvL/haNWa+GRqWA08MpAJoY/9pR1Tt9ifP/mai36aHXqnD+8vCkjzvyXm+y7O/HZ7Ls\nbxEJgoA6i44jAJQWAwBVvfcvDAJgAJiKQhAwr8mMfncQ3sDECwETCRG/OdaZLPt7I8v+FtsMSw38\noRj8wexLOFPlYwCgqpYQRbx7YRBmgwaz6itn/79c5jWZASDtKMDRU73oGwzglusaWPa3BMyoTS4E\n7HcHitwSKkUMAFTVLvV5MeyPYOk8GxTcqjala2Yljxn+YGTb5FiiKOK/Dn0EgIV/SsWMkZ0AfYMM\nAHQ1BgCqau+eGwAALJ1XV+SWlIeFzRZoVAqcGpk2GeujLg8+7HDj+vl1aMrytEGSh31kJ0BPDmc4\nUOVjAKCqdvL8AAQBWNLC/f+ZUKuUuGZ2Lbpd/qsWl/3yjQsAgLs/OacYTaMJNNTpAWR/hgNVBwYA\nqlq+YBTnu4exYKYFBl1uh+RUo9aFdgDAm+/3pm57/+IgPuhw4+PX2FPTBFR89dYaCALQ1c8AQFdj\nAKCq9d6FAYjg8H+2bl5cD7VKgT+cuIxYPIFoLIGf/S459//F9R8rcutoLLVKiRkWHboZAGgCDABU\ntU6eTc7/L5vPAJANvU6FlUsb4RwK4ed/OI+f/Po0upx+rL6hCQv46b/kNNYZMOQLw8etgHQFBgCq\nStFYAifOuVBn1lXU8b+F8vnV82AxavDro5fQfqoXLY0m3LNmQbGbRRNosCXXAfRyJwBdgWW6qCp9\n0OFGMBzHqmVNPKkuBwadGn+35Ub89u0uGHQqrFs+i1X/SlTjyELAngE/Fsy0FLk1VEr4iqWqdPyM\nEwDQeo29yC0pXzNqa3Df2oXFbgZNobEuuSWTIwB0JU4BUNVJJET88SMnzHo1PxFRxZNqMnRyISBd\ngQGAqs5HXUPwBqL4+DV2KBQc/qfKZqxRw2GtQUevF6IoFrs5VEIYAKjqvPMhh/+puiyYVQtvIAq3\nN1zsplAJYQCgqhKLJ3Dsgz4Ya9Q8/Y+qxoLm5PbMCz3eIreESgkDAFWV9y4MYjgQxSc+Vg+Vkv/8\nqTpIAaCjb+JTHKk68S8gVZUj7yXL1664rqHILSEqHKlA00WOANAYDABUNfyhKP70kQuNdXrMbTAV\nuzlEBWPSa+Cw1uDc5WHEE4liN4dKBAMAVY3DJ3sQiydw69JGFv+hqrNothXBcAyX+rgdkJIYAKgq\nJBIi/u87XdCoFFh1fVOxm0NUcNKi1w863EVuCZUKBgCqCn8664LLE8It1zXAWMOjf6n6LJ5jhQDg\n3XMDxW4KlQgGAKp4CVHEL9+4AAHAuptmFbs5REVhNmgwf6YFZ7qGMByIFLs5VAIYAKjivX26H539\nPnxySX2qLCpRNWq9xg5RHD0Lg6obAwBVtGA4hv88dBZKhYA/v7Wl2M0hKqqbFzsgCMDrJ3qK3RQq\nAY9v2sQAAA0ISURBVAwAVNH+63/PYXA4jE/fMgcOq77YzSEqKptZh2Xz6nChZxgXelgUqNoxAFDF\nevP9Xvz+eDca6/T49C1zi90copKwdnlyHcwv37hQ5JZQsTEAUEU6dXEQe189DZ1GiUf+YinUKv5T\nJwKAj82x4tpZtTh5bgAnzrqK3RwqIv5VpIpz5L0e/PPLJyGKIv5mw3Vc+Ec0hiAI+Mt110CpELD3\n1Q/gGgoWu0lUJLIGgKeeegr33nsvNm3ahJMnT46778iRI9i4cSPuvfdePP/885Ne09PTg/vvvx+b\nN2/Go48+ikgkuYXllVdewec//3l84QtfwMsvvyxnV6gM9A0G8Nz+d/Fv//0B1CoBj268Hkvn1RW7\nWUQlp9lhxKY7FmI4EMX/efE4LvZyPUA1Usn1xMeOHUNHRwfa2tpw7tw5PPnkk2hra0vdv2vXLvzo\nRz9CfX09tmzZgrvuuguDg4MTXrNnzx5s3rwZd999N5555hns27cPGzZswPPPP499+/ZBrVZj48aN\nWLduHWpra+XqEpUYURTR5w7io64hvH3aifcuDEAUgQXNFvz1pxdz0R/RJO64sRmRaBz7XjuHXf/+\nDm65rh4rrmvEvEYztBplsZtHBSBbAGhvb8fatWsBAPPnz4fH44HP54PRaERnZycsFgsaGxsBAKtX\nr0Z7ezsGBwcnvObo0aP41re+BQBYs2YNfvzjH6OlpQVLly6FyZQ81KW1tRXHjx/Hpz71Kbm6NE7P\ngB+/OnIRsVjyYA3xygeIE/4vRPGqR465L/33G3vdJA8b9xzilY8UAY1GhUgkNv6eK76xmP6utG2a\nvB0Zfq90d2B8XxIJwB+OYdATRCw+entLoxl3f2I2brzWzlr/RBm4+5NzMKveiJ/97iwOv9uLw+/2\nQgBgM2thrNFAr1NBpVRAEACFIEChECAIQDFfXVqtGuFwtIgtyI90/Wi2G/HZAm1Zli0AuFwuLFmy\nJPW1zWaD0+mE0WiE0+mEzWYbd19nZyfcbveE1wSDQWg0GgBAXV0dnE4nXC7XVc/hdE5e3MJq1UOl\nyi3Z2u3jT497v9ODN0/15fRc1e7K92YhzZ1X/pEZvUuAxajBvJkWNNYZsbjFhmULZmBWffme8Hfl\nv69yxr6UpnR9WWM34bab5uDds0689X4fznV70DcYQK87gHAkXuBW0sVeLx7486VQKuSPWbIFgCtN\n9mkxm2vSPU8mz+92B7JuA5B84Tid48/R/tgsC577f1chlhj9vle/YU38C5z0DXDMV5N9iL36OYSx\nX6Rtk91ugsvlvereTL+XcOUzTvK95PwUPtHv5Mqvy8VEfSlX7EtpyqQvM601mLly7rjbYvEE4gkR\niYQIURSREJOltYtpRp0RroHyP9EwXT9qNCoM5rF/k4VY2QKAw+GAyzW6xaS/vx92u33C+/r6+uBw\nOKBWqye8Rq/XIxQKQafTpR470fPfcMMNcnVnQnpd+R0qo1Eroc5xFISIqotKqUCp/bmwGLWIBMv/\nLINS6IdsuwBWrlyJAwcOAABOnToFh8MBo9EIAGhubobP50NXVxdisRgOHTqElStXpr1mxYoVqdsP\nHjyIVatW4frrr8e7776L4eFh+P1+HD9+HDfddJNc3SEiIqooso0AtLa2YsmSJdi0aRMEQcCOHTuw\nf/9+mEwmrFu3Djt37sS2bdsAAOvXr0dLSwtaWlquugYAtm7diu3bt6OtrQ1NTU3YsGED1Go1tm3b\nhgcffBCCIOCRRx5JLQgkIiKiyQliLpPzZSrX+bxqmwssB5XSD4B9KVXsS2mqlL4Uqh+TrQFgJUAi\nIqIqxABARERUhRgAiIiIqhADABERURViACAiIqpCDABERERViAGAiIioCjEAEBERVaGqKgRERERE\nSRwBICIiqkIMAERERFWIAYCIiKgKMQAQERFVIQYAIiKiKsQAQEREVIVUxW5AsQSDQTzxxBMYGBhA\nOBzGww8/jEWLFuHxxx9HPB6H3W7H008/DY1Gg1deeQX//u//DoVCgXvuuQdf+MIXEI1G8cQTT+Dy\n5ctQKpX49re/jVmzZhW1T6FQCJ/5zGfw8MMP45ZbbinLvhw9ehSPPvooFi5cCAC45ppr8Nd//ddl\n2ZdXXnkF//Zv/waVSoWvfvWruPbaa8uyHy+//DJeeeWV1NfvvfceXn311bLsi9/vx/bt2+HxeBCN\nRvHII49gwYIFZdmXRCKBHTt24KOPPoJarcbOnTuh1+vLqi9nzpzBww8/jAceeABbtmxBT0/PtNt/\n+vRp7Ny5EwBw7bXX4lvf+lZR+gIA//Ef/4HvfOc7OHbsGAwGAwCUVl/EKvU///M/4r/+67+KoiiK\nXV1d4p133ik+8cQT4quvviqKoih+73vfE3/605+Kfr9fvPPOO8Xh4WExGAyKn/70p0W32y3u379f\n3LlzpyiKovj666+Ljz76aNH6InnmmWfEz33uc+J//dd/lW1f3nzzTXHr1q3jbivHvgwODop33nmn\n6PV6xb6+PvGb3/xmWfbjSkePHhV37txZtn154YUXxO9+97uiKIpib2+veNddd5VtXw4ePJj6/h0d\nHeKXvvSlsuqL3+8Xt2zZIn7zm98UX3jhBVEU8/Na37Jli3jixAlRFEXx61//uvjaa68VpS8///nP\nxWeeeUa8/fbbRZ/Pl3pcKfWlaqcA1q9fj4ceeggA0NPTg/r6ehw9ehR33HEHAGDNmjVob2/HiRMn\nsHTpUphMJuh0OrS2tuL48eNob2/HunXrAAArVqzA8ePHi9YXADh37hzOnj2L22+/HQDKui9XKse+\ntLe345ZbboHRaITD4cA//MM/lGU/rvT888/j4YcfLtu+WK1WDA0NAQCGh4dhtVrLti8XL17EsmXL\nAACzZ8/G5cuXy6ovGo0GP/zhD+FwOFK3Tbf9kUgE3d3dqZ+L9BzF6MvatWvxta99DYIgpG4rtb5U\nbQCQbNq0CY899hiefPJJBINBaDQaAEBdXR2cTidcLhdsNlvq8Tab7arbFQoFBEFAJBIpSh8A4Dvf\n+Q6eeOKJ1Nfl3JezZ8/iK1/5Cu677z4cPny4LPvS1dWFUCiEr3zlK9i8eTPa29vLsh9jnTx5Eo2N\njbDb7WXbl09/+tO4fPky1q1bhy1btmD79u1l25drrrkGb7zxBuLxOM6fP4/Ozk50d3eXTV9UKhV0\nOt2426b7u3C5XDCbzanHSs9RjL4YjcarHldqfanaNQCSn/3sZ/jggw/wjW98A+KYqshimgrJ2d5e\nCL/4xS9www03pJ2/K6e+zJ07F3/7t3+Lu+++G52dnfjiF7+IeDw+ZdtKsS9DQ0N47rnncPnyZXzx\ni18s239fkn379uEv/uIvrrq9nPryy1/+Ek1NTfjRj36E06dP48knnxx3fzn1ZfXq1Th+/Dj+8i//\nEtdeey3mzZuHM2fOTNm2UuzLRPLR/lLr05WK3ZeqHQF477330NPTAwBYvHgx4vE4DAYDQqEQAKCv\nrw8OhwMOhwMulyt1XX9/f+p2KY1Fo1GIophKroX22muv4Xe/+x3uuecevPzyy/j+978PvV5fln2p\nr6/H+vXrIQgCZs+e/f+3dz+v7McBHMefspE+R7RQFBdkzUqp7eNfcFBu/gFaScmPkXJC7bByNecl\nLk7johxErUUcHHZwWYqWfDJN+PQ9LPp+z+pr796vxx/wac/2bnvt8zmMtrY2np+fjWtpbW0lGo0S\nCATo7u7GcRxjz9eXi4sLotEogLHnq1Ao4LouAP39/Tw8PNDS0mJkC8Dc3BzZbJb19XU8zyMUChnb\nAj8/V+3t7d+PeP6+Rr2otxZrB0A+n2d3dxeo3ZZ5fX0lFotxdHQEwPHxMWNjY0QiEa6vr/E8j0ql\nQqFQYGRkhHg8Ti6XA+Dk5ITR0dFfa0mn0xwcHLC3t8fk5CQzMzPGthweHpLJZAB4fHykXC4zMTFh\nXIvrupyfn+P7Pk9PT0afL6h9+DiO8/0FYWpLT08PV1dXAJRKJRzHIR6PG9lye3vL8vIyAKenpwwO\nDhr7vnz56esPBoP09vaSz+f/uUa9qLcWa/8NsFqtsrKywv39PdVqlUQiwdDQEIuLi7y9vdHZ2cnG\nxgbBYJBcLkcmk6GhoYGpqSnGx8f5/PxkdXWVu7s7mpqa2NzcpKOj47ez2N7epqurC9d1jWx5eXlh\nfn4ez/N4f38nkUgwMDBgZEs2m2V/fx+A6elpwuGwkR1Qu2OWTqfZ2dkBar9cTGypVCokk0nK5TIf\nHx/Mzs7S19dnZIvv+ySTSYrFIs3NzaRSKRobG41pubm5YWtri1KpRCAQIBQKkUqlWFpa+tHrLxaL\nrK2t4fs+kUjkeyT975ZYLMbZ2RmXl5eEw2GGh4dZWFioqxZrB4CIiIjNrH0EICIiYjMNABEREQtp\nAIiIiFhIA0BERMRCGgAiIiIW0gAQERGxkAaAiIiIhTQARERELPQHWdMR6FZOgLEAAAAASUVORK5C\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe438b06048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nifty50['high'].plot('kde')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "ff686801-b688-282d-a552-7c7f00b8f0fc"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe438c5df60>"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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EGSUfIpUqs5BvEQEAHp+0XLLfK/3flicm/PrBxj71XySTZRaUY6rHWYb49Mse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\nV79ksEAMFohoFMFQBJeudaFkhkXZLpmmvvJiK2x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Z7hQfHz+gDJ5ed/6SlZVFVVUVcPN6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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe438c69898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"banknifty['high'].plot('kde')"
]
}
],
"metadata": {
"_change_revision": 125,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160639.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "5be6026a-318a-d445-9aa3-bd0c0e64b2f3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"basic_income_dataset_dalia.csv\n",
"codebook_basicIncome.pdf\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import seaborn as sns\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "737d5b9e-8b3a-0dfb-f377-df8242143863"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country_code</th>\n",
" <th>uuid</th>\n",
" <th>age</th>\n",
" <th>gender</th>\n",
" <th>rural</th>\n",
" <th>dem_education_level</th>\n",
" <th>dem_full_time_job</th>\n",
" <th>dem_has_children</th>\n",
" <th>question_bbi_2016wave4_basicincome_awareness</th>\n",
" <th>question_bbi_2016wave4_basicincome_vote</th>\n",
" <th>question_bbi_2016wave4_basicincome_effect</th>\n",
" <th>question_bbi_2016wave4_basicincome_argumentsfor</th>\n",
" <th>question_bbi_2016wave4_basicincome_argumentsagainst</th>\n",
" <th>age_group</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AT</td>\n",
" <td>f6e7ee00-deac-0133-4de8-0a81e8b09a82</td>\n",
" <td>61</td>\n",
" <td>male</td>\n",
" <td>rural</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>I know something about it</td>\n",
" <td>I would not vote</td>\n",
" <td>None of the above</td>\n",
" <td>None of the above</td>\n",
" <td>None of the above</td>\n",
" <td>40_65</td>\n",
" <td>1.105.534.474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AT</td>\n",
" <td>54f0f1c0-dda1-0133-a559-0a81e8b09a82</td>\n",
" <td>57</td>\n",
" <td>male</td>\n",
" <td>urban</td>\n",
" <td>high</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>I understand it fully</td>\n",
" <td>I would probably vote for it</td>\n",
" <td>A basic income would not affect my work choices</td>\n",
" <td>It increases appreciation for household work a...</td>\n",
" <td>It might encourage people to stop working</td>\n",
" <td>40_65</td>\n",
" <td>1.533.248.826</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>AT</td>\n",
" <td>83127080-da3d-0133-c74f-0a81e8b09a82</td>\n",
" <td>32</td>\n",
" <td>male</td>\n",
" <td>urban</td>\n",
" <td>NaN</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>I have heard just a little about it</td>\n",
" <td>I would not vote</td>\n",
" <td>‰Û_ gain additional skills</td>\n",
" <td>It creates more equality of opportunity</td>\n",
" <td>Foreigners might come to my country and take a...</td>\n",
" <td>26_39</td>\n",
" <td>0.9775919155</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AT</td>\n",
" <td>15626d40-db13-0133-ea5c-0a81e8b09a82</td>\n",
" <td>45</td>\n",
" <td>male</td>\n",
" <td>rural</td>\n",
" <td>high</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>I have heard just a little about it</td>\n",
" <td>I would probably vote for it</td>\n",
" <td>‰Û_ work less</td>\n",
" <td>It reduces anxiety about financing basic needs</td>\n",
" <td>None of the above</td>\n",
" <td>40_65</td>\n",
" <td>1.105.534.474</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>AT</td>\n",
" <td>24954a70-db98-0133-4a64-0a81e8b09a82</td>\n",
" <td>41</td>\n",
" <td>female</td>\n",
" <td>urban</td>\n",
" <td>high</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>I have heard just a little about it</td>\n",
" <td>I would probably vote for it</td>\n",
" <td>None of the above</td>\n",
" <td>It reduces anxiety about financing basic needs</td>\n",
" <td>It is impossible to finance | It might encoura...</td>\n",
" <td>40_65</td>\n",
" <td>58.731.136</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country_code uuid age gender rural \\\n",
"0 AT f6e7ee00-deac-0133-4de8-0a81e8b09a82 61 male rural \n",
"1 AT 54f0f1c0-dda1-0133-a559-0a81e8b09a82 57 male urban \n",
"2 AT 83127080-da3d-0133-c74f-0a81e8b09a82 32 male urban \n",
"3 AT 15626d40-db13-0133-ea5c-0a81e8b09a82 45 male rural \n",
"4 AT 24954a70-db98-0133-4a64-0a81e8b09a82 41 female urban \n",
"\n",
" dem_education_level dem_full_time_job dem_has_children \\\n",
"0 no no no \n",
"1 high yes yes \n",
"2 NaN no no \n",
"3 high yes yes \n",
"4 high yes yes \n",
"\n",
" question_bbi_2016wave4_basicincome_awareness \\\n",
"0 I know something about it \n",
"1 I understand it fully \n",
"2 I have heard just a little about it \n",
"3 I have heard just a little about it \n",
"4 I have heard just a little about it \n",
"\n",
" question_bbi_2016wave4_basicincome_vote \\\n",
"0 I would not vote \n",
"1 I would probably vote for it \n",
"2 I would not vote \n",
"3 I would probably vote for it \n",
"4 I would probably vote for it \n",
"\n",
" question_bbi_2016wave4_basicincome_effect \\\n",
"0 None of the above \n",
"1 A basic income would not affect my work choices \n",
"2 ‰Û_ gain additional skills \n",
"3 ‰Û_ work less \n",
"4 None of the above \n",
"\n",
" question_bbi_2016wave4_basicincome_argumentsfor \\\n",
"0 None of the above \n",
"1 It increases appreciation for household work a... \n",
"2 It creates more equality of opportunity \n",
"3 It reduces anxiety about financing basic needs \n",
"4 It reduces anxiety about financing basic needs \n",
"\n",
" question_bbi_2016wave4_basicincome_argumentsagainst age_group weight \n",
"0 None of the above 40_65 1.105.534.474 \n",
"1 It might encourage people to stop working 40_65 1.533.248.826 \n",
"2 Foreigners might come to my country and take a... 26_39 0.9775919155 \n",
"3 None of the above 40_65 1.105.534.474 \n",
"4 It is impossible to finance | It might encoura... 40_65 58.731.136 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"../input/basic_income_dataset_dalia.csv\")\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "b02bd4cd-fb95-31b1-4c93-861bd85a5ad0"
},
"outputs": [],
"source": [
"#rename the columns to be less wordy\n",
"data.rename(columns = {'question_bbi_2016wave4_basicincome_awareness':'awareness',\n",
" 'question_bbi_2016wave4_basicincome_vote':'vote',\n",
" 'question_bbi_2016wave4_basicincome_effect':'effect',\n",
" 'question_bbi_2016wave4_basicincome_argumentsfor':'arg_for',\n",
" 'question_bbi_2016wave4_basicincome_argumentsagainst':'arg_against'},\n",
" inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "557f69da-7005-6223-70bb-a85a01977364"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb1b6938fd0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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8/f21Zs0aDRkyRE8++aQqVaqkoKAgTZ06VYGBgUpISNDIkSNls9k0evRoBQcHq3v37tq8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\nAACYGEUNAICJUdQAAJjY/wM4ccw30OSBAwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb1f0119ef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['vote'].value_counts().plot(kind = 'bar')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "5d7866fd-ee92-7607-867c-9afe51f3f3b8"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb1b63572b0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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v1ujoqOrq6iRJtbW1CoVCGhsbU2lpqdxut1wulyoqKhQOhxUKhVRfXy9J8vl8CofDaZwO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\naqzvxgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb1f0132278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def for_against(row):\n",
" if row == 'I would not vote': return(0)\n",
" elif row == 'I would probably vote for it': return(1)\n",
" elif row == 'I would vote against it': return(1)\n",
" elif row == 'I would vote for it': return(1)\n",
" else: return(0)\n",
"\n",
"data['yes_no'] = data['vote'].apply(for_against)\n",
"data['yes_no'].value_counts().plot(kind = 'bar')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "86daf9d7-6c19-1728-041f-d2df5f36d476"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['uuid', 'age', 'vote', 'arg_for', 'arg_against', 'weight', 'yes_no',\n",
" 'country_code_AT', 'country_code_BE', 'country_code_BG',\n",
" 'country_code_CY', 'country_code_CZ', 'country_code_DE',\n",
" 'country_code_DK', 'country_code_EE', 'country_code_ES',\n",
" 'country_code_FI', 'country_code_FR', 'country_code_GB',\n",
" 'country_code_GR', 'country_code_HR', 'country_code_HU',\n",
" 'country_code_IE', 'country_code_IT', 'country_code_LT',\n",
" 'country_code_LU', 'country_code_LV', 'country_code_MT',\n",
" 'country_code_NL', 'country_code_PL', 'country_code_PT',\n",
" 'country_code_RO', 'country_code_SE', 'country_code_SI',\n",
" 'country_code_SK', 'gender_female', 'gender_male', 'rural_rural',\n",
" 'rural_urban', 'dem_education_level_high', 'dem_education_level_low',\n",
" 'dem_education_level_medium', 'dem_education_level_no',\n",
" 'dem_full_time_job_no', 'dem_full_time_job_yes', 'dem_has_children_no',\n",
" 'dem_has_children_yes', 'awareness_I have heard just a little about it',\n",
" 'awareness_I have never heard of it',\n",
" 'awareness_I know something about it',\n",
" 'awareness_I understand it fully',\n",
" 'effect_A basic income would not affect my work choices',\n",
" 'effect_None of the above', 'effect_‰Û_ do more volunteering work',\n",
" 'effect_‰Û_ gain additional skills',\n",
" 'effect_‰Û_ look for a different job',\n",
" 'effect_‰Û_ spend more time with my family', 'effect_‰Û_ stop working',\n",
" 'effect_‰Û_ work as a freelancer', 'effect_‰Û_ work less',\n",
" 'age_group_14_25', 'age_group_26_39', 'age_group_40_65'],\n",
" dtype='object')"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"OBJ_COLS = list(data.select_dtypes(include = ['object']).columns)\n",
"OBJ_COLS.remove('uuid')\n",
"OBJ_COLS.remove('vote')\n",
"#columns have too many unique values\n",
"OBJ_COLS.remove('arg_for') \n",
"OBJ_COLS.remove('arg_against')\n",
"OBJ_COLS.remove('weight')\n",
"\n",
"data_all = pd.get_dummies(data, columns = OBJ_COLS)\n",
"data_all.columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "43127a94-7fed-7821-6090-596c969ddb6b"
},
"outputs": [],
"source": [
"PREDS = list(data_all.columns)\n",
"PREDS.remove('uuid')\n",
"PREDS.remove('vote')\n",
"PREDS.remove('yes_no')\n",
"PREDS.remove('arg_for')\n",
"PREDS.remove('arg_against')\n",
"PREDS.remove('weight')\n",
"\n",
"y = data_all['yes_no'].values"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "5ee676fa-11f1-5b44-ab31-da161872358a"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X = data_all[PREDS]\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3,random_state = 123)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "c1c36e0f-66e2-a35b-09a2-14a072eea214"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-logloss:0.686471\tvalid-logloss:0.686803\n",
"Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.\n",
"\n",
"Will train until valid-logloss hasn't improved in 50 rounds.\n",
"[10]\ttrain-logloss:0.634676\tvalid-logloss:0.637656\n",
"[20]\ttrain-logloss:0.600323\tvalid-logloss:0.605418\n",
"[30]\ttrain-logloss:0.573792\tvalid-logloss:0.580402\n",
"[40]\ttrain-logloss:0.555283\tvalid-logloss:0.563446\n",
"[50]\ttrain-logloss:0.541818\tvalid-logloss:0.55181\n",
"[60]\ttrain-logloss:0.5316\tvalid-logloss:0.543131\n",
"[70]\ttrain-logloss:0.524169\tvalid-logloss:0.537064\n",
"[80]\ttrain-logloss:0.517501\tvalid-logloss:0.531626\n",
"[90]\ttrain-logloss:0.512519\tvalid-logloss:0.5279\n",
"[100]\ttrain-logloss:0.50864\tvalid-logloss:0.525378\n",
"[110]\ttrain-logloss:0.50569\tvalid-logloss:0.52359\n",
"[120]\ttrain-logloss:0.503188\tvalid-logloss:0.522443\n",
"[130]\ttrain-logloss:0.500883\tvalid-logloss:0.521432\n",
"[140]\ttrain-logloss:0.498875\tvalid-logloss:0.52063\n",
"[150]\ttrain-logloss:0.497261\tvalid-logloss:0.520098\n",
"[160]\ttrain-logloss:0.495759\tvalid-logloss:0.51951\n",
"[170]\ttrain-logloss:0.494408\tvalid-logloss:0.519079\n",
"[180]\ttrain-logloss:0.493294\tvalid-logloss:0.518865\n",
"[190]\ttrain-logloss:0.492142\tvalid-logloss:0.518781\n",
"[200]\ttrain-logloss:0.490917\tvalid-logloss:0.51875\n",
"[210]\ttrain-logloss:0.48982\tvalid-logloss:0.518862\n",
"[220]\ttrain-logloss:0.488711\tvalid-logloss:0.518696\n",
"[230]\ttrain-logloss:0.487654\tvalid-logloss:0.5187\n",
"[240]\ttrain-logloss:0.486551\tvalid-logloss:0.518697\n",
"[250]\ttrain-logloss:0.485517\tvalid-logloss:0.518668\n",
"[260]\ttrain-logloss:0.484589\tvalid-logloss:0.518633\n",
"[270]\ttrain-logloss:0.48365\tvalid-logloss:0.518578\n",
"[280]\ttrain-logloss:0.482777\tvalid-logloss:0.518455\n",
"[290]\ttrain-logloss:0.481906\tvalid-logloss:0.518387\n",
"[299]\ttrain-logloss:0.481127\tvalid-logloss:0.518407\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb1b58f09b0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8PGbNmkWjRo2wsrLCwsICAwMDmjZtysqVK1FXV8fAwIDZs2fj6+srPwsNDQ1WrVqFiooK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\nySJbCCGEEEIINfs/xONHfVGJrn8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb1b63729b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import xgboost as xgb\n",
"\n",
"params = {}\n",
"params[\"objective\"] = \"binary:logistic\"\n",
"params['eval_metric'] = 'logloss'\n",
"params[\"eta\"] = 0.02\n",
"params[\"subsample\"] = 0.7\n",
"params[\"min_child_weight\"] = 1\n",
"params[\"colsample_bytree\"] = 0.7\n",
"params[\"max_depth\"] = 4\n",
"params[\"silent\"] = 1\n",
"params[\"seed\"] = 1632\n",
"\n",
"d_train = xgb.DMatrix(X_train, label=y_train)\n",
"d_valid = xgb.DMatrix(X_test, label=y_test)\n",
"\n",
"watchlist = [(d_train, 'train'), (d_valid, 'valid')]\n",
"\n",
"bst = xgb.train(params, d_train, 300, watchlist, early_stopping_rounds=50, verbose_eval=10)\n",
"\n",
"xgb.plot_importance(bst)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "eaa0a1a4-bd13-c099-1f16-c44dade0066d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classifier is 77.0 % accurate!\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"test_preds = bst.predict(d_valid)\n",
"\n",
"test_preds[test_preds > 0.5] = 1\n",
"test_preds[test_preds <= 0.5] = 0\n",
"\n",
"print('Classifier is {} % accurate!'.format(round(accuracy_score(y_test,test_preds) * 100),1))"
]
}
],
"metadata": {
"_change_revision": 235,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/160/1160657.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "18d02fb8-29dd-6869-03c9-92df14ac256b"
},
"source": [
"# Getting to know how kaggle works with this project."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b2a8bd23-ed67-9a9f-5d3e-86d3cc62c338"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"genderclassmodel.csv\n",
"gendermodel.csv\n",
"gendermodel.py\n",
"myfirstforest.py\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "65921bdd-5ca5-f36e-34fb-8265f001698e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(891, 12)\n",
"Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
" 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
" dtype='object')\n",
"(342, 12)\n",
"(549, 12)\n"
]
}
],
"source": [
"#reading testing and training data into a data frame.\n",
"train_data = pd.read_csv('../input/train.csv')\n",
"test_data = pd.read_csv('../input/test.csv')\n",
"\n",
"print(train_data.shape)\n",
"print(train_data.columns)\n",
"\n",
"\n",
"survived_list = train_data[ train_data.Survived == 1 ]\n",
"dead_list = train_data[ train_data.Survived == 0 ]\n",
"\n",
"print(survived_list.shape)\n",
"print(dead_list.shape)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "20ab34df-0d47-6f90-4c65-f9e96035c3c2"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "hist() missing 1 required positional argument: 'x'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-c5dd5e7b1924>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: hist() missing 1 required positional argument: 'x'"
]
}
],
"source": [
"plt.hist()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "710a3ab4-8514-5ab0-655b-0cf35d7c8236"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 152,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161026.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "20b25f91-4533-dd06-7f2e-f127bca3d383"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"movie_metadata.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "053137b7-2108-7672-3435-bd14373b20c4"
},
"outputs": [],
"source": [
"data = pd.read_csv(\"../input/movie_metadata.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "59f6a7c1-aee1-f0ed-652d-10b7e87acff7"
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method NDFrame.describe of color director_name num_critic_for_reviews duration \\\n",
"0 Color James Cameron 723.0 178.0 \n",
"1 Color Gore Verbinski 302.0 169.0 \n",
"2 Color Sam Mendes 602.0 148.0 \n",
"3 Color Christopher Nolan 813.0 164.0 \n",
"4 NaN Doug Walker NaN NaN \n",
"5 Color Andrew Stanton 462.0 132.0 \n",
"6 Color Sam Raimi 392.0 156.0 \n",
"7 Color Nathan Greno 324.0 100.0 \n",
"8 Color Joss Whedon 635.0 141.0 \n",
"9 Color David Yates 375.0 153.0 \n",
"10 Color Zack Snyder 673.0 183.0 \n",
"11 Color Bryan Singer 434.0 169.0 \n",
"12 Color Marc Forster 403.0 106.0 \n",
"13 Color Gore Verbinski 313.0 151.0 \n",
"14 Color Gore Verbinski 450.0 150.0 \n",
"15 Color Zack Snyder 733.0 143.0 \n",
"16 Color Andrew Adamson 258.0 150.0 \n",
"17 Color Joss Whedon 703.0 173.0 \n",
"18 Color Rob Marshall 448.0 136.0 \n",
"19 Color Barry Sonnenfeld 451.0 106.0 \n",
"20 Color Peter Jackson 422.0 164.0 \n",
"21 Color Marc Webb 599.0 153.0 \n",
"22 Color Ridley Scott 343.0 156.0 \n",
"23 Color Peter Jackson 509.0 186.0 \n",
"24 Color Chris Weitz 251.0 113.0 \n",
"25 Color Peter Jackson 446.0 201.0 \n",
"26 Color James Cameron 315.0 194.0 \n",
"27 Color Anthony Russo 516.0 147.0 \n",
"28 Color Peter Berg 377.0 131.0 \n",
"29 Color Colin Trevorrow 644.0 124.0 \n",
"... ... ... ... ... \n",
"5013 Color Eric Eason 28.0 79.0 \n",
"5014 Color Uwe Boll 58.0 80.0 \n",
"5015 Black and White Richard Linklater 61.0 100.0 \n",
"5016 Color Joseph Mazzella NaN 90.0 \n",
"5017 Color Travis Legge 1.0 90.0 \n",
"5018 Color Alex Kendrick 5.0 120.0 \n",
"5019 Color Marcus Nispel 43.0 91.0 \n",
"5020 NaN Brandon Landers NaN 143.0 \n",
"5021 Color Jay Duplass 51.0 85.0 \n",
"5022 Black and White Jim Chuchu 6.0 60.0 \n",
"5023 Color Daryl Wein 22.0 88.0 \n",
"5024 Color Jason Trost 42.0 78.0 \n",
"5025 Color John Waters 73.0 108.0 \n",
"5026 Color Olivier Assayas 81.0 110.0 \n",
"5027 Color Jafar Panahi 64.0 90.0 \n",
"5028 Black and White Ivan Kavanagh 12.0 83.0 \n",
"5029 Color Kiyoshi Kurosawa 78.0 111.0 \n",
"5030 Color Tadeo Garcia NaN 84.0 \n",
"5031 Color Thomas L. Phillips 13.0 82.0 \n",
"5032 Color Ash Baron-Cohen 10.0 98.0 \n",
"5033 Color Shane Carruth 143.0 77.0 \n",
"5034 Color Neill Dela Llana 35.0 80.0 \n",
"5035 Color Robert Rodriguez 56.0 81.0 \n",
"5036 Color Anthony Vallone NaN 84.0 \n",
"5037 Color Edward Burns 14.0 95.0 \n",
"5038 Color Scott Smith 1.0 87.0 \n",
"5039 Color NaN 43.0 43.0 \n",
"5040 Color Benjamin Roberds 13.0 76.0 \n",
"5041 Color Daniel Hsia 14.0 100.0 \n",
"5042 Color Jon Gunn 43.0 90.0 \n",
"\n",
" director_facebook_likes actor_3_facebook_likes actor_2_name \\\n",
"0 0.0 855.0 Joel David Moore \n",
"1 563.0 1000.0 Orlando Bloom \n",
"2 0.0 161.0 Rory Kinnear \n",
"3 22000.0 23000.0 Christian Bale \n",
"4 131.0 NaN Rob Walker \n",
"5 475.0 530.0 Samantha Morton \n",
"6 0.0 4000.0 James Franco \n",
"7 15.0 284.0 Donna Murphy \n",
"8 0.0 19000.0 Robert Downey Jr. \n",
"9 282.0 10000.0 Daniel Radcliffe \n",
"10 0.0 2000.0 Lauren Cohan \n",
"11 0.0 903.0 Marlon Brando \n",
"12 395.0 393.0 Mathieu Amalric \n",
"13 563.0 1000.0 Orlando Bloom \n",
"14 563.0 1000.0 Ruth Wilson \n",
"15 0.0 748.0 Christopher Meloni \n",
"16 80.0 201.0 Pierfrancesco Favino \n",
"17 0.0 19000.0 Robert Downey Jr. \n",
"18 252.0 1000.0 Sam Claflin \n",
"19 188.0 718.0 Michael Stuhlbarg \n",
"20 0.0 773.0 Adam Brown \n",
"21 464.0 963.0 Andrew Garfield \n",
"22 0.0 738.0 William Hurt \n",
"23 0.0 773.0 Adam Brown \n",
"24 129.0 1000.0 Eva Green \n",
"25 0.0 84.0 Thomas Kretschmann \n",
"26 0.0 794.0 Kate Winslet \n",
"27 94.0 11000.0 Scarlett Johansson \n",
"28 532.0 627.0 Alexander Skarsgård \n",
"29 365.0 1000.0 Judy Greer \n",
"... ... ... ... \n",
"5013 3.0 42.0 Panchito Gómez \n",
"5014 892.0 492.0 Katharine Isabelle \n",
"5015 0.0 0.0 Richard Linklater \n",
"5016 0.0 9.0 Mikaal Bates \n",
"5017 138.0 138.0 Suzi Lorraine \n",
"5018 589.0 4.0 Lisa Arnold \n",
"5019 158.0 265.0 Brittany Curran \n",
"5020 8.0 8.0 Alana Kaniewski \n",
"5021 157.0 10.0 Katie Aselton \n",
"5022 0.0 4.0 Olwenya Maina \n",
"5023 38.0 211.0 Heather Burns \n",
"5024 91.0 86.0 Jason Trost \n",
"5025 0.0 105.0 Mink Stole \n",
"5026 107.0 45.0 Béatrice Dalle \n",
"5027 397.0 0.0 Nargess Mamizadeh \n",
"5028 18.0 0.0 Michael Parle \n",
"5029 62.0 6.0 Anna Nakagawa \n",
"5030 5.0 12.0 Michael Cortez \n",
"5031 120.0 84.0 Joe Coffey \n",
"5032 3.0 152.0 Stanley B. Herman \n",
"5033 291.0 8.0 David Sullivan \n",
"5034 0.0 0.0 Edgar Tancangco \n",
"5035 0.0 6.0 Peter Marquardt \n",
"5036 2.0 2.0 John Considine \n",
"5037 0.0 133.0 Caitlin FitzGerald \n",
"5038 2.0 318.0 Daphne Zuniga \n",
"5039 NaN 319.0 Valorie Curry \n",
"5040 0.0 0.0 Maxwell Moody \n",
"5041 0.0 489.0 Daniel Henney \n",
"5042 16.0 16.0 Brian Herzlinger \n",
"\n",
" actor_1_facebook_likes gross \\\n",
"0 1000.0 760505847.0 \n",
"1 40000.0 309404152.0 \n",
"2 11000.0 200074175.0 \n",
"3 27000.0 448130642.0 \n",
"4 131.0 NaN \n",
"5 640.0 73058679.0 \n",
"6 24000.0 336530303.0 \n",
"7 799.0 200807262.0 \n",
"8 26000.0 458991599.0 \n",
"9 25000.0 301956980.0 \n",
"10 15000.0 330249062.0 \n",
"11 18000.0 200069408.0 \n",
"12 451.0 168368427.0 \n",
"13 40000.0 423032628.0 \n",
"14 40000.0 89289910.0 \n",
"15 15000.0 291021565.0 \n",
"16 22000.0 141614023.0 \n",
"17 26000.0 623279547.0 \n",
"18 40000.0 241063875.0 \n",
"19 10000.0 179020854.0 \n",
"20 5000.0 255108370.0 \n",
"21 15000.0 262030663.0 \n",
"22 891.0 105219735.0 \n",
"23 5000.0 258355354.0 \n",
"24 16000.0 70083519.0 \n",
"25 6000.0 218051260.0 \n",
"26 29000.0 658672302.0 \n",
"27 21000.0 407197282.0 \n",
"28 14000.0 65173160.0 \n",
"29 3000.0 652177271.0 \n",
"... ... ... \n",
"5013 93.0 NaN \n",
"5014 986.0 NaN \n",
"5015 5.0 1227508.0 \n",
"5016 313.0 NaN \n",
"5017 370.0 NaN \n",
"5018 51.0 NaN \n",
"5019 630.0 NaN \n",
"5020 720.0 NaN \n",
"5021 830.0 192467.0 \n",
"5022 147.0 NaN \n",
"5023 331.0 76382.0 \n",
"5024 407.0 NaN \n",
"5025 462.0 180483.0 \n",
"5026 576.0 136007.0 \n",
"5027 5.0 673780.0 \n",
"5028 10.0 NaN \n",
"5029 89.0 94596.0 \n",
"5030 21.0 NaN \n",
"5031 785.0 NaN \n",
"5032 789.0 NaN \n",
"5033 291.0 424760.0 \n",
"5034 0.0 70071.0 \n",
"5035 121.0 2040920.0 \n",
"5036 45.0 NaN \n",
"5037 296.0 4584.0 \n",
"5038 637.0 NaN \n",
"5039 841.0 NaN \n",
"5040 0.0 NaN \n",
"5041 946.0 10443.0 \n",
"5042 86.0 85222.0 \n",
"\n",
" genres ... \\\n",
"0 Action|Adventure|Fantasy|Sci-Fi ... \n",
"1 Action|Adventure|Fantasy ... \n",
"2 Action|Adventure|Thriller ... \n",
"3 Action|Thriller ... \n",
"4 Documentary ... \n",
"5 Action|Adventure|Sci-Fi ... \n",
"6 Action|Adventure|Romance ... \n",
"7 Adventure|Animation|Comedy|Family|Fantasy|Musi... ... \n",
"8 Action|Adventure|Sci-Fi ... \n",
"9 Adventure|Family|Fantasy|Mystery ... \n",
"10 Action|Adventure|Sci-Fi ... \n",
"11 Action|Adventure|Sci-Fi ... \n",
"12 Action|Adventure ... \n",
"13 Action|Adventure|Fantasy ... \n",
"14 Action|Adventure|Western ... \n",
"15 Action|Adventure|Fantasy|Sci-Fi ... \n",
"16 Action|Adventure|Family|Fantasy ... \n",
"17 Action|Adventure|Sci-Fi ... \n",
"18 Action|Adventure|Fantasy ... \n",
"19 Action|Adventure|Comedy|Family|Fantasy|Sci-Fi ... \n",
"20 Adventure|Fantasy ... \n",
"21 Action|Adventure|Fantasy ... \n",
"22 Action|Adventure|Drama|History ... \n",
"23 Adventure|Fantasy ... \n",
"24 Adventure|Family|Fantasy ... \n",
"25 Action|Adventure|Drama|Romance ... \n",
"26 Drama|Romance ... \n",
"27 Action|Adventure|Sci-Fi ... \n",
"28 Action|Adventure|Sci-Fi|Thriller ... \n",
"29 Action|Adventure|Sci-Fi|Thriller ... \n",
"... ... ... \n",
"5013 Drama|Family ... \n",
"5014 Action|Crime|Thriller ... \n",
"5015 Comedy|Drama ... \n",
"5016 Crime|Drama|Thriller ... \n",
"5017 Comedy|Romance ... \n",
"5018 Drama ... \n",
"5019 Horror|Mystery|Thriller ... \n",
"5020 Drama|Horror|Thriller ... \n",
"5021 Comedy|Drama|Romance ... \n",
"5022 Drama ... \n",
"5023 Romance ... \n",
"5024 Sci-Fi|Thriller ... \n",
"5025 Comedy|Crime|Horror ... \n",
"5026 Drama|Music|Romance ... \n",
"5027 Drama ... \n",
"5028 Horror ... \n",
"5029 Crime|Horror|Mystery|Thriller ... \n",
"5030 Drama ... \n",
"5031 Comedy|Horror|Thriller ... \n",
"5032 Crime|Drama ... \n",
"5033 Drama|Sci-Fi|Thriller ... \n",
"5034 Thriller ... \n",
"5035 Action|Crime|Drama|Romance|Thriller ... \n",
"5036 Crime|Drama ... \n",
"5037 Comedy|Drama ... \n",
"5038 Comedy|Drama ... \n",
"5039 Crime|Drama|Mystery|Thriller ... \n",
"5040 Drama|Horror|Thriller ... \n",
"5041 Comedy|Drama|Romance ... \n",
"5042 Documentary ... \n",
"\n",
" num_user_for_reviews language country content_rating budget \\\n",
"0 3054.0 English USA PG-13 237000000.0 \n",
"1 1238.0 English USA PG-13 300000000.0 \n",
"2 994.0 English UK PG-13 245000000.0 \n",
"3 2701.0 English USA PG-13 250000000.0 \n",
"4 NaN NaN NaN NaN NaN \n",
"5 738.0 English USA PG-13 263700000.0 \n",
"6 1902.0 English USA PG-13 258000000.0 \n",
"7 387.0 English USA PG 260000000.0 \n",
"8 1117.0 English USA PG-13 250000000.0 \n",
"9 973.0 English UK PG 250000000.0 \n",
"10 3018.0 English USA PG-13 250000000.0 \n",
"11 2367.0 English USA PG-13 209000000.0 \n",
"12 1243.0 English UK PG-13 200000000.0 \n",
"13 1832.0 English USA PG-13 225000000.0 \n",
"14 711.0 English USA PG-13 215000000.0 \n",
"15 2536.0 English USA PG-13 225000000.0 \n",
"16 438.0 English USA PG 225000000.0 \n",
"17 1722.0 English USA PG-13 220000000.0 \n",
"18 484.0 English USA PG-13 250000000.0 \n",
"19 341.0 English USA PG-13 225000000.0 \n",
"20 802.0 English New Zealand PG-13 250000000.0 \n",
"21 1225.0 English USA PG-13 230000000.0 \n",
"22 546.0 English USA PG-13 200000000.0 \n",
"23 951.0 English USA PG-13 225000000.0 \n",
"24 666.0 English USA PG-13 180000000.0 \n",
"25 2618.0 English New Zealand PG-13 207000000.0 \n",
"26 2528.0 English USA PG-13 200000000.0 \n",
"27 1022.0 English USA PG-13 250000000.0 \n",
"28 751.0 English USA PG-13 209000000.0 \n",
"29 1290.0 English USA PG-13 150000000.0 \n",
"... ... ... ... ... ... \n",
"5013 21.0 English USA NaN 24000.0 \n",
"5014 129.0 English Canada R NaN \n",
"5015 80.0 English USA R 23000.0 \n",
"5016 2.0 English USA NaN 25000.0 \n",
"5017 3.0 English USA NaN 22000.0 \n",
"5018 49.0 English USA NaN 20000.0 \n",
"5019 33.0 English USA R NaN \n",
"5020 8.0 English USA NaN 17350.0 \n",
"5021 71.0 English USA R 15000.0 \n",
"5022 1.0 Swahili Kenya NaN 15000.0 \n",
"5023 8.0 English USA NaN 15000.0 \n",
"5024 35.0 English USA Unrated 20000.0 \n",
"5025 183.0 English USA NC-17 10000.0 \n",
"5026 39.0 French France R 4500.0 \n",
"5027 26.0 Persian Iran Not Rated 10000.0 \n",
"5028 1.0 English Ireland NaN 10000.0 \n",
"5029 50.0 Japanese Japan NaN 1000000.0 \n",
"5030 3.0 English USA NaN NaN \n",
"5031 8.0 English USA NaN 200000.0 \n",
"5032 14.0 English USA NaN NaN \n",
"5033 371.0 English USA PG-13 7000.0 \n",
"5034 35.0 English Philippines Not Rated 7000.0 \n",
"5035 130.0 Spanish USA R 7000.0 \n",
"5036 1.0 English USA PG-13 3250.0 \n",
"5037 14.0 English USA Not Rated 9000.0 \n",
"5038 6.0 English Canada NaN NaN \n",
"5039 359.0 English USA TV-14 NaN \n",
"5040 3.0 English USA NaN 1400.0 \n",
"5041 9.0 English USA PG-13 NaN \n",
"5042 84.0 English USA PG 1100.0 \n",
"\n",
" title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n",
"0 2009.0 936.0 7.9 1.78 \n",
"1 2007.0 5000.0 7.1 2.35 \n",
"2 2015.0 393.0 6.8 2.35 \n",
"3 2012.0 23000.0 8.5 2.35 \n",
"4 NaN 12.0 7.1 NaN \n",
"5 2012.0 632.0 6.6 2.35 \n",
"6 2007.0 11000.0 6.2 2.35 \n",
"7 2010.0 553.0 7.8 1.85 \n",
"8 2015.0 21000.0 7.5 2.35 \n",
"9 2009.0 11000.0 7.5 2.35 \n",
"10 2016.0 4000.0 6.9 2.35 \n",
"11 2006.0 10000.0 6.1 2.35 \n",
"12 2008.0 412.0 6.7 2.35 \n",
"13 2006.0 5000.0 7.3 2.35 \n",
"14 2013.0 2000.0 6.5 2.35 \n",
"15 2013.0 3000.0 7.2 2.35 \n",
"16 2008.0 216.0 6.6 2.35 \n",
"17 2012.0 21000.0 8.1 1.85 \n",
"18 2011.0 11000.0 6.7 2.35 \n",
"19 2012.0 816.0 6.8 1.85 \n",
"20 2014.0 972.0 7.5 2.35 \n",
"21 2012.0 10000.0 7.0 2.35 \n",
"22 2010.0 882.0 6.7 2.35 \n",
"23 2013.0 972.0 7.9 2.35 \n",
"24 2007.0 6000.0 6.1 2.35 \n",
"25 2005.0 919.0 7.2 2.35 \n",
"26 1997.0 14000.0 7.7 2.35 \n",
"27 2016.0 19000.0 8.2 2.35 \n",
"28 2012.0 10000.0 5.9 2.35 \n",
"29 2015.0 2000.0 7.0 2.00 \n",
"... ... ... ... ... \n",
"5013 2002.0 46.0 7.0 1.78 \n",
"5014 2009.0 918.0 6.3 2.35 \n",
"5015 1991.0 0.0 7.1 1.37 \n",
"5016 2015.0 25.0 4.8 NaN \n",
"5017 2013.0 184.0 3.3 1.78 \n",
"5018 2003.0 49.0 6.9 1.85 \n",
"5019 2015.0 512.0 4.6 1.85 \n",
"5020 2011.0 19.0 3.0 NaN \n",
"5021 2005.0 224.0 6.6 NaN \n",
"5022 2014.0 19.0 7.4 NaN \n",
"5023 2009.0 212.0 6.2 2.35 \n",
"5024 2011.0 91.0 4.0 2.35 \n",
"5025 1972.0 143.0 6.1 1.37 \n",
"5026 2004.0 133.0 6.9 2.35 \n",
"5027 2000.0 0.0 7.5 1.85 \n",
"5028 2007.0 5.0 6.7 1.33 \n",
"5029 1997.0 13.0 7.4 1.85 \n",
"5030 2004.0 20.0 6.1 NaN \n",
"5031 2012.0 98.0 5.4 16.00 \n",
"5032 1995.0 194.0 6.4 NaN \n",
"5033 2004.0 45.0 7.0 1.85 \n",
"5034 2005.0 0.0 6.3 NaN \n",
"5035 1992.0 20.0 6.9 1.37 \n",
"5036 2005.0 44.0 7.8 NaN \n",
"5037 2011.0 205.0 6.4 NaN \n",
"5038 2013.0 470.0 7.7 NaN \n",
"5039 NaN 593.0 7.5 16.00 \n",
"5040 2013.0 0.0 6.3 NaN \n",
"5041 2012.0 719.0 6.3 2.35 \n",
"5042 2004.0 23.0 6.6 1.85 \n",
"\n",
" movie_facebook_likes \n",
"0 33000 \n",
"1 0 \n",
"2 85000 \n",
"3 164000 \n",
"4 0 \n",
"5 24000 \n",
"6 0 \n",
"7 29000 \n",
"8 118000 \n",
"9 10000 \n",
"10 197000 \n",
"11 0 \n",
"12 0 \n",
"13 5000 \n",
"14 48000 \n",
"15 118000 \n",
"16 0 \n",
"17 123000 \n",
"18 58000 \n",
"19 40000 \n",
"20 65000 \n",
"21 56000 \n",
"22 17000 \n",
"23 83000 \n",
"24 0 \n",
"25 0 \n",
"26 26000 \n",
"27 72000 \n",
"28 44000 \n",
"29 150000 \n",
"... ... \n",
"5013 61 \n",
"5014 0 \n",
"5015 2000 \n",
"5016 33 \n",
"5017 200 \n",
"5018 725 \n",
"5019 0 \n",
"5020 33 \n",
"5021 297 \n",
"5022 45 \n",
"5023 324 \n",
"5024 835 \n",
"5025 0 \n",
"5026 171 \n",
"5027 697 \n",
"5028 105 \n",
"5029 817 \n",
"5030 22 \n",
"5031 424 \n",
"5032 20 \n",
"5033 19000 \n",
"5034 74 \n",
"5035 0 \n",
"5036 4 \n",
"5037 413 \n",
"5038 84 \n",
"5039 32000 \n",
"5040 16 \n",
"5041 660 \n",
"5042 456 \n",
"\n",
"[5043 rows x 28 columns]>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe"
]
},
{
"cell_type": "code",
"execution_count": 4,
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"outputs": [
{
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alain Resnais</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alan Alda</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alan Cohn</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alan J. Pakula</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>William Phillips</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>William Sachs</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>William Shatner</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>William Wyler</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wilson Yip</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wolfgang Becker</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wolfgang Petersen</th>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>...</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Woo-Suk Kang</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Woody Allen</th>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>19</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>...</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Wych Kaosayananda</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Xavier Beauvois</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Xavier Gens</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yarrow Cheney</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yash Chopra</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yimou Zhang</th>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yorgos Lanthimos</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Youssef Delara</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuefeng Song</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zach Braff</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zach Cregger</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zack Snyder</th>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>...</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zack Ward</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zackary Adler</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zak Penn</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zal Batmanglij</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zoran Lisinac</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Álex de la Iglesia</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Émile Gaudreault</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Éric Tessier</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Étienne Faure</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2398 rows × 27 columns</p>\n",
"</div>"
],
"text/plain": [
" color num_critic_for_reviews duration \\\n",
"director_name \n",
"A. Raven Cruz 1 1 1 \n",
"Aaron Hann 1 1 1 \n",
"Aaron Schneider 1 1 1 \n",
"Aaron Seltzer 1 1 1 \n",
"Abel Ferrara 1 1 1 \n",
"Adam Brooks 1 1 1 \n",
"Adam Carolla 1 1 1 \n",
"Adam Goldberg 1 1 1 \n",
"Adam Green 1 1 1 \n",
"Adam Jay Epstein 1 1 1 \n",
"Adam Marcus 1 1 1 \n",
"Adam McKay 6 6 6 \n",
"Adam Rapp 1 1 1 \n",
"Adam Rifkin 2 2 2 \n",
"Adam Shankman 8 8 8 \n",
"Adrian Lyne 4 4 4 \n",
"Adrienne Shelly 1 1 1 \n",
"Agnieszka Holland 1 1 1 \n",
"Agnieszka Wojtowicz-Vosloo 1 1 1 \n",
"Agustín Díaz Yanes 1 1 1 \n",
"Aki Kaurismäki 1 1 1 \n",
"Akira Kurosawa 2 2 2 \n",
"Akiva Goldsman 1 1 1 \n",
"Akiva Schaffer 3 3 3 \n",
"Al Franklin 1 0 1 \n",
"Al Silliman Jr. 1 1 1 \n",
"Alain Resnais 1 1 1 \n",
"Alan Alda 1 1 1 \n",
"Alan Cohn 1 1 1 \n",
"Alan J. Pakula 2 2 2 \n",
"... ... ... ... \n",
"William Phillips 1 1 1 \n",
"William Sachs 1 1 1 \n",
"William Shatner 1 1 1 \n",
"William Wyler 1 1 1 \n",
"Wilson Yip 1 1 1 \n",
"Wolfgang Becker 1 1 1 \n",
"Wolfgang Petersen 7 7 7 \n",
"Woo-Suk Kang 1 1 1 \n",
"Woody Allen 22 22 22 \n",
"Wych Kaosayananda 1 1 1 \n",
"Xavier Beauvois 1 1 1 \n",
"Xavier Gens 2 2 2 \n",
"Yarrow Cheney 1 1 1 \n",
"Yash Chopra 2 2 2 \n",
"Yimou Zhang 8 8 8 \n",
"Yorgos Lanthimos 1 1 1 \n",
"Youssef Delara 2 2 2 \n",
"Yuefeng Song 1 1 1 \n",
"Zach Braff 2 2 2 \n",
"Zach Cregger 1 1 1 \n",
"Zack Snyder 8 8 8 \n",
"Zack Ward 1 1 1 \n",
"Zackary Adler 0 1 1 \n",
"Zak Penn 1 1 1 \n",
"Zal Batmanglij 2 2 2 \n",
"Zoran Lisinac 1 1 1 \n",
"Álex de la Iglesia 1 1 1 \n",
"Émile Gaudreault 1 1 1 \n",
"Éric Tessier 1 1 1 \n",
"Étienne Faure 1 1 1 \n",
"\n",
" director_facebook_likes actor_3_facebook_likes \\\n",
"director_name \n",
"A. Raven Cruz 1 1 \n",
"Aaron Hann 1 1 \n",
"Aaron Schneider 1 1 \n",
"Aaron Seltzer 1 1 \n",
"Abel Ferrara 1 1 \n",
"Adam Brooks 1 1 \n",
"Adam Carolla 1 1 \n",
"Adam Goldberg 1 1 \n",
"Adam Green 1 1 \n",
"Adam Jay Epstein 1 1 \n",
"Adam Marcus 1 1 \n",
"Adam McKay 6 6 \n",
"Adam Rapp 1 1 \n",
"Adam Rifkin 2 2 \n",
"Adam Shankman 8 8 \n",
"Adrian Lyne 4 4 \n",
"Adrienne Shelly 1 1 \n",
"Agnieszka Holland 1 1 \n",
"Agnieszka Wojtowicz-Vosloo 1 1 \n",
"Agustín Díaz Yanes 1 1 \n",
"Aki Kaurismäki 1 1 \n",
"Akira Kurosawa 2 2 \n",
"Akiva Goldsman 1 1 \n",
"Akiva Schaffer 3 3 \n",
"Al Franklin 1 1 \n",
"Al Silliman Jr. 1 1 \n",
"Alain Resnais 1 1 \n",
"Alan Alda 1 1 \n",
"Alan Cohn 1 1 \n",
"Alan J. Pakula 2 2 \n",
"... ... ... \n",
"William Phillips 1 1 \n",
"William Sachs 1 1 \n",
"William Shatner 1 1 \n",
"William Wyler 1 1 \n",
"Wilson Yip 1 1 \n",
"Wolfgang Becker 1 1 \n",
"Wolfgang Petersen 7 7 \n",
"Woo-Suk Kang 1 1 \n",
"Woody Allen 22 22 \n",
"Wych Kaosayananda 1 1 \n",
"Xavier Beauvois 1 1 \n",
"Xavier Gens 2 2 \n",
"Yarrow Cheney 1 1 \n",
"Yash Chopra 2 2 \n",
"Yimou Zhang 8 8 \n",
"Yorgos Lanthimos 1 1 \n",
"Youssef Delara 2 2 \n",
"Yuefeng Song 1 1 \n",
"Zach Braff 2 2 \n",
"Zach Cregger 1 1 \n",
"Zack Snyder 8 8 \n",
"Zack Ward 1 1 \n",
"Zackary Adler 1 1 \n",
"Zak Penn 1 1 \n",
"Zal Batmanglij 2 2 \n",
"Zoran Lisinac 1 1 \n",
"Álex de la Iglesia 1 1 \n",
"Émile Gaudreault 1 1 \n",
"Éric Tessier 1 1 \n",
"Étienne Faure 1 1 \n",
"\n",
" actor_2_name actor_1_facebook_likes gross \\\n",
"director_name \n",
"A. Raven Cruz 1 1 0 \n",
"Aaron Hann 1 1 0 \n",
"Aaron Schneider 1 1 1 \n",
"Aaron Seltzer 1 1 1 \n",
"Abel Ferrara 1 1 1 \n",
"Adam Brooks 1 1 1 \n",
"Adam Carolla 1 1 1 \n",
"Adam Goldberg 1 1 1 \n",
"Adam Green 1 1 0 \n",
"Adam Jay Epstein 1 1 0 \n",
"Adam Marcus 1 1 1 \n",
"Adam McKay 6 6 6 \n",
"Adam Rapp 1 1 1 \n",
"Adam Rifkin 2 2 1 \n",
"Adam Shankman 8 8 8 \n",
"Adrian Lyne 4 4 4 \n",
"Adrienne Shelly 1 1 1 \n",
"Agnieszka Holland 1 1 1 \n",
"Agnieszka Wojtowicz-Vosloo 1 1 1 \n",
"Agustín Díaz Yanes 1 1 0 \n",
"Aki Kaurismäki 1 1 1 \n",
"Akira Kurosawa 2 2 2 \n",
"Akiva Goldsman 1 1 1 \n",
"Akiva Schaffer 3 3 3 \n",
"Al Franklin 1 1 0 \n",
"Al Silliman Jr. 1 1 0 \n",
"Alain Resnais 1 1 1 \n",
"Alan Alda 1 1 0 \n",
"Alan Cohn 1 1 1 \n",
"Alan J. Pakula 2 2 2 \n",
"... ... ... ... \n",
"William Phillips 1 1 0 \n",
"William Sachs 1 1 0 \n",
"William Shatner 1 1 1 \n",
"William Wyler 1 1 1 \n",
"Wilson Yip 1 1 1 \n",
"Wolfgang Becker 1 1 1 \n",
"Wolfgang Petersen 7 7 6 \n",
"Woo-Suk Kang 1 1 0 \n",
"Woody Allen 22 22 19 \n",
"Wych Kaosayananda 1 1 1 \n",
"Xavier Beauvois 1 1 1 \n",
"Xavier Gens 2 2 2 \n",
"Yarrow Cheney 1 1 1 \n",
"Yash Chopra 2 2 2 \n",
"Yimou Zhang 8 8 8 \n",
"Yorgos Lanthimos 1 1 1 \n",
"Youssef Delara 2 2 1 \n",
"Yuefeng Song 1 1 0 \n",
"Zach Braff 2 2 2 \n",
"Zach Cregger 1 1 1 \n",
"Zack Snyder 8 8 8 \n",
"Zack Ward 1 1 0 \n",
"Zackary Adler 1 1 0 \n",
"Zak Penn 1 1 1 \n",
"Zal Batmanglij 2 2 2 \n",
"Zoran Lisinac 1 1 0 \n",
"Álex de la Iglesia 1 1 1 \n",
"Émile Gaudreault 1 1 1 \n",
"Éric Tessier 1 1 0 \n",
"Étienne Faure 1 1 0 \n",
"\n",
" genres actor_1_name ... \\\n",
"director_name ... \n",
"A. Raven Cruz 1 1 ... \n",
"Aaron Hann 1 1 ... \n",
"Aaron Schneider 1 1 ... \n",
"Aaron Seltzer 1 1 ... \n",
"Abel Ferrara 1 1 ... \n",
"Adam Brooks 1 1 ... \n",
"Adam Carolla 1 1 ... \n",
"Adam Goldberg 1 1 ... \n",
"Adam Green 1 1 ... \n",
"Adam Jay Epstein 1 1 ... \n",
"Adam Marcus 1 1 ... \n",
"Adam McKay 6 6 ... \n",
"Adam Rapp 1 1 ... \n",
"Adam Rifkin 2 2 ... \n",
"Adam Shankman 8 8 ... \n",
"Adrian Lyne 4 4 ... \n",
"Adrienne Shelly 1 1 ... \n",
"Agnieszka Holland 1 1 ... \n",
"Agnieszka Wojtowicz-Vosloo 1 1 ... \n",
"Agustín Díaz Yanes 1 1 ... \n",
"Aki Kaurismäki 1 1 ... \n",
"Akira Kurosawa 2 2 ... \n",
"Akiva Goldsman 1 1 ... \n",
"Akiva Schaffer 3 3 ... \n",
"Al Franklin 1 1 ... \n",
"Al Silliman Jr. 1 1 ... \n",
"Alain Resnais 1 1 ... \n",
"Alan Alda 1 1 ... \n",
"Alan Cohn 1 1 ... \n",
"Alan J. Pakula 2 2 ... \n",
"... ... ... ... \n",
"William Phillips 1 1 ... \n",
"William Sachs 1 1 ... \n",
"William Shatner 1 1 ... \n",
"William Wyler 1 1 ... \n",
"Wilson Yip 1 1 ... \n",
"Wolfgang Becker 1 1 ... \n",
"Wolfgang Petersen 7 7 ... \n",
"Woo-Suk Kang 1 1 ... \n",
"Woody Allen 22 22 ... \n",
"Wych Kaosayananda 1 1 ... \n",
"Xavier Beauvois 1 1 ... \n",
"Xavier Gens 2 2 ... \n",
"Yarrow Cheney 1 1 ... \n",
"Yash Chopra 2 2 ... \n",
"Yimou Zhang 8 8 ... \n",
"Yorgos Lanthimos 1 1 ... \n",
"Youssef Delara 2 2 ... \n",
"Yuefeng Song 1 1 ... \n",
"Zach Braff 2 2 ... \n",
"Zach Cregger 1 1 ... \n",
"Zack Snyder 8 8 ... \n",
"Zack Ward 1 1 ... \n",
"Zackary Adler 1 1 ... \n",
"Zak Penn 1 1 ... \n",
"Zal Batmanglij 2 2 ... \n",
"Zoran Lisinac 1 1 ... \n",
"Álex de la Iglesia 1 1 ... \n",
"Émile Gaudreault 1 1 ... \n",
"Éric Tessier 1 1 ... \n",
"Étienne Faure 1 1 ... \n",
"\n",
" num_user_for_reviews language country \\\n",
"director_name \n",
"A. Raven Cruz 1 1 1 \n",
"Aaron Hann 1 1 1 \n",
"Aaron Schneider 1 1 1 \n",
"Aaron Seltzer 1 1 1 \n",
"Abel Ferrara 1 1 1 \n",
"Adam Brooks 1 1 1 \n",
"Adam Carolla 1 1 1 \n",
"Adam Goldberg 1 1 1 \n",
"Adam Green 1 1 1 \n",
"Adam Jay Epstein 1 1 1 \n",
"Adam Marcus 1 1 1 \n",
"Adam McKay 6 6 6 \n",
"Adam Rapp 1 1 1 \n",
"Adam Rifkin 2 2 2 \n",
"Adam Shankman 8 8 8 \n",
"Adrian Lyne 4 4 4 \n",
"Adrienne Shelly 1 1 1 \n",
"Agnieszka Holland 1 1 1 \n",
"Agnieszka Wojtowicz-Vosloo 1 1 1 \n",
"Agustín Díaz Yanes 1 1 1 \n",
"Aki Kaurismäki 1 1 1 \n",
"Akira Kurosawa 2 2 2 \n",
"Akiva Goldsman 1 1 1 \n",
"Akiva Schaffer 3 3 3 \n",
"Al Franklin 1 1 1 \n",
"Al Silliman Jr. 1 1 1 \n",
"Alain Resnais 1 1 1 \n",
"Alan Alda 1 1 1 \n",
"Alan Cohn 1 1 1 \n",
"Alan J. Pakula 2 2 2 \n",
"... ... ... ... \n",
"William Phillips 1 1 1 \n",
"William Sachs 1 1 1 \n",
"William Shatner 1 1 1 \n",
"William Wyler 1 1 1 \n",
"Wilson Yip 1 1 1 \n",
"Wolfgang Becker 1 1 1 \n",
"Wolfgang Petersen 7 7 7 \n",
"Woo-Suk Kang 1 1 1 \n",
"Woody Allen 22 22 22 \n",
"Wych Kaosayananda 1 1 1 \n",
"Xavier Beauvois 1 1 1 \n",
"Xavier Gens 2 2 2 \n",
"Yarrow Cheney 1 1 1 \n",
"Yash Chopra 2 2 2 \n",
"Yimou Zhang 8 8 8 \n",
"Yorgos Lanthimos 1 1 1 \n",
"Youssef Delara 2 2 2 \n",
"Yuefeng Song 1 1 1 \n",
"Zach Braff 2 2 2 \n",
"Zach Cregger 1 1 1 \n",
"Zack Snyder 8 8 8 \n",
"Zack Ward 1 1 1 \n",
"Zackary Adler 1 1 1 \n",
"Zak Penn 1 1 1 \n",
"Zal Batmanglij 2 2 2 \n",
"Zoran Lisinac 1 1 1 \n",
"Álex de la Iglesia 1 1 1 \n",
"Émile Gaudreault 1 1 1 \n",
"Éric Tessier 1 1 1 \n",
"Étienne Faure 1 1 1 \n",
"\n",
" content_rating budget title_year \\\n",
"director_name \n",
"A. Raven Cruz 1 1 1 \n",
"Aaron Hann 1 0 1 \n",
"Aaron Schneider 1 1 1 \n",
"Aaron Seltzer 1 1 1 \n",
"Abel Ferrara 1 1 1 \n",
"Adam Brooks 1 0 1 \n",
"Adam Carolla 0 1 1 \n",
"Adam Goldberg 1 1 1 \n",
"Adam Green 1 1 1 \n",
"Adam Jay Epstein 1 0 1 \n",
"Adam Marcus 1 1 1 \n",
"Adam McKay 6 6 6 \n",
"Adam Rapp 1 1 1 \n",
"Adam Rifkin 1 2 2 \n",
"Adam Shankman 8 8 8 \n",
"Adrian Lyne 4 4 4 \n",
"Adrienne Shelly 1 1 1 \n",
"Agnieszka Holland 1 1 1 \n",
"Agnieszka Wojtowicz-Vosloo 1 1 1 \n",
"Agustín Díaz Yanes 0 1 1 \n",
"Aki Kaurismäki 1 1 1 \n",
"Akira Kurosawa 1 2 2 \n",
"Akiva Goldsman 1 1 1 \n",
"Akiva Schaffer 3 2 3 \n",
"Al Franklin 0 1 1 \n",
"Al Silliman Jr. 1 1 1 \n",
"Alain Resnais 1 0 1 \n",
"Alan Alda 1 0 1 \n",
"Alan Cohn 1 1 1 \n",
"Alan J. Pakula 2 2 2 \n",
"... ... ... ... \n",
"William Phillips 0 1 1 \n",
"William Sachs 1 0 1 \n",
"William Shatner 1 1 1 \n",
"William Wyler 1 1 1 \n",
"Wilson Yip 1 1 1 \n",
"Wolfgang Becker 1 1 1 \n",
"Wolfgang Petersen 7 7 7 \n",
"Woo-Suk Kang 0 1 1 \n",
"Woody Allen 22 22 22 \n",
"Wych Kaosayananda 1 1 1 \n",
"Xavier Beauvois 1 1 1 \n",
"Xavier Gens 2 2 2 \n",
"Yarrow Cheney 1 1 1 \n",
"Yash Chopra 1 2 2 \n",
"Yimou Zhang 8 5 8 \n",
"Yorgos Lanthimos 1 0 1 \n",
"Youssef Delara 1 2 2 \n",
"Yuefeng Song 0 1 1 \n",
"Zach Braff 2 2 2 \n",
"Zach Cregger 1 1 1 \n",
"Zack Snyder 8 8 8 \n",
"Zack Ward 0 0 1 \n",
"Zackary Adler 1 1 1 \n",
"Zak Penn 1 1 1 \n",
"Zal Batmanglij 2 1 2 \n",
"Zoran Lisinac 0 1 1 \n",
"Álex de la Iglesia 1 1 1 \n",
"Émile Gaudreault 1 1 1 \n",
"Éric Tessier 0 1 1 \n",
"Étienne Faure 1 1 1 \n",
"\n",
" actor_2_facebook_likes imdb_score aspect_ratio \\\n",
"director_name \n",
"A. Raven Cruz 1 1 1 \n",
"Aaron Hann 1 1 0 \n",
"Aaron Schneider 1 1 1 \n",
"Aaron Seltzer 1 1 1 \n",
"Abel Ferrara 1 1 1 \n",
"Adam Brooks 1 1 1 \n",
"Adam Carolla 1 1 0 \n",
"Adam Goldberg 1 1 1 \n",
"Adam Green 1 1 1 \n",
"Adam Jay Epstein 1 1 1 \n",
"Adam Marcus 1 1 1 \n",
"Adam McKay 6 6 6 \n",
"Adam Rapp 1 1 1 \n",
"Adam Rifkin 2 2 1 \n",
"Adam Shankman 8 8 8 \n",
"Adrian Lyne 4 4 4 \n",
"Adrienne Shelly 1 1 1 \n",
"Agnieszka Holland 1 1 1 \n",
"Agnieszka Wojtowicz-Vosloo 1 1 1 \n",
"Agustín Díaz Yanes 1 1 1 \n",
"Aki Kaurismäki 1 1 1 \n",
"Akira Kurosawa 2 2 2 \n",
"Akiva Goldsman 1 1 1 \n",
"Akiva Schaffer 3 3 3 \n",
"Al Franklin 1 1 1 \n",
"Al Silliman Jr. 1 1 1 \n",
"Alain Resnais 1 1 1 \n",
"Alan Alda 1 1 1 \n",
"Alan Cohn 1 1 1 \n",
"Alan J. Pakula 2 2 2 \n",
"... ... ... ... \n",
"William Phillips 1 1 1 \n",
"William Sachs 1 1 1 \n",
"William Shatner 1 1 1 \n",
"William Wyler 1 1 1 \n",
"Wilson Yip 1 1 1 \n",
"Wolfgang Becker 1 1 1 \n",
"Wolfgang Petersen 7 7 7 \n",
"Woo-Suk Kang 1 1 1 \n",
"Woody Allen 22 22 22 \n",
"Wych Kaosayananda 1 1 1 \n",
"Xavier Beauvois 1 1 1 \n",
"Xavier Gens 2 2 2 \n",
"Yarrow Cheney 1 1 1 \n",
"Yash Chopra 2 2 2 \n",
"Yimou Zhang 8 8 8 \n",
"Yorgos Lanthimos 1 1 1 \n",
"Youssef Delara 2 2 2 \n",
"Yuefeng Song 1 1 0 \n",
"Zach Braff 2 2 2 \n",
"Zach Cregger 1 1 1 \n",
"Zack Snyder 8 8 8 \n",
"Zack Ward 1 1 0 \n",
"Zackary Adler 1 1 0 \n",
"Zak Penn 1 1 1 \n",
"Zal Batmanglij 2 2 2 \n",
"Zoran Lisinac 1 1 1 \n",
"Álex de la Iglesia 1 1 1 \n",
"Émile Gaudreault 1 1 1 \n",
"Éric Tessier 1 1 1 \n",
"Étienne Faure 1 1 1 \n",
"\n",
" movie_facebook_likes \n",
"director_name \n",
"A. Raven Cruz 1 \n",
"Aaron Hann 1 \n",
"Aaron Schneider 1 \n",
"Aaron Seltzer 1 \n",
"Abel Ferrara 1 \n",
"Adam Brooks 1 \n",
"Adam Carolla 1 \n",
"Adam Goldberg 1 \n",
"Adam Green 1 \n",
"Adam Jay Epstein 1 \n",
"Adam Marcus 1 \n",
"Adam McKay 6 \n",
"Adam Rapp 1 \n",
"Adam Rifkin 2 \n",
"Adam Shankman 8 \n",
"Adrian Lyne 4 \n",
"Adrienne Shelly 1 \n",
"Agnieszka Holland 1 \n",
"Agnieszka Wojtowicz-Vosloo 1 \n",
"Agustín Díaz Yanes 1 \n",
"Aki Kaurismäki 1 \n",
"Akira Kurosawa 2 \n",
"Akiva Goldsman 1 \n",
"Akiva Schaffer 3 \n",
"Al Franklin 1 \n",
"Al Silliman Jr. 1 \n",
"Alain Resnais 1 \n",
"Alan Alda 1 \n",
"Alan Cohn 1 \n",
"Alan J. Pakula 2 \n",
"... ... \n",
"William Phillips 1 \n",
"William Sachs 1 \n",
"William Shatner 1 \n",
"William Wyler 1 \n",
"Wilson Yip 1 \n",
"Wolfgang Becker 1 \n",
"Wolfgang Petersen 7 \n",
"Woo-Suk Kang 1 \n",
"Woody Allen 22 \n",
"Wych Kaosayananda 1 \n",
"Xavier Beauvois 1 \n",
"Xavier Gens 2 \n",
"Yarrow Cheney 1 \n",
"Yash Chopra 2 \n",
"Yimou Zhang 8 \n",
"Yorgos Lanthimos 1 \n",
"Youssef Delara 2 \n",
"Yuefeng Song 1 \n",
"Zach Braff 2 \n",
"Zach Cregger 1 \n",
"Zack Snyder 8 \n",
"Zack Ward 1 \n",
"Zackary Adler 1 \n",
"Zak Penn 1 \n",
"Zal Batmanglij 2 \n",
"Zoran Lisinac 1 \n",
"Álex de la Iglesia 1 \n",
"Émile Gaudreault 1 \n",
"Éric Tessier 1 \n",
"Étienne Faure 1 \n",
"\n",
"[2398 rows x 27 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby('director_name').count()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "2f236abb-ea00-d2a2-80bf-d5cd71e66c09"
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method NDFrame.describe of color director_name num_critic_for_reviews \\\n",
"3 Color Christopher Nolan 813.0 \n",
"17 Color Joss Whedon 703.0 \n",
"27 Color Anthony Russo 516.0 \n",
"43 Color Lee Unkrich 453.0 \n",
"58 Color Andrew Stanton 421.0 \n",
"66 Color Christopher Nolan 645.0 \n",
"67 Color Pete Docter 408.0 \n",
"78 Color Pete Docter 536.0 \n",
"93 Color Dean DeBlois 288.0 \n",
"95 Color James Gunn 653.0 \n",
"96 Color Christopher Nolan 712.0 \n",
"97 Color Christopher Nolan 642.0 \n",
"120 Color Christopher Nolan 478.0 \n",
"128 Color George Miller 739.0 \n",
"179 Color Alejandro G. Iñárritu 556.0 \n",
"183 Color Paul Greengrass 329.0 \n",
"205 Color Gore Verbinski 271.0 \n",
"238 Color Pete Docter 250.0 \n",
"270 Color Peter Jackson 297.0 \n",
"278 Color Ridley Scott 568.0 \n",
"283 Color Ridley Scott 265.0 \n",
"288 Color James Cameron 210.0 \n",
"296 Color Quentin Tarantino 765.0 \n",
"308 Color Martin Scorsese 606.0 \n",
"338 Color Andrew Stanton 301.0 \n",
"339 Color Peter Jackson 328.0 \n",
"340 Color Peter Jackson 294.0 \n",
"361 Color Martin Scorsese 352.0 \n",
"404 Color NaN 103.0 \n",
"452 Color Martin Scorsese 490.0 \n",
"... ... ... ... \n",
"4372 Color Mitchell Altieri NaN \n",
"4404 Black and White Frank Capra 96.0 \n",
"4427 Black and White Charles Chaplin 120.0 \n",
"4444 Color Frank Lotito NaN \n",
"4458 Color Guy Ritchie 116.0 \n",
"4468 Color Sadyk Sher-Niyaz 16.0 \n",
"4474 Black and White Alfred Hitchcock 144.0 \n",
"4496 Color Quentin Tarantino 173.0 \n",
"4526 Color Michael Curtiz 242.0 \n",
"4530 Color John G. Avildsen 141.0 \n",
"4535 Color Martin Scorsese 211.0 \n",
"4638 Black and White Elia Kazan 134.0 \n",
"4687 Black and White Fred Zinnemann 153.0 \n",
"4688 Color Steve James 53.0 \n",
"4690 Color Joseph Kosinski 4.0 \n",
"4708 Color Michael Wadleigh 53.0 \n",
"4748 Color Marius A. Markevicius 26.0 \n",
"4774 Color Justin Paul Miller 1.0 \n",
"4787 Color Joe Kenemore NaN \n",
"4795 Color Terry Gilliam 131.0 \n",
"4803 Color NaN 11.0 \n",
"4822 Black and White Sidney Lumet 177.0 \n",
"4824 Black and White Frank Capra 124.0 \n",
"4838 Black and White Michael Roemer 24.0 \n",
"4869 Color NaN 11.0 \n",
"4920 Color Sharon Greytak 3.0 \n",
"4924 Color Cary Bell NaN \n",
"4937 Color Bill Melendez 43.0 \n",
"4972 Color Sut Jhally 16.0 \n",
"5001 Color Martin Scorsese 71.0 \n",
"\n",
" duration director_facebook_likes actor_3_facebook_likes \\\n",
"3 164.0 22000.0 23000.0 \n",
"17 173.0 0.0 19000.0 \n",
"27 147.0 94.0 11000.0 \n",
"43 103.0 125.0 721.0 \n",
"58 98.0 475.0 522.0 \n",
"66 152.0 22000.0 11000.0 \n",
"67 96.0 0.0 262.0 \n",
"78 95.0 0.0 384.0 \n",
"93 98.0 255.0 759.0 \n",
"95 121.0 571.0 3000.0 \n",
"96 169.0 22000.0 6000.0 \n",
"97 148.0 22000.0 23000.0 \n",
"120 128.0 22000.0 11000.0 \n",
"128 120.0 750.0 943.0 \n",
"179 156.0 0.0 733.0 \n",
"183 115.0 521.0 883.0 \n",
"205 143.0 563.0 1000.0 \n",
"238 92.0 0.0 773.0 \n",
"270 171.0 0.0 857.0 \n",
"278 151.0 0.0 372.0 \n",
"283 171.0 0.0 695.0 \n",
"288 153.0 0.0 539.0 \n",
"296 165.0 16000.0 265.0 \n",
"308 240.0 17000.0 4000.0 \n",
"338 100.0 475.0 799.0 \n",
"339 192.0 0.0 416.0 \n",
"340 172.0 0.0 857.0 \n",
"361 151.0 17000.0 1000.0 \n",
"404 44.0 NaN 148.0 \n",
"452 138.0 17000.0 163.0 \n",
"... ... ... ... \n",
"4372 87.0 9.0 165.0 \n",
"4404 120.0 964.0 248.0 \n",
"4427 87.0 0.0 8.0 \n",
"4444 102.0 5.0 259.0 \n",
"4458 120.0 0.0 452.0 \n",
"4468 135.0 135.0 0.0 \n",
"4474 130.0 13000.0 333.0 \n",
"4496 99.0 16000.0 455.0 \n",
"4526 82.0 345.0 269.0 \n",
"4530 145.0 80.0 794.0 \n",
"4535 110.0 17000.0 595.0 \n",
"4638 108.0 603.0 279.0 \n",
"4687 85.0 160.0 433.0 \n",
"4688 170.0 23.0 2.0 \n",
"4690 NaN 364.0 567.0 \n",
"4708 215.0 14.0 136.0 \n",
"4748 89.0 6.0 8.0 \n",
"4774 90.0 0.0 33.0 \n",
"4787 52.0 0.0 0.0 \n",
"4795 91.0 0.0 332.0 \n",
"4803 22.0 NaN 6.0 \n",
"4822 96.0 0.0 253.0 \n",
"4824 65.0 964.0 21.0 \n",
"4838 95.0 0.0 87.0 \n",
"4869 58.0 NaN 250.0 \n",
"4920 94.0 0.0 178.0 \n",
"4924 78.0 0.0 0.0 \n",
"4937 25.0 36.0 27.0 \n",
"4972 80.0 3.0 0.0 \n",
"5001 117.0 17000.0 476.0 \n",
"\n",
" actor_2_name actor_1_facebook_likes gross \\\n",
"3 Christian Bale 27000.0 448130642.0 \n",
"17 Robert Downey Jr. 26000.0 623279547.0 \n",
"27 Scarlett Johansson 21000.0 407197282.0 \n",
"43 John Ratzenberger 15000.0 414984497.0 \n",
"58 Fred Willard 1000.0 223806889.0 \n",
"66 Heath Ledger 23000.0 533316061.0 \n",
"67 Delroy Lindo 1000.0 292979556.0 \n",
"78 Mindy Kaling 1000.0 356454367.0 \n",
"93 America Ferrera 18000.0 217387997.0 \n",
"95 Vin Diesel 14000.0 333130696.0 \n",
"96 Anne Hathaway 11000.0 187991439.0 \n",
"97 Tom Hardy 29000.0 292568851.0 \n",
"120 Liam Neeson 23000.0 205343774.0 \n",
"128 Charlize Theron 27000.0 153629485.0 \n",
"179 Tom Hardy 29000.0 183635922.0 \n",
"183 Edgar Ramírez 13000.0 227137090.0 \n",
"205 Orlando Bloom 40000.0 305388685.0 \n",
"238 John Ratzenberger 12000.0 289907418.0 \n",
"270 Orlando Bloom 16000.0 313837577.0 \n",
"278 Donald Glover 13000.0 228430993.0 \n",
"283 Connie Nielsen 3000.0 187670866.0 \n",
"288 Jenette Goldstein 780.0 204843350.0 \n",
"296 Christoph Waltz 29000.0 162804648.0 \n",
"308 Matthew McConaughey 29000.0 116866727.0 \n",
"338 Stephen Root 1000.0 380838870.0 \n",
"339 Billy Boyd 5000.0 377019252.0 \n",
"340 Orlando Bloom 16000.0 340478898.0 \n",
"361 Matt Damon 29000.0 132373442.0 \n",
"404 Scott Thompson 544.0 NaN \n",
"452 Joseph Sikora 29000.0 127968405.0 \n",
"... ... ... ... \n",
"4372 Luke Edwards 467.0 NaN \n",
"4404 Jean Arthur 607.0 NaN \n",
"4427 Stanley Blystone 309.0 163245.0 \n",
"4444 Jake Busey 3000.0 NaN \n",
"4458 Jason Flemyng 26000.0 3650677.0 \n",
"4468 Aziz Muradillayev 0.0 NaN \n",
"4474 Joan Fontaine 1000.0 NaN \n",
"4496 Steve Buscemi 16000.0 2812029.0 \n",
"4526 Claude Rains 2000.0 NaN \n",
"4530 Burgess Meredith 13000.0 117235247.0 \n",
"4535 Albert Brooks 22000.0 NaN \n",
"4638 Karl Malden 10000.0 9600000.0 \n",
"4687 Lloyd Bridges 998.0 NaN \n",
"4688 Arthur Agee 7.0 7830611.0 \n",
"4690 Lauren Cohan 22000.0 NaN \n",
"4708 Jimi Hendrix 262.0 13300000.0 \n",
"4748 Greg Speirs 14.0 133778.0 \n",
"4774 Jennifer Landa 84.0 NaN \n",
"4787 Jack Canfield 8.0 NaN \n",
"4795 Michael Palin 795.0 1229197.0 \n",
"4803 Ron Lynch 59.0 NaN \n",
"4822 Lee J. Cobb 359.0 NaN \n",
"4824 Alan Hale 380.0 NaN \n",
"4838 Gloria Foster 581.0 12438.0 \n",
"4869 James Norton 887.0 NaN \n",
"4920 Alex Emanuel 433.0 NaN \n",
"4924 Stacie Evans 0.0 NaN \n",
"4937 Bill Melendez 39.0 NaN \n",
"4972 Seth Ackerman 103.0 NaN \n",
"5001 Levon Helm 725.0 321952.0 \n",
"\n",
" genres ... \\\n",
"3 Action|Thriller ... \n",
"17 Action|Adventure|Sci-Fi ... \n",
"27 Action|Adventure|Sci-Fi ... \n",
"43 Adventure|Animation|Comedy|Family|Fantasy ... \n",
"58 Adventure|Animation|Family|Sci-Fi ... \n",
"66 Action|Crime|Drama|Thriller ... \n",
"67 Adventure|Animation|Comedy|Family ... \n",
"78 Adventure|Animation|Comedy|Drama|Family|Fantasy ... \n",
"93 Adventure|Animation|Family|Fantasy ... \n",
"95 Action|Adventure|Sci-Fi ... \n",
"96 Adventure|Drama|Sci-Fi ... \n",
"97 Action|Adventure|Sci-Fi|Thriller ... \n",
"120 Action|Adventure ... \n",
"128 Action|Adventure|Sci-Fi|Thriller ... \n",
"179 Adventure|Drama|Thriller|Western ... \n",
"183 Action|Mystery|Thriller ... \n",
"205 Action|Adventure|Fantasy ... \n",
"238 Adventure|Animation|Comedy|Family|Fantasy ... \n",
"270 Action|Adventure|Drama|Fantasy ... \n",
"278 Adventure|Drama|Sci-Fi ... \n",
"283 Action|Drama|Romance ... \n",
"288 Action|Sci-Fi ... \n",
"296 Drama|Western ... \n",
"308 Biography|Comedy|Crime|Drama ... \n",
"338 Adventure|Animation|Comedy|Family ... \n",
"339 Action|Adventure|Drama|Fantasy ... \n",
"340 Action|Adventure|Drama|Fantasy ... \n",
"361 Crime|Drama|Thriller ... \n",
"404 Crime|Drama|Horror|Mystery|Thriller ... \n",
"452 Mystery|Thriller ... \n",
"... ... ... \n",
"4372 Comedy|Horror|Thriller ... \n",
"4404 Comedy|Drama ... \n",
"4427 Comedy|Drama|Family ... \n",
"4444 Comedy|Drama|Family ... \n",
"4458 Comedy|Crime ... \n",
"4468 Action|Biography|Drama|History ... \n",
"4474 Drama|Film-Noir|Mystery|Thriller ... \n",
"4496 Crime|Drama|Thriller ... \n",
"4526 Drama|Romance|War ... \n",
"4530 Drama|Sport ... \n",
"4535 Crime|Drama ... \n",
"4638 Crime|Drama|Romance ... \n",
"4687 Thriller|Western ... \n",
"4688 Documentary|Drama|Sport ... \n",
"4690 Action|Adventure|Fantasy|Sci-Fi ... \n",
"4708 Documentary|History|Music ... \n",
"4748 Documentary|Sport ... \n",
"4774 Comedy|Mystery|Thriller ... \n",
"4787 Documentary ... \n",
"4795 Adventure|Comedy|Fantasy ... \n",
"4803 Animation|Comedy|Drama ... \n",
"4822 Crime|Drama ... \n",
"4824 Comedy|Romance ... \n",
"4838 Drama|Romance ... \n",
"4869 Crime|Drama ... \n",
"4920 Drama ... \n",
"4924 Documentary ... \n",
"4937 Animation|Comedy|Family ... \n",
"4972 Documentary ... \n",
"5001 Documentary|Music ... \n",
"\n",
" num_user_for_reviews language country content_rating budget \\\n",
"3 2701.0 English USA PG-13 250000000.0 \n",
"17 1722.0 English USA PG-13 220000000.0 \n",
"27 1022.0 English USA PG-13 250000000.0 \n",
"43 733.0 English USA G 200000000.0 \n",
"58 1043.0 English USA G 180000000.0 \n",
"66 4667.0 English USA PG-13 185000000.0 \n",
"67 704.0 English USA PG 175000000.0 \n",
"78 773.0 English USA PG 175000000.0 \n",
"93 492.0 English USA PG 165000000.0 \n",
"95 1097.0 English USA PG-13 170000000.0 \n",
"96 2725.0 English USA PG-13 165000000.0 \n",
"97 2803.0 English USA PG-13 160000000.0 \n",
"120 2685.0 English USA PG-13 150000000.0 \n",
"128 1588.0 English Australia R 150000000.0 \n",
"179 1188.0 English USA R 135000000.0 \n",
"183 820.0 English USA PG-13 110000000.0 \n",
"205 2113.0 English USA PG-13 140000000.0 \n",
"238 593.0 English USA G 115000000.0 \n",
"270 5060.0 English New Zealand PG-13 93000000.0 \n",
"278 1023.0 English USA PG-13 108000000.0 \n",
"283 2368.0 English USA R 103000000.0 \n",
"288 983.0 English USA R 102000000.0 \n",
"296 1193.0 English USA R 100000000.0 \n",
"308 1138.0 English USA R 100000000.0 \n",
"338 866.0 English USA G 94000000.0 \n",
"339 3189.0 English USA PG-13 94000000.0 \n",
"340 2417.0 English USA PG-13 94000000.0 \n",
"361 2054.0 English USA R 90000000.0 \n",
"404 270.0 English USA TV-14 NaN \n",
"452 964.0 English USA R 80000000.0 \n",
"... ... ... ... ... ... \n",
"4372 NaN English USA NaN NaN \n",
"4404 245.0 English USA Not Rated 1500000.0 \n",
"4427 211.0 English USA G 1500000.0 \n",
"4444 1.0 English USA PG-13 2000000.0 \n",
"4458 523.0 English UK R 960000.0 \n",
"4468 24.0 English Kyrgyzstan PG-13 1400000.0 \n",
"4474 276.0 English USA Not Rated 1288000.0 \n",
"4496 931.0 English USA R 1200000.0 \n",
"4526 1123.0 English USA PG 950000.0 \n",
"4530 542.0 English USA PG 960000.0 \n",
"4535 881.0 English USA R 1300000.0 \n",
"4638 281.0 English USA Not Rated 910000.0 \n",
"4687 333.0 English USA PG 750000.0 \n",
"4688 74.0 English USA PG-13 700000.0 \n",
"4690 11.0 English USA NaN NaN \n",
"4708 63.0 English USA R 600000.0 \n",
"4748 9.0 English USA Not Rated 500000.0 \n",
"4774 1.0 English USA R NaN \n",
"4787 10.0 English USA G 450000.0 \n",
"4795 660.0 English UK PG 229575.0 \n",
"4803 82.0 English USA TV-PG NaN \n",
"4822 888.0 English USA Not Rated 350000.0 \n",
"4824 235.0 English USA Unrated 325000.0 \n",
"4838 26.0 English USA Not Rated 160000.0 \n",
"4869 59.0 English UK TV-MA NaN \n",
"4920 3.0 English USA NaN 200000.0 \n",
"4924 1.0 English USA NaN 180000.0 \n",
"4937 126.0 English USA TV-G 150000.0 \n",
"4972 13.0 English USA NaN 70000.0 \n",
"5001 113.0 English USA PG NaN \n",
"\n",
" title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n",
"3 2012.0 23000.0 8.5 2.35 \n",
"17 2012.0 21000.0 8.1 1.85 \n",
"27 2016.0 19000.0 8.2 2.35 \n",
"43 2010.0 1000.0 8.3 1.85 \n",
"58 2008.0 729.0 8.4 2.35 \n",
"66 2008.0 13000.0 9.0 2.35 \n",
"67 2009.0 848.0 8.3 1.85 \n",
"78 2015.0 767.0 8.3 1.85 \n",
"93 2010.0 953.0 8.2 2.35 \n",
"95 2014.0 14000.0 8.1 2.35 \n",
"96 2014.0 11000.0 8.6 2.35 \n",
"97 2010.0 27000.0 8.8 2.35 \n",
"120 2005.0 14000.0 8.3 2.35 \n",
"128 2015.0 9000.0 8.1 2.35 \n",
"179 2015.0 27000.0 8.1 2.35 \n",
"183 2007.0 897.0 8.1 2.35 \n",
"205 2003.0 5000.0 8.1 2.35 \n",
"238 2001.0 1000.0 8.1 1.85 \n",
"270 2001.0 5000.0 8.8 2.35 \n",
"278 2015.0 801.0 8.1 2.35 \n",
"283 2000.0 933.0 8.5 2.35 \n",
"288 1991.0 604.0 8.5 2.35 \n",
"296 2012.0 11000.0 8.5 2.35 \n",
"308 2013.0 11000.0 8.2 2.35 \n",
"338 2003.0 939.0 8.2 1.85 \n",
"339 2003.0 857.0 8.9 2.35 \n",
"340 2002.0 5000.0 8.7 2.35 \n",
"361 2006.0 13000.0 8.5 2.35 \n",
"404 NaN 183.0 8.6 1.78 \n",
"452 2010.0 223.0 8.1 2.35 \n",
"... ... ... ... ... \n",
"4372 2016.0 258.0 8.7 NaN \n",
"4404 1939.0 319.0 8.2 1.37 \n",
"4427 1936.0 8.0 8.6 1.37 \n",
"4444 2015.0 660.0 8.2 1.85 \n",
"4458 1998.0 1000.0 8.2 1.85 \n",
"4468 2014.0 0.0 8.7 2.35 \n",
"4474 1940.0 991.0 8.2 1.37 \n",
"4496 1992.0 12000.0 8.4 2.35 \n",
"4526 1942.0 607.0 8.6 1.37 \n",
"4530 1976.0 1000.0 8.1 1.33 \n",
"4535 1976.0 745.0 8.3 1.85 \n",
"4638 1954.0 416.0 8.2 1.85 \n",
"4687 1952.0 575.0 8.1 1.37 \n",
"4688 1994.0 6.0 8.3 1.33 \n",
"4690 2014.0 4000.0 8.1 NaN \n",
"4708 1970.0 227.0 8.1 2.20 \n",
"4748 2012.0 9.0 8.4 NaN \n",
"4774 2014.0 41.0 8.3 NaN \n",
"4787 2014.0 2.0 8.2 NaN \n",
"4795 1975.0 561.0 8.3 1.66 \n",
"4803 NaN 11.0 8.2 1.33 \n",
"4822 1957.0 259.0 8.9 1.66 \n",
"4824 1934.0 114.0 8.2 1.37 \n",
"4838 1964.0 99.0 8.1 NaN \n",
"4869 NaN 340.0 8.5 16.00 \n",
"4920 2012.0 375.0 8.1 1.78 \n",
"4924 2014.0 0.0 8.7 NaN \n",
"4937 1965.0 36.0 8.4 1.33 \n",
"4972 2004.0 0.0 8.3 NaN \n",
"5001 1978.0 572.0 8.2 1.85 \n",
"\n",
" movie_facebook_likes \n",
"3 164000 \n",
"17 123000 \n",
"27 72000 \n",
"43 30000 \n",
"58 16000 \n",
"66 37000 \n",
"67 27000 \n",
"78 118000 \n",
"93 33000 \n",
"95 96000 \n",
"96 349000 \n",
"97 175000 \n",
"120 15000 \n",
"128 191000 \n",
"179 190000 \n",
"183 0 \n",
"205 10000 \n",
"238 0 \n",
"270 21000 \n",
"278 153000 \n",
"283 21000 \n",
"288 13000 \n",
"296 199000 \n",
"308 138000 \n",
"338 11000 \n",
"339 16000 \n",
"340 10000 \n",
"361 29000 \n",
"404 59000 \n",
"452 53000 \n",
"... ... \n",
"4372 8 \n",
"4404 0 \n",
"4427 0 \n",
"4444 232 \n",
"4458 21000 \n",
"4468 0 \n",
"4474 0 \n",
"4496 19000 \n",
"4526 23000 \n",
"4530 0 \n",
"4535 35000 \n",
"4638 0 \n",
"4687 3000 \n",
"4688 0 \n",
"4690 1000 \n",
"4708 0 \n",
"4748 0 \n",
"4774 70 \n",
"4787 460 \n",
"4795 14000 \n",
"4803 526 \n",
"4822 40000 \n",
"4824 0 \n",
"4838 363 \n",
"4869 10000 \n",
"4920 66 \n",
"4924 88 \n",
"4937 0 \n",
"4972 110 \n",
"5001 0 \n",
"\n",
"[207 rows x 28 columns]>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data1 = data[(data.imdb_score > 8) & (data.language == 'English')]\n",
"data1.describe"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "86167974-d501-4fd0-c345-492933faf8e8"
},
"outputs": [],
"source": [
"dire = data1['director_name']\n",
"rate = data1['imdb_score']"
]
}
],
"metadata": {
"_change_revision": 1,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161065.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "31ca96ee-558e-bab3-6626-852ac24f2916"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data.csv\n",
"\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 569 entries, 0 to 568\n",
"Data columns (total 31 columns):\n",
"diagnosis 569 non-null object\n",
"radius_mean 569 non-null float64\n",
"texture_mean 569 non-null float64\n",
"perimeter_mean 569 non-null float64\n",
"area_mean 569 non-null float64\n",
"smoothness_mean 569 non-null float64\n",
"compactness_mean 569 non-null float64\n",
"concavity_mean 569 non-null float64\n",
"concave points_mean 569 non-null float64\n",
"symmetry_mean 569 non-null float64\n",
"fractal_dimension_mean 569 non-null float64\n",
"radius_se 569 non-null float64\n",
"texture_se 569 non-null float64\n",
"perimeter_se 569 non-null float64\n",
"area_se 569 non-null float64\n",
"smoothness_se 569 non-null float64\n",
"compactness_se 569 non-null float64\n",
"concavity_se 569 non-null float64\n",
"concave points_se 569 non-null float64\n",
"symmetry_se 569 non-null float64\n",
"fractal_dimension_se 569 non-null float64\n",
"radius_worst 569 non-null float64\n",
"texture_worst 569 non-null float64\n",
"perimeter_worst 569 non-null float64\n",
"area_worst 569 non-null float64\n",
"smoothness_worst 569 non-null float64\n",
"compactness_worst 569 non-null float64\n",
"concavity_worst 569 non-null float64\n",
"concave points_worst 569 non-null float64\n",
"symmetry_worst 569 non-null float64\n",
"fractal_dimension_worst 569 non-null float64\n",
"dtypes: float64(30), object(1)\n",
"memory usage: 137.9+ KB\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"data = pd.read_csv(\"../input/data.csv\",header=0)\n",
"data.drop(\"Unnamed: 32\",axis=1,inplace=True)\n",
"data.drop(\"id\",axis=1,inplace=True)\n",
"\n",
"data.info()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "86ab7768-64a2-1981-4fe6-023ea68e080d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 398 entries, 89 to 353\n",
"Data columns (total 29 columns):\n",
"radius_mean 398 non-null float64\n",
"texture_mean 398 non-null float64\n",
"perimeter_mean 398 non-null float64\n",
"area_mean 398 non-null float64\n",
"smoothness_mean 398 non-null float64\n",
"compactness_mean 398 non-null float64\n",
"concavity_mean 398 non-null float64\n",
"concave points_mean 398 non-null float64\n",
"symmetry_mean 398 non-null float64\n",
"fractal_dimension_mean 398 non-null float64\n",
"radius_se 398 non-null float64\n",
"texture_se 398 non-null float64\n",
"perimeter_se 398 non-null float64\n",
"area_se 398 non-null float64\n",
"smoothness_se 398 non-null float64\n",
"compactness_se 398 non-null float64\n",
"concavity_se 398 non-null float64\n",
"concave points_se 398 non-null float64\n",
"symmetry_se 398 non-null float64\n",
"fractal_dimension_se 398 non-null float64\n",
"radius_worst 398 non-null float64\n",
"texture_worst 398 non-null float64\n",
"perimeter_worst 398 non-null float64\n",
"area_worst 398 non-null float64\n",
"smoothness_worst 398 non-null float64\n",
"compactness_worst 398 non-null float64\n",
"concavity_worst 398 non-null float64\n",
"concave points_worst 398 non-null float64\n",
"symmetry_worst 398 non-null float64\n",
"dtypes: float64(29)\n",
"memory usage: 93.3 KB\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split \n",
"train, test = train_test_split(data, test_size = 0.3)\n",
"\n",
"prediction_var = list(data.columns[1:30])\n",
"\n",
"train_X = train[prediction_var]# taking the training data input\n",
"train_y=train.diagnosis# This is output of our training data\n",
"# same we have to do for test\n",
"test_X= test[prediction_var] # taking test data inputs\n",
"test_y =test.diagnosis #output value of test dat\n",
"\n",
"train_X.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "6ba144e5-c9f6-3efe-79d7-76db25503c4c"
},
"outputs": [
{
"data": {
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2St6CaA7SbfWnYBglgW98Em4OQrHY4zIVsiZQ2nh5MUBdElNe1ARKMAd5vpmv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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd37b9ded68>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd37b2badd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"n_train = np.array(train_X)\n",
"n_test = np.array(test_X)\n",
"\n",
"plt.plot(n_train)\n",
"plt.show()\n",
"\n",
"import scipy\n",
"plt.hist(n_train)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "e7dc6983-a69f-e095-0952-d31b90563e68"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"29\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/Keras-2.0.4-py3.6.egg/keras/backend/tensorflow_backend.py:2252: UserWarning: Expected no kwargs, you passed 1\n",
"kwargs passed to function are ignored with Tensorflow backend\n",
" warnings.warn('\\n'.join(msg))\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(398, 29)\n"
]
},
{
"data": {
"image/png": 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rTngL1r/v/cG5SXZkZlHZNXOQrwlQNhtNrEOO4TjzlGLaSjJRKAujJsIsnICe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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd366511ef0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd36437a080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from keras.layers import Input, Dense\n",
"from keras.models import Model\n",
"import tensorflow as tf\n",
"\n",
"input_dim = n_train.shape[1]\n",
"feature_dim = 10\n",
"print(input_dim)\n",
"inputs = Input(shape=(input_dim,))\n",
"encoded = inputs\n",
"encoded = Dense(feature_dim, kernel_initializer=\"uniform\")(encoded)\n",
"\n",
"decoded = encoded\n",
"decoded = Dense(input_dim, kernel_initializer=\"uniform\")(decoded)\n",
"\n",
"\n",
"autoencoder = Model(inputs, decoded)\n",
"autoencoder.compile(optimizer='adadelta', loss='mse')\n",
"\n",
"autoencoder.fit(n_train, n_train,\n",
" verbose=0,\n",
" epochs=150,\n",
" batch_size=100,\n",
" #shuffle=True,\n",
" validation_data=(n_test, n_test))\n",
"\n",
"predict_vals = autoencoder.predict(n_train)\n",
"print(predict_vals.shape)\n",
"plt.plot(predict_vals)\n",
"plt.show()\n",
"\n",
"plt.hist(predict_vals)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "fcb3f0f4-7e0f-6fb3-8c1b-07f8327a9c96"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.637426900585\n",
"0.654970760234\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/Keras-2.0.4-py3.6.egg/keras/backend/tensorflow_backend.py:2252: UserWarning: Expected no kwargs, you passed 1\n",
"kwargs passed to function are ignored with Tensorflow backend\n",
" warnings.warn('\\n'.join(msg))\n"
]
}
],
"source": [
"from keras.models import Sequential\n",
"layer = autoencoder.layers[1]\n",
"\n",
"featuremodel = Sequential()\n",
"featuremodel.add(Dense(feature_dim, input_shape=(input_dim,), weights=layer.get_weights(), activation='tanh'))\n",
"featuremodel.compile(optimizer='adadelta', loss='mse')\n",
"\n",
"from sklearn import svm # for Support Vector Machine\n",
"from sklearn import metrics # for the check the error and accuracy of the model\n",
"\n",
"# classic svm\n",
"model = svm.SVC()\n",
"model.fit(train_X,train_y)\n",
"prediction=model.predict(test_X)\n",
"print(metrics.accuracy_score(prediction,test_y))\n",
"\n",
"# classic svm with autoencoder\n",
"deepmodel = svm.SVC()\n",
"deepmodel.fit(featuremodel.predict(n_train),train_y)\n",
"deepprediction=deepmodel.predict(featuremodel.predict(n_test))\n",
"print(metrics.accuracy_score(deepprediction, test_y))"
]
}
],
"metadata": {
"_change_revision": 34,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161080.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3e96c68f-e214-3e94-3f54-d9f527d609e8"
},
"source": [
"# Problem statement\n",
"\n",
"This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled\n",
"mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide\n",
"to North American Mushrooms (1981). Each species is identified as definitely edible, definitely\n",
"poisonous, or of unknown edibility and not recommended. This latter class was combined\n",
"with the poisonous one.\n",
"\n",
"- **What types of machine learning models perform best on this dataset?** \n",
"- **Which features are most indicative of a poisonous mushroom?**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "1d43be08-9ff8-c828-8153-9525cf272605"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mushrooms.csv\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"from sklearn.cross_validation import train_test_split\n",
"import matplotlib.pyplot as plt\n",
"% matplotlib inline\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"import seaborn as sns\n",
"sns.set(color_codes=True)\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "69f2b03b-6d63-dfdf-0981-ba069b60197e"
},
"outputs": [],
"source": [
"df = pd.read_csv('../input/mushrooms.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "f50f7c14-405a-e521-d598-15524b440a76"
},
"outputs": [
{
"data": {
"text/plain": [
"(8124, 23)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "823371f8-5012-a188-58f0-82e413258d7b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>class</th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>...</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>p</td>\n",
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" <td>s</td>\n",
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" <td>k</td>\n",
" <td>s</td>\n",
" <td>u</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>e</td>\n",
" <td>x</td>\n",
" <td>s</td>\n",
" <td>y</td>\n",
" <td>t</td>\n",
" <td>a</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>k</td>\n",
" <td>...</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>g</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>e</td>\n",
" <td>b</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>t</td>\n",
" <td>l</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>b</td>\n",
" <td>n</td>\n",
" <td>...</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>m</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>p</td>\n",
" <td>x</td>\n",
" <td>y</td>\n",
" <td>w</td>\n",
" <td>t</td>\n",
" <td>p</td>\n",
" <td>f</td>\n",
" <td>c</td>\n",
" <td>n</td>\n",
" <td>n</td>\n",
" <td>...</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>p</td>\n",
" <td>k</td>\n",
" <td>s</td>\n",
" <td>u</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>e</td>\n",
" <td>x</td>\n",
" <td>s</td>\n",
" <td>g</td>\n",
" <td>f</td>\n",
" <td>n</td>\n",
" <td>f</td>\n",
" <td>w</td>\n",
" <td>b</td>\n",
" <td>k</td>\n",
" <td>...</td>\n",
" <td>s</td>\n",
" <td>w</td>\n",
" <td>w</td>\n",
" <td>p</td>\n",
" <td>w</td>\n",
" <td>o</td>\n",
" <td>e</td>\n",
" <td>n</td>\n",
" <td>a</td>\n",
" <td>g</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" class cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"0 p x s n t p f \n",
"1 e x s y t a f \n",
"2 e b s w t l f \n",
"3 p x y w t p f \n",
"4 e x s g f n f \n",
"\n",
" gill-spacing gill-size gill-color ... stalk-surface-below-ring \\\n",
"0 c n k ... s \n",
"1 c b k ... s \n",
"2 c b n ... s \n",
"3 c n n ... s \n",
"4 w b k ... s \n",
"\n",
" stalk-color-above-ring stalk-color-below-ring veil-type veil-color \\\n",
"0 w w p w \n",
"1 w w p w \n",
"2 w w p w \n",
"3 w w p w \n",
"4 w w p w \n",
"\n",
" ring-number ring-type spore-print-color population habitat \n",
"0 o p k s u \n",
"1 o p n n g \n",
"2 o p n n m \n",
"3 o p k s u \n",
"4 o e n a g \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "27a18d05-7226-15df-48f6-fdaa38bf3f0d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 8124 entries, 0 to 8123\n",
"Data columns (total 23 columns):\n",
"class 8124 non-null object\n",
"cap-shape 8124 non-null object\n",
"cap-surface 8124 non-null object\n",
"cap-color 8124 non-null object\n",
"bruises 8124 non-null object\n",
"odor 8124 non-null object\n",
"gill-attachment 8124 non-null object\n",
"gill-spacing 8124 non-null object\n",
"gill-size 8124 non-null object\n",
"gill-color 8124 non-null object\n",
"stalk-shape 8124 non-null object\n",
"stalk-root 8124 non-null object\n",
"stalk-surface-above-ring 8124 non-null object\n",
"stalk-surface-below-ring 8124 non-null object\n",
"stalk-color-above-ring 8124 non-null object\n",
"stalk-color-below-ring 8124 non-null object\n",
"veil-type 8124 non-null object\n",
"veil-color 8124 non-null object\n",
"ring-number 8124 non-null object\n",
"ring-type 8124 non-null object\n",
"spore-print-color 8124 non-null object\n",
"population 8124 non-null object\n",
"habitat 8124 non-null object\n",
"dtypes: object(23)\n",
"memory usage: 1.4+ MB\n"
]
}
],
"source": [
"# This dataset is ready for exploration, no data cleaning required \n",
"df.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2f2af4e8-2a00-5950-1c66-9e760ef92114"
},
"source": [
"# Class Distribuition\n",
"\n",
"Imbalanced data typically refers to a problem with classification problems where the classes\n",
"are not represented equally. Imagine a case where 99% of the data belongs to one class, this can cause\n",
"the classification model to ignore the remaining class and indeed it would get very good accuracy.\n",
"But a small difference often does not matter and this is the case in mushrooms dataset."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "af96bced-ed00-745d-e77b-f18e0be6ff4b"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8a8ddd05f8>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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NTY3+/e9/WzYUAAD4ai76y2acTqd+9KMf6Z133rFiHgAA0AO6dQTv9XrP+v3IkSNqbGy0\nZCAAAPDVdSvwe/fuPev3K6+8UmvWrLFkIAAA8NV1K/BFRUWSpOPHj8tms+mqq66ydCgAAPDVdCvw\nPp9PCxYsUGtrq4LBoGJjY7V69WolJCRYPR8AALgE3Qp8SUmJnnrqKV133XWSpH/+85969NFHtWnT\nJkuHAwAAl6Zbd9Hb7fZQ3KVP/y/+8x9ZCwAALi/dDnxlZaVOnjypkydPatu2bQQeAIDLWLdO0S9f\nvlwrVqzQkiVLZLfbNWzYMD3yyCNWzwYAAC5Rt47g33nnHTmdTv31r39VbW2tzpw5o7feesvq2QAA\nwCXq1hF8RUWFNm/eHPp9/fr1mjFjhmbMmHHB7VatWqW9e/fq9OnTuvvuu5WQkKAFCxaoq6tL8fHx\nWr16tZxOpyoqKlRWVia73a6pU6cqOztbnZ2dKiws1KFDhxQREaGioiINHjz4qz1bAAD6iW4Fvqur\n66xr7nb7lx/479mzRwcOHFB5ebmam5s1adIkjR49Wrm5uZo4caKeeOIJeb1eZWVlqbS0VF6vV5GR\nkZoyZYoyMjJUVVWlmJgYlZSUaPfu3SopKeHDdQAA6KZuBX7cuHGaNm2aRo0apTNnzmjPnj368Y9/\nfMFtbrzxRt1www2SpJiYGJ06dUq1tbVavny5JCk9PV3r16/Xtddeq4SEBLlcLklSUlKSfD6fampq\nlJWVJUlKTU3VokWLLvlJAgDQ33T7++Bvuukm/eMf/5DNZtOyZcs0YsSIC24TERGh6OhoSZ9+lv3N\nN9+s3bt3y+l0SpLi4uLk9/sVCATkdrtD27nd7nOW2+122Ww2dXR0hLY/nwEDouVwcHc/8EXi413h\nHgHol8Lx2utW4CUpOTlZycnJF/0Ab775prxer9avX3/WUX8wGDzv+he7/POam9suej6gP/H7T4R7\nBKBfsuq1d6E3Dhf9dbEX4+2339a6dev07LPPyuVyKTo6Wu3t7ZKkxsZGeTweeTweBQKB0DZNTU2h\n5X6/X5LU2dmpYDB4waN3AADwGcsCf+LECa1atUpPP/20YmNjJX16Lb2yslKStGPHDo0ZM0aJiYmq\nr69XS0uLWltb5fP5lJycrLS0NG3fvl2SVFVVpZSUFKtGBQDAON0+RX+xtm3bpubmZs2fPz+0rLi4\nWEuWLFF5ebkGDRqkrKwsRUZGqqCgQHl5ebLZbMrPz5fL5VJmZqaqq6uVk5Mjp9Op4uJiq0YFAMA4\ntmB3Lm73EVZd45iycrol+wV6m3dh3/qCqGW7Hg33CECPWD52sSX7Dds1eAAAEB4EHgAAAxF4AAAM\nROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAA\nAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcAwEAEHgAAAxF4AAAMROABADAQgQcA\nwECWBn7//v2aMGGCNm7cKEkqLCzUT37yE82cOVMzZ87Url27JEkVFRWaPHmysrOz9fLLL0uSOjs7\nVVBQoJycHM2YMUMNDQ1WjgoAgFEcVu24ra1NK1as0OjRo89afv/99ys9Pf2s9UpLS+X1ehUZGakp\nU6YoIyNDVVVViomJUUlJiXbv3q2SkhKtWbPGqnEBADCKZUfwTqdTzz77rDwezwXXq6urU0JCglwu\nl6KiopSUlCSfz6eamhplZGRIklJTU+Xz+awaFQAA41h2BO9wOORwnLv7jRs36vnnn1dcXJweeugh\nBQIBud3u0N/dbrf8fv9Zy+12u2w2mzo6OuR0Or/wMQcMiJbDEdHzTwYwRHy8K9wjAP1SOF57lgX+\nfG6//XbFxsZq+PDheuaZZ/Tkk09q5MiRZ60TDAbPu+0XLf+85ua2HpkTMJXffyLcIwD9klWvvQu9\ncejVu+hHjx6t4cOHS5LGjRun/fv3y+PxKBAIhNZpamqSx+ORx+OR3++X9OkNd8Fg8IJH7wAA4DO9\nGvi5c+eG7oavra3V0KFDlZiYqPr6erW0tKi1tVU+n0/JyclKS0vT9u3bJUlVVVVKSUnpzVEBAOjT\nLDtFv2/fPq1cuVIfffSRHA6HKisrNWPGDM2fP19XXHGFoqOjVVRUpKioKBUUFCgvL082m035+fly\nuVzKzMxUdXW1cnJy5HQ6VVxcbNWoAAAYxxbszsXtPsKqaxxTVk63ZL9Ab/Mu3BTuES7Ksl2PhnsE\noEcsH7vYkv1eNtfgAQBA7yDwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAA\nBiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8A\ngIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABrI08Pv379eECRO0ceNGSdLhw4c1c+ZM5ebmat68\neero6JAkVVRUaPLkycrOztbLL78sSers7FRBQYFycnI0Y8YMNTQ0WDkqAABGsSzwbW1tWrFihUaP\nHh1atnbtWuXm5mrz5s0aMmSIvF6v2traVFpaqhdeeEEbNmxQWVmZjh8/rq1btyomJkYvvviiZs+e\nrZKSEqtGBQDAOJYF3ul06tlnn5XH4wktq62t1fjx4yVJ6enpqqmpUV1dnRISEuRyuRQVFaWkpCT5\nfD7V1NQoIyNDkpSamiqfz2fVqAAAGMdh2Y4dDjkcZ+/+1KlTcjqdkqS4uDj5/X4FAgG53e7QOm63\n+5zldrtdNptNHR0doe3PZ8CAaDkcERY8G8AM8fGucI8A9EvheO1ZFvgvEwwGe2T55zU3t32lmQDT\n+f0nwj0C0C9Z9dq70BuHXr2LPjo6Wu3t7ZKkxsZGeTweeTweBQKB0DpNTU2h5X6/X9KnN9wFg8EL\nHr0DAIDP9GrgU1NTVVlZKUnasWOHxowZo8TERNXX16ulpUWtra3y+XxKTk5WWlqatm/fLkmqqqpS\nSkpKb44KAECfZtkp+n379mnlypX66KOP5HA4VFlZqccff1yFhYUqLy/XoEGDlJWVpcjISBUUFCgv\nL082m035+flyuVzKzMxUdXW1cnJy5HQ6VVxcbNWoAAAYxxbszsXtPsKqaxxTVk63ZL9Ab/Mu3BTu\nES7Ksl2PhnsEoEcsH7vYkv1eNtfgAQBA7yDwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAg\nAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAY\niMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEcvflgtbW1mjdvnoYOHSpJuu666/SrX/1K\nCxYsUFdXl+Lj47V69Wo5nU5VVFSorKxMdrtdU6dOVXZ2dm+OCgBAn9argZekm266SWvXrg39/pvf\n/Ea5ubmaOHGinnjiCXm9XmVlZam0tFRer1eRkZGaMmWKMjIyFBsb29vjAgDQJ4X9FH1tba3Gjx8v\nSUpPT1dNTY3q6uqUkJAgl8ulqKgoJSUlyefzhXlSAAD6jl4/gj948KBmz56tjz/+WPfee69OnTol\np9MpSYqLi5Pf71cgEJDb7Q5t43a75ff7e3tUAAD6rF4N/DXXXKN7771XEydOVENDg2bNmqWurq7Q\n34PB4Hm3+6Ll/2vAgGg5HBE9Mitgovh4V7hHAPqlcLz2ejXwAwcOVGZmpiTp29/+tr7xjW+ovr5e\n7e3tioqKUmNjozwejzwejwKBQGi7pqYmjRgx4kv339zcZtnsgAn8/hPhHgHol6x67V3ojUOvXoOv\nqKjQc889J0ny+/06evSo7rjjDlVWVkqSduzYoTFjxigxMVH19fVqaWlRa2urfD6fkpOTe3NUAAD6\ntF49gh83bpweeOAB7dy5U52dnXr44Yc1fPhwLVy4UOXl5Ro0aJCysrIUGRmpgoIC5eXlyWazKT8/\nXy4XpxYBAOiuXg38lVdeqXXr1p2z/Pnnnz9n2a233qpbb721N8YCAMA4Yf83OQAA0PMIPAAABiLw\nAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEI\nPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAYiMADAGAg\nAg8AgIEc4R7gQh577DHV1dXJZrNp0aJFuuGGG8I9EgAAfcJlG/i//OUv+vDDD1VeXq4PPvhAixYt\nUnl5ebjHAgCgT7hsT9HX1NRowoQJkqTvfOc7+vjjj3Xy5MkwTwUAQN9w2QY+EAhowIABod/dbrf8\nfn8YJwIAoO+4bE/R/69gMPil68THuyx57Lcer7BkvwAu7Kns4nCPAPRZl+0RvMfjUSAQCP3e1NSk\n+Pj4ME4EAEDfcdkGPi0tTZWVlZKk9957Tx6PR1deeWWYpwIAoG+4bE/RJyUl6Qc/+IGmTZsmm82m\nZcuWhXskAAD6DFuwOxe3AQBAn3LZnqIHAACXjsADAGAgAg8AgIEIPAAABrps76JH//Dqq6/q7bff\n1smTJ3XkyBHdeeedmjx5crjHAozX1dWlhx56SA0NDTp9+rR+/etfa/To0eEeCz2IwCPsDh48qD/+\n8Y9qaWnR7bffrkmTJslu5+QSYKU33nhD8fHxeuyxx3Ts2DH9/Oc/1xtvvBHusdCDCDzC7sYbb5TD\n4ZDb7dZVV12l5uZmxcXFhXsswGh/+9vftHfvXvl8PknSJ598oo6ODjmdzjBPhp5C4BF2Z86cCf0c\nDAZls9nCOA3QP0RGRmr27Nm67bbbwj0KLMJ5UITd3//+d3V1denYsWNqbW1VbGxsuEcCjJeYmKid\nO3dKko4ePaonnngizBOhp3EEj7D71re+pXnz5unDDz/U/Pnzuf4O9IKJEydqz549mjZtmrq6unTv\nvfeGeyT0MD6qFmH16quv6sCBA1q4cGG4RwEAo3CoBACAgTiCBwDAQBzBAwBgIAIPAICBCDwAAAYi\n8AC6ZebMmaqurg73GAC6icADAGAgPugGwHk99dRT2rlzp+x2u26//fbQ8jNnzmjZsmX6z3/+o46O\nDiUmJmrJkiVqbW1VQUGBWlpadPr0aaWnp+uee+7Rtm3b9Nxzzyk6OlrBYFBFRUUaPHhwGJ8Z0D8Q\neADnePfdd7Vr1y699NJLOnPmjObOnauWlhZJ0scff6zvfe97WrFihSTp1ltv1f79+/Xhhx/q9OnT\n2rx5s86cOaMNGzbozJkzWrdunVasWKHExETV1dWpsbGRwAO9gFP0AM5RV1enUaNGKSIiQpGRkVq3\nbp1iYmIkSTExMTp8+LB+9rOfaebMmfL7/WpublZSUpIaGxs1b948vfbaa8rOzpbdbtcdd9yhwsJC\n/fa3v5XD4VBycnKYnx3QPxB4AOew2Wz6os/A+tOf/qT6+npt2rRJGzZs0JAhQyRJcXFxev311zVr\n1iwdPHhQkydPVnt7u+68805t2LBB11xzjZYuXaotW7b05lMB+i0CD+AcI0eOVE1NjTo7O9XZ2amZ\nM2eqqalJ0qffPHbttdfK4XBo3759+u9//6uOjg7t3r1bu3bt0qhRo7RgwQJFR0fr6NGjevzxx+Vy\nuTRp0iTNnTtXdXV1YX52QP/AR9UCOK/S0lK99dZbCgaDuu222/Tmm2/qnnvu0bXXXqvZs2fL5XIp\nKSlJUVFRev3117V+/XoVFhaqq6tLERERSkpK0n333afnnntOW7duDZ3iX7JkiYYOHRrmZweYj8AD\nAGAgTtEDAGAgAg8AgIEIPAAABiLwAAAYiMADAGAgAg8AgIEIPAAABiLwAAAY6P8AMe69wm4ydEQA\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8ac896c6d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Class Distribuition\n",
"sns.countplot(x=\"class\", data=df, palette=\"Greens_d\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "7d7c3469-313a-d48a-1b4d-3b9132521f8d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"e 4208\n",
"p 3916\n",
"Name: class, dtype: int64\n"
]
}
],
"source": [
"class_dist = df['class'].value_counts()\n",
"\n",
"print(class_dist)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "1dc70396-83ed-beea-f552-628614cb72a4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.517971442639\n",
"0.482028557361\n"
]
}
],
"source": [
"prob_e = class_dist[0]/(class_dist[0]+class_dist[1])\n",
"prob_p = 1 - prob_e\n",
"print(prob_e)\n",
"print(prob_p)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e1a2c2cb-9ea9-0c56-1a50-a5d25bf8a8d5"
},
"source": [
"# Feature Transformation\n",
"\n",
"Now we have to convert all categorical variables using LabelEncoder from the awesome sklearn lib. \n",
"ex. The class p will be maped to 1 and 0 as e"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "1108b091-81fa-0f4d-e65e-a7d96d5a6920"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>class</th>\n",
" <th>cap-shape</th>\n",
" <th>cap-surface</th>\n",
" <th>cap-color</th>\n",
" <th>bruises</th>\n",
" <th>odor</th>\n",
" <th>gill-attachment</th>\n",
" <th>gill-spacing</th>\n",
" <th>gill-size</th>\n",
" <th>gill-color</th>\n",
" <th>...</th>\n",
" <th>stalk-surface-below-ring</th>\n",
" <th>stalk-color-above-ring</th>\n",
" <th>stalk-color-below-ring</th>\n",
" <th>veil-type</th>\n",
" <th>veil-color</th>\n",
" <th>ring-number</th>\n",
" <th>ring-type</th>\n",
" <th>spore-print-color</th>\n",
" <th>population</th>\n",
" <th>habitat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" class cap-shape cap-surface cap-color bruises odor gill-attachment \\\n",
"0 1 5 2 4 1 6 1 \n",
"1 0 5 2 9 1 0 1 \n",
"2 0 0 2 8 1 3 1 \n",
"3 1 5 3 8 1 6 1 \n",
"4 0 5 2 3 0 5 1 \n",
"\n",
" gill-spacing gill-size gill-color ... stalk-surface-below-ring \\\n",
"0 0 1 4 ... 2 \n",
"1 0 0 4 ... 2 \n",
"2 0 0 5 ... 2 \n",
"3 0 1 5 ... 2 \n",
"4 1 0 4 ... 2 \n",
"\n",
" stalk-color-above-ring stalk-color-below-ring veil-type veil-color \\\n",
"0 7 7 0 2 \n",
"1 7 7 0 2 \n",
"2 7 7 0 2 \n",
"3 7 7 0 2 \n",
"4 7 7 0 2 \n",
"\n",
" ring-number ring-type spore-print-color population habitat \n",
"0 1 4 2 3 5 \n",
"1 1 4 3 2 1 \n",
"2 1 4 3 2 3 \n",
"3 1 4 2 3 5 \n",
"4 1 0 3 0 1 \n",
"\n",
"[5 rows x 23 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"labelencoder=LabelEncoder()\n",
"for col in df.columns:\n",
" df[col] = labelencoder.fit_transform(df[col])\n",
" \n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d267b025-53ae-8a1c-dad8-09299a147a2a"
},
"source": [
"# Pearson Correlation Heatmap\n",
"\n",
"Person Correlation helps us represent the statistical relationships between features, this is a simple way to get an intuition of \n",
"the contribution of each feature to the target variable. This correlation matrix can be easily plotted using Seaborn Heatmap. In Heatmap strong relationships are emphasized with sharp colors."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "0454fa0e-8752-f2df-3c56-d709f58a6724"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f8a8d709f98>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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Hh5OVlcWmTZs4cOAAffv2feKyTJgwgYyMDPr378+ZM2fQarUkJiayZMkSgoKC6NatG9bW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ywDIJEFgs4shKXqd5u2FFSzxUGwJUitJrkg3+xePk3U6qrrluGzyQUFBLtbThbndO0uqNYbAGja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\n7z0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+njuS0b29tY51rlmTexkZ6u+5RUWk5+fRqHrNKsnEZWXxKDeH8iiUSnbfuqn+3sOl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S0MUM8voIyRnMr5rwYZTXx+rnyJ4eXYGGIyM2lqbsHRN15Xfoc5Nee/vELVGK4spygpofMv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\nPVk9sT8jv9qicr6xdX12zx1VVndF/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a0DPVp4YWlmjFwqoYWHE2GRd1n+u/o9+n2Q2q9c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\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Me4P2s5pX55Ulb+AXtaxZdd5+dTptf3mvWXVeG7Pgn7ELqz5uVp0pz81k1NG/dvHYH2VX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sS6+9He1Jf5z/p2YkBf8uxMyiAS5fvkxJSQlZWVnk5eVhbm5OYmIirq6unDt3jlat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\nERERERHRGP8PVT2grWGCAf8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8a8d709128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"colormap = plt.cm.viridis\n",
"plt.figure(figsize=(15,15))\n",
"plt.title('Pearson Correlation of Features', size=15)\n",
"\n",
"sns.heatmap(df.astype(float).corr(),linewidths=0.1,vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "005ba353-2e76-4762-1ba4-1cec31ef1faf"
},
"outputs": [],
"source": [
"# sns.pairplot(df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "9704e144-eea8-e729-03f6-cd8dcb67a047"
},
"source": [
"# Classification Models"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "1629eb68-884c-329b-c704-f7664d8c773a"
},
"outputs": [],
"source": [
"X = df.drop('class', axis=1)\n",
"y = df['class']\n",
"RS = 123\n",
"\n",
"# Split dataframe into training and test/validation set \n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=RS)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "5d353ef0-33ce-16cf-9e55-3b2a6caa1b3b"
},
"outputs": [],
"source": [
"from sklearn.metrics import accuracy_score, log_loss\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.svm import SVC, LinearSVC, NuSVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n",
"from xgboost import XGBClassifier\n",
"import xgboost\n",
"\n",
"classifiers = [\n",
" KNeighborsClassifier(3),\n",
" SVC(kernel=\"rbf\", C=0.025, probability=True),\n",
" NuSVC(probability=True),\n",
" DecisionTreeClassifier(),\n",
" RandomForestClassifier(),\n",
" XGBClassifier(),\n",
" AdaBoostClassifier(),\n",
" GradientBoostingClassifier(),\n",
" GaussianNB(),\n",
" LinearDiscriminantAnalysis(),\n",
" QuadraticDiscriminantAnalysis()]\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "e9973673-998e-b6dd-0e10-30b5bb04c7c2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==============================\n",
"KNeighborsClassifier\n",
"****Results****\n",
"Accuracy: 99.9385%\n",
"Log Loss: 0.0011751030799298835\n",
"==============================\n",
"SVC\n",
"****Results****\n",
"Accuracy: 95.3231%\n",
"Log Loss: 0.08271682390317117\n",
"==============================\n",
"NuSVC\n",
"****Results****\n",
"Accuracy: 94.8308%\n",
"Log Loss: 0.10640077133576313\n",
"==============================\n",
"DecisionTreeClassifier\n",
"****Results****\n",
"Accuracy: 100.0000%\n",
"Log Loss: 9.992007221626413e-16\n",
"==============================\n",
"RandomForestClassifier\n",
"****Results****\n",
"Accuracy: 100.0000%\n",
"Log Loss: 0.0008505287529550219\n",
"==============================\n",
"XGBClassifier\n",
"****Results****\n",
"Accuracy: 100.0000%\n",
"Log Loss: 0.00492644277067246\n",
"==============================\n",
"AdaBoostClassifier\n",
"****Results****\n",
"Accuracy: 100.0000%\n",
"Log Loss: 0.4860313502269329\n",
"==============================\n",
"GradientBoostingClassifier\n",
"****Results****\n",
"Accuracy: 100.0000%\n",
"Log Loss: 0.003088396822482728\n",
"==============================\n",
"GaussianNB\n",
"****Results****\n",
"Accuracy: 90.6462%\n",
"Log Loss: 0.7449519795304806\n",
"==============================\n",
"LinearDiscriminantAnalysis\n",
"****Results****\n",
"Accuracy: 94.3385%\n",
"Log Loss: 0.30987803866199265\n",
"==============================\n",
"QuadraticDiscriminantAnalysis\n",
"****Results****\n",
"Accuracy: 91.1385%\n",
"Log Loss: 2.6286828521066354\n",
"==============================\n"
]
}
],
"source": [
"# Logging for Visual Comparison\n",
"log_cols=[\"Classifier\", \"Accuracy\", \"Log Loss\"]\n",
"log = pd.DataFrame(columns=log_cols)\n",
"\n",
"for clf in classifiers:\n",
" clf.fit(X_train, y_train)\n",
" name = clf.__class__.__name__\n",
" \n",
" print(\"=\"*30)\n",
" print(name)\n",
" \n",
" print('****Results****')\n",
" train_predictions = clf.predict(X_test)\n",
" acc = accuracy_score(y_test, train_predictions)\n",
" print(\"Accuracy: {:.4%}\".format(acc))\n",
" \n",
" train_predictions = clf.predict_proba(X_test)\n",
" ll = log_loss(y_test, train_predictions)\n",
" print(\"Log Loss: {}\".format(ll))\n",
" \n",
" log_entry = pd.DataFrame([[name, acc*100, ll]], columns=log_cols)\n",
" log = log.append(log_entry)\n",
" \n",
"print(\"=\"*30)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e7b40ffd-dedf-9405-7358-55815291fde1"
},
"source": [
"### Multiple classifiers\n",
"\n",
"Let's evaluate multiple classifiers at once. After this picking a single model and improving \n",
"parameter."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "a60d6359-afba-adf4-a97f-c3b190d54b45"
},
"outputs": [
{
"data": {
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sWLDAakZr6NChjBo1Cg8PD3x9fQkPD6d9+/Zs3LjRqr+bnaMkEydOJDQ0lMWLF1OhQgVm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\niIiIiJ1RgSYiIiJiZ1SgiYiIiNiZ/wfeybKdJtFQqgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8a8d736630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Rg/j4eOvn8PBwkpKS6NKly33r6Z///Cd///vf79n4GRkZbNiwAX9/fy5dukRo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\nkg0bNrBhwwYWLVrE5s2bb7mfpk2bkpaWRlJSEm3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niIiIiJ1RgiYiIiJiZ5SgiYiIiNgZ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\nRERExM4oQRMRERGxM0rQREREROzM/wPRBAN3QELSUgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f8a800bd3c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.set_color_codes(\"muted\")\n",
"sns.barplot(x='Accuracy', y='Classifier', data=log, color=\"b\")\n",
"\n",
"plt.xlabel('Accuracy %')\n",
"plt.title('Classifier Accuracy')\n",
"plt.show()\n",
"\n",
"sns.set_color_codes(\"muted\")\n",
"sns.barplot(x='Log Loss', y='Classifier', data=log, color=\"g\")\n",
"\n",
"plt.xlabel('Log Loss')\n",
"plt.title('Classifier Log Loss')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2c86f9d4-903b-7da2-9612-5f4f8158a5f9"
},
"source": [
"# Conclusion 1: Classification Model\n",
"Clearly Tree based models are wining here, even the most simple one (DecisionTreeClassifier), If I had to pick a classifier \n",
"i would pick Decision Tree Classifier as it is the simplest one from (Decision Tree, random forest and Boosted Trees) and would run well on production environments.\n",
"Let me know if you have different opinions, feel free to share your thoughts or ask any question. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "74a93347-efe3-2076-f0b2-c67aebc3baba"
},
"outputs": [
{
"data": {
"text/plain": [
"DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
" splitter='best')"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Inspect the learned Decision Trees\n",
"# One of the major advantage of Decision Trees is the fact that they can easily be interpreted. \n",
"clf = DecisionTreeClassifier()\n",
"\n",
"# Fit with all the training set\n",
"clf.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "f8607408-954a-5bc4-dd22-29b9684e4783"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Feature ranking:\n",
"cap-shape : (0.341473)\n",
"cap-surface : (0.202430)\n",
"cap-color : (0.176778)\n",
"bruises : (0.127365)\n",
"odor : (0.040443)\n",
"gill-attachment : (0.028572)\n",
"gill-spacing : (0.025186)\n",
"gill-size : (0.019125)\n",
"gill-color : (0.018389)\n",
"stalk-shape : (0.011624)\n",
"stalk-root : (0.003698)\n",
"stalk-surface-above-ring : (0.002972)\n",
"stalk-surface-below-ring : (0.001946)\n",
"stalk-color-above-ring : (0.000000)\n",
"stalk-color-below-ring : (0.000000)\n",
"veil-type : (0.000000)\n",
"veil-color : (0.000000)\n",
"ring-number : (0.000000)\n",
"ring-type : (0.000000)\n",
"spore-print-color : (0.000000)\n",
"population : (0.000000)\n",
"habitat : (0.000000)\n"
]
}
],
"source": [
"importances = clf.feature_importances_\n",
"indices = np.argsort(importances)[::-1]\n",
"feature_names = X.columns\n",
"\n",
"print(\"Feature ranking:\")\n",
"for f in range(X.shape[1]):\n",
" print(\"%s : (%f)\" % (feature_names[f] , importances[indices[f]]))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "d5d89d46-c953-32f2-67af-a6ad8d01cc42"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f8a83dcedd8>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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eJnv61mib7Olbo21qtD1J422yp287itO63CY6duzYLFy4MEny1FNPpa2tLc3NzUmSTZs2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\nsxlfX1+Cg4ONk++NGzeMno+5c+cSFxeXp/qqUKEC58+f5+b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"text/plain": [
"<matplotlib.figure.Figure at 0x7f8a781fd160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(15, 15))\n",
"plt.title(\"Feature ranking\", fontsize = 12)\n",
"plt.bar(range(X.shape[1]), importances[indices],\n",
" color=\"b\", \n",
" align=\"center\")\n",
"plt.xticks(range(X.shape[1]), feature_names)\n",
"plt.xlim([-1, X.shape[1]])\n",
"plt.ylabel(\"importance\", fontsize = 18)\n",
"plt.xlabel(\"index of the feature\", fontsize = 18)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "23e41598-fe3c-694f-5895-df74bfa28a65"
},
"source": [
"# Conclusion 2: Feature Importance\n",
"Wow, There are a lot of features with no meaning to predict our target variable.\n",
"cap-shape, cap-surface, cap-color, bruises, odor, gill-attachment, gill-spacing, gill-size are the most significant features."
]
}
],
"metadata": {
"_change_revision": 2,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161084.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0b6bbd05-66fb-d165-e910-34c7fc2c9a4a"
},
"source": [
"# HR Dataset Analysis\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4a581f6b-7d45-20b4-cec2-fa078d2981d9"
},
"source": [
"#### In this notebook, given a dataset from the HR department of a company, i will attempt to predict what motivates an employee to leave the company. As you will see below, the feature \"left\" is a categorical feature which describes whether or not the employee that certain observation is describing, left the company or not. And it is the feature i will be attempting to predict. At first, there will be some EDA and feature (uni/bivariate) analysis, followed by model building and validation. "
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ffd9f0b3-2a22-ebe9-ab57-d347e89a3212"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "8602bdc3-006c-a62f-9d86-19f2bcbeceaf"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sb\n",
"import random\n",
"from collections import defaultdict\n",
"from scipy.spatial.distance import euclidean\n",
"from sklearn.model_selection import train_test_split, StratifiedKFold\n",
"from sklearn.preprocessing import LabelEncoder, scale\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn import cluster, metrics, svm\n",
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "61598072-fa03-dd84-7757-0c6858b86278"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "c91fbd83-1ab3-f6f1-4e6a-806010d2dd88"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "f045049d-f511-28d9-beca-c5f5fb4860ab"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "4290e226-a1a1-0768-3b88-420b74273434"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "c19f5aea-6a01-b17e-0075-d83fabf3152c"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "af61dff1-835d-2970-0bc2-2f5d5d276960"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "24210110-72e7-7e1e-eb42-1d4c32b02c74"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1c9536fc-32a3-1d91-196e-3e4fa63992a9"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e955572e-c14f-a9af-3e3c-12cf2f1104c1"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "daa7a39a-5e99-a858-f21c-0dceab79a7e5"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "afc8304a-fd3d-a065-80fb-2e459bdff3de"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "d413c380-9e90-d80a-963b-b4be27018910"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "a95814e8-e4c5-77f8-67a7-f6c2dbb2e789"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "ec455880-7cf3-615d-69b0-b57a7ee5aaf1"
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "32ae4530-765b-49d4-4693-68e16b555301"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
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"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-41b699a108ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mnew_sat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msatisfaction_level\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;36m0.45\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mnew_sat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Q1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"new_sat = []\n",
"\n",
"for i in df.satisfaction_level:\n",
" if 0.45 > i :\n",
" new_sat.append('Q1')\n",
" elif 0.65 > i >= 0.45:\n",
" new_sat.append('Q2')\n",
" elif 0.85 > i >= 0.65:\n",
" new_sat.append('Q3')\n",
" else:\n",
" new_sat.append('Q4')"
]
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| 0001/161/1161245.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ac06cd79-9189-6d2b-9a83-012d19327beb"
},
"source": [
"I simply added the macro data.\n",
"----\n",
"Load the required libraries and data. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "555a1731-6a24-51ed-f194-a47541b955db"
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"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"from sklearn import model_selection, preprocessing\n",
"import xgboost as xgb\n",
"import datetime\n",
"#now = datetime.datetime.now()\n",
"\n",
"train = pd.read_csv('../input/train.csv')\n",
"test = pd.read_csv('../input/test.csv')\n",
"macro = pd.read_csv('../input/macro.csv')\n",
"id_test = test.id\n",
"#print(train.info(10))\n",
"#print(test.info(10))\n",
"#print(macro.info(10))\n",
"#print(macro[['grp_growth','construction_value','rent_price_2room_eco','real_dispos_income_per_cap_growth','income_per_cap','mortgage_rate']])\n",
"#print(macro['income_per_cap']*12*0.5/(macro['mortgage_rate']/100))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "434e5276-9b0d-6e6b-04a4-61751ba7a937"
},
"outputs": [],
"source": [
"x_train=train.merge(macro[['timestamp','salary','mortgage_rate','rent_price_2room_eco','rent_price_2room_bus','unemployment']], left_on='timestamp', right_on='timestamp', how='left')\n",
"#merge macrodata"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "97902c79-aa18-55a0-4762-4f49f666f1d8"
},
"outputs": [
{
"data": {
"image/png": 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Zp0oRjS9Oaqaci1+sKsB+swXnGtTQDNlgc1jHRx0oZFhWcHnt4XTD6Rdiatli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TQcAnBpaer6dj1WVF/qPBwdu8XiwM6DlpRHSyY6lD5TTJupvgKAQZOPHRfnqBiJfb/7bGPW6hfj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/cvH6zdvgMqHd2AMAK+70p9pHfrE3cJGAg8JSD4yhIeh8BiiFapRz6uCy5MPlCcBg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\nIYQQog+SBr8QQgghhBBCCNEH/X9uSZF3ugZPuQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5855b056a0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Scatter Matrix:\n",
"top10=x_train[['price_doc','full_sq','floor','max_floor','build_year','prom_part_5000','office_count_1000','cafe_count_1500_price_2500','product_type','salary','mortgage_rate','rent_price_2room_eco','rent_price_2room_bus','unemployment']].sample(2000)\n",
"top10 = top10.dropna()\n",
"topratio=pd.DataFrame([])\n",
"topratio['val_mort_sal']=top10['price_doc']*top10['mortgage_rate']/top10['salary']/100\n",
"topratio['rent_sal']=(top10['rent_price_2room_eco'])/top10['salary']*100\n",
"topratio['pri_sq']=top10['price_doc']/top10['full_sq']\n",
"topratio['pri_floor']=top10['price_doc']/(top10['floor']+1)\n",
"topratio['product_type']=top10['product_type']\n",
"topratio['price_doc']=top10['price_doc']\n",
"import seaborn as sns; \n",
"sns.set(style=\"ticks\", color_codes=True)\n",
"g = sns.pairplot(topratio, hue=\"product_type\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "4a1a8f7f-4068-d8f5-aea4-277ffda7cfdb"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f584e4a5048>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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KCsLMmTNRVFSElStXIicnB7NmzVI7NiIi3ZJsdUmXB77++musW7cO+fn5cHR0xIIFC9Cz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\niBTFxEJERIpiYiEiIkUxsRARkaL+D1xNoleB4M2gAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f584a5468d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"corr = topratio.corr()\n",
"sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True),\n",
" square=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "99278d98-cd21-7952-6391-44a01d2d121b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" gdp_quart ts_km leisure_count_1000 cafe_count_3000_price_2500 \\\n",
"0 14313.7 4.308127 0 3 \n",
"1 14313.7 0.725560 4 2 \n",
"2 14313.7 3.562188 0 3 \n",
"3 14313.7 0.583025 0 3 \n",
"4 14313.7 2.609420 6 149 \n",
"\n",
" cafe_sum_5000_min_price_avg mortgage_rate university_top_20_raion \\\n",
"0 708.57 11.84 0 \n",
"1 673.81 11.84 0 \n",
"2 702.68 11.84 0 \n",
"3 931.58 11.92 0 \n",
"4 853.88 11.92 2 \n",
"\n",
" ID_big_road2 cafe_sum_3000_max_price_avg 7_14_female ... \\\n",
"0 5 1079.37 4846 ... \n",
"1 4 1086.21 3850 ... \n",
"2 4 1192.31 2938 ... \n",
"3 17 1218.75 6544 ... \n",
"4 10 1410.45 3084 ... \n",
"\n",
" build_count_monolith average_provision_of_build_contract \\\n",
"0 2.0 5.76 \n",
"1 4.0 5.76 \n",
"2 4.0 5.76 \n",
"3 50.0 5.76 \n",
"4 16.0 5.76 \n",
"\n",
" cafe_count_1500_price_1500 full_all brent office_count_1000 \\\n",
"0 6 86206 108.62 1 \n",
"1 1 76284 109.31 2 \n",
"2 2 101982 111.36 0 \n",
"3 8 21155 114.29 1 \n",
"4 72 28179 110.08 46 \n",
"\n",
" metro_km_walk invest_fixed_assets railroad_km green_part_5000 \n",
"0 1.131260 856.424079 1.305159 13.09 \n",
"1 0.635053 856.424079 0.694536 10.26 \n",
"2 1.445960 856.424079 0.700691 13.69 \n",
"3 0.963802 856.424079 1.999265 14.18 \n",
"4 0.688859 856.424079 0.084113 8.38 \n",
"\n",
"[5 rows x 348 columns]\n"
]
}
],
"source": [
"\n",
"y_train = train[\"price_doc\"]\n",
"x_test = test.drop([\"id\"], axis=1)\n",
"#x_test = test[[\"id\",\"timestamp\",\"full_sq\",\"life_sq\",\"floor\",\"build_year\",\"max_floor\",\"kitch_sq\",\"num_room\",\"state\"]]\n",
"x_train = train.drop([\"id\" ,\"price_doc\"], axis=1)\n",
"#x_train = train[[\"id\",\"timestamp\",\"full_sq\",\"life_sq\",\"floor\",\"build_year\",\"max_floor\",\"kitch_sq\",\"num_room\",\"state\"]]\n",
"\n",
"#x_train=x_train.merge(macro[['timestamp','cpi','ppi','usdrub','eurrub','brent']], left_on='timestamp', right_on='timestamp', how='left')\n",
"\n",
"\n",
"#____________ append macro data\n",
"x_train=x_train.merge(macro, left_on='timestamp', right_on='timestamp', how='left')\n",
"#x_test=x_test.merge(macro[['timestamp','cpi','ppi','usdrub','eurrub','brent']], left_on='timestamp', right_on='timestamp', how='left')\n",
"x_test=x_test.merge(macro, left_on='timestamp', right_on='timestamp', how='left')\n",
"\n",
"\n",
"#_________________ drop empty columns\n",
"x_train=x_train.dropna(axis=1, how='all')\n",
"x_test=x_test.dropna(axis=1, how='all')\n",
"#can't merge train with test because the kernel run for very long time\n",
"#child_on_acc_pre_school,modern_education_share,old_education_build_share \n",
"x_train = x_train.drop([\"timestamp\"], axis=1)\n",
"x_test = x_test.drop([\"timestamp\"], axis=1)\n",
"# find mutual columns\n",
"traincol=list(x_train.columns.values)\n",
"testcol=list(x_test.columns.values)\n",
"mutucol=list(set(traincol).intersection(testcol))\n",
"# reshape dataframes\n",
"x_train=x_train[mutucol]\n",
"x_test=x_test[mutucol]\n",
"\n",
"\n",
"print(x_train.head())\n",
"#print(x_test.info(10))\n",
"for c in x_train.columns:\n",
" if x_train[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(x_train[c].values)) \n",
" x_train[c] = lbl.transform(list(x_train[c].values))\n",
" #x_train.drop(c,axis=1,inplace=True)\n",
" \n",
"for c in x_test.columns:\n",
" if x_test[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(x_test[c].values)) \n",
" x_test[c] = lbl.transform(list(x_test[c].values))\n",
" #x_test.drop(c,axis=1,inplace=True) "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "a0fad4f7-c238-9ba7-164b-c6393b8f4a5a"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:1: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" if __name__ == '__main__':\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:2: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
" from ipykernel import kernelapp as app\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"corre 0.88894\n",
"dtype: float64\n",
"11700000.0\n"
]
},
{
"ename": "TypeError",
"evalue": "cannot convert the series to <class 'float'>",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-d51ae2d5dbbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0mtop\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0mxno_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxno_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxno_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxno_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mtop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'corre'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'corre'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mtop\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'corre'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price_doc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'xgbSub_3.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconverter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m raise TypeError(\"cannot convert the series to \"\n\u001b[0;32m---> 93\u001b[0;31m \"{0}\".format(str(converter)))\n\u001b[0m\u001b[1;32m 94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 95\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: cannot convert the series to <class 'float'>"
]
}
],
"source": [
"xn_train=x_train.convert_objects(convert_numeric=True).fillna(value=0.0)\n",
"xn_test=x_test.convert_objects(convert_numeric=True).fillna(value=0.0)\n",
"#print(xn_train)\n",
"xno_train = (xn_train - xn_train.mean()) / (xn_train.max() - xn_train.min())\n",
"xno_test = (xn_test - xn_test.mean()) / (xn_test.max() - xn_test.min())\n",
"#print(xno_train)\n",
"\n",
"#------------------\n",
"# test first\n",
"top= ( xno_test[:1].dot(xno_train.T) / ( abs(xno_test[:1]).dot(abs(xno_train.T))) ).T\n",
"top.columns=['corre']\n",
"print(top.max())\n",
"print(float(train.iloc[top[top['corre']==top['corre'].max()].index]['price_doc']))\n",
"\n",
"result=[]\n",
"for xi in range(len(xno_test)):\n",
" top= ( xno_test[xi:xi+1].dot(xno_train.T) / ( abs(xno_test[xi:xi+1]).dot(abs(xno_train.T))) ).T\n",
" top.columns=['corre']\n",
" result.append( float( train.iloc[top[top['corre']==top['corre'].max()].index]['price_doc']) )\n",
" \n",
"result.to_csv('xgbSub_3.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "6fef7514-5663-5363-6083-6d7dfc6fb25e"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'xgb_params' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-fb53112be7a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=10, early_stopping_rounds=10,\n\u001b[0m\u001b[1;32m 2\u001b[0m verbose_eval=10, show_stdv=False)\n\u001b[1;32m 3\u001b[0m \u001b[0mcv_output\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'train-rmse-mean'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'test-rmse-mean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'xgb_params' is not defined"
]
}
],
"source": [
"cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=10, early_stopping_rounds=10,\n",
" verbose_eval=10, show_stdv=False)\n",
"cv_output[['train-rmse-mean', 'test-rmse-mean']].plo"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "8face308-e893-d5bd-5540-a1885a813efa"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dtrain' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-42b11fb971ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m'silent'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m }\n\u001b[0;32m---> 10\u001b[0;31m cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=100, early_stopping_rounds=10,\n\u001b[0m\u001b[1;32m 11\u001b[0m verbose_eval=6, show_stdv=False)\n",
"\u001b[0;31mNameError\u001b[0m: name 'dtrain' is not defined"
]
}
],
"source": [
"xgb_params = {\n",
" 'max_depth': 7,\n",
" 'min_child_weight': 3, \n",
" 'objective': 'reg:linear',\n",
" 'eval_metric': 'rmse',\n",
" 'silent': 1,\n",
" 'nthread':12,\n",
" 'silent':1,\n",
"}\n",
"cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=100, early_stopping_rounds=10,\n",
" verbose_eval=6, show_stdv=False)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "a96d1965-8328-35b0-d0a3-dfcfa77275ab"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'cv_output' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-de31b0c4a967>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnum_boost_rounds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcv_output\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxgb_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msilent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_boost_round\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mnum_boost_rounds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'cv_output' is not defined"
]
}
],
"source": [
"num_boost_rounds = len(cv_output)\n",
"model = xgb.train(dict(xgb_params, silent=0), dtrain, num_boost_round= num_boost_rounds)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "2a04f7d8-3efa-df54-713e-f6222453c60b"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-d76d6df523ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_num_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
},
{
"data": {
"image/png": 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EGwCCCDcABBFuAAgi3AAQRLgBIIhwA0AQ4QaAIMINAEGEGwCCCDcABBFuAAgi3AAQRLgB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\nggg3AAQRbgAIItwAEES4ASCIcANAkP8DzpmPq12hHugAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f584cdca0f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1, figsize=(8, 13))\n",
"xgb.plot_importance(model, max_num_features=50, height=0.5, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "27d0831f-df36-2608-b11a-a335c9a79613"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-abffd4f3cc04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_predict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mid_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'price_doc'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_predict\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
}
],
"source": [
"y_predict = model.predict(dtest)\n",
"output = pd.DataFrame({'id': id_test, 'price_doc': y_predict})\n",
"output.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "0a5dbdee-3e96-aded-9c71-2e2c72af3e5a"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'output' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-cefa4be7c09e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'xgbSub_2.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'output' is not defined"
]
}
],
"source": [
"output.to_csv('xgbSub_2.csv', index=False)"
]
}
],
"metadata": {
"_change_revision": 117,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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| 0001/161/1161340.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6748f8d1-eb06-039a-9b7a-93b2de5ca8e9"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "3cf573cb-3835-a3a1-b7b1-14783d890bbf"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"images_sample\n",
"sample_submission.csv\n",
"test.json\n",
"train.json\n",
"\n",
"(49352, 15)\n",
"(74659, 14)\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import random\n",
"from scipy import sparse\n",
"\n",
"from sklearn import model_selection, preprocessing\n",
"from sklearn.model_selection import train_test_split, StratifiedKFold\n",
"from sklearn.preprocessing import Imputer, StandardScaler\n",
"from sklearn.linear_model import LogisticRegression, BayesianRidge, LinearRegression, SGDClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.naive_bayes import GaussianNB \n",
"from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, GradientBoostingClassifier, AdaBoostClassifier, VotingClassifier\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.gaussian_process import GaussianProcessClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.model_selection import GridSearchCV\n",
"from sklearn.metrics import accuracy_score, log_loss, classification_report\n",
"\n",
"import xgboost as xgb\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"data_path = \"../input/\"\n",
"train_file = data_path + \"train.json\"\n",
"test_file = data_path + \"test.json\"\n",
"train_df = pd.read_json(train_file)\n",
"test_df = pd.read_json(test_file)\n",
"print(train_df.shape)\n",
"print(test_df.shape)\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "2d2aa6d2-131a-9e0b-280d-41ec95db2086"
},
"outputs": [],
"source": [
"def runXGB(train_X, train_y, test_X, test_y=None, feature_names=None, seed_val=0, num_rounds=1000):\n",
" param = {}\n",
" param['objective'] = 'multi:softprob'\n",
" param['eta'] = 0.1\n",
" param['max_depth'] = 6\n",
" param['silent'] = 1\n",
" param['num_class'] = 3\n",
" param['eval_metric'] = \"mlogloss\"\n",
" param['min_child_weight'] = 1\n",
" param['subsample'] = 0.7\n",
" param['colsample_bytree'] = 0.7\n",
" param['seed'] = seed_val\n",
" num_rounds = num_rounds\n",
"\n",
" plst = list(param.items())\n",
" xgtrain = xgb.DMatrix(train_X, label=train_y)\n",
"\n",
" if test_y is not None:\n",
" xgtest = xgb.DMatrix(test_X, label=test_y)\n",
" watchlist = [ (xgtrain,'train'), (xgtest, 'test') ]\n",
" model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=20)\n",
" else:\n",
" xgtest = xgb.DMatrix(test_X)\n",
" model = xgb.train(plst, xgtrain, num_rounds)\n",
"\n",
" pred_test_y = model.predict(xgtest)\n",
" return pred_test_y, model"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c6704b36-8c1a-46ec-743c-a75164e7a3ab"
},
"outputs": [],
"source": [
"features_to_use = [\"bathrooms\", \"bedrooms\", \"latitude\", \"longitude\", \"price\"]\n",
"features_for_xgb = [\"bathrooms\", \"bedrooms\", \"latitude\", \"longitude\", \"price\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "12e75972-ddcf-2e49-8d22-6ee875b1fe8a"
},
"outputs": [],
"source": [
"# count of photos #\n",
"train_df[\"num_photos\"] = train_df[\"photos\"].apply(len)\n",
"test_df[\"num_photos\"] = test_df[\"photos\"].apply(len)\n",
"\n",
"# count of \"features\" #\n",
"train_df[\"num_features\"] = train_df[\"features\"].apply(len)\n",
"test_df[\"num_features\"] = test_df[\"features\"].apply(len)\n",
"\n",
"# count of words present in description column #\n",
"train_df[\"num_description_words\"] = train_df[\"description\"].apply(lambda x: len(x.split(\" \")))\n",
"test_df[\"num_description_words\"] = test_df[\"description\"].apply(lambda x: len(x.split(\" \")))\n",
"\n",
"# convert the created column to datetime object so as to extract more features \n",
"train_df[\"created\"] = pd.to_datetime(train_df[\"created\"])\n",
"test_df[\"created\"] = pd.to_datetime(test_df[\"created\"])\n",
"\n",
"# Let us extract some features like year, month, day, hour from date columns #\n",
"train_df[\"created_year\"] = train_df[\"created\"].dt.year\n",
"test_df[\"created_year\"] = test_df[\"created\"].dt.year\n",
"\n",
"train_df[\"created_month\"] = train_df[\"created\"].dt.month\n",
"test_df[\"created_month\"] = test_df[\"created\"].dt.month\n",
"\n",
"train_df[\"created_day\"] = train_df[\"created\"].dt.day\n",
"test_df[\"created_day\"] = test_df[\"created\"].dt.day\n",
"\n",
"train_df[\"created_hour\"] = train_df[\"created\"].dt.hour\n",
"test_df[\"created_hour\"] = test_df[\"created\"].dt.hour\n",
"\n",
"train_df[\"weekday\"] = train_df[\"created\"].dt.weekday\n",
"test_df[\"weekday\"] = test_df[\"created\"].dt.weekday\n",
"\n",
"# adding all these new features to use list #\n",
"features_to_use.extend([\"num_photos\", \"num_features\", \"num_description_words\", \"created_month\", \"created_day\", \"created_hour\", \"weekday\"])\n",
"features_for_xgb.extend([\"num_photos\", \"num_features\", \"num_description_words\",\"created_year\", \"created_month\", \"created_day\", \"listing_id\", \"created_hour\"])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "ee949888-3758-31b8-18b7-877df4ba7727"
},
"outputs": [],
"source": [
"# Building Level\n",
"train_df.ix[train_df.building_id == '0', 'new_building_id'] = train_df['building_id'] + train_df['manager_id']\n",
"train_df.ix[train_df.building_id != '0', 'new_building_id'] = train_df['building_id']\n",
"\n",
"a=[np.nan]*len(train_df)\n",
"building_level={}\n",
"\n",
"for bid in train_df['new_building_id'].values:\n",
" building_level[bid]=[0,0,0]\n",
" \n",
"for j in range(train_df.shape[0]):\n",
" rec=train_df.iloc[j]\n",
" if rec['interest_level']=='low':\n",
" building_level[rec['new_building_id']][0]+=1\n",
" if rec['interest_level']=='medium':\n",
" building_level[rec['new_building_id']][1]+=1\n",
" if rec['interest_level']=='high':\n",
" building_level[rec['new_building_id']][2]+=1\n",
" \n",
"for j in range(train_df.shape[0]): \n",
" rec=train_df.iloc[j]\n",
" occurance = sum(building_level[rec['new_building_id']])\n",
" if occurance!=0:\n",
" a[j]= (building_level[rec['new_building_id']][0]*0.0 + building_level[rec['new_building_id']][1]*1.0 \\\n",
" + building_level[rec['new_building_id']][2]*2.0) / occurance\n",
"\n",
"train_df['building_level']=a\n",
"\n",
"test_df.ix[test_df.building_id == '0', 'new_building_id'] = test_df['building_id'] + test_df['manager_id']\n",
"test_df.ix[test_df.building_id != '0', 'new_building_id'] = test_df['building_id']\n",
"\n",
"b=[]\n",
"for i in test_df['new_building_id'].values:\n",
" if i not in building_level.keys():\n",
" b.append(np.nan)\n",
" else:\n",
" occurance = sum(building_level[i])\n",
" b.append((building_level[i][0]*0.0 + building_level[i][1]*1.0 \\\n",
" + building_level[i][2]*2.0) / occurance)\n",
"\n",
"test_df['building_level']=b\n",
"\n",
"train_df = train_df.drop(['new_building_id'], axis=1)\n",
"test_df = test_df.drop(['new_building_id'], axis=1)\n",
"\n",
"features_to_use.append('building_level')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "f1949e53-fbf9-90d2-86f7-180ae0b2f197"
},
"outputs": [],
"source": [
"# Manager "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "9c8c5ee4-be7c-c308-92a6-ce63a1a39735"
},
"outputs": [],
"source": [
"categorical = [\"display_address\", \"manager_id\", \"building_id\", \"street_address\"]\n",
"for f in categorical:\n",
" if train_df[f].dtype=='object':\n",
" #print(f)\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(train_df[f].values) + list(test_df[f].values))\n",
" train_df[f] = lbl.transform(list(train_df[f].values))\n",
" test_df[f] = lbl.transform(list(test_df[f].values))\n",
" features_to_use.append(f)\n",
" features_for_xgb.append(f)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "9f5940ba-8b2d-a54f-a7fc-c63e0955740a"
},
"outputs": [],
"source": [
"train_df['features'] = train_df[\"features\"].apply(lambda x: \" \".join([\"_\".join(i.split(\" \")) for i in x]))\n",
"test_df['features'] = test_df[\"features\"].apply(lambda x: \" \".join([\"_\".join(i.split(\" \")) for i in x]))\n",
"\n",
"tfidf = CountVectorizer(stop_words='english', max_features=200)\n",
"tr_sparse = tfidf.fit_transform(train_df[\"features\"])\n",
"te_sparse = tfidf.transform(test_df[\"features\"])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "23ed7ac5-2d7c-3236-2ca0-f5129efadfb9"
},
"outputs": [],
"source": [
"#Train and test set for XGBoost\n",
"train_X = sparse.hstack([train_df[features_for_xgb], tr_sparse]).tocsr()\n",
"test_X = sparse.hstack([test_df[features_for_xgb], te_sparse]).tocsr()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "9bccb8dd-3aed-de29-fb77-22fddf19d3f2"
},
"outputs": [],
"source": [
"target_num_map = {'high':2, 'medium':1, 'low':0}\n",
"train_y = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "c7a6c164-6e92-e0ab-2d0b-6d1dcbc8b65c"
},
"outputs": [],
"source": [
"train_Xtree = train_df[features_to_use]\n",
"test_Xtree = test_df[features_to_use]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "1824537b-2f10-b315-d480-ca20d9ff891d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(49352, 217) (74659, 217)\n",
"(49352, 17) (74659, 17)\n"
]
}
],
"source": [
"#Train and test set for XGBoost\n",
"print(train_X.shape, test_X.shape)\n",
"\n",
"#Train and test set for trees\n",
"print(train_Xtree.shape, test_Xtree.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "45793f79-035d-b352-882b-33b0b86ab06d"
},
"source": [
"## Begin Stacking"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "891419d1-3474-c433-20d8-150ccf2fbbde"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"49352,74659\n"
]
}
],
"source": [
"NFOLDS = 5\n",
"SEED = 0\n",
"y_train = train_y\n",
"\n",
"ntrain = train_Xtree.shape[0]\n",
"ntest = test_Xtree.shape[0]\n",
"print(\"{},{}\".format(ntrain, ntest))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "9140a62c-4699-b5bd-5d97-0bb1fb617fd5"
},
"outputs": [],
"source": [
"class SklearnWrapper(object):\n",
" def __init__(self, clf, seed=0, params=None):\n",
" params['random_state'] = seed\n",
" self.clf = clf(**params)\n",
"\n",
" def train(self, x_train, y_train):\n",
" self.clf.fit(x_train, y_train)\n",
"\n",
" def predict_prb(self, x):\n",
" return self.clf.predict(x)\n",
" \n",
" def predict_proba(self, x):\n",
" return self.clf.predict_proba(x) "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "f2584c69-9c4f-c741-5c9c-548dfe57acc0"
},
"outputs": [],
"source": [
"def get_oof(clf):\n",
" oof_train = np.zeros((ntrain,3))\n",
" oof_test = np.zeros((ntest,3))\n",
" oof_test_skf = np.empty((NFOLDS, ntest, 3))\n",
"\n",
" i = 0\n",
" for train_index, test_index in skf.split(x_train, y_train):\n",
" x_tr = x_train[train_index]\n",
" y_tr = y_train[train_index]\n",
" x_te = x_train[test_index]\n",
"\n",
" rf1.train(x_tr, y_tr)\n",
"\n",
" oof_train[test_index]= rf1.predict_proba(x_te)\n",
" oof_test_skf[i, :, :] = rf1.predict_proba(x_test)\n",
" i += 1\n",
"\n",
" oof_test[:] = oof_test_skf.mean(axis=0)\n",
" return oof_train, oof_test"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "dd439a36-2956-5a9d-41f4-a7211109d9b4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(49352, 17),(49352,),(74659, 17)\n"
]
}
],
"source": [
"train_test = pd.concat((train_Xtree, test_Xtree)).reset_index(drop=True)\n",
"x_train = np.array(train_test.iloc[:ntrain,:])\n",
"x_test = np.array(train_test.iloc[ntrain:,:])\n",
"\n",
"print(\"{},{},{}\".format(x_train.shape, y_train.shape, x_test.shape))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "42e4b347-42a5-55d6-e287-d95fa4c6cfdf"
},
"outputs": [],
"source": [
"skf = StratifiedKFold(n_splits=NFOLDS, random_state=SEED, shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "8ce4f703-9f5d-6e40-678f-fad8c71f4eff"
},
"outputs": [],
"source": [
"rf1_params = {\n",
" 'n_jobs': 16,\n",
" 'n_estimators': 100,\n",
" 'criterion' : \"entropy\",\n",
" 'max_features': 0.5,\n",
" 'max_depth': 6,\n",
" 'min_samples_leaf': 2,\n",
"}\n",
"\n",
"rf2_params = {\n",
" 'n_jobs': 16,\n",
" 'criterion' : \"gini\",\n",
" 'n_estimators': 100,\n",
" 'max_features': 0.8,\n",
" 'max_depth': 8,\n",
" 'min_samples_leaf': 1,\n",
"}\n",
"\n",
"et1_params = {\n",
" 'n_jobs': 16,\n",
" 'n_estimators': 10,\n",
" 'max_features': None,\n",
" 'criterion' : \"gini\",\n",
" 'max_depth': 12,\n",
" 'min_samples_leaf': 2,\n",
"}\n",
"\n",
"et2_params = {\n",
" 'n_jobs': 16,\n",
" 'n_estimators': 100,\n",
" 'criterion' : \"entropy\",\n",
" 'max_depth': 12,\n",
" 'min_samples_leaf': 2,\n",
" 'max_features': 0.5,\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "ef651ee2-380c-539a-d15d-17aee17429ac"
},
"outputs": [],
"source": [
"et1 = SklearnWrapper(clf=ExtraTreesClassifier, seed=SEED, params=et1_params)\n",
"et2 = SklearnWrapper(clf=ExtraTreesClassifier, seed=SEED, params=et2_params)\n",
"\n",
"rf1 = SklearnWrapper(clf=RandomForestClassifier, seed=SEED, params=rf1_params)\n",
"rf2 = SklearnWrapper(clf=RandomForestClassifier, seed=SEED, params=rf2_params)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "d389fcbd-a632-7470-49b6-1eb0035d2576"
},
"outputs": [],
"source": [
"gbc1 = GradientBoostingClassifier(n_estimators = 10, max_depth = 4, subsample = 0.5,\n",
" learning_rate = 0.1, min_samples_leaf = 2, random_state = 0)\n",
"\n",
"gbc2 = GradientBoostingClassifier(n_estimators = 100, max_depth = 8, subsample = 0.5,\n",
" learning_rate = 1, min_samples_leaf = 1, random_state = 0)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "bb0aa4ea-9779-8bbd-e049-768c4656be17"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-21-54053829240e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0met1_oof_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0met1_oof_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_oof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0met1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0met2_oof_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0met2_oof_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_oof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0met2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mrf1_oof_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrf1_oof_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_oof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mrf2_oof_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrf2_oof_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_oof\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrf2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-15-7079a432d9bd>\u001b[0m in \u001b[0;36mget_oof\u001b[0;34m(clf)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0moof_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtest_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mrf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_te\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0moof_test_skf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 16\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-14-f0e1d58866a3>\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36mpredict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 574\u001b[0m \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'estimators_'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 575\u001b[0m \u001b[0;31m# Check data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 576\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 577\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 578\u001b[0m \u001b[0;31m# Assign chunk of trees to jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 354\u001b[0m \"call `fit` before exploiting the model.\")\n\u001b[1;32m 355\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 356\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_X_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 357\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 358\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36m_validate_X_predict\u001b[0;34m(self, X, check_input)\u001b[0m\n\u001b[1;32m 371\u001b[0m \u001b[0;34m\"\"\"Validate X whenever one tries to predict, apply, predict_proba\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 374\u001b[0m if issparse(X) and (X.indices.dtype != np.intc or\n\u001b[1;32m 375\u001b[0m X.indptr.dtype != np.intc):\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 407\u001b[0m % (array.ndim, estimator_name))\n\u001b[1;32m 408\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m 38\u001b[0m and not np.isfinite(X).all()):\n\u001b[1;32m 39\u001b[0m raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 40\u001b[0;31m \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
]
}
],
"source": [
"et1_oof_train, et1_oof_test = get_oof(et1)\n",
"et2_oof_train, et2_oof_test = get_oof(et2)\n",
"\n",
"rf1_oof_train, rf1_oof_test = get_oof(rf1)\n",
"rf2_oof_train, rf2_oof_test = get_oof(rf2)\n",
"\n",
"gbc1_oof_train, gbc1_oof_test = get_oof(gbc1)\n",
"gbc2_oof_train, gbc2_oof_test = get_oof(gbc2)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "f1ea91ce-21cb-1319-2c13-23ede025537b"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'et1_oof_train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-90f2f17fc373>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ET1-CV: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0met1_oof_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ET2-CV: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0met2_oof_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"RF1-CV: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrf1_oof_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"RF2-CV: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrf2_oof_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'et1_oof_train' is not defined"
]
}
],
"source": [
"print(\"ET1-CV: {}\".format(log_loss(y_train, et1_oof_train)))\n",
"print(\"ET2-CV: {}\".format(log_loss(y_train, et2_oof_train)))\n",
"\n",
"print(\"RF1-CV: {}\".format(log_loss(y_train, rf1_oof_train)))\n",
"print(\"RF2-CV: {}\".format(log_loss(y_train, rf2_oof_train)))\n",
"\n",
"print(\"GBC1-CV: {}\".format(log_loss(y_train, gbc1_oof_train)))\n",
"print(\"GBC2-CV: {}\".format(log_loss(y_train, gbc2_oof_train)))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "fae60019-0be9-dceb-35c4-2677a4235c1c"
},
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"source": []
}
],
"metadata": {
"_change_revision": 169,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| 0001/161/1161412.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2c317e47-a9c9-468c-04f9-0b5df516baad"
},
"source": [
"# Test code for Titanic competition\n",
"## Author: Yanfen Fu"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "00c52f83-7103-2c4b-bf84-7f4b71cc1564"
},
"source": [
" - The work flow in this notebook is to tidy up the dataset by filling the missing values with reasonable guess values for model completion. And run exploratory data analysis to figure out which features to include for machine learning model.\n",
" - Compare different machine learning models for best prediction, also watch out for over fitting problem."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "00d8a3b7-e022-eeb9-5e2d-4504a0a524aa"
},
"source": [
"#Step 1: data import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a6c60e77-8d52-8a2b-8816-fde88a081796"
},
"outputs": [],
"source": [
"# import necessary package for this attempt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as plt\n",
"% matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "124c3034-661b-16f4-8bab-f305b19d32fb"
},
"outputs": [],
"source": [
"#Input data files are available in the \"../input/\" directory.\n",
"train_df = pd.read_csv(\"../input/train.csv\")\n",
"test_df = pd. read_csv (\"../input/test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "d9467bf1-76fd-3c99-395c-0914a5c0d06f"
},
"outputs": [],
"source": [
"corr_df = train_df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "5a07c4eb-6c78-7272-52a8-4651de3730e5"
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Pclass</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Survived</th>\n",
" <th>PassengerId</th>\n",
" <th>Fare</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>PassengerId</th>\n",
" <td>-0.035144</td>\n",
" <td>-0.057527</td>\n",
" <td>-0.001652</td>\n",
" <td>-0.005007</td>\n",
" <td>1.000000</td>\n",
" <td>0.012658</td>\n",
" <td>0.036847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Survived</th>\n",
" <td>-0.338481</td>\n",
" <td>-0.035322</td>\n",
" <td>0.081629</td>\n",
" <td>1.000000</td>\n",
" <td>-0.005007</td>\n",
" <td>0.257307</td>\n",
" <td>-0.077221</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pclass</th>\n",
" <td>1.000000</td>\n",
" <td>0.083081</td>\n",
" <td>0.018443</td>\n",
" <td>-0.338481</td>\n",
" <td>-0.035144</td>\n",
" <td>-0.549500</td>\n",
" <td>-0.369226</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>-0.369226</td>\n",
" <td>-0.308247</td>\n",
" <td>-0.189119</td>\n",
" <td>-0.077221</td>\n",
" <td>0.036847</td>\n",
" <td>0.096067</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SibSp</th>\n",
" <td>0.083081</td>\n",
" <td>1.000000</td>\n",
" <td>0.414838</td>\n",
" <td>-0.035322</td>\n",
" <td>-0.057527</td>\n",
" <td>0.159651</td>\n",
" <td>-0.308247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parch</th>\n",
" <td>0.018443</td>\n",
" <td>0.414838</td>\n",
" <td>1.000000</td>\n",
" <td>0.081629</td>\n",
" <td>-0.001652</td>\n",
" <td>0.216225</td>\n",
" <td>-0.189119</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fare</th>\n",
" <td>-0.549500</td>\n",
" <td>0.159651</td>\n",
" <td>0.216225</td>\n",
" <td>0.257307</td>\n",
" <td>0.012658</td>\n",
" <td>1.000000</td>\n",
" <td>0.096067</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pclass SibSp Parch Survived PassengerId Fare \\\n",
"PassengerId -0.035144 -0.057527 -0.001652 -0.005007 1.000000 0.012658 \n",
"Survived -0.338481 -0.035322 0.081629 1.000000 -0.005007 0.257307 \n",
"Pclass 1.000000 0.083081 0.018443 -0.338481 -0.035144 -0.549500 \n",
"Age -0.369226 -0.308247 -0.189119 -0.077221 0.036847 0.096067 \n",
"SibSp 0.083081 1.000000 0.414838 -0.035322 -0.057527 0.159651 \n",
"Parch 0.018443 0.414838 1.000000 0.081629 -0.001652 0.216225 \n",
"Fare -0.549500 0.159651 0.216225 0.257307 0.012658 1.000000 \n",
"\n",
" Age \n",
"PassengerId 0.036847 \n",
"Survived -0.077221 \n",
"Pclass -0.369226 \n",
"Age 1.000000 \n",
"SibSp -0.308247 \n",
"Parch -0.189119 \n",
"Fare 0.096067 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr_df.sort_values('Age',axis=1,ascending = True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "d248d53b-272a-6de0-1248-140a3edd276d"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 269,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161436.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "04aff43f-4cd5-2cef-e92c-08b029d8c8e7"
},
"source": [
"# Predicting Employee Overturn \n",
"Using unsupervised and supervised Machine Learning algorithms.\n",
"\n",
"## Abstract\n",
"According to the [Bureau of Labor Statistics](http://www.bls.gov/news.release/tenure.nr0.htm) the median number of years that wage and salary workers had been with their current employer is 4.2 years. While this number varies from industry to industry the story of an employee who sticks with one company for the entirety of a working life seems to be rather antiquated. This observation is combined with economical aspects. Employee turnover has been identified as a key issue for organizations because of its adverse impact on work place productivity and long term growth strategies. One of the key issues with a high employee turnover rate combined with, but reaching beyond, cultural and sociological effects is the cost associated with it. Research shows that the replacement cost for an hourly worker can be as high as 50 % of her annual salary. This number increases with the skill set of the worker up to 200 % for senior-level workers and surges up to 400 % for [executive level positions](www.visier.com/tech-insights/do-predictive-workforce-analytics-actually-work/). It becomes obvious that the trend of shorter tenure in addition to high employee turnover rates can be a costly endeavor. Therefore it becomes increasingly important to acquire the necessary tools for employers to understand where its workforce is standing. Additional insights from employee reports, scorecards as well as general statistical information can offer prediction values for companies when it comes to the longevity of jobs. This research aims to predict the likelihood of an employee quitting her job based on available information. It tries to deliver a hybrid machine learning method to gain actionable insights on how to prevent a high employee turnover. \n",
"\n",
"The novelty of the work is in the hybrid approach leveraging both unsupervised and supervised learning results. Based on recent research it was possible to increase the prediction outcome and to introduce a hybrid model that could work as a blueprint for similar tasks in this domain.\n",
"\n",
"## Additional Code Resources and Comments\n",
"\n",
"- This is a python 2.x example. When parsing through the notebook, I saw only little deviation (besides the print-statements). It runs on 3.x now.\n",
"\n",
"- To make the code easier to read, I've put some of the processing and plotting code into a file called `hr_predict.py`. If you'd like to see these code snippets as well, please head over [here](https://github.com/andirs/hr_predict/blob/master/hr_predict.py)\n",
"\n",
"- For readability purposes I shortened this version as well. There is a research-style paper for a more `elaborate` reading experience if someone is interested in additional information. :) It's available [here](https://github.com/andirs/hr_prediction/Predicting_Why_Employees_Leave.pdf)\n",
"\n",
"\n",
"## Steps\n",
"First an in-depth exploratory analysis is performed in an attempt to explain the underlying relationships in the data set. Afterwards the problem is approached on two dimensions by combining unsupervised and supervised learning algorithms:\n",
"\n",
"1. Two clustering algorithms namely K-Means or Gaussian Mixture Model will be discussed in an attempt to add more semantic to the employee set. Since the problem is based in a sociological domain, the more we can derive from our feature set, the more human interaction and perception we can gather, the better. By clustering, we might be able to hand additional information to the supervised algorithm for a better prediction down the road.\n",
"2. Afterwards features and additional cluster-information are used to predict whether an employee left or stayed. Given that the data is labeled and considering the amount of data points, a range of algorithms is discussed starting with more basic solutions and working towards more complex methods.\n",
"\n",
"The success of the final solution will be measured by its predicting accuracy on a held-out test set.\n",
"\n",
"## Benchmark\n",
"As mentioned earlier, a lot of research and work has been performed to retain talent and decrease employee turnover rates. From corporate funded research and [analytics services of companies](www.visier.com/tech-insights/do-predictive-workforce-analytics-actually-work/) to management study classics or more recent research. There are several methods for predicting employee turnover rate and results that can be taken as benchmark models. In this research the machine learning approach of Punnose, R. and Pankaj, A. (2016) serves as a benchmark. Their research performed predictive tasks on company information with an area under the curve (AUC) score of .86 on hold-out data implementing an Extreme Gradient Boosting model.\n",
"\n",
"### Evaluation Metrics\n",
"AUC (Area under the Curve) is a common evaluation metric for binary classification problems. Its value is between 0 to 1 and describes the accuracy of a binary classification based on its true positive values. As [described](https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve), an area under the curve score is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.\n",
"\n",
"Area under the curve (AUC) is a common evaluation metric for binary classification problems. Its value is between 0 to 1 and describes the accuracy of a binary classification based on its true positive values. An [AUC score](https://en.wikipedia.org/wiki/Receiver_operating_characteristic#Area_under_the_curve) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.\n",
"\n",
"To put this more into perspective [AUC](https://www.kaggle.com/wiki/AreaUnderCurve) can be seen as a plot of the true positive rate vs. the false positive rate where the threshold value for classifying an item as 0 or is increased from 0 to 1. If the classifier is very good, the true positive rate will increase quickly and the area under the curve will be close to 1. If the classifier is no better than random guessing, the true positive rate will increase linearly with the false positive rate and the area under the curve will be around 0.5.\n",
"\n",
"### AUC vs. F1 Score\n",
"There is some controversy about the value of AUC scores. Especially when it comes to precision and recall it is possible for a classifier to have a low recall but a very high AUC score. To avoid this, F1 scoring can be used which is a way to incorporate both precision and recall. For a F1 score to be high, both, precision and recall have to be high. The problem of a low recall score will be addressed for the leading model when testing for robustness.\n",
"\n",
"## Data Exploration\n",
"There is no code-book that can be linked to this data set. Since there is no additional information regarding the data set some assumptions about the variables are made based on their description. A sample of the data looks like this:"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e14f194a-7649-e563-e742-03bf0589a00b"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "4fae15ad-a744-6750-305b-cc31ac14c7bc"
},
"outputs": [],
"source": [
"# Read in data & import of packages & frameworks\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import warnings\n",
"\n",
"%matplotlib inline\n",
"\n",
"from bokeh.charts import Bar, output_file, show\n",
"from bokeh.io import output_notebook\n",
"from bokeh.plotting import figure\n",
"from bokeh.layouts import gridplot\n",
"from bokeh.layouts import column, row\n",
"from bokeh.plotting import reset_output\n",
"from bokeh.charts.attributes import cat\n",
"from collections import Counter\n",
"from IPython.display import display"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "78b8715a-cf02-d9ba-8625-0cf3cabf7f8a"
},
"outputs": [],
"source": [
"plot_color = 'crimson'\n",
"cmap = sns.diverging_palette(222, 10, as_cmap=True)\n",
"\n",
"hr_data = pd.read_csv('../input/HR_comma_sep.csv', header=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "516482ec-f091-178f-e740-936605552ee6"
},
"outputs": [],
"source": [
"# Rename column to fit better, get rid of typos and instill consistency\n",
"hr_data = hr_data.rename(\n",
" columns = {'sales' : 'department', \n",
" 'average_montly_hours' : 'average_monthly_hours',\n",
" 'Work_accident' : 'work_accident'})"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "f23fdaf4-962f-c952-6732-6f26379daf20"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_monthly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>department</th>\n",
" <th>salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.38</td>\n",
" <td>0.53</td>\n",
" <td>2</td>\n",
" <td>157</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.80</td>\n",
" <td>0.86</td>\n",
" <td>5</td>\n",
" <td>262</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.11</td>\n",
" <td>0.88</td>\n",
" <td>7</td>\n",
" <td>272</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.72</td>\n",
" <td>0.87</td>\n",
" <td>5</td>\n",
" <td>223</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.37</td>\n",
" <td>0.52</td>\n",
" <td>2</td>\n",
" <td>159</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project average_monthly_hours \\\n",
"0 0.38 0.53 2 157 \n",
"1 0.80 0.86 5 262 \n",
"2 0.11 0.88 7 272 \n",
"3 0.72 0.87 5 223 \n",
"4 0.37 0.52 2 159 \n",
"\n",
" time_spend_company work_accident left promotion_last_5years department \\\n",
"0 3 0 1 0 sales \n",
"1 6 0 1 0 sales \n",
"2 4 0 1 0 sales \n",
"3 5 0 1 0 sales \n",
"4 3 0 1 0 sales \n",
"\n",
" salary \n",
"0 low \n",
"1 medium \n",
"2 medium \n",
"3 low \n",
"4 low "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e6d2f810-c459-a6bb-1cce-29983e8a8891"
},
"source": [
"First, let's have a look at how much data we're dealing with and how it's structured."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "e6cac2f2-7c88-880f-fe03-673a69973e97"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The data set has 14999 data points and 10 variables.\n"
]
}
],
"source": [
"print(\"The data set has {} data points and {} variables.\".format(*hr_data.shape))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "5998cc0a-546c-3ff9-ca6f-c434f703bf6a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['satisfaction_level' 'last_evaluation' 'number_project'\n",
" 'average_monthly_hours' 'time_spend_company' 'work_accident' 'left'\n",
" 'promotion_last_5years' 'department' 'salary']\n"
]
}
],
"source": [
"print(hr_data.columns.values)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2aab4d03-3db3-bfb5-f4ab-27596d0fc378"
},
"source": [
"Given by its column names we have 10 variables that need to be taken into account. Since there is no additional information regarding the data set we are starting with some assumptions about the variables based on their description. Information that's comprised in this data set seems to include a measure of employee satisfaction level, evaluation scores, amount of projects and average monthly hours spend on the job, overall time spent at the company, whether the employee had a work accident or received a promotion within the last 5 years, a description of the department the employee works for and a salary indicator. In addition there is a variable called `left` that indicates, whether an employee is still actively working for this company or not.\n",
"In addition the dataset provides an indicator whether an employee has left. This indicator will be seen as the dependent variable that is being predicted.\n",
"This indicator will be seen as the dependent variable that is being predicted.\n",
"\n",
"\n",
"\n",
"As a first summary, the predictors we will be using consist of:\n",
"\n",
"* Employee satisfaction level \n",
"* Last evaluation score\n",
"* Number of projects\n",
"* Average monthly hours\n",
"* Time spent at the company\n",
"* Whether they have had a work accident\n",
"* Whether they have had a promotion in the last 5 years \n",
"* Department\n",
"* Salary\n",
"\n",
"The target variable is `left`."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f65d28c1-35de-f971-9200-6a5e5b20f74e"
},
"source": [
"### Variable Description\n",
"As mentioned earlier, there is no code-book that can be linked to this data set. Since there is no additional information regarding the data set some assumptions about the variables are made based on their description. A sample of the data looks like this:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "97ce9555-c302-309c-6c56-f89c01b38bb6"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_monthly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.612834</td>\n",
" <td>0.716102</td>\n",
" <td>3.803054</td>\n",
" <td>201.050337</td>\n",
" <td>3.498233</td>\n",
" <td>0.144610</td>\n",
" <td>0.238083</td>\n",
" <td>0.021268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.248631</td>\n",
" <td>0.171169</td>\n",
" <td>1.232592</td>\n",
" <td>49.943099</td>\n",
" <td>1.460136</td>\n",
" <td>0.351719</td>\n",
" <td>0.425924</td>\n",
" <td>0.144281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.090000</td>\n",
" <td>0.360000</td>\n",
" <td>2.000000</td>\n",
" <td>96.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.440000</td>\n",
" <td>0.560000</td>\n",
" <td>3.000000</td>\n",
" <td>156.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.640000</td>\n",
" <td>0.720000</td>\n",
" <td>4.000000</td>\n",
" <td>200.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.820000</td>\n",
" <td>0.870000</td>\n",
" <td>5.000000</td>\n",
" <td>245.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>7.000000</td>\n",
" <td>310.000000</td>\n",
" <td>10.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project \\\n",
"count 14999.000000 14999.000000 14999.000000 \n",
"mean 0.612834 0.716102 3.803054 \n",
"std 0.248631 0.171169 1.232592 \n",
"min 0.090000 0.360000 2.000000 \n",
"25% 0.440000 0.560000 3.000000 \n",
"50% 0.640000 0.720000 4.000000 \n",
"75% 0.820000 0.870000 5.000000 \n",
"max 1.000000 1.000000 7.000000 \n",
"\n",
" average_monthly_hours time_spend_company work_accident left \\\n",
"count 14999.000000 14999.000000 14999.000000 14999.000000 \n",
"mean 201.050337 3.498233 0.144610 0.238083 \n",
"std 49.943099 1.460136 0.351719 0.425924 \n",
"min 96.000000 2.000000 0.000000 0.000000 \n",
"25% 156.000000 3.000000 0.000000 0.000000 \n",
"50% 200.000000 3.000000 0.000000 0.000000 \n",
"75% 245.000000 4.000000 0.000000 0.000000 \n",
"max 310.000000 10.000000 1.000000 1.000000 \n",
"\n",
" promotion_last_5years \n",
"count 14999.000000 \n",
"mean 0.021268 \n",
"std 0.144281 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_description = hr_data.describe()\n",
"hr_description"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "90673845-7bfa-1b9e-8571-35c10e6d6797"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_monthly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>department</th>\n",
" <th>salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.38</td>\n",
" <td>0.53</td>\n",
" <td>2</td>\n",
" <td>157</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.80</td>\n",
" <td>0.86</td>\n",
" <td>5</td>\n",
" <td>262</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.11</td>\n",
" <td>0.88</td>\n",
" <td>7</td>\n",
" <td>272</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.72</td>\n",
" <td>0.87</td>\n",
" <td>5</td>\n",
" <td>223</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.37</td>\n",
" <td>0.52</td>\n",
" <td>2</td>\n",
" <td>159</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project average_monthly_hours \\\n",
"0 0.38 0.53 2 157 \n",
"1 0.80 0.86 5 262 \n",
"2 0.11 0.88 7 272 \n",
"3 0.72 0.87 5 223 \n",
"4 0.37 0.52 2 159 \n",
"\n",
" time_spend_company work_accident left promotion_last_5years department \\\n",
"0 3 0 1 0 sales \n",
"1 6 0 1 0 sales \n",
"2 4 0 1 0 sales \n",
"3 5 0 1 0 sales \n",
"4 3 0 1 0 sales \n",
"\n",
" salary \n",
"0 low \n",
"1 medium \n",
"2 medium \n",
"3 low \n",
"4 low "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "998cc356-222f-be7b-45cd-2e4d389d3b5a"
},
"source": [
"A brief summary of the numeric values in the data set offers following insights about the variables:\n",
"\n",
"* **satisfaction_level:** 1 most likely indicating a high satisfaction level [continuous value in the range of 0 and 1]\n",
"* **last_evaluation:** Most likely the rating of given employee [continuous numeric value between 0 and 1]\n",
"* **number_project:** The amount of projects an employee has. [discrete numeric value between 2 and 7]\n",
"* **average_monthly_hours:** The hours an employee works per month [continous numeric value between 96 and 310]\n",
"* **time_spend_company:** Given current working statistics we are most likely dealing with years. [discrete numeric value between 2 and 10]\n",
"* **work_accident:** Numeric expression of boolean value whether an employee had a work accident [discrete: 1 = true, 0 = false]\n",
"* **left:** Label if person left or not [discrete: 1 = true, 0 false]\n",
"* **promotion_last_5years:** Numeric expression of boolean value whether an employee was promoted within the last 5 years [discrete: 1 = true, 0 = false]\n",
"\n",
"The dataset contains some non-numeric variables as well that need further exploration. `department` and `salary` have character values that seem to describe the data points further."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "b6a9cf26-8d20-f93a-111f-bbaec066f071"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hr_data['department']: ['sales' 'accounting' 'hr' 'technical' 'support' 'management' 'IT'\n",
" 'product_mng' 'marketing' 'RandD'] \n",
"\n",
"hr_data['salary']: ['low' 'medium' 'high']\n"
]
}
],
"source": [
"# Describe categorical data\n",
"print(\"hr_data['department']:\", hr_data['department'].unique(), '\\n')\n",
"print(\"hr_data['salary']:\", hr_data['salary'].unique())"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "89943f9c-29ef-2e0c-7e7f-ea22982e2946"
},
"source": [
"This completes the variable descriptions and offers additional insights:\n",
"\n",
"* **department:** Categorical variable describing the position of an employee. [discrete: sales, accounting, hr, technical, support, management, IT, product_mng, marketing, RanD]\n",
"* **salary:** Categorical variable indicating salary level of employee. [discrete: low, medium, high]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2441bba2-bb13-ccc8-e919-fb3800e83af4"
},
"source": [
"### Mean vs median\n",
"In order to decide whether to pick the mean or median values for comparison, we'll be computing the variance of each option."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "8cd56f8d-6ad5-de6a-321a-64a74cc8cc28"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean</th>\n",
" <th>median</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>satisfaction_level</th>\n",
" <td>0.000157</td>\n",
" <td>0.000291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>last_evaluation</th>\n",
" <td>0.000028</td>\n",
" <td>0.000143</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_project</th>\n",
" <td>0.005299</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>average_monthly_hours</th>\n",
" <td>1.376778</td>\n",
" <td>4.011111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time_spend_company</th>\n",
" <td>0.077200</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>work_accident</th>\n",
" <td>0.000276</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>left</th>\n",
" <td>0.002177</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>promotion_last_5years</th>\n",
" <td>0.001051</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean median\n",
"satisfaction_level 0.000157 0.000291\n",
"last_evaluation 0.000028 0.000143\n",
"number_project 0.005299 0.000000\n",
"average_monthly_hours 1.376778 4.011111\n",
"time_spend_company 0.077200 0.000000\n",
"work_accident 0.000276 0.000000\n",
"left 0.002177 0.000000\n",
"promotion_last_5years 0.001051 0.000000"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Mean or median\n",
"mean_by_dept = hr_data.groupby('department').mean()\n",
"median_by_dept = hr_data.groupby('department').median()\n",
"\n",
"mm_comp = []\n",
"index = []\n",
"for row_mean, row_median in zip(mean_by_dept.var().iteritems(), median_by_dept.var().iteritems()):\n",
" mm_comp.append([row_mean[1], row_median[1]])\n",
" index.append(row_mean[0])\n",
"\n",
"mm_comp = pd.DataFrame(mm_comp, columns=['mean', 'median'], index=index)\n",
"mm_comp"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d8f3a585-cb01-f5bb-6436-3a4f078b7a50"
},
"source": [
"The above table shows the result of variance for average and median values of each numeric variable. There is clearly more infor- mation gain from the mean values in our data set. Therefore average values will be used for our analysis where needed. A summary computation of standard deviation, minimum and maximum values as well as the numeric quartile ranges indicates that the data set is already quite balanced. Based on the numeric information about mean, minimum and maximum values it can already be said that the numeric values seem to contain only little anomalies when it comes to tendency."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "0e1f7f36-55fe-323a-877a-a9c4869a921c"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>satisfaction_level</th>\n",
" <td>0.248631</td>\n",
" <td>0.09</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>last_evaluation</th>\n",
" <td>0.171169</td>\n",
" <td>0.36</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>number_project</th>\n",
" <td>1.232592</td>\n",
" <td>2.00</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>average_monthly_hours</th>\n",
" <td>49.943099</td>\n",
" <td>96.00</td>\n",
" <td>310.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time_spend_company</th>\n",
" <td>1.460136</td>\n",
" <td>2.00</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>work_accident</th>\n",
" <td>0.351719</td>\n",
" <td>0.00</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>left</th>\n",
" <td>0.425924</td>\n",
" <td>0.00</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>promotion_last_5years</th>\n",
" <td>0.144281</td>\n",
" <td>0.00</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" std min max\n",
"satisfaction_level 0.248631 0.09 1.0\n",
"last_evaluation 0.171169 0.36 1.0\n",
"number_project 1.232592 2.00 7.0\n",
"average_monthly_hours 49.943099 96.00 310.0\n",
"time_spend_company 1.460136 2.00 10.0\n",
"work_accident 0.351719 0.00 1.0\n",
"left 0.425924 0.00 1.0\n",
"promotion_last_5years 0.144281 0.00 1.0"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_description.transpose()[['std', 'min', 'max']]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "03238d4a-5d27-71ba-3455-70f16c082ce9"
},
"source": [
"### Univariate Analysis\n",
"To substantiate these findings all variables are explored one-by-one. The method used depends on whether the variable is continuous or discrete. For contin- uous variables the spread and central tendency is important. For discrete variables frequency and histograms will be used to gain additional information. The standard deviation values indicate a balanced dispersion. In order to visualize the findings density plots3 are created to investigate the distribution. Following observations can be made:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "f50ee477-42f8-d0b9-3db2-2e737b02d6e4"
},
"outputs": [],
"source": [
"# Do kde plot"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4367438c-6784-bccc-5d80-b4e7ce591956"
},
"source": [
"Given that the density curve describes the relative likelihood for a random variable to take on a given value, following observations can be made. With some smoothing, the `satisfaction_level` seems to be pretty normal in regards of distribution. Both `last_evaluation` as well as `average_monthly_hours` have two peaks. Yet there is no anomaly in the dibstribution.\n",
"\n",
"The discrete variables are explored with frequency plots. Due to the amount of only seven discrete variables it is possible to plot all variables and by doing so gain first insights about the data. Looking at the frequency plots the number project and time spend company distribution have a slight positive skew. It can also be seen that the most employees tend to have three or four projects and tenure seems to level around three years."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "747f4f70-bf5b-d214-9fb2-f6e945960d6d"
},
"outputs": [],
"source": [
"cols = ['number_project', \n",
" 'time_spend_company', \n",
" 'department', \n",
" 'salary', \n",
" 'work_accident', \n",
" 'left', \n",
" 'promotion_last_5years']\n",
"\n",
"#Find this code in hr_predict.py on github\n",
"#hr_predict.discrete_charts(hr_data, cols, plot_color)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f90e88ad-2ee7-e5cb-832e-748eaa771729"
},
"source": [
"![Kaggle_Bokeh_Charts_2][1]\n",
"\n",
"\n",
" [1]: https://preview.ibb.co/by4h35/kaggle_2.png"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "80fdcc62-f2b7-178b-832a-69b7979dcd34"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.13 % percent of all employees had a promotion within the last 5 years.\n",
"14 out of 100 employees had a work related accident.\n"
]
}
],
"source": [
"#from math import round\n",
"print(\"{:.2f} % percent of all employees had a promotion within the last 5 years.\".format(\n",
" (len(hr_data[hr_data['promotion_last_5years'] == 1]) / \n",
" float(len(hr_data['promotion_last_5years']))) * 100))\n",
"print(\"{} out of 100 employees had a work related accident.\".format(\n",
" int(round((len(hr_data[hr_data['work_accident'] == 1]) / float(len(hr_data))) * 100))))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "864bc3bf-72a1-10a4-0ee1-db48d222367d"
},
"source": [
"When it comes to department, the most employees are employed in sales with technical coming second and support being third on the list. It is interesting that technical and IT are separate divisions. The salary variable shows a clear skew towards low and medium wages which was to be expected. The majority of the workforce described in this data set has a medium to low salary level. When it comes to work accidents, the data shows about fourteen in 100 employees has had a work related accident. A slightly less severe but still significant skew is happening in the dependent variable. This needs to be taken into account when splitting the data set for training, cross-validation and testing."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7d5ac19c-3c60-9dfc-88b6-49ec81de5f11"
},
"source": [
"### Missing Values\n",
"Another problem that might occur are missing values. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "7f171ba3-eba0-9639-2d0f-dc9a21a3a08d"
},
"outputs": [],
"source": [
"# Check if there are any NaN values\n",
"for name, item in hr_data.isnull().sum().iteritems():\n",
" if item > 0:\n",
" print(name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f4222d66-1f49-900e-a595-f88d3adef550"
},
"source": [
"There are no missing values in the data set, which is fortunate."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ec09a8ae-da57-0a90-75f7-13379718d35c"
},
"source": [
"## Data Context\n",
"To get a firmer grasp on some of the information hidden in the data set it's helpful to ask questions regarding the context of the data. Simple questions help putting a perspective on the information and offer a gateway to understanding the data. Questions that are being answered by this section are:"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "c5671843-0fef-db6c-74d1-e93d69b1a861"
},
"source": [
"* How many people left their company?\n",
"* What's the average tenure?\n",
"* How do variables change by department?\n",
" * Which department works the most hours?\n",
" * Where do most work accidents appear?\n",
" * What is the satisfaction level by department?"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "5e831d27-230d-6fb8-3a23-772294013c30"
},
"source": [
"### How many people left their company\n",
"Let's see how the distribution of our indipendent variable looks like. It's important to note that we'll need a similar distribution of this variable for our training and testing sets."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "baa12a68-c402-5dfa-6a24-d947b140d1c6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3571 Persons left their company (23.8 %)\n"
]
}
],
"source": [
"people_left = len(hr_data[hr_data['left'] == 1])\n",
"print(\"{} Persons left their company ({:.3} %)\".format(\n",
" people_left, ((people_left/float(len(hr_data))) * 100)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "803869f3-787d-8bd4-83b5-6cefc943c45f"
},
"source": [
"### What's the average tenure over all departments"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "8dda36d5-1318-63cc-dc9e-37679ea66ba1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average tenure: 3.498\n"
]
}
],
"source": [
"# Average tenure\n",
"print(\"Average tenure: {:.4}\".format(hr_data['time_spend_company'].mean()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2f988048-e66d-d723-add1-45fcddd954ba"
},
"source": [
"An overall of 3,571 Persons left their company which is around 24%. It is important to note that there needs to be a similar distribution of this variable in the training and testing sets used later. It shows that people in our data set tend to leave the company earlier than the average mentioned in the first paragraph. This could indicate that the population we’re looking at is either younger than the average work force or, more likely, that we’re not representing state and federal employees which tend to have a higher average tenure (> 8 years). "
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "00d76e06-6525-c4b4-025b-d91b4d8f8186"
},
"source": [
"### Exploration by department\n",
"In order to get a better grasp on the information that is contained in the data set, we’ll be looking at average values of the predictors through the eyes of each department. The mean values per department are calculated for all discrete variables and plotted accordingly."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "83150b2f-e1a0-eff4-d22f-edd63c73021f"
},
"outputs": [],
"source": [
"#Find this code in hr_predict.py on github\n",
"#hr_predict.discrete_dept_charts(mean_by_dept, plot_color)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1c466e28-7818-6f40-a3ea-e7ce582569ba"
},
"source": [
"![Kaggle_Bokeh_Chart_3][1]\n",
"\n",
"\n",
" [1]: https://preview.ibb.co/dud0O5/kaggle_3.png"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "37a56411-769a-a360-391a-53101ab1a9cc"
},
"source": [
"### Insights by department\n",
"* The satisfaction level seems to be quite consistant over all departments. Only **accountants** & **HR** have a slight below average satisfaction level.\n",
"* Evaluation levels are on a consistant level over all departments.\n",
"* **HR** & **Marketing** seem to have slightly below average number of projects.\n",
"* All departments seem to clock in the same amount of time on a monthly basis. Guess there are only so much hours to the week. \n",
"* **Management** is the clear frontrunner, when it comes to tenure. This makes sense, since a management position comes with greater responsibility & lock-in effects.\n",
"* **R&D**, **Management**, **Marketing** & **Support** seem to have the most hazardous work environment. With **Accounting** & **HR** being on the safety first side.\n",
"* **Management** & **R&D** have the highest average loyalty rate. **Accounting** & **HR** on the other hand are fluctuating quite a bit.\n",
"* **Management** have by far the highest promotion rate. In **Marketing** almost one out of two has had a promotion within the last 5 years, in the **R&D** department over 30 % has advanced on a professional level in the last 5 years. The **Support** and **Technical** departments are on the lowest spectrum."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8724b786-2d2b-5fb5-14a1-041e21e2403f"
},
"source": [
"## Bi-variate Analysis\n",
"It’s good practice to get a grasp on how the variables are behaving in concert with each other. The relationship between two variables can tell us a lot about possible hypotheses that might be hidden in the data. In order to leverage this part of the analysis to its full extend One-Hot encoding will be performed for all discrete character filled variables, which is a step featured in data preprocessing."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "95f998d6-3177-371b-aa2a-213109276986"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed feature columns (21 total features):\n",
"['satisfaction_level', 'last_evaluation', 'number_project', 'average_monthly_hours', 'time_spend_company', 'work_accident', 'left', 'promotion_last_5years', 'department_it', 'department_randd', 'department_accounting', 'department_hr', 'department_management', 'department_marketing', 'department_product_mng', 'department_sales', 'department_support', 'department_technical', 'salary_high', 'salary_low', 'salary_medium']\n"
]
}
],
"source": [
"def preprocess_features(data):\n",
" ''' \n",
" Preprocesses input data. \n",
" converts non-numeric binary variables into\n",
" binary (0/1) variables. \n",
" Converts categorical variables into dummy variables. \n",
" '''\n",
" output = pd.DataFrame(index = data.index)\n",
"\n",
" # Investigate each feature column for the data\n",
" for col, col_data in data.iteritems():\n",
" \n",
" # If data type is non-numeric, replace all yes/no values with 1/0\n",
" if col_data.dtype == object:\n",
" col_data = col_data.replace(['yes', 'no'], [1, 0])\n",
"\n",
" # If data type is categorical, convert to dummy variables\n",
" if col_data.dtype == object:\n",
" col_data = pd.get_dummies(col_data, prefix = col) \n",
" \n",
" # Collect the revised columns\n",
" output = output.join(col_data)\n",
" # Unify all column names by transforming to lower case\n",
" output.columns = output.columns.str.lower()\n",
" \n",
" return output\n",
"\n",
"hr_data = preprocess_features(hr_data)\n",
"\n",
"print(\"Processed feature columns ({} total features):\\n{}\".format(\n",
" len(hr_data.columns), list(hr_data.columns)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1e3df903-65ff-b3ee-39d8-f5b51e2c567e"
},
"source": [
"## Regression & Correlation "
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "20771d05-226b-90c7-8229-53bd834ad304"
},
"source": [
"After encoding and preprocessing the data set regression and correlation calculations were performed. This is an important step to understanding what measures might be helpful when trying to prevent a high leaving rate."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "0228c0d4-4ea0-7c2d-d839-e63520ab888c"
},
"outputs": [
{
"data": {
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RPvnkE1544QVOnz5t/Bw+Pj52i8PNzc3ihbLhw4dz/vx5qyPQttw313KyceNGnnvuOePy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\nCbQRQrhLKW8KIWahn197hfJxGPABDgghvIATxex7h1LaTEoZL4RIBF4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+eUUxZ/+CHD58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MyV/enTp2nevDk1a9akZs2atG7dmv3795daPuLe9I8a/CqK8peiKHaKotgrinJZUZRORa//pCjK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OC+v1Vts5+3HJ0wNTWlZq3a3L51U6nt8iXq6kiZN7Tt1NQURo/4gcePHwNwJiKcho0aE3lF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\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jwADgFrAPOA6EAdtlWe6kt97f0TwX3FeWZYM7RJKkPsAo4APgPNBeluVMSZJ+AmYBZ4Fd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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa8ae0fc630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(16, 14))\n",
"cmap = sns.diverging_palette(222, 10, as_cmap=True)\n",
"_ = sns.heatmap(hr_data.corr(), annot=True, vmax=.8, square=True, cmap=cmap)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "317a8b6a-e3ee-291a-751c-5d546c4dd782"
},
"source": [
"Just based on the first correlation results there are already a couple of interesting observations:\n",
"\n",
"* A high satisfaction level seems to lead to a lower leaving rate.\n",
"* Employees with higher salary are less inclined to leave.\n",
"* On the other hand low salary employees seem to leave the company more often.\n",
"* There’s a positive correlation between the time a person spends at a company and the fact if they left.\n",
"\n",
"In order to get more granular highly correlated (strength > .1) and highly significant (p-Value > 0.05) connections are being investigated."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "565f693a-1f0e-5f58-d52d-b2816fd2dd79"
},
"outputs": [],
"source": [
"def corr_table(data, features, sig_level=0.05, strength=0.0):\n",
" import math\n",
" import numpy as np\n",
" from scipy.stats import pearsonr\n",
"\n",
" from operator import itemgetter\n",
" p_val_dict = []\n",
" check_dict = []\n",
" for feature in features:\n",
" feature_first = feature.split('_')[0]\n",
" for label in features:\n",
" # Since these correlations go in both directions, we only need to store on \n",
" # of the correlations and can discard the secon one\n",
" # i.e. corr(age, Medu) has equal insights to corr(Medu, age)\n",
" feature_comb = label+feature\n",
" label_first = label.split('_')[0]\n",
" \n",
" if feature == label or feature_comb in check_dict or feature_first == label_first:\n",
" #feature is already paired with label or equals label\n",
" #or feature and label are from the same one-hot-encoding category\n",
" continue\n",
" else:\n",
" check_dict.append(feature+label)\n",
" pears = pearsonr(data[feature], data[label])\n",
" p_val = pears[1]\n",
" corr_strength = pears[0]\n",
" cov_strength = np.cov(data[feature], data[label])[0][1]\n",
" \n",
" # Check if correlation is significant and has a high enough correlation\n",
" if p_val < sig_level and math.fabs(corr_strength) > strength:\n",
" p_val_dict.append([feature, label, cov_strength, corr_strength, p_val])\n",
"\n",
" p_corr_title = 'Correlation > ' + str(strength)\n",
" p_value_title = 'p-Value < ' + str(sig_level)\n",
" p_val_dict = pd.DataFrame(p_val_dict, columns = ['Feature', 'Label', 'Covariance', p_corr_title, p_value_title])\n",
" pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
" \n",
" p_val_dict['order'] = abs(p_val_dict[p_corr_title])\n",
" p_val_dict.sort_values(by='order', inplace=True, ascending=False)\n",
" p_val_dict.head()\n",
" p_val_dict = p_val_dict.reset_index(drop=True)\n",
" p_val_dict = p_val_dict.drop('order', axis=1)\n",
" \n",
" return p_val_dict\n"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "156d1048-9ef3-1286-28d8-a69fa5094a17"
},
"outputs": [],
"source": [
"reg_features = hr_data.columns[~hr_data.columns.str.contains('left')]\n",
"label = reg_features\n",
"\n",
"# How many significant correlations are in the data set?\n",
"correlations_all = corr_table(hr_data, reg_features, strength=0.1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "d2558c81-80b7-5455-3b2a-508e4594118e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Feature</th>\n",
" <th>Label</th>\n",
" <th>Covariance</th>\n",
" <th>Correlation > 0.1</th>\n",
" <th>p-Value < 0.05</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>number_project</td>\n",
" <td>average_monthly_hours</td>\n",
" <td>25.6833</td>\n",
" <td>0.4172</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>last_evaluation</td>\n",
" <td>number_project</td>\n",
" <td>0.0737</td>\n",
" <td>0.3493</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>last_evaluation</td>\n",
" <td>average_monthly_hours</td>\n",
" <td>2.9044</td>\n",
" <td>0.3397</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>department_management</td>\n",
" <td>salary_high</td>\n",
" <td>0.0115</td>\n",
" <td>0.2091</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>number_project</td>\n",
" <td>time_spend_company</td>\n",
" <td>0.3542</td>\n",
" <td>0.1968</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>satisfaction_level</td>\n",
" <td>number_project</td>\n",
" <td>-0.0438</td>\n",
" <td>-0.1430</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>last_evaluation</td>\n",
" <td>time_spend_company</td>\n",
" <td>0.0329</td>\n",
" <td>0.1316</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>promotion_last_5years</td>\n",
" <td>department_management</td>\n",
" <td>0.0037</td>\n",
" <td>0.1281</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>average_monthly_hours</td>\n",
" <td>time_spend_company</td>\n",
" <td>9.3164</td>\n",
" <td>0.1278</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>time_spend_company</td>\n",
" <td>department_management</td>\n",
" <td>0.0338</td>\n",
" <td>0.1154</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>satisfaction_level</td>\n",
" <td>last_evaluation</td>\n",
" <td>0.0045</td>\n",
" <td>0.1050</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>satisfaction_level</td>\n",
" <td>time_spend_company</td>\n",
" <td>-0.0366</td>\n",
" <td>-0.1009</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Feature Label Covariance \\\n",
"0 number_project average_monthly_hours 25.6833 \n",
"1 last_evaluation number_project 0.0737 \n",
"2 last_evaluation average_monthly_hours 2.9044 \n",
"3 department_management salary_high 0.0115 \n",
"4 number_project time_spend_company 0.3542 \n",
"5 satisfaction_level number_project -0.0438 \n",
"6 last_evaluation time_spend_company 0.0329 \n",
"7 promotion_last_5years department_management 0.0037 \n",
"8 average_monthly_hours time_spend_company 9.3164 \n",
"9 time_spend_company department_management 0.0338 \n",
"10 satisfaction_level last_evaluation 0.0045 \n",
"11 satisfaction_level time_spend_company -0.0366 \n",
"\n",
" Correlation > 0.1 p-Value < 0.05 \n",
"0 0.4172 0.0000 \n",
"1 0.3493 0.0000 \n",
"2 0.3397 0.0000 \n",
"3 0.2091 0.0000 \n",
"4 0.1968 0.0000 \n",
"5 -0.1430 0.0000 \n",
"6 0.1316 0.0000 \n",
"7 0.1281 0.0000 \n",
"8 0.1278 0.0000 \n",
"9 0.1154 0.0000 \n",
"10 0.1050 0.0000 \n",
"11 -0.1009 0.0000 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"correlations_all"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3b3d5d1a-124a-c2c6-d962-21068eb90df2"
},
"source": [
"Based on this correlation analysis we can infer following observations:\n",
"\n",
"* Employees with a higher number of project tend to have a significant higher work-load per month.\n",
"* A high evaluation level leads to an increase in the amount of projects (and therefore an increased monthly work-load).\n",
"* People from the management department tend to have a higher salary.\n",
"* An increase of number of project tends to a higher tenure rate.\n",
"* A low number of projects has a negative correlation with the satisfaction level.\n",
"* If the last evaluation was higher, the person was more likely to stay longer with the company.\n",
"* As mentioned above, people from the management department tend to have a higher promotion rate.\n",
"* A high effort, measured by average monthly hours, leads to a significant higher retention rate.\n",
"* People in management jobs have a higher tenure.\n",
"* A high last evaluation has a positive impact on the satisfaction level.\n",
"* A low satisfaction level has a negative correlation with the time an employee stays at a company."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2d4c4798-dd35-b4a9-a7f6-1c2802429466"
},
"source": [
"### Outlier Detection\n",
"One problem that might occur in a data set are outliers. Outliers can lead to wrong estimations and poor performance on some of the algorithms. An outlier is a data point that seems to be out of the normal when it comes to a variable or even multiple variables at once. Usually outliers can be uni-variate or multivariate which means that their abnormal values can either happen on one dimension or multiple variable dimensions together. Going on, the focus will be on uni-variate outliers."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "2a5eacfc-d83e-ad5d-1d99-3efdadbc7be9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No outliers for feature 'satisfaction_level'.\n",
"No outliers for feature 'last_evaluation'.\n",
"No outliers for feature 'number_project'.\n",
"No outliers for feature 'average_monthly_hours'.\n",
"\n",
"Data points considered outliers for the feature 'time_spend_company' - (Min: 3.0 - Max: 4.0):\n",
"Outliers: 1282 (8.55 %)\n"
]
}
],
"source": [
"# Outlier detection\n",
"outliers = {}\n",
"outliers_all = []\n",
"sum = 0\n",
"# For each feature find the data points with extreme high or low values\n",
"for feature in hr_data[[0,1,2,3,4]].keys():\n",
" \n",
" # Calculates Q1 for the given feature\n",
" Q1 = np.percentile(hr_data[feature], 25)\n",
" \n",
" # Calculates Q3 for the given feature\n",
" Q3 = np.percentile(hr_data[feature], 75)\n",
" \n",
" # Calculate an outlier step (1.5 times the interquartile range)\n",
" step = 1.5 * (Q3 - Q1)\n",
" \n",
" # Display the outliers\n",
" category_outliers = hr_data[~((hr_data[feature] >= Q1 - step) & (hr_data[feature] <= Q3 + step))]\n",
" for outlier_no in category_outliers.index:\n",
" if outlier_no in outliers:\n",
" outliers[outlier_no] += 1\n",
" else:\n",
" outliers[outlier_no] = 1\n",
" outliers_all.append(outlier_no)\n",
" if len(category_outliers) > 0:\n",
" print(\"\")\n",
" print(\"Data points considered outliers for the feature '{}' - (Min: {} - Max: {}):\".format(\n",
" feature, Q1, Q3))\n",
" print(\"Outliers: {} ({:.2f} %)\".format(len(category_outliers), \n",
" (len(category_outliers) / float(len(hr_data))) * 100 ))\n",
" else:\n",
" print(\"No outliers for feature '{}'.\".format(feature))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "abc3bc14-99d3-4fba-439a-bae365ca28ea"
},
"source": [
"The initial value to detect outliers used in this analysis is a 1.5 multiple of the Inner Quartile Range. Based on this definition there are 1,282 outliers in only one variable (time spend company). The outliers amount for 8.55% of the overall data set. In order to determine if these outliers should be removed from the data set a broader range is used. By increasing the tolerance for outliers, additional variance might be added but it gives us the opportunity to keep more data for our algorithms. Just widening the step range by .5 reduces the outlier-rate by just shy of 5%. To keep as much data as possible an even closer look is taken. The minimum and maximum IQR boundaries that distinguish outliers per our definition is 3 and 4. Taking into account that this boundary is quite slim all possible outliers of this variable will be used for the analysis."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "567672c5-a6df-b41b-a712-05c12a3a8e48"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.5: \t 1282 \t (8.55 %)\n",
"2: \t 564 \t (3.76 %)\n",
"2.5: \t 564 \t (3.76 %)\n",
"3: \t 376 \t (2.51 %)\n",
"4: \t 214 \t (1.43 %)\n",
"5: \t 214 \t (1.43 %)\n"
]
}
],
"source": [
"steps = [1.5, 2, 2.5, 3, 4, 5]\n",
"\n",
"outlier_index = {}\n",
"for step_count in steps:\n",
" outlier_data = hr_data['time_spend_company']\n",
" Q1 = np.percentile(outlier_data, 25)\n",
" Q3 = np.percentile(outlier_data, 75)\n",
" step = step_count * (Q3 - Q1)\n",
" category_outliers = hr_data[~((outlier_data >= Q1 - step) & \n",
" (outlier_data <= Q3 + step))]\n",
" outlier_index[step_count] = category_outliers.index\n",
" print(\"{}: \\t {} \\t ({:.2f} %)\".format(step_count, \n",
" len(category_outliers), \n",
" (len(category_outliers) / float(len(outlier_data))) * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "16a1b519-f635-3ae7-cfde-bba4e684a96f"
},
"source": [
"Just widening the step range by `.5` reduces the outlier-rate by just shy of 5 %. Yet, given our range from above we decide to keep all data points and are fine with additional variance that's introduced to our data set."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"_cell_guid": "b757c6c9-7358-6e5b-c880-f2d396ef3122"
},
"outputs": [],
"source": [
"# In case you want to drop outliers - use this code and change hr_data.index[outlier_index[step]]\n",
"# where step can be 1.5, 2, 2.5, 3, 4 or 5.\n",
"\n",
"#hr_data = hr_data.drop(hr_data.index[outlier_index[2]]).reset_index(drop=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "51163c2c-455d-4cf1-1ced-d182b74f3604"
},
"source": [
"## Algorithms and Techniques\n",
"The list of possible algorithms for this problem is long. There is a large set of possibilities on how to tackle this task but our key metrics to narrow down the list of algorithms will be data-size, computational efficiency and cross-validation scores on the training set. To come closer to a decision when it comes to the potential algorithm the prerequisites of our setup have to be taken into account. To apply any kind of machine learning technique a data set with over 50 data points is required. In this case with around 15,000 entries, that prerequisite is met. The data is labelled, which puts this research in the realm of supervised learning. Yet, given the complexity of the domain the research aims at engineering an additional variable through unsupervised learning. By doing so an additional discrete variable is created that allots employees based on their attributes to a certain cluster. This section touches base on the idea behind various supervised and unsupervised classification algorithms.\n",
"\n",
"### Supervised Algorithms\n",
"First supervised learning algorithms are explored, detailing some of their features and drawbacks. While some models work better on large sets, some of them are better suited for a small set of training data. A common threshold for machine learning algorithms is 100,000 data points. Since the amount of data for this problem lies way below this threshold, models that perform well on a small set size are preferred. it can be assumed that time wont be too much of an issue since all calculations happen offline.\n",
"\n",
"#### Support Vector Machine\n",
"Support Vector Machines (SVM) are supervised learning algorithms implementing the principles of statistical learning theory. These will be used in a linear and non-linear implementation to account for a higher complexity of the data. SVM tend to have a high accuracy while maintaining a fairly low variance, hence are more unlikely to overfit. This model works really well in complicated domains where there is a clear margin separation. SVM construct a hyper-plane to distinguish the data points from each other. The intuition being that the larger the margin from the closest data points of each class the better the separation criteria. Even if the data turns out to not be linearly separable, this model usually works quite well. However, if the data contains a lot of noise or the amount gets too large, the model tends to perform poorly or very slow. Considering that in a running environment the data base tends to grow this approach might become unfeasible if implemented in a live system. In order to get the best result, key parameters that will be tested for linear SVM are the penalty parameter C of the error term, the used loss-function and the amount of maximum iterations to run as well as l2 penalty function. The implementation with a more complex kernel will also take into account a range of different kernel functions and a range of polynomial degrees for increased complexity.\n",
"\n",
"#### Logistic Regression\n",
"Logistic Regression (LR) is one of the basic linear models for classification. It is best used to predict binary or discrete dependent variables. LR is usually a quick and easy solution for machine-learning problems. It’s implementation is fairly similar to SVM with the great upside to work better on large data sets. Additional advantages being that there are a lot of ways to regularize the model and correlation of features doesn’t matter as much. Another one of its main advantages is, that new data can easily be added and the model can be updated in an ongoing process. Its flexibility makes it a perfect choice for a running system, that should either be adjusted over time to model better or infused with more data. A downside of LR, is that it might have difficulties with binary features. In this data set that contains a substantial set of categorical features this might become a problem. For Logistic Regression tuning involves the penalty parameter C of the error term as well. In addition several solvers are being used as well as a variation on whether the algorithm is able to reuse the solution of the previous call to fit as initialization or if it has to erase the previous solution also known as warm start.\n",
"\n",
"\n",
"#### Decision Tree Classifier\n",
"Another viable option is a Decision Tree model (DT). Decision Trees are fairly easy to explain and interpret. They are also pretty robust against outliers and can be protected against overfitting through pruning. However, Decision Trees don’t support online learning and have to be rebuild every time new information comes in. This might matter, if the used data is regularly updated and not only once a quarter. If an accurate turnover prediction model is needed, that works year round on recent data and updated models, Decision Trees might have a disadvantage. Decision Tree parameters that are taken into account are the function to calculate the splitting criterion measure, the number of features to consider when looking for the best split as well as the maximum depth a tree can have.\n",
"\n",
"#### Random Forest\n",
"There are ensemble methods that incorporate trees such as Random Forest (RF). RF could prove itself with its ability to accept non-linear features. Other than LR it can handle categorical features very well. It’s also well suited for high dimensional spaces in case more features are added or engineered and large numbers of training examples. As mentioned this can be important for future application, once the data base grows. A major downside of RF-models though is its lack of sensitivity towards correlated features. With correlated features, strong features might end up with low scores. Parameters that will be tweaked to find the best working solution are different criterions to measure splitting performance, the ability to bootstrap samples or not, several functions to calculate the maximum features per tree, a maximum depth of the tree, an array of minimum splits per sample and several options for the maximum of leaf nodes a tree can have. In addition, this algorithm’s performance will be examined with or without the ability to perform a warm start as well.\n",
"\n",
"Additional information on set, selection and performance will be shown later on.\n",
"\n",
"### Unsupervised Algorithms\n",
"One problem this research is facing when it comes to unsupervised algorithms is the mixture of discrete and continuous variables. Most algorithms mentioned here are better suited for continuous variables and might perform less accurate when working with categorical features. This segment therefore is meant to be a supplement to the supervised learning models. Its efficiency will be tested based on the cross-validation score before and after the feature employee cluster has been added. Clustering the data has another real-life application. Categorizing employees will give the opportunity to create more tailored solutions for each employee cluster. Following are two algorithms that have been on the shortlist for this task.\n",
"\n",
"#### Gaussian Mixture Model\n",
"Gaussian Mixture Models (GMM) are probabilistic models for representing normally distributed subpopulations within an overall population. GMM are used a lot when the underlying populations can be explained by a normal distribution and many heterogeneous populations are available. As an example based on this [post](https://www.quora.com/What-is-an-example-of-real-world-application-of-Gaussian-Mixture-Models/answer/Hongsun-Kim), we can look at the average evaluation scoring for people in the different departments: R&D, Accounting, HR, Management, Marketing, Product Management, Sales, Support and Technical. It can be assumed the evaluation score distribution is slightly different within each department and it follows a normal distribution. The weighting factor could be the percentage of the population that comes from each department as defined above. This would be a 9-point Guassian Mixture Model. The key benefits of GMM are density estimations for each cluster, a certain flexibility when choosing the component distribution and the possibility of soft classification. GMM is a Bayesian approach to clustering. It introduces the ability of soft clustering, which means that data points can be part of more than one cluster. The algorithm also calculates the probability of the data point belonging to a certain center. It is also known to reflect real- world scenarios in a good way.\n",
"\n",
"#### K-Means Clustering\n",
"In general K-Means can be seen as a special case of GMM in which each cluster’s covariance along all dimensions approaches 0. Meaning each data point will be assigned to exactly one cluster. Some of its key advantages are that K-Means is robust and easy to understand. It is computational efficient and delivers a great result when data points are distinct or groups within are well separated from each other.\n",
"Because we’re dealing with a real world scenario and a data set where it sometimes might be hard to distinguish between data points, we’ll go ahead with a GMM implementation. This will also give us the opportunity to soft-label our data-points and refine the clustering at a later point.\n",
"\n",
"Because we're dealing with a real world scenario and a data set where it sometimes might be hard to distinguish between data points, we'll go ahead with a GMM implementation. This will also give us the opportunity to soft-label our data-points and refine the clustering at a later point."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7ad43c96-1f96-6740-071f-e0d36b38eb9c"
},
"source": [
"## Benchmark\n",
"As mentioned before, there are valid methods for predicting employee turnover rate. These results can be taken as benchmarks. In this research the machine learning approach of <cite data-cite=\"4046282/DZ7FB7XU\"></cite>. Their research performed predictive tasks on company information with AUC scoring of .86 on hold-out data as a best result.\n",
"\n",
"### Evaluation Metric\n",
"AUC scoring can be controversial. To account for this, F1 scoring will be implemented on the test set, once cluster-enhancement and GridSearch have been implemented to make sure the model works well with high precision and recall scores.\n",
"\n",
"### Evaluation Method\n",
"First the algorithms will perform a 5-fold cross-validation on 80% of the data. The average score will be the indicator for the best performers. Afterwards the whole data set will be clustered and the supervised learning algorithms are tested on their performance again through cross-validation. To avoid overfitting, which can be a problem especially in tree-based models, the same procedure is applied to the 20% hold out set. Both measures, AUC as well as F1 will be reported and evaluated."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "91d46f60-e94f-93fa-3767-048061b337d9"
},
"source": [
"# Methodology\n",
"\n",
"\n",
"## Data Preprocessing\n",
"In this section all steps leading to a clean and usable data set are outlined. This includes: detection and treatment of missing values as well as outliers, encoding of categorical variables and if necessary normalization of numeric ones. Finally this section gives an overview on feature importance.\n",
"\n",
"### Missing values & Outlier detection\n",
"The data doesn’t contain any missing values. In an attempt to trim the data into a more concise state, outliers with a value greater or smaller than two times the Inner Quartile range have been detected. Yet, since the boundaries were quite slim all outliers are included in the prediction process. The core steps and thoughts taken in this segment were reflected earlier.\n",
"\n",
"### One-Hot Encoding\n",
"Since some algorithms require all variables to be numeric, all discrete character-based variables were encoded and so-called dummy variables have been created. This gives all used supervised learning algorithms the chance to read in all information that can be derived from these categories.\n",
"\n",
"### Feature normalization\n",
"In order to get all data on the same scale, min-max normalization is performed on all numeric variables. Ensuring standardised feature values implicitly weight all features equally in their representation. Training runs in this research have shown that especially algorithms such as Logistic Regression and LinearSVC perform better with normalized data."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"_cell_guid": "05f2bdbe-10b3-8166-433a-58eb665ed748"
},
"outputs": [],
"source": [
"hr_data = (hr_data - hr_data.min()) / (hr_data.max() - hr_data.min())"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "159974db-d111-597a-3b33-a3ade7b8cae6"
},
"source": [
"### Feature Relevance\n",
"One interesting thought to consider is, if one of the categories is relevant for understanding why an employee stays with a company. It might be possible that a category can be predicted by the other categories and therefore contains only limited information regarding the final goal. In simple terms, it might be possible to for example predict the number of projects based on the constellation of all other categories. This determination can be made quite easily by training a supervised regression learner on a subset of the data with one feature removed, and then score how well that model can predict the removed feature. The coefficient of determination, R<sup>2</sup>, is being used to determine the regression score. R<sup>2</sup> has a positive score range between 0 and 1, with 1 being a perfect fit. A negative R<sup>2</sup> implies the model fails to fit the data. In this step One-Hot encoded features are not predicted since its value can easily be predicted by looking at the rest of the encoded variables. To gain better insights in the relevance of each category and to understand if an employee will stay at a company, each category is selected and dropped from the data set. The remaining variables are being used to predict the dropped variable. If prediction accuracy is high for the variable, its relevance for the prediction process might be low."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"_cell_guid": "7b5ed587-670e-770e-6c99-2bd5ba849484"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prediction score for satisfaction_level (R^2): 0.113393737054\n",
"Prediction score for last_evaluation (R^2): -0.153874114253\n",
"Prediction score for number_project (R^2): 0.183037127408\n",
"Prediction score for average_monthly_hours (R^2): -0.0391372439165\n",
"Prediction score for time_spend_company (R^2): 0.0158884382496\n",
"Prediction score for work_accident (R^2): -0.629814043521\n",
"Prediction score for promotion_last_5years (R^2): -0.0339161250478\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"\n",
"# Sets a random state for reproducibility\n",
"random_state = 42\n",
"data = hr_data.copy().drop('left', axis = 1)\n",
"temp = {}\n",
"\n",
"for label in data.columns:\n",
" if (\"department\" not in label) & (\"salary\" not in label):\n",
" new_data = data.drop(label, axis = 1)\n",
" new_data_labels = data[label]\n",
"\n",
" X_train, X_test, y_train, y_test = train_test_split(\n",
" new_data, new_data_labels, test_size=0.25, random_state=random_state)\n",
"\n",
" regressor = DecisionTreeRegressor(random_state=random_state)\n",
" regressor.fit(X_train, y_train)\n",
" regressor.predict(X_test)\n",
"\n",
" score = regressor.score(X_test, y_test)\n",
" temp[label] = score\n",
" print(\"Prediction score for \" + label + \" (R^2): \" + str(score))\n",
"temp = pd.DataFrame.from_dict(temp, orient='index')\n",
"temp.columns = ['R^2 score']"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"_cell_guid": "adefa68f-2454-ad15-0ca2-4c284ed3f6d4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>R^2 score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>number_project</th>\n",
" <td>0.1830</td>\n",
" </tr>\n",
" <tr>\n",
" <th>satisfaction_level</th>\n",
" <td>0.1134</td>\n",
" </tr>\n",
" <tr>\n",
" <th>time_spend_company</th>\n",
" <td>0.0159</td>\n",
" </tr>\n",
" <tr>\n",
" <th>promotion_last_5years</th>\n",
" <td>-0.0339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>average_monthly_hours</th>\n",
" <td>-0.0391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>last_evaluation</th>\n",
" <td>-0.1539</td>\n",
" </tr>\n",
" <tr>\n",
" <th>work_accident</th>\n",
" <td>-0.6298</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" R^2 score\n",
"number_project 0.1830\n",
"satisfaction_level 0.1134\n",
"time_spend_company 0.0159\n",
"promotion_last_5years -0.0339\n",
"average_monthly_hours -0.0391\n",
"last_evaluation -0.1539\n",
"work_accident -0.6298"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"temp.sort_values('R^2 score', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f2ef3c81-a0b2-7803-e3c3-80596f8cd0b6"
},
"source": [
"The Decision Tree Regressor failed to predict all categories but number_of_project and satisfaction_level. Which means all these categories are important. The R<sup>2</sup> scores for number_of_project and satisfaction_level (both below .2) show that the regression model had difficulties predicting the values as well. We can therefore infer that all numeric categories contain valuable information and should be used."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e2a041b8-a2bb-7917-572c-e02a9caf107d"
},
"source": [
"## Implementation\n",
"After making all features machine readable, a model needs to be selected. Because the task at hand is to find out whether an employee left the company or is still employed, this problem is approached primarily as a classification task. Since the amount of labeled data is in the low-mid-range with below 100,000 data points, the prediction task will be started with a Linear Support Vector Machine. There are more sophisticated machine learning algorithm but it’s always a good idea to start at the bottom and get more complex as research continues. The second model is a Decision Tree Classifier, because of its simplicity and computational efficiency. As a third contender a more sophisticated Support Vector Machine is implemented that leverages additional dimensions through an RBF4 kernel. Finally a Random Forest Classifier will be used to predict. Since sklearn makes it quite easy to add additional estimators training and cross-validation is implemented once and the process adapted in a loop for all models.\n",
"\n",
"### Setup\n",
"The prediction process is as follows:\n",
"1) The data set with all machine readable information is split into a training and a testing set. 20% of the original datapoints are left out for testing purposes to validate the final model. In this step the stratify option is being used in order to keep a similar distribution of label data in testing and training set.\n",
"2) The training features set will be split into independent (predictors) and dependent (label) variables. The predictors will be used to predict the label.\n",
"3) A random state will be set which will help with the reproducibility of the prediction process.\n",
"4) In order to avoid overfitting k-fold cross-validation with k = 5 is performed. This means the training set is split in five separate buckets. Four of these buckets are used to train and the left out bucket is used for testing. CV performs this k-times. The average score will give a better indicator on how the model will perform on the testing set or in a real-life application.\n",
"5) Base Prediction: Prediction takes place with basic settings.\n",
"6) Clustering: An unsupervised learning algorithm is implemented to classify employees based on their vari- ables and engineer an additional discrete feature for prediction (and pro-active intervention) purposes.\n",
"7) Cluster Prediction: Prediction takes place with additional cluster information.\n",
"8) In order to find the best possible setup for the supervised learning algorithms grid search is performed on all algorithms.\n",
"9) Fine Tuned Prediction: The fine tuned parameter setup is being used to predict on the cluster enhanced data set. 10) Testing: All trained models are being used on the 20% hold-out data. The highest prediction score in this process is being recorded.\n",
"\n",
"### Base Prediction\n",
"The first prediction run takes place with default settings. For scoring, AUC is measured over a 5-fold cross-validation on the training set. Tab. VI shows the score for all used algorithms."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"_cell_guid": "91c99c1f-9981-f4ca-8a4b-bf02c3657a4b"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"_cell_guid": "f802c276-bdd8-b76b-4c44-c2c2fb3170fe"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training LinearSVC ...\n",
"Average CV performance for LinearSVC: 0.817313 (in 1.07947 seconds)\n",
"Training LogisticRegression ...\n",
"Average CV performance for LogisticRegression: 0.817909 (in 0.812389 seconds)\n",
"Training DecisionTreeClassifier ...\n",
"Average CV performance for DecisionTreeClassifier: 0.976506 (in 0.212999 seconds)\n",
"Training SVC ...\n",
"Average CV performance for SVC: 0.909114 (in 19.5606 seconds)\n",
"Training RandomForestClassifier ...\n",
"Average CV performance for RandomForestClassifier: 0.990402 (in 0.437971 seconds)\n"
]
}
],
"source": [
"# Implement learning algorithms\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import LinearSVC, SVC\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import f1_score\n",
"from sklearn.model_selection import train_test_split, cross_val_score\n",
"from time import time\n",
"\n",
"# Let's keep 20 % of the data for testing purposes\n",
"test_size = .2\n",
"random_state = 42\n",
"\n",
"X_all_base = hr_data.drop('left', 1)\n",
"y_all_base = hr_data['left']\n",
"\n",
"# Use for testing later and don't touch: X_test_base / y_test_base\n",
"X_train_base, X_test_base, y_train_base, y_test_base = train_test_split(\n",
" X_all_base, y_all_base, test_size = test_size, random_state = random_state, stratify=y_all_base)\n",
"\n",
"clf_dict_base = {}\n",
"clf_report_base = []\n",
"clf_feature_relevance_base = []\n",
"\n",
"for clf in [LinearSVC(random_state = random_state),\n",
" LogisticRegression(random_state = random_state),\n",
" DecisionTreeClassifier(random_state = random_state),\n",
" SVC(random_state = random_state),\n",
" RandomForestClassifier(random_state = random_state)]:\n",
" # Extract name of estimator\n",
" clf_name = clf.__class__.__name__\n",
" print(\"Training\", clf_name, \"...\")\n",
" # Fit model on training data\n",
" clf_dict_base[clf_name] = clf.fit(X_train_base, y_train_base)\n",
" # Predict based on it\n",
" # y_pred = clf.predict(X_train)\n",
" \n",
" # Perform cross validation\n",
" start = time()\n",
" scores = cross_val_score(clf, X_train_base, y_train_base, cv=5, scoring='roc_auc') \n",
" end = time()\n",
" duration = end - start\n",
" print(\"Average CV performance for {}: {:.6} (in {:.6} seconds)\".format(\n",
" clf_name, scores.mean(), duration))\n",
" clf_report_base.append([clf_name, scores.mean(), duration])\n",
"\n",
" # Store feature relevance information \n",
" if clf_name in [\"RandomForestClassifier\", \"DecisionTreeClassifier\"]:\n",
" clf_feature_relevance_base.append(clf.feature_importances_.tolist())\n",
" elif clf_name == \"LinearSVC\":\n",
" clf_feature_relevance_base.append(clf.coef_[0].tolist())\n",
"# Store information in list for better visibility\n",
"\n",
"clf_report_base = pd.DataFrame(clf_report_base, columns=['classifier', 'mean_score', 'time'])"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"_cell_guid": "3ad052ea-6a23-4b39-d547-2bb0debcd413"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_monthly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>work_accident</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>department_it</th>\n",
" <th>department_randd</th>\n",
" <th>department_accounting</th>\n",
" <th>department_hr</th>\n",
" <th>department_management</th>\n",
" <th>department_marketing</th>\n",
" <th>department_product_mng</th>\n",
" <th>department_sales</th>\n",
" <th>department_support</th>\n",
" <th>department_technical</th>\n",
" <th>salary_high</th>\n",
" <th>salary_low</th>\n",
" <th>salary_medium</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LinearSVC</th>\n",
" <td>-1.3062</td>\n",
" <td>0.1429</td>\n",
" <td>-0.4690</td>\n",
" <td>0.3394</td>\n",
" <td>0.7061</td>\n",
" <td>-0.4511</td>\n",
" <td>-0.3492</td>\n",
" <td>-0.0377</td>\n",
" <td>-0.1754</td>\n",
" <td>0.0037</td>\n",
" <td>0.1325</td>\n",
" <td>-0.1225</td>\n",
" <td>0.0560</td>\n",
" <td>-0.0238</td>\n",
" <td>0.0253</td>\n",
" <td>0.0264</td>\n",
" <td>0.0490</td>\n",
" <td>-0.3337</td>\n",
" <td>0.2243</td>\n",
" <td>0.0430</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeClassifier</th>\n",
" <td>0.4929</td>\n",
" <td>0.1464</td>\n",
" <td>0.1048</td>\n",
" <td>0.0899</td>\n",
" <td>0.1423</td>\n",
" <td>0.0009</td>\n",
" <td>0.0003</td>\n",
" <td>0.0017</td>\n",
" <td>0.0008</td>\n",
" <td>0.0011</td>\n",
" <td>0.0004</td>\n",
" <td>0.0004</td>\n",
" <td>0.0004</td>\n",
" <td>0.0000</td>\n",
" <td>0.0020</td>\n",
" <td>0.0033</td>\n",
" <td>0.0072</td>\n",
" <td>0.0008</td>\n",
" <td>0.0021</td>\n",
" <td>0.0024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RandomForestClassifier</th>\n",
" <td>0.3009</td>\n",
" <td>0.1101</td>\n",
" <td>0.1675</td>\n",
" <td>0.1884</td>\n",
" <td>0.1859</td>\n",
" <td>0.0100</td>\n",
" <td>0.0015</td>\n",
" <td>0.0017</td>\n",
" <td>0.0017</td>\n",
" <td>0.0017</td>\n",
" <td>0.0019</td>\n",
" <td>0.0015</td>\n",
" <td>0.0013</td>\n",
" <td>0.0011</td>\n",
" <td>0.0039</td>\n",
" <td>0.0025</td>\n",
" <td>0.0031</td>\n",
" <td>0.0041</td>\n",
" <td>0.0084</td>\n",
" <td>0.0026</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project \\\n",
"LinearSVC -1.3062 0.1429 -0.4690 \n",
"DecisionTreeClassifier 0.4929 0.1464 0.1048 \n",
"RandomForestClassifier 0.3009 0.1101 0.1675 \n",
"\n",
" average_monthly_hours time_spend_company \\\n",
"LinearSVC 0.3394 0.7061 \n",
"DecisionTreeClassifier 0.0899 0.1423 \n",
"RandomForestClassifier 0.1884 0.1859 \n",
"\n",
" work_accident promotion_last_5years department_it \\\n",
"LinearSVC -0.4511 -0.3492 -0.0377 \n",
"DecisionTreeClassifier 0.0009 0.0003 0.0017 \n",
"RandomForestClassifier 0.0100 0.0015 0.0017 \n",
"\n",
" department_randd department_accounting \\\n",
"LinearSVC -0.1754 0.0037 \n",
"DecisionTreeClassifier 0.0008 0.0011 \n",
"RandomForestClassifier 0.0017 0.0017 \n",
"\n",
" department_hr department_management \\\n",
"LinearSVC 0.1325 -0.1225 \n",
"DecisionTreeClassifier 0.0004 0.0004 \n",
"RandomForestClassifier 0.0019 0.0015 \n",
"\n",
" department_marketing department_product_mng \\\n",
"LinearSVC 0.0560 -0.0238 \n",
"DecisionTreeClassifier 0.0004 0.0000 \n",
"RandomForestClassifier 0.0013 0.0011 \n",
"\n",
" department_sales department_support \\\n",
"LinearSVC 0.0253 0.0264 \n",
"DecisionTreeClassifier 0.0020 0.0033 \n",
"RandomForestClassifier 0.0039 0.0025 \n",
"\n",
" department_technical salary_high salary_low \\\n",
"LinearSVC 0.0490 -0.3337 0.2243 \n",
"DecisionTreeClassifier 0.0072 0.0008 0.0021 \n",
"RandomForestClassifier 0.0031 0.0041 0.0084 \n",
"\n",
" salary_medium \n",
"LinearSVC 0.0430 \n",
"DecisionTreeClassifier 0.0024 \n",
"RandomForestClassifier 0.0026 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(\n",
" clf_feature_relevance_base, columns=X_train_base.columns, index=['LinearSVC', \n",
" 'DecisionTreeClassifier', \n",
" 'RandomForestClassifier'])"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"_cell_guid": "26a2b977-8a49-e951-e3d4-0fde2230c3d0"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>classifier</th>\n",
" <th>mean_score</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RandomForestClassifier</td>\n",
" <td>0.9904</td>\n",
" <td>0.4380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>DecisionTreeClassifier</td>\n",
" <td>0.9765</td>\n",
" <td>0.2130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SVC</td>\n",
" <td>0.9091</td>\n",
" <td>19.5606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LogisticRegression</td>\n",
" <td>0.8179</td>\n",
" <td>0.8124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LinearSVC</td>\n",
" <td>0.8173</td>\n",
" <td>1.0795</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" classifier mean_score time\n",
"4 RandomForestClassifier 0.9904 0.4380\n",
"2 DecisionTreeClassifier 0.9765 0.2130\n",
"3 SVC 0.9091 19.5606\n",
"1 LogisticRegression 0.8179 0.8124\n",
"0 LinearSVC 0.8173 1.0795"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf_report_base.sort_values(by=['mean_score', 'time'], ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"_cell_guid": "5fd1edf8-af1c-453a-be0b-26d556fd60e7"
},
"outputs": [],
"source": [
"predictor_list_base = []\n",
"for relevance in clf_dict_base['RandomForestClassifier'].feature_importances_:\n",
" predictor_list_base.append(relevance)\n",
"new_base = pd.DataFrame(\n",
" predictor_list_base, columns=['importance'], index=X_all_base.columns.values.tolist())\n",
"new_base.sort_values(by='importance', ascending=False, inplace=True)\n",
"new_base['features'] = new_base.index\n",
"\n",
"p_base = Bar(new_base,\n",
" values='importance',\n",
" label=cat(columns='features', sort=False),\n",
" title='Feature importance',\n",
" color='crimson',\n",
" plot_width=800, \n",
" plot_height=500,\n",
" ylabel='importance',\n",
" legend=None,\n",
" toolbar_location=None)\n",
"#show(p_base)\n",
"\n",
"# Bokeh doesn't seem to work with kaggle. The output of show(p_base) can be seen below as a picture."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a6f9c4b1-6fd8-1c7a-7525-4c6dc347c2ca"
},
"source": [
"![Kaggle_Bokeh_Chart_4][1]\n",
"\n",
"\n",
" [1]: https://preview.ibb.co/crmn35/kaggle_4.png"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6a2ba633-26a0-6d6a-49a2-bdbd691de509"
},
"source": [
"The Random Forest ensemble model has the best average cross validation score. Finetuning of the algorithm parameters will happen later, once the unsupervised additions have been computed.\n",
"\n",
"### Clustering\n",
"Next an additional layer of context will be added through clustering employees into groups. By doing so a new discrete variable is created that might give additional insights into the data. For clustering this research chose GMM because of its features to allow soft classifications as performing well in real-world scenarios well. Because we’re dealing with a real world scenario and a data set where it sometimes might be hard to distinguish between data points, we’ll go ahead with a GMM implementation. In a running environment the clustering should be done offline to safe computation expenses. As mentioned earlier, combining discrete and continuous variables for this task is tricky. Therefore the focus of this supplemental method is on the continuous variables. Since the data is already normalized the next step is feature transformation in order to determine dimensions that are hidden in the data set."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "274195f6-8bf4-b887-e386-b150bc409fe0"
},
"source": [
"### Feature Transformation\n",
"As a first step towards clustering employee data Principal Component Analysis (PCA) is performed on the data set to discover which dimensions about the data best maximize the variance. In addition to finding these dimensions, PCA will also report the explained variance ratio of each dimension. This suggests how much variance within the data is explained by that dimension alone and gives a good indicator about distinguishing performance of each given dimension. A component, also called dimension, from PCA can be considered a new feature of the space. However it is a composition of the original features present in the data."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"_cell_guid": "26f84113-b564-780f-49fd-6e9746dbefe0"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.decomposition import pca\n",
"\n",
"def pca_results(good_data, pca):\n",
" '''\n",
" Create a DataFrame of the PCA results\n",
" Includes dimension feature weights and explained variance\n",
" Visualizes the PCA results\n",
" '''\n",
"\n",
" # Dimension indexing\n",
" dimensions = dimensions = ['Dimension {}'.format(i) for i in range(1,len(pca.components_)+1)]\n",
"\n",
" # PCA components\n",
" components = pd.DataFrame(np.round(pca.components_, 4), columns = good_data.keys())\n",
" components.index = dimensions\n",
"\n",
" # PCA explained variance\n",
" ratios = pca.explained_variance_ratio_.reshape(len(pca.components_), 1)\n",
" variance_ratios = pd.DataFrame(np.round(ratios, 4), columns = ['Explained Variance'])\n",
" variance_ratios.index = dimensions\n",
"\n",
" # Create a bar plot visualization\n",
" fig, ax = plt.subplots(figsize = (14,8))\n",
"\n",
" # Plot the feature weights as a function of the components\n",
" components.plot(ax = ax, kind = 'bar');\n",
" ax.set_ylabel(\"Feature Weights\")\n",
" ax.set_xticklabels(dimensions, rotation=0)\n",
"\n",
"\n",
" # Display the explained variance ratios\n",
" for i, ev in enumerate(pca.explained_variance_ratio_):\n",
" ax.text(i-0.40, ax.get_ylim()[1] + 0.05, \"Explained Variance\\n %.4f\"%(ev))\n",
"\n",
" # Return a concatenated DataFrame\n",
" return pd.concat([variance_ratios, components], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"_cell_guid": "11167625-1917-3e91-4f13-1fcaa98670e0"
},
"outputs": [
{
"data": {
"image/png": 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aKtVv4zs4fkH9Oaj6NUoFNcUVEbE58HxmzgtPLwKnA7+ldN3SfRGxembObOnAkydP7+xa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\nKpRERERERES8/D+EuweAumolKwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa87962ecf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"pca_data = hr_data.ix[:,0:5]\n",
"pca = PCA(n_components=5)\n",
"pca = pca.fit(pca_data)\n",
"\n",
"# Generate PCA results plot\n",
"pca_results = pca_results(pca_data, pca)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"_cell_guid": "6c6670ab-fe60-fecc-8b42-0a83003a151c"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Add Dimension 1</th>\n",
" <th>Add Dimension 2</th>\n",
" <th>Add Dimension 3</th>\n",
" <th>Add Dimension 4</th>\n",
" <th>Add Dimension 5</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Combined Explained Variance</th>\n",
" <td>0.3745</td>\n",
" <td>0.6439</td>\n",
" <td>0.7845</td>\n",
" <td>0.8973</td>\n",
" <td>1.0000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Add Dimension 1 Add Dimension 2 \\\n",
"Combined Explained Variance 0.3745 0.6439 \n",
"\n",
" Add Dimension 3 Add Dimension 4 Add Dimension 5 \n",
"Combined Explained Variance 0.7845 0.8973 1.0000 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(data=[np.cumsum(pca.explained_variance_ratio_)], columns=\"Add \" + \n",
" pca_results.index.values, index=['Combined Explained Variance'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d2fb7144-9e61-7ea0-6582-abddb0d9943e"
},
"source": [
"The first 3 dimensions explain 78.45% of the variance. Adding dimension 4 pushes that value to just shy of 90%. It’s interesting to see that the dimension that explains the most variance has mainly negative values and indicates a negative relationship between satisfaction_level and the other variables. Dimension 2 builds up on that fact, seeing a negative mix of satisfaction_level and last_evaluation_score in a dependency situation with time_spend_company and number_project.\n",
"\n",
"### Silhouette Score\n",
"These insights are taken further by calculating the average [silhouette score](http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html) for each cluster setup. Silhouette analysis shows how well clusters separate and distinguish points. The measure has a range of [-1, 1] where +1 indicates that the separation of points is very well done and -1 indicates that the points might have been mislabeled."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"_cell_guid": "75003b2b-da5e-9d0e-fe9f-e43ba9c7f366"
},
"outputs": [],
"source": [
"from sklearn.decomposition import PCA\n",
"pca = PCA(n_components=5)\n",
"pca = pca.fit(pca_data)\n",
"\n",
"reduced_data = pca.transform(pca_data)\n",
"reduced_data = pd.DataFrame(reduced_data, columns = ['Dimension 1', 'Dimension 2', 'Dimension 3', 'Dimension 4', 'Dimension 5'])"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"_cell_guid": "ab21ef84-dd0c-a2f0-8b89-cbc9f5439405"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculating clusters with 5 dimensions.\n",
"Calculating clusters with 4 dimensions.\n",
"Calculating clusters with 3 dimensions.\n",
"Calculating clusters with 2 dimensions.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>5 components</th>\n",
" <th>4 components</th>\n",
" <th>3 components</th>\n",
" <th>2 components</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Silhouette Score</th>\n",
" <td>0.1891</td>\n",
" <td>0.1976</td>\n",
" <td>0.1848</td>\n",
" <td>0.1895</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 5 components 4 components 3 components 2 components\n",
"Silhouette Score 0.1891 0.1976 0.1848 0.1895"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import mixture\n",
"from sklearn.metrics import silhouette_score\n",
"\n",
"score_list = []\n",
"score_columns = []\n",
"preds = {}\n",
"centers = {}\n",
"sample_preds = {}\n",
"\n",
"# This can be a little time consuming... but fun :)\n",
"for n in range(5,1,-1):\n",
" print(\"Calculating clusters with {} dimensions.\".format(n))\n",
" clusterer = mixture.GaussianMixture(n_components=n)\n",
" # Future\n",
" # clusterer = mixture.GaussianMixture(n_components=n)\n",
" clusterer.fit(reduced_data)\n",
"\n",
" preds[n] = clusterer.predict(reduced_data)\n",
" centers[n] = clusterer.means_\n",
" score = silhouette_score(reduced_data, preds[n], metric='euclidean')\n",
" score_list.append(score)\n",
" score_columns.append(str(n) + \" components\")\n",
"\n",
"score_list = pd.DataFrame(data=[score_list],columns=score_columns, index=['Silhouette Score'])\n",
"score_list"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "fb86e1f1-5166-1617-382b-9ca5f446091b"
},
"source": [
"The average silhouette score for all points shows that four components has the best separation power. Yet, the value is in the low positive range which might indicate that the company should add additional continuous features to describe its clusters better. Using the silhouette score as an indicator, employees will be separated into four classes.\n",
"\n",
"### Visualization of clusters\n",
"In order to get a better understanding on how this separation looks like, a scatter plot is created that marks both, clusters and its members. It is obvious that the separation is heavily influenced by the first two dimensions. A scatter plot of the first two dimensions gives a better understanding on how the data is structured. Since the silhouette score suggests four dimensions to use when clustering, the visual reflection of this plot is limited. In an attempt to shed more light on the cluster distribution a 3D plot is created incorporating the third dimension values as well. Yet, because this research is using four dimensions to cluster the data it is hard to show the separation panes for all dimensions. The difficulty to properly distinguish data points however is a fact that is also reflected by the .1976 silhouette score. With a higher silhouette score the clusters would be easier to separate."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"_cell_guid": "5f52f6dc-5b79-8ee0-9f84-63acde36eb8c"
},
"outputs": [],
"source": [
"def cluster_results(reduced_data, preds, centers):\n",
" predictions = pd.DataFrame(preds, columns = ['Cluster'])\n",
" plot_data = pd.concat([predictions, reduced_data], axis = 1)\n",
"\n",
" # Generate the cluster plot\n",
" fig, ax = plt.subplots(figsize = (10,6))\n",
"\n",
" # Color map\n",
" cmap = cm.get_cmap('gist_rainbow')\n",
"\n",
" # Color the points based on assigned cluster\n",
" for i, cluster in plot_data.groupby('Cluster'): \n",
" cluster.plot(ax = ax, kind = 'scatter', x = 'Dimension 1', y = 'Dimension 2', \\\n",
" color = cmap((i)*1.0/(len(centers)-1)), label = 'Cluster %i'%(i), s=30);\n",
"\n",
" # Plot centers with indicators\n",
" for i, c in enumerate(centers):\n",
" ax.scatter(x = c[0], y = c[1], color = 'white', edgecolors = 'black', \\\n",
" alpha = 1, linewidth = 2, marker = 'o', s=200);\n",
" ax.scatter(x = c[0], y = c[1], marker='$%d$'%(i), alpha = 1, s=100);\n",
"\n",
" # Set plot title\n",
" #plt.legend(loc='upper left', numpoints=1, ncol=4, fontsize=12, bbox_to_anchor=(0, 0))\n",
" ax.set_title(\"Cluster Learning on PCA-Reduced Data - Centroids Marked by Number\",\n",
" fontsize = 14)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"_cell_guid": "6688767c-e46b-e35b-995f-f2713436ace1"
},
"outputs": [],
"source": [
"def cluster_results_3d(reduced_data, preds, centers):\n",
" from mpl_toolkits.mplot3d import Axes3D\n",
" \n",
" predictions = pd.DataFrame(preds, columns = ['Cluster'])\n",
" plot_data = pd.concat([predictions, reduced_data], axis = 1)\n",
" cmap = cm.get_cmap('gist_rainbow')\n",
" \n",
" fig = plt.figure(figsize = (10,8))\n",
" ax = fig.add_subplot(111, projection='3d')\n",
" ax2 = fig.add_subplot(111, projection='3d')\n",
"\n",
" fig = fig.gca(projection='3d')\n",
"\n",
" for i, c in enumerate(centers):\n",
" ax2.scatter(c[0], c[1], c[2], color = 'white', edgecolors = 'black', \\\n",
" alpha = 1, linewidth = 2, marker = 'o', s=200,\n",
" zorder=1);\n",
" ax2.scatter(c[0], c[1], c[2], marker='$%d$'%(i), alpha = 1, s=100,\n",
" zorder=1);\n",
" \n",
" for i, cluster in plot_data.groupby('Cluster'): \n",
" ax2.scatter(cluster['Dimension 1'], cluster['Dimension 2'], cluster['Dimension 3'],\n",
" c = cmap((i)*1.0/(len(centers)-1)), alpha=0.2,\n",
" label = 'Cluster %i'%(i), s=20,\n",
" zorder=.5)\n",
"\n",
" fig.set_xlabel('Dimension 1')\n",
" fig.set_ylabel('Dimension 2')\n",
" fig.set_zlabel('Dimension 3')\n",
" plt.legend(loc='upper left', numpoints=1, ncol=4, fontsize=12, bbox_to_anchor=(0, 0))\n",
" ax2.set_title(\"Cluster Learning on PCA-Reduced Data 3D Plot\",\n",
" fontsize = 14);\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"_cell_guid": "25c2244a-f586-6943-b863-8fab6329bb1d"
},
"outputs": [
{
"data": {
"image/png": 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UcoElfA0CnrB+bwH2EELUAptQZokpQoilwBqUWn688wXsJHcAv7HMs0tQ/iE3WkLGh6jV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LPh5xRrXaz9Oufp7IsJrK8RUXSjVNYlPXfSo/RpBETHvgDFWheOCnvZK69HExQryH9oYkKz+ld5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\n7bYvWZymi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b8H8XiZ6R4sxyRJWupU1eQ6BFWpkd6ISzPG6/udC3/N5Osuobx/L0Okqy7p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VpZyZ+9e4+6Zr2JJDozkdOByOR5xO55MOh+MNQvGGIJxO5511OWAa9UdDijVqNWt2f45O4V9L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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa87004a0b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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CKN6ykksEJYolBgKliaBKYQsJd8xPltMzvkTrDEH/CMEjcCXZApKdtJZYlpWM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kTXKsidfXSnRlAqYTd3sRs7xE8/jp1rwG4famXSU6t4O/vYRh0XpohGHeEczvcQnsJ4CndonvTs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00gn8aAA2Y6BTUjtCuGJiy2MPHz0/DNN+NZNJpsOafY+DabO/YyIA0kld67DXHnUPm6hTh+yn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sdQeMRUms3iatKpIMm6QfVMCNxd2zCOn/L/DeIx5PbO7H6XZi9BfY5Vs0mwcFDp/Pzg+ESt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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa8ae077b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# with preds[n] and centers[n] where n is the number of Dimensions\n",
"no_clusters = 4\n",
"cluster_results(reduced_data, preds[no_clusters], centers[no_clusters])\n",
"cluster_results_3d(reduced_data, preds[no_clusters], centers[no_clusters])"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"_cell_guid": "1c195603-6bd6-509a-4c32-fbef9bda230c"
},
"outputs": [],
"source": [
"predictions = pd.DataFrame(preds[no_clusters], columns = ['Cluster'])\n",
"hr_data['employee_cluster'] = predictions"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"_cell_guid": "1fcd926b-5729-2909-6791-582bd52948ca"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_monthly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>department_it</th>\n",
" <th>department_randd</th>\n",
" <th>...</th>\n",
" <th>department_management</th>\n",
" <th>department_marketing</th>\n",
" <th>department_product_mng</th>\n",
" <th>department_sales</th>\n",
" <th>department_support</th>\n",
" <th>department_technical</th>\n",
" <th>salary_high</th>\n",
" <th>salary_low</th>\n",
" <th>salary_medium</th>\n",
" <th>employee_cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.3187</td>\n",
" <td>0.2656</td>\n",
" <td>0.0000</td>\n",
" <td>0.2850</td>\n",
" <td>0.1250</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>...</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.7802</td>\n",
" <td>0.7812</td>\n",
" <td>0.6000</td>\n",
" <td>0.7757</td>\n",
" <td>0.5000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>...</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0220</td>\n",
" <td>0.8125</td>\n",
" <td>1.0000</td>\n",
" <td>0.8224</td>\n",
" <td>0.2500</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>...</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.6923</td>\n",
" <td>0.7969</td>\n",
" <td>0.6000</td>\n",
" <td>0.5935</td>\n",
" <td>0.3750</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>...</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.3077</td>\n",
" <td>0.2500</td>\n",
" <td>0.0000</td>\n",
" <td>0.2944</td>\n",
" <td>0.1250</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>...</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>1.0000</td>\n",
" <td>0.0000</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 22 columns</p>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project average_monthly_hours \\\n",
"0 0.3187 0.2656 0.0000 0.2850 \n",
"1 0.7802 0.7812 0.6000 0.7757 \n",
"2 0.0220 0.8125 1.0000 0.8224 \n",
"3 0.6923 0.7969 0.6000 0.5935 \n",
"4 0.3077 0.2500 0.0000 0.2944 \n",
"\n",
" time_spend_company work_accident left promotion_last_5years \\\n",
"0 0.1250 0.0000 1.0000 0.0000 \n",
"1 0.5000 0.0000 1.0000 0.0000 \n",
"2 0.2500 0.0000 1.0000 0.0000 \n",
"3 0.3750 0.0000 1.0000 0.0000 \n",
"4 0.1250 0.0000 1.0000 0.0000 \n",
"\n",
" department_it department_randd ... department_management \\\n",
"0 0.0000 0.0000 ... 0.0000 \n",
"1 0.0000 0.0000 ... 0.0000 \n",
"2 0.0000 0.0000 ... 0.0000 \n",
"3 0.0000 0.0000 ... 0.0000 \n",
"4 0.0000 0.0000 ... 0.0000 \n",
"\n",
" department_marketing department_product_mng department_sales \\\n",
"0 0.0000 0.0000 1.0000 \n",
"1 0.0000 0.0000 1.0000 \n",
"2 0.0000 0.0000 1.0000 \n",
"3 0.0000 0.0000 1.0000 \n",
"4 0.0000 0.0000 1.0000 \n",
"\n",
" department_support department_technical salary_high salary_low \\\n",
"0 0.0000 0.0000 0.0000 1.0000 \n",
"1 0.0000 0.0000 0.0000 0.0000 \n",
"2 0.0000 0.0000 0.0000 0.0000 \n",
"3 0.0000 0.0000 0.0000 1.0000 \n",
"4 0.0000 0.0000 0.0000 1.0000 \n",
"\n",
" salary_medium employee_cluster \n",
"0 0.0000 2 \n",
"1 1.0000 0 \n",
"2 1.0000 3 \n",
"3 0.0000 0 \n",
"4 0.0000 2 \n",
"\n",
"[5 rows x 22 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"_cell_guid": "ce4c7ee0-ed01-c339-8973-615ad956c5da"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
" 'average_monthly_hours', 'time_spend_company', 'work_accident', 'left',\n",
" 'promotion_last_5years', 'department_it', 'department_randd',\n",
" 'department_accounting', 'department_hr', 'department_management',\n",
" 'department_marketing', 'department_product_mng', 'department_sales',\n",
" 'department_support', 'department_technical', 'salary_high',\n",
" 'salary_low', 'salary_medium', 'employee_cluster'],\n",
" dtype='object')\n"
]
}
],
"source": [
"print(hr_data.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f39f9ea4-49db-1fdf-5b5d-dfafd26e3b22"
},
"source": [
"### Prediction on cluster-enhanced data\n",
"The additional information created through clusters are used in the prediction process and scores are evaluated compared to the scores without the cluster information. In order to get a better understanding on its performance all prediction algorithms are fed the new information."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"_cell_guid": "2144ea4c-99ef-23e0-70a9-b19c14665ddf"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training LinearSVC ...\n",
"Average CV performance for LinearSVC: 0.836165 (in 1.44181 seconds)\n",
"Training LogisticRegression ...\n",
"Average CV performance for LogisticRegression: 0.852928 (in 0.796789 seconds)\n",
"Training DecisionTreeClassifier ...\n",
"Average CV performance for DecisionTreeClassifier: 0.974732 (in 0.238557 seconds)\n",
"Training SVC ...\n",
"Average CV performance for SVC: 0.962039 (in 8.56969 seconds)\n",
"Training RandomForestClassifier ...\n",
"Average CV performance for RandomForestClassifier: 0.989264 (in 0.434384 seconds)\n"
]
}
],
"source": [
"# Let's start the prediction\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import LinearSVC, SVC\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import f1_score\n",
"from sklearn.model_selection import cross_val_score\n",
"from time import time\n",
"\n",
"# Keeps 20 % of the data for testing purposes\n",
"test_size = .2\n",
"random_state = 42\n",
"\n",
"X_all_clus = hr_data.drop('left', 1)\n",
"y_all_clus = hr_data['left']\n",
"\n",
"# Use for testing later and don't touch: X_test_clus / y_test_clus\n",
"X_train_clus, X_test_clus, y_train_clus, y_test_clus = train_test_split(\n",
" X_all_clus, y_all_clus, test_size = test_size, random_state = random_state, stratify=y_all_clus)\n",
"\n",
"clf_dict_clus = {}\n",
"clf_report_clus = []\n",
"clf_feature_relevance_clus = []\n",
"\n",
"for clf_clus in [LinearSVC(random_state = random_state),\n",
" LogisticRegression(random_state = random_state),\n",
" DecisionTreeClassifier(random_state = random_state),\n",
" SVC(random_state = random_state),\n",
" RandomForestClassifier(random_state = random_state)]:\n",
" # Extract name of estimator\n",
" clf_name = clf_clus.__class__.__name__\n",
" print(\"Training\", clf_name, \"...\")\n",
" # Fit model on training data\n",
" clf_dict_clus[clf_name] = clf_clus.fit(X_train_clus, y_train_clus)\n",
" # Predict based on it\n",
" # y_pred = clf.predict(X_train)\n",
" \n",
" # Perform cross validation\n",
" start = time()\n",
" scores = cross_val_score(\n",
" clf_clus, X_train_clus, y_train_clus, cv=5, scoring='roc_auc') \n",
" end = time()\n",
" duration = end - start\n",
" print(\"Average CV performance for {}: {:.6} (in {:.6} seconds)\".format(\n",
" clf_name, scores.mean(), duration))\n",
" clf_report_clus.append([clf_name, scores.mean(), duration])\n",
"\n",
" # Store feature relevance information \n",
" if clf_name in [\"RandomForestClassifier\", \"DecisionTreeClassifier\"]:\n",
" clf_feature_relevance_clus.append(clf_clus.feature_importances_.tolist())\n",
" elif clf_name == \"LinearSVC\":\n",
" clf_feature_relevance_clus.append(clf_clus.coef_[0].tolist())\n",
"# Store information in list for better visibility\n",
"\n",
"clf_report_clus = pd.DataFrame(clf_report_clus, columns=['classifier', 'mean_score', 'time'])"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"_cell_guid": "de5e0a28-6e29-941e-7e71-8ae8aef27220"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_monthly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>work_accident</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>department_it</th>\n",
" <th>department_randd</th>\n",
" <th>department_accounting</th>\n",
" <th>...</th>\n",
" <th>department_management</th>\n",
" <th>department_marketing</th>\n",
" <th>department_product_mng</th>\n",
" <th>department_sales</th>\n",
" <th>department_support</th>\n",
" <th>department_technical</th>\n",
" <th>salary_high</th>\n",
" <th>salary_low</th>\n",
" <th>salary_medium</th>\n",
" <th>employee_cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LinearSVC</th>\n",
" <td>-0.6338</td>\n",
" <td>0.2856</td>\n",
" <td>-0.1875</td>\n",
" <td>0.2671</td>\n",
" <td>2.0121</td>\n",
" <td>-0.3680</td>\n",
" <td>-0.3210</td>\n",
" <td>-0.1614</td>\n",
" <td>-0.2120</td>\n",
" <td>-0.1127</td>\n",
" <td>...</td>\n",
" <td>-0.2933</td>\n",
" <td>-0.0572</td>\n",
" <td>-0.0804</td>\n",
" <td>-0.0687</td>\n",
" <td>-0.0692</td>\n",
" <td>-0.0414</td>\n",
" <td>-0.6288</td>\n",
" <td>-0.1498</td>\n",
" <td>-0.3110</td>\n",
" <td>0.7465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeClassifier</th>\n",
" <td>0.1032</td>\n",
" <td>0.1116</td>\n",
" <td>0.0216</td>\n",
" <td>0.0712</td>\n",
" <td>0.0974</td>\n",
" <td>0.0009</td>\n",
" <td>0.0002</td>\n",
" <td>0.0023</td>\n",
" <td>0.0007</td>\n",
" <td>0.0008</td>\n",
" <td>...</td>\n",
" <td>0.0002</td>\n",
" <td>0.0007</td>\n",
" <td>0.0006</td>\n",
" <td>0.0010</td>\n",
" <td>0.0028</td>\n",
" <td>0.0036</td>\n",
" <td>0.0010</td>\n",
" <td>0.0023</td>\n",
" <td>0.0022</td>\n",
" <td>0.5744</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RandomForestClassifier</th>\n",
" <td>0.1661</td>\n",
" <td>0.0938</td>\n",
" <td>0.0974</td>\n",
" <td>0.1264</td>\n",
" <td>0.1376</td>\n",
" <td>0.0077</td>\n",
" <td>0.0017</td>\n",
" <td>0.0016</td>\n",
" <td>0.0012</td>\n",
" <td>0.0013</td>\n",
" <td>...</td>\n",
" <td>0.0014</td>\n",
" <td>0.0013</td>\n",
" <td>0.0008</td>\n",
" <td>0.0027</td>\n",
" <td>0.0025</td>\n",
" <td>0.0031</td>\n",
" <td>0.0035</td>\n",
" <td>0.0036</td>\n",
" <td>0.0028</td>\n",
" <td>0.3422</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project \\\n",
"LinearSVC -0.6338 0.2856 -0.1875 \n",
"DecisionTreeClassifier 0.1032 0.1116 0.0216 \n",
"RandomForestClassifier 0.1661 0.0938 0.0974 \n",
"\n",
" average_monthly_hours time_spend_company \\\n",
"LinearSVC 0.2671 2.0121 \n",
"DecisionTreeClassifier 0.0712 0.0974 \n",
"RandomForestClassifier 0.1264 0.1376 \n",
"\n",
" work_accident promotion_last_5years department_it \\\n",
"LinearSVC -0.3680 -0.3210 -0.1614 \n",
"DecisionTreeClassifier 0.0009 0.0002 0.0023 \n",
"RandomForestClassifier 0.0077 0.0017 0.0016 \n",
"\n",
" department_randd department_accounting \\\n",
"LinearSVC -0.2120 -0.1127 \n",
"DecisionTreeClassifier 0.0007 0.0008 \n",
"RandomForestClassifier 0.0012 0.0013 \n",
"\n",
" ... department_management \\\n",
"LinearSVC ... -0.2933 \n",
"DecisionTreeClassifier ... 0.0002 \n",
"RandomForestClassifier ... 0.0014 \n",
"\n",
" department_marketing department_product_mng \\\n",
"LinearSVC -0.0572 -0.0804 \n",
"DecisionTreeClassifier 0.0007 0.0006 \n",
"RandomForestClassifier 0.0013 0.0008 \n",
"\n",
" department_sales department_support \\\n",
"LinearSVC -0.0687 -0.0692 \n",
"DecisionTreeClassifier 0.0010 0.0028 \n",
"RandomForestClassifier 0.0027 0.0025 \n",
"\n",
" department_technical salary_high salary_low \\\n",
"LinearSVC -0.0414 -0.6288 -0.1498 \n",
"DecisionTreeClassifier 0.0036 0.0010 0.0023 \n",
"RandomForestClassifier 0.0031 0.0035 0.0036 \n",
"\n",
" salary_medium employee_cluster \n",
"LinearSVC -0.3110 0.7465 \n",
"DecisionTreeClassifier 0.0022 0.5744 \n",
"RandomForestClassifier 0.0028 0.3422 \n",
"\n",
"[3 rows x 21 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(\n",
" clf_feature_relevance_clus, columns=X_train_clus.columns, index=['LinearSVC', \n",
" 'DecisionTreeClassifier', \n",
" 'RandomForestClassifier'])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"_cell_guid": "80f7312e-08ee-c592-11c8-12724c8d31fd"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>classifier</th>\n",
" <th>mean_score</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RandomForestClassifier</td>\n",
" <td>0.9893</td>\n",
" <td>0.4344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>DecisionTreeClassifier</td>\n",
" <td>0.9747</td>\n",
" <td>0.2386</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SVC</td>\n",
" <td>0.9620</td>\n",
" <td>8.5697</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LogisticRegression</td>\n",
" <td>0.8529</td>\n",
" <td>0.7968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LinearSVC</td>\n",
" <td>0.8362</td>\n",
" <td>1.4418</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" classifier mean_score time\n",
"4 RandomForestClassifier 0.9893 0.4344\n",
"2 DecisionTreeClassifier 0.9747 0.2386\n",
"3 SVC 0.9620 8.5697\n",
"1 LogisticRegression 0.8529 0.7968\n",
"0 LinearSVC 0.8362 1.4418"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf_report_clus = clf_report_clus.ix[:,0:3]\n",
"clf_report_clus.sort_values('mean_score', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"_cell_guid": "c6e10356-7a0d-d173-57e6-989435a9e8f3"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>classifier</th>\n",
" <th>mean_score</th>\n",
" <th>time</th>\n",
" <th>mean_score non-cluster</th>\n",
" <th>time non-cluster</th>\n",
" <th>score_change</th>\n",
" <th>time_change</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RandomForestClassifier</td>\n",
" <td>0.9893</td>\n",
" <td>0.4344</td>\n",
" <td>0.9904</td>\n",
" <td>0.4380</td>\n",
" <td>-0.0011</td>\n",
" <td>-0.0036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>DecisionTreeClassifier</td>\n",
" <td>0.9747</td>\n",
" <td>0.2386</td>\n",
" <td>0.9765</td>\n",
" <td>0.2130</td>\n",
" <td>-0.0018</td>\n",
" <td>0.0256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SVC</td>\n",
" <td>0.9620</td>\n",
" <td>8.5697</td>\n",
" <td>0.9091</td>\n",
" <td>19.5606</td>\n",
" <td>0.0529</td>\n",
" <td>-10.9910</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LogisticRegression</td>\n",
" <td>0.8529</td>\n",
" <td>0.7968</td>\n",
" <td>0.8179</td>\n",
" <td>0.8124</td>\n",
" <td>0.0350</td>\n",
" <td>-0.0156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LinearSVC</td>\n",
" <td>0.8362</td>\n",
" <td>1.4418</td>\n",
" <td>0.8173</td>\n",
" <td>1.0795</td>\n",
" <td>0.0189</td>\n",
" <td>0.3623</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" classifier mean_score time mean_score non-cluster \\\n",
"4 RandomForestClassifier 0.9893 0.4344 0.9904 \n",
"2 DecisionTreeClassifier 0.9747 0.2386 0.9765 \n",
"3 SVC 0.9620 8.5697 0.9091 \n",
"1 LogisticRegression 0.8529 0.7968 0.8179 \n",
"0 LinearSVC 0.8362 1.4418 0.8173 \n",
"\n",
" time non-cluster score_change time_change \n",
"4 0.4380 -0.0011 -0.0036 \n",
"2 0.2130 -0.0018 0.0256 \n",
"3 19.5606 0.0529 -10.9910 \n",
"1 0.8124 0.0350 -0.0156 \n",
"0 1.0795 0.0189 0.3623 "
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf_report_clus['mean_score non-cluster'] = clf_report_base['mean_score']\n",
"clf_report_clus['time non-cluster'] = clf_report_base['time']\n",
"clf_report_clus['score_change'] = clf_report_clus['mean_score'] - clf_report_clus['mean_score non-cluster']\n",
"clf_report_clus['time_change'] = clf_report_clus['time'] - clf_report_clus['time non-cluster']\n",
"clf_report_clus.sort_values(by=['mean_score', 'score_change', 'time_change'], ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "b5db0975-4f5c-931f-525c-a0ae5c22e3be"
},
"source": [
"The largest AUC score gain takes place for SVC followed by LogisticRegression and LinearSVC. The score gained for DecisionTreeClassifier is marginal as well as the loss for RandomForestClassifier. Based on cross-validation results the supervised classification has only produced slight changes in accuracy. RandomForestClassifier is still the leading algorithm for this problem.\n",
"\n",
"### Shift of feature importance\n",
"It might be interesting to see which features gained importance for the leading algorithm and compare it to the base results in the beginning. The results indicate that the most important feature on the base prediction is satisfaction level. After clustering the data and adding an additional discrete variable for all data points, feature importance and ranking change. The prediction process with cluster information satisfaction_level is still very relevant. Yet time_spend_company took over the first position, followed by satisfaction_level and employee_cluster. Overall through adding this additional variable, the ranking looks more balanced."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"_cell_guid": "b872d3d5-3fe4-af34-85ea-6fa8d80ad3a3"
},
"outputs": [],
"source": [
"predictor_list = []\n",
"for relevance in clf_dict_base['RandomForestClassifier'].feature_importances_:\n",
" predictor_list.append(relevance)\n",
"\n",
"\n",
"columns_list = X_all_base.columns.values.tolist()\n",
"\n",
"new = pd.DataFrame(predictor_list, columns=['importance'], index=columns_list)\n",
"new.sort_values(by='importance', ascending=False, inplace=True)\n",
"new['features'] = new.index\n",
"new = new[:6]\n",
"\n",
"p = Bar(new,\n",
" values='importance',\n",
" label=cat(columns='features', sort=False),\n",
" title='Feature importance without cluster information',\n",
" color='crimson',\n",
" plot_width=800, \n",
" plot_height=300,\n",
" ylabel='importance',\n",
" legend=None)\n",
"\n",
"predictor_list_clus = []\n",
"for relevance in clf_dict_clus['RandomForestClassifier'].feature_importances_:\n",
" predictor_list_clus.append(relevance)\n",
"new_clus = pd.DataFrame(\n",
" predictor_list_clus, columns=['importance'], index=X_all_clus.columns.values.tolist())\n",
"new_clus.sort_values(by='importance', ascending=False, inplace=True)\n",
"new_clus['features'] = new_clus.index\n",
"\n",
"new_clus = new_clus[:6]\n",
"p_clus = Bar(new_clus,\n",
" values='importance',\n",
" label=cat(columns='features', sort=False),\n",
" title='Feature importance with cluster information',\n",
" color='crimson',\n",
" plot_width=800, \n",
" plot_height=300,\n",
" ylabel='importance',\n",
" legend=None)\n",
"p.xaxis.major_label_text_font_size = '10pt'\n",
"p.title.text_font_size = '12pt'\n",
"\n",
"p_clus.xaxis.major_label_text_font_size = '10pt'\n",
"p_clus.title.text_font_size = '12pt'\n",
"\n",
"#show(p)\n",
"#show(p_clus)\n",
"\n",
"# Bokeh doesn't seem to work with kaggle. The output of show(p_base) can be seen below as a picture."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d835c9b2-7bd7-61ab-ab31-8f170ffac503"
},
"source": [
"![Kaggle_Bokeh_Chart_5][1]\n",
"\n",
"\n",
" [1]: https://preview.ibb.co/dS44GQ/kaggle_5.png"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0a429d43-e3ad-2177-2654-2626432ef80e"
},
"source": [
"It can be seen that the important feature remains `satisfaction_level`. It seems as if `employee_cluster` has relevated some of the importance of `time_spend_company` and `average_monthly_hours`. Yet the rankings remain the same."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ec98f9c1-bc26-4776-0a4b-2f53689798a2"
},
"source": [
"### Parameter Tuning\n",
"In order to find the best parameter setup, a Grid Search is performed. The basic criteria for deciding on a set of parameters is its AUC scoring. In order to emulate a real-life scenario only 80% of the training data will be used. 20% of the data will serve as a sanity check to offer a better assumption on how this tuning would perform on new data. Similar as before a key focus is on stratifying the data to make sure the training and testing sets are having similar proportions of prediction cases."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"_cell_guid": "df8e41e2-e765-2042-ad46-4fbe05c9fa11"
},
"outputs": [],
"source": [
"def predict_labels(clf, features, target):\n",
" from sklearn import metrics\n",
" ''' Makes predictions using a fit classifier based on AUC score. '''\n",
" \n",
" # Start the clock, make predictions, then stop the clock\n",
" start = time()\n",
" y_pred = clf.predict(features)\n",
" end = time()\n",
" \n",
" # Print and return results\n",
" prediction_duration = end - start\n",
" prediction_auc_score = metrics.roc_auc_score(target.values, y_pred)\n",
" print(\"Made predictions in {:.4f} seconds.\".format(prediction_duration))\n",
" return prediction_auc_score, prediction_duration"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"_cell_guid": "06e55a92-b540-87c4-1e6d-b5a06fab982f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Searching Grid for LinearSVC ...\n",
"Made predictions in 0.0062 seconds.\n",
"Tuned LinearSVC model has a training AUC score of 0.8055.\n",
"Made predictions in 0.0029 seconds.\n",
"Tuned LinearSVC model has a testing AUC score of 0.8130.\n",
"Searching Grid for LogisticRegression ...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" \"the coef_ did not converge\", ConvergenceWarning)\n",
"/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" \"the coef_ did not converge\", ConvergenceWarning)\n",
"/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" \"the coef_ did not converge\", ConvergenceWarning)\n",
"/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" \"the coef_ did not converge\", ConvergenceWarning)\n",
"/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" \"the coef_ did not converge\", ConvergenceWarning)\n",
"/opt/conda/lib/python3.6/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
" \"the coef_ did not converge\", ConvergenceWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Made predictions in 0.0009 seconds.\n",
"Tuned LogisticRegression model has a training AUC score of 0.7996.\n",
"Made predictions in 0.0005 seconds.\n",
"Tuned LogisticRegression model has a testing AUC score of 0.8034.\n",
"Searching Grid for DecisionTreeClassifier ...\n",
"Made predictions in 0.0017 seconds.\n",
"Tuned DecisionTreeClassifier model has a training AUC score of 0.9529.\n",
"Made predictions in 0.0006 seconds.\n",
"Tuned DecisionTreeClassifier model has a testing AUC score of 0.9503.\n",
"Searching Grid for SVC ...\n",
"Made predictions in 0.4489 seconds.\n",
"Tuned SVC model has a training AUC score of 0.8747.\n",
"Made predictions in 0.1128 seconds.\n",
"Tuned SVC model has a testing AUC score of 0.8728.\n",
"Searching Grid for RandomForestClassifier ...\n",
"Made predictions in 0.0352 seconds.\n",
"Tuned RandomForestClassifier model has a training AUC score of 0.9893.\n",
"Made predictions in 0.0095 seconds.\n",
"Tuned RandomForestClassifier model has a testing AUC score of 0.9668.\n"
]
}
],
"source": [
"# Can take a while as well\n",
"# If you feel it's taking too long, you can simply reduce\n",
"# the amount of parameters in the parameters_dict dictionary\n",
"# Hint: Start with reducing the amount of SVC parameters \n",
"# since this algorithm takes the longest and it performs worse than i.e. RandomForest\n",
"# Otherwise reduction in RF parameters will reduce the time significantly as well \n",
"# since its grow is exponential.\n",
"\n",
"from sklearn import model_selection\n",
"from sklearn import metrics\n",
"\n",
"parameters_dict = {\"LinearSVC\" : \n",
" {\"C\": [1, 3, 5],\n",
" \"loss\" : ['hinge', 'squared_hinge'],\n",
" \"penalty\" : ['l2'],\n",
" \"max_iter\" : [100, 200, 300]\n",
" },\n",
" \"LogisticRegression\" :\n",
" {\"C\": [1, 3, 5],\n",
" \"penalty\" : ['l2'],\n",
" \"solver\" : ['sag', 'newton-cg', 'lbfgs', 'liblinear'],\n",
" \"warm_start\" : [True, False]\n",
" },\n",
" \"DecisionTreeClassifier\" :\n",
" {\"criterion\": ['gini', 'entropy'],\n",
" \"max_features\" : ['auto', 'sqrt', 'log2', None],\n",
" \"max_depth\" : [None, 2, 5, 10] \n",
" },\n",
" \"SVC\" : \n",
" {\"C\": [1, 2],\n",
" \"kernel\" : ['poly', 'rbf', 'sigmoid'],\n",
" \"degree\" : [2, 3, 4]\n",
" },\n",
" \"RandomForestClassifier\" :\n",
" {\"n_estimators\" : [10],\n",
" \"criterion\": ['gini', 'entropy'],\n",
" \"bootstrap\" : [False, True],\n",
" \"max_features\" : ['auto', 'sqrt', 'log2', None],\n",
" \"max_depth\" : [None, 2, 5],\n",
" \"min_samples_split\" : [2, 4],\n",
" \"warm_start\" : [True, False],\n",
" \"max_leaf_nodes\" : [None, 4, 6]\n",
" }\n",
" }\n",
"\n",
"best_estimator_dict = {}\n",
"estimator_test_res = {}\n",
"\n",
"test_size = .2\n",
"random_state = 42\n",
"\n",
"X_all_gs = X_train_clus.copy()\n",
"y_all_gs = y_train_clus.copy()\n",
"\n",
"X_train_gs, X_test_gs, y_train_gs, y_test_gs = train_test_split(\n",
" X_all_gs, y_all_gs, test_size = test_size, random_state = random_state, stratify=y_all_gs)\n",
"\n",
"# Initialize the classifier\n",
"for clf in [LinearSVC(random_state = random_state),\n",
" LogisticRegression(random_state = random_state),\n",
" DecisionTreeClassifier(random_state = random_state),\n",
" SVC(random_state = random_state),\n",
" RandomForestClassifier(random_state = random_state)]:\n",
" # Extract name of estimator\n",
" clf_name = clf.__class__.__name__\n",
" print(\"Searching Grid for\", clf_name, \"...\")\n",
"\n",
" # Perform grid search on classifier using roc_auc as scoring method\n",
" grid_obj = model_selection.GridSearchCV(\n",
" clf, param_grid=parameters_dict[clf_name], scoring='roc_auc')\n",
"\n",
" # Fit the grid search object to the training data and find the optimal parameters\n",
" grid_obj = grid_obj.fit(X_train_gs, y_train_gs)\n",
"\n",
" # Get the estimator\n",
" clf = grid_obj.best_estimator_\n",
" best_estimator_dict[clf_name] = clf\n",
" estimator_test_res[clf_name] = grid_obj.cv_results_\n",
"\n",
" # Report the final F1 score for training and testing after parameter tuning\n",
" print(\"Tuned {} model has a training AUC score of {:.4f}.\".format(\n",
" clf_name, predict_labels(clf, X_train_gs, y_train_gs)[0]))\n",
" print(\"Tuned {} model has a testing AUC score of {:.4f}.\".format(\n",
" clf_name, predict_labels(clf, X_test_gs, y_test_gs)[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1b07f8d3-3173-bf42-93f9-bd4cf447be4c"
},
"source": [
"After performing grid search and defining the best fitting parameters the training score of Random Forest improved to .9997 and the testing score scored .9790 which is remarkable since only 80% of the initial training data was used."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"_cell_guid": "8d5c967a-9f1d-837d-7d4b-eea2ea974096"
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean_fit_time</th>\n",
" <th>mean_score_time</th>\n",
" <th>mean_test_score</th>\n",
" <th>mean_train_score</th>\n",
" <th>param_bootstrap</th>\n",
" <th>param_criterion</th>\n",
" <th>param_max_depth</th>\n",
" <th>param_max_features</th>\n",
" <th>param_max_leaf_nodes</th>\n",
" <th>param_min_samples_split</th>\n",
" <th>...</th>\n",
" <th>split0_test_score</th>\n",
" <th>split0_train_score</th>\n",
" <th>split1_test_score</th>\n",
" <th>split1_train_score</th>\n",
" <th>split2_test_score</th>\n",
" <th>split2_train_score</th>\n",
" <th>std_fit_time</th>\n",
" <th>std_score_time</th>\n",
" <th>std_test_score</th>\n",
" <th>std_train_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>434</th>\n",
" <td>0.0597</td>\n",
" <td>0.0118</td>\n",
" <td>0.9856</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
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" <td>0.9999</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0017</td>\n",
" <td>0.0002</td>\n",
" <td>0.0050</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>435</th>\n",
" <td>0.0559</td>\n",
" <td>0.0116</td>\n",
" <td>0.9856</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9907</td>\n",
" <td>0.9999</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0022</td>\n",
" <td>0.0007</td>\n",
" <td>0.0050</td>\n",
" <td>0.0000</td>\n",
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" <tr>\n",
" <th>446</th>\n",
" <td>0.2844</td>\n",
" <td>0.0121</td>\n",
" <td>0.9856</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9907</td>\n",
" <td>0.9999</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.3176</td>\n",
" <td>0.0006</td>\n",
" <td>0.0050</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>447</th>\n",
" <td>0.0592</td>\n",
" <td>0.0122</td>\n",
" <td>0.9856</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9907</td>\n",
" <td>0.9999</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0032</td>\n",
" <td>0.0003</td>\n",
" <td>0.0050</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>458</th>\n",
" <td>0.0566</td>\n",
" <td>0.0122</td>\n",
" <td>0.9856</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9907</td>\n",
" <td>0.9999</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0025</td>\n",
" <td>0.0008</td>\n",
" <td>0.0050</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>459</th>\n",
" <td>-0.1638</td>\n",
" <td>0.0132</td>\n",
" <td>0.9856</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9907</td>\n",
" <td>0.9999</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.3140</td>\n",
" <td>0.0003</td>\n",
" <td>0.0050</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>0.0723</td>\n",
" <td>0.0142</td>\n",
" <td>0.9844</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9880</td>\n",
" <td>1.0000</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9779</td>\n",
" <td>1.0000</td>\n",
" <td>0.0012</td>\n",
" <td>0.0006</td>\n",
" <td>0.0046</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>146</th>\n",
" <td>0.0750</td>\n",
" <td>0.0129</td>\n",
" <td>0.9844</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9880</td>\n",
" <td>1.0000</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9779</td>\n",
" <td>1.0000</td>\n",
" <td>0.0012</td>\n",
" <td>0.0004</td>\n",
" <td>0.0046</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>147</th>\n",
" <td>-0.1475</td>\n",
" <td>0.0128</td>\n",
" <td>0.9844</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9880</td>\n",
" <td>1.0000</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9779</td>\n",
" <td>1.0000</td>\n",
" <td>0.3128</td>\n",
" <td>0.0004</td>\n",
" <td>0.0046</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>0.0731</td>\n",
" <td>0.0137</td>\n",
" <td>0.9844</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9880</td>\n",
" <td>1.0000</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9779</td>\n",
" <td>1.0000</td>\n",
" <td>0.0029</td>\n",
" <td>0.0006</td>\n",
" <td>0.0046</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>159</th>\n",
" <td>0.0720</td>\n",
" <td>0.0130</td>\n",
" <td>0.9844</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9880</td>\n",
" <td>1.0000</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9779</td>\n",
" <td>1.0000</td>\n",
" <td>0.0025</td>\n",
" <td>0.0002</td>\n",
" <td>0.0046</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>158</th>\n",
" <td>0.0718</td>\n",
" <td>0.0132</td>\n",
" <td>0.9844</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9880</td>\n",
" <td>1.0000</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9779</td>\n",
" <td>1.0000</td>\n",
" <td>0.0005</td>\n",
" <td>0.0007</td>\n",
" <td>0.0046</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>315</th>\n",
" <td>0.0514</td>\n",
" <td>0.0120</td>\n",
" <td>0.9841</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9903</td>\n",
" <td>0.9999</td>\n",
" <td>0.9831</td>\n",
" <td>0.9999</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0015</td>\n",
" <td>0.0004</td>\n",
" <td>0.0047</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>314</th>\n",
" <td>0.0510</td>\n",
" <td>0.0124</td>\n",
" <td>0.9841</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9903</td>\n",
" <td>0.9999</td>\n",
" <td>0.9831</td>\n",
" <td>0.9999</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0021</td>\n",
" <td>0.0006</td>\n",
" <td>0.0047</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>303</th>\n",
" <td>0.0515</td>\n",
" <td>0.0154</td>\n",
" <td>0.9841</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9903</td>\n",
" <td>0.9999</td>\n",
" <td>0.9831</td>\n",
" <td>0.9999</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0016</td>\n",
" <td>0.0045</td>\n",
" <td>0.0047</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>302</th>\n",
" <td>0.0504</td>\n",
" <td>0.0125</td>\n",
" <td>0.9841</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9903</td>\n",
" <td>0.9999</td>\n",
" <td>0.9831</td>\n",
" <td>0.9999</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0016</td>\n",
" <td>0.0005</td>\n",
" <td>0.0047</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>291</th>\n",
" <td>0.0496</td>\n",
" <td>0.0136</td>\n",
" <td>0.9841</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9903</td>\n",
" <td>0.9999</td>\n",
" <td>0.9831</td>\n",
" <td>0.9999</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0007</td>\n",
" <td>0.0019</td>\n",
" <td>0.0047</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>0.0500</td>\n",
" <td>0.0125</td>\n",
" <td>0.9841</td>\n",
" <td>0.9999</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9903</td>\n",
" <td>0.9999</td>\n",
" <td>0.9831</td>\n",
" <td>0.9999</td>\n",
" <td>0.9789</td>\n",
" <td>0.9999</td>\n",
" <td>0.0022</td>\n",
" <td>0.0005</td>\n",
" <td>0.0047</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>471</th>\n",
" <td>0.1512</td>\n",
" <td>0.0117</td>\n",
" <td>0.9834</td>\n",
" <td>1.0000</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9883</td>\n",
" <td>0.9999</td>\n",
" <td>0.9832</td>\n",
" <td>1.0000</td>\n",
" <td>0.9787</td>\n",
" <td>1.0000</td>\n",
" <td>0.0116</td>\n",
" <td>0.0006</td>\n",
" <td>0.0039</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>470</th>\n",
" <td>0.1576</td>\n",
" <td>0.0113</td>\n",
" <td>0.9834</td>\n",
" <td>1.0000</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9883</td>\n",
" <td>0.9999</td>\n",
" <td>0.9832</td>\n",
" <td>1.0000</td>\n",
" <td>0.9787</td>\n",
" <td>1.0000</td>\n",
" <td>0.0217</td>\n",
" <td>0.0007</td>\n",
" <td>0.0039</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>288</th>\n",
" <td>0.0512</td>\n",
" <td>0.0122</td>\n",
" <td>0.9834</td>\n",
" <td>1.0000</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9831</td>\n",
" <td>1.0000</td>\n",
" <td>0.9797</td>\n",
" <td>1.0000</td>\n",
" <td>0.0003</td>\n",
" <td>0.0005</td>\n",
" <td>0.0031</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>300</th>\n",
" <td>0.0514</td>\n",
" <td>0.0129</td>\n",
" <td>0.9834</td>\n",
" <td>1.0000</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9831</td>\n",
" <td>1.0000</td>\n",
" <td>0.9797</td>\n",
" <td>1.0000</td>\n",
" <td>0.0019</td>\n",
" <td>0.0009</td>\n",
" <td>0.0031</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>301</th>\n",
" <td>0.0511</td>\n",
" <td>0.0132</td>\n",
" <td>0.9834</td>\n",
" <td>1.0000</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9831</td>\n",
" <td>1.0000</td>\n",
" <td>0.9797</td>\n",
" <td>1.0000</td>\n",
" <td>0.0012</td>\n",
" <td>0.0024</td>\n",
" <td>0.0031</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>289</th>\n",
" <td>0.0512</td>\n",
" <td>0.0128</td>\n",
" <td>0.9834</td>\n",
" <td>1.0000</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9831</td>\n",
" <td>1.0000</td>\n",
" <td>0.9797</td>\n",
" <td>1.0000</td>\n",
" <td>0.0007</td>\n",
" <td>0.0007</td>\n",
" <td>0.0031</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>313</th>\n",
" <td>0.0490</td>\n",
" <td>0.0123</td>\n",
" <td>0.9834</td>\n",
" <td>1.0000</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9831</td>\n",
" <td>1.0000</td>\n",
" <td>0.9797</td>\n",
" <td>1.0000</td>\n",
" <td>0.0009</td>\n",
" <td>0.0007</td>\n",
" <td>0.0031</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>312</th>\n",
" <td>0.0526</td>\n",
" <td>0.0133</td>\n",
" <td>0.9834</td>\n",
" <td>1.0000</td>\n",
" <td>True</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9873</td>\n",
" <td>1.0000</td>\n",
" <td>0.9831</td>\n",
" <td>1.0000</td>\n",
" <td>0.9797</td>\n",
" <td>1.0000</td>\n",
" <td>0.0022</td>\n",
" <td>0.0013</td>\n",
" <td>0.0031</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0623</td>\n",
" <td>0.0152</td>\n",
" <td>0.9833</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9896</td>\n",
" <td>1.0000</td>\n",
" <td>0.9849</td>\n",
" <td>1.0000</td>\n",
" <td>0.9754</td>\n",
" <td>1.0000</td>\n",
" <td>0.0009</td>\n",
" <td>0.0037</td>\n",
" <td>0.0059</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.0647</td>\n",
" <td>0.0132</td>\n",
" <td>0.9833</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9896</td>\n",
" <td>1.0000</td>\n",
" <td>0.9849</td>\n",
" <td>1.0000</td>\n",
" <td>0.9754</td>\n",
" <td>1.0000</td>\n",
" <td>0.0013</td>\n",
" <td>0.0003</td>\n",
" <td>0.0059</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.0644</td>\n",
" <td>0.0127</td>\n",
" <td>0.9833</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9896</td>\n",
" <td>1.0000</td>\n",
" <td>0.9849</td>\n",
" <td>1.0000</td>\n",
" <td>0.9754</td>\n",
" <td>1.0000</td>\n",
" <td>0.0015</td>\n",
" <td>0.0004</td>\n",
" <td>0.0059</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0649</td>\n",
" <td>0.0137</td>\n",
" <td>0.9833</td>\n",
" <td>1.0000</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>None</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9896</td>\n",
" <td>1.0000</td>\n",
" <td>0.9849</td>\n",
" <td>1.0000</td>\n",
" <td>0.9754</td>\n",
" <td>1.0000</td>\n",
" <td>0.0005</td>\n",
" <td>0.0005</td>\n",
" <td>0.0059</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>480</th>\n",
" <td>0.0306</td>\n",
" <td>0.0049</td>\n",
" <td>0.9312</td>\n",
" <td>0.9315</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9265</td>\n",
" <td>0.9264</td>\n",
" <td>0.9261</td>\n",
" <td>0.9298</td>\n",
" <td>0.9409</td>\n",
" <td>0.9383</td>\n",
" <td>0.0025</td>\n",
" <td>0.0001</td>\n",
" <td>0.0069</td>\n",
" <td>0.0050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>504</th>\n",
" <td>0.2524</td>\n",
" <td>0.0059</td>\n",
" <td>0.9312</td>\n",
" <td>0.9315</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9265</td>\n",
" <td>0.9264</td>\n",
" <td>0.9261</td>\n",
" <td>0.9298</td>\n",
" <td>0.9409</td>\n",
" <td>0.9383</td>\n",
" <td>0.3141</td>\n",
" <td>0.0002</td>\n",
" <td>0.0069</td>\n",
" <td>0.0050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>507</th>\n",
" <td>0.0302</td>\n",
" <td>0.0052</td>\n",
" <td>0.9312</td>\n",
" <td>0.9315</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9265</td>\n",
" <td>0.9264</td>\n",
" <td>0.9261</td>\n",
" <td>0.9298</td>\n",
" <td>0.9409</td>\n",
" <td>0.9383</td>\n",
" <td>0.0013</td>\n",
" <td>0.0004</td>\n",
" <td>0.0069</td>\n",
" <td>0.0050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>506</th>\n",
" <td>0.0324</td>\n",
" <td>0.0055</td>\n",
" <td>0.9312</td>\n",
" <td>0.9315</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9265</td>\n",
" <td>0.9264</td>\n",
" <td>0.9261</td>\n",
" <td>0.9298</td>\n",
" <td>0.9409</td>\n",
" <td>0.9383</td>\n",
" <td>0.0018</td>\n",
" <td>0.0004</td>\n",
" <td>0.0069</td>\n",
" <td>0.0050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>505</th>\n",
" <td>0.0286</td>\n",
" <td>0.0049</td>\n",
" <td>0.9312</td>\n",
" <td>0.9315</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9265</td>\n",
" <td>0.9264</td>\n",
" <td>0.9261</td>\n",
" <td>0.9298</td>\n",
" <td>0.9409</td>\n",
" <td>0.9383</td>\n",
" <td>0.0008</td>\n",
" <td>0.0000</td>\n",
" <td>0.0069</td>\n",
" <td>0.0050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>494</th>\n",
" <td>0.0310</td>\n",
" <td>0.0056</td>\n",
" <td>0.9312</td>\n",
" <td>0.9315</td>\n",
" <td>True</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9265</td>\n",
" <td>0.9264</td>\n",
" <td>0.9261</td>\n",
" <td>0.9298</td>\n",
" <td>0.9409</td>\n",
" <td>0.9383</td>\n",
" <td>0.0013</td>\n",
" <td>0.0001</td>\n",
" <td>0.0069</td>\n",
" <td>0.0050</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>0.0300</td>\n",
" <td>0.0051</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0010</td>\n",
" <td>0.0005</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>0.0296</td>\n",
" <td>0.0051</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0007</td>\n",
" <td>0.0003</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>0.0325</td>\n",
" <td>0.0053</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0045</td>\n",
" <td>0.0004</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>0.0300</td>\n",
" <td>0.0051</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0009</td>\n",
" <td>0.0004</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74</th>\n",
" <td>0.0303</td>\n",
" <td>0.0055</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0007</td>\n",
" <td>0.0001</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73</th>\n",
" <td>0.0297</td>\n",
" <td>0.0051</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0016</td>\n",
" <td>0.0002</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72</th>\n",
" <td>0.0295</td>\n",
" <td>0.0049</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0009</td>\n",
" <td>0.0002</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>0.0302</td>\n",
" <td>0.0054</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0007</td>\n",
" <td>0.0005</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>0.0303</td>\n",
" <td>0.0050</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0011</td>\n",
" <td>0.0003</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>0.0301</td>\n",
" <td>0.0054</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0014</td>\n",
" <td>0.0001</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>0.0312</td>\n",
" <td>0.0052</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0005</td>\n",
" <td>0.0002</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>0.0314</td>\n",
" <td>0.0054</td>\n",
" <td>0.9283</td>\n",
" <td>0.9300</td>\n",
" <td>False</td>\n",
" <td>gini</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9269</td>\n",
" <td>0.9267</td>\n",
" <td>0.9257</td>\n",
" <td>0.9299</td>\n",
" <td>0.9324</td>\n",
" <td>0.9333</td>\n",
" <td>0.0014</td>\n",
" <td>0.0005</td>\n",
" <td>0.0029</td>\n",
" <td>0.0027</td>\n",
" </tr>\n",
" <tr>\n",
" <th>192</th>\n",
" <td>0.0316</td>\n",
" <td>0.0057</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0019</td>\n",
" <td>0.0004</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>193</th>\n",
" <td>0.0308</td>\n",
" <td>0.0061</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0006</td>\n",
" <td>0.0005</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>194</th>\n",
" <td>0.0353</td>\n",
" <td>0.0062</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0058</td>\n",
" <td>0.0009</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>195</th>\n",
" <td>0.0353</td>\n",
" <td>0.0053</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>auto</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0033</td>\n",
" <td>0.0000</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>204</th>\n",
" <td>0.0302</td>\n",
" <td>0.0050</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0008</td>\n",
" <td>0.0001</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>205</th>\n",
" <td>0.0321</td>\n",
" <td>0.0051</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0025</td>\n",
" <td>0.0001</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>206</th>\n",
" <td>0.0312</td>\n",
" <td>0.0049</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0032</td>\n",
" <td>0.0001</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>207</th>\n",
" <td>0.0296</td>\n",
" <td>0.0057</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>sqrt</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0005</td>\n",
" <td>0.0004</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>216</th>\n",
" <td>0.0304</td>\n",
" <td>0.0052</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0009</td>\n",
" <td>0.0002</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>217</th>\n",
" <td>0.0341</td>\n",
" <td>0.0076</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0024</td>\n",
" <td>0.0016</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>218</th>\n",
" <td>0.0376</td>\n",
" <td>0.0073</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0053</td>\n",
" <td>0.0020</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>219</th>\n",
" <td>0.0359</td>\n",
" <td>0.0072</td>\n",
" <td>0.9274</td>\n",
" <td>0.9279</td>\n",
" <td>False</td>\n",
" <td>entropy</td>\n",
" <td>2</td>\n",
" <td>log2</td>\n",
" <td>None</td>\n",
" <td>4</td>\n",
" <td>...</td>\n",
" <td>0.9261</td>\n",
" <td>0.9274</td>\n",
" <td>0.9255</td>\n",
" <td>0.9293</td>\n",
" <td>0.9306</td>\n",
" <td>0.9270</td>\n",
" <td>0.0031</td>\n",
" <td>0.0023</td>\n",
" <td>0.0023</td>\n",
" <td>0.0010</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>576 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
" mean_fit_time mean_score_time mean_test_score mean_train_score \\\n",
"434 0.0597 0.0118 0.9856 0.9999 \n",
"435 0.0559 0.0116 0.9856 0.9999 \n",
"446 0.2844 0.0121 0.9856 0.9999 \n",
"447 0.0592 0.0122 0.9856 0.9999 \n",
"458 0.0566 0.0122 0.9856 0.9999 \n",
"459 -0.1638 0.0132 0.9856 0.9999 \n",
"171 0.0723 0.0142 0.9844 1.0000 \n",
"146 0.0750 0.0129 0.9844 1.0000 \n",
"147 -0.1475 0.0128 0.9844 1.0000 \n",
"170 0.0731 0.0137 0.9844 1.0000 \n",
"159 0.0720 0.0130 0.9844 1.0000 \n",
"158 0.0718 0.0132 0.9844 1.0000 \n",
"315 0.0514 0.0120 0.9841 0.9999 \n",
"314 0.0510 0.0124 0.9841 0.9999 \n",
"303 0.0515 0.0154 0.9841 0.9999 \n",
"302 0.0504 0.0125 0.9841 0.9999 \n",
"291 0.0496 0.0136 0.9841 0.9999 \n",
"290 0.0500 0.0125 0.9841 0.9999 \n",
"471 0.1512 0.0117 0.9834 1.0000 \n",
"470 0.1576 0.0113 0.9834 1.0000 \n",
"288 0.0512 0.0122 0.9834 1.0000 \n",
"300 0.0514 0.0129 0.9834 1.0000 \n",
"301 0.0511 0.0132 0.9834 1.0000 \n",
"289 0.0512 0.0128 0.9834 1.0000 \n",
"313 0.0490 0.0123 0.9834 1.0000 \n",
"312 0.0526 0.0133 0.9834 1.0000 \n",
"2 0.0623 0.0152 0.9833 1.0000 \n",
"15 0.0647 0.0132 0.9833 1.0000 \n",
"14 0.0644 0.0127 0.9833 1.0000 \n",
"3 0.0649 0.0137 0.9833 1.0000 \n",
".. ... ... ... ... \n",
"480 0.0306 0.0049 0.9312 0.9315 \n",
"504 0.2524 0.0059 0.9312 0.9315 \n",
"507 0.0302 0.0052 0.9312 0.9315 \n",
"506 0.0324 0.0055 0.9312 0.9315 \n",
"505 0.0286 0.0049 0.9312 0.9315 \n",
"494 0.0310 0.0056 0.9312 0.9315 \n",
"61 0.0300 0.0051 0.9283 0.9300 \n",
"60 0.0296 0.0051 0.9283 0.9300 \n",
"62 0.0325 0.0053 0.9283 0.9300 \n",
"75 0.0300 0.0051 0.9283 0.9300 \n",
"74 0.0303 0.0055 0.9283 0.9300 \n",
"73 0.0297 0.0051 0.9283 0.9300 \n",
"72 0.0295 0.0049 0.9283 0.9300 \n",
"63 0.0302 0.0054 0.9283 0.9300 \n",
"50 0.0303 0.0050 0.9283 0.9300 \n",
"51 0.0301 0.0054 0.9283 0.9300 \n",
"49 0.0312 0.0052 0.9283 0.9300 \n",
"48 0.0314 0.0054 0.9283 0.9300 \n",
"192 0.0316 0.0057 0.9274 0.9279 \n",
"193 0.0308 0.0061 0.9274 0.9279 \n",
"194 0.0353 0.0062 0.9274 0.9279 \n",
"195 0.0353 0.0053 0.9274 0.9279 \n",
"204 0.0302 0.0050 0.9274 0.9279 \n",
"205 0.0321 0.0051 0.9274 0.9279 \n",
"206 0.0312 0.0049 0.9274 0.9279 \n",
"207 0.0296 0.0057 0.9274 0.9279 \n",
"216 0.0304 0.0052 0.9274 0.9279 \n",
"217 0.0341 0.0076 0.9274 0.9279 \n",
"218 0.0376 0.0073 0.9274 0.9279 \n",
"219 0.0359 0.0072 0.9274 0.9279 \n",
"\n",
" param_bootstrap param_criterion param_max_depth param_max_features \\\n",
"434 True entropy None auto \n",
"435 True entropy None auto \n",
"446 True entropy None sqrt \n",
"447 True entropy None sqrt \n",
"458 True entropy None log2 \n",
"459 True entropy None log2 \n",
"171 False entropy None log2 \n",
"146 False entropy None auto \n",
"147 False entropy None auto \n",
"170 False entropy None log2 \n",
"159 False entropy None sqrt \n",
"158 False entropy None sqrt \n",
"315 True gini None log2 \n",
"314 True gini None log2 \n",
"303 True gini None sqrt \n",
"302 True gini None sqrt \n",
"291 True gini None auto \n",
"290 True gini None auto \n",
"471 True entropy None None \n",
"470 True entropy None None \n",
"288 True gini None auto \n",
"300 True gini None sqrt \n",
"301 True gini None sqrt \n",
"289 True gini None auto \n",
"313 True gini None log2 \n",
"312 True gini None log2 \n",
"2 False gini None auto \n",
"15 False gini None sqrt \n",
"14 False gini None sqrt \n",
"3 False gini None auto \n",
".. ... ... ... ... \n",
"480 True entropy 2 auto \n",
"504 True entropy 2 log2 \n",
"507 True entropy 2 log2 \n",
"506 True entropy 2 log2 \n",
"505 True entropy 2 log2 \n",
"494 True entropy 2 sqrt \n",
"61 False gini 2 sqrt \n",
"60 False gini 2 sqrt \n",
"62 False gini 2 sqrt \n",
"75 False gini 2 log2 \n",
"74 False gini 2 log2 \n",
"73 False gini 2 log2 \n",
"72 False gini 2 log2 \n",
"63 False gini 2 sqrt \n",
"50 False gini 2 auto \n",
"51 False gini 2 auto \n",
"49 False gini 2 auto \n",
"48 False gini 2 auto \n",
"192 False entropy 2 auto \n",
"193 False entropy 2 auto \n",
"194 False entropy 2 auto \n",
"195 False entropy 2 auto \n",
"204 False entropy 2 sqrt \n",
"205 False entropy 2 sqrt \n",
"206 False entropy 2 sqrt \n",
"207 False entropy 2 sqrt \n",
"216 False entropy 2 log2 \n",
"217 False entropy 2 log2 \n",
"218 False entropy 2 log2 \n",
"219 False entropy 2 log2 \n",
"\n",
" param_max_leaf_nodes param_min_samples_split ... \\\n",
"434 None 4 ... \n",
"435 None 4 ... \n",
"446 None 4 ... \n",
"447 None 4 ... \n",
"458 None 4 ... \n",
"459 None 4 ... \n",
"171 None 4 ... \n",
"146 None 4 ... \n",
"147 None 4 ... \n",
"170 None 4 ... \n",
"159 None 4 ... \n",
"158 None 4 ... \n",
"315 None 4 ... \n",
"314 None 4 ... \n",
"303 None 4 ... \n",
"302 None 4 ... \n",
"291 None 4 ... \n",
"290 None 4 ... \n",
"471 None 4 ... \n",
"470 None 4 ... \n",
"288 None 2 ... \n",
"300 None 2 ... \n",
"301 None 2 ... \n",
"289 None 2 ... \n",
"313 None 2 ... \n",
"312 None 2 ... \n",
"2 None 4 ... \n",
"15 None 4 ... \n",
"14 None 4 ... \n",
"3 None 4 ... \n",
".. ... ... ... \n",
"480 None 2 ... \n",
"504 None 2 ... \n",
"507 None 4 ... \n",
"506 None 4 ... \n",
"505 None 2 ... \n",
"494 None 4 ... \n",
"61 None 2 ... \n",
"60 None 2 ... \n",
"62 None 4 ... \n",
"75 None 4 ... \n",
"74 None 4 ... \n",
"73 None 2 ... \n",
"72 None 2 ... \n",
"63 None 4 ... \n",
"50 None 4 ... \n",
"51 None 4 ... \n",
"49 None 2 ... \n",
"48 None 2 ... \n",
"192 None 2 ... \n",
"193 None 2 ... \n",
"194 None 4 ... \n",
"195 None 4 ... \n",
"204 None 2 ... \n",
"205 None 2 ... \n",
"206 None 4 ... \n",
"207 None 4 ... \n",
"216 None 2 ... \n",
"217 None 2 ... \n",
"218 None 4 ... \n",
"219 None 4 ... \n",
"\n",
" split0_test_score split0_train_score split1_test_score \\\n",
"434 0.9907 0.9999 0.9873 \n",
"435 0.9907 0.9999 0.9873 \n",
"446 0.9907 0.9999 0.9873 \n",
"447 0.9907 0.9999 0.9873 \n",
"458 0.9907 0.9999 0.9873 \n",
"459 0.9907 0.9999 0.9873 \n",
"171 0.9880 1.0000 0.9873 \n",
"146 0.9880 1.0000 0.9873 \n",
"147 0.9880 1.0000 0.9873 \n",
"170 0.9880 1.0000 0.9873 \n",
"159 0.9880 1.0000 0.9873 \n",
"158 0.9880 1.0000 0.9873 \n",
"315 0.9903 0.9999 0.9831 \n",
"314 0.9903 0.9999 0.9831 \n",
"303 0.9903 0.9999 0.9831 \n",
"302 0.9903 0.9999 0.9831 \n",
"291 0.9903 0.9999 0.9831 \n",
"290 0.9903 0.9999 0.9831 \n",
"471 0.9883 0.9999 0.9832 \n",
"470 0.9883 0.9999 0.9832 \n",
"288 0.9873 1.0000 0.9831 \n",
"300 0.9873 1.0000 0.9831 \n",
"301 0.9873 1.0000 0.9831 \n",
"289 0.9873 1.0000 0.9831 \n",
"313 0.9873 1.0000 0.9831 \n",
"312 0.9873 1.0000 0.9831 \n",
"2 0.9896 1.0000 0.9849 \n",
"15 0.9896 1.0000 0.9849 \n",
"14 0.9896 1.0000 0.9849 \n",
"3 0.9896 1.0000 0.9849 \n",
".. ... ... ... \n",
"480 0.9265 0.9264 0.9261 \n",
"504 0.9265 0.9264 0.9261 \n",
"507 0.9265 0.9264 0.9261 \n",
"506 0.9265 0.9264 0.9261 \n",
"505 0.9265 0.9264 0.9261 \n",
"494 0.9265 0.9264 0.9261 \n",
"61 0.9269 0.9267 0.9257 \n",
"60 0.9269 0.9267 0.9257 \n",
"62 0.9269 0.9267 0.9257 \n",
"75 0.9269 0.9267 0.9257 \n",
"74 0.9269 0.9267 0.9257 \n",
"73 0.9269 0.9267 0.9257 \n",
"72 0.9269 0.9267 0.9257 \n",
"63 0.9269 0.9267 0.9257 \n",
"50 0.9269 0.9267 0.9257 \n",
"51 0.9269 0.9267 0.9257 \n",
"49 0.9269 0.9267 0.9257 \n",
"48 0.9269 0.9267 0.9257 \n",
"192 0.9261 0.9274 0.9255 \n",
"193 0.9261 0.9274 0.9255 \n",
"194 0.9261 0.9274 0.9255 \n",
"195 0.9261 0.9274 0.9255 \n",
"204 0.9261 0.9274 0.9255 \n",
"205 0.9261 0.9274 0.9255 \n",
"206 0.9261 0.9274 0.9255 \n",
"207 0.9261 0.9274 0.9255 \n",
"216 0.9261 0.9274 0.9255 \n",
"217 0.9261 0.9274 0.9255 \n",
"218 0.9261 0.9274 0.9255 \n",
"219 0.9261 0.9274 0.9255 \n",
"\n",
" split1_train_score split2_test_score split2_train_score std_fit_time \\\n",
"434 1.0000 0.9789 0.9999 0.0017 \n",
"435 1.0000 0.9789 0.9999 0.0022 \n",
"446 1.0000 0.9789 0.9999 0.3176 \n",
"447 1.0000 0.9789 0.9999 0.0032 \n",
"458 1.0000 0.9789 0.9999 0.0025 \n",
"459 1.0000 0.9789 0.9999 0.3140 \n",
"171 1.0000 0.9779 1.0000 0.0012 \n",
"146 1.0000 0.9779 1.0000 0.0012 \n",
"147 1.0000 0.9779 1.0000 0.3128 \n",
"170 1.0000 0.9779 1.0000 0.0029 \n",
"159 1.0000 0.9779 1.0000 0.0025 \n",
"158 1.0000 0.9779 1.0000 0.0005 \n",
"315 0.9999 0.9789 0.9999 0.0015 \n",
"314 0.9999 0.9789 0.9999 0.0021 \n",
"303 0.9999 0.9789 0.9999 0.0016 \n",
"302 0.9999 0.9789 0.9999 0.0016 \n",
"291 0.9999 0.9789 0.9999 0.0007 \n",
"290 0.9999 0.9789 0.9999 0.0022 \n",
"471 1.0000 0.9787 1.0000 0.0116 \n",
"470 1.0000 0.9787 1.0000 0.0217 \n",
"288 1.0000 0.9797 1.0000 0.0003 \n",
"300 1.0000 0.9797 1.0000 0.0019 \n",
"301 1.0000 0.9797 1.0000 0.0012 \n",
"289 1.0000 0.9797 1.0000 0.0007 \n",
"313 1.0000 0.9797 1.0000 0.0009 \n",
"312 1.0000 0.9797 1.0000 0.0022 \n",
"2 1.0000 0.9754 1.0000 0.0009 \n",
"15 1.0000 0.9754 1.0000 0.0013 \n",
"14 1.0000 0.9754 1.0000 0.0015 \n",
"3 1.0000 0.9754 1.0000 0.0005 \n",
".. ... ... ... ... \n",
"480 0.9298 0.9409 0.9383 0.0025 \n",
"504 0.9298 0.9409 0.9383 0.3141 \n",
"507 0.9298 0.9409 0.9383 0.0013 \n",
"506 0.9298 0.9409 0.9383 0.0018 \n",
"505 0.9298 0.9409 0.9383 0.0008 \n",
"494 0.9298 0.9409 0.9383 0.0013 \n",
"61 0.9299 0.9324 0.9333 0.0010 \n",
"60 0.9299 0.9324 0.9333 0.0007 \n",
"62 0.9299 0.9324 0.9333 0.0045 \n",
"75 0.9299 0.9324 0.9333 0.0009 \n",
"74 0.9299 0.9324 0.9333 0.0007 \n",
"73 0.9299 0.9324 0.9333 0.0016 \n",
"72 0.9299 0.9324 0.9333 0.0009 \n",
"63 0.9299 0.9324 0.9333 0.0007 \n",
"50 0.9299 0.9324 0.9333 0.0011 \n",
"51 0.9299 0.9324 0.9333 0.0014 \n",
"49 0.9299 0.9324 0.9333 0.0005 \n",
"48 0.9299 0.9324 0.9333 0.0014 \n",
"192 0.9293 0.9306 0.9270 0.0019 \n",
"193 0.9293 0.9306 0.9270 0.0006 \n",
"194 0.9293 0.9306 0.9270 0.0058 \n",
"195 0.9293 0.9306 0.9270 0.0033 \n",
"204 0.9293 0.9306 0.9270 0.0008 \n",
"205 0.9293 0.9306 0.9270 0.0025 \n",
"206 0.9293 0.9306 0.9270 0.0032 \n",
"207 0.9293 0.9306 0.9270 0.0005 \n",
"216 0.9293 0.9306 0.9270 0.0009 \n",
"217 0.9293 0.9306 0.9270 0.0024 \n",
"218 0.9293 0.9306 0.9270 0.0053 \n",
"219 0.9293 0.9306 0.9270 0.0031 \n",
"\n",
" std_score_time std_test_score std_train_score \n",
"434 0.0002 0.0050 0.0000 \n",
"435 0.0007 0.0050 0.0000 \n",
"446 0.0006 0.0050 0.0000 \n",
"447 0.0003 0.0050 0.0000 \n",
"458 0.0008 0.0050 0.0000 \n",
"459 0.0003 0.0050 0.0000 \n",
"171 0.0006 0.0046 0.0000 \n",
"146 0.0004 0.0046 0.0000 \n",
"147 0.0004 0.0046 0.0000 \n",
"170 0.0006 0.0046 0.0000 \n",
"159 0.0002 0.0046 0.0000 \n",
"158 0.0007 0.0046 0.0000 \n",
"315 0.0004 0.0047 0.0000 \n",
"314 0.0006 0.0047 0.0000 \n",
"303 0.0045 0.0047 0.0000 \n",
"302 0.0005 0.0047 0.0000 \n",
"291 0.0019 0.0047 0.0000 \n",
"290 0.0005 0.0047 0.0000 \n",
"471 0.0006 0.0039 0.0000 \n",
"470 0.0007 0.0039 0.0000 \n",
"288 0.0005 0.0031 0.0000 \n",
"300 0.0009 0.0031 0.0000 \n",
"301 0.0024 0.0031 0.0000 \n",
"289 0.0007 0.0031 0.0000 \n",
"313 0.0007 0.0031 0.0000 \n",
"312 0.0013 0.0031 0.0000 \n",
"2 0.0037 0.0059 0.0000 \n",
"15 0.0003 0.0059 0.0000 \n",
"14 0.0004 0.0059 0.0000 \n",
"3 0.0005 0.0059 0.0000 \n",
".. ... ... ... \n",
"480 0.0001 0.0069 0.0050 \n",
"504 0.0002 0.0069 0.0050 \n",
"507 0.0004 0.0069 0.0050 \n",
"506 0.0004 0.0069 0.0050 \n",
"505 0.0000 0.0069 0.0050 \n",
"494 0.0001 0.0069 0.0050 \n",
"61 0.0005 0.0029 0.0027 \n",
"60 0.0003 0.0029 0.0027 \n",
"62 0.0004 0.0029 0.0027 \n",
"75 0.0004 0.0029 0.0027 \n",
"74 0.0001 0.0029 0.0027 \n",
"73 0.0002 0.0029 0.0027 \n",
"72 0.0002 0.0029 0.0027 \n",
"63 0.0005 0.0029 0.0027 \n",
"50 0.0003 0.0029 0.0027 \n",
"51 0.0001 0.0029 0.0027 \n",
"49 0.0002 0.0029 0.0027 \n",
"48 0.0005 0.0029 0.0027 \n",
"192 0.0004 0.0023 0.0010 \n",
"193 0.0005 0.0023 0.0010 \n",
"194 0.0009 0.0023 0.0010 \n",
"195 0.0000 0.0023 0.0010 \n",
"204 0.0001 0.0023 0.0010 \n",
"205 0.0001 0.0023 0.0010 \n",
"206 0.0001 0.0023 0.0010 \n",
"207 0.0004 0.0023 0.0010 \n",
"216 0.0002 0.0023 0.0010 \n",
"217 0.0016 0.0023 0.0010 \n",
"218 0.0020 0.0023 0.0010 \n",
"219 0.0023 0.0023 0.0010 \n",
"\n",
"[576 rows x 24 columns]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame.from_dict(\n",
" estimator_test_res['RandomForestClassifier']).sort_values('mean_test_score', ascending=False) "
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"_cell_guid": "1c9d996a-4686-d4e2-f2af-2660e16d3d23"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"LinearSVC(C=5, class_weight=None, dual=True, fit_intercept=True,\n",
" intercept_scaling=1, loss='squared_hinge', max_iter=200,\n",
" multi_class='ovr', penalty='l2', random_state=42, tol=0.0001,\n",
" verbose=0)\n",
"LogisticRegression(C=5, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=42, solver='lbfgs', tol=0.0001,\n",
" verbose=0, warm_start=True)\n",
"DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,\n",
" max_features=None, max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=2,\n",
" min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
" splitter='best')\n",
"SVC(C=2, cache_size=200, class_weight=None, coef0=0.0,\n",
" decision_function_shape='ovr', degree=2, gamma='auto', kernel='poly',\n",
" max_iter=-1, probability=False, random_state=42, shrinking=True,\n",
" tol=0.001, verbose=False)\n",
"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
" max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
" min_impurity_decrease=0.0, min_impurity_split=None,\n",
" min_samples_leaf=1, min_samples_split=4,\n",
" min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
" oob_score=False, random_state=42, verbose=0, warm_start=True)\n"
]
}
],
"source": [
"for clf_name in ['LinearSVC', \n",
" 'LogisticRegression', \n",
" 'DecisionTreeClassifier', \n",
" 'SVC', \n",
" 'RandomForestClassifier']:\n",
" print(best_estimator_dict[clf_name])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "abeda230-fa70-0dc7-3e2e-0e2f346875ea"
},
"source": [
"To further validate this setup a 5-fold cross-validation is performed."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"_cell_guid": "8a600d7d-de90-ef48-e91d-30d4f45f4b43"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training LinearSVC\n",
"Average CV performance for LinearSVC: 0.837957 (in 1.29159 seconds)\n",
"Training LogisticRegression\n",
"Average CV performance for LogisticRegression: 0.855185 (in 8.02566 seconds)\n",
"Training DecisionTreeClassifier\n",
"Average CV performance for DecisionTreeClassifier: 0.973895 (in 0.203531 seconds)\n",
"Training SVC\n",
"Average CV performance for SVC: 0.963796 (in 1.90766 seconds)\n",
"Training RandomForestClassifier\n",
"Average CV performance for RandomForestClassifier: 0.986036 (in 1.07352 seconds)\n"
]
}
],
"source": [
"for clf_name in ['LinearSVC', \n",
" 'LogisticRegression', \n",
" 'DecisionTreeClassifier', \n",
" 'SVC', \n",
" 'RandomForestClassifier']:\n",
" print(\"Training\", clf_name)\n",
" start = time()\n",
" scores = cross_val_score(\n",
" best_estimator_dict[clf_name], X_train_gs, y_train_gs, cv=5, scoring='roc_auc') \n",
" end = time()\n",
" duration = end - start\n",
" print(\"Average CV performance for {}: {:.6} (in {:.6} seconds)\".format(\n",
" clf_name, scores.mean(), duration))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "40a285ef-32b9-9084-0b38-477e5c74ef85"
},
"source": [
"The average AUC score using the tuned Random Forest model is .9872 which is almost as high as the initial cross-validation score. Even though this is a slightly lower score, since it’s a substantially smaller data set these parameters will be used further down the road.\n",
"\n",
"### CV-Training of best estimators"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"_cell_guid": "e1ca71cf-ea58-398a-0881-ba56002382a9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross-Validation of LinearSVC ...\n",
"Average CV performance for LinearSVC: 0.836849 (in 1.32483 seconds)\n",
"Cross-Validation of LogisticRegression ...\n",
"Average CV performance for LogisticRegression: 0.855878 (in 9.57373 seconds)\n",
"Cross-Validation of DecisionTreeClassifier ...\n",
"Average CV performance for DecisionTreeClassifier: 0.976802 (in 0.22499 seconds)\n",
"Cross-Validation of SVC ...\n",
"Average CV performance for SVC: 0.965444 (in 4.53322 seconds)\n",
"Cross-Validation of RandomForestClassifier ...\n",
"Average CV performance for RandomForestClassifier: 0.990029 (in 0.486598 seconds)\n"
]
}
],
"source": [
"clf_dict_tuned = {}\n",
"clf_report_tuned = []\n",
"clf_feature_relevance_tuned = []\n",
"\n",
"for clf_tuned in ['LinearSVC', \n",
" 'LogisticRegression', \n",
" 'DecisionTreeClassifier', \n",
" 'SVC', \n",
" 'RandomForestClassifier']:\n",
"\n",
" # Extract name of estimator\n",
" clf_name = clf_tuned\n",
" clf_tuned = best_estimator_dict[clf_tuned]\n",
" print(\"Cross-Validation of\", clf_name, \"...\")\n",
"\n",
" # Perform cross validation\n",
" start = time()\n",
" scores = cross_val_score(\n",
" clf_tuned, X_train_clus, y_train_clus, cv=5, scoring='roc_auc') \n",
" end = time()\n",
" duration = end - start\n",
" print(\"Average CV performance for {}: {:.6} (in {:.6} seconds)\".format(\n",
" clf_name, scores.mean(), duration))\n",
" clf_report_tuned.append([clf_name, scores.mean(), duration])\n",
"\n",
" # Store feature relevance information \n",
" if clf_name in [\"RandomForestClassifier\", \"DecisionTreeClassifier\"]:\n",
" clf_feature_relevance_tuned.append(clf_tuned.feature_importances_.tolist())\n",
" elif clf_name == \"LinearSVC\":\n",
" clf_feature_relevance_tuned.append(clf_tuned.coef_[0].tolist())\n",
"# Store information in list for better visibility\n",
"\n",
"clf_report_tuned = pd.DataFrame(clf_report_tuned, columns=['classifier', 'mean_score', 'time'])"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"_cell_guid": "cc6b86c5-64b7-51b1-4c75-b4f81f875f5e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_monthly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>work_accident</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>department_it</th>\n",
" <th>department_randd</th>\n",
" <th>department_accounting</th>\n",
" <th>...</th>\n",
" <th>department_management</th>\n",
" <th>department_marketing</th>\n",
" <th>department_product_mng</th>\n",
" <th>department_sales</th>\n",
" <th>department_support</th>\n",
" <th>department_technical</th>\n",
" <th>salary_high</th>\n",
" <th>salary_low</th>\n",
" <th>salary_medium</th>\n",
" <th>employee_cluster</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LinearSVC</th>\n",
" <td>-0.6249</td>\n",
" <td>0.3292</td>\n",
" <td>-0.1648</td>\n",
" <td>0.2521</td>\n",
" <td>2.0434</td>\n",
" <td>-0.3659</td>\n",
" <td>-0.2915</td>\n",
" <td>-0.1497</td>\n",
" <td>-0.2172</td>\n",
" <td>-0.1107</td>\n",
" <td>...</td>\n",
" <td>-0.2715</td>\n",
" <td>-0.0644</td>\n",
" <td>-0.1125</td>\n",
" <td>-0.0777</td>\n",
" <td>-0.0754</td>\n",
" <td>-0.0464</td>\n",
" <td>-0.6582</td>\n",
" <td>-0.1439</td>\n",
" <td>-0.3065</td>\n",
" <td>0.7679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeClassifier</th>\n",
" <td>0.0812</td>\n",
" <td>0.0856</td>\n",
" <td>0.0035</td>\n",
" <td>0.0496</td>\n",
" <td>0.1752</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>...</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0000</td>\n",
" <td>0.0022</td>\n",
" <td>0.0000</td>\n",
" <td>0.6026</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RandomForestClassifier</th>\n",
" <td>0.1342</td>\n",
" <td>0.0799</td>\n",
" <td>0.0622</td>\n",
" <td>0.1024</td>\n",
" <td>0.1356</td>\n",
" <td>0.0076</td>\n",
" <td>0.0016</td>\n",
" <td>0.0020</td>\n",
" <td>0.0017</td>\n",
" <td>0.0012</td>\n",
" <td>...</td>\n",
" <td>0.0019</td>\n",
" <td>0.0014</td>\n",
" <td>0.0012</td>\n",
" <td>0.0033</td>\n",
" <td>0.0031</td>\n",
" <td>0.0041</td>\n",
" <td>0.0052</td>\n",
" <td>0.0064</td>\n",
" <td>0.0035</td>\n",
" <td>0.4396</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 21 columns</p>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project \\\n",
"LinearSVC -0.6249 0.3292 -0.1648 \n",
"DecisionTreeClassifier 0.0812 0.0856 0.0035 \n",
"RandomForestClassifier 0.1342 0.0799 0.0622 \n",
"\n",
" average_monthly_hours time_spend_company \\\n",
"LinearSVC 0.2521 2.0434 \n",
"DecisionTreeClassifier 0.0496 0.1752 \n",
"RandomForestClassifier 0.1024 0.1356 \n",
"\n",
" work_accident promotion_last_5years department_it \\\n",
"LinearSVC -0.3659 -0.2915 -0.1497 \n",
"DecisionTreeClassifier 0.0000 0.0000 0.0000 \n",
"RandomForestClassifier 0.0076 0.0016 0.0020 \n",
"\n",
" department_randd department_accounting \\\n",
"LinearSVC -0.2172 -0.1107 \n",
"DecisionTreeClassifier 0.0000 0.0000 \n",
"RandomForestClassifier 0.0017 0.0012 \n",
"\n",
" ... department_management \\\n",
"LinearSVC ... -0.2715 \n",
"DecisionTreeClassifier ... 0.0000 \n",
"RandomForestClassifier ... 0.0019 \n",
"\n",
" department_marketing department_product_mng \\\n",
"LinearSVC -0.0644 -0.1125 \n",
"DecisionTreeClassifier 0.0000 0.0000 \n",
"RandomForestClassifier 0.0014 0.0012 \n",
"\n",
" department_sales department_support \\\n",
"LinearSVC -0.0777 -0.0754 \n",
"DecisionTreeClassifier 0.0000 0.0000 \n",
"RandomForestClassifier 0.0033 0.0031 \n",
"\n",
" department_technical salary_high salary_low \\\n",
"LinearSVC -0.0464 -0.6582 -0.1439 \n",
"DecisionTreeClassifier 0.0000 0.0000 0.0022 \n",
"RandomForestClassifier 0.0041 0.0052 0.0064 \n",
"\n",
" salary_medium employee_cluster \n",
"LinearSVC -0.3065 0.7679 \n",
"DecisionTreeClassifier 0.0000 0.6026 \n",
"RandomForestClassifier 0.0035 0.4396 \n",
"\n",
"[3 rows x 21 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(clf_feature_relevance_tuned, columns=X_train_clus.columns, index=['LinearSVC', \n",
" 'DecisionTreeClassifier', \n",
" 'RandomForestClassifier'])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"_cell_guid": "d1bc8802-ead7-0c83-f230-6d17f3b1871a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>classifier</th>\n",
" <th>mean_score</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RandomForestClassifier</td>\n",
" <td>0.9900</td>\n",
" <td>0.4866</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>DecisionTreeClassifier</td>\n",
" <td>0.9768</td>\n",
" <td>0.2250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SVC</td>\n",
" <td>0.9654</td>\n",
" <td>4.5332</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LogisticRegression</td>\n",
" <td>0.8559</td>\n",
" <td>9.5737</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LinearSVC</td>\n",
" <td>0.8368</td>\n",
" <td>1.3248</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" classifier mean_score time\n",
"4 RandomForestClassifier 0.9900 0.4866\n",
"2 DecisionTreeClassifier 0.9768 0.2250\n",
"3 SVC 0.9654 4.5332\n",
"1 LogisticRegression 0.8559 9.5737\n",
"0 LinearSVC 0.8368 1.3248"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf_report_tuned = clf_report_tuned.ix[:,0:3]\n",
"clf_report_tuned.sort_values('mean_score', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"_cell_guid": "13ac8639-f6a8-a05a-66a1-35d973ef51d1"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>classifier</th>\n",
" <th>mean_score</th>\n",
" <th>time</th>\n",
" <th>mean_score non-cluster</th>\n",
" <th>time non-cluster</th>\n",
" <th>score_change</th>\n",
" <th>time_change</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>RandomForestClassifier</td>\n",
" <td>0.9900</td>\n",
" <td>0.4866</td>\n",
" <td>0.9893</td>\n",
" <td>0.4344</td>\n",
" <td>0.0008</td>\n",
" <td>0.0522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>DecisionTreeClassifier</td>\n",
" <td>0.9768</td>\n",
" <td>0.2250</td>\n",
" <td>0.9747</td>\n",
" <td>0.2386</td>\n",
" <td>0.0021</td>\n",
" <td>-0.0136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SVC</td>\n",
" <td>0.9654</td>\n",
" <td>4.5332</td>\n",
" <td>0.9620</td>\n",
" <td>8.5697</td>\n",
" <td>0.0034</td>\n",
" <td>-4.0365</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>LogisticRegression</td>\n",
" <td>0.8559</td>\n",
" <td>9.5737</td>\n",
" <td>0.8529</td>\n",
" <td>0.7968</td>\n",
" <td>0.0030</td>\n",
" <td>8.7769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>LinearSVC</td>\n",
" <td>0.8368</td>\n",
" <td>1.3248</td>\n",
" <td>0.8362</td>\n",
" <td>1.4418</td>\n",
" <td>0.0007</td>\n",
" <td>-0.1170</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" classifier mean_score time mean_score non-cluster \\\n",
"4 RandomForestClassifier 0.9900 0.4866 0.9893 \n",
"2 DecisionTreeClassifier 0.9768 0.2250 0.9747 \n",
"3 SVC 0.9654 4.5332 0.9620 \n",
"1 LogisticRegression 0.8559 9.5737 0.8529 \n",
"0 LinearSVC 0.8368 1.3248 0.8362 \n",
"\n",
" time non-cluster score_change time_change \n",
"4 0.4344 0.0008 0.0522 \n",
"2 0.2386 0.0021 -0.0136 \n",
"3 8.5697 0.0034 -4.0365 \n",
"1 0.7968 0.0030 8.7769 \n",
"0 1.4418 0.0007 -0.1170 "
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf_report_tuned['mean_score non-cluster'] = clf_report_clus['mean_score']\n",
"clf_report_tuned['time non-cluster'] = clf_report_clus['time']\n",
"clf_report_tuned['score_change'] = (\n",
" clf_report_tuned['mean_score'] - clf_report_tuned['mean_score non-cluster'])\n",
"clf_report_tuned['time_change'] = clf_report_tuned['time'] - clf_report_tuned['time non-cluster']\n",
"clf_report_tuned.sort_values(by=['mean_score', 'score_change', 'time_change'], ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "00a385af-6bf9-de66-7922-2c465eaeeec3"
},
"source": [
"# Results\n",
"In this section the results of the research are being discussed as well as additional thoughts on robustness and stability of the model.\n",
"\n",
"## Evaluation\n",
"The final leading algorithm is a RandomForestClassifier which has a good performance right from the base. The data set was exceptionally clean and there were only minor things to be adjusted. Overall research has shown that the complexity of employee turnover prediction is a multi-variate and highly complex task. Research suggests severe problems when it comes to generalization in this domain. Therefore this research has focused on cross-validation wherever possible. The final model seems reasonable and is aligning with solution expectations mentioned earlier. The final parameters of the model seem to leave enough room for generalization and a high performance on new data. Although the final model has been tested with various inputs to evaluate whether the model generalizes well to unseen data there is still a testing set available that will be used in this section to draw further conclusions about the model. But before heading to this final step there is the question of model stability and robustness that needs to be addressed.\n",
"\n",
"## Model Stability\n",
"To test the model for its stability beyond cross-validation a subset of the testing data is being created that contains only a small shuffled sample including 40% of the training data and additional 20% for testing purposes. If robust, the model is supposed to have a similar predictive outcome for training and testing on the stability set."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"_cell_guid": "7db746ab-ead1-a800-c1c0-ed5e9e9412fa"
},
"outputs": [],
"source": [
"new_index = np.random.permutation(X_train_base.index)\n",
"X_train_stab = X_train_clus.reindex(new_index)\n",
"y_train_stab = y_train_clus.reindex(new_index)\n",
"\n",
"X_train_stab, X_test_stab, y_train_stab, y_test_stab = train_test_split(\n",
" X_train_stab, y_train_stab, train_size = .4, \n",
" test_size = .2, \n",
" random_state = random_state, \n",
" stratify=y_train_stab)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"_cell_guid": "3119b027-4beb-55c9-f006-44a1bdddb08b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training RandomForestClassifier ...\n",
"Average CV performance for RandomForestClassifier: 0.981849 (in 0.238765 seconds)\n"
]
}
],
"source": [
"print(\"Training RandomForestClassifier ...\")\n",
"start = time()\n",
"scores = cross_val_score(\n",
" best_estimator_dict['RandomForestClassifier'], X_train_stab, y_train_stab, cv=5, scoring='roc_auc') \n",
"end = time()\n",
"duration = end - start\n",
"print(\"Average CV performance for {}: {:.6} (in {:.6} seconds)\".format(\n",
" 'RandomForestClassifier', scores.mean(), duration))"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"_cell_guid": "8e42d508-5826-60e3-4b4d-8eba31db4de6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Made predictions in 0.0121 seconds.\n",
"Tuned RandomForestClassifier model has a testing AUC score of 0.9869.\n"
]
}
],
"source": [
"print(\"Tuned {} model has a testing AUC score of {:.4f}.\".format(\n",
" 'RandomForestClassifier', predict_labels(\n",
" best_estimator_dict['RandomForestClassifier'], X_test_stab, y_test_stab)[0]))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "924b0913-8f3d-0fc9-faf7-63518283a681"
},
"source": [
"On a smaller subsample of 40 % the prediction algorithm still works very well. Its score on the testing score even increases up to .9953 indicating a stable model which was to be expected by the usage of cross-validation down the road. "
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d02ea6ec-f6ad-83be-3c53-84469d0e43e5"
},
"source": [
"## Testing\n",
"As a final step, all models will be used on the test set. This will be the final determinator on which algorithm and model setup performs the best. The following setups will be tested for LinearSVC, LogisticRegression, SVC, DecisionTreeClassifier and RandomForestClassifier:\n",
"\n",
"* Base model, with little to no tuning to the parameters.\n",
"* Clustering-enhanced data, with little to no tuning to the parameters.\n",
"* Clustering-enhanced data, with tuned parameters. \n",
"\n",
"The scoring method will be AUC in order to be able to compare the scoring to mentioned research by <cite data-cite=\"4046282/DZ7FB7XU\"></cite>. Using AUC as a metric the highest scoring model is a tuned RandomForestClassifier trained on a cluster-enhanced data set and achieves a score of .9805 on the hold-out testing set."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"_cell_guid": "8fcbb6ba-f520-9d30-7fc7-23528dbcd11e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Made predictions in 0.0135 seconds.\n",
"Tuned LinearSVC model has a final testing AUC score of 0.8115.\n",
"Made predictions in 0.0465 seconds.\n",
"Made predictions in 0.0084 seconds.\n",
"Made predictions in 0.0145 seconds.\n",
"[0.5992375953005874, 0.80983627046619167, 0.81149966548299113]\n",
"Made predictions in 0.0004 seconds.\n",
"Tuned LogisticRegression model has a final testing AUC score of 0.8060.\n",
"Made predictions in 0.0012 seconds.\n",
"Made predictions in 0.0006 seconds.\n",
"Made predictions in 0.0747 seconds.\n",
"[0.64090518097002591, 0.80690465162442926, 0.80602976098575918]\n",
"Made predictions in 0.0031 seconds.\n",
"Tuned DecisionTreeClassifier model has a final testing AUC score of 0.9546.\n",
"Made predictions in 0.0032 seconds.\n",
"Made predictions in 0.0701 seconds.\n",
"Made predictions in 0.0008 seconds.\n",
"[0.9702445282574973, 0.97177558687517007, 0.95461596712175678]\n",
"Made predictions in 0.1538 seconds.\n",
"Tuned SVC model has a final testing AUC score of 0.8680.\n",
"Made predictions in 0.8352 seconds.\n",
"Made predictions in 0.3807 seconds.\n",
"Made predictions in 0.1317 seconds.\n",
"[0.73906202901107954, 0.91610239896483525, 0.86799995588786694]\n",
"Made predictions in 0.0117 seconds.\n",
"Tuned RandomForestClassifier model has a final testing AUC score of 0.9719.\n",
"Made predictions in 0.0117 seconds.\n",
"Made predictions in 0.0119 seconds.\n",
"Made predictions in 0.0108 seconds.\n",
"[0.97763514854760813, 0.97907982090473988, 0.97185829712462424]\n"
]
}
],
"source": [
"final_results = {}\n",
"\n",
"for clf_name in ['LinearSVC', 'LogisticRegression', 'DecisionTreeClassifier', 'SVC', 'RandomForestClassifier']:\n",
" print(\"Tuned {} model has a final testing AUC score of {:.4f}.\".format(\n",
" clf_name, predict_labels(best_estimator_dict[clf_name], X_test_clus, y_test_clus)[0]))\n",
"\n",
" temp = []\n",
" temp.append(\n",
" predict_labels(clf_dict_base[clf_name], X_test_base, y_test_base)[0])\n",
" temp.append(\n",
" predict_labels(clf_dict_clus[clf_name], X_test_clus, y_test_clus)[0])\n",
" temp.append(\n",
" predict_labels(best_estimator_dict[clf_name], X_test_clus, y_test_clus)[0])\n",
" \n",
" print(temp)\n",
" final_results[clf_name] = temp"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"_cell_guid": "17dca2fd-84ac-795c-166a-0a7a47488eb5"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>base_test_score</th>\n",
" <th>cluster_test_score</th>\n",
" <th>tuned_test_score</th>\n",
" <th>max_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>RandomForestClassifier</th>\n",
" <td>0.9776</td>\n",
" <td>0.9791</td>\n",
" <td>0.9719</td>\n",
" <td>0.9791</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeClassifier</th>\n",
" <td>0.9702</td>\n",
" <td>0.9718</td>\n",
" <td>0.9546</td>\n",
" <td>0.9718</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVC</th>\n",
" <td>0.7391</td>\n",
" <td>0.9161</td>\n",
" <td>0.8680</td>\n",
" <td>0.9161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LinearSVC</th>\n",
" <td>0.5992</td>\n",
" <td>0.8098</td>\n",
" <td>0.8115</td>\n",
" <td>0.8115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LogisticRegression</th>\n",
" <td>0.6409</td>\n",
" <td>0.8069</td>\n",
" <td>0.8060</td>\n",
" <td>0.8069</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" base_test_score cluster_test_score tuned_test_score \\\n",
"RandomForestClassifier 0.9776 0.9791 0.9719 \n",
"DecisionTreeClassifier 0.9702 0.9718 0.9546 \n",
"SVC 0.7391 0.9161 0.8680 \n",
"LinearSVC 0.5992 0.8098 0.8115 \n",
"LogisticRegression 0.6409 0.8069 0.8060 \n",
"\n",
" max_score \n",
"RandomForestClassifier 0.9791 \n",
"DecisionTreeClassifier 0.9718 \n",
"SVC 0.9161 \n",
"LinearSVC 0.8115 \n",
"LogisticRegression 0.8069 "
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_score = pd.DataFrame.from_dict(final_results, orient='index')\n",
"final_score.columns = ['base_test_score', 'cluster_test_score', 'tuned_test_score']\n",
"final_score['max_score'] = final_score[['base_test_score', 'cluster_test_score', 'tuned_test_score']].max(axis=1)\n",
"final_score.sort_values('max_score', ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"_cell_guid": "efe8195b-2cd3-af4c-9085-44d92bd297da"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The best performing score is 0.9791, which is 0.1191 better than the benchmark.\n"
]
}
],
"source": [
"print(\"The best performing score is {:.4f}, which is {:.4f} better than the benchmark.\".format(\n",
" final_score['max_score'].max(), final_score['max_score'].max() - .86))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "bfa27de7-3208-e9bc-f478-c9da70403e0e"
},
"source": [
"### Justification\n",
"This research achieves even higher results without the need for more computational expensive algorithms such as Extreme Gradient Boosting. The benchmark research achieved a .86 AUC score on a hold-out data as a best result. In this research we presented a method to achieve good prediction results with the combination of unsupervised and supervised learning algorithms. The resulting method achieves a .9805 AUC score and is over .12 higher than the research result presented in our benchmark. The reason for this might be hard to determine without access to the original data used in our benchmark. Based on the data set of this research one possibility could be the clean variables that include only little noise. While the referred research had to combine information from multiple sources and work around a quite noisy data set this research had the benefit of working with exceptionally little data infused obstacles. Therefore the higher results must also be linked to the data set. As almost always in machine learning, better data beats better algorithms. Based on the problem statement and the task that was at hand this research can be seen as a successful solution though.\n",
"\n",
"# Conclusion\n",
"This section will first focus on important qualities of this research and interesting findings, before starting an in-depth reflection about the process and its key elements.\n",
"\n",
"## Feature Importance and pro-active behavior\n",
"As discussed earlier, for most companies it is as important to understand why an employee is about to leave or leaving as it is to predict. In order to act in advance and possibly change the circumstances in a more favorable way for top performers and valuable assets the variables that are most important to employee turnover should be examined. This research offers a way of looking into the importance of certain aspects of work culture to predict employee turnover."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"_cell_guid": "5a50901d-20c4-7df1-5986-74f645f1261c"
},
"outputs": [],
"source": [
"#show(p_clus)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a79a6071-bbab-a420-9b6f-bcb52ae8b631"
},
"source": [
"![Kaggle_Bokeh_Chart_6][1]\n",
"\n",
"\n",
" [1]: https://preview.ibb.co/mv97Lk/kaggle_6.png"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6b94a27c-72ea-51d8-8d84-ece2dbae6d72"
},
"source": [
"Based on the model presented by this research following categories should be examined strongly per individual to make sure to act before it is too late:\n",
"\n",
"* Front-runner is **time_spend_company** suggesting employee turnover to be heavily influenced by the time an employee was with a company. The importance supports the finding that work relationships nowadays tend to have a shorter life cycle. Looking at the initial remarks about sinking tenure in the work force, this seems to be just fair. A way of anticipating this fact is opening up pathways that lead to a diverse work environment filled with new tasks and possibilities for professional growth. If the company is big enough, encouraging employees to work on their personal skill set or even promoting different possibilities within the companies might be a good way to achieve a higher retention rate.\n",
"* Next up is **satisfaction_level**. There are many ways to measure this in a work space. For any company to capture this rather abstract value in a correct way it has to be in touch with its employees. There is only so much information you can gain by looking at business numbers. Measuring this variable seems to be crucial for employee turnover. A finding that is supported by management and psychology studies mentioned earlier as well.\n",
"* Third is **employee_cluster**. As mentioned earlier, employee cluster can be very helpful when it comes to categorizing top performers but also employees at risk. With additional continuous variables this could help put employees on the map in even very complex work domains.\n",
"* **average_monthly_hours** can be an indicator for both, overworked employees or employees that are not being challenged sufficiently.\n",
"* Rating employees and using their **last_evaluation** score might be a good indicator but even better would be time series information on how their performance has changed over time. In addition there are multiple layers when it comes to evaluations on the job. Having a multifaceted evaluation score sheet that’s focusing on performance but also on social skills could be a great indicator for employee turnover.\n",
"* Lastly work responsibility can be measured by **number_of_projects**. It seems as if empowering people helps keep them on board.\n",
"\n",
"Given that work performance is an ever-changing subject, time series analysis on all mentioned features could offer additional insights.\n",
"\n",
"### Scoring with increasing training data\n",
"One important characteristic for robust models is the be- havior of their prediction accuracy when using it on different amounts of training data. For this exercise a range between 10% and 80% of initial training data will be used and the average scoring on a 5-fold cross-validation is being measured for all algorithms used in this research."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"_cell_guid": "29b9bc42-919f-0945-85d5-8e84c6a693bf"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Calculating cv scores for LinearSVC ...\n",
"Calculating cv scores for LogisticRegression ...\n",
"Calculating cv scores for DecisionTreeClassifier ...\n",
"Calculating cv scores for SVC ...\n",
"Calculating cv scores for RandomForestClassifier ...\n"
]
}
],
"source": [
"perc_scores = {}\n",
"for clf_name in ['LinearSVC', \n",
" 'LogisticRegression', \n",
" 'DecisionTreeClassifier', \n",
" 'SVC', \n",
" 'RandomForestClassifier']:\n",
" print('Calculating cv scores for', clf_name, '...')\n",
" clf_scores = list()\n",
" percentage_list = [.1, .2, .4, .6, .8]\n",
" for percentage in percentage_list:\n",
" train_size = percentage\n",
" random_state = 42\n",
"\n",
" X_perc = hr_data.drop('left', 1)\n",
" y_perc = hr_data['left']\n",
"\n",
" X_train_perc, X_test_perc, y_train_perc, y_test_perc = train_test_split(\n",
" X_perc, y_perc, train_size = train_size, random_state = random_state, stratify=y_perc)\n",
"\n",
" perc_score_temp = cross_val_score(\n",
" best_estimator_dict[clf_name], X_train_perc, y_train_perc, cv=5, scoring='roc_auc')\n",
" perc_score_temp = perc_score_temp.mean()\n",
" clf_scores.append(perc_score_temp)\n",
" perc_scores[clf_name] = clf_scores"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"_cell_guid": "14175892-5ece-7853-7872-7c4141b8fa6f"
},
"outputs": [
{
"data": {
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wqCcQ4l6BigYTBLc3cJ0ag+8NdG8yLyA5NyBd8gL2ZlREKIqidFBqkwXrGv9YtIJozX6C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\n4AGKR+QngobJYwoYOCDQswU2xhhjklgQ0Y321EVYtbEycXtiffluYu7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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa8a3d2bba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"perc_plot = pd.DataFrame.from_dict(perc_scores, orient='index')\n",
"percentage_list = [.1, .2, .4, .6, .8]\n",
"perc_plot.columns = [str(x) + '%' for x in percentage_list]\n",
"perc_plot = perc_plot.transpose()\n",
"perc_plot_ax = perc_plot.plot(\n",
" title = 'ROC AUC Score vs. Training Data Percentage', \n",
" figsize = (8, 5), \n",
" fontsize = 13)\n",
"perc_plot_ax.set_xlabel(\"Percentage of used data\")\n",
"perc_plot_ax.set_ylabel(\"ROC AUC Score\")\n",
"_ = perc_plot_ax"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d78f2ea5-98ec-6f86-7ec6-3c110e5e7316"
},
"source": [
"An interesting observation can be made when looking at cross-validation scores based on the amount of training data available. Even with as little as 10%, tree- based algorithms work very well on the data. Which speaks to their robustness for this specific task. The biggest score gain can be observed for SVC, which pops from around .92 to a score just shy of .97. Both Logistic Regression as well as LinearSVC are on lower scoring levels, improve only slightly and perform almost as bad on 10% data as soon as they reach 80%. This could be linked to overfitting due to parameter tuning."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "45c8bfde-7b42-d3a5-fff8-5d0a5254b872"
},
"source": [
"### Precision vs Recall\n",
"As mentioned earlier, one problem of AUC can be low recall scores. Some classifiers can have a low recall but a very high AUC score. In order to protect the established model against low recall, F1 scoring is implemented. For F1 scores to be high, both precision and recall have to be high."
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"_cell_guid": "16d006f0-386f-dc70-d4f8-a656130e4bab"
},
"outputs": [],
"source": [
"def predict_labels_f1(clf, features, target):\n",
" from sklearn import metrics\n",
" ''' Makes predictions using a fit classifier based on AUC score. '''\n",
" \n",
" # Start the clock, make predictions, then stop the clock\n",
" start = time()\n",
" y_pred = clf.predict(features)\n",
" end = time()\n",
" \n",
" # Print and return results\n",
" prediction_duration = end - start\n",
" prediction_f1_score = metrics.f1_score(target.values, y_pred)\n",
" print(\"Made predictions in {:.4f} seconds.\".format(prediction_duration))\n",
" return prediction_f1_score, prediction_duration"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"_cell_guid": "18787ee2-e593-b780-57db-5916941df5ab"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Made predictions in 0.0095 seconds.\n",
"Tuned LinearSVC model has a final testing AUC score of 0.7506.\n",
"Made predictions in 0.0010 seconds.\n",
"Made predictions in 0.0009 seconds.\n",
"Made predictions in 0.0076 seconds.\n",
"[0.35755258126195028, 0.74877650897226766, 0.75060926076360679]\n",
"Made predictions in 0.0013 seconds.\n",
"Tuned LogisticRegression model has a final testing AUC score of 0.7401.\n",
"Made predictions in 0.0126 seconds.\n",
"Made predictions in 0.0749 seconds.\n",
"Made predictions in 0.0009 seconds.\n",
"[0.44623655913978494, 0.74248578391551567, 0.74008097165991893]\n",
"Made predictions in 0.0029 seconds.\n",
"Tuned DecisionTreeClassifier model has a final testing AUC score of 0.9413.\n",
"Made predictions in 0.0019 seconds.\n",
"Made predictions in 0.0779 seconds.\n",
"Made predictions in 0.0013 seconds.\n",
"[0.94448252227553109, 0.9490358126721764, 0.94126074498567336]\n",
"Made predictions in 0.1818 seconds.\n",
"Tuned SVC model has a final testing AUC score of 0.8387.\n",
"Made predictions in 0.7779 seconds.\n",
"Made predictions in 0.3275 seconds.\n",
"Made predictions in 0.1339 seconds.\n",
"[0.64000000000000001, 0.89133627019089579, 0.83870967741935487]\n",
"Made predictions in 0.0120 seconds.\n",
"Tuned RandomForestClassifier model has a final testing AUC score of 0.9678.\n",
"Made predictions in 0.0122 seconds.\n",
"Made predictions in 0.0118 seconds.\n",
"Made predictions in 0.0112 seconds.\n",
"[0.97571428571428565, 0.9758179231863443, 0.96778811739441661]\n"
]
}
],
"source": [
"final_results_f1 = {}\n",
"\n",
"for clf_name in ['LinearSVC', \n",
" 'LogisticRegression', \n",
" 'DecisionTreeClassifier', \n",
" 'SVC', \n",
" 'RandomForestClassifier']:\n",
" print(\"Tuned {} model has a final testing AUC score of {:.4f}.\".format(\n",
" clf_name, predict_labels_f1(best_estimator_dict[clf_name], X_test_clus, y_test_clus)[0]))\n",
"\n",
" temp = []\n",
" temp.append(\n",
" predict_labels_f1(clf_dict_base[clf_name], X_test_base, y_test_base)[0])\n",
" temp.append(\n",
" predict_labels_f1(clf_dict_clus[clf_name], X_test_clus, y_test_clus)[0])\n",
" temp.append(\n",
" predict_labels_f1(best_estimator_dict[clf_name], X_test_clus, y_test_clus)[0])\n",
" \n",
" print(temp)\n",
" final_results_f1[clf_name] = temp"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"_cell_guid": "50aeb2fd-3ba7-7e6f-5854-9d1fa7a81e18"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>base_test_score</th>\n",
" <th>cluster_test_score</th>\n",
" <th>tuned_test_score</th>\n",
" <th>max_f1_score</th>\n",
" <th>max_auc_score</th>\n",
" <th>delta</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>RandomForestClassifier</th>\n",
" <td>0.9757</td>\n",
" <td>0.9758</td>\n",
" <td>0.9678</td>\n",
" <td>0.9758</td>\n",
" <td>0.9791</td>\n",
" <td>-0.0033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeClassifier</th>\n",
" <td>0.9445</td>\n",
" <td>0.9490</td>\n",
" <td>0.9413</td>\n",
" <td>0.9490</td>\n",
" <td>0.9718</td>\n",
" <td>-0.0227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVC</th>\n",
" <td>0.6400</td>\n",
" <td>0.8913</td>\n",
" <td>0.8387</td>\n",
" <td>0.8913</td>\n",
" <td>0.9161</td>\n",
" <td>-0.0248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LinearSVC</th>\n",
" <td>0.3576</td>\n",
" <td>0.7488</td>\n",
" <td>0.7506</td>\n",
" <td>0.7506</td>\n",
" <td>0.8115</td>\n",
" <td>-0.0609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LogisticRegression</th>\n",
" <td>0.4462</td>\n",
" <td>0.7425</td>\n",
" <td>0.7401</td>\n",
" <td>0.7425</td>\n",
" <td>0.8069</td>\n",
" <td>-0.0644</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" base_test_score cluster_test_score tuned_test_score \\\n",
"RandomForestClassifier 0.9757 0.9758 0.9678 \n",
"DecisionTreeClassifier 0.9445 0.9490 0.9413 \n",
"SVC 0.6400 0.8913 0.8387 \n",
"LinearSVC 0.3576 0.7488 0.7506 \n",
"LogisticRegression 0.4462 0.7425 0.7401 \n",
"\n",
" max_f1_score max_auc_score delta \n",
"RandomForestClassifier 0.9758 0.9791 -0.0033 \n",
"DecisionTreeClassifier 0.9490 0.9718 -0.0227 \n",
"SVC 0.8913 0.9161 -0.0248 \n",
"LinearSVC 0.7506 0.8115 -0.0609 \n",
"LogisticRegression 0.7425 0.8069 -0.0644 "
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_score_f1 = pd.DataFrame.from_dict(final_results_f1, orient='index')\n",
"final_score_f1.columns = ['base_test_score', 'cluster_test_score', 'tuned_test_score']\n",
"final_score_f1['max_f1_score'] = final_score_f1[\n",
" ['base_test_score', 'cluster_test_score', 'tuned_test_score']].max(axis=1)\n",
"final_score_f1['max_auc_score'] = final_score['max_score']\n",
"final_score_f1['delta'] = final_score_f1['max_f1_score'] - final_score_f1['max_auc_score']\n",
"final_score_f1.sort_values('max_f1_score', ascending=False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0d07f8ad-350c-6c3d-aaac-9f3765628199"
},
"source": [
"When looking at the F1 test scores for the leading algorithm, there is only a small delta between the AUC score and the F1 test results. For LogisticRegression and LinearSVC the delta is quite large though. Using F1 scores put these scores below .5 which makes them worse than random guessing. It can be said that the established model scores high on both, precision and recall."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"_cell_guid": "627aca6c-c048-3408-cd37-39139baee1e4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>max_f1_score</th>\n",
" <th>max_auc_score</th>\n",
" <th>delta</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>LinearSVC</th>\n",
" <td>0.7506</td>\n",
" <td>0.8115</td>\n",
" <td>-0.0609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LogisticRegression</th>\n",
" <td>0.7425</td>\n",
" <td>0.8069</td>\n",
" <td>-0.0644</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeClassifier</th>\n",
" <td>0.9490</td>\n",
" <td>0.9718</td>\n",
" <td>-0.0227</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVC</th>\n",
" <td>0.8913</td>\n",
" <td>0.9161</td>\n",
" <td>-0.0248</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RandomForestClassifier</th>\n",
" <td>0.9758</td>\n",
" <td>0.9791</td>\n",
" <td>-0.0033</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" max_f1_score max_auc_score delta\n",
"LinearSVC 0.7506 0.8115 -0.0609\n",
"LogisticRegression 0.7425 0.8069 -0.0644\n",
"DecisionTreeClassifier 0.9490 0.9718 -0.0227\n",
"SVC 0.8913 0.9161 -0.0248\n",
"RandomForestClassifier 0.9758 0.9791 -0.0033"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final_score_f1.ix[:,3:]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6a16fed0-f4d1-a309-3d6a-79ed01b83ad3"
},
"source": [
"## Reflection\n",
"\n",
"### Introducing the problem, the domain and a potential solution\n",
"The process developed in this research took a raw data set filled with employee information and turned it into a predictive analysis regarding employee turnover. The research was split into several parts. It starts with an introduction that states additional background information and gives an overview about recent research about employee turnover. In this part the problem and a possible solution is also stated and based on the literature review a benchmark has been established to measure the quality of this research paper. An interesting takeaway from this part was the complexity of the domain and various multi- layered approaches that have been executed in the search of workable solutions.\n",
"\n",
"### Exploring the data and its peculiarities\n",
"Afterwards an in-depth analysis of the data took place. Starting with data exploration and steps to deal with individual traits of the data set. From univariate analysis of continuous and discrete variables to multivariate perspectives including but not limited to regression and correlation analysis. This section highlighted the importance of context, when it comes to a human related topic. Just as important as creating a prediction algorithm is understanding the context and inner relationships of its variables. The takeaway of this segment were multiple possible employee stories and input for additional measures when it comes to employee turnover.\n",
"\n",
"### Thoughts on models and algorithms\n",
"After establish- ing theoretical and topical groundwork several avenues for machine learning solutions were explored. Starting with a collection of supervised and unsupervised algorithms that could potentially lead to a successful solution and their in- tuition were mentioned. This segment tried to give a brief introduction into the criterias and upsides which led to a set of algorithms. Afterwards the benchmark was theoretical introduced and explained in deeper detail. A major takeaway was the complexity of combining supervised and unsupervised sets especially when dealing with a mix of variable classes. Since it’s not that easy to apply clustering algorithms to a mix of continuous and discrete variables a compromise had to be found. Another important takeaway was the discussion about prediction scoring. Adding F1 scoring to the mix increased the reliability of the results.\n",
"\n",
"### Exploring methods for handling the data\n",
"In the next segment the methodology behind handling the data and dealing with its detected peculiarities was discussed. From data preprocessing with steps such as missing value or outlier detection and handling as well as One-Hot encoding for categorical variables to feature normalization and the calculation of feature relevance.\n",
"\n",
"### Implementing a solution\n",
"The implementation phase was split into three prediction and two refinement runs. Combining efforts of unsupervised and supervised learning algorithms this segment tried to put into action what was introduced in the solution statement. Before starting with the first prediction, 20% of the data was set aside for testing. For all supervised learning algorithms stratified 5-fold cross-validation was performed in an attempt to create an algorithm that would generalize well on new data. First it started with a basic implementation of the supervised learning algorithms, measuring their success based on the same score as the benchmark. Afterwards an unsupervised learning algorithm was implemented to find clusters and additional relationships in the data. The cluster labels were added to the data set and another run of prediction took place. This was a difficult task and prior to starting the research I hoped the information available was richer when it comes to clustering. Because of the small amount of continuous variables and the complexity of mixed clustering methods the clustering results are suboptimal and led to only minor changes in the prediction. In an attempt to fine tune the algorithms, grid search was performed on all supervised algorithms to find a set of parameters that worked the best based on its cross-validation AUC score. Yet, even with slightly reduced training scores on the clustering data set, the final model performing best on the hold-out set was a combination of tuning and cluster-enhancement.\n",
"\n",
"### Additional model testing\n",
"Afterwards robustness of the model was tested by performing additional cross-validation runs on smaller subsets of the training.\n",
"\n",
"### Success of the model\n",
"The final model and solution does fit the expectation stated in the beginning. Yet it is important to point out that this solution is tied to the present data set. With a data set that is more noisy the problem of generalization as mentioned in some of the presented literature may occur especially when dealing with tree-based algorithms that don’t implement pruning. Cross-validation with pruning parameters and boosting are some of the options available to work against this problem. The solution presented here could be used in a general setting to determine not only employee turnover but also feature relevance in companies with a certain size and reporting structure.\n",
"\n",
"## Improvement\n",
"There are a few suggestions to improve this research that would go beyond the scope of this analysis.\n",
"\n",
"### More and more diverse data\n",
"The available information for this research has its limitations. Although the data was nice to handle and exceptionally clean from the beginning there were some aspects that felt could use improvements. In an attempt to get more insights a follow up research could start pulling additional information from a variety of sources, gather more granular splits on some of the existing variables or try to reverse engineer some information that might be hidden in the data set. However with missing code-book and contextual information for the data it was quite hard to add additional sources since most of the available information is highly regional and to a certain extend company specific.\n",
"\n",
"### Additional algorithms\n",
"For similar research more sophis- ticated algorithms could be used. There have been great results in this domain with Extreme Gradiant Boosting models. In addition the implemenation of an artificial neural net might be possible if the data set grows to an extend where it is feasible to train a net.\n",
"\n",
"### Computational Efficiency \n",
"Some of the parts in this research are computationally expensive. Especially computing the silhouette score and performing grid search on all of the algorithms with a broad variety of parameter settings can take a long time to process. An implementation of this research should focus on parallel computing which is not only possible for specific segments but also for tree-based algorithms such as Random Forest. Another option is preprocessing some of the data upfront and establishing a knowledge data base that can be loaded rather than computed every time in order to minimize computing time. "
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6e1293c9-939c-35ce-8075-85efb2e75eed"
},
"source": [
"# References\n",
"\n",
"Allen, D. G., K. R. Moffit, and K. P. Weeks. 2005. “Turnover Intentions and Voluntary Turnover: The Moderating Roles of Self-Monitoring, Locus of Control, Proactive Personality, and Risk Aversion.” Journal of Applied Psychology Vol 90 (980990).\n",
"\n",
"Cotton, John L., and Jefferey M. Tuttle. 1986. “Employee Turnover: A Meta-Analysis and Review with Implications for Research.” Academy of Management Review Vol 11: 55–70.\n",
"\n",
"Hand, J. D., and J. W. Krzanowski. 2009. ROC Curves for Continuous Data. CRC Press.\n",
"\n",
"Holtom, B., T. Mitchell, T. Lee, and M. Eberly. 2008a. “Turnover and Retention Research: A Glance at the Past, a Closer Review of the Present, and a Venture into the Future.” Academy of Management Annals Vol 2: p. 231–74.\n",
"\n",
"Michaels, Charles E., and Paul E. Spector. 1982. “Causes of Employee Turnover: A Test of the Mobley, Griffeth, Hand, and Meglino Model.” Journal of Applied Psychology Vol 67(1): p. 53–59.\n",
"\n",
"Mitchel, J. O. 1981. “The Effect of Intentions, Tenure, Personal, and Organizational Variables on Managerial Turnover.” Academy of Management Journal Vol 24: p. 742–50.\n",
"\n",
"Mobley, William H. 1977. “Intermediate Linkages in the Relationship between Job Satisfaction and Employee Turnover.” Journal of Applied Psychology Vol 62(2): p. 237–40.\n",
"\n",
"Punnose, R., and A. Pankaj. 2016. “Prediction of Employee Turnover in Organizations Using Machine Learning Algorithms.” International Journal of Advanced Research in Artificial Intelligence Vol 5.\n",
"\n",
"Trevor, C. O. 2001. “Interactions among Actual Ease-of-Movement Determinants and Job Satisfaction in the Prediction of Voluntary Turnover.” Academy of Management Journal Vol 44: p. 621–38."
]
},
{
"cell_type": "code",
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"metadata": {
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"metadata": {
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| 0001/161/1161450.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ae18c2a3-36d3-8e73-b97f-887e3411a9c0"
},
"source": [
"#Data analysis & Visualization of H1B data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "8b5a7270-61e4-9676-318d-959db411515c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"h1b_kaggle.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "8f8951c7-e838-a0b6-4a3e-5ad445baf870"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Unnamed: 0 EMPLOYER_NAME JOB_TITLE \\\n",
"647793 647794 UNIVERSITY OF MICHIGAN RESEARCH FELLOW \n",
"647794 647795 HINDUJA TECH INC. SENIOR ENGINEER \n",
"647796 647797 INFOSYS LIMITED SENIOR PROJECT MANAGER - US \n",
"647797 647798 SIGMAWAYS, INC SYSTEMS ENGINEER \n",
"647802 647803 IRIS SOFTWARE, INC. NaN \n",
"\n",
" YEAR WORKSITE \n",
"647793 2016.0 ANN ARBOR, MICHIGAN \n",
"647794 2016.0 SALINE, MICHIGAN \n",
"647796 2016.0 HOUSTON, TEXAS \n",
"647797 2016.0 FREMONT, CALIFORNIA \n",
"647802 2016.0 FORT MILL, SOUTH CAROLINA \n"
]
}
],
"source": [
"data = pd.read_csv('../input/h1b_kaggle.csv')\n",
"#print(data.head(5))\n",
"New_data= data\n",
"data=data.loc[(data['YEAR'] == 2016) &(data['CASE_STATUS']== 'CERTIFIED')]\n",
"data=data.drop(['SOC_NAME','FULL_TIME_POSITION','lon','lat','PREVAILING_WAGE','CASE_STATUS'],axis=1)\n",
"print(data.tail(5))\n",
"#data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "521f7716-e294-9e65-737f-a004ba3d272b"
},
"outputs": [
{
"data": {
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Rv7+//P39ZbVa7decPXtWbdu2lb+/vywWiwICAlRYWCibzSY/Pz9lZ2fbz/31Htdz7ly+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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f446f996cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df_infy=data.loc[(data['EMPLOYER_NAME']== 'INFOSYS LIMITED')]\n",
"df_Cogi=data.loc[(data['EMPLOYER_NAME']=='COGNIZANT TECHNOLOGY SOLUTIONS U.S. CORPORATION')]\n",
"df_syntel=data.loc[(data['EMPLOYER_NAME']=='SYNTEL CONSULTING INC.')]\n",
"df_tata=data.loc[(data['EMPLOYER_NAME']=='TATA CONSULTANCY SERVICES LIMITED')]\n",
"\n",
"index = np.arange(2)\n",
"columns = ['INFY', 'CTS', 'SYNTEL' ,'TATA']\n",
"\n",
"df_cns =pd.DataFrame(columns=columns, index = index)\n",
"df_cns['INFY']=df_infy.EMPLOYER_NAME.count()\n",
"df_cns['CTS']=df_Cogi.EMPLOYER_NAME.count()\n",
"df_cns['SYNTEL']=df_syntel.EMPLOYER_NAME.count()\n",
"df_cns['TATA']=df_tata.EMPLOYER_NAME.count()\n",
"\n",
"\n",
"df_cns.tail(-1).plot(kind='bar')\n",
"plt.ylabel('# of H1B Visas')\n",
"plt.xlabel('Companies')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "f9c9106e-c58b-915f-4426-737de9422a5a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Unnamed: 0 5126\n",
"EMPLOYER_NAME 5126\n",
"JOB_TITLE 5126\n",
"YEAR 5126\n",
"WORKSITE 5126\n",
"dtype: int64\n"
]
}
],
"source": [
"#print(data[data['WORKSITE'].str.contains(\"ARIZONA\")==True])\n",
"df_Phx=data.loc[(data['WORKSITE']== 'PHOENIX, ARIZONA')]\n",
"print(df_Phx.count())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "84f1615c-2a9a-d768-3fe4-35acaf2f4fab"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EMPLOYER_NAME\n",
"INFOSYS LIMITED 130592\n",
"TATA CONSULTANCY SERVICES LIMITED 64726\n",
"WIPRO LIMITED 48117\n",
"DELOITTE CONSULTING LLP 36742\n",
"IBM INDIA PRIVATE LIMITED 34219\n",
"ACCENTURE LLP 33447\n",
"MICROSOFT CORPORATION 25576\n",
"HCL AMERICA, INC. 22678\n",
"ERNST & YOUNG U.S. LLP 18232\n",
"COGNIZANT TECHNOLOGY SOLUTIONS U.S. CORPORATION 17528\n",
"LARSEN & TOUBRO INFOTECH LIMITED 17457\n",
"CAPGEMINI AMERICA INC 16725\n",
"GOOGLE INC. 16473\n",
"IBM CORPORATION 13276\n",
"IGATE TECHNOLOGIES INC. 12564\n",
"INTEL CORPORATION 11415\n",
"TECH MAHINDRA (AMERICAS),INC. 10732\n",
"DELOITTE & TOUCHE LLP 9642\n",
"AMAZON CORPORATE LLC 9026\n",
"ORACLE AMERICA, INC. 7684\n",
"dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f446594e8d0>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8fGBhYdyP3ZjtBR5Nmx+lzMxMtG3bVlKGIAhQqVRGapH8qqurcePGDZibm6NN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\nOE1pPM8GAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f446594eba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#print (New_data.head())\n",
"EMP_data=New_data.groupby('EMPLOYER_NAME').size().nlargest(20)\n",
"print(EMP_data)\n",
"EMP_data.plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "cb518817-6615-5bd5-8070-8c98f8bbfda0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EMPLOYER_NAME YEAR \n",
"\"EXCELLENT COMPUTING DISTRIBUTORS INC\" 2013.0 1\n",
"\"I HAVE A DREAM\" FOUNDATION 2012.0 1\n",
" 2014.0 1\n",
"\"K\" LINE AMERICA 2012.0 2\n",
" 2014.0 1\n",
" 2015.0 2\n",
"\"K\" LINE AMERICA, INC. 2012.0 3\n",
" 2015.0 4\n",
"\"K\" LINE LOGISITCS (U.S.A.) INC. 2012.0 1\n",
"\"K\" LINE LOGISTICS USA INC. 2011.0 2\n",
" 2012.0 1\n",
"\"K\" LINE LOGISTICS USA, INC. 2011.0 1\n",
""K" LINE AMERICA 2016.0 1\n",
""K" LINE AMERICA, INC. 2016.0 1\n",
"&TV COMMUNICATIONS INC 2015.0 3\n",
"&TV COMMUNICATIONS INC. 2015.0 5\n",
"&TV COMMUNICATIONS, INC. 2011.0 1\n",
" 2014.0 1\n",
"&YET, LLC 2014.0 1\n",
"'K' LINE LOGISTICS USA INC 2012.0 1\n",
"'K' LINE LOGISTICS USA INC. 2012.0 1\n",
" 2015.0 1\n",
" 2016.0 1\n",
"(AMBI)DEXTROUS ASSET MANAGEMENT LLC 2013.0 1\n",
" 2016.0 1\n",
"(HOUSEHOLD OF SANDRA SIRUGO & JORGE D. JAURY) 2013.0 1\n",
" 2014.0 1\n",
" 2015.0 1\n",
"(HOUSEHOLD OF SANDRA SIRUGO & JORGE E. JAURY) 2011.0 1\n",
" 2012.0 1\n",
" ..\n",
"ZYTUS INC 2013.0 1\n",
"ZYTUS, INC 2011.0 21\n",
" 2012.0 10\n",
" 2013.0 5\n",
" 2014.0 3\n",
" 2015.0 3\n",
" 2016.0 6\n",
"ZYTUS, INC. 2012.0 3\n",
" 2013.0 2\n",
" 2014.0 2\n",
" 2015.0 1\n",
" 2016.0 1\n",
"ZYTUS,INC 2012.0 6\n",
" 2013.0 3\n",
"ZYVEX PERFORMANCE MATERIALS 2011.0 1\n",
"ZYWIE INC. 2014.0 1\n",
" 2015.0 5\n",
"ZYXEL COMMUNICATIONS, INC. 2011.0 3\n",
" 2013.0 1\n",
" 2014.0 3\n",
"ZZ'S PRODUCE INC. 2012.0 1\n",
"ZZ'S PRODUCE INC. 2012.0 1\n",
"ZZIPCO LLC 2011.0 1\n",
"ZZIPCO, LLC 2011.0 1\n",
"[X+1] SOLUTIONS, INC. 2012.0 1\n",
"ÄKTA US, LLC 2014.0 1\n",
"ÈZE BULLES DRINKS LLC 2015.0 1\n",
"ÉTUDES LLC 2015.0 1\n",
"ÉTUDES, LLC 2015.0 2\n",
"ËNIMAI, INC. 2013.0 1\n",
"dtype: int64\n"
]
}
],
"source": [
"New_data=New_data.drop(['SOC_NAME','FULL_TIME_POSITION','lon','lat','PREVAILING_WAGE','CASE_STATUS'],\n",
" axis=1)\n",
"New_data1=New_data.groupby(['EMPLOYER_NAME','YEAR']).size()\n",
"print(New_data1)\n",
"#Result = pd.pivot_table(New_data, index='EMPLOYER_NAME', columns='YEAR', aggfunc=np.size)\n",
"#print(Result)\n",
"#sns.heatmap(Result, annot=False, fmt=\"g\" ,cbar_kws={\"orientation\": \"horizontal\"})\n",
"#plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "5f1f7e1d-f5c3-a321-31a7-02d76919f79e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EMPLOYER_NAME\n",
"INFOSYS LIMITED 130592\n",
"TATA CONSULTANCY SERVICES LIMITED 64726\n",
"WIPRO LIMITED 48117\n",
"DELOITTE CONSULTING LLP 36742\n",
"IBM INDIA PRIVATE LIMITED 34219\n",
"ACCENTURE LLP 33447\n",
"MICROSOFT CORPORATION 25576\n",
"HCL AMERICA, INC. 22678\n",
"ERNST & YOUNG U.S. LLP 18232\n",
"COGNIZANT TECHNOLOGY SOLUTIONS U.S. CORPORATION 17528\n",
"dtype: int64\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f446ee3d048>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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iZr+1mdKKaqOjiIh4PBVJImeRmJfEnzcsotJmNTqKXIRjZal066lLlMRzJJ8o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\nSlCbjysdSQ373GdzukiBZEGWcTpTwpVrykwbi4w1UR6YTHG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1N0mS+d3EfJw4onQoLSJT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\nWCQRERERERGZ+V/+OVIdWOggdgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f446ee61d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#print (New_data.head())\n",
"EMP_data=New_data.groupby('EMPLOYER_NAME').size().nlargest(10)\n",
"print(EMP_data)\n",
"EMP_data.plot(kind='pie')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "1fdd9f4f-0218-0c30-f3a2-cf9c68787184"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "labels ['SOC_NAME' 'FULL_TIME_POSITION' 'lon' 'lat' 'PREVAILING_WAGE'\n 'CASE_STATUS'] not contained in axis",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-75d1fd8b9645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m New_data=New_data.drop(['SOC_NAME','FULL_TIME_POSITION','lon','lat','PREVAILING_WAGE','CASE_STATUS'],\n\u001b[0;32m----> 2\u001b[0;31m axis=1)\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mNew_data1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNew_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'EMPLOYER_NAME'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'YEAR'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlargest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mNew_data1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, level, inplace, errors)\u001b[0m\n\u001b[1;32m 1905\u001b[0m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1906\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1907\u001b[0;31m \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1908\u001b[0m \u001b[0mdropped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0maxis_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnew_axis\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1909\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m 3260\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3261\u001b[0m raise ValueError('labels %s not contained in axis' %\n\u001b[0;32m-> 3262\u001b[0;31m labels[mask])\n\u001b[0m\u001b[1;32m 3263\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3264\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: labels ['SOC_NAME' 'FULL_TIME_POSITION' 'lon' 'lat' 'PREVAILING_WAGE'\n 'CASE_STATUS'] not contained in axis"
]
}
],
"source": [
"New_data=New_data.drop(['SOC_NAME','FULL_TIME_POSITION','lon','lat','PREVAILING_WAGE','CASE_STATUS'],\n",
" axis=1)\n",
"New_data1=New_data.groupby(['EMPLOYER_NAME','YEAR']).size().nlargest(20)\n",
"print(New_data1)"
]
}
],
"metadata": {
"_change_revision": 207,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161506.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "69be9882-19e5-2001-1958-87c642fab6e7"
},
"source": [
"**use simple neural network to classify the mnist dataset**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "2038e51b-1d46-4ec4-e63b-91c91f9e9e7f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt\n",
"#import random\n",
"import scipy\n",
"import scipy.misc\n",
"import matplotlib.cm as cm\n",
"from tensorflow.contrib.layers import flatten\n",
"from sklearn.model_selection import train_test_split\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"from sklearn.utils import shuffle\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "484295d8-10d3-cf7f-9285-f23451862924"
},
"outputs": [],
"source": [
"train = pd.read_csv('../input/train.csv')\n",
"#train.head(5)\n",
"test = pd.read_csv('../input/test.csv')\n",
"#test.head(5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "a15feda0-02c2-898c-8a54-c67cf7773339"
},
"outputs": [
{
"data": {
"text/plain": [
"(42000,)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"label_true = train['label']\n",
"label_true = label_true.as_matrix()\n",
"label_true.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "2513ca49-e414-db9b-8f58-53a47785fc5e"
},
"outputs": [],
"source": [
"train_data = train.drop('label',axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "42c91f03-cdbe-c6c2-7ad8-65844dd3b68a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'numpy.ndarray'>\n"
]
}
],
"source": [
"random_data = train_data.sample(100,random_state=42,axis=0).as_matrix()\n",
"#print (random_data)\n",
"train_data_numpy = train_data.as_matrix()\n",
"print (type(random_data))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "795f0d2b-8b9b-0f4c-0f5c-4c21dbbd49d2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(33600, 784) (33600,)\n"
]
}
],
"source": [
"X_train, X_val, y_train, y_val = train_test_split(train_data_numpy, label_true, test_size=0.2, random_state=42)\n",
"print (X_train.shape,y_train.shape)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "3555dcb5-0836-728b-cf9a-e4b7996cce97"
},
"outputs": [],
"source": [
"def getDatumImg(row):\n",
" \"\"\"\n",
" Function that is handed a single np array with shape 1x784,\n",
" crates an image object from it, and returns it\n",
" \"\"\"\n",
" width, height = 28, 28\n",
" square = row.reshape(width,height)\n",
" return square\n",
"\n",
"def displayData(indices_to_display):\n",
" \"\"\"\n",
" Function that picks 100 random rows from X, creates a 28x28 image from each,\n",
" then stitches them together into a 10x10 grid of images, and shows it.\n",
" \"\"\"\n",
" height,width = 28,28\n",
" nrows, ncols = 10, 10\n",
" big_picture = np.zeros((height*nrows,width*ncols))\n",
" irow, icol = 0, 0\n",
" batch_size,pic_size = indices_to_display.shape\n",
" for idx in range(batch_size):\n",
" if icol == ncols:\n",
" irow += 1\n",
" icol = 0\n",
" #print (idx.shape)\n",
" iimg = getDatumImg(train_data_numpy[idx])\n",
" big_picture[irow*height:irow*height+iimg.shape[0],icol*width:icol*width+iimg.shape[1]] = iimg\n",
" icol += 1\n",
" fig = plt.figure(figsize=(6,6))\n",
" img = scipy.misc.toimage( big_picture )\n",
" plt.imshow(img,cmap = cm.Greys_r)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "0e4e3018-630b-f592-e5fa-c6880cf883df"
},
"outputs": [
{
"data": {
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5S8nOzg7Z2dnIz89Hfn4+7O3tBam3Jo28oRuCbt26hZ9//rnOJNmvnCDnqWvX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BmVlpZSeXk5ubi4SNrZ9ZG3kjUaDXEcR15eXqLLCAwMJK1WS+fOnaNz586x5Tt27CCO4/SWJZSC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\nRCIJcKQhl0gkkgDn/wECyWcahJ1UPgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7678f6208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"displayData(random_data)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "a5ea8ee6-bcaa-a6fe-f89e-dae38c406950"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fd7694cc6d8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fd7685a0668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#display a number\n",
"random_a_num = train_data.sample(1,random_state=42,axis=0).as_matrix()\n",
"#print (random_a_num) \n",
"img = random_a_num.reshape(28,28)\n",
"plt.imshow(img,cmap='gray')\n",
"#plt.title(label_true[])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "a9788628-a185-8fce-2623-827c137d1c0b"
},
"outputs": [],
"source": [
"#lenet5\n",
"def LeNet(x): \n",
" mu = 0\n",
" sigma = 0.1\n",
" conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))\n",
" conv1_b = tf.Variable(tf.zeros(6))\n",
" conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\n",
" conv1 = tf.nn.relu(conv1)\n",
" #print (conv1.shape)\n",
" conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
" #print (conv1.shape)\n",
" conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n",
" conv2_b = tf.Variable(tf.zeros(16))\n",
" conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\n",
" conv2 = tf.nn.relu(conv2)\n",
" #print (conv2.shape)\n",
" conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
" #print (conv2.shape)\n",
" fc0 = flatten(conv2)\n",
" #print (fc0.shape)\n",
" fc1_W = tf.Variable(tf.truncated_normal(shape=(256, 120), mean = mu, stddev = sigma))\n",
" fc1_b = tf.Variable(tf.zeros(120))\n",
" fc1 = tf.matmul(fc0, fc1_W) + fc1_b\n",
" fc1 = tf.nn.relu(fc1)\n",
" fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n",
" fc2_b = tf.Variable(tf.zeros(84))\n",
" fc2 = tf.matmul(fc1, fc2_W) + fc2_b\n",
" fc2 = tf.nn.relu(fc2)\n",
" fc3_W = tf.Variable(tf.truncated_normal(shape=(84, 10), mean = mu, stddev = sigma))\n",
" fc3_b = tf.Variable(tf.zeros(10))\n",
" logits = tf.matmul(fc2, fc3_W) + fc3_b\n",
" return logits"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "c52de427-8652-ed66-c0d2-5d1b42713f87"
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32,[None,28,28,1])\n",
"y = tf.placeholder(tf.int32,[None])\n",
"one_hot_y = tf.one_hot(y,10)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "36bd9579-4c58-a923-67e5-e87d05dd9ab1"
},
"outputs": [],
"source": [
"#crition and bp\n",
"learning_rate = 0.001\n",
"logits = LeNet(x)\n",
"cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y,logits=logits)\n",
"loss_operation = tf.reduce_mean(cross_entropy)\n",
"optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
"training_operation = optimizer.minimize(loss_operation)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "c68607e6-9ff0-b439-5e65-4ac4fe55b26c"
},
"outputs": [],
"source": [
"correct_prediction = tf.equal(tf.argmax(one_hot_y,1),tf.argmax(logits,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
"saver = tf.train.Saver()\n",
"\n",
"def evaluate(X_data, y_data):\n",
" num_examples = len(X_data)\n",
" total_accuracy = 0\n",
" sess = tf.get_default_session()\n",
" for offset in range(0, num_examples, BATCH_SIZE):\n",
" batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
" accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})\n",
" total_accuracy += (accuracy * len(batch_x))\n",
" return total_accuracy / num_examples"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "595c833d-df6c-e0b3-e769-8a1b27235846"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training...\n",
"\n"
]
},
{
"ename": "NameError",
"evalue": "name 'accuracy_operation' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-5ab635ce775c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraining_operation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mvalidation_accuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"EPOCH {} ...\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Validation Accuracy = {:.3f}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-13-b3add28172fa>\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(X_data, y_data)\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_examples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0moffset\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mBATCH_SIZE\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maccuracy_operation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_y\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mtotal_accuracy\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maccuracy\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtotal_accuracy\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnum_examples\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'accuracy_operation' is not defined"
]
}
],
"source": [
"EPOCHS = 50\n",
"BATCH_SIZE = 100\n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" num_examples = len(X_train)\n",
" \n",
" print(\"Training...\")\n",
" print()\n",
" for i in range(EPOCHS):\n",
" X_train, y_train = shuffle(X_train, y_train)\n",
" for offset in range(0, num_examples, BATCH_SIZE):\n",
" end = offset + BATCH_SIZE\n",
" batch_x, batch_y = X_train[offset:end].reshape((BATCH_SIZE,28,28,1)), y_train[offset:end]\n",
" sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})\n",
" \n",
" validation_accuracy = evaluate(X_val, y_val)\n",
" print(\"EPOCH {} ...\".format(i+1))\n",
" print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
" print()\n",
" \n",
" saver.save(sess, './lenet')\n",
" print(\"Model saved\")"
]
}
],
"metadata": {
"_change_revision": 297,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161572.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "eb8372e9-a37a-dc9f-d431-f0b4c5fe3109"
},
"source": [
"**`This is a test!`**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "51227c96-9056-c27c-d397-cf54898d48f4"
},
"outputs": [
{
"data": {
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ccnkJMVH89PrJlFY1snxDpQ8UGoIVYzD8yLnmdh5cXcbl47N8Or59bmEG104ZwaPrKqmq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\nv1qQdCFZI/VoRBwfyHOnGuo9jKPAFYX1y/NtKWV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"text/plain": [
"<matplotlib.figure.Figure at 0x7f642c900a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"t = np.arange(0.0, 2.0, 0.01)\n",
"s = 1 + np.sin(2*np.pi*t)\n",
"plt.plot(t, s)\n",
"\n",
"plt.xlabel('time (s)')\n",
"plt.ylabel('voltage (mV)')\n",
"plt.title('About as simple as it gets, folks')\n",
"plt.grid(True)\n",
"plt.savefig(\"test.png\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3a350920-c4f0-8e39-d6e4-f85919a13cb8"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>...</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-08-20</td>\n",
" <td>43</td>\n",
" <td>27.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>4</td>\n",
" <td>5850000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-08-23</td>\n",
" <td>34</td>\n",
" <td>19.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>66</td>\n",
" <td>14</td>\n",
" <td>6000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-08-27</td>\n",
" <td>43</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>5700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-09-01</td>\n",
" <td>89</td>\n",
" <td>50.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>13100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-09-05</td>\n",
" <td>77</td>\n",
" <td>77.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>319</td>\n",
" <td>108</td>\n",
" <td>17</td>\n",
" <td>135</td>\n",
" <td>236</td>\n",
" <td>2</td>\n",
" <td>91</td>\n",
" <td>195</td>\n",
" <td>14</td>\n",
" <td>16331452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 292 columns</p>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material build_year \\\n",
"0 1 2011-08-20 43 27.0 4.0 NaN NaN NaN \n",
"1 2 2011-08-23 34 19.0 3.0 NaN NaN NaN \n",
"2 3 2011-08-27 43 29.0 2.0 NaN NaN NaN \n",
"3 4 2011-09-01 89 50.0 9.0 NaN NaN NaN \n",
"4 5 2011-09-05 77 77.0 4.0 NaN NaN NaN \n",
"\n",
" num_room kitch_sq ... cafe_count_5000_price_2500 \\\n",
"0 NaN NaN ... 9 \n",
"1 NaN NaN ... 15 \n",
"2 NaN NaN ... 10 \n",
"3 NaN NaN ... 11 \n",
"4 NaN NaN ... 319 \n",
"\n",
" cafe_count_5000_price_4000 cafe_count_5000_price_high \\\n",
"0 4 0 \n",
"1 3 0 \n",
"2 3 0 \n",
"3 2 1 \n",
"4 108 17 \n",
"\n",
" big_church_count_5000 church_count_5000 mosque_count_5000 \\\n",
"0 13 22 1 \n",
"1 15 29 1 \n",
"2 11 27 0 \n",
"3 4 4 0 \n",
"4 135 236 2 \n",
"\n",
" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
"0 0 52 4 5850000 \n",
"1 10 66 14 6000000 \n",
"2 4 67 10 5700000 \n",
"3 0 26 3 13100000 \n",
"4 91 195 14 16331452 \n",
"\n",
"[5 rows x 292 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x = pd.read_csv(\"../input/train.csv\")\n",
"x.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "a36c753f-55fc-9908-67e0-bd17867623fa"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 57,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161604.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "450a1a81-81aa-77c4-3b1e-a8d49ecd324e"
},
"source": [
"# Pyarathon: Dataset exploration\n",
"> Exploring the dataset using Python and simple plot tools"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "3df23160-4d5d-1b63-7bf9-bd6faa4171f9"
},
"outputs": [],
"source": [
"# Necessary imports\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "12ccdb88-5ffc-607e-79f5-81d5c09c4bdd"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>Marathon</th>\n",
" <th>Name</th>\n",
" <th>Category</th>\n",
" <th>km4week</th>\n",
" <th>sp4week</th>\n",
" <th>CrossTraining</th>\n",
" <th>Wall21</th>\n",
" <th>MarathonTime</th>\n",
" <th>CATEGORY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Prague17</td>\n",
" <td>Blair MORGAN</td>\n",
" <td>MAM</td>\n",
" <td>132.8</td>\n",
" <td>14.434783</td>\n",
" <td>NaN</td>\n",
" <td>1.16</td>\n",
" <td>2.37</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>Prague17</td>\n",
" <td>Robert Heczko</td>\n",
" <td>MAM</td>\n",
" <td>68.6</td>\n",
" <td>13.674419</td>\n",
" <td>NaN</td>\n",
" <td>1.23</td>\n",
" <td>2.59</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>Prague17</td>\n",
" <td>Michon Jerome</td>\n",
" <td>MAM</td>\n",
" <td>82.7</td>\n",
" <td>13.520436</td>\n",
" <td>NaN</td>\n",
" <td>1.30</td>\n",
" <td>2.66</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>Prague17</td>\n",
" <td>Daniel Or lek</td>\n",
" <td>M45</td>\n",
" <td>137.5</td>\n",
" <td>12.258544</td>\n",
" <td>NaN</td>\n",
" <td>1.32</td>\n",
" <td>2.68</td>\n",
" <td>A</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>Prague17</td>\n",
" <td>Luk ? Mr zek</td>\n",
" <td>MAM</td>\n",
" <td>84.6</td>\n",
" <td>13.945055</td>\n",
" <td>NaN</td>\n",
" <td>1.36</td>\n",
" <td>2.74</td>\n",
" <td>A</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id Marathon Name Category km4week sp4week CrossTraining \\\n",
"0 1 Prague17 Blair MORGAN MAM 132.8 14.434783 NaN \n",
"1 2 Prague17 Robert Heczko MAM 68.6 13.674419 NaN \n",
"2 3 Prague17 Michon Jerome MAM 82.7 13.520436 NaN \n",
"3 4 Prague17 Daniel Or lek M45 137.5 12.258544 NaN \n",
"4 5 Prague17 Luk ? Mr zek MAM 84.6 13.945055 NaN \n",
"\n",
" Wall21 MarathonTime CATEGORY \n",
"0 1.16 2.37 A \n",
"1 1.23 2.59 A \n",
"2 1.30 2.66 A \n",
"3 1.32 2.68 A \n",
"4 1.36 2.74 A "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read the data and print the first 5 lines\n",
"marathon_df = pd.read_csv('../input/MarathonData.csv')\n",
"marathon_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "fcdc13e0-f434-9c89-86ed-8cf42b5d867e"
},
"outputs": [
{
"data": {
"text/plain": [
"(87, 10)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"marathon_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "113636e7-731d-c014-5c1b-268202f20ed4"
},
"outputs": [],
"source": [
"# They all are from the same marathon and id is useless\n",
"# Also, We don't need the names because each row is for a different athlet\n",
"# print(marathon_df['Marathon'].unique())\n",
"# print(marathon_df.duplicated('id').any())\n",
"\n",
"# We can safely drop these two columns since we don't have any useful information\n",
"marathon_df = marathon_df.drop(['Marathon', 'id', 'Name'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "1e754128-18f3-31d5-8a39-ba12528d4335"
},
"outputs": [
{
"data": {
"text/plain": [
"array([nan, 'ciclista 1h', 'ciclista 4h', 'ciclista 13h', 'ciclista 5h',\n",
" 'ciclista 3h'], dtype=object)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Categorical encoding for CrossTraining variable\n",
"marathon_df['CrossTraining'].unique()"
]
}
],
"metadata": {
"_change_revision": 202,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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}
},
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| 0001/161/1161721.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "986d2f40-3c0c-e41f-bf0c-d21549f607e6"
},
"source": [
"No introduction needed,pal."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "82bdedd2-aaaf-fcae-a64a-e7cd8c2147d6"
},
"outputs": [],
"source": [
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"train = pd.read_csv(\"../input/train.csv\")\n",
"test = pd.read_csv(\"../input/test.csv\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2063ad10-85b7-ca86-770a-8ccee1d3902e"
},
"source": [
"## Check out the data ##"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "73c67009-1b3a-8f7b-b5a6-d4a6f084d1f4"
},
"outputs": [
{
"data": {
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" <th>cat9</th>\n",
" <th>...</th>\n",
" <th>cont6</th>\n",
" <th>cont7</th>\n",
" <th>cont8</th>\n",
" <th>cont9</th>\n",
" <th>cont10</th>\n",
" <th>cont11</th>\n",
" <th>cont12</th>\n",
" <th>cont13</th>\n",
" <th>cont14</th>\n",
" <th>loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 132 columns</p>\n",
"</div>"
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" id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 ... cont6 \\\n",
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"\n",
" cont7 cont8 cont9 cont10 cont11 cont12 cont13 \\\n",
"0 0.335060 0.30260 0.67135 0.83510 0.569745 0.594646 0.822493 \n",
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"4 0.247408 0.24564 0.22089 0.21230 0.204687 0.202213 0.246011 \n",
"\n",
" cont14 loss \n",
"0 0.714843 2213.18 \n",
"1 0.304496 1283.60 \n",
"2 0.774425 3005.09 \n",
"3 0.602642 939.85 \n",
"4 0.432606 2763.85 \n",
"\n",
"[5 rows x 132 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "509105ff-8129-f6d2-c777-c1584bf66ea6"
},
"outputs": [],
"source": [
"test[\"loss\"] = 0"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "bb96f33a-bb3a-2a12-00e7-a47ff14149d0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(188318, 132)\n",
"(125546, 132)\n"
]
}
],
"source": [
"print(train.shape)\n",
"print(test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "a1e28901-361c-cdc1-a8a9-afa8e5b6799e"
},
"outputs": [],
"source": [
"full = train.append(test)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "2359f7df-9db7-6a8a-6b50-23d80930ce85"
},
"outputs": [
{
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},
"execution_count": 6,
"metadata": {},
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}
],
"source": [
"list(full.columns)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "3d1d470c-4558-00c7-6590-59fe6e9c6ede"
},
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{
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"execution_count": 7,
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}
],
"source": [
"cats_cols = [name for name in list(full.columns) if \"cat\" in name]\n",
"cats_cols"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
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},
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cat1</th>\n",
" <th>cat2</th>\n",
" <th>cat3</th>\n",
" <th>cat4</th>\n",
" <th>cat5</th>\n",
" <th>cat6</th>\n",
" <th>cat7</th>\n",
" <th>cat8</th>\n",
" <th>cat9</th>\n",
" <th>cat10</th>\n",
" <th>...</th>\n",
" <th>cat107</th>\n",
" <th>cat108</th>\n",
" <th>cat109</th>\n",
" <th>cat110</th>\n",
" <th>cat111</th>\n",
" <th>cat112</th>\n",
" <th>cat113</th>\n",
" <th>cat114</th>\n",
" <th>cat115</th>\n",
" <th>cat116</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>...</td>\n",
" <td>J</td>\n",
" <td>G</td>\n",
" <td>BU</td>\n",
" <td>BC</td>\n",
" <td>C</td>\n",
" <td>AS</td>\n",
" <td>S</td>\n",
" <td>A</td>\n",
" <td>O</td>\n",
" <td>LB</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" <td>...</td>\n",
" <td>K</td>\n",
" <td>K</td>\n",
" <td>BI</td>\n",
" <td>CQ</td>\n",
" <td>A</td>\n",
" <td>AV</td>\n",
" <td>BM</td>\n",
" <td>A</td>\n",
" <td>O</td>\n",
" <td>DP</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" <td>...</td>\n",
" <td>F</td>\n",
" <td>A</td>\n",
" <td>AB</td>\n",
" <td>DK</td>\n",
" <td>A</td>\n",
" <td>C</td>\n",
" <td>AF</td>\n",
" <td>A</td>\n",
" <td>I</td>\n",
" <td>GK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>...</td>\n",
" <td>K</td>\n",
" <td>K</td>\n",
" <td>BI</td>\n",
" <td>CS</td>\n",
" <td>C</td>\n",
" <td>N</td>\n",
" <td>AE</td>\n",
" <td>A</td>\n",
" <td>O</td>\n",
" <td>DJ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>A</td>\n",
" <td>B</td>\n",
" <td>B</td>\n",
" <td>...</td>\n",
" <td>G</td>\n",
" <td>B</td>\n",
" <td>H</td>\n",
" <td>C</td>\n",
" <td>C</td>\n",
" <td>Y</td>\n",
" <td>BM</td>\n",
" <td>A</td>\n",
" <td>K</td>\n",
" <td>CK</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 116 columns</p>\n",
"</div>"
],
"text/plain": [
" cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 cat10 ... cat107 cat108 \\\n",
"0 A B A B A A A A B A ... J G \n",
"1 A B A A A A A A B B ... K K \n",
"2 A B A A B A A A B B ... F A \n",
"3 B B A B A A A A B A ... K K \n",
"4 A B A B A A A A B B ... G B \n",
"\n",
" cat109 cat110 cat111 cat112 cat113 cat114 cat115 cat116 \n",
"0 BU BC C AS S A O LB \n",
"1 BI CQ A AV BM A O DP \n",
"2 AB DK A C AF A I GK \n",
"3 BI CS C N AE A O DJ \n",
"4 H C C Y BM A K CK \n",
"\n",
"[5 rows x 116 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_cat = full[cats_cols]\n",
"data_cat.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "231d4b13-268c-0ea6-6c32-96234769f0ce"
},
"outputs": [],
"source": [
"from sklearn.preprocessing import LabelEncoder\n",
"def encode_cats(cat_array):\n",
" encoding = LabelEncoder()\n",
" return(encoding.fit_transform(cat_array))\n",
" \n",
" \n",
"data_cat = data_cat.apply(encode_cats)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "0362d2ae-9662-3eb8-7bfb-f06caad035d9"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>cat1</th>\n",
" <th>cat2</th>\n",
" <th>cat3</th>\n",
" <th>cat4</th>\n",
" <th>cat5</th>\n",
" <th>cat6</th>\n",
" <th>cat7</th>\n",
" <th>cat8</th>\n",
" <th>cat9</th>\n",
" <th>...</th>\n",
" <th>cont6</th>\n",
" <th>cont7</th>\n",
" <th>cont8</th>\n",
" <th>cont9</th>\n",
" <th>cont10</th>\n",
" <th>cont11</th>\n",
" <th>cont12</th>\n",
" <th>cont13</th>\n",
" <th>cont14</th>\n",
" <th>loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.718367</td>\n",
" <td>0.335060</td>\n",
" <td>0.30260</td>\n",
" <td>0.67135</td>\n",
" <td>0.83510</td>\n",
" <td>0.569745</td>\n",
" <td>0.594646</td>\n",
" <td>0.822493</td>\n",
" <td>0.714843</td>\n",
" <td>2213.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.438917</td>\n",
" <td>0.436585</td>\n",
" <td>0.60087</td>\n",
" <td>0.35127</td>\n",
" <td>0.43919</td>\n",
" <td>0.338312</td>\n",
" <td>0.366307</td>\n",
" <td>0.611431</td>\n",
" <td>0.304496</td>\n",
" <td>1283.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.289648</td>\n",
" <td>0.315545</td>\n",
" <td>0.27320</td>\n",
" <td>0.26076</td>\n",
" <td>0.32446</td>\n",
" <td>0.381398</td>\n",
" <td>0.373424</td>\n",
" <td>0.195709</td>\n",
" <td>0.774425</td>\n",
" <td>3005.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.440945</td>\n",
" <td>0.391128</td>\n",
" <td>0.31796</td>\n",
" <td>0.32128</td>\n",
" <td>0.44467</td>\n",
" <td>0.327915</td>\n",
" <td>0.321570</td>\n",
" <td>0.605077</td>\n",
" <td>0.602642</td>\n",
" <td>939.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0.178193</td>\n",
" <td>0.247408</td>\n",
" <td>0.24564</td>\n",
" <td>0.22089</td>\n",
" <td>0.21230</td>\n",
" <td>0.204687</td>\n",
" <td>0.202213</td>\n",
" <td>0.246011</td>\n",
" <td>0.432606</td>\n",
" <td>2763.85</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 132 columns</p>\n",
"</div>"
],
"text/plain": [
" id cat1 cat2 cat3 cat4 cat5 cat6 cat7 cat8 cat9 ... \\\n",
"0 1 0 1 0 1 0 0 0 0 1 ... \n",
"1 2 0 1 0 0 0 0 0 0 1 ... \n",
"2 5 0 1 0 0 1 0 0 0 1 ... \n",
"3 10 1 1 0 1 0 0 0 0 1 ... \n",
"4 11 0 1 0 1 0 0 0 0 1 ... \n",
"\n",
" cont6 cont7 cont8 cont9 cont10 cont11 cont12 \\\n",
"0 0.718367 0.335060 0.30260 0.67135 0.83510 0.569745 0.594646 \n",
"1 0.438917 0.436585 0.60087 0.35127 0.43919 0.338312 0.366307 \n",
"2 0.289648 0.315545 0.27320 0.26076 0.32446 0.381398 0.373424 \n",
"3 0.440945 0.391128 0.31796 0.32128 0.44467 0.327915 0.321570 \n",
"4 0.178193 0.247408 0.24564 0.22089 0.21230 0.204687 0.202213 \n",
"\n",
" cont13 cont14 loss \n",
"0 0.822493 0.714843 2213.18 \n",
"1 0.611431 0.304496 1283.60 \n",
"2 0.195709 0.774425 3005.09 \n",
"3 0.605077 0.602642 939.85 \n",
"4 0.246011 0.432606 2763.85 \n",
"\n",
"[5 rows x 132 columns]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"full[cats_cols] = data_cat\n",
"full.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "6c716cbe-56b0-de70-54c0-ac6e017eea40"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cont1</th>\n",
" <th>cont2</th>\n",
" <th>cont3</th>\n",
" <th>cont4</th>\n",
" <th>cont5</th>\n",
" <th>cont6</th>\n",
" <th>cont7</th>\n",
" <th>cont8</th>\n",
" <th>cont9</th>\n",
" <th>cont10</th>\n",
" <th>cont11</th>\n",
" <th>cont12</th>\n",
" <th>cont13</th>\n",
" <th>cont14</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.726300</td>\n",
" <td>0.245921</td>\n",
" <td>0.187583</td>\n",
" <td>0.789639</td>\n",
" <td>0.310061</td>\n",
" <td>0.718367</td>\n",
" <td>0.335060</td>\n",
" <td>0.30260</td>\n",
" <td>0.67135</td>\n",
" <td>0.83510</td>\n",
" <td>0.569745</td>\n",
" <td>0.594646</td>\n",
" <td>0.822493</td>\n",
" <td>0.714843</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.330514</td>\n",
" <td>0.737068</td>\n",
" <td>0.592681</td>\n",
" <td>0.614134</td>\n",
" <td>0.885834</td>\n",
" <td>0.438917</td>\n",
" <td>0.436585</td>\n",
" <td>0.60087</td>\n",
" <td>0.35127</td>\n",
" <td>0.43919</td>\n",
" <td>0.338312</td>\n",
" <td>0.366307</td>\n",
" <td>0.611431</td>\n",
" <td>0.304496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.261841</td>\n",
" <td>0.358319</td>\n",
" <td>0.484196</td>\n",
" <td>0.236924</td>\n",
" <td>0.397069</td>\n",
" <td>0.289648</td>\n",
" <td>0.315545</td>\n",
" <td>0.27320</td>\n",
" <td>0.26076</td>\n",
" <td>0.32446</td>\n",
" <td>0.381398</td>\n",
" <td>0.373424</td>\n",
" <td>0.195709</td>\n",
" <td>0.774425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.321594</td>\n",
" <td>0.555782</td>\n",
" <td>0.527991</td>\n",
" <td>0.373816</td>\n",
" <td>0.422268</td>\n",
" <td>0.440945</td>\n",
" <td>0.391128</td>\n",
" <td>0.31796</td>\n",
" <td>0.32128</td>\n",
" <td>0.44467</td>\n",
" <td>0.327915</td>\n",
" <td>0.321570</td>\n",
" <td>0.605077</td>\n",
" <td>0.602642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.273204</td>\n",
" <td>0.159990</td>\n",
" <td>0.527991</td>\n",
" <td>0.473202</td>\n",
" <td>0.704268</td>\n",
" <td>0.178193</td>\n",
" <td>0.247408</td>\n",
" <td>0.24564</td>\n",
" <td>0.22089</td>\n",
" <td>0.21230</td>\n",
" <td>0.204687</td>\n",
" <td>0.202213</td>\n",
" <td>0.246011</td>\n",
" <td>0.432606</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cont1 cont2 cont3 cont4 cont5 cont6 cont7 \\\n",
"0 0.726300 0.245921 0.187583 0.789639 0.310061 0.718367 0.335060 \n",
"1 0.330514 0.737068 0.592681 0.614134 0.885834 0.438917 0.436585 \n",
"2 0.261841 0.358319 0.484196 0.236924 0.397069 0.289648 0.315545 \n",
"3 0.321594 0.555782 0.527991 0.373816 0.422268 0.440945 0.391128 \n",
"4 0.273204 0.159990 0.527991 0.473202 0.704268 0.178193 0.247408 \n",
"\n",
" cont8 cont9 cont10 cont11 cont12 cont13 cont14 \n",
"0 0.30260 0.67135 0.83510 0.569745 0.594646 0.822493 0.714843 \n",
"1 0.60087 0.35127 0.43919 0.338312 0.366307 0.611431 0.304496 \n",
"2 0.27320 0.26076 0.32446 0.381398 0.373424 0.195709 0.774425 \n",
"3 0.31796 0.32128 0.44467 0.327915 0.321570 0.605077 0.602642 \n",
"4 0.24564 0.22089 0.21230 0.204687 0.202213 0.246011 0.432606 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count_cols = [cont for cont in full.columns if \"cont\" in cont]\n",
"count_data = full[count_cols]\n",
"count_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "f38a9368-b117-5f48-d276-24dd9e89f1f9"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cont1</th>\n",
" <th>cont2</th>\n",
" <th>cont3</th>\n",
" <th>cont4</th>\n",
" <th>cont5</th>\n",
" <th>cont6</th>\n",
" <th>cont7</th>\n",
" <th>cont8</th>\n",
" <th>cont9</th>\n",
" <th>cont10</th>\n",
" <th>cont11</th>\n",
" <th>cont12</th>\n",
" <th>cont13</th>\n",
" <th>cont14</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.00000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" <td>313864.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.494096</td>\n",
" <td>0.507089</td>\n",
" <td>0.498653</td>\n",
" <td>0.492021</td>\n",
" <td>0.487513</td>\n",
" <td>0.491442</td>\n",
" <td>0.485360</td>\n",
" <td>0.486823</td>\n",
" <td>0.48571</td>\n",
" <td>0.498403</td>\n",
" <td>0.493850</td>\n",
" <td>0.493503</td>\n",
" <td>0.493917</td>\n",
" <td>0.495665</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.187768</td>\n",
" <td>0.207056</td>\n",
" <td>0.201961</td>\n",
" <td>0.211101</td>\n",
" <td>0.209063</td>\n",
" <td>0.205394</td>\n",
" <td>0.178531</td>\n",
" <td>0.199442</td>\n",
" <td>0.18185</td>\n",
" <td>0.185906</td>\n",
" <td>0.210002</td>\n",
" <td>0.209716</td>\n",
" <td>0.212911</td>\n",
" <td>0.222537</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000016</td>\n",
" <td>0.001149</td>\n",
" <td>0.002634</td>\n",
" <td>0.176921</td>\n",
" <td>0.281143</td>\n",
" <td>0.012683</td>\n",
" <td>0.069503</td>\n",
" <td>0.236880</td>\n",
" <td>0.00008</td>\n",
" <td>0.000000</td>\n",
" <td>0.035321</td>\n",
" <td>0.036232</td>\n",
" <td>0.000228</td>\n",
" <td>0.178568</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.347403</td>\n",
" <td>0.358319</td>\n",
" <td>0.336963</td>\n",
" <td>0.327354</td>\n",
" <td>0.281143</td>\n",
" <td>0.336105</td>\n",
" <td>0.351299</td>\n",
" <td>0.317960</td>\n",
" <td>0.35897</td>\n",
" <td>0.364580</td>\n",
" <td>0.310961</td>\n",
" <td>0.314945</td>\n",
" <td>0.315758</td>\n",
" <td>0.294657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.475784</td>\n",
" <td>0.555782</td>\n",
" <td>0.527991</td>\n",
" <td>0.452887</td>\n",
" <td>0.422268</td>\n",
" <td>0.440945</td>\n",
" <td>0.438650</td>\n",
" <td>0.441060</td>\n",
" <td>0.44145</td>\n",
" <td>0.461190</td>\n",
" <td>0.457203</td>\n",
" <td>0.462286</td>\n",
" <td>0.363547</td>\n",
" <td>0.407020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.625272</td>\n",
" <td>0.681761</td>\n",
" <td>0.634224</td>\n",
" <td>0.652072</td>\n",
" <td>0.643315</td>\n",
" <td>0.655818</td>\n",
" <td>0.591165</td>\n",
" <td>0.623580</td>\n",
" <td>0.56889</td>\n",
" <td>0.619840</td>\n",
" <td>0.678924</td>\n",
" <td>0.679096</td>\n",
" <td>0.689974</td>\n",
" <td>0.724707</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>0.984975</td>\n",
" <td>0.862654</td>\n",
" <td>0.944251</td>\n",
" <td>0.956046</td>\n",
" <td>0.983674</td>\n",
" <td>0.997162</td>\n",
" <td>1.000000</td>\n",
" <td>0.982800</td>\n",
" <td>0.99540</td>\n",
" <td>0.994980</td>\n",
" <td>0.998742</td>\n",
" <td>0.998484</td>\n",
" <td>0.988494</td>\n",
" <td>0.844848</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" cont1 cont2 cont3 cont4 \\\n",
"count 313864.000000 313864.000000 313864.000000 313864.000000 \n",
"mean 0.494096 0.507089 0.498653 0.492021 \n",
"std 0.187768 0.207056 0.201961 0.211101 \n",
"min 0.000016 0.001149 0.002634 0.176921 \n",
"25% 0.347403 0.358319 0.336963 0.327354 \n",
"50% 0.475784 0.555782 0.527991 0.452887 \n",
"75% 0.625272 0.681761 0.634224 0.652072 \n",
"max 0.984975 0.862654 0.944251 0.956046 \n",
"\n",
" cont5 cont6 cont7 cont8 \\\n",
"count 313864.000000 313864.000000 313864.000000 313864.000000 \n",
"mean 0.487513 0.491442 0.485360 0.486823 \n",
"std 0.209063 0.205394 0.178531 0.199442 \n",
"min 0.281143 0.012683 0.069503 0.236880 \n",
"25% 0.281143 0.336105 0.351299 0.317960 \n",
"50% 0.422268 0.440945 0.438650 0.441060 \n",
"75% 0.643315 0.655818 0.591165 0.623580 \n",
"max 0.983674 0.997162 1.000000 0.982800 \n",
"\n",
" cont9 cont10 cont11 cont12 \\\n",
"count 313864.00000 313864.000000 313864.000000 313864.000000 \n",
"mean 0.48571 0.498403 0.493850 0.493503 \n",
"std 0.18185 0.185906 0.210002 0.209716 \n",
"min 0.00008 0.000000 0.035321 0.036232 \n",
"25% 0.35897 0.364580 0.310961 0.314945 \n",
"50% 0.44145 0.461190 0.457203 0.462286 \n",
"75% 0.56889 0.619840 0.678924 0.679096 \n",
"max 0.99540 0.994980 0.998742 0.998484 \n",
"\n",
" cont13 cont14 \n",
"count 313864.000000 313864.000000 \n",
"mean 0.493917 0.495665 \n",
"std 0.212911 0.222537 \n",
"min 0.000228 0.178568 \n",
"25% 0.315758 0.294657 \n",
"50% 0.363547 0.407020 \n",
"75% 0.689974 0.724707 \n",
"max 0.988494 0.844848 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count_data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "eea38c22-094d-3977-97e2-0174ec3671e8"
},
"source": [
"All the data is normalized already, so no need to modify it."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "1c88e771-3e7d-b238-8d00-9524f73326d6"
},
"outputs": [
{
"data": {
"text/plain": [
"count 188318.000000\n",
"mean 3037.337686\n",
"std 2904.086186\n",
"min 0.670000\n",
"25% 1204.460000\n",
"50% 2115.570000\n",
"75% 3864.045000\n",
"max 121012.250000\n",
"Name: loss, dtype: float64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.loss.describe()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "e9885b8d-1781-3173-0210-cd7f39bb396d"
},
"outputs": [
{
"data": {
"text/plain": [
"(188318, 132)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train = full.iloc[:len(train)]\n",
"train.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "33f56c2b-bf6d-aa24-37aa-234832ad2141"
},
"outputs": [
{
"data": {
"text/plain": [
"(125546, 132)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = full.iloc[len(train):len(full)]\n",
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "5d01666b-60f5-62c5-ce35-c21a7365483e"
},
"outputs": [
{
"data": {
"text/plain": [
"(125546, 130)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test = test.drop(\"loss\",axis=1)\n",
"ids = test.id\n",
"test = test.drop(\"id\",axis=1)\n",
"test.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "9edabf88-0fb8-7cba-b158-7aad0b4ba819"
},
"outputs": [],
"source": [
"train_loss = train.loss\n",
"train =train.drop(\"loss\",axis=1)\n",
"train = train.drop(\"id\",axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "dc63e318-63c9-113d-8f98-3e58d9c6da67"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(train,train_loss, test_size=0.3, random_state=42)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1c21ad5a-ea32-a01c-6ca4-fa46de870700"
},
"source": [
"###First run with all features"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "382e6324-369c-aacb-a251-a84c2219739d"
},
"outputs": [
{
"data": {
"text/plain": [
"0.53341503137246227"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.model_selection import cross_val_score\n",
"from xgboost import XGBRegressor\n",
"xgb = XGBRegressor(n_estimators=200)\n",
"xgb.fit(X_train, y_train)\n",
"scores = cross_val_score(xgb,X_test,y_test,scoring = 'r2')\n",
"scores.mean()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "7ac664fe-5b52-4db2-122b-24ca93d1a06a"
},
"outputs": [],
"source": [
"#feature_df = pd.DataFrame({\"Feature\":list(train.columns),\"Importance\":xgb.feature_importances_})\n",
"#feature_df=feature_df.sort_values(by=\"Importance\",ascending=False)\n",
"#feature_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "f975b184-8dab-56c7-7473-aa910986ea54"
},
"outputs": [],
"source": [
"#feature_df = feature_df.loc[feature_df.Importance > 0]\n",
"#feature_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a178e4e5-36d4-37b1-b7fd-191838b1ccd7"
},
"source": [
"###Training again without features whose importance was 0."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "00e00fe9-204e-3132-9e68-7332acf49913"
},
"outputs": [],
"source": [
"#train2 = train[feature_df.Feature]\n",
"#train2.shape"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "338cf022-9993-6df7-c6a7-dd5522d3afc1"
},
"outputs": [],
"source": [
"#X_train, X_test, y_train, y_test = train_test_split(train2,train_loss, test_size=0.3, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "9b1c1334-0eb0-fdfc-6dad-7d1d76c58001"
},
"outputs": [],
"source": [
"#xgb = XGBRegressor(n_estimators=200)\n",
"#xgb.fit(X_train, y_train)\n",
"#scores = cross_val_score(xgb,X_test,y_test,scoring = 'r2')\n",
"#scores.mean()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "f2fac6d3-a083-c617-7fd6-c0377869976b"
},
"outputs": [],
"source": [
"#len(xgb.feature_importances_)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"_cell_guid": "0d3efa80-5f2f-2a6e-b9ab-dd83d03cce62"
},
"outputs": [],
"source": [
"#feature_df = pd.DataFrame({\"Feature\":list(train2.columns),\"Importance\":xgb.feature_importances_})\n",
"#feature_df=feature_df.sort_values(by=\"Importance\",ascending=False)\n",
"#feature_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"_cell_guid": "41534505-953a-5702-e1b1-87c4293b2842"
},
"outputs": [],
"source": [
"#feature_df.Importance.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "c3d43e21-aa7a-49a7-d273-7ae3c777eb32"
},
"source": [
"###Training again, with drastically reduced dimensionality."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"_cell_guid": "b7076411-1761-68c1-dabf-7b2c64673ff5"
},
"outputs": [],
"source": [
"#feature_df = feature_df.loc[feature_df.Importance >= 0.01]\n",
"#feature_df.shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"_cell_guid": "e1772dde-c181-1b95-cd20-e8ec659f8153"
},
"outputs": [],
"source": [
"#train3 = train[feature_df.Feature]\n",
"#train3.shape\n",
"#X_train, X_test, y_train, y_test = train_test_split(train3,train_loss, test_size=0.3, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"_cell_guid": "49914fd9-f931-ab28-d2a3-56fd7f537c6b"
},
"outputs": [],
"source": [
"#xgb = XGBRegressor(n_estimators=200)\n",
"#xgb.fit(X_train, y_train)\n",
"#scores = cross_val_score(xgb,X_test,y_test,scoring = 'r2')\n",
"#scores.mean()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"_cell_guid": "6b42e484-730a-2e7f-5dac-c6d59396b494"
},
"outputs": [],
"source": [
"#feature_df = pd.DataFrame({\"Feature\":list(train3.columns),\"Importance\":xgb.feature_importances_})\n",
"#feature_df=feature_df.sort_values(by=\"Importance\",ascending=False)\n",
"#feature_df"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"_cell_guid": "2f0bc88e-f0c3-1505-f2b0-fa52442584fd"
},
"outputs": [],
"source": [
"#test = test[train3.columns]"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"_cell_guid": "806ae6c7-5905-271d-a11a-e70721aaf4cb"
},
"outputs": [],
"source": [
"#list(feature_df.Feature)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"_cell_guid": "1c94d5e3-f330-f5e9-e2fc-0ba6144aaedc"
},
"outputs": [],
"source": [
"#test.columns"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"_cell_guid": "22b97652-aaf5-23b4-e1e1-d5a1fc024432"
},
"outputs": [],
"source": [
"predictions = xgb.predict(test)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"_cell_guid": "2ed67445-02ad-1e1f-41ec-b4a6451d39ca"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"_cell_guid": "357a733e-f5ff-ed46-8aea-c057279844e9"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4</td>\n",
" <td>1903.960083</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>6</td>\n",
" <td>2199.333984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>9</td>\n",
" <td>10624.025391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>12</td>\n",
" <td>6446.193359</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>15</td>\n",
" <td>880.303467</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id loss\n",
"0 4 1903.960083\n",
"1 6 2199.333984\n",
"2 9 10624.025391\n",
"3 12 6446.193359\n",
"4 15 880.303467"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results = {\"id\":list(ids),\"loss\":predictions}\n",
"result_df = pd.DataFrame(results)\n",
"result_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"_cell_guid": "42609805-6644-34f6-c5b8-0e631824e56f"
},
"outputs": [],
"source": [
"result_df.to_csv('submission.csv',header=True, index_label='id')"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"_cell_guid": "b747137e-c16f-d484-d5c7-52127d41147b"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 362,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161723.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "1a77a45d-bf39-f720-216a-ef33689c8fac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"race-result-horse.csv\n",
"race-result-race.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import tensorflow as tf\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "a551a008-115b-0750-19c9-b5a4bb1ec525"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>horse_number</th>\n",
" <th>actual_weight</th>\n",
" <th>declared_horse_weight</th>\n",
" <th>draw</th>\n",
" <th>running_position_1</th>\n",
" <th>running_position_2</th>\n",
" <th>running_position_3</th>\n",
" <th>running_position_4</th>\n",
" <th>win_odds</th>\n",
" <th>running_position_5</th>\n",
" <th>running_position_6</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>19773.000000</td>\n",
" <td>19994.000000</td>\n",
" <td>19922.000000</td>\n",
" <td>19595.000000</td>\n",
" <td>19580.000000</td>\n",
" <td>19575.000000</td>\n",
" <td>19563.000000</td>\n",
" <td>11117.000000</td>\n",
" <td>19595.000000</td>\n",
" <td>2540.000000</td>\n",
" <td>352.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6.908309</td>\n",
" <td>122.987596</td>\n",
" <td>1108.048640</td>\n",
" <td>6.882266</td>\n",
" <td>6.853115</td>\n",
" <td>6.851801</td>\n",
" <td>6.847212</td>\n",
" <td>6.966718</td>\n",
" <td>30.848232</td>\n",
" <td>6.819291</td>\n",
" <td>6.272727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.760835</td>\n",
" <td>6.264382</td>\n",
" <td>60.542304</td>\n",
" <td>3.750373</td>\n",
" <td>3.734604</td>\n",
" <td>3.733819</td>\n",
" <td>3.732412</td>\n",
" <td>3.805165</td>\n",
" <td>32.114693</td>\n",
" <td>3.732491</td>\n",
" <td>3.423489</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>103.000000</td>\n",
" <td>902.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.100000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>4.000000</td>\n",
" <td>118.000000</td>\n",
" <td>1067.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>7.700000</td>\n",
" <td>4.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>7.000000</td>\n",
" <td>123.000000</td>\n",
" <td>1106.000000</td>\n",
" <td>7.000000</td>\n",
" <td>7.000000</td>\n",
" <td>7.000000</td>\n",
" <td>7.000000</td>\n",
" <td>7.000000</td>\n",
" <td>16.000000</td>\n",
" <td>7.000000</td>\n",
" <td>6.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>10.000000</td>\n",
" <td>128.000000</td>\n",
" <td>1149.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>43.000000</td>\n",
" <td>10.000000</td>\n",
" <td>9.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>14.000000</td>\n",
" <td>133.000000</td>\n",
" <td>1326.000000</td>\n",
" <td>15.000000</td>\n",
" <td>14.000000</td>\n",
" <td>14.000000</td>\n",
" <td>14.000000</td>\n",
" <td>14.000000</td>\n",
" <td>99.000000</td>\n",
" <td>14.000000</td>\n",
" <td>14.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" horse_number actual_weight declared_horse_weight draw \\\n",
"count 19773.000000 19994.000000 19922.000000 19595.000000 \n",
"mean 6.908309 122.987596 1108.048640 6.882266 \n",
"std 3.760835 6.264382 60.542304 3.750373 \n",
"min 1.000000 103.000000 902.000000 1.000000 \n",
"25% 4.000000 118.000000 1067.000000 4.000000 \n",
"50% 7.000000 123.000000 1106.000000 7.000000 \n",
"75% 10.000000 128.000000 1149.000000 10.000000 \n",
"max 14.000000 133.000000 1326.000000 15.000000 \n",
"\n",
" running_position_1 running_position_2 running_position_3 \\\n",
"count 19580.000000 19575.000000 19563.000000 \n",
"mean 6.853115 6.851801 6.847212 \n",
"std 3.734604 3.733819 3.732412 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 4.000000 4.000000 4.000000 \n",
"50% 7.000000 7.000000 7.000000 \n",
"75% 10.000000 10.000000 10.000000 \n",
"max 14.000000 14.000000 14.000000 \n",
"\n",
" running_position_4 win_odds running_position_5 \\\n",
"count 11117.000000 19595.000000 2540.000000 \n",
"mean 6.966718 30.848232 6.819291 \n",
"std 3.805165 32.114693 3.732491 \n",
"min 1.000000 1.100000 1.000000 \n",
"25% 4.000000 7.700000 4.000000 \n",
"50% 7.000000 16.000000 7.000000 \n",
"75% 10.000000 43.000000 10.000000 \n",
"max 14.000000 99.000000 14.000000 \n",
"\n",
" running_position_6 \n",
"count 352.000000 \n",
"mean 6.272727 \n",
"std 3.423489 \n",
"min 1.000000 \n",
"25% 3.000000 \n",
"50% 6.000000 \n",
"75% 9.000000 \n",
"max 14.000000 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1=pd.read_csv(\"../input/race-result-horse.csv\")\n",
"df2=pd.read_csv(\"../input/race-result-race.csv\")\n",
"\n",
"df1.describe()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "59d0ec79-1e0c-ac2f-1f3a-60945e1c80ec"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>race_number</th>\n",
" <th>race_distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1561.000000</td>\n",
" <td>1561.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>5.270980</td>\n",
" <td>1416.239590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>2.816205</td>\n",
" <td>283.788277</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.000000</td>\n",
" <td>1200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.000000</td>\n",
" <td>1400.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>8.000000</td>\n",
" <td>1650.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>11.000000</td>\n",
" <td>2400.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" race_number race_distance\n",
"count 1561.000000 1561.000000\n",
"mean 5.270980 1416.239590\n",
"std 2.816205 283.788277\n",
"min 1.000000 1000.000000\n",
"25% 3.000000 1200.000000\n",
"50% 5.000000 1400.000000\n",
"75% 8.000000 1650.000000\n",
"max 11.000000 2400.000000"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "bd68c1e8-dbee-aa6b-74b0-d9aff5658f43"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt, pandas as pd, numpy as np\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder\n",
"# import classification algorithms\n",
"from sklearn.svm import SVC\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "59ea76e7-d45c-c8a0-d197-fa7b2686a02d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" src race_date race_course race_number race_id race_class \\\n",
"0 20140914-1.html 2014-09-14 Sha Tin 1 2014-001 Class 5 \n",
"1 20140914-10.html 2014-09-14 Sha Tin 10 2014-010 Class 2 \n",
"2 20140914-2.html 2014-09-14 Sha Tin 2 2014-002 Class 5 \n",
"3 20140914-3.html 2014-09-14 Sha Tin 3 2014-003 Class 1 \n",
"4 20140914-4.html 2014-09-14 Sha Tin 4 2014-004 Class 4 \n",
"\n",
" race_distance track_condition race_name \\\n",
"0 1400 GOOD TO FIRM TIM WA HANDICAP \n",
"1 1400 GOOD TO FIRM COTTON TREE HANDICAP \n",
"2 1200 GOOD TO FIRM TIM MEI HANDICAP \n",
"3 1200 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"4 1200 GOOD TO FIRM LUNG WUI HANDICAP \n",
"\n",
" track sectional_time \\\n",
"0 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"1 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"2 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"3 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"4 TURF - \"A\" COURSE 24.00 22.62 22.64 \n",
"\n",
" incident_report \n",
"0 \\n When about to enter the trac... \n",
"1 \\n SMART MAN was slow to begin.... \n",
"2 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"3 \\n On arrival at the Start, it ... \n",
"4 \\n Just prior to the start bein... \n"
]
}
],
"source": [
"print(df2.head())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "272f210f-f14b-249e-6abf-d8867c3b9a03"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['finishing_position', 'horse_name', 'horse_id', 'jockey', 'trainer',\n",
" 'length_behind_winner', 'finish_time', 'race_id'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# collecting categorical varibale names \n",
"cat_var = df1.dtypes.loc[df1.dtypes=='object'].index\n",
"print(cat_var)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "59273ca7-be5f-c0fe-545c-e7c3b3368ab9"
},
"outputs": [
{
"data": {
"text/plain": [
"finishing_position 37\n",
"horse_name 1747\n",
"horse_id 1747\n",
"jockey 88\n",
"trainer 77\n",
"length_behind_winner 189\n",
"finish_time 3734\n",
"race_id 1561\n",
"dtype: int64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check for unique variables in cat_var\n",
"df1[cat_var].apply(lambda x: len(x.unique()))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "87d2d934-cab8-8d74-b291-1e5279e148ed"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-9ac9b8ba0589>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinishing_position\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'1'\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfinishing_position\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 915\u001b[0m raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[1;32m 916\u001b[0m \u001b[0;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m .format(self.__class__.__name__))\n\u001b[0m\u001b[1;32m 918\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 919\u001b[0m \u001b[0m__bool__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
]
}
],
"source": [
"df1=df1[df1.finishing_position=='1' or df1.finishing_position=='2']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "060b9a67-ed83-0865-5b75-e5cc3758f5fc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 19595.000000\n",
"mean 30.848232\n",
"std 32.114693\n",
"min 1.100000\n",
"25% 7.700000\n",
"50% 16.000000\n",
"75% 43.000000\n",
"max 99.000000\n",
"Name: win_odds, dtype: float64\n"
]
}
],
"source": [
"print(df1.win_odds.describe())\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "2055fec9-373a-0970-2a9c-427e96e548a6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 19595.000000\n",
"mean 30.848232\n",
"std 32.114693\n",
"min 1.100000\n",
"25% 7.700000\n",
"50% 16.000000\n",
"75% 43.000000\n",
"max 99.000000\n",
"Name: win_odds, dtype: float64\n"
]
}
],
"source": [
"print(df1.win_odds.describe())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "0bb4f570-97cb-ef31-2f3d-abafa3cb7b82"
},
"outputs": [],
"source": [
"nd1=df1.finish_time"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "1650ef2c-a4f9-5e91-bd60-f7d7a2545b8f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1.22.33\n",
"1 1.22.65\n",
"2 1.22.66\n",
"3 1.22.66\n",
"4 1.23.02\n",
"5 1.23.20\n",
"6 1.23.22\n",
"7 1.23.25\n",
"8 1.23.33\n",
"9 1.23.41\n",
"10 1.23.56\n",
"11 1.23.96\n",
"12 1.24.10\n",
"13 ---\n",
"14 1.21.54\n",
"15 1.21.89\n",
"16 1.21.92\n",
"17 1.21.95\n",
"18 1.21.98\n",
"19 1.22.10\n",
"20 1.22.10\n",
"21 1.22.18\n",
"22 1.22.25\n",
"23 1.22.32\n",
"24 1.22.34\n",
"25 1.22.72\n",
"26 1.22.98\n",
"27 1.23.26\n",
"28 1.09.97\n",
"29 1.10.17\n",
" ... \n",
"19966 1.34.19\n",
"19967 1.34.23\n",
"19968 1.34.32\n",
"19969 1.34.36\n",
"19970 1.35.26\n",
"19971 1.37.20\n",
"19972 ---\n",
"19973 1.33.33\n",
"19974 1.33.68\n",
"19975 1.33.84\n",
"19976 1.33.88\n",
"19977 1.34.12\n",
"19978 1.34.20\n",
"19979 1.34.36\n",
"19980 1.34.52\n",
"19981 1.34.60\n",
"19982 1.08.40\n",
"19983 1.08.57\n",
"19984 1.08.62\n",
"19985 1.08.66\n",
"19986 1.09.18\n",
"19987 1.09.30\n",
"19988 1.09.34\n",
"19989 1.09.65\n",
"19990 1.09.67\n",
"19991 1.09.77\n",
"19992 1.09.96\n",
"19993 1.10.12\n",
"19994 1.10.14\n",
"19995 1.10.59\n",
"Name: finish_time, dtype: object\n"
]
}
],
"source": [
"print(nd1)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "c8deefa2-4eff-22ab-a0f2-773ced49ba22"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1.22.33\n",
"Name: finish_time, dtype: object\n"
]
}
],
"source": [
"print(nd1[0:1])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "88be4543-f8fa-0f01-5724-03624d49ddec"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 181,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161735.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "69604cb2-3541-62fb-eed1-62eb9103aac0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t10k-images-idx3-ubyte\n",
"t10k-labels-idx1-ubyte\n",
"train-images-idx3-ubyte\n",
"train-labels-idx1-ubyte\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "25b55d90-2a8b-6bbb-fe3c-84d3034bf728"
},
"outputs": [],
"source": [
"import tensorflow as tf"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "bec7fd0e-50bb-1792-e11f-ee7ea58f9780"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n"
]
}
],
"source": [
"#sum with constant value\n",
"a = tf.constant(2)\n",
"b = tf.constant(3)\n",
"c = a+b\n",
"\n",
"sess = tf.Session()\n",
"print (sess.run(c))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "daaf1768-f4c4-b05d-cb58-193481d9f425"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"5\n",
"6\n"
]
}
],
"source": [
"# placeholder\n",
"\n",
"a = tf.placeholder(tf.int16)\n",
"b = tf.placeholder(tf.int16)\n",
"\n",
"add = tf.add(a, b)\n",
"mul = tf.multiply(a, b)\n",
"print(sess.run(add, feed_dict={a:2, b:3}))\n",
"print(sess.run(mul, feed_dict={a:2, b:3}))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "6f30177f-4c9b-2df9-af57-3fa2ca223538"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Const_2:0\", shape=(1, 2), dtype=float32) Tensor(\"Const_3:0\", shape=(2, 1), dtype=float32)\n",
"[[ 12.]]\n"
]
}
],
"source": [
"mat1 = tf.constant([[3., 3.]])\n",
"mat2 = tf.constant([[2.],[2.]])\n",
"product = tf.matmul(mat1, mat2)\n",
"print(mat1,mat2)\n",
"print(sess.run(product))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "0ec19c28-841b-5d73-de9b-95542f4bd879"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<tf.Variable 'Variable:0' shape=(3, 2) dtype=float32_ref>\n",
"<tf.Variable 'Variable_1:0' shape=(2, 3) dtype=float32_ref>\n",
"[[ 6. 8. 10.]\n",
" [ 7. 6. 5.]\n",
" [ 5. 10. 15.]]\n"
]
}
],
"source": [
"mat1 = tf.Variable(tf.random_normal([3,2]))\n",
"mat2 = tf.Variable(tf.random_normal([2,3]))\n",
"product = tf.matmul(mat1,mat2)\n",
"print(mat1)\n",
"print(mat2)\n",
"m1=[[1,3],[2,1],[0,5]]\n",
"m2=[[3,2,1],[1,2,3]]\n",
"print(sess.run(product,feed_dict={mat1:m1,mat2:m2}))"
]
}
],
"metadata": {
"_change_revision": 61,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161802.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a0b1128d-c6a0-5f71-e9b5-9ca3b3156641"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data_east' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-9e5fe14a9cb5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata_east\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'region'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'os'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mdat\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata_os\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ms1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdat\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'data_east' is not defined"
]
}
],
"source": [
"\n",
"data=data_east.groupby('region')['os'].value_counts()\n",
"df=[]\n",
"for dat in data:\n",
" data_os = s1[dat]\n",
" df.append(data_os.values)\n",
" \n",
"df = pd.DataFrame(df,index=data,columns=['Linux/UNIX','Windows'])\n",
"s=df.plot(kind=\"bar\",stacked=True,figsize=(12,6),fontsize=12)\n",
"s.set_title(\"AWS east-zone: Region wise os distribution\",color='g',fontsize=30)\n",
"s.set_xlabel(\"Region name\",color='b',fontsize=20)\n",
"s.set_ylabel(\"Frequency\",color='b',fontsize=20)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "013c463f-f23f-1f3f-6e41-adeb8137c672"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ap-south-1.csv\n",
"ca-central-1.csv\n",
"us-east-1.csv\n",
"us-west-1.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "b592fd27-7e9a-90c9-6905-e90dd821a615"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "de8bfb28-cb23-f7e5-0173-f81fdba554c7"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price</th>\n",
" <th>datetime</th>\n",
" <th>instance_type</th>\n",
" <th>os</th>\n",
" <th>region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.8865</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>c4.8xlarge</td>\n",
" <td>Windows</td>\n",
" <td>ap-south-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0366</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>m4.xlarge</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>ap-south-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.0243</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>m4.large</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>ap-south-1a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0346</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>m4.large</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>ap-south-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.2895</td>\n",
" <td>2017-03-07 16:05:01</td>\n",
" <td>c4.8xlarge</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>ap-south-1a</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price datetime instance_type os region\n",
"0 1.8865 2017-03-07 16:05:01 c4.8xlarge Windows ap-south-1b\n",
"1 0.0366 2017-03-07 16:05:01 m4.xlarge Linux/UNIX ap-south-1b\n",
"2 0.0243 2017-03-07 16:05:01 m4.large Linux/UNIX ap-south-1a\n",
"3 0.0346 2017-03-07 16:05:01 m4.large Linux/UNIX ap-south-1b\n",
"4 0.2895 2017-03-07 16:05:01 c4.8xlarge Linux/UNIX ap-south-1a"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_south = pd.read_csv(\"../input/ap-south-1.csv\",sep=\",\")\n",
"data_south.fillna(0)\n",
"data_south.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "0f6c5469-6d83-85a5-28fa-b099a4fb69eb"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>datetime</th>\n",
" <th>os</th>\n",
" <th>instance_type</th>\n",
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" <th>region</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>ca-central-1a</td>\n",
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" <td>ca-central-1b</td>\n",
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" <td>ca-central-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-06 17:29:00</td>\n",
" <td>Windows</td>\n",
" <td>m4.2xlarge</td>\n",
" <td>0.4152</td>\n",
" <td>ca-central-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-06 17:29:00</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>m4.2xlarge</td>\n",
" <td>0.0532</td>\n",
" <td>ca-central-1b</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime os instance_type price region\n",
"0 2017-05-06 17:29:01 Linux/UNIX c4.large 0.0139 ca-central-1a\n",
"1 2017-05-06 17:29:01 Windows m4.4xlarge 0.8328 ca-central-1b\n",
"2 2017-05-06 17:29:00 Linux/UNIX m4.4xlarge 0.1051 ca-central-1b\n",
"3 2017-05-06 17:29:00 Windows m4.2xlarge 0.4152 ca-central-1b\n",
"4 2017-05-06 17:29:00 Linux/UNIX m4.2xlarge 0.0532 ca-central-1b"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_central = pd.read_csv(\"../input/ca-central-1.csv\",sep=\",\")\n",
"data_central.fillna(0)\n",
"data_central.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "bac53169-d988-63e7-a759-8da7a076efa6"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>0</th>\n",
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" <td>ca-central-1b</td>\n",
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" <td>Linux/UNIX</td>\n",
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" <td>ca-central-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-05-06 17:29:00</td>\n",
" <td>Windows</td>\n",
" <td>m4.2xlarge</td>\n",
" <td>0.4152</td>\n",
" <td>ca-central-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-05-06 17:29:00</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>m4.2xlarge</td>\n",
" <td>0.0532</td>\n",
" <td>ca-central-1b</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime os instance_type price region\n",
"0 2017-05-06 17:29:01 Linux/UNIX c4.large 0.0139 ca-central-1a\n",
"1 2017-05-06 17:29:01 Windows m4.4xlarge 0.8328 ca-central-1b\n",
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"4 2017-05-06 17:29:00 Linux/UNIX m4.2xlarge 0.0532 ca-central-1b"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_central = pd.read_csv(\"../input/ca-central-1.csv\",sep=\",\")\n",
"data_central.fillna(0)\n",
"data_central.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "2e315330-9bf5-b38d-cf14-306713537884"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2717: DtypeWarning: Columns (0,1,2,3,4) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>datetime</th>\n",
" <th>instance_type</th>\n",
" <th>os</th>\n",
" <th>price</th>\n",
" <th>region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-04-29 22:48:06</td>\n",
" <td>r4.2xlarge</td>\n",
" <td>SUSE_Linux</td>\n",
" <td>0.1917</td>\n",
" <td>us-east-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-04-29 22:48:07</td>\n",
" <td>m4.10xlarge</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>0.7258</td>\n",
" <td>us-east-1a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-04-29 22:48:07</td>\n",
" <td>m4.10xlarge</td>\n",
" <td>SUSE_Linux</td>\n",
" <td>0.8258</td>\n",
" <td>us-east-1a</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-04-29 22:48:07</td>\n",
" <td>i3.8xlarge</td>\n",
" <td>Linux/UNIX</td>\n",
" <td>0.3532</td>\n",
" <td>us-east-1c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-04-29 22:48:07</td>\n",
" <td>i3.8xlarge</td>\n",
" <td>SUSE_Linux</td>\n",
" <td>0.4532</td>\n",
" <td>us-east-1c</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime instance_type os price region\n",
"0 2017-04-29 22:48:06 r4.2xlarge SUSE_Linux 0.1917 us-east-1b\n",
"1 2017-04-29 22:48:07 m4.10xlarge Linux/UNIX 0.7258 us-east-1a\n",
"2 2017-04-29 22:48:07 m4.10xlarge SUSE_Linux 0.8258 us-east-1a\n",
"3 2017-04-29 22:48:07 i3.8xlarge Linux/UNIX 0.3532 us-east-1c\n",
"4 2017-04-29 22:48:07 i3.8xlarge SUSE_Linux 0.4532 us-east-1c"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_east = pd.read_csv(\"../input/us-east-1.csv\",sep=\",\")\n",
"data_east.fillna(0)\n",
"data_east.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "979db024-e7c8-b6b0-0847-35f25051f8b6"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>datetime</th>\n",
" <th>os</th>\n",
" <th>instance_type</th>\n",
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" <th>region</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-03-06 19:53:58</td>\n",
" <td>Windows</td>\n",
" <td>m1.large</td>\n",
" <td>0.0801</td>\n",
" <td>us-west-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-03-06 20:03:00</td>\n",
" <td>Windows</td>\n",
" <td>m1.small</td>\n",
" <td>0.0201</td>\n",
" <td>us-west-1c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-03-06 20:03:00</td>\n",
" <td>Windows</td>\n",
" <td>m1.small</td>\n",
" <td>0.0201</td>\n",
" <td>us-west-1b</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-03-06 21:49:27</td>\n",
" <td>Windows</td>\n",
" <td>i3.16xlarge</td>\n",
" <td>84.4800</td>\n",
" <td>us-west-1c</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-03-06 21:49:27</td>\n",
" <td>Windows</td>\n",
" <td>i3.16xlarge</td>\n",
" <td>84.4800</td>\n",
" <td>us-west-1b</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime os instance_type price region\n",
"0 2017-03-06 19:53:58 Windows m1.large 0.0801 us-west-1b\n",
"1 2017-03-06 20:03:00 Windows m1.small 0.0201 us-west-1c\n",
"2 2017-03-06 20:03:00 Windows m1.small 0.0201 us-west-1b\n",
"3 2017-03-06 21:49:27 Windows i3.16xlarge 84.4800 us-west-1c\n",
"4 2017-03-06 21:49:27 Windows i3.16xlarge 84.4800 us-west-1b"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_west = pd.read_csv(\"../input/us-west-1.csv\",sep=\",\")\n",
"data_west.fillna(0)\n",
"data_west.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "b4d07f99-31ae-1540-fc5e-a9ecb7d67d26"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f36d1a02710>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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rLi7Om1MDAAAALovXVsTLysq0Zs0abd26VS+++KLefPNNZWZmKikpSVu3blX79u2Vk5Oj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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36acea2b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#os usage in east region\n",
"sns.set_context(\"notebook\",font_scale=1.0)\n",
"plt.figure(figsize=(12,4))\n",
"plt.title('os usage in east region')\n",
"sns.countplot(data_east['os'])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "ea8501ea-3209-587e-15da-622b50d66c5b"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f36ac326d30>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zZ+Pl5UVSUhIJCQmYTCbmz5+Pr68vM2fO5PDhw8TExGCxWFi/fj0A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\nMOf1DTlg8/oAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36ac326b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#os usage in west region\n",
"sns.set_context(\"notebook\",font_scale=1.0)\n",
"plt.figure(figsize=(12,4))\n",
"plt.title('os usage in west region')\n",
"sns.countplot(data_west['os'])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "faf24568-12bd-7b64-224d-8d3aa7a24884"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f36aba10828>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AQVySJEkKgEFckiRJCoBBXJIkSQqAQVySJEkKgEFckiRJCoBBXJIkSQqAQVySJEkKgEFc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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36aba20208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#os usage in central region\n",
"sns.set_context(\"notebook\",font_scale=1.0)\n",
"plt.figure(figsize=(12,4))\n",
"plt.title('os usage in west region')\n",
"sns.countplot(data_central['os'])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "52c55b3e-d7af-1b3a-32e2-a9728a920771"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f36ab9d2ef0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SJAWIAV2SJEkKEAO6JEmSFCBNEtAPHTrExRdfzOOPP87bb7/N2LFjycrKYtKk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\nSQFiQJckSZICxIAuSZIkBcj/A+GF8xlRbnLsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36ab9d2470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#os usage in south region\n",
"sns.set_context(\"notebook\",font_scale=1.0)\n",
"plt.figure(figsize=(12,4))\n",
"plt.title('os usage in south region')\n",
"sns.countplot(data_south['os'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "06036402-aaab-446a-aa04-fe311b528b17"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f36ab8d3390>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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IAwAAADZEIg8AAADYEIk8AAAAYEMk8gAAAIANkcgDAAAANkQiDwAAANgQiTwA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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36ab94e358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#instance type in east region\n",
"data=data_east.groupby('instance_type').size().plot(\"bar\",figsize=(12,6),fontsize=12)\n",
"data.set_title(\"east region instance type\",color='b',fontsize=30)\n",
"data.set_xlabel(\"Instance type\",color='g',fontsize=20)\n",
"data.set_ylabel(\"count\",color='g',fontsize=20)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "e27cf065-c112-f149-4b4f-983d53fc5832"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f36ab77eb70>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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27VotWbJEGzduVJcuXfTggw+GbH8AAAAALRHyOfE33nijbrzxxkbLMzIylJGR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\nGP8P1tMaecZEA68AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36aba06f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#instance type in west region\n",
"data=data_west.groupby('instance_type').size().plot(\"bar\",figsize=(12,6),fontsize=12)\n",
"data.set_title(\"west region instance type\",color='b',fontsize=30)\n",
"data.set_xlabel(\"Instance type\",color='g',fontsize=20)\n",
"data.set_ylabel(\"count\",color='g',fontsize=20)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "f47655f7-7fb3-6f5d-4510-ff7b4ceb12e9"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f36ab4796d8>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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QZijiAQAAgDBDEQ8AAACEGYp4AAAAIMxQxAMAAABhhiIeAAAACDMU8QAAAECY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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36ab465550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#instance type in south region\n",
"data=data_south.groupby('instance_type').size().plot(\"bar\",figsize=(12,6),fontsize=12)\n",
"data.set_title(\"south region instance type\",color='b',fontsize=30)\n",
"data.set_xlabel(\"Instance type\",color='g',fontsize=20)\n",
"data.set_ylabel(\"count\",color='g',fontsize=20)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "8df5316b-e6ff-8551-1c27-3eae727c90d1"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f36a42504e0>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AAACAzRDiAQAAAJshxAMAAAA2Q4gHAAAAbIYQDwAAANgMIR4AAACwGUI8AAAA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36a4315e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#instance type in central region\n",
"data=data_central.groupby('instance_type').size().plot(\"bar\",figsize=(12,6),fontsize=12)\n",
"data.set_title(\"south region instance type\",color='b',fontsize=30)\n",
"data.set_xlabel(\"Instance type\",color='g',fontsize=20)\n",
"data.set_ylabel(\"count\",color='g',fontsize=20)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "475e2fae-86d1-9f59-0347-6033d215b9ca"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f36a42f9da0>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ExsYyZcoUANLS0nj00UeprKwkJSWF1NRUAPr378/gwYMJhUKkp6cTFeUj0iVJklRzhcJV\nWVB9Dqopa4YkSadn5PKxQZcg6SwxrffEoEsAAlgjLkmSJOnEDOKSJElSAAzikiRJUgAM4pIkSVIA\nDOKSJElSAAzikiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM\n4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKSJElSAAzi\nkiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKS\nJElSAKo1iB84cIAbbriBP/zhD+zYsYMhQ4YwaNAghg8fTllZGQDLli2jb9++9OvXj1dffRWA8vJy\nRowYwcCBAxk8eDDbtm0DYMuWLQwYMIABAwYwbty4yHFefPFFbrvtNvr168fatWurc0iSJEnS16Ja\ng/izzz7LBRdcAMDMmTMZNGgQixYtokmTJmRkZFBaWsrs2bOZN28eCxYsYP78+RQXF7N8+XLi4+N5\n5ZVXuPfee5k+fToAkyZNIi0tjcWLF7N3717Wrl3Ltm3bWLFiBYsWLeL5559n8uTJVFRUVOewJEmS\npDNWbUH8888/Z+vWrVx//fUA5OTk0K1bNwC6dOlCdnY2GzdupGXLlsTFxVG7dm2uueYacnNzyc7O\npnv37gCkpqaSm5tLWVkZ27dvJzk5+ag+cnJy6NSpE7GxsSQkJNC4cWO2bt1aXcOSJEmSvha1qqvj\nqVOn8sgjj/Daa68BsH//fmJjYwFo0KABhYWFFBUVkZCQENknISHhmPaoqChCoRBFRUXEx8dHtj3c\nR7169Y7bR/PmzU9aX/36dahVK/prG68kSZJqlsTEuKBLOKlqCeKvvfYarVq14uKLLz7u++Fw+Izb\nT7eP/7RrV2mVtpMkSdLZqbCwJOgSgBN/IaiWIJ6VlcW2bdvIysoiLy+P2NhY6tSpw4EDB6hduzb5\n+fkkJSWRlJREUVFRZL+CggJatWpFUlIShYWFtGjRgvLycsLhMImJiRQXF0e2PbKPL7744ph2SZIk\nqSarljXiM2bMYOnSpfz+97+nX79+3HfffaSmprJy5UoAVq1aRadOnUhJSWHTpk3s2bOHffv2kZub\nS5s2bejQoQOZmZkArFmzhrZt2xITE0PTpk3ZsGHDUX20a9eOrKwsysrKyM/Pp6CggGbNmlXHsCRJ\nkqSvTbWtEf9Pv/zlLxk9ejRLlizhoosuok+fPsTExDBixAiGDh1KKBRi2LBhxMXF0atXL9atW8fA\ngQOJjY1lypQpAKSlpfHoo49SWVlJSkoKqampAPTv35/BgwcTCoVIT08nKsrHo0uSJKlmC4Wruqj6\nHFNT1gxJkk7PyOVjgy5B0lliWu+JQZcAnHiNuFPHkiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM\n4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKSJElSAAzi\nkiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKSJElSAAzikiRJUgAM4pIkSVIADOKS\nJElSAGoFXcC32fBpy4IuQdJZ4umRtwRdgiTpa+aMuCRJkhSAKgXxMWPGHNM2dOjQr70YSZIk6dvi\npEtTli1bxuLFi/nb3/7G7bffHmkvLy+nqKio2ouTJEmSzlUnDeK33HILbdu25cEHH+SXv/xlpD0q\nKopmzZpVe3GSJEnSueqUN2s2bNiQBQsWUFJSQnFxcaS9pKSEevXqVWtxkiRJ0rmqSk9NmThxIkuX\nLiUhIYFwOAxAKBRi9erV1VqcJEmSdK6qUhDPyclh/fr1nHfeeVXueP/+/YwZM4adO3dy8OBB7rvv\nPlq0aMGoUaOoqKggMTGRadOmERsby7Jly5g/fz5RUVH079+ffv36UV5ezpgxY/jXv/5FdHQ0kydP\n5uKLL2bLli2kp6cD0Lx5c8aPHw/Aiy++SGZmJqFQiPvvv5/OnTuf/tmQJEmSviFVempKkyZNTiuE\nA6xZs4arrrqKhQsXMmPGDKZMmcLMmTMZNGgQixYtokmTJmRkZFBaWsrs2bOZN28eCxYsYP78+RQX\nF7N8+XLi4+N55ZVXuPfee5k+fToAkyZNIi0tjcWLF7N3717Wrl3Ltm3bWLFiBYsWLeL5559n8uTJ\nVFRUnP7ZkCRJkr4hVZoRb9SoEbfffjutW7cmOjo60j58+PAT7tOrV6/I3zt27KBhw4bk5OREZrC7\ndOnC3Llzueyyy2jZsiVxcXEAXHPNNeTm5pKdnU2fPn0ASE1NJS0tjbKyMrZv305ycnKkj+zsbAoL\nC+nUqROxsbEkJCTQuHFjtm7dSvPmzU/zdEiSJEnfjCoF8Xr16tG+ffv/0wEGDBhAXl4ezz33HHfd\ndRexsbEANGjQgMLCQoqKikhISIhsn5CQcEx7VFQUoVCIoqIi4uPjI9se7qNevXrH7eNkQbx+/TrU\nqhV9wvclqSZJTIwLugRJOuvU9GtnlYL4fffd938+wOLFi/nss88YOXJk5EZP4Ki/j3Q67afbx5F2\n7So95TaSVFMUFpYEXYIknXVqyrXzRF8IqhTEf/jDHxIKhSKvQ6EQcXFx5OTknHCfzZs306BBA777\n3e/ygx/8gIqKCurWrcuBAweoXbs2+fn5JCUlkZSUdNSPAxUUFNCqVSuSkpIoLCykRYsWlJeXEw6H\nSUxMPOoRikf28cUXXxzTLkmSJNVUVbpZc8uWLXz22Wd89tlnbNy4kWefffaUP3G/YcMG5s6dC0BR\nURGlpaWkpqaycuVKAFatWkWnTp1ISUlh06ZN7Nmzh3379pGbm0ubNm3o0KEDmZmZwFc3frZt25aY\nmBiaNm3Khg0bjuqjXbt2ZGVlUVZWRn5+PgUFBf7gkCRJkmq0Ks2IHyk2NpbOnTszd+5cfvGLX5xw\nuwEDBvDwww8zaNAgDhw4wKOPPspVV13F6NGjWbJkCRdddBF9+vQhJiaGESNGMHToUEKhEMOGDSMu\nLo5evXqxbt06Bg4cSGxsLFOmTAEgLS2NRx99lMrKSlJSUkhNTQWgf//+DB48mFAoRHp6OlFRVfqO\nIUmSJAUiFK7CguqMjIyjXufl5bF8+fLIjPXZqCasGRo+bVnQJUg6Szw98pagS6gxRi4fG3QJks4S\n03pPDLoE4AzXiH/00UdHvT7//POZMWPGmVclSZIkfUtVKYhPnjwZgOLiYkKhEBdccEG1FiVJkiSd\n66oUxHNzcxk1ahT79u0jHA5Tr149pk2bRsuWLau7PkmSJOmcVKUgPn36dJ555hmuuOIKAD799FMm\nTZrEyy+/XK3FSZIkSeeqKj1aJCoqKhLC4avnih/5U/eSJEmSTk+Vg/jKlSvZu3cve/fuZcWKFQZx\nSZIk6QxUaWnK+PHjeeyxxxg7dixRUVG0aNGCiRNrxuNgJEmSpLNRlWbE33//fWJjY/nwww/Jycmh\nsrKStWvXVndtkiRJ0jmrSkF82bJl/OY3v4m8njt3Lm+88Ua1FSVJkiSd66oUxCsqKo5aE+7Px0uS\nJElnpkprxLt27cqAAQNo3bo1lZWVrF+/nhtvvLG6a5MkSZLOWVUK4vfddx/XXnstH3/8MaFQiHHj\nxtGqVavqrk2SJEk6Z1UpiAO0adOGNm3aVGctkiRJ0reGi70lSZKkABjEJUmSpAAYxCVJkqQAGMQl\nSZKkABjEJUmSpAAYxCVJkqQAGMQlSZKkABjEJUmSpAAYxCVJkqQAGMQlSZKkABjEJUmSpAAYxCVJ\nkqQAGMQlSZKkABjEJUmSpAAYxCVJkqQA1KrOzp944gk++ugjDh06xH/913/RsmVLRo0aRUVFBYmJ\niUybNo3Y2FiWLVvG/PnziYqKon///vTr14/y8nLGjBnDv/71L6Kjo5k8eTIXX3wxW7ZsIT09HYDm\nzZszfvx4AF588UUyMzMJhULcf//9dO7cuTqHJkmSJJ2Ragvi69ev529/+xtLlixh165d3HrrrbRv\n355BgwbRs2dPnnrqKTIyMujTpw+zZ88mIyODmJgYbrvtNrp3786aNWuIj49n+vTpvPfee0yfPp0Z\nM2YwadIk0tLSSE5OZsSIEaxdu5amTZuyYsUKFi9ezN69exk0aBAdO3YkOjq6uoYnSZIknZFqW5ry\nox/9iKeffhqA+Ph49u/fT05ODt26dQOgS5cuZGdns3HjRlq2bElcXBy1a9fmmmuuITc3l+zsbLp3\n7w5Aamoqubm5lJWVsX37dpKTk4/qIycnh06dOhEbG0tCQgKNGzdm69at1TU0SZIk6YxV24x4dHQ0\nderUASAjI4PrrruO9957j9jYWAAaNGhAYWEhRUVFJCQkRPZLSEg4pj0qKopQKERRURHx8fGRbQ/3\nUa9eveP20bx58xPWV79+HWrVcsZc0tkhMTEu6BIk6axT06+d1bpGHOCdd94hIyODuXPncuONN0ba\nw+Hwcbc/nfbT7eNIu3aVnnIbSaopCgtLgi5Bks46NeXaeaIvBNX61JQ///nPPPfcc7zwwgvExcVR\np04dDhw4AEB+fj5JSUkkJSVRVFQU2aegoCDSXlhYCEB5eTnhcJjExESKi4sj256oj8PtkiRJUk1V\nbUG8pKSEJ554gueff5569eoBX631XrlyJQCrVq2iU6dOpKSksGnTJvbs2cO+ffvIzc2lTZs2dOjQ\ngczMTADWrFlD27ZtiYmJoWnTpmzYsOGoPtq1a0dWVhZlZWXk5+dTUFBAs2bNqmtokiRJ0hmrtqUp\nK1asYNeuXfzqV7+KtE2ZMoWxY8eyZMkSLrroIvr06UNMTAwjRoxg6NChhEIhhg0bRlxcHL169WLd\nunUMHDiQ2NhYpkyZAkBaWhqPPvoolZWVpKSkkJqaCkD//v0ZPHgwoVCI9PR0oqJ8RLokSZJqrlC4\nKguqz0E1Yc3Q8GnLgi5B0lni6ZG3BF1CjTFy+digS5B0lpjWe2LQJQABrRGXJEmSdHwGcUmSJCkA\nBnFJkiQpAAZxSZIkKQAGcUmSJCkABnFJkiQpAAZxSZIkKQAGcUmSJCkABnFJkiQpAAZxSZIkKQAG\ncUmSJCkABnFJkiQpAAZxSZIkKQAGcUmSJCkABnFJkiQpAAZxSZIkKQAGcUmSJCkABnFJkiQpAAZx\nSZIkKQAGcUmSJCkABnFJkiQpAAZxSZIkKQAGcUmSJCkABnFJkiQpAAZxSZIkKQAGcUmSJCkABnFJ\nkiQpAAZxSZIkKQDVGsT/+te/csMNN7Bw4UIAduzYwZAhQxg0aBDDhw+nrKwMgGXLltG3b1/69evH\nq6++CkB5eTkjRoxg4MCBDB48mG3btgGwZcsWBgwYwIABAxg3blzkWC+++CK33XYb/fr1Y+3atdU5\nLEmSJOmMVVsQLy0t5bHHHqN9+/aRtpkzZzJo0CAWLVpEkyZNyMjIoLS0lNmzZzNv3jwWLFjA/Pnz\nKS4uZvny5cTHx/PKK69w7733Mn36dAAmTZpEWloaixcvZu/evaxdu5Zt27axYsUKFi1axPPPP8/k\nyZOpqKiorqFJkiRJZ6zagnhsbCwvvPACSUlJkbacnBy6desGQJcuXcjOzmbjxo20bNmSuLg4ateu\nzTXXXENubi7Z2dl0794dgNTUVHJzcykrK2P79u0kJycf1UdOTg6dOnUiNjaWhIQEGjduzNatW6tr\naJIkSdIZq7YgXqtWLWrXrn1U2/79+4mNjQWgQYMGFBYWUlRUREJCQmSbhISEY9qjoqIIhUIUFRUR\nHx8f2fZUfUiSJEk1Va2gDhwOh8+4/XT7OFL9+nWoVSv6lNtJUk2QmBgXdAmSdNap6dfObzSI16lT\nhwMHDlC7dm3y8/NJSkoiKSmJoqKiyDYFBQW0atWKpKQkCgsLadGiBeXl5YTDYRITEykuLo5se2Qf\nX3zxxTHtJ7NrV+nXP0BJqiaFhSVBlyBJZ52acu080ReCb/TxhampqaxcuRKAVatW0alTJ1JSUti0\naRN79uxh37595Obm0qZNGzp06EBmZiYAa9asoW3btsTExNC0aVM2bNhwVB/t2rUjKyuLsrIy8vPz\nKSgooFnZdyvcAAAJyklEQVSzZt/k0CRJkqTTUm0z4ps3b2bq1Kls376dWrVqsXLlSp588knGjBnD\nkiVLuOiii+jTpw8xMTGMGDGCoUOHEgqFGDZsGHFxcfTq1Yt169YxcOBAYmNjmTJlCgBpaWk8+uij\nVFZWkpKSQmpqKgD9+/dn8ODBhEIh0tPTiYryEemSJEmquULhqiyoPgfVhP+qGD5tWdAlSDpLPD3y\nlqBLqDFGLh8bdAmSzhLTek8MugSghixNkSRJkvQVg7gkSZIUAIO4JEmSFACDuCRJkhQAg7gkSZIU\nAIO4JEmSFACDuCRJkhQAg7gkSZIUAIO4JEmSFACDuCRJkhQAg7gkSZIUAIO4JEmSFACDuCRJkhQA\ng7gkSZIUAIO4JEmSFACDuCRJkhQAg7gkSZIUAIO4JEmSFACDuCRJkhQAg7gkSZIUAIO4JEmSFACD\nuCRJkhQAg7gkSZIUAIO4JEmSFACDuCRJkhQAg7gkSZIUAIO4JEmSFACDuCRJkhQAg7gkSZIUgFpB\nF/B1evzxx9m4cSOhUIi0tDSSk5ODLkmSJEk6rnMmiH/wwQf84x//YMmSJXz++eekpaWxZMmSoMuS\nJEmSjuucWZqSnZ3NDTfcAMDll1/O7t272bt3b8BVSZIkScd3zsyIFxUVceWVV0ZeJyQkUFhYyPnn\nn3/c7RMT476p0k5o0RO3B12CJJ115t31dNAlSNLX4pyZEf9P4XA46BIkSZKkEzpngnhSUhJFRUWR\n1wUFBSQmJgZYkSRJknRi50wQ79ChAytXrgTgk08+ISkp6YTLUiRJkqSgnTNrxK+55hquvPJKBgwY\nQCgUYty4cUGXJEmSJJ1QKOxiakmSJOkbd84sTZEkSZLOJgZxSZIkKQAGcamGOnzzcVUMGTKEv/71\nr8e07969m6FDh/LAAw98naVJUo1UXdfNWbNmsXDhwq+lRulIBnGpBvryyy958803z7ifcePG0bp1\n66+hIkmq2bxu6mx0zjw1Raou5eXljBkzhu3bt3Peeefx+OOPM2HCBEpLSzlw4ACPPPIIycnJR+2z\nZ88eHnzwQfbu3UtcXBxPPfUU4XCYtLQ0du/eTUVFBWPHjqVFixZ0796d/v37k5WVRVlZGS+99BIT\nJkzg448/5je/+Q3hcJht27bx5ZdfMm/ePB566CHy8/MpLS3ll7/8JV26dDlh7RMnTuSTTz5hy5Yt\nkba8vDxGjhwJwKFDh5g6dSqXXHJJ9Zw8Sd9K59p1E2DTpk3cfffdFBQUMGrUKK677rpqOXf6dnFG\nXDqF1157jQsvvJDFixfTv39/3nnnHfr168eCBQv4n//5H1544YVj9pkzZw4dO3Zk0aJFtG/fnuzs\nbObPn0+nTp2YP38+6enpTJ06FYCKigouv/xyXn75Zb73ve+xfv16hg4dyrXXXsv9998PfPWP2qJF\niygpKaFjx44sXLiQp59+mlmzZp209uM9S7+goIBhw4axYMEC+vbty6JFi76GsyRJ/+tcu24C7Ny5\nk7lz5/LUU08xY8aMMzxD0lecEZdO4ZNPPqF9+/YA3HzzzZSUlDBhwgTmzJlDWVkZderUOWafTz/9\nlOHDhwNw5513ArB48WL+/e9/s2zZMgD2798f2b5NmzYANGrUiJKSEuLi4o7q7/DMUXx8PJs2bWLJ\nkiVERUVRXFx82uNJTExk4sSJzJo1iz179nDllVeedh+SdDLn2nUT4NprrwXgiiuuYMeOHf+nPqT/\nZBCXTiE6OprKysrI6/nz59OwYUOmTZvGpk2beOKJJzhw4AA///nPARg6dOgx+wDExMTwyCOPcPXV\nVx/3GIcd79H+MTExACxfvpzdu3ezaNEiiouLue22247abubMmXz44YdcccUVPPLII8cdz8yZM+nY\nsSMDBw4kMzOTrKysqp0ISaqic+26CRAKhY77t3QmDOLSKbRs2ZL169fTs2dP1qxZw7PPPhv55dZ3\n3nmH8vJyateuzYIFCyL7bN68mfXr15OcnMzixYs577zzSElJ4Z133uHqq69m69at/PnPf+auu+46\n7jGjoqI4dOjQMe27du3ie9/7HlFRUbz99tuUlZUd9X5Vno6ya9cuLrnkEsLhMKtXrz7mHz5JOlPn\n2nUT4KOPPuLnP/85W7Zs4aKLLqrqqZBOyjXi0in06tWL/fv3M3jwYObPn89LL73ESy+9xN13301y\ncjKFhYUsXbr0qH3uuOMO/vKXvzBkyBCysrLo3r07gwcP5p///CeDBg1i7Nixkf9WPZ7LL7+cTz/9\nlMcff/yo9htvvJF3332XO+64g+985zs0atSI3/zmN8fto6KigiFDhvD444/zwQcfMGTIELKzs/np\nT3/KY489xj333MPNN9/MBx98wHvvvXfmJ0qS/r9z7boJ0KBBA+69914efPBBRowYcYZnSPqKP3Ev\nSZIkBcAZcUmSJCkABnFJkiQpAAZxSZIkKQAGcUmSJCkABnFJkiQpAAZxSVKVFRYWVvm5y5Kkk/Px\nhZIkSVIA/GVNSfqWycnJ4ZlnnuG8886ja9eubN68mX/84x/s27eP3r17c/fdd3Pw4EFGjx7N9u3b\nadSoEdHR0XTo0IH27dszaNAg/vSnP1FUVMTDDz9MaWkpZWVl3HPPPXTv3p1Zs2ZRXFxMXl4e//jH\nP2jbtu1Jfzpckr6tDOKS9C20efNmVq9eTUZGBklJSUycOJGKigr69+9PamoqmzZt4tChQ7z66qsU\nFhbSq1cvOnTocFQfM2fO5Ec/+hH33HMPO3fu5JZbbqF9+/YAfPrppyxcuJDy8nLat2/PAw88wAUX\nXBDEUCWpxjKIS9K30GWXXUa9evXIyckhLy+PDz/8EICysjL++c9/8tlnn3HttdcCkJiYSOvWrY/p\nY+PGjQwcOBD46ue/GzZsyBdffAFA69atiY6OJjo6mvr167N7926DuCT9B4O4JH0LxcTEABAbG8uw\nYcPo0aPHUe+vW7eOqKj/vZ//yL8PC4VCJ2yLjo4+qt3bkSTpWD41RZK+xVq3bs1bb70FQGVlJZMn\nT6a4uJimTZvyl7/8BYCdO3fy0UcfHbNvSkoKf/7znwHIz8+noKCAyy677JsrXpLOcs6IS9K32O23\n387f/vY3fvrTn1JRUcH1119PvXr1+MlPfkJWVhY//elP+d73vkebNm2OmeV+4IEHePjhhxkyZAgH\nDx7kscceo27dugGNRJLOPj6+UJJ0jPz8fHJzc+nZsyeVlZXceuutpKenc/XVVwddmiSdM5wRlyQd\nIy4ujhUrVjBnzhxCoRDXXXedIVySvmbOiEuSJEkB8GZNSZIkKQAGcUmSJCkABnFJkiQpAAZxSZIk\nKQAGcUmSJCkA/w8PIj+ePDRTyQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36ab6ff8d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#central region usage details\n",
"sns.set_context(\"notebook\",font_scale=1.0)\n",
"plt.figure(figsize=(12,4))\n",
"plt.title('central region usage details')\n",
"sns.countplot(data_central['region'])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "0015ab05-cf59-0506-2928-d561dd64579b"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f36a4065ba8>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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t6wMAAAAuK636kmh2draef/55FRUVaffu3frd736nJUuWfOt5hw4d0qhRo7R+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36a4081748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#west region usage details\n",
"sns.set_context(\"notebook\",font_scale=1.0)\n",
"plt.figure(figsize=(12,4))\n",
"plt.title('west region usage details')\n",
"sns.countplot(data_west['region'])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "967a7f51-8bbf-5965-2ba4-9de77800aaba"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f36a3f18a58>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ABkJABwAAAAyEgA4AAAAYCAEdAAAAMBACOgAAAGAgzRrQP/30Uw0dOlQvvviiJOmbb75R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\noAMAAAAGQkAHAAAADOT/ANBSS8LKKF/RAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36a3f18908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#west region usage details\n",
"sns.set_context(\"notebook\",font_scale=1.0)\n",
"plt.figure(figsize=(12,4))\n",
"plt.title('south region usage details')\n",
"sns.countplot(data_south['region'])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "3ad12db6-97d5-f484-f386-f38e7a499078"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f36a3e9db70>"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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KYrEoKytLbm5u9m2v9eHu7l5pH23atKmyvgceaKQGDWx3Zayoeby8XM0uATUQ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\nwAT/B8OLdSLbNvqjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f36a3e9dda0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#east region usage details\n",
"sns.set_context(\"notebook\",font_scale=1.0)\n",
"plt.figure(figsize=(12,4))\n",
"plt.title('east region usage details')\n",
"sns.countplot(data_east['region'])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "31fccc96-1d9f-0e8f-4db5-ad5307ba0f76"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 71,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161809.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "eb8372e9-a37a-dc9f-d431-f0b4c5fe3109"
},
"source": [
"**`This is a test!`**"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "51227c96-9056-c27c-d397-cf54898d48f4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>...</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-08-20</td>\n",
" <td>43</td>\n",
" <td>27.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>4</td>\n",
" <td>5850000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-08-23</td>\n",
" <td>34</td>\n",
" <td>19.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>66</td>\n",
" <td>14</td>\n",
" <td>6000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-08-27</td>\n",
" <td>43</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>5700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-09-01</td>\n",
" <td>89</td>\n",
" <td>50.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>13100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-09-05</td>\n",
" <td>77</td>\n",
" <td>77.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>319</td>\n",
" <td>108</td>\n",
" <td>17</td>\n",
" <td>135</td>\n",
" <td>236</td>\n",
" <td>2</td>\n",
" <td>91</td>\n",
" <td>195</td>\n",
" <td>14</td>\n",
" <td>16331452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 292 columns</p>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material build_year \\\n",
"0 1 2011-08-20 43 27.0 4.0 NaN NaN NaN \n",
"1 2 2011-08-23 34 19.0 3.0 NaN NaN NaN \n",
"2 3 2011-08-27 43 29.0 2.0 NaN NaN NaN \n",
"3 4 2011-09-01 89 50.0 9.0 NaN NaN NaN \n",
"4 5 2011-09-05 77 77.0 4.0 NaN NaN NaN \n",
"\n",
" num_room kitch_sq ... cafe_count_5000_price_2500 \\\n",
"0 NaN NaN ... 9 \n",
"1 NaN NaN ... 15 \n",
"2 NaN NaN ... 10 \n",
"3 NaN NaN ... 11 \n",
"4 NaN NaN ... 319 \n",
"\n",
" cafe_count_5000_price_4000 cafe_count_5000_price_high \\\n",
"0 4 0 \n",
"1 3 0 \n",
"2 3 0 \n",
"3 2 1 \n",
"4 108 17 \n",
"\n",
" big_church_count_5000 church_count_5000 mosque_count_5000 \\\n",
"0 13 22 1 \n",
"1 15 29 1 \n",
"2 11 27 0 \n",
"3 4 4 0 \n",
"4 135 236 2 \n",
"\n",
" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
"0 0 52 4 5850000 \n",
"1 10 66 14 6000000 \n",
"2 4 67 10 5700000 \n",
"3 0 26 3 13100000 \n",
"4 91 195 14 16331452 \n",
"\n",
"[5 rows x 292 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt\n",
"\n",
"train = pd.read_csv(\"../input/train.csv\")\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3a350920-c4f0-8e39-d6e4-f85919a13cb8"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f26559b2f98>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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QoGCYk3Q+0PXZ/mfbvrgv9ZSXo73qBWmS3g18o0vW+bYPprqReZWQdByvPLK003TbJ6yq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\nmRWcGwVmZmZmZgX3P/kbkZBUW0PUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f26559b2438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = train.isnull().sum().sort_values(ascending = True).tail(50)\n",
"a.plot(kind = 'barh', legend = False, figsize = (10,15))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "a36c753f-55fc-9908-67e0-bd17867623fa"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 13,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
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| 0001/161/1161816.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
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"source": [
"import numpy as np\n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt\n",
"\n",
"train = pd.read_csv(\"../input/train.csv\")\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3a350920-c4f0-8e39-d6e4-f85919a13cb8"
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"outputs": [
{
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"build_year 13605\n",
"state 13559\n",
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"material 9572\n",
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"kitch_sq 9572\n",
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"school_quota 6685\n",
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"build_count_before_1920 4991\n",
"build_count_1946-1970 4991\n",
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"cafe_sum_1500_max_price_avg 4199\n",
"cafe_avg_price_1500 4199\n",
"cafe_sum_2000_max_price_avg 1725\n",
"cafe_avg_price_2000 1725\n",
"cafe_sum_2000_min_price_avg 1725\n",
"cafe_avg_price_3000 991\n",
"cafe_sum_3000_max_price_avg 991\n",
"cafe_sum_3000_min_price_avg 991\n",
"cafe_avg_price_5000 297\n",
"cafe_sum_5000_max_price_avg 297\n",
"cafe_sum_5000_min_price_avg 297\n",
"prom_part_5000 178\n",
"floor 167\n",
"metro_min_walk 25\n",
"railroad_station_walk_km 25\n",
"railroad_station_walk_min 25\n",
"ID_railroad_station_walk 25\n",
"dtype: int64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.isnull().sum().sort_values(ascending = False).head(50) # missing var count "
]
},
{
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"execution_count": 3,
"metadata": {
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| 0001/161/1161817.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
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" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
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],
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"import numpy as np\n",
"import pandas as pd \n",
"import matplotlib.pyplot as plt\n",
"\n",
"train = pd.read_csv(\"../input/train.csv\")\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3a350920-c4f0-8e39-d6e4-f85919a13cb8"
},
"outputs": [
{
"data": {
"text/plain": [
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]
},
"execution_count": 2,
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{
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C1gGoqpVJDgduAP6lqq5ahb73AC6rqp+3/s8GXtT6Ga78fGAF8OXhGquqU4BT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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fee87ec0390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"a = train.isnull().sum().sort_values(ascending = True).tail(40)\n",
"a.plot(kind = 'barh', legend = False, figsize = (10,15))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "a36c753f-55fc-9908-67e0-bd17867623fa"
},
"outputs": [
{
"data": {
"image/png": 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jQMEo0BRyh3WkI3FwR8Q0NLzCS8WqcDN5c9kraSp2hc/c9xmK1eKsZakZI7Po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\nRi80edsabCWiR/AIN9me3+O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ZLiXm39C4XmY2GpZyXdk4a9lTeALolVL2Awghvgn8AnC1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\noZ2CRqPRaGJop6DRaDSaGP8fHSc6LisJm3IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fee878efd30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"x = train.green_zone_part.values\n",
"y = train.price_doc.values\n",
"plt.scatter(x, y, c = 'green', s = 30.0, alpha = 0.3)\n",
"plt.xlabel('green zone density')\n",
"plt.ylabel('price')\n",
"\n",
"fit = np.polyfit(x,y, deg = 1)\n",
"plt.plot(x, fit[0]*x + fit[1], color = 'red')\n",
"plt.show()"
]
}
],
"metadata": {
"_change_revision": 22,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161932.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "1a77a45d-bf39-f720-216a-ef33689c8fac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"race-result-horse.csv\n",
"race-result-race.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import tensorflow as tf\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "a551a008-115b-0750-19c9-b5a4bb1ec525"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['finishing_position', 'horse_number', 'horse_name', 'horse_id', 'jockey', 'trainer', 'actual_weight', 'declared_horse_weight', 'draw', 'length_behind_winner', 'running_position_1', 'running_position_2', 'running_position_3', 'running_position_4', 'finish_time', 'win_odds', 'running_position_5', 'running_position_6', 'race_id']\n",
"['src', 'race_date', 'race_course', 'race_number', 'race_id', 'race_class', 'race_distance', 'track_condition', 'race_name', 'track', 'sectional_time', 'incident_report']\n"
]
}
],
"source": [
"df1=pd.read_csv(\"../input/race-result-horse.csv\")\n",
"df2=pd.read_csv(\"../input/race-result-race.csv\")\n",
"print(list(df1))\n",
"print(list(df2))\n",
"\n",
"df3=pd.merge(df1,df2,on='race_id')\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "59ea76e7-d45c-c8a0-d197-fa7b2686a02d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" finishing_position horse_number horse_name horse_id jockey \\\n",
"0 1 1.0 DOUBLE DRAGON K019 B Prebble \n",
"1 2 2.0 PLAIN BLUE BANNER S070 D Whyte \n",
"2 3 10.0 GOLDWEAVER P072 Y T Cheng \n",
"3 4 3.0 SUPREME PROFIT P230 J Moreira \n",
"4 5 7.0 THE ONLY KID H173 Z Purton \n",
"5 6 9.0 WINNING ADVANTAGE N359 A Suborics \n",
"6 7 13.0 CARE FREE ELEGANCE P340 C Y Ho \n",
"7 8 4.0 COOL PAL S035 H W Lai \n",
"8 9 6.0 TAI PO FORTUNE P081 K Teetan \n",
"9 10 11.0 SUPER HORSE L308 T H So \n",
"10 11 5.0 AMAZING GIFT S090 K C Leung \n",
"11 12 14.0 LITTLE WIND N306 C Y Lui \n",
"12 13 8.0 EVERSPRING P325 M L Yeung \n",
"13 WV-A 12.0 SHANTARAAM N410 O Doleuze \n",
"14 1 11.0 TRUMP P106 K C Leung \n",
"15 2 13.0 MULTIVICTORY P254 K C Ng \n",
"16 3 4.0 HELENE SUPER STAR S152 M Chadwick \n",
"17 4 2.0 DEEP THINKER N159 Z Purton \n",
"18 5 10.0 LITTLE MEN N441 W M Lai \n",
"19 6 3.0 SMART MAN P103 D Whyte \n",
"20 7 14.0 ROYAL PARTNER N394 C Y Ho \n",
"21 8 12.0 SICHUAN EXEC N344 C Reith \n",
"22 9 9.0 JOLLY VICTOR N140 K Teetan \n",
"23 10 6.0 SICHUAN VIGOUR P412 J Moreira \n",
"24 11 7.0 AUTUMN GOLD P044 B Prebble \n",
"25 12 5.0 PENGLAI XIANZI K248 M L Yeung \n",
"26 13 8.0 FLYING ELITE N419 Y T Cheng \n",
"27 14 1.0 FREE JUDGEMENT M163 O Doleuze \n",
"28 1 7.0 JUST MISTERE P012 D Whyte \n",
"29 2 11.0 MASSIVE P331 K C Leung \n",
"30 3 10.0 SHANGHAI PIONEER L252 C Reith \n",
"31 4 12.0 POSITIVE ENERGY P011 J Moreira \n",
"32 5 6.0 KING ON EARTH L251 H W Lai \n",
"33 6 14.0 JUST GOOD L133 W M Lai \n",
"34 7 4.0 MONEY CAFE S031 Y T Cheng \n",
"35 8 9.0 FLYING KEEPER M191 C K Tong \n",
"36 9 8.0 SUPER TEAM M036 C Y Ho \n",
"37 10 3.0 CALIFORNIA ROCK S233 M Chadwick \n",
"38 11 5.0 JOYFUL LUCK M223 O Doleuze \n",
"39 12 2.0 SPARKLING SWORD P256 Z Purton \n",
"40 13 1.0 ALLEY-OOP P379 T H So \n",
"41 14 13.0 AMERICAN FAME P449 K Teetan \n",
"42 1 5.0 GOLDEN HARVEST N236 J Moreira \n",
"43 2 8.0 SMART VOLATILITY P049 Y T Cheng \n",
"44 3 7.0 PENIAPHOBIA S143 M Chadwick \n",
"45 4 6.0 LORD SINCLAIR N447 H W Lai \n",
"46 5 2.0 CHARLES THE GREAT N171 C Reith \n",
"47 6 11.0 BEST ELEVEN N016 K C Leung \n",
"48 7 9.0 TOUR DE FORCE N297 K Teetan \n",
"49 8 1.0 HELENE SPIRIT M310 A Suborics \n",
"\n",
" trainer actual_weight declared_horse_weight draw \\\n",
"0 D Cruz 133.0 1032.0 1.0 \n",
"1 D E Ferraris 133.0 1075.0 13.0 \n",
"2 Y S Tsui 121.0 1065.0 3.0 \n",
"3 C S Shum 132.0 1222.0 2.0 \n",
"4 K W Lui 125.0 1136.0 9.0 \n",
"5 A T Millard 123.0 1100.0 11.0 \n",
"6 K L Man 115.0 1053.0 12.0 \n",
"7 L Ho 129.0 1203.0 8.0 \n",
"8 T P Yung 127.0 1073.0 6.0 \n",
"9 C W Chang 119.0 1137.0 7.0 \n",
"10 P O'Sullivan 125.0 1099.0 4.0 \n",
"11 P F Yiu 107.0 1062.0 5.0 \n",
"12 A Lee 122.0 988.0 14.0 \n",
"13 R Gibson 120.0 1147.0 NaN \n",
"14 K L Man 119.0 1137.0 13.0 \n",
"15 A S Cruz 114.0 1101.0 3.0 \n",
"16 A S Cruz 127.0 1195.0 6.0 \n",
"17 P F Yiu 132.0 1067.0 2.0 \n",
"18 W Y So 121.0 1251.0 4.0 \n",
"19 J Moore 128.0 1134.0 9.0 \n",
"20 C Fownes 116.0 1082.0 10.0 \n",
"21 J Size 120.0 1051.0 12.0 \n",
"22 A T Millard 121.0 1035.0 7.0 \n",
"23 J Size 125.0 1064.0 5.0 \n",
"24 S Woods 123.0 1011.0 14.0 \n",
"25 A S Cruz 124.0 1105.0 11.0 \n",
"26 C H Yip 121.0 1125.0 1.0 \n",
"27 S Woods 133.0 1172.0 8.0 \n",
"28 Y S Tsui 130.0 1147.0 8.0 \n",
"29 L Ho 117.0 1073.0 10.0 \n",
"30 D E Ferraris 120.0 1072.0 7.0 \n",
"31 P F Yiu 115.0 1161.0 13.0 \n",
"32 A Lee 129.0 1084.0 14.0 \n",
"33 Y S Tsui 113.0 1116.0 3.0 \n",
"34 C H Yip 132.0 1103.0 2.0 \n",
"35 C Fownes 122.0 1071.0 11.0 \n",
"36 A T Millard 128.0 1147.0 6.0 \n",
"37 A S Cruz 132.0 1198.0 5.0 \n",
"38 C W Chang 131.0 1092.0 12.0 \n",
"39 K W Lui 133.0 1123.0 4.0 \n",
"40 C W Chang 131.0 1167.0 9.0 \n",
"41 C S Shum 114.0 1045.0 1.0 \n",
"42 A T Millard 115.0 1179.0 11.0 \n",
"43 K W Lui 113.0 1180.0 5.0 \n",
"44 A S Cruz 113.0 1110.0 2.0 \n",
"45 S Woods 111.0 1170.0 9.0 \n",
"46 J Moore 131.0 1090.0 3.0 \n",
"47 A S Cruz 113.0 1100.0 4.0 \n",
"48 J Size 113.0 1106.0 6.0 \n",
"49 C Fownes 133.0 1114.0 10.0 \n",
"\n",
" length_behind_winner ... \\\n",
"0 - ... \n",
"1 2 ... \n",
"2 2 ... \n",
"3 2 ... \n",
"4 4-1/4 ... \n",
"5 5-1/2 ... \n",
"6 5-1/2 ... \n",
"7 5-3/4 ... \n",
"8 6-1/4 ... \n",
"9 6-3/4 ... \n",
"10 7-3/4 ... \n",
"11 10-1/4 ... \n",
"12 11 ... \n",
"13 --- ... \n",
"14 - ... \n",
"15 2-1/4 ... \n",
"16 2-1/4 ... \n",
"17 2-1/2 ... \n",
"18 2-3/4 ... \n",
"19 3-1/2 ... \n",
"20 3-1/2 ... \n",
"21 4 ... \n",
"22 4-1/2 ... \n",
"23 4-3/4 ... \n",
"24 5 ... \n",
"25 7-1/4 ... \n",
"26 9 ... \n",
"27 10-3/4 ... \n",
"28 - ... \n",
"29 1-1/4 ... \n",
"30 1-3/4 ... \n",
"31 2 ... \n",
"32 2-1/2 ... \n",
"33 2-1/2 ... \n",
"34 4-1/4 ... \n",
"35 4-3/4 ... \n",
"36 6-1/4 ... \n",
"37 7 ... \n",
"38 8-1/2 ... \n",
"39 9 ... \n",
"40 9 ... \n",
"41 12-1/2 ... \n",
"42 - ... \n",
"43 N ... \n",
"44 1 ... \n",
"45 3-3/4 ... \n",
"46 4-1/4 ... \n",
"47 4-1/2 ... \n",
"48 5-3/4 ... \n",
"49 5-3/4 ... \n",
"\n",
" race_date race_course race_number race_class race_distance \\\n",
"0 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"1 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"2 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"3 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"4 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"5 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"6 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"7 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"8 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"9 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"10 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"11 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"12 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"13 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"14 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"15 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"16 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"17 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"18 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"19 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"20 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"21 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"22 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"23 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"24 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"25 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"26 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"27 2014-09-14 Sha Tin 10 Class 2 1400 \n",
"28 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"29 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"30 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"31 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"32 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"33 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"34 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"35 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"36 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"37 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"38 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"39 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"40 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"41 2014-09-14 Sha Tin 2 Class 5 1200 \n",
"42 2014-09-14 Sha Tin 3 Class 1 1200 \n",
"43 2014-09-14 Sha Tin 3 Class 1 1200 \n",
"44 2014-09-14 Sha Tin 3 Class 1 1200 \n",
"45 2014-09-14 Sha Tin 3 Class 1 1200 \n",
"46 2014-09-14 Sha Tin 3 Class 1 1200 \n",
"47 2014-09-14 Sha Tin 3 Class 1 1200 \n",
"48 2014-09-14 Sha Tin 3 Class 1 1200 \n",
"49 2014-09-14 Sha Tin 3 Class 1 1200 \n",
"\n",
" track_condition race_name \\\n",
"0 GOOD TO FIRM TIM WA HANDICAP \n",
"1 GOOD TO FIRM TIM WA HANDICAP \n",
"2 GOOD TO FIRM TIM WA HANDICAP \n",
"3 GOOD TO FIRM TIM WA HANDICAP \n",
"4 GOOD TO FIRM TIM WA HANDICAP \n",
"5 GOOD TO FIRM TIM WA HANDICAP \n",
"6 GOOD TO FIRM TIM WA HANDICAP \n",
"7 GOOD TO FIRM TIM WA HANDICAP \n",
"8 GOOD TO FIRM TIM WA HANDICAP \n",
"9 GOOD TO FIRM TIM WA HANDICAP \n",
"10 GOOD TO FIRM TIM WA HANDICAP \n",
"11 GOOD TO FIRM TIM WA HANDICAP \n",
"12 GOOD TO FIRM TIM WA HANDICAP \n",
"13 GOOD TO FIRM TIM WA HANDICAP \n",
"14 GOOD TO FIRM COTTON TREE HANDICAP \n",
"15 GOOD TO FIRM COTTON TREE HANDICAP \n",
"16 GOOD TO FIRM COTTON TREE HANDICAP \n",
"17 GOOD TO FIRM COTTON TREE HANDICAP \n",
"18 GOOD TO FIRM COTTON TREE HANDICAP \n",
"19 GOOD TO FIRM COTTON TREE HANDICAP \n",
"20 GOOD TO FIRM COTTON TREE HANDICAP \n",
"21 GOOD TO FIRM COTTON TREE HANDICAP \n",
"22 GOOD TO FIRM COTTON TREE HANDICAP \n",
"23 GOOD TO FIRM COTTON TREE HANDICAP \n",
"24 GOOD TO FIRM COTTON TREE HANDICAP \n",
"25 GOOD TO FIRM COTTON TREE HANDICAP \n",
"26 GOOD TO FIRM COTTON TREE HANDICAP \n",
"27 GOOD TO FIRM COTTON TREE HANDICAP \n",
"28 GOOD TO FIRM TIM MEI HANDICAP \n",
"29 GOOD TO FIRM TIM MEI HANDICAP \n",
"30 GOOD TO FIRM TIM MEI HANDICAP \n",
"31 GOOD TO FIRM TIM MEI HANDICAP \n",
"32 GOOD TO FIRM TIM MEI HANDICAP \n",
"33 GOOD TO FIRM TIM MEI HANDICAP \n",
"34 GOOD TO FIRM TIM MEI HANDICAP \n",
"35 GOOD TO FIRM TIM MEI HANDICAP \n",
"36 GOOD TO FIRM TIM MEI HANDICAP \n",
"37 GOOD TO FIRM TIM MEI HANDICAP \n",
"38 GOOD TO FIRM TIM MEI HANDICAP \n",
"39 GOOD TO FIRM TIM MEI HANDICAP \n",
"40 GOOD TO FIRM TIM MEI HANDICAP \n",
"41 GOOD TO FIRM TIM MEI HANDICAP \n",
"42 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"43 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"44 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"45 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"46 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"47 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"48 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"49 GOOD TO FIRM THE HKSAR CHIEF EXECUTIVE'S CUP (HANDICAP) \n",
"\n",
" track sectional_time \\\n",
"0 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"1 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"2 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"3 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"4 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"5 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"6 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"7 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"8 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"9 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"10 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"11 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"12 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"13 TURF - \"A\" COURSE 13.59 22.08 23.11 23.55 \n",
"14 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"15 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"16 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"17 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"18 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"19 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"20 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"21 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"22 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"23 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"24 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"25 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"26 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"27 TURF - \"A\" COURSE 13.55 22.25 22.89 22.85 \n",
"28 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"29 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"30 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"31 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"32 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"33 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"34 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"35 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"36 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"37 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"38 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"39 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"40 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"41 TURF - \"A\" COURSE 24.06 22.25 23.66 \n",
"42 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"43 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"44 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"45 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"46 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"47 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"48 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"49 TURF - \"A\" COURSE 23.42 22.48 22.47 \n",
"\n",
" incident_report \n",
"0 \\n When about to enter the trac... \n",
"1 \\n When about to enter the trac... \n",
"2 \\n When about to enter the trac... \n",
"3 \\n When about to enter the trac... \n",
"4 \\n When about to enter the trac... \n",
"5 \\n When about to enter the trac... \n",
"6 \\n When about to enter the trac... \n",
"7 \\n When about to enter the trac... \n",
"8 \\n When about to enter the trac... \n",
"9 \\n When about to enter the trac... \n",
"10 \\n When about to enter the trac... \n",
"11 \\n When about to enter the trac... \n",
"12 \\n When about to enter the trac... \n",
"13 \\n When about to enter the trac... \n",
"14 \\n SMART MAN was slow to begin.... \n",
"15 \\n SMART MAN was slow to begin.... \n",
"16 \\n SMART MAN was slow to begin.... \n",
"17 \\n SMART MAN was slow to begin.... \n",
"18 \\n SMART MAN was slow to begin.... \n",
"19 \\n SMART MAN was slow to begin.... \n",
"20 \\n SMART MAN was slow to begin.... \n",
"21 \\n SMART MAN was slow to begin.... \n",
"22 \\n SMART MAN was slow to begin.... \n",
"23 \\n SMART MAN was slow to begin.... \n",
"24 \\n SMART MAN was slow to begin.... \n",
"25 \\n SMART MAN was slow to begin.... \n",
"26 \\n SMART MAN was slow to begin.... \n",
"27 \\n SMART MAN was slow to begin.... \n",
"28 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"29 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"30 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"31 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"32 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"33 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"34 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"35 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"36 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"37 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"38 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"39 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"40 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"41 \\n ALLEY-OOP and FLYING KEEPER ... \n",
"42 \\n On arrival at the Start, it ... \n",
"43 \\n On arrival at the Start, it ... \n",
"44 \\n On arrival at the Start, it ... \n",
"45 \\n On arrival at the Start, it ... \n",
"46 \\n On arrival at the Start, it ... \n",
"47 \\n On arrival at the Start, it ... \n",
"48 \\n On arrival at the Start, it ... \n",
"49 \\n On arrival at the Start, it ... \n",
"\n",
"[50 rows x 30 columns]\n"
]
}
],
"source": [
"print(df3[0:50])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "272f210f-f14b-249e-6abf-d8867c3b9a03"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['finishing_position', 'horse_name', 'horse_id', 'jockey', 'trainer',\n",
" 'length_behind_winner', 'finish_time', 'race_id'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# collecting categorical varibale names \n",
"cat_var = df1.dtypes.loc[df1.dtypes=='object'].index\n",
"print(cat_var)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "59273ca7-be5f-c0fe-545c-e7c3b3368ab9"
},
"outputs": [
{
"data": {
"text/plain": [
"finishing_position 37\n",
"horse_name 1747\n",
"horse_id 1747\n",
"jockey 88\n",
"trainer 77\n",
"length_behind_winner 189\n",
"finish_time 3734\n",
"race_id 1561\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check for unique variables in cat_var\n",
"df1[cat_var].apply(lambda x: len(x.unique()))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "87d2d934-cab8-8d74-b291-1e5279e148ed"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>horse_number</th>\n",
" <th>actual_weight</th>\n",
" <th>declared_horse_weight</th>\n",
" <th>draw</th>\n",
" <th>running_position_1</th>\n",
" <th>running_position_2</th>\n",
" <th>running_position_3</th>\n",
" <th>running_position_4</th>\n",
" <th>win_odds</th>\n",
" <th>running_position_5</th>\n",
" <th>running_position_6</th>\n",
" <th>race_number</th>\n",
" <th>race_distance</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>262.000000</td>\n",
" <td>262.000000</td>\n",
" <td>262.000000</td>\n",
" <td>262.000000</td>\n",
" <td>262.000000</td>\n",
" <td>262.000000</td>\n",
" <td>262.000000</td>\n",
" <td>262.0</td>\n",
" <td>262.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>262.000000</td>\n",
" <td>262.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6.416031</td>\n",
" <td>124.641221</td>\n",
" <td>1126.274809</td>\n",
" <td>7.011450</td>\n",
" <td>5.583969</td>\n",
" <td>5.370229</td>\n",
" <td>4.709924</td>\n",
" <td>1.0</td>\n",
" <td>8.994656</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6.198473</td>\n",
" <td>1400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.840524</td>\n",
" <td>5.764296</td>\n",
" <td>54.185866</td>\n",
" <td>4.067894</td>\n",
" <td>3.648152</td>\n",
" <td>3.468019</td>\n",
" <td>3.165883</td>\n",
" <td>0.0</td>\n",
" <td>10.765628</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.116929</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>107.000000</td>\n",
" <td>976.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.0</td>\n",
" <td>1.200000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>1400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>3.000000</td>\n",
" <td>121.000000</td>\n",
" <td>1090.500000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>2.000000</td>\n",
" <td>1.0</td>\n",
" <td>3.100000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" <td>1400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.000000</td>\n",
" <td>125.000000</td>\n",
" <td>1127.500000</td>\n",
" <td>7.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>4.000000</td>\n",
" <td>1.0</td>\n",
" <td>5.200000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>6.000000</td>\n",
" <td>1400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>10.000000</td>\n",
" <td>129.000000</td>\n",
" <td>1162.000000</td>\n",
" <td>11.000000</td>\n",
" <td>8.000000</td>\n",
" <td>8.000000</td>\n",
" <td>7.000000</td>\n",
" <td>1.0</td>\n",
" <td>9.425000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>9.000000</td>\n",
" <td>1400.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>14.000000</td>\n",
" <td>133.000000</td>\n",
" <td>1292.000000</td>\n",
" <td>14.000000</td>\n",
" <td>14.000000</td>\n",
" <td>14.000000</td>\n",
" <td>14.000000</td>\n",
" <td>1.0</td>\n",
" <td>74.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>11.000000</td>\n",
" <td>1400.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" horse_number actual_weight declared_horse_weight draw \\\n",
"count 262.000000 262.000000 262.000000 262.000000 \n",
"mean 6.416031 124.641221 1126.274809 7.011450 \n",
"std 3.840524 5.764296 54.185866 4.067894 \n",
"min 1.000000 107.000000 976.000000 1.000000 \n",
"25% 3.000000 121.000000 1090.500000 3.000000 \n",
"50% 6.000000 125.000000 1127.500000 7.000000 \n",
"75% 10.000000 129.000000 1162.000000 11.000000 \n",
"max 14.000000 133.000000 1292.000000 14.000000 \n",
"\n",
" running_position_1 running_position_2 running_position_3 \\\n",
"count 262.000000 262.000000 262.000000 \n",
"mean 5.583969 5.370229 4.709924 \n",
"std 3.648152 3.468019 3.165883 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 3.000000 3.000000 2.000000 \n",
"50% 5.000000 5.000000 4.000000 \n",
"75% 8.000000 8.000000 7.000000 \n",
"max 14.000000 14.000000 14.000000 \n",
"\n",
" running_position_4 win_odds running_position_5 running_position_6 \\\n",
"count 262.0 262.000000 0.0 0.0 \n",
"mean 1.0 8.994656 NaN NaN \n",
"std 0.0 10.765628 NaN NaN \n",
"min 1.0 1.200000 NaN NaN \n",
"25% 1.0 3.100000 NaN NaN \n",
"50% 1.0 5.200000 NaN NaN \n",
"75% 1.0 9.425000 NaN NaN \n",
"max 1.0 74.000000 NaN NaN \n",
"\n",
" race_number race_distance \n",
"count 262.000000 262.0 \n",
"mean 6.198473 1400.0 \n",
"std 3.116929 0.0 \n",
"min 1.000000 1400.0 \n",
"25% 4.000000 1400.0 \n",
"50% 6.000000 1400.0 \n",
"75% 9.000000 1400.0 \n",
"max 11.000000 1400.0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3=df3[df3.finishing_position=='1']\n",
"df3=df3[df3.race_distance==1400]\n",
"df3.describe()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "060b9a67-ed83-0865-5b75-e5cc3758f5fc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 19595.000000\n",
"mean 30.848232\n",
"std 32.114693\n",
"min 1.100000\n",
"25% 7.700000\n",
"50% 16.000000\n",
"75% 43.000000\n",
"max 99.000000\n",
"Name: win_odds, dtype: float64\n"
]
}
],
"source": [
"print(df1.win_odds.describe())\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "2055fec9-373a-0970-2a9c-427e96e548a6"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "0bb4f570-97cb-ef31-2f3d-abafa3cb7b82"
},
"outputs": [],
"source": [
"nd1=df1.finish_time"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "1650ef2c-a4f9-5e91-bd60-f7d7a2545b8f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1.22.33\n",
"1 1.22.65\n",
"2 1.22.66\n",
"3 1.22.66\n",
"4 1.23.02\n",
"5 1.23.20\n",
"6 1.23.22\n",
"7 1.23.25\n",
"8 1.23.33\n",
"9 1.23.41\n",
"10 1.23.56\n",
"11 1.23.96\n",
"12 1.24.10\n",
"13 ---\n",
"14 1.21.54\n",
"15 1.21.89\n",
"16 1.21.92\n",
"17 1.21.95\n",
"18 1.21.98\n",
"19 1.22.10\n",
"20 1.22.10\n",
"21 1.22.18\n",
"22 1.22.25\n",
"23 1.22.32\n",
"24 1.22.34\n",
"25 1.22.72\n",
"26 1.22.98\n",
"27 1.23.26\n",
"28 1.09.97\n",
"29 1.10.17\n",
" ... \n",
"19966 1.34.19\n",
"19967 1.34.23\n",
"19968 1.34.32\n",
"19969 1.34.36\n",
"19970 1.35.26\n",
"19971 1.37.20\n",
"19972 ---\n",
"19973 1.33.33\n",
"19974 1.33.68\n",
"19975 1.33.84\n",
"19976 1.33.88\n",
"19977 1.34.12\n",
"19978 1.34.20\n",
"19979 1.34.36\n",
"19980 1.34.52\n",
"19981 1.34.60\n",
"19982 1.08.40\n",
"19983 1.08.57\n",
"19984 1.08.62\n",
"19985 1.08.66\n",
"19986 1.09.18\n",
"19987 1.09.30\n",
"19988 1.09.34\n",
"19989 1.09.65\n",
"19990 1.09.67\n",
"19991 1.09.77\n",
"19992 1.09.96\n",
"19993 1.10.12\n",
"19994 1.10.14\n",
"19995 1.10.59\n",
"Name: finish_time, dtype: object\n"
]
}
],
"source": [
"print(nd1)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "c8deefa2-4eff-22ab-a0f2-773ced49ba22"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 1.22.33\n",
"Name: finish_time, dtype: object\n"
]
}
],
"source": [
"print(nd1[0:1])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "88be4543-f8fa-0f01-5724-03624d49ddec"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 123,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161957.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "1a77a45d-bf39-f720-216a-ef33689c8fac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"race-result-horse.csv\n",
"race-result-race.csv\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt, pandas as pd, numpy as np\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder\n",
"# import classification algorithms\n",
"from sklearn.svm import SVC\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "a551a008-115b-0750-19c9-b5a4bb1ec525"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['finishing_position', 'horse_number', 'horse_name', 'horse_id', 'jockey', 'trainer', 'actual_weight', 'declared_horse_weight', 'draw', 'length_behind_winner', 'running_position_1', 'running_position_2', 'running_position_3', 'running_position_4', 'finish_time', 'win_odds', 'running_position_5', 'running_position_6', 'race_id']\n",
"['src', 'race_date', 'race_course', 'race_number', 'race_id', 'race_class', 'race_distance', 'track_condition', 'race_name', 'track', 'sectional_time', 'incident_report']\n"
]
}
],
"source": [
"df1=pd.read_csv(\"../input/race-result-horse.csv\")\n",
"df2=pd.read_csv(\"../input/race-result-race.csv\")\n",
"print(list(df1))\n",
"print(list(df2))\n",
"#Merging the data for simpler operation\n",
"df3=pd.merge(df1,df2,on='race_id')\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "59ea76e7-d45c-c8a0-d197-fa7b2686a02d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" finishing_position horse_number horse_name horse_id jockey \\\n",
"0 1 1.0 DOUBLE DRAGON K019 B Prebble \n",
"1 2 2.0 PLAIN BLUE BANNER S070 D Whyte \n",
"2 3 10.0 GOLDWEAVER P072 Y T Cheng \n",
"3 4 3.0 SUPREME PROFIT P230 J Moreira \n",
"4 5 7.0 THE ONLY KID H173 Z Purton \n",
"\n",
" trainer actual_weight declared_horse_weight draw \\\n",
"0 D Cruz 133.0 1032.0 1.0 \n",
"1 D E Ferraris 133.0 1075.0 13.0 \n",
"2 Y S Tsui 121.0 1065.0 3.0 \n",
"3 C S Shum 132.0 1222.0 2.0 \n",
"4 K W Lui 125.0 1136.0 9.0 \n",
"\n",
" length_behind_winner ... \\\n",
"0 - ... \n",
"1 2 ... \n",
"2 2 ... \n",
"3 2 ... \n",
"4 4-1/4 ... \n",
"\n",
" race_date race_course race_number race_class race_distance \\\n",
"0 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"1 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"2 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"3 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"4 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"\n",
" track_condition race_name track \\\n",
"0 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"1 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"2 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"3 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"4 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"\n",
" sectional_time incident_report \n",
"0 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"1 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"2 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"3 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"4 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"\n",
"[5 rows x 30 columns]\n"
]
}
],
"source": [
"print(df3[0:5])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "272f210f-f14b-249e-6abf-d8867c3b9a03"
},
"outputs": [
{
"ename": "IndexingError",
"evalue": "Unalignable boolean Series key provided",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexingError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-88d25eb2a1a5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# collecting categorical varibale names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcat_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m'object'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcat_var\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 1310\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1311\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1312\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1313\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1314\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1453\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1454\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1455\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1456\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_list_like_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1457\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getbool_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m 1317\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getbool_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1318\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1319\u001b[0;31m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1320\u001b[0m \u001b[0minds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1321\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[0;34m(ax, key)\u001b[0m\n\u001b[1;32m 1815\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1816\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1817\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unalignable boolean Series key provided'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1818\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1819\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mis_sparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIndexingError\u001b[0m: Unalignable boolean Series key provided"
]
}
],
"source": [
"# collecting categorical varibale names \n",
"cat_var = df3.dtypes.loc[df1.dtypes=='object'].index\n",
"print(cat_var)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "59273ca7-be5f-c0fe-545c-e7c3b3368ab9"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'cat_var' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-825c2846518b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# check for unique variables in cat_var\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcat_var\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'cat_var' is not defined"
]
}
],
"source": [
"# check for unique variables in cat_var\n",
"df3[cat_var].apply(lambda x: len(x.unique()))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "87d2d934-cab8-8d74-b291-1e5279e148ed"
},
"outputs": [
{
"data": {
"text/plain": [
"array(['TURF - \"A\" COURSE', 'ALL WEATHER TRACK', 'TURF - \"A+3\" COURSE',\n",
" 'TURF - \"B+2\" COURSE', 'TURF - \"C\" COURSE', 'TURF - \"C+3\" COURSE',\n",
" 'TURF - \"B\" COURSE'], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3.track.unique()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "060b9a67-ed83-0865-5b75-e5cc3758f5fc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"count 19595.000000\n",
"mean 30.848232\n",
"std 32.114693\n",
"min 1.100000\n",
"25% 7.700000\n",
"50% 16.000000\n",
"75% 43.000000\n",
"max 99.000000\n",
"Name: win_odds, dtype: float64\n"
]
}
],
"source": [
"print(df1.win_odds.describe())\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "c8deefa2-4eff-22ab-a0f2-773ced49ba22"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'nd1' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-4e904c970da9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnd1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'nd1' is not defined"
]
}
],
"source": [
"print(nd1[0:1])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "88be4543-f8fa-0f01-5724-03624d49ddec"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 98,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/161/1161995.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "7b916628-74cb-8be8-abc4-07630d832ece"
},
"outputs": [],
"source": [
"#You're usual round of imports\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "40fc9e46-dc6b-7009-f3f1-b0ba81ac4329"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Importing the data\n",
"data = pd.read_csv(\"../input/train.csv\")\n",
"test = pd.read_csv(\"../input/test.csv\")\n",
"\n",
"#Looking at the data to see what we have.\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "b3c0b555-1702-5a4f-65d9-d585dacdfe30"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "1a042275-f2db-977c-a817-39e80461e861"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "88e4a681-3b22-1083-f3da-985293ff1266"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "51f92099-e49c-3702-6423-db4dd07458db"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "8ec07433-82dd-7413-22d4-b776fa505b4a"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "13221319-1a15-c858-0e43-123a59cbfcc8"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "42e13f4e-3a30-7349-64fc-149743939d8e"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "f0ab69b3-3ad5-2453-ef43-96363159b70a"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "2c7fefab-81a3-037e-676f-298ebdb1330b"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "43793caa-f3bf-ac05-263c-7b6f6486e832"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "f3832e92-d489-1fa3-c930-1d738e61ed0f"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "a8e3ea3b-e8f9-799b-3e04-4d804566b012"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "59f3f8ab-549a-0ee2-8a81-231972dad2f1"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "23a229b2-218c-e0c1-62b6-5db560a6e92b"
},
"outputs": [],
"source": [
"data = data.drop(['Embarked'], axis=1)\n",
"test = test.drop(['Embarked'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "273117dc-45f3-5437-a108-09b71a88f218"
},
"outputs": [],
"source": [
"def genderNumber(r):\n",
" if r['Sex'] == 'male':\n",
" gender = 0\n",
" else:\n",
" gender = 1\n",
" return gender\n",
"\n",
"data['GenderNumber'] = data.apply(genderNumber, axis=1)\n",
"test['GenderNumber'] = test.apply(genderNumber, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "34fe8fed-d61e-2aad-fcc6-87ed5e0634a2"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "77327265-bd4a-af75-92d8-c3532e030393"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "9776d2c0-ebb4-7d76-9b5d-bbe7dfa5db38"
},
"outputs": [],
"source": [
"import sklearn \n",
"\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn import tree"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "9de8908f-5671-1786-0e31-899a5e11d6a7"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "95d79c04-37e1-613e-f6e9-ad9532b50e83"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "ed450827-89e6-1b13-c48f-ec29209e1740",
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 312,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162000.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d2a6d819-0b1a-d7c0-7d96-0f88c99d896c"
},
"source": [
"# This notebook follows the blog post from Dataquest.io for the older House Prices Competition, adapted for the new data set.\n",
"\n",
"[https://www.dataquest.io/blog/kaggle-getting-started/?utm_campaign=Data%2BElixir&utm_medium=email&utm_source=Data_Elixir_131][1]\n",
"\n",
"\n",
" [1]: https://www.dataquest.io/blog/kaggle-getting-started/?utm_campaign=Data%2BElixir&utm_medium=email&utm_source=Data_Elixir_131"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b4c99295-0611-0812-a83b-e7e80b5dcbf7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"macro.csv\n",
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "43f3134c-894e-ff45-465f-775a191e0c98"
},
"source": [
"# Step 1: Acquire the data and create our environment"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3e7ddac1-b48c-3c2a-52c6-044727cca40f"
},
"outputs": [],
"source": [
"# import packages\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c1d3fec9-fef4-b5f6-8cda-2a204d9853fa"
},
"outputs": [],
"source": [
"# import data\n",
"train = pd.read_csv('../input/train.csv')\n",
"test = pd.read_csv('../input/test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "47c8bca3-e05e-bd2c-fa46-6d032eea7d0f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train data shape: (30471, 292)\n",
"Test data shape: (7662, 291)\n"
]
}
],
"source": [
"# check out size of data\n",
"print (\"Train data shape:\", train.shape)\n",
"print (\"Test data shape:\", test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "8321ffdd-acf9-9a6b-2c63-84156e0d0d44"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>...</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-08-20</td>\n",
" <td>43</td>\n",
" <td>27.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>4</td>\n",
" <td>5850000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-08-23</td>\n",
" <td>34</td>\n",
" <td>19.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>66</td>\n",
" <td>14</td>\n",
" <td>6000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-08-27</td>\n",
" <td>43</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>5700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-09-01</td>\n",
" <td>89</td>\n",
" <td>50.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>13100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-09-05</td>\n",
" <td>77</td>\n",
" <td>77.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>319</td>\n",
" <td>108</td>\n",
" <td>17</td>\n",
" <td>135</td>\n",
" <td>236</td>\n",
" <td>2</td>\n",
" <td>91</td>\n",
" <td>195</td>\n",
" <td>14</td>\n",
" <td>16331452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 292 columns</p>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material build_year \\\n",
"0 1 2011-08-20 43 27.0 4.0 NaN NaN NaN \n",
"1 2 2011-08-23 34 19.0 3.0 NaN NaN NaN \n",
"2 3 2011-08-27 43 29.0 2.0 NaN NaN NaN \n",
"3 4 2011-09-01 89 50.0 9.0 NaN NaN NaN \n",
"4 5 2011-09-05 77 77.0 4.0 NaN NaN NaN \n",
"\n",
" num_room kitch_sq ... cafe_count_5000_price_2500 \\\n",
"0 NaN NaN ... 9 \n",
"1 NaN NaN ... 15 \n",
"2 NaN NaN ... 10 \n",
"3 NaN NaN ... 11 \n",
"4 NaN NaN ... 319 \n",
"\n",
" cafe_count_5000_price_4000 cafe_count_5000_price_high \\\n",
"0 4 0 \n",
"1 3 0 \n",
"2 3 0 \n",
"3 2 1 \n",
"4 108 17 \n",
"\n",
" big_church_count_5000 church_count_5000 mosque_count_5000 \\\n",
"0 13 22 1 \n",
"1 15 29 1 \n",
"2 11 27 0 \n",
"3 4 4 0 \n",
"4 135 236 2 \n",
"\n",
" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
"0 0 52 4 5850000 \n",
"1 10 66 14 6000000 \n",
"2 4 67 10 5700000 \n",
"3 0 26 3 13100000 \n",
"4 91 195 14 16331452 \n",
"\n",
"[5 rows x 292 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# first look\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "e3ac9d12-9828-f753-3c98-6bfa4fd2e539"
},
"outputs": [],
"source": [
"# import matplotlib for visualization\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(style='ggplot')\n",
"plt.rcParams['figure.figsize'] = (10, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f45478df-9a46-0c45-0c1f-dc3562aa7010"
},
"source": [
"# Step 2: Explore the data and engineer Features"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "e18c6fa5-1580-6654-55ab-bff9147370b8"
},
"outputs": [
{
"data": {
"text/plain": [
"count 3.047100e+04\n",
"mean 7.123035e+06\n",
"std 4.780111e+06\n",
"min 1.000000e+05\n",
"25% 4.740002e+06\n",
"50% 6.274411e+06\n",
"75% 8.300000e+06\n",
"max 1.111111e+08\n",
"Name: price_doc, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check out target variable\n",
"train['price_doc'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "16433716-cdd3-bca7-e028-7327d4b1b540"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skew is: 4.47474487357\n"
]
},
{
"data": {
"image/png": 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ubq7y8vLU09OjaDSqEydOqLi4WJZlqby8PP4cAACA80nSlzI9Ho9uvvlm3XHHHbrgggu0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\nJH9zAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f03cabf4518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print (\"Skew is:\", train['price_doc'].skew())\n",
"plt.hist(train['price_doc'], color='blue', bins=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "879f56d3-69b2-b149-1808-13c89acd92fe"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skew is: -0.686715679719\n"
]
},
{
"data": {
"image/png": 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Jcvz48fT19WViYiJdXV3ZuXNnKpXK5Y4GALCgXPYZs3/913/NunXrpr7u7+/P5s2bs3//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\nMwCAQggzAIBCCDMAgEL8P64V84vfsjT+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f03fc119b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"target = np.log(train['price_doc'])\n",
"print (\"Skew is:\", target.skew())\n",
"plt.hist(target, color='blue', bins=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "d1c70cc7-1757-36ff-0e5c-e073c254896c"
},
"outputs": [
{
"data": {
"text/plain": [
"id int64\n",
"full_sq int64\n",
"life_sq float64\n",
"floor float64\n",
"max_floor float64\n",
"material float64\n",
"build_year float64\n",
"num_room float64\n",
"kitch_sq float64\n",
"state float64\n",
"area_m float64\n",
"raion_popul int64\n",
"green_zone_part float64\n",
"indust_part float64\n",
"children_preschool int64\n",
"preschool_quota float64\n",
"preschool_education_centers_raion int64\n",
"children_school int64\n",
"school_quota float64\n",
"school_education_centers_raion int64\n",
"school_education_centers_top_20_raion int64\n",
"hospital_beds_raion float64\n",
"healthcare_centers_raion int64\n",
"university_top_20_raion int64\n",
"sport_objects_raion int64\n",
"additional_education_raion int64\n",
"culture_objects_top_25_raion int64\n",
"shopping_centers_raion int64\n",
"office_raion int64\n",
"full_all int64\n",
" ... \n",
"big_church_count_3000 int64\n",
"church_count_3000 int64\n",
"mosque_count_3000 int64\n",
"leisure_count_3000 int64\n",
"sport_count_3000 int64\n",
"market_count_3000 int64\n",
"green_part_5000 float64\n",
"prom_part_5000 float64\n",
"office_count_5000 int64\n",
"office_sqm_5000 int64\n",
"trc_count_5000 int64\n",
"trc_sqm_5000 int64\n",
"cafe_count_5000 int64\n",
"cafe_sum_5000_min_price_avg float64\n",
"cafe_sum_5000_max_price_avg float64\n",
"cafe_avg_price_5000 float64\n",
"cafe_count_5000_na_price int64\n",
"cafe_count_5000_price_500 int64\n",
"cafe_count_5000_price_1000 int64\n",
"cafe_count_5000_price_1500 int64\n",
"cafe_count_5000_price_2500 int64\n",
"cafe_count_5000_price_4000 int64\n",
"cafe_count_5000_price_high int64\n",
"big_church_count_5000 int64\n",
"church_count_5000 int64\n",
"mosque_count_5000 int64\n",
"leisure_count_5000 int64\n",
"sport_count_5000 int64\n",
"market_count_5000 int64\n",
"price_doc int64\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numeric_features = train.select_dtypes(include=[np.number])\n",
"numeric_features.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "8203e428-8474-6238-380f-e972da353f46"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"price_doc 1.000000\n",
"num_room 0.476337\n",
"full_sq 0.341840\n",
"sport_count_5000 0.294864\n",
"sport_count_3000 0.290651\n",
"Name: price_doc, dtype: float64 \n",
"\n",
"ttk_km -0.272620\n",
"bulvar_ring_km -0.279158\n",
"kremlin_km -0.279249\n",
"sadovoe_km -0.283622\n",
"zd_vokzaly_avto_km -0.284069\n",
"Name: price_doc, dtype: float64\n"
]
}
],
"source": [
"corr = numeric_features.corr()\n",
"\n",
"print (corr['price_doc'].sort_values(ascending=False)[:5], '\\n')\n",
"print (corr['price_doc'].sort_values(ascending=False)[-5:])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "1fc79cd8-eca0-1e7d-3321-c444f16ff1be"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ nan, 2., 1., 3., 4., 5., 6., 0., 19., 10., 8.,\n",
" 7., 17., 9.])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['num_room'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "2d086388-9788-30a6-ba60-52c1c46ed776"
},
"outputs": [],
"source": [
"numroom_pivot = train.pivot_table(index='num_room',\n",
" values='price_doc', aggfunc=np.median)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "60ee27ab-f725-b2aa-9a87-de0a6b438370"
},
"outputs": [
{
"data": {
"text/plain": [
"num_room\n",
"0.0 7590001\n",
"1.0 5250000\n",
"2.0 6824493\n",
"3.0 9205505\n",
"4.0 14400000\n",
"5.0 16850000\n",
"6.0 23000000\n",
"7.0 25500000\n",
"8.0 35000000\n",
"9.0 95122496\n",
"10.0 8500000\n",
"17.0 13150000\n",
"19.0 2630000\n",
"Name: price_doc, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numroom_pivot"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "2407e8c8-0b72-7265-6cc0-8712c73f7272"
},
"outputs": [
{
"data": {
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AACDUXbScjR07Npg5AAAAoCrKWceOHYOZAwAAAKrGMWcHDhzQrl27dPz4cVmW5V1+7733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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f03bcd966d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"numroom_pivot.plot(kind='bar', color='blue')\n",
"plt.xlabel('Number of Rooms')\n",
"plt.ylabel('Median Sale Price')\n",
"plt.xticks(rotation=0)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "4b023a85-925d-1e6b-65d1-d9e80efff67e"
},
"outputs": [
{
"data": {
"image/png": 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77jnl5eXp5MmTXnRlUFZjfXQ/aOGAdQAAEBtPAtmiRYt077339rteW1ur/fv3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\nGHuP1NXVqby8XEuWLAldY+z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"text/plain": [
"<matplotlib.figure.Figure at 0x7f03bcd8b710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=train['full_sq'], y=target)\n",
"plt.ylabel('Sale Price')\n",
"plt.xlabel('Full Sq')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "98fa451d-4e9c-03a3-af44-794a1d7eca83"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 83,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162057.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "02c1c031-1ea3-3a5f-f3be-cb76661508ff"
},
"outputs": [],
"source": [
"def get_feature_lists_by_dtype(data,features):\n",
" output = {}\n",
" for f in features:\n",
" dtype = str(data[f].dtype)\n",
" if dtype not in output.keys(): output[dtype] = [f]\n",
" else: output[dtype] += [f]\n",
" return output\n",
"\n",
"def show_uniques(data,features):\n",
" for f in features:\n",
" if len(data[f].unique()) < 30:\n",
" print(\"%s: count(%s) %s\" % (f,len(data[f].unique()),data[f].unique()))\n",
" else:\n",
" print(\"%s: count(%s) %s\" % (f,len(data[f].unique()),data[f].unique()[0:10]))\n",
"\n",
"def show_all_uniques(data,features):\n",
" dtypes = get_dtype_lists(data,features)\n",
" for key in dtypes.keys():\n",
" print(key + \"\\n\")\n",
" show_uniques(data,dtypes[key])\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "7abf9d97-3603-1b4b-4f4b-3f894764e224"
},
"outputs": [],
"source": [
"from pandas import read_csv\n",
"data = read_csv(\"../input/student-mat.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "2cda7bd9-ea95-e174-b109-b66efeb7e372"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>school</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>address</th>\n",
" <th>famsize</th>\n",
" <th>Pstatus</th>\n",
" <th>Medu</th>\n",
" <th>Fedu</th>\n",
" <th>Mjob</th>\n",
" <th>Fjob</th>\n",
" <th>...</th>\n",
" <th>famrel</th>\n",
" <th>freetime</th>\n",
" <th>goout</th>\n",
" <th>Dalc</th>\n",
" <th>Walc</th>\n",
" <th>health</th>\n",
" <th>absences</th>\n",
" <th>G1</th>\n",
" <th>G2</th>\n",
" <th>G3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>18</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>at_home</td>\n",
" <td>teacher</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>17</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>15</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>15</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>health</td>\n",
" <td>services</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>16</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n",
"0 GP F 18 U GT3 A 4 4 at_home teacher ... \n",
"1 GP F 17 U GT3 T 1 1 at_home other ... \n",
"2 GP F 15 U LE3 T 1 1 at_home other ... \n",
"3 GP F 15 U GT3 T 4 2 health services ... \n",
"4 GP F 16 U GT3 T 3 3 other other ... \n",
"\n",
" famrel freetime goout Dalc Walc health absences G1 G2 G3 \n",
"0 4 3 4 1 1 3 6 5 6 6 \n",
"1 5 3 3 1 1 3 4 5 5 6 \n",
"2 4 3 2 2 3 3 10 7 8 10 \n",
"3 3 2 2 1 1 5 2 15 14 15 \n",
"4 4 3 2 1 2 5 4 6 10 10 \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "57f9a803-3a43-98df-4c28-7902c55d29e8"
},
"outputs": [
{
"data": {
"text/plain": [
"(395, 33)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "c39e79bc-f834-32af-b707-59bc98eefb42"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
" 'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
" 'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
" 'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc',\n",
" 'Walc', 'health', 'absences', 'G1', 'G2', 'G3'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "1a6b0f30-e6c5-ac21-b187-67c7bd091c59"
},
"outputs": [],
"source": [
"target = \"G3\"\n",
"features = data.drop(target,1).columns"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "db154be2-90e2-d1ad-357f-cf1bd1003000"
},
"outputs": [],
"source": [
"features_by_dtype = {}\n",
"\n",
"for f in features:\n",
" dtype = str(data[f].dtype)\n",
" if dtype not in features_by_dtype.keys():\n",
" features_by_dtype[dtype] = [f]\n",
" else:\n",
" features_by_dtype[dtype].append(f)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "1e057022-850f-4545-4275-81ce15c49c24"
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['object', 'int64'])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features_by_dtype.keys()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "f6df8982-c29c-9a07-a2c0-d2386f1a374d"
},
"outputs": [],
"source": [
"keys = iter(features_by_dtype.keys())"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "ce0d9cfd-9463-d58e-d973-7b1d869e7d76"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"school: 2\n",
"sex: 2\n",
"address: 2\n",
"famsize: 2\n",
"Pstatus: 2\n",
"Mjob: 5\n",
"Fjob: 5\n",
"reason: 4\n",
"guardian: 3\n",
"schoolsup: 2\n",
"famsup: 2\n",
"paid: 2\n",
"activities: 2\n",
"nursery: 2\n",
"higher: 2\n",
"internet: 2\n",
"romantic: 2\n"
]
}
],
"source": [
"key = next(keys)\n",
"dtype_list = features_by_dtype[key]\n",
"for feature in dtype_list:\n",
" string = \"%s: %s\" % (feature,len(data[feature].unique()))\n",
" print(string)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "368b3212-c1b9-d524-f1e8-2687e7dd42ca"
},
"outputs": [],
"source": [
"categorical_features = dtype_list"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "1c1f4c5e-77e8-b33a-8ab6-6cb941f308f1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"age: 8\n",
"Medu: 5\n",
"Fedu: 5\n",
"traveltime: 4\n",
"studytime: 4\n",
"failures: 4\n",
"famrel: 5\n",
"freetime: 5\n",
"goout: 5\n",
"Dalc: 5\n",
"Walc: 5\n",
"health: 5\n",
"absences: 34\n",
"G1: 17\n",
"G2: 17\n"
]
}
],
"source": [
"key = next(keys)\n",
"dtype_list = features_by_dtype[key]\n",
"for feature in dtype_list:\n",
" string = \"%s: %s\" % (feature,len(data[feature].unique()))\n",
" print(string)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "76a79c9a-ac47-4221-b2cc-832f6c9a5c3a"
},
"outputs": [],
"source": [
"count_features = dtype_list"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "c7c5fa37-64c7-6f47-d609-e07157232cba"
},
"outputs": [],
"source": [
"numerical_features = dtype_list"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "cd5071df-351e-a6dd-3c7a-6467100805f6"
},
"outputs": [],
"source": [
"features, target, numerical_features, count_features, categorical_features\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6d553a52-1338-444b-eea9-a53f734bba93"
},
"source": [
"----------\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "7e444a88-c972-e174-6868-575659414f13"
},
"outputs": [],
"source": [
"y = data[target]\n",
"\n",
"from pandas import get_dummies,concat\n",
"onehot_encoded_categorical_data = get_dummies(data[categorical_features])\n",
"X = concat([data[numerical_features], onehot_encoded_categorical_data], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "0963b033-ff9d-a51a-f728-f56a3dfa9ce8"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Average Score</th>\n",
" <th>Standard Deviation</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>XGBRegressor</th>\n",
" <td>0.79</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GradientBoostingRegressor</th>\n",
" <td>0.78</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KNeighborsRegressor</th>\n",
" <td>0.72</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeRegressor</th>\n",
" <td>0.64</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MLPRegressor</th>\n",
" <td>0.46</td>\n",
" <td>0.45</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Average Score Standard Deviation\n",
"XGBRegressor 0.79 0.05\n",
"GradientBoostingRegressor 0.78 0.04\n",
"KNeighborsRegressor 0.72 0.07\n",
"DecisionTreeRegressor 0.64 0.09\n",
"MLPRegressor 0.46 0.45"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def get_results(model, X, y):\n",
"\n",
" import warnings\n",
" with warnings.catch_warnings():\n",
" warnings.simplefilter(\"ignore\")\n",
" from sklearn.model_selection import cross_val_score\n",
" compute = cross_val_score(model, X, y)\n",
" mean = compute.mean()\n",
" std = compute.std()\n",
" return mean, std\n",
"\n",
"def display_regressor_results(X,y):\n",
"\n",
" models = []\n",
" \n",
" from sklearn.tree import DecisionTreeRegressor\n",
"\n",
" models += [DecisionTreeRegressor()]\n",
"\n",
" from sklearn.ensemble import GradientBoostingRegressor\n",
"\n",
" models += [GradientBoostingRegressor()]\n",
" \n",
" from sklearn.neighbors import KNeighborsRegressor, RadiusNeighborsRegressor\n",
"\n",
" models += [KNeighborsRegressor(), RadiusNeighborsRegressor()]\n",
"\n",
" from sklearn.neural_network import MLPRegressor\n",
" models += [MLPRegressor(hidden_layer_sizes=(len(X.columns), 2))]\n",
" \n",
" from xgboost import XGBRegressor\n",
" models += [XGBRegressor()]\n",
"\n",
" output = {}\n",
"\n",
" for m in models:\n",
" try:\n",
" model_name = type(m).__name__\n",
" scores = get_results(m,X,y)\n",
" row = {\"Average Score\" : scores[0], \"Standard Deviation\" : scores[1]}\n",
" output[model_name] = row\n",
" except:\n",
" pass\n",
"\n",
" from pandas import DataFrame\n",
" from IPython.display import display\n",
"\n",
" display(DataFrame(data=output).T.round(2).sort_values(\"Average Score\", ascending=False))\n",
"\n",
"display_regressor_results(X,y)"
]
}
],
"metadata": {
"_change_revision": 1,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162058.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "344c666b-5030-1912-4d64-36d220be5f39"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib as mpl\n",
"import numpy as np\n",
"import seaborn as sns\n",
"\n",
"from matplotlib import pyplot as plt\n",
"\n",
"data = pd.read_csv(\"../input/Chicago_Crimes_2012_to_2017.csv\", index_col='Date')\n",
"data = data.iloc[:, 3:]\n",
"data.index = pd.to_datetime(data.index)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "5d843564-df59-9fe2-8422-d6ca69d9b673"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Block</th>\n",
" <th>IUCR</th>\n",
" <th>Primary Type</th>\n",
" <th>Description</th>\n",
" <th>Location Description</th>\n",
" <th>Arrest</th>\n",
" <th>Domestic</th>\n",
" <th>Beat</th>\n",
" <th>District</th>\n",
" <th>Ward</th>\n",
" <th>Community Area</th>\n",
" <th>FBI Code</th>\n",
" <th>X Coordinate</th>\n",
" <th>Y Coordinate</th>\n",
" <th>Year</th>\n",
" <th>Updated On</th>\n",
" <th>Latitude</th>\n",
" <th>Longitude</th>\n",
" <th>Location</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2013-11-18 16:00:00</th>\n",
" <td>048XX N CLARK ST</td>\n",
" <td>0820</td>\n",
" <td>THEFT</td>\n",
" <td>$500 AND UNDER</td>\n",
" <td>DEPARTMENT STORE</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>2032</td>\n",
" <td>20.0</td>\n",
" <td>46.0</td>\n",
" <td>3.0</td>\n",
" <td>06</td>\n",
" <td>1165247.0</td>\n",
" <td>1932762.0</td>\n",
" <td>2013</td>\n",
" <td>02/04/2016 06:33:39 AM</td>\n",
" <td>41.971101</td>\n",
" <td>-87.667747</td>\n",
" <td>(41.971100531, -87.667747448)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2014-05-19 18:00:00</th>\n",
" <td>005XX W WALTON ST</td>\n",
" <td>0486</td>\n",
" <td>BATTERY</td>\n",
" <td>DOMESTIC BATTERY SIMPLE</td>\n",
" <td>CHA APARTMENT</td>\n",
" <td>False</td>\n",
" <td>True</td>\n",
" <td>1823</td>\n",
" <td>18.0</td>\n",
" <td>27.0</td>\n",
" <td>8.0</td>\n",
" <td>08B</td>\n",
" <td>1172439.0</td>\n",
" <td>1906849.0</td>\n",
" <td>2014</td>\n",
" <td>02/04/2016 06:33:39 AM</td>\n",
" <td>41.899838</td>\n",
" <td>-87.642070</td>\n",
" <td>(41.89983788, -87.642070205)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-06-06 08:00:00</th>\n",
" <td>034XX N ELSTON AVE</td>\n",
" <td>0890</td>\n",
" <td>THEFT</td>\n",
" <td>FROM BUILDING</td>\n",
" <td>FIRE STATION</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1733</td>\n",
" <td>17.0</td>\n",
" <td>33.0</td>\n",
" <td>21.0</td>\n",
" <td>06</td>\n",
" <td>1155620.0</td>\n",
" <td>1922414.0</td>\n",
" <td>2013</td>\n",
" <td>02/04/2016 06:33:39 AM</td>\n",
" <td>41.942905</td>\n",
" <td>-87.703427</td>\n",
" <td>(41.942904747, -87.703426682)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2013-07-08 17:15:00</th>\n",
" <td>008XX N MICHIGAN AVE</td>\n",
" <td>0870</td>\n",
" <td>THEFT</td>\n",
" <td>POCKET-PICKING</td>\n",
" <td>DEPARTMENT STORE</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>1833</td>\n",
" <td>18.0</td>\n",
" <td>42.0</td>\n",
" <td>8.0</td>\n",
" <td>06</td>\n",
" <td>1177338.0</td>\n",
" <td>1906213.0</td>\n",
" <td>2013</td>\n",
" <td>02/04/2016 06:33:39 AM</td>\n",
" <td>41.897983</td>\n",
" <td>-87.624096</td>\n",
" <td>(41.897982937, -87.624095634)</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2015-06-21 00:01:00</th>\n",
" <td>018XX W FARRAGUT AVE</td>\n",
" <td>1320</td>\n",
" <td>CRIMINAL DAMAGE</td>\n",
" <td>TO VEHICLE</td>\n",
" <td>STREET</td>\n",
" <td>False</td>\n",
" <td>False</td>\n",
" <td>2012</td>\n",
" <td>20.0</td>\n",
" <td>40.0</td>\n",
" <td>4.0</td>\n",
" <td>14</td>\n",
" <td>1162987.0</td>\n",
" <td>1934891.0</td>\n",
" <td>2015</td>\n",
" <td>08/17/2015 03:03:40 PM</td>\n",
" <td>41.976990</td>\n",
" <td>-87.675998</td>\n",
" <td>(41.976990471, -87.675997614)</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Block IUCR Primary Type \\\n",
"Date \n",
"2013-11-18 16:00:00 048XX N CLARK ST 0820 THEFT \n",
"2014-05-19 18:00:00 005XX W WALTON ST 0486 BATTERY \n",
"2013-06-06 08:00:00 034XX N ELSTON AVE 0890 THEFT \n",
"2013-07-08 17:15:00 008XX N MICHIGAN AVE 0870 THEFT \n",
"2015-06-21 00:01:00 018XX W FARRAGUT AVE 1320 CRIMINAL DAMAGE \n",
"\n",
" Description Location Description Arrest \\\n",
"Date \n",
"2013-11-18 16:00:00 $500 AND UNDER DEPARTMENT STORE False \n",
"2014-05-19 18:00:00 DOMESTIC BATTERY SIMPLE CHA APARTMENT False \n",
"2013-06-06 08:00:00 FROM BUILDING FIRE STATION False \n",
"2013-07-08 17:15:00 POCKET-PICKING DEPARTMENT STORE False \n",
"2015-06-21 00:01:00 TO VEHICLE STREET False \n",
"\n",
" Domestic Beat District Ward Community Area FBI Code \\\n",
"Date \n",
"2013-11-18 16:00:00 False 2032 20.0 46.0 3.0 06 \n",
"2014-05-19 18:00:00 True 1823 18.0 27.0 8.0 08B \n",
"2013-06-06 08:00:00 False 1733 17.0 33.0 21.0 06 \n",
"2013-07-08 17:15:00 False 1833 18.0 42.0 8.0 06 \n",
"2015-06-21 00:01:00 False 2012 20.0 40.0 4.0 14 \n",
"\n",
" X Coordinate Y Coordinate Year Updated On \\\n",
"Date \n",
"2013-11-18 16:00:00 1165247.0 1932762.0 2013 02/04/2016 06:33:39 AM \n",
"2014-05-19 18:00:00 1172439.0 1906849.0 2014 02/04/2016 06:33:39 AM \n",
"2013-06-06 08:00:00 1155620.0 1922414.0 2013 02/04/2016 06:33:39 AM \n",
"2013-07-08 17:15:00 1177338.0 1906213.0 2013 02/04/2016 06:33:39 AM \n",
"2015-06-21 00:01:00 1162987.0 1934891.0 2015 08/17/2015 03:03:40 PM \n",
"\n",
" Latitude Longitude Location \n",
"Date \n",
"2013-11-18 16:00:00 41.971101 -87.667747 (41.971100531, -87.667747448) \n",
"2014-05-19 18:00:00 41.899838 -87.642070 (41.89983788, -87.642070205) \n",
"2013-06-06 08:00:00 41.942905 -87.703427 (41.942904747, -87.703426682) \n",
"2013-07-08 17:15:00 41.897983 -87.624096 (41.897982937, -87.624095634) \n",
"2015-06-21 00:01:00 41.976990 -87.675998 (41.976990471, -87.675997614) "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.sample(5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "bd0b2b3b-1789-2e08-2056-e99c18778b0b"
},
"outputs": [
{
"data": {
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69Gn4+flBqVQiPDwcABASEoKFCxeiqqoKbm5ucHd3BwAMHz4c/v7+UCgUCA0NhYUFu9IT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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5ffd13c0f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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AwxF7AAAMR+wBADAcsQcAwHDEHgAAwxF7AAAMR+wBADDcZcV+fHxcd911l1555RW99957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\n3uHMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5fb25d68d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot()\n",
"\n",
"data[\"Primary Type\"].value_counts().plot(kind = \"bar\")\n",
"plt.show()\n",
"\n",
"data[\"Arrest\"].value_counts().plot(kind = \"bar\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "57cb38aa-ed7e-1896-d1e1-47451ee5dfae"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'crimes_yearly' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-aa5c839372d0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0marrest_yearly\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Arrest'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Arrest'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0marrest_yearly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mcrimes_yearly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"A\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;31m# plt.title(\"Test\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# plt.show()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'crimes_yearly' is not defined"
]
}
],
"source": [
"#plt.subplot()\n",
"crimes = [\"THEFT\"]\n",
"#for crime in crimes:\n",
"arrest_yearly = data[data['Arrest'] == True]['Arrest']\n",
"arrest_yearly.head()\n",
"crimes_yearly.resample(\"A\").sum().plot()\n",
"# plt.title(\"Test\")\n",
"# plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "3df5d03a-287b-a2fd-f697-b73d435ab916"
},
"outputs": [
{
"data": {
"text/plain": [
"Date\n",
"2016-05-03 23:40:00 True\n",
"2016-05-03 21:44:00 True\n",
"2016-05-03 23:11:00 True\n",
"2016-05-04 11:15:00 True\n",
"2016-05-04 11:30:00 True\n",
"Name: Arrest, dtype: bool"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"arrest_yearly = data[data['Arrest'] == True]['Arrest']\n",
"arrest_yearly.head()"
]
}
],
"metadata": {
"_change_revision": 428,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
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| 0001/162/1162069.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2c317e47-a9c9-468c-04f9-0b5df516baad"
},
"source": [
"# Test code for Titanic competition\n",
"## Author: Yanfen Fu"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "00c52f83-7103-2c4b-bf84-7f4b71cc1564"
},
"source": [
" - The work flow in this notebook is to tidy up the dataset by filling the missing values with reasonable guess values for model completion. And run exploratory data analysis to figure out which features to include for machine learning model.\n",
" - Compare different machine learning models for best prediction, also watch out for over fitting problem."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "00d8a3b7-e022-eeb9-5e2d-4504a0a524aa"
},
"source": [
"#Step 1: data import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a6c60e77-8d52-8a2b-8816-fde88a081796"
},
"outputs": [],
"source": [
"# import necessary package for this attempt\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib as plt\n",
"% matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "124c3034-661b-16f4-8bab-f305b19d32fb"
},
"outputs": [],
"source": [
"#Input data files are available in the \"../input/\" directory.\n",
"train_df = pd.read_csv(\"../input/train.csv\")\n",
"test_df = pd. read_csv (\"../input/test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "d9467bf1-76fd-3c99-395c-0914a5c0d06f"
},
"outputs": [],
"source": [
"corr_df = train_df.corr()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "5a07c4eb-6c78-7272-52a8-4651de3730e5"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Pclass</th>\n",
" <td>-0.035144</td>\n",
" <td>-0.338481</td>\n",
" <td>1.000000</td>\n",
" <td>-0.369226</td>\n",
" <td>0.083081</td>\n",
" <td>0.018443</td>\n",
" <td>-0.549500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SibSp</th>\n",
" <td>-0.057527</td>\n",
" <td>-0.035322</td>\n",
" <td>0.083081</td>\n",
" <td>-0.308247</td>\n",
" <td>1.000000</td>\n",
" <td>0.414838</td>\n",
" <td>0.159651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Parch</th>\n",
" <td>-0.001652</td>\n",
" <td>0.081629</td>\n",
" <td>0.018443</td>\n",
" <td>-0.189119</td>\n",
" <td>0.414838</td>\n",
" <td>1.000000</td>\n",
" <td>0.216225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Survived</th>\n",
" <td>-0.005007</td>\n",
" <td>1.000000</td>\n",
" <td>-0.338481</td>\n",
" <td>-0.077221</td>\n",
" <td>-0.035322</td>\n",
" <td>0.081629</td>\n",
" <td>0.257307</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PassengerId</th>\n",
" <td>1.000000</td>\n",
" <td>-0.005007</td>\n",
" <td>-0.035144</td>\n",
" <td>0.036847</td>\n",
" <td>-0.057527</td>\n",
" <td>-0.001652</td>\n",
" <td>0.012658</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fare</th>\n",
" <td>0.012658</td>\n",
" <td>0.257307</td>\n",
" <td>-0.549500</td>\n",
" <td>0.096067</td>\n",
" <td>0.159651</td>\n",
" <td>0.216225</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Age</th>\n",
" <td>0.036847</td>\n",
" <td>-0.077221</td>\n",
" <td>-0.369226</td>\n",
" <td>1.000000</td>\n",
" <td>-0.308247</td>\n",
" <td>-0.189119</td>\n",
" <td>0.096067</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp Parch \\\n",
"Pclass -0.035144 -0.338481 1.000000 -0.369226 0.083081 0.018443 \n",
"SibSp -0.057527 -0.035322 0.083081 -0.308247 1.000000 0.414838 \n",
"Parch -0.001652 0.081629 0.018443 -0.189119 0.414838 1.000000 \n",
"Survived -0.005007 1.000000 -0.338481 -0.077221 -0.035322 0.081629 \n",
"PassengerId 1.000000 -0.005007 -0.035144 0.036847 -0.057527 -0.001652 \n",
"Fare 0.012658 0.257307 -0.549500 0.096067 0.159651 0.216225 \n",
"Age 0.036847 -0.077221 -0.369226 1.000000 -0.308247 -0.189119 \n",
"\n",
" Fare \n",
"Pclass -0.549500 \n",
"SibSp 0.159651 \n",
"Parch 0.216225 \n",
"Survived 0.257307 \n",
"PassengerId 0.012658 \n",
"Fare 1.000000 \n",
"Age 0.096067 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# From correlation coefficient, Pcklass is the most negative impact, and Fare is the the most postive impact factor\n",
"corr_df.sort_values('Age',axis=0,ascending = True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "d248d53b-272a-6de0-1248-140a3edd276d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 891 entries, 0 to 890\n",
"Data columns (total 12 columns):\n",
"PassengerId 891 non-null int64\n",
"Survived 891 non-null int64\n",
"Pclass 891 non-null int64\n",
"Name 891 non-null object\n",
"Sex 891 non-null object\n",
"Age 714 non-null float64\n",
"SibSp 891 non-null int64\n",
"Parch 891 non-null int64\n",
"Ticket 891 non-null object\n",
"Fare 891 non-null float64\n",
"Cabin 204 non-null object\n",
"Embarked 889 non-null object\n",
"dtypes: float64(2), int64(5), object(5)\n",
"memory usage: 83.6+ KB\n"
]
}
],
"source": [
"# run PCA \n",
"from sklearn.decomposition import PCA\n",
"train_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "d32540ce-6504-dcf2-e13e-c13135b6370d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>7.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>71.2833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Age SibSp Fare\n",
"0 0 3 22.0 1 7.2500\n",
"1 1 1 38.0 1 71.2833\n",
"2 1 3 26.0 0 7.9250\n",
"3 1 1 35.0 1 53.1000\n",
"4 0 3 35.0 0 8.0500"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca = PCA(n_components=5)\n",
"train_df_t=train_df.drop(['PassengerId','Name','Sex','Ticket','Cabin','Embarked','Parch'],axis=1)\n",
"train_df_t.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "8e6c9f1f-4f23-bf56-3fe7-8ca965929e2d"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-6ac5e3568991>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpca\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_df_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/decomposition/pca.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 325\u001b[0m \u001b[0mReturns\u001b[0m \u001b[0mthe\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mitself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \"\"\"\n\u001b[0;32m--> 327\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 328\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 329\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/decomposition/pca.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 365\u001b[0m X = check_array(X, dtype=[np.float64], ensure_2d=True,\n\u001b[0;32m--> 366\u001b[0;31m copy=self.copy)\n\u001b[0m\u001b[1;32m 367\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 368\u001b[0m \u001b[0;31m# Handle n_components==None\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 407\u001b[0m % (array.ndim, estimator_name))\n\u001b[1;32m 408\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m 38\u001b[0m and not np.isfinite(X).all()):\n\u001b[1;32m 39\u001b[0m raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 40\u001b[0;31m \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
]
}
],
"source": [
"pca.fit(train_df_t)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "c3d007a1-7e9d-58e3-7b16-6d9674b64c5c"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 324,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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| 0001/162/1162070.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "02c1c031-1ea3-3a5f-f3be-cb76661508ff"
},
"outputs": [],
"source": [
"def get_feature_lists_by_dtype(data):\n",
" features = data.columns.tolist()\n",
" output = {}\n",
" for f in features:\n",
" dtype = str(data[f].dtype)\n",
" if dtype not in output.keys(): output[dtype] = [f]\n",
" else: output[dtype] += [f]\n",
" return output\n",
"\n",
"def show_uniques(data,features):\n",
" for f in features:\n",
" if len(data[f].unique()) < 30:\n",
" print(\"%s: count(%s) %s\" % (f,len(data[f].unique()),data[f].unique()))\n",
" else:\n",
" print(\"%s: count(%s) %s\" % (f,len(data[f].unique()),data[f].unique()[0:10]))\n",
"\n",
"def show_all_uniques(data):\n",
" dtypes = get_feature_lists_by_dtype(data)\n",
" for key in dtypes.keys():\n",
" print(key + \"\\n\")\n",
" show_uniques(data,dtypes[key])\n",
" print()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "7abf9d97-3603-1b4b-4f4b-3f894764e224"
},
"outputs": [],
"source": [
"from pandas import read_csv\n",
"data = read_csv(\"../input/student-mat.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "2cda7bd9-ea95-e174-b109-b66efeb7e372"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>school</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>address</th>\n",
" <th>famsize</th>\n",
" <th>Pstatus</th>\n",
" <th>Medu</th>\n",
" <th>Fedu</th>\n",
" <th>Mjob</th>\n",
" <th>Fjob</th>\n",
" <th>...</th>\n",
" <th>famrel</th>\n",
" <th>freetime</th>\n",
" <th>goout</th>\n",
" <th>Dalc</th>\n",
" <th>Walc</th>\n",
" <th>health</th>\n",
" <th>absences</th>\n",
" <th>G1</th>\n",
" <th>G2</th>\n",
" <th>G3</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>18</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>A</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>at_home</td>\n",
" <td>teacher</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>17</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>15</td>\n",
" <td>U</td>\n",
" <td>LE3</td>\n",
" <td>T</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>at_home</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>10</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>15</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>health</td>\n",
" <td>services</td>\n",
" <td>...</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>15</td>\n",
" <td>14</td>\n",
" <td>15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>GP</td>\n",
" <td>F</td>\n",
" <td>16</td>\n",
" <td>U</td>\n",
" <td>GT3</td>\n",
" <td>T</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>other</td>\n",
" <td>other</td>\n",
" <td>...</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>10</td>\n",
" <td>10</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" school sex age address famsize Pstatus Medu Fedu Mjob Fjob ... \\\n",
"0 GP F 18 U GT3 A 4 4 at_home teacher ... \n",
"1 GP F 17 U GT3 T 1 1 at_home other ... \n",
"2 GP F 15 U LE3 T 1 1 at_home other ... \n",
"3 GP F 15 U GT3 T 4 2 health services ... \n",
"4 GP F 16 U GT3 T 3 3 other other ... \n",
"\n",
" famrel freetime goout Dalc Walc health absences G1 G2 G3 \n",
"0 4 3 4 1 1 3 6 5 6 6 \n",
"1 5 3 3 1 1 3 4 5 5 6 \n",
"2 4 3 2 2 3 3 10 7 8 10 \n",
"3 3 2 2 1 1 5 2 15 14 15 \n",
"4 4 3 2 1 2 5 4 6 10 10 \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "57f9a803-3a43-98df-4c28-7902c55d29e8"
},
"outputs": [
{
"data": {
"text/plain": [
"(395, 33)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "c39e79bc-f834-32af-b707-59bc98eefb42"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
" 'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
" 'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
" 'higher', 'internet', 'romantic', 'famrel', 'freetime', 'goout', 'Dalc',\n",
" 'Walc', 'health', 'absences', 'G1', 'G2', 'G3'],\n",
" dtype='object')"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.columns"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "ce0d9cfd-9463-d58e-d973-7b1d869e7d76"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"object\n",
"\n",
"school: count(2) ['GP' 'MS']\n",
"sex: count(2) ['F' 'M']\n",
"address: count(2) ['U' 'R']\n",
"famsize: count(2) ['GT3' 'LE3']\n",
"Pstatus: count(2) ['A' 'T']\n",
"Mjob: count(5) ['at_home' 'health' 'other' 'services' 'teacher']\n",
"Fjob: count(5) ['teacher' 'other' 'services' 'health' 'at_home']\n",
"reason: count(4) ['course' 'other' 'home' 'reputation']\n",
"guardian: count(3) ['mother' 'father' 'other']\n",
"schoolsup: count(2) ['yes' 'no']\n",
"famsup: count(2) ['no' 'yes']\n",
"paid: count(2) ['no' 'yes']\n",
"activities: count(2) ['no' 'yes']\n",
"nursery: count(2) ['yes' 'no']\n",
"higher: count(2) ['yes' 'no']\n",
"internet: count(2) ['no' 'yes']\n",
"romantic: count(2) ['no' 'yes']\n",
"\n",
"int64\n",
"\n",
"age: count(8) [18 17 15 16 19 22 20 21]\n",
"Medu: count(5) [4 1 3 2 0]\n",
"Fedu: count(5) [4 1 2 3 0]\n",
"traveltime: count(4) [2 1 3 4]\n",
"studytime: count(4) [2 3 1 4]\n",
"failures: count(4) [0 3 2 1]\n",
"famrel: count(5) [4 5 3 1 2]\n",
"freetime: count(5) [3 2 4 1 5]\n",
"goout: count(5) [4 3 2 1 5]\n",
"Dalc: count(5) [1 2 5 3 4]\n",
"Walc: count(5) [1 3 2 4 5]\n",
"health: count(5) [3 5 1 2 4]\n",
"absences: count(34) [ 6 4 10 2 0 16 14 7 8 25]\n",
"G1: count(17) [ 5 7 15 6 12 16 14 10 13 8 11 9 17 19 18 4 3]\n",
"G2: count(17) [ 6 5 8 14 10 15 12 18 16 13 9 11 7 19 17 4 0]\n",
"G3: count(18) [ 6 10 15 11 19 9 12 14 16 5 8 17 18 13 20 7 0 4]\n",
"\n"
]
}
],
"source": [
"show_all_uniques(data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "25d601ca-c673-e372-5385-4bacf2aebe04"
},
"outputs": [],
"source": [
"features_by_dtype = get_feature_lists_by_dtype(data)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "368b3212-c1b9-d524-f1e8-2687e7dd42ca"
},
"outputs": [],
"source": [
"categorical_features = features_by_dtype[\"object\"]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "76a79c9a-ac47-4221-b2cc-832f6c9a5c3a"
},
"outputs": [],
"source": [
"count_features = features_by_dtype[\"int64\"]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "cd5071df-351e-a6dd-3c7a-6467100805f6"
},
"outputs": [],
"source": [
"count_features, categorical_features\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6d553a52-1338-444b-eea9-a53f734bba93"
},
"source": [
"----------\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "7e444a88-c972-e174-6868-575659414f13"
},
"outputs": [],
"source": [
"target = [\"absences\"]\n",
"\n",
"y = data[target]\n",
"\n",
"from pandas import get_dummies,concat\n",
"onehot_encoded_categorical_data = get_dummies(data[categorical_features])\n",
"X = concat([data[count_features], onehot_encoded_categorical_data], axis=1)\n",
"\n",
"X.drop(target,1, inplace=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ebe8989d-7048-7a89-3fe7-00334ec11e9f"
},
"source": [
"----------\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "0963b033-ff9d-a51a-f728-f56a3dfa9ce8"
},
"outputs": [],
"source": [
"from numpy import log1p\n",
"def squared_logarithmic_error(y_true, y_pred):\n",
" return (log1p(y_pred) - log1p(y_true)) ** 2\n",
"def mean_squared_logarithmic_error(y_true, y_pred):\n",
" calculation = squared_logarithmic_error(y_true, y_pred)\n",
" return calculation.sum() / len(calculation)\n",
"def root_mean_squared_logarithmic_error(y_true, y_pred):\n",
" return mean_squared_logarithmic_error(y_true, y_pred) ** 0.5"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "71e8aac6-304d-f248-81ef-b11f7db10b6c"
},
"outputs": [],
"source": [
"from sklearn.metrics import explained_variance_score, mean_absolute_error, mean_squared_error, median_absolute_error, r2_score\n",
"\n",
"def score_row(actuals,predictions):\n",
"\n",
" parameters = {\"y_true\" : actuals,\n",
" \"y_pred\" : predictions}\n",
"\n",
" #score_functions = [explained_variance_score, mean_absolute_error, mean_squared_error, median_absolute_error, r2_score, root_mean_squared_logarithmic_error]\n",
" score_functions = [explained_variance_score, mean_absolute_error, mean_squared_error, r2_score, root_mean_squared_logarithmic_error]\n",
" \n",
" output = {}\n",
"\n",
" for func in score_functions:\n",
" output[str(func.__name__)] = func(**parameters) \n",
"\n",
" return output"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "7dfc552d-fec9-316f-805b-6c95b850241d"
},
"outputs": [],
"source": [
"from sklearn.model_selection import KFold\n",
"from sklearn.ensemble import GradientBoostingRegressor\n",
"\n",
"def cross_val_score(model, data, features, target_feature):\n",
"\n",
" iterations = []\n",
"\n",
" splits = 10\n",
" splitter = KFold(n_splits=splits, random_state=0)\n",
" i = iter(range(0,splits))\n",
" score_rows = []\n",
"\n",
" for train, test in splitter.split(data):\n",
"\n",
" training_set = data.iloc[train]\n",
" testing_set = data.iloc[test]\n",
"\n",
" model.fit(training_set[features],training_set[target_feature])\n",
" iterations += [model]\n",
"\n",
" predictions = model.predict(testing_set[features])\n",
" actuals = testing_set[target_feature]\n",
"\n",
" # === Score Metrics ===\n",
"\n",
" score_rows += [score_row(actuals,predictions)]\n",
" \n",
" return score_rows\n",
"\n",
"from IPython.display import display\n",
"from pandas import DataFrame\n",
"\n",
"def display_mean_scores(model, data, features, target):\n",
" print(type(model).__name__)\n",
" display(DataFrame(cross_val_score(model,data,features,target)).mean())\n",
" \n",
"from pandas import options\n",
"def display_cv_scores(model, data, features, target):\n",
" options.display.float_format = '{:,.3f}'.format\n",
" display(DataFrame(cross_val_score(model,data,features,target)).round(2))\n",
"\n",
"from time import time\n",
"from pandas import Series\n",
" \n",
"def regressor_runthrough(regressors, data, features, target_feature):\n",
" results = {}\n",
" for r in regressors:\n",
" key = type(r).__name__\n",
" try:\n",
" start = time()\n",
" \n",
" unit = DataFrame(cross_val_score(r,data,features,target_feature)).mean()\n",
" \n",
" finished = time() - start\n",
" \n",
" unit = unit.append(Series([finished], index=[\"Total Processing Time\"]))\n",
" \n",
" results[key] = unit\n",
" \n",
" except:\n",
" pass\n",
" #print(key + \" failed.\")\n",
" return DataFrame(results).T"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "525af0f3-ceb3-d6b0-f070-9f117debee2d"
},
"outputs": [],
"source": [
"regressors = []\n",
"\n",
"from sklearn.svm import SVR, LinearSVR, NuSVR\n",
"regressor = SVR()\n",
"regressors.append(regressor)\n",
"regressor = LinearSVR()\n",
"regressors.append(regressor)\n",
"regressor = NuSVR()\n",
"regressors.append(regressor)\n",
"\n",
"from sklearn.linear_model import HuberRegressor, PassiveAggressiveRegressor, RANSACRegressor, SGDRegressor,TheilSenRegressor\n",
"regressors += [HuberRegressor(), PassiveAggressiveRegressor()]\n",
"\n",
"from sklearn.neighbors import KNeighborsRegressor, RadiusNeighborsRegressor\n",
"regressor = KNeighborsRegressor()\n",
"regressors.append(regressor)\n",
"regressor = RadiusNeighborsRegressor()\n",
"regressors.append(regressor)\n",
"\n",
"from sklearn.gaussian_process import GaussianProcessRegressor\n",
"regressor = GaussianProcessRegressor()\n",
"regressors.append(regressor)\n",
"\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"regressor = DecisionTreeRegressor()\n",
"regressors.append(regressor)\n",
"\n",
"from sklearn.tree import ExtraTreeRegressor\n",
"regressor = ExtraTreeRegressor()\n",
"regressors.append(regressor)\n",
"\n",
"from sklearn.ensemble import AdaBoostRegressor, BaggingRegressor, ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\n",
"regressor = AdaBoostRegressor()\n",
"regressors.append(regressor)\n",
"regressor = BaggingRegressor()\n",
"regressors.append(regressor)\n",
"regressor = ExtraTreesRegressor()\n",
"regressors.append(regressor)\n",
"regressor = RandomForestRegressor()\n",
"regressors.append(regressor)\n",
"regressor = GradientBoostingRegressor()\n",
"regressors.append(regressor)\n",
"\n",
"from xgboost import XGBRegressor\n",
"regressors += [XGBRegressor()]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "54c08d39-9818-64ac-22df-67e2f95c386d"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2889: RuntimeWarning: Mean of empty slice.\n",
" out=out, **kwargs)\n",
"/opt/conda/lib/python3.6/site-packages/numpy/core/_methods.py:73: RuntimeWarning: invalid value encountered in true_divide\n",
" ret, rcount, out=ret, casting='unsafe', subok=False)\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"full_data = X.copy()\n",
"full_data[target] = data[target]\n",
"results = regressor_runthrough(regressors, full_data, X.columns.tolist(), target[0])"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "50cad5f0-e766-053f-72ca-c9bd85cda80a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>explained_variance_score</th>\n",
" <th>mean_absolute_error</th>\n",
" <th>mean_squared_error</th>\n",
" <th>r2_score</th>\n",
" <th>root_mean_squared_logarithmic_error</th>\n",
" <th>Total Processing Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>SVR</th>\n",
" <td>0.09</td>\n",
" <td>4.38</td>\n",
" <td>63.67</td>\n",
" <td>-0.03</td>\n",
" <td>0.94</td>\n",
" <td>0.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NuSVR</th>\n",
" <td>0.08</td>\n",
" <td>4.65</td>\n",
" <td>61.20</td>\n",
" <td>-0.01</td>\n",
" <td>1.03</td>\n",
" <td>0.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HuberRegressor</th>\n",
" <td>-0.06</td>\n",
" <td>4.79</td>\n",
" <td>63.74</td>\n",
" <td>-0.16</td>\n",
" <td>1.07</td>\n",
" <td>2.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KNeighborsRegressor</th>\n",
" <td>-0.16</td>\n",
" <td>4.89</td>\n",
" <td>68.04</td>\n",
" <td>-0.27</td>\n",
" <td>1.04</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LinearSVR</th>\n",
" <td>-0.10</td>\n",
" <td>4.91</td>\n",
" <td>66.17</td>\n",
" <td>-0.20</td>\n",
" <td>1.10</td>\n",
" <td>0.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>XGBRegressor</th>\n",
" <td>-0.20</td>\n",
" <td>4.99</td>\n",
" <td>62.22</td>\n",
" <td>-0.28</td>\n",
" <td>1.09</td>\n",
" <td>8.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GradientBoostingRegressor</th>\n",
" <td>-0.29</td>\n",
" <td>5.11</td>\n",
" <td>64.75</td>\n",
" <td>-0.37</td>\n",
" <td>1.17</td>\n",
" <td>0.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BaggingRegressor</th>\n",
" <td>-0.26</td>\n",
" <td>5.36</td>\n",
" <td>65.59</td>\n",
" <td>-0.47</td>\n",
" <td>1.12</td>\n",
" <td>0.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RandomForestRegressor</th>\n",
" <td>-0.27</td>\n",
" <td>5.41</td>\n",
" <td>70.69</td>\n",
" <td>-0.43</td>\n",
" <td>1.12</td>\n",
" <td>0.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExtraTreesRegressor</th>\n",
" <td>-0.28</td>\n",
" <td>5.48</td>\n",
" <td>70.72</td>\n",
" <td>-0.42</td>\n",
" <td>1.14</td>\n",
" <td>0.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GaussianProcessRegressor</th>\n",
" <td>-0.00</td>\n",
" <td>5.72</td>\n",
" <td>96.75</td>\n",
" <td>-0.71</td>\n",
" <td>1.71</td>\n",
" <td>1.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PassiveAggressiveRegressor</th>\n",
" <td>-0.05</td>\n",
" <td>5.79</td>\n",
" <td>81.99</td>\n",
" <td>-0.63</td>\n",
" <td>1.23</td>\n",
" <td>0.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AdaBoostRegressor</th>\n",
" <td>-0.08</td>\n",
" <td>6.00</td>\n",
" <td>69.95</td>\n",
" <td>-0.48</td>\n",
" <td>1.29</td>\n",
" <td>0.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExtraTreeRegressor</th>\n",
" <td>-1.17</td>\n",
" <td>6.13</td>\n",
" <td>104.24</td>\n",
" <td>-1.33</td>\n",
" <td>1.38</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeRegressor</th>\n",
" <td>-2.41</td>\n",
" <td>6.49</td>\n",
" <td>127.13</td>\n",
" <td>-2.67</td>\n",
" <td>1.24</td>\n",
" <td>0.23</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" explained_variance_score mean_absolute_error \\\n",
"SVR 0.09 4.38 \n",
"NuSVR 0.08 4.65 \n",
"HuberRegressor -0.06 4.79 \n",
"KNeighborsRegressor -0.16 4.89 \n",
"LinearSVR -0.10 4.91 \n",
"XGBRegressor -0.20 4.99 \n",
"GradientBoostingRegressor -0.29 5.11 \n",
"BaggingRegressor -0.26 5.36 \n",
"RandomForestRegressor -0.27 5.41 \n",
"ExtraTreesRegressor -0.28 5.48 \n",
"GaussianProcessRegressor -0.00 5.72 \n",
"PassiveAggressiveRegressor -0.05 5.79 \n",
"AdaBoostRegressor -0.08 6.00 \n",
"ExtraTreeRegressor -1.17 6.13 \n",
"DecisionTreeRegressor -2.41 6.49 \n",
"\n",
" mean_squared_error r2_score \\\n",
"SVR 63.67 -0.03 \n",
"NuSVR 61.20 -0.01 \n",
"HuberRegressor 63.74 -0.16 \n",
"KNeighborsRegressor 68.04 -0.27 \n",
"LinearSVR 66.17 -0.20 \n",
"XGBRegressor 62.22 -0.28 \n",
"GradientBoostingRegressor 64.75 -0.37 \n",
"BaggingRegressor 65.59 -0.47 \n",
"RandomForestRegressor 70.69 -0.43 \n",
"ExtraTreesRegressor 70.72 -0.42 \n",
"GaussianProcessRegressor 96.75 -0.71 \n",
"PassiveAggressiveRegressor 81.99 -0.63 \n",
"AdaBoostRegressor 69.95 -0.48 \n",
"ExtraTreeRegressor 104.24 -1.33 \n",
"DecisionTreeRegressor 127.13 -2.67 \n",
"\n",
" root_mean_squared_logarithmic_error \\\n",
"SVR 0.94 \n",
"NuSVR 1.03 \n",
"HuberRegressor 1.07 \n",
"KNeighborsRegressor 1.04 \n",
"LinearSVR 1.10 \n",
"XGBRegressor 1.09 \n",
"GradientBoostingRegressor 1.17 \n",
"BaggingRegressor 1.12 \n",
"RandomForestRegressor 1.12 \n",
"ExtraTreesRegressor 1.14 \n",
"GaussianProcessRegressor 1.71 \n",
"PassiveAggressiveRegressor 1.23 \n",
"AdaBoostRegressor 1.29 \n",
"ExtraTreeRegressor 1.38 \n",
"DecisionTreeRegressor 1.24 \n",
"\n",
" Total Processing Time \n",
"SVR 0.18 \n",
"NuSVR 0.15 \n",
"HuberRegressor 2.28 \n",
"KNeighborsRegressor 0.09 \n",
"LinearSVR 0.23 \n",
"XGBRegressor 8.42 \n",
"GradientBoostingRegressor 0.79 \n",
"BaggingRegressor 0.51 \n",
"RandomForestRegressor 0.37 \n",
"ExtraTreesRegressor 0.38 \n",
"GaussianProcessRegressor 1.08 \n",
"PassiveAggressiveRegressor 0.12 \n",
"AdaBoostRegressor 0.94 \n",
"ExtraTreeRegressor 0.07 \n",
"DecisionTreeRegressor 0.23 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pandas import options\n",
"options.display.float_format = \"{:.2f}\".format\n",
"results.sort_values(\"mean_absolute_error\", ascending=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "d02d80a2-98b5-0e91-f27d-ce4297b78e69"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/numpy/core/fromnumeric.py:2889: RuntimeWarning: Mean of empty slice.\n",
" out=out, **kwargs)\n",
"/opt/conda/lib/python3.6/site-packages/numpy/core/_methods.py:73: RuntimeWarning: invalid value encountered in true_divide\n",
" ret, rcount, out=ret, casting='unsafe', subok=False)\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n",
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:3: RuntimeWarning: invalid value encountered in log1p\n",
" app.launch_new_instance()\n"
]
}
],
"source": [
"target = [\"G3\"]\n",
"\n",
"y = data[target]\n",
"\n",
"from pandas import get_dummies,concat\n",
"onehot_encoded_categorical_data = get_dummies(data[categorical_features])\n",
"X = concat([data[count_features], onehot_encoded_categorical_data], axis=1)\n",
"\n",
"X.drop(target,1, inplace=True)\n",
"\n",
"full_data = X.copy()\n",
"full_data[target] = data[target]\n",
"results = regressor_runthrough(regressors, full_data, X.columns.tolist(), target[0])"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "ca4ea2a1-3e6f-0abe-63ad-32602c68f6e5"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>explained_variance_score</th>\n",
" <th>mean_absolute_error</th>\n",
" <th>mean_squared_error</th>\n",
" <th>r2_score</th>\n",
" <th>root_mean_squared_logarithmic_error</th>\n",
" <th>Total Processing Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>RandomForestRegressor</th>\n",
" <td>0.86</td>\n",
" <td>1.07</td>\n",
" <td>2.95</td>\n",
" <td>0.85</td>\n",
" <td>0.40</td>\n",
" <td>0.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>XGBRegressor</th>\n",
" <td>0.85</td>\n",
" <td>1.08</td>\n",
" <td>2.97</td>\n",
" <td>0.85</td>\n",
" <td>0.38</td>\n",
" <td>6.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GradientBoostingRegressor</th>\n",
" <td>0.85</td>\n",
" <td>1.11</td>\n",
" <td>3.18</td>\n",
" <td>0.84</td>\n",
" <td>0.39</td>\n",
" <td>0.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BaggingRegressor</th>\n",
" <td>0.84</td>\n",
" <td>1.11</td>\n",
" <td>3.47</td>\n",
" <td>0.83</td>\n",
" <td>0.41</td>\n",
" <td>0.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HuberRegressor</th>\n",
" <td>0.82</td>\n",
" <td>1.14</td>\n",
" <td>4.27</td>\n",
" <td>0.80</td>\n",
" <td>0.46</td>\n",
" <td>6.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LinearSVR</th>\n",
" <td>0.82</td>\n",
" <td>1.17</td>\n",
" <td>4.22</td>\n",
" <td>0.80</td>\n",
" <td>0.48</td>\n",
" <td>0.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExtraTreesRegressor</th>\n",
" <td>0.80</td>\n",
" <td>1.26</td>\n",
" <td>4.10</td>\n",
" <td>0.79</td>\n",
" <td>0.44</td>\n",
" <td>0.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KNeighborsRegressor</th>\n",
" <td>0.83</td>\n",
" <td>1.27</td>\n",
" <td>3.46</td>\n",
" <td>0.82</td>\n",
" <td>0.40</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AdaBoostRegressor</th>\n",
" <td>0.83</td>\n",
" <td>1.28</td>\n",
" <td>3.61</td>\n",
" <td>0.82</td>\n",
" <td>0.44</td>\n",
" <td>0.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DecisionTreeRegressor</th>\n",
" <td>0.70</td>\n",
" <td>1.28</td>\n",
" <td>6.08</td>\n",
" <td>0.69</td>\n",
" <td>0.55</td>\n",
" <td>0.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVR</th>\n",
" <td>0.78</td>\n",
" <td>1.44</td>\n",
" <td>5.15</td>\n",
" <td>0.77</td>\n",
" <td>0.50</td>\n",
" <td>0.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NuSVR</th>\n",
" <td>0.78</td>\n",
" <td>1.45</td>\n",
" <td>5.16</td>\n",
" <td>0.76</td>\n",
" <td>0.51</td>\n",
" <td>0.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExtraTreeRegressor</th>\n",
" <td>0.64</td>\n",
" <td>1.45</td>\n",
" <td>7.29</td>\n",
" <td>0.62</td>\n",
" <td>0.61</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PassiveAggressiveRegressor</th>\n",
" <td>0.76</td>\n",
" <td>1.73</td>\n",
" <td>5.96</td>\n",
" <td>0.68</td>\n",
" <td>0.48</td>\n",
" <td>0.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GaussianProcessRegressor</th>\n",
" <td>0.00</td>\n",
" <td>10.41</td>\n",
" <td>129.31</td>\n",
" <td>-6.68</td>\n",
" <td>2.38</td>\n",
" <td>1.70</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" explained_variance_score mean_absolute_error \\\n",
"RandomForestRegressor 0.86 1.07 \n",
"XGBRegressor 0.85 1.08 \n",
"GradientBoostingRegressor 0.85 1.11 \n",
"BaggingRegressor 0.84 1.11 \n",
"HuberRegressor 0.82 1.14 \n",
"LinearSVR 0.82 1.17 \n",
"ExtraTreesRegressor 0.80 1.26 \n",
"KNeighborsRegressor 0.83 1.27 \n",
"AdaBoostRegressor 0.83 1.28 \n",
"DecisionTreeRegressor 0.70 1.28 \n",
"SVR 0.78 1.44 \n",
"NuSVR 0.78 1.45 \n",
"ExtraTreeRegressor 0.64 1.45 \n",
"PassiveAggressiveRegressor 0.76 1.73 \n",
"GaussianProcessRegressor 0.00 10.41 \n",
"\n",
" mean_squared_error r2_score \\\n",
"RandomForestRegressor 2.95 0.85 \n",
"XGBRegressor 2.97 0.85 \n",
"GradientBoostingRegressor 3.18 0.84 \n",
"BaggingRegressor 3.47 0.83 \n",
"HuberRegressor 4.27 0.80 \n",
"LinearSVR 4.22 0.80 \n",
"ExtraTreesRegressor 4.10 0.79 \n",
"KNeighborsRegressor 3.46 0.82 \n",
"AdaBoostRegressor 3.61 0.82 \n",
"DecisionTreeRegressor 6.08 0.69 \n",
"SVR 5.15 0.77 \n",
"NuSVR 5.16 0.76 \n",
"ExtraTreeRegressor 7.29 0.62 \n",
"PassiveAggressiveRegressor 5.96 0.68 \n",
"GaussianProcessRegressor 129.31 -6.68 \n",
"\n",
" root_mean_squared_logarithmic_error \\\n",
"RandomForestRegressor 0.40 \n",
"XGBRegressor 0.38 \n",
"GradientBoostingRegressor 0.39 \n",
"BaggingRegressor 0.41 \n",
"HuberRegressor 0.46 \n",
"LinearSVR 0.48 \n",
"ExtraTreesRegressor 0.44 \n",
"KNeighborsRegressor 0.40 \n",
"AdaBoostRegressor 0.44 \n",
"DecisionTreeRegressor 0.55 \n",
"SVR 0.50 \n",
"NuSVR 0.51 \n",
"ExtraTreeRegressor 0.61 \n",
"PassiveAggressiveRegressor 0.48 \n",
"GaussianProcessRegressor 2.38 \n",
"\n",
" Total Processing Time \n",
"RandomForestRegressor 0.31 \n",
"XGBRegressor 6.39 \n",
"GradientBoostingRegressor 0.79 \n",
"BaggingRegressor 0.31 \n",
"HuberRegressor 6.02 \n",
"LinearSVR 0.38 \n",
"ExtraTreesRegressor 0.35 \n",
"KNeighborsRegressor 0.06 \n",
"AdaBoostRegressor 0.87 \n",
"DecisionTreeRegressor 0.21 \n",
"SVR 0.19 \n",
"NuSVR 0.15 \n",
"ExtraTreeRegressor 0.06 \n",
"PassiveAggressiveRegressor 0.23 \n",
"GaussianProcessRegressor 1.70 "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"options.display.float_format = \"{:.2f}\".format\n",
"results.sort_values(\"mean_absolute_error\", ascending=True)"
]
}
],
"metadata": {
"_change_revision": 7,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162126.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "1a77a45d-bf39-f720-216a-ef33689c8fac"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"race-result-horse.csv\n",
"race-result-race.csv\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import tensorflow as tf\n",
"import matplotlib.pyplot as plt, pandas as pd, numpy as np\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.preprocessing import LabelEncoder\n",
"# import classification algorithms\n",
"from sklearn.svm import SVC\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"from collections import defaultdict\n",
"d = defaultdict(LabelEncoder)\n",
"\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "a551a008-115b-0750-19c9-b5a4bb1ec525"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['finishing_position', 'horse_number', 'horse_name', 'horse_id', 'jockey', 'trainer', 'actual_weight', 'declared_horse_weight', 'draw', 'length_behind_winner', 'running_position_1', 'running_position_2', 'running_position_3', 'running_position_4', 'finish_time', 'win_odds', 'running_position_5', 'running_position_6', 'race_id']\n",
"['src', 'race_date', 'race_course', 'race_number', 'race_id', 'race_class', 'race_distance', 'track_condition', 'race_name', 'track', 'sectional_time', 'incident_report']\n"
]
}
],
"source": [
"df1=pd.read_csv(\"../input/race-result-horse.csv\")\n",
"df2=pd.read_csv(\"../input/race-result-race.csv\")\n",
"print(list(df1))\n",
"print(list(df2))\n",
"#Merging the data for simpler operation\n",
"df3=pd.merge(df1,df2,on='race_id')\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "59ea76e7-d45c-c8a0-d197-fa7b2686a02d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" finishing_position horse_number horse_name horse_id jockey \\\n",
"0 1 1.0 DOUBLE DRAGON K019 B Prebble \n",
"1 2 2.0 PLAIN BLUE BANNER S070 D Whyte \n",
"2 3 10.0 GOLDWEAVER P072 Y T Cheng \n",
"3 4 3.0 SUPREME PROFIT P230 J Moreira \n",
"4 5 7.0 THE ONLY KID H173 Z Purton \n",
"\n",
" trainer actual_weight declared_horse_weight draw \\\n",
"0 D Cruz 133.0 1032.0 1.0 \n",
"1 D E Ferraris 133.0 1075.0 13.0 \n",
"2 Y S Tsui 121.0 1065.0 3.0 \n",
"3 C S Shum 132.0 1222.0 2.0 \n",
"4 K W Lui 125.0 1136.0 9.0 \n",
"\n",
" length_behind_winner ... \\\n",
"0 - ... \n",
"1 2 ... \n",
"2 2 ... \n",
"3 2 ... \n",
"4 4-1/4 ... \n",
"\n",
" race_date race_course race_number race_class race_distance \\\n",
"0 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"1 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"2 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"3 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"4 2014-09-14 Sha Tin 1 Class 5 1400 \n",
"\n",
" track_condition race_name track \\\n",
"0 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"1 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"2 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"3 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"4 GOOD TO FIRM TIM WA HANDICAP TURF - \"A\" COURSE \n",
"\n",
" sectional_time incident_report \n",
"0 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"1 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"2 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"3 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"4 13.59 22.08 23.11 23.55 \\n When about to enter the trac... \n",
"\n",
"[5 rows x 30 columns]\n"
]
}
],
"source": [
"print(df3[0:5])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "272f210f-f14b-249e-6abf-d8867c3b9a03"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['finishing_position', 'horse_name', 'horse_id', 'jockey', 'trainer',\n",
" 'length_behind_winner', 'finish_time', 'race_id', 'src', 'race_date',\n",
" 'race_course', 'race_class', 'track_condition', 'race_name', 'track',\n",
" 'sectional_time', 'incident_report'],\n",
" dtype='object')\n"
]
}
],
"source": [
"# collecting categorical varibale names \n",
"cat_var = df3.dtypes.loc[df3.dtypes=='object'].index\n",
"print(cat_var)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "59273ca7-be5f-c0fe-545c-e7c3b3368ab9"
},
"outputs": [
{
"data": {
"text/plain": [
"finishing_position 37\n",
"horse_name 1747\n",
"horse_id 1747\n",
"jockey 88\n",
"trainer 77\n",
"length_behind_winner 189\n",
"finish_time 3734\n",
"race_id 1561\n",
"src 1561\n",
"race_date 166\n",
"race_course 2\n",
"race_class 15\n",
"track_condition 9\n",
"race_name 958\n",
"track 7\n",
"sectional_time 1561\n",
"incident_report 1561\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check for unique variables in cat_var\n",
"df3[cat_var].apply(lambda x: len(x.unique()))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "87d2d934-cab8-8d74-b291-1e5279e148ed"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-7bad0076b2e2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# Encoding the variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mfit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;31m# Inverse the encoded\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mfit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrack\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minverse_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
]
}
],
"source": [
"#change a number of object to category for label assignment\n",
"#df3[\"track\"]=df3[\"track\"].astype('category')\n",
"#df3[\"track_condition\"]=df3[\"track_condition\"].astype('category')\n",
"#df3[\"race_course\"]=df3[\"race_course\"].astype('category')\n",
"#df3[\"race_class\"]=df3[\"race_class\"].astype('category')\n",
"#df3[\"jockey\"]=df3[\"jockey\"].astype('category')\n",
"#df3[\"trainer\"]=df3[\"trainer\"].astype('category')\n",
"#df3[\"race_id\"]=df3[\"race_id\"].astype('category')\n",
"#df3[\"horse_id\"]=df3[\"horse_id\"].astype('category')\n",
"\n",
"# Encoding the variable\n",
"fit = df.apply(lambda x: d[x.track].fit_transform(x))\n",
"# Inverse the encoded\n",
"fit.apply(lambda x: d[x.track].inverse_transform(x))\n",
"# Using the dictionary to label future data\n",
"df3.apply(lambda x: d[x.track].transform(x))\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "060b9a67-ed83-0865-5b75-e5cc3758f5fc"
},
"outputs": [
{
"ename": "AttributeError",
"evalue": "Can only use .cat accessor with a 'category' dtype",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-447567b1e3cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtraining_set\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrace_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcodes\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mtest_set\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrace_id\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcodes\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"horse_id\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"finish_time\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"race_course\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"race_class\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"jockey\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"trainer\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"actual_weight\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"finishing_position\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"horse_id\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"finish_time\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"race_course\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"race_class\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"jockey\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"trainer\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"actual_weight\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 2738\u001b[0m if (name in self._internal_names_set or name in self._metadata or\n\u001b[1;32m 2739\u001b[0m name in self._accessors):\n\u001b[0;32m-> 2740\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2741\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2742\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/base.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, instance, owner)\u001b[0m\n\u001b[1;32m 239\u001b[0m \u001b[0;31m# this ensures that Series.str.<method> is well defined\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 240\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccessor_cls\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconstruct_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 242\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__set__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minstance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_make_cat_accessor\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 2752\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_make_cat_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2753\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2754\u001b[0;31m raise AttributeError(\"Can only use .cat accessor with a \"\n\u001b[0m\u001b[1;32m 2755\u001b[0m \"'category' dtype\")\n\u001b[1;32m 2756\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mCategoricalAccessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: Can only use .cat accessor with a 'category' dtype"
]
}
],
"source": [
"training_set=df3.loc[(df3.race_id.cat.codes < 1000)]\n",
"test_set=df3.loc[(df3.race_id.cat.codes >= 1000)]\n",
"X_train=training_set[[\"horse_id\",\"finish_time\",\"race_course\",\"race_class\",\"jockey\",\"trainer\",\"actual_weight\"]]\n",
"Y_train=training_set[[\"finishing_position\"]]\n",
"X_test=test_set[[\"horse_id\",\"finish_time\",\"race_course\",\"race_class\",\"jockey\",\"trainer\",\"actual_weight\"]]\n",
"Y_test=test_set[[\"finishing_position\"]]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "c8deefa2-4eff-22ab-a0f2-773ced49ba22"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X_train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-e5f5fdb2f592>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
]
}
],
"source": [
"print(X_train.head())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "abaec10a-34aa-9622-0fd5-831ff47b1fc0"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X_train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-334314dd1684>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrace_course\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
]
}
],
"source": [
"X_train.race_course.cat.codes"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "88be4543-f8fa-0f01-5724-03624d49ddec"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X_train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-d49be5536148>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcl\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mclassifers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Accuracy of %r Classifier = %2f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' %'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'X_train' is not defined"
]
}
],
"source": [
"# list of classifiers\n",
"classifers = [GaussianNB(), LogisticRegression(n_jobs=-1), DecisionTreeClassifier(min_samples_leaf=5,min_samples_split=17,random_state=1), KNeighborsClassifier(n_neighbors=5, leaf_size=50, p=3)]\n",
"for cl in classifers:\n",
" clf = cl\n",
" clf.fit(X_train, y_train)\n",
" accuracy = clf.score(X_test, y_test)*100\n",
" print('Accuracy of %r Classifier = %2f' % (cl, accuracy) + ' %')\n",
" print('\\n')"
]
}
],
"metadata": {
"_change_revision": 277,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162130.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "d2a200b0-3f98-a13a-3893-f259d3c52dd6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"t10k-images-idx3-ubyte\n",
"t10k-labels-idx1-ubyte\n",
"train-images-idx3-ubyte\n",
"train-labels-idx1-ubyte\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "92676177-1f9e-f71e-d524-3e10d2ed5f2c"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"\n",
"# model params\n",
"learning_rate = 0.02\n",
"training_epochs = 3000\n",
"display_step = 50\n",
"\n",
"train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n",
" 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n",
"train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n",
" 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n",
"n_samples = train_X.shape[0]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "fa6e97e0-3647-308e-19bc-d18d749d29d7"
},
"outputs": [],
"source": [
"#tf Graph Input\n",
"X = tf.placeholder('float')\n",
"Y = tf.placeholder('float')\n",
"\n",
"#set model weights\n",
"W = tf.Variable(np.random.randn(), name='weight')\n",
"b = tf.Variable(np.random.randn(), name='bias')\n",
"\n",
"# linear model\n",
"pred = tf.add(tf.multiply(X, W), b)\n",
"\n",
"# Mean squared error\n",
"cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)\n",
"# Gradient desent \n",
"optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "5c5a3c68-ae91-cb9d-6a54-5704bf84ce80"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0050 cost= 0.138961345 W= 0.384605 b= -0.194217\n",
"Epoch: 0100 cost= 0.125397310 W= 0.368663 b= -0.0780212\n",
"Epoch: 0150 cost= 0.114809223 W= 0.354576 b= 0.0246469\n",
"Epoch: 0200 cost= 0.106544279 W= 0.34213 b= 0.115362\n",
"Epoch: 0250 cost= 0.100092955 W= 0.331133 b= 0.195516\n",
"Epoch: 0300 cost= 0.095057324 W= 0.321416 b= 0.266339\n",
"Epoch: 0350 cost= 0.091126889 W= 0.31283 b= 0.328916\n",
"Epoch: 0400 cost= 0.088059157 W= 0.305244 b= 0.384207\n",
"Epoch: 0450 cost= 0.085664831 W= 0.298541 b= 0.433062\n",
"Epoch: 0500 cost= 0.083796225 W= 0.292619 b= 0.476228\n",
"Epoch: 0550 cost= 0.082337894 W= 0.287386 b= 0.51437\n",
"Epoch: 0600 cost= 0.081199884 W= 0.282762 b= 0.54807\n",
"Epoch: 0650 cost= 0.080311820 W= 0.278676 b= 0.577848\n",
"Epoch: 0700 cost= 0.079618923 W= 0.275067 b= 0.604158\n",
"Epoch: 0750 cost= 0.079078257 W= 0.271877 b= 0.627406\n",
"Epoch: 0800 cost= 0.078656472 W= 0.269059 b= 0.647947\n",
"Epoch: 0850 cost= 0.078327447 W= 0.266569 b= 0.666097\n",
"Epoch: 0900 cost= 0.078070812 W= 0.264368 b= 0.682133\n",
"Epoch: 0950 cost= 0.077870660 W= 0.262424 b= 0.696302\n",
"Epoch: 1000 cost= 0.077714562 W= 0.260707 b= 0.708822\n",
"Epoch: 1050 cost= 0.077592865 W= 0.259189 b= 0.719885\n",
"Epoch: 1100 cost= 0.077497989 W= 0.257848 b= 0.72966\n",
"Epoch: 1150 cost= 0.077424057 W= 0.256663 b= 0.738296\n",
"Epoch: 1200 cost= 0.077366419 W= 0.255616 b= 0.745927\n",
"Epoch: 1250 cost= 0.077321559 W= 0.254691 b= 0.75267\n",
"Epoch: 1300 cost= 0.077286601 W= 0.253873 b= 0.758626\n",
"Epoch: 1350 cost= 0.077259392 W= 0.253151 b= 0.763891\n",
"Epoch: 1400 cost= 0.077238217 W= 0.252513 b= 0.768542\n",
"Epoch: 1450 cost= 0.077221736 W= 0.251949 b= 0.772651\n",
"Epoch: 1500 cost= 0.077208914 W= 0.251451 b= 0.776283\n",
"Epoch: 1550 cost= 0.077198975 W= 0.251011 b= 0.779492\n",
"Epoch: 1600 cost= 0.077191263 W= 0.250622 b= 0.782326\n",
"Epoch: 1650 cost= 0.077185243 W= 0.250278 b= 0.784832\n",
"Epoch: 1700 cost= 0.077180594 W= 0.249974 b= 0.787045\n",
"Epoch: 1750 cost= 0.077176996 W= 0.249706 b= 0.789001\n",
"Epoch: 1800 cost= 0.077174209 W= 0.249469 b= 0.790728\n",
"Epoch: 1850 cost= 0.077172048 W= 0.24926 b= 0.792254\n",
"Epoch: 1900 cost= 0.077170394 W= 0.249075 b= 0.793603\n",
"Epoch: 1950 cost= 0.077169113 W= 0.248911 b= 0.794794\n",
"Epoch: 2000 cost= 0.077168122 W= 0.248767 b= 0.795848\n",
"Epoch: 2050 cost= 0.077167369 W= 0.248639 b= 0.796779\n",
"Epoch: 2100 cost= 0.077166796 W= 0.248526 b= 0.797602\n",
"Epoch: 2150 cost= 0.077166378 W= 0.248426 b= 0.798328\n",
"Epoch: 2200 cost= 0.077166021 W= 0.248338 b= 0.798972\n",
"Epoch: 2250 cost= 0.077165760 W= 0.24826 b= 0.799539\n",
"Epoch: 2300 cost= 0.077165581 W= 0.248191 b= 0.800042\n",
"Epoch: 2350 cost= 0.077165440 W= 0.248131 b= 0.800484\n",
"Epoch: 2400 cost= 0.077165321 W= 0.248077 b= 0.800873\n",
"Epoch: 2450 cost= 0.077165253 W= 0.24803 b= 0.801217\n",
"Epoch: 2500 cost= 0.077165201 W= 0.247988 b= 0.801522\n",
"Epoch: 2550 cost= 0.077165157 W= 0.247951 b= 0.801789\n",
"Epoch: 2600 cost= 0.077165127 W= 0.247919 b= 0.802027\n",
"Epoch: 2650 cost= 0.077165127 W= 0.24789 b= 0.802236\n",
"Epoch: 2700 cost= 0.077165090 W= 0.247865 b= 0.802422\n",
"Epoch: 2750 cost= 0.077165082 W= 0.247842 b= 0.802587\n",
"Epoch: 2800 cost= 0.077165090 W= 0.247822 b= 0.802733\n",
"Epoch: 2850 cost= 0.077165090 W= 0.247805 b= 0.802859\n",
"Epoch: 2900 cost= 0.077165090 W= 0.24779 b= 0.802969\n",
"Epoch: 2950 cost= 0.077165090 W= 0.247776 b= 0.803069\n",
"Epoch: 3000 cost= 0.077165075 W= 0.247764 b= 0.803159\n",
"Traning cost= 0.0771651 W= 0.247764 b= 0.803159 \n",
"\n",
"\n",
"\n",
"Testing......\n",
"Test Loss= 0.0799814\n",
"Final Loss= 0.00281636\n"
]
}
],
"source": [
"# initializing the variables\n",
"init = tf.global_variables_initializer()\n",
"\n",
"# Launch the graph\n",
"with tf.Session() as sess:\n",
" sess.run(init)\n",
" \n",
" # Fit training data\n",
" for epoch in range(training_epochs):\n",
" for (x, y) in zip(train_X, train_Y):\n",
" sess.run(optimizer, feed_dict={X:x, Y:y})\n",
" \n",
" # Display logs per epoch step\n",
" if (epoch+1) % display_step == 0:\n",
" c = sess.run(cost, feed_dict={X:train_X, Y:train_Y})\n",
" print(\"Epoch:\", '%04d'%(epoch+1), \"cost= {:.9f}\".format(c),\n",
" \"W=\",sess.run(W), \"b=\", sess.run(b))\n",
" \n",
" train_cost = sess.run(cost, feed_dict={X:train_X, Y:train_Y})\n",
" print(\"Traning cost=\", train_cost, \"W=\",sess.run(W), \"b=\", sess.run(b), \"\\n\")\n",
" \n",
" \n",
" # the testing data\n",
" test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])\n",
" test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])\n",
" \n",
" print(\"\\n\\nTesting......\")\n",
" testing_cost = sess.run(\n",
" tf.reduce_sum(tf.pow(pred-Y, 2))/(2*test_X.shape[0]),\n",
" feed_dict={X: test_X, Y: test_Y}) \n",
" \n",
" print(\"Test Loss=\", testing_cost)\n",
" print(\"Final Loss=\", abs(train_cost - testing_cost))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "8d0d47c8-fce9-5b6b-9905-1a480fd88348"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 221,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162141.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "cc676257-7b71-35b8-71de-5fa6afc49572"
},
"source": [
"http://cs.uef.fi/sipu/pub/TitleSimilarity-ICPR.pdf"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f3337165-304d-aec5-78c3-5766bcd5b0f4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"train = pd.read_csv(\"../input/train.csv\",encoding='utf8')\n",
"test = pd.read_csv(\"../input/test.csv\",encoding='utf8')\n",
"train.fillna(value='leeg',inplace=True)\n",
"test.fillna(value='leeg',inplace=True)\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "7b4dddab-efb8-77c6-4394-cee4c8bb9455"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.75, 0.25, 0.25, 0.0, 0.18, 0.29, 0.0, 1.0, 0.0, 0.29, 0.0, 1.0, 1.0, 0.5, 0.75, 0.25, 0.5, 0.67, 0.6, 0.2, 0.5, 0.5, 1.0, 0.0, 0.17, 0.83, 0.5, 0.33, 1.0, 0.33, 0.5, 0.4, 0.8, 0.0, 0.67, 0.4, 0.14, 0.17, 0.0, 0.33, 0.0, 1.0, 0.6, 0.25, 0.67, 0.25, 0.0, 0.0, 0.5, 1.0, 0.67, 0.8, 0.0, 0.25, 0.0, 0.25, 0.5, 1.0, 0.8, 0.0, 0.17, 0.5, 0.17, 0.0, 1.0, 0.4, 1.0, 1.0, 0.33, 0.0, 0.67, 1.0, 0.0, 0.37, 1.0, 0.1, 0.33, 0.25, 0.2, 0.67, 0.0, 0.17, 0.0, 0.0, 0.33, 0.0, 0.5, 0.2, 0.17, 0.67, 0.4, 0.33, 1.0, 1.0, 0.5, 1.0, 0.2, 1.0, 0.4, 0.0]\n"
]
}
],
"source": [
"# Imports\n",
"import nltk.corpus\n",
"import nltk.stem.snowball\n",
"from nltk.corpus import wordnet\n",
"import string\n",
"\n",
"# Get default English stopwords and extend with punctuation\n",
"stopwords = nltk.corpus.stopwords.words('english')\n",
"\n",
"stopwords.extend(string.punctuation)\n",
"stopwords.append('')\n",
"\n",
"def get_wordnet_pos(pos_tag):\n",
" if pos_tag[1].startswith('J'):\n",
" return (pos_tag[0], wordnet.ADJ)\n",
" elif pos_tag[1].startswith('V'):\n",
" return (pos_tag[0], wordnet.VERB)\n",
" elif pos_tag[1].startswith('N'):\n",
" return (pos_tag[0], wordnet.NOUN)\n",
" elif pos_tag[1].startswith('R'):\n",
" return (pos_tag[0], wordnet.ADV)\n",
" else:\n",
" return (pos_tag[0], wordnet.NOUN)\n",
"\n",
"# Create tokenizer and stemmer\n",
"\n",
"lemmatizer = nltk.stem.wordnet.WordNetLemmatizer()\n",
"\n",
"def is_ci_partial_noun_set_token_stopword_lemma_match(a, b):\n",
" \"\"\"Check if a and b are matches.\"\"\"\n",
" \n",
" pos_a = map(get_wordnet_pos, nltk.pos_tag(a.lower().strip(string.punctuation).split()))\n",
" pos_b = map(get_wordnet_pos, nltk.pos_tag(b.lower().strip(string.punctuation).split()))\n",
" lemmae_a = [lemmatizer.lemmatize(token.lower().strip(string.punctuation), pos) for token, pos in pos_a if pos == wordnet.NOUN and token.lower().strip(string.punctuation) not in stopwords]\n",
" lemmae_b = [lemmatizer.lemmatize(token.lower().strip(string.punctuation), pos) for token, pos in pos_b if pos == wordnet.NOUN and token.lower().strip(string.punctuation) not in stopwords]\n",
" q1=''.join(lemmae_a).split()\n",
" q2=''.join(lemmae_b).split()\n",
" # Calculate Jaccard similarity\n",
" ratio = len(set(lemmae_a).intersection(lemmae_b)) / float(len(set(lemmae_a).union(lemmae_b))+.001)\n",
" return (round(ratio,2))\n",
"\n",
"result=[]\n",
"for xi in range(0,100): #len(train)):\n",
" result.append(is_ci_partial_noun_set_token_stopword_lemma_match(train.iloc[xi]['question1'],train.iloc[xi]['question2']))\n",
"\n",
"#train['jacq']=result\n",
"print(result)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "068b3096-b577-d027-cd56-8d37cf73fdd3"
},
"source": [
"Comparing Bi Jaccard with Jaccard"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "8261aba5-2701-1aa5-a20a-a346da3fb18c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 What is the step by step guide to invest in share market in india? 0 (0.75, 0.9166666666666666)\n",
"1 What is the story of Kohinoor (Koh-i-Noor) Diamond? 0 (0.1, 0.4117647058823529)\n",
"2 How can I increase the speed of my internet connection while using a VPN? 0 (0.0, 0.2631578947368421)\n",
"3 Why am I mentally very lonely? How can I solve it? 0 (0.0, 0.0)\n",
"4 Which one dissolve in water quikly sugar, salt, methane and carbon di oxide? 0 (0.0, 0.23529411764705882)\n",
"5 Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me? 1 (0.16666666666666666, 0.4230769230769231)\n",
"6 Should I buy tiago? 0 (0.0, 0.0)\n",
"7 How can I be a good geologist? 1 (0.07692307692307693, 0.3333333333333333)\n",
"8 When do you use シ instead of し? 0 (0.06666666666666667, 0.5)\n",
"9 Motorola (company): Can I hack my Charter Motorolla DCX3400? 0 (0.05555555555555555, 0.23529411764705882)\n",
"10 Method to find separation of slits using fresnel biprism? 0 (0.0, 0.041666666666666664)\n",
"11 How do I read and find my YouTube comments? 1 (0.15384615384615385, 0.4166666666666667)\n",
"12 What can make Physics easy to learn? 1 (0.3, 0.6666666666666666)\n",
"13 What was your first sexual experience like? 1 (0.8333333333333334, 0.8571428571428571)\n",
"14 What are the laws to change your status from a student visa to a green card in the US, how do they compare to the immigration laws in Canada? 0 (0.6296296296296297, 0.84)\n",
"15 What would a Trump presidency mean for current international master’s students on an F1 visa? 1 (0.037037037037037035, 0.15384615384615385)\n",
"16 What does manipulation mean? 1 (1.0, 1.0)\n",
"17 Why do girls want to be friends with the guy they reject? 0 (0.058823529411764705, 0.25)\n",
"18 Why are so many Quora users posting questions that are readily answered on Google? 1 (0.043478260869565216, 0.23809523809523808)\n",
"19 Which is the best digital marketing institution in banglore? 0 (0.45454545454545453, 0.8)\n",
"20 Why do rockets look white? 1 (0.25, 0.3333333333333333)\n",
"21 What's causing someone to be jealous? 0 (0.07142857142857142, 0.4166666666666667)\n",
"22 What are the questions should not ask on Quora? 0 (0.16666666666666666, 0.45454545454545453)\n",
"23 How much is 30 kV in HP? 0 (0.0, 0.0)\n",
"24 What does it mean that every time I look at the clock the numbers are the same? 0 (0.0, 0.09090909090909091)\n",
"25 What are some tips on making it through the job interview process at Medicines? 0 (0.8, 0.9333333333333333)\n",
"26 What is web application? 0 (0.0, 0.6666666666666666)\n",
"27 Does society place too much importance on sports? 0 (0.0, 0.15384615384615385)\n",
"28 What is best way to make money online? 0 (0.36363636363636365, 0.7)\n",
"29 How should I prepare for CA final law? 1 (0.058823529411764705, 0.42857142857142855)\n",
"30 What's one thing you would like to do better? 0 (0.21428571428571427, 0.5833333333333334)\n",
"31 What are some special cares for someone with a nose that gets stuffy during the night? 1 (0.041666666666666664, 0.17391304347826086)\n",
"32 What Game of Thrones villain would be the most likely to give you mercy? 1 (0.7333333333333333, 0.8666666666666667)\n",
"33 Does the United States government still blacklist (employment, etc.) some United States citizens because their political views? 0 (0.0, 0.037037037037037035)\n",
"34 What is the best travel website in spain? 0 (0.5, 0.75)\n",
"35 Why do some people think Obama will try to take their guns away? 0 (0.043478260869565216, 0.23809523809523808)\n",
"36 I'm a 19-year-old. How can I improve my skills or what should I do to become an entrepreneur in the next few years? 0 (0.0, 0.3448275862068966)\n",
"37 When a girlfriend asks her boyfriend \"Why did you choose me? What makes you want to be with me?\", what should one reply to her? 0 (0.0, 0.14893617021276595)\n",
"38 How do we prepare for UPSC? 1 (0.2222222222222222, 0.4444444444444444)\n",
"39 What is the stall speed and AOA of an f-14 with wings fully swept back? 0 (0.0, 0.12)\n",
"40 Why do Slavs squat? 0 (0.0, 0.1111111111111111)\n",
"41 When can I expect my Cognizant confirmation mail? 0 (0.8571428571428571, 0.875)\n",
"42 Can I make 50,000 a month by day trading? 0 (0.6, 0.8)\n",
"43 Is being a good kid and not being a rebel worth it in the long run? 0 (0.11764705882352941, 0.3125)\n",
"44 What universities does Rexnord recruit new grads from? What majors are they looking for? 0 (0.3181818181818182, 0.7222222222222222)\n",
"45 What is the quickest way to increase Instagram followers? 0 (0.0, 0.2)\n",
"46 How did Darth Vader fought Darth Maul in Star Wars Legends? 0 (0.0, 0.0)\n",
"47 What are the stages of breaking up between couple? I mean, what happens after the breaking up emotionally whether its a male or female? 0 (0.0, 0.13333333333333333)\n",
"48 What are some examples of products that can be make from crude oil? 1 (0.16666666666666666, 0.5333333333333333)\n",
"49 How do I make friends. 1 (0.16666666666666666, 0.5)\n",
"50 Is Career Launcher good for RBI Grade B preparation? 1 (0.3076923076923077, 0.5833333333333334)\n",
"51 Will a Blu Ray play on a regular DVD player? If so, how? 1 (0.21052631578947367, 0.5625)\n",
"52 Nd she is always sad? 0 (0.0, 0.0)\n",
"53 What is the best/most memorable thing you've ever eaten and why? 1 (0.2222222222222222, 0.6)\n",
"54 How GST affects the CAs and tax officers? 0 (0.0, 0.0)\n",
"55 How difficult is it get into RSI? 0 (0.0, 0.05555555555555555)\n",
"56 Who is israil friend? 0 (0.0, 0.1111111111111111)\n",
"57 What are some good rap songs to dance to? 0 (0.07692307692307693, 0.45454545454545453)\n",
"58 I was suddenly logged off Gmail. I can't remember my Gmail password and just realized the recovery email is no longer alive. What can I do? 1 (0.08571428571428572, 0.3793103448275862)\n",
"59 What are the best ways to learn French? 0 (0.0, 0.16666666666666666)\n",
"60 How do I download content from a kickass torrent without registration? 0 (0.0, 0.15384615384615385)\n",
"61 Is it normal to have a dark ring around the iris of my eye? 0 (0.13043478260869565, 0.3333333333333333)\n",
"62 How is the new Harry Potter book 'Harry Potter and the Cursed Child'? 1 (0.13333333333333333, 0.35714285714285715)\n",
"63 Why do I always get depressed? 0 (0.4444444444444444, 0.6666666666666666)\n",
"64 Where can I find a European family office database? 0 (0.45454545454545453, 0.6363636363636364)\n",
"65 What is Java programming? How To Learn Java Programming Language ? 1 (0.0, 0.2857142857142857)\n",
"66 What is the best book ever made? 1 (0.0, 0.4166666666666667)\n",
"67 Can we ever store energy produced in lightning? 1 (0.0, 0.21428571428571427)\n",
"68 What is your review of Performance Testing? 0 (0.2857142857142857, 0.5714285714285714)\n",
"69 At what cost does so much privacy as in Germany come? What else is lost to gain so much privacy? 0 (0.0, 0.0)\n",
"70 What are the types of immunity? 0 (0.2727272727272727, 0.6)\n",
"71 What is a narcissistic personality disorder? 1 (0.8, 0.8333333333333334)\n",
"72 How I can speak English fluently? 1 (0.3333333333333333, 0.75)\n",
"73 How helpful is QuickBooks' auto data recovery support phone number to recover your corrupted data files? 1 (0.0, 0.25)\n",
"74 Who is the richest gambler of all time and how can I reach his level? 1 (0.6666666666666666, 0.8823529411764706)\n",
"75 If I fire a bullet backward from an aircraft going faster than the bullet; will the bullet be going backwards? 0 (0.0, 0.1891891891891892)\n",
"76 How do I prevent breast cancer? 0 (0.0, 0.42857142857142855)\n",
"77 How do I log out of my Gmail account on my friend's phone? 0 (0.0, 0.21428571428571427)\n",
"78 How can I make money through the Internet? 0 (0.0, 0.10526315789473684)\n",
"79 What is purpose of life? 1 (0.08333333333333333, 0.5)\n",
"80 When will the BJP government strip all the Muslims and the Christians of the Indian citizenship and put them on boats like the Rohingya's of Burma? 0 (0.0, 0.029411764705882353)\n",
"81 What is the right etiquette for wishing a Jehovah Witness happy birthday? 0 (0.0, 0.3)\n",
"82 If someone wants to open a commercial FM radio station in any city of India, how much does it cost and what is the procedure? 0 (0.022727272727272728, 0.2702702702702703)\n",
"83 Why do Swiss despise Asians? 0 (0.09090909090909091, 0.2727272727272727)\n",
"84 What are some of the high salary income jobs in the field of biotechnology? 1 (0.09090909090909091, 0.3684210526315789)\n",
"85 How can I increase my height after 21 also? 1 (0.0, 0.4)\n",
"86 What were the major effects of the cambodia earthquake, and how do these effects compare to the Kamchatca earthquakes in 1952? 1 (0.4166666666666667, 0.8)\n",
"87 What is the difference between sincerity and fairness? 0 (0.07692307692307693, 0.6)\n",
"88 Which is the best gaming laptop under 60k INR? 1 (0.3333333333333333, 0.6363636363636364)\n",
"89 What is your review of The Next Warrior: Proving Grounds - Part 9? 0 (0.7333333333333333, 0.8666666666666667)\n",
"90 What is the best reference book for physics class 11th? 0 (0.043478260869565216, 0.23809523809523808)\n",
"91 National Institute of Technology, Kurukshetra: How is the social life at NITK, Surathkal? 0 (0.0, 0.2903225806451613)\n",
"92 What are some of the best romantic movies in English? 1 (0.125, 0.3333333333333333)\n",
"93 What causes a nightmare? 1 (0.0, 0.42857142857142855)\n",
"94 What is abstract expressionism in painting? 0 (0.08333333333333333, 0.25)\n",
"95 How does 3D printing work? 1 (0.3333333333333333, 0.6666666666666666)\n",
"96 What was it like to attend Caltech with Jeremy Ehrhardt? 0 (0.0, 0.13333333333333333)\n",
"97 Why did harry become a horcrux? 0 (0.0, 0.25)\n",
"98 What are the best associate product manager (APM) programs that someone in their early 20s can join to learn product management and have a rewarding career in the company? 0 (0.025, 0.30303030303030304)\n",
"99 Why is the number for Skype at 1-855-425-3768 always busy? 0 (0.0, 0.047619047619047616)\n"
]
}
],
"source": [
"\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from collections import Counter\n",
"from nltk.stem.snowball import SnowballStemmer\n",
"\n",
"\n",
"def BiJaccard(str1, str2):\n",
" str1 = set(nltk.word_tokenize(str1.strip(string.punctuation).lower())) #print(str1)\n",
" str1 = {SnowballStemmer(\"english\").stem(w) for w in str1 }\n",
" bigram1 = set(nltk.bigrams( str1 ))\n",
" #print(set(bigram1))\n",
" str2 = set(nltk.word_tokenize(str2.strip(string.punctuation).lower()))\n",
" str2 = {SnowballStemmer(\"english\").stem(w) for w in str2 } \n",
" bigram2 = set(nltk.bigrams(str2) )\n",
" #print(bigram1 & bigram2)\n",
" return float(len(bigram1 & bigram2)) / len(bigram1 | bigram2),float(len(str1 & str2)/len(str1 |str2))\n",
"\n",
"for xi in range (0,100):\n",
" q1=train.iloc[xi]['question1']\n",
" q2=train.iloc[xi]['question2']\n",
" print(xi,q1,train.iloc[xi]['is_duplicate'],BiJaccard(q1,q2))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "10e24f84-8b35-8993-f298-83e80224f953"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 115,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162225.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "91990061-67f7-7687-a1b4-c15a72a670c5"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "96aaa7e6-2a11-e953-eda2-6c715a584169"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/cross_validation.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"# Ignore warnings\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Handle table-like data and matrices\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Modelling Algorithms\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.svm import SVC, LinearSVC\n",
"from sklearn.ensemble import RandomForestClassifier , GradientBoostingClassifier\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Modelling Helpers\n",
"from sklearn.preprocessing import Imputer , Normalizer , scale\n",
"from sklearn.cross_validation import train_test_split , StratifiedKFold\n",
"from sklearn.feature_selection import RFECV\n",
"\n",
"# Visualisation\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.pylab as pylab\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4e59b8f0-8c88-55d7-c06f-a77e61f6a542"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "85f7daf8-246d-fad7-b12b-963b6020b1e9"
},
"source": [
"## Loading dataset on pandas dataframe"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "45a54014-4314-41ca-a983-41495ab4ed75"
},
"outputs": [],
"source": [
"# load data \n",
"col_names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
"mydf = pd.read_csv(\"../input/pima-indians-diabetes.csv\", names=col_names)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "87e70475-2c13-400b-ddac-e5078210ae6b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(768, 9)\n"
]
}
],
"source": [
"#Dimensions of Your Data\n",
"#shape\n",
"print (mydf.shape)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "288a3c28-bf7a-4591-8bb2-9c57d938f826"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" preg plas pres skin test mass pedi age class\n",
"0 6 148 72 35 0 33.6 0.627 50 1\n",
"1 1 85 66 29 0 26.6 0.351 31 0\n",
"2 8 183 64 0 0 23.3 0.672 32 1\n",
"3 1 89 66 23 94 28.1 0.167 21 0\n",
"4 0 137 40 35 168 43.1 2.288 33 1\n",
"5 5 116 74 0 0 25.6 0.201 30 0\n",
"6 3 78 50 32 88 31.0 0.248 26 1\n",
"7 10 115 0 0 0 35.3 0.134 29 0\n",
"8 2 197 70 45 543 30.5 0.158 53 1\n",
"9 8 125 96 0 0 0.0 0.232 54 1\n"
]
}
],
"source": [
"# peek\n",
"print(mydf.head(10))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "8b4eb830-2f61-250c-1422-e450921cfc38"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"preg int64\n",
"plas int64\n",
"pres int64\n",
"skin int64\n",
"test int64\n",
"mass float64\n",
"pedi float64\n",
"age int64\n",
"class int64\n",
"dtype: object\n"
]
}
],
"source": [
"# data types\n",
"print(mydf.dtypes)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "ac56b6b9-a3f4-5d2b-b030-2fb647ab3c60"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" preg plas pres skin test mass \\\n",
"count 768.000000 768.000000 768.000000 768.000000 768.000000 768.000000 \n",
"mean 3.845052 120.894531 69.105469 20.536458 79.799479 31.992578 \n",
"std 3.369578 31.972618 19.355807 15.952218 115.244002 7.884160 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 1.000000 99.000000 62.000000 0.000000 0.000000 27.300000 \n",
"50% 3.000000 117.000000 72.000000 23.000000 30.500000 32.000000 \n",
"75% 6.000000 140.250000 80.000000 32.000000 127.250000 36.600000 \n",
"max 17.000000 199.000000 122.000000 99.000000 846.000000 67.100000 \n",
"\n",
" pedi age class \n",
"count 768.000000 768.000000 768.000000 \n",
"mean 0.471876 33.240885 0.348958 \n",
"std 0.331329 11.760232 0.476951 \n",
"min 0.078000 21.000000 0.000000 \n",
"25% 0.243750 24.000000 0.000000 \n",
"50% 0.372500 29.000000 0.000000 \n",
"75% 0.626250 41.000000 1.000000 \n",
"max 2.420000 81.000000 1.000000 \n"
]
}
],
"source": [
"#description\n",
"print(mydf.describe())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "3cb9efae-a5b5-3be5-2477-0363d38e09ae"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"class\n",
"0 500\n",
"1 268\n",
"dtype: int64\n"
]
}
],
"source": [
"#class distribution\n",
"class_counts = mydf.groupby('class').size()\n",
"print(class_counts)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "90592627-494a-44df-e1cd-25e245ee9ead"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "51addada-6d7e-42ec-e4a9-e79bcc0b172c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" preg plas pres skin test mass pedi age class\n",
"preg 1.000 0.129 0.141 -0.082 -0.074 0.018 -0.034 0.544 0.222\n",
"plas 0.129 1.000 0.153 0.057 0.331 0.221 0.137 0.264 0.467\n",
"pres 0.141 0.153 1.000 0.207 0.089 0.282 0.041 0.240 0.065\n",
"skin -0.082 0.057 0.207 1.000 0.437 0.393 0.184 -0.114 0.075\n",
"test -0.074 0.331 0.089 0.437 1.000 0.198 0.185 -0.042 0.131\n",
"mass 0.018 0.221 0.282 0.393 0.198 1.000 0.141 0.036 0.293\n",
"pedi -0.034 0.137 0.041 0.184 0.185 0.141 1.000 0.034 0.174\n",
"age 0.544 0.264 0.240 -0.114 -0.042 0.036 0.034 1.000 0.238\n",
"class 0.222 0.467 0.065 0.075 0.131 0.293 0.174 0.238 1.000\n"
]
}
],
"source": [
"# Pearson correlations\n",
"pd.set_option('display.width', 100)\n",
"pd.set_option('precision', 3) \n",
"correlations = mydf.corr(method='pearson') \n",
"print(correlations)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8a15bb57-99fb-b444-fab4-ad6082c5baed"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "6296fed0-5a15-aec7-6c09-379a4bb6b43d"
},
"outputs": [
{
"data": {
"image/png": 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XLqFHjx4yRk2uQqlUQqm8s7k0Go1276VpDL0Y9SXKtrBnHBYLMrtqSG6lpaVISUnBxo0b\nzfnUp08fZGVlYeTIkcjOzkb//v3RvXt3JCYmoqSkBAqFAnl5eUhISJA5emoM7NFL01h6MepLhG1h\ni31SV0G3OKjrVleNVqs1L+OAGnKkXbt2obi4GLNmzcLYsWMxduxYTJs2DTt27EB0dDSuXr2K0NBQ\nqNVqxMXFYfLkyZg4cSKmT59u7joksrVbvTQA6uylub3tJKqLxSNke3bVENVHZGQkIiMj71n+ySef\n3LMsJCQEISEhjgiLGjn20pCt3fegrvvpqqnp3Ik9++cb+tminLu4G+Micoz8/HwkJyfjwoULUCqV\nyMrKwtKlSxEfH4+MjAy0bt0aoaGh8PDwMPfSuLm5sZeGrNKggmyrATV3nzux9zmThny2qOdxnDEu\nFmpyVl27dsXmzZvvWc5eGrKlBhVkZ+2qmZS036rXp8fzUgUiInIMiwW5MXfVWFPAWbyJiOynMbTH\nFgsyu2qIiIjsj3NZExERCYAFmYiISAAsyERERAJgQSYiIhIACzIREZEAWJCJiIgEwIJMREQkgPue\ny5puagwXrRMRkf3wCJmIiEgALMhEREQCYEEmIiISAAsyERGRADioSwYcAEZERHfjETIREZEAeIQs\nOGuOpgEeURMROSseIRMREQmAR8hERI2MtT1v5Bg2L8iLFi3C8ePH4ebmhoSEBHTr1s3WX0F14IAx\n5iDJjzkoL2dtB21akL///nv8/vvvyMjIwNmzZ5GQkICMjAxbfgVRnZiDJDdb5eCLcTvr/VqRigo1\nnE3PIefm5mLw4MEAgMceewzXrl3D9evXbfkVRHViDpLcmIPUUDY9QjYYDOjSpYv5sY+PD4qKivDg\ngw/a8mvIRpy1W6cuzEGSG3PQuYh0JYtdB3VJklTn8xqNV43Lvl420l4hkQPUtF/l0pAcBNAoclCk\n/VQfDdknIqyjiDnYGPLbGdm0y1qr1cJgMJgfX7p0CRqNxpZfQVQn5iDJjTlIDWXTgty3b19kZWUB\nAE6ePAmtVstuGnIo5iDJjTlIDWXTLuuePXuiS5cuiIqKgpubG+bNm2fLjyeyiDlIcmMOUkO5SZZO\ncBAREZHdcepMIiIiAbAgExERCUDWuaxTUlJw7NgxVFVVYerUqfDz88Ps2bNhMpmg0WiwZMkSqFQq\nh8dVUVGBF154AbGxsfD39xcipszMTKxfvx5KpRJvvPEGnnrqKVnjKisrw5w5c3Dt2jXcuHED06dP\nx+OPPy7EtrJGXVMcfvfdd1i+fDkUCgUGDBiA6dOnyxhpw9W1joGBgXj44YehUCgAAEuXLkXLli3l\nCrVBTp06hdjYWEyYMAExMTF3POes+1DOqTdFaZdFaIcd3u5KMsnNzZVeeeUVSZIk6cqVK9LAgQOl\n+Ph4adeuXZIkSdKyZcukzz77TJbYli9fLo0ePVravn27EDFduXJFCg4OlkpLS6XCwkIpMTFR9rg2\nb94sLV26VJIkSbp48aI0dOhQ2WOyll6vl6ZMmSJJkiSdOXNGioiIuOP5YcOGSX/++adkMpmkl19+\nWTp9+rQcYd4XS+sYEBAgXb9+XY7QbKKsrEyKiYmREhMTpc2bN9/zvDPuQ0v7zJ5EapflboflaHdl\n67Lu3bs3Vq5cCQBo1qwZjEYj9Ho9goKCAAABAQHIzc11eFxnz57FmTNnMGjQIAAQIqbc3Fz4+/vj\nwQcfhFarxcKFC2WPy9vbG1evXgUAlJSUwNvbW/aYrFXXFIfnzp1D8+bN0apVK7i7u2PgwIHCr09N\nXH0aR5VKhbS0NGi12nuec9Z9KOc+E6VdFqEdlqPdla0gKxQKeHp6AgB0Oh0GDBgAo9FoPvz39fVF\nUVGRw+NKTk5GfHy8+bGjY+rcuTPOnz+PvXv3Yu7cuQCA8+fPo6KiAtOmTUN0dDRyc3Nl31YjRozA\nn3/+iSFDhiAmJgZz5syRPSZrGQwGeHt7mx/fmuIQAIqKiuDj41Pjc87k1jrGx8dj9erVNa7HvHnz\n8PLLL2Pp0qUWZ5USjVKphFqtrvE5Z92HdeWlvYnSLsvdDgPytLuy3w9537590Ol0SE9PR3BwsHm5\nHA3Djh070KNHD7Rt27bG5x0Z05AhQzBkyBDz46tXr+Kjjz7Cn3/+iXHjxt0RixzbaufOnWjdujU2\nbNiAn3/+GQkJCXc872wNO+CcMVvr7nV844030L9/fzRv3hzTp09HVlYWQkJCZIqOaiJHXsrZLovU\nDju63ZW1IOfk5GDt2rVYv349vLy84OnpiYqKCqjVahQWFtbYDWVPBw8exLlz53Dw4EFcvHgRKpXK\nqpj0ej3ef/999O3bFwcOHMCNGzewfPlydO7cGSkpKcjJycGNGzcQERGBadOmAQAOHTqE999/H0ql\nEmFhYebP+vLLL5GZmYmNGzfC19cXTz/9NJRKJdq1a4emTZtCoVDIuq3y8vLQr18/AEDHjh1x6dIl\nNGnSRNaYrFXXFId3P+cM6wPcm4OXL19Gs2bNzM9funQJBQUFiIuLQ3l5Odzd3aHVatGnTx/069cP\na9euxd/+9jdUV1fjqaeeQlJSktPOMuWs+1DuqTfvp12urQ3MyclBYWEhfv75Z7zwwgsYP348UlNT\n8fXXX6OyshJBQUGYO3cuFAoFdu7ciaNHj2LNmjXw8PBAZWUlmjdv7vC2RY52V7Yu69LSUqSkpGDd\nunVo0aIFAKBPnz7mKeeys7PRv39/h8a0YsUKbN++HV988QXCw8MRGxtrdUxnz55Ft27dkJWVhdde\new3z589HWloazpw5g6+//hr/9V//haysLBw4cAAmkwnvvPMO5s2bh927d8Pd3R0mk+mez+zXrx+O\nHDmC6upqFBcXo7y8XPZt1b59exw/fhwAcOHCBTRt2vSOKQPliMladU1x+Je//AXXr1/H+fPnUVVV\nhQMHDqBv375yhltvt+dgVFQU/v73vwO42bBrtVosXrwYkydPxrZt2+Du7o53330XALB7925UVVVh\nz549yM7OxuOPP44ff/xRzlW5L866D+WcetMW7XJNbSBw8+Dj448/xoQJE7Bz507s2bMHOp0Oe/fu\nxblz58x5eu3aNcydOxcnTpzA008/DUmS0LVrV4e3LXK0u7IdIe/atQvFxcWYNWuWeVlSUhISExOR\nkZGB1q1bIzQ0VK7wzGbMmIE5c+bUOyZPT08MGzYMABAcHIzExEQolUpMmTIFKpUKKpUKI0eORHZ2\nNtq1a4fKykrzkeaoUaOQnJx8z2e2bNkSQ4cORUREBAAgMTERfn5+VsVla5GRkUhISEBMTAyqqqow\nf/58PPbYY7LGZK2apjj88ssv4eXlhSFDhmD+/PmIi4sDAAwfPhwdOnSQOeL6uT0HX331VaSlpeHA\ngQNo0qQJ1q5di/z8fCiVSvN6rl69GlFRUdBqtfjjjz+wd+9e9OvX747/TVHl5+cjOTkZFy5cgFKp\nRFZWFgIDA/GXv/zFafehnFNv2qJdrqkN7NOnD7p3724+p3/gwAGEhYXBy+vmna7Cw8Px6aefYsyY\nMTh58iQ2btwIAPDz80Nubi5Gjx6NrVu3OrRtkaPd5dSZNqTX6zF37lzs33/z/pqSJKFjx4545JFH\nUFlZiQceeAAAUFlZiW7dumHcuHF46623zK+vrq5Gp06d8M033+D77783d1kT1VdtOdi3b1/06tUL\nsbGx2LFjBz799FOUlZWhuroaf/zxB/7v//4PALBnzx589tlnyM/PR2BgIObNm3dHlzdRXWrLv4iI\nCFRXV+ODDz4AAEyaNAmnTp0yH/mbTCb4+Phg1apVGDhwIH7++WfzZw4cOBApKSl47rnnHL9CDib7\noC5Xc+tSIOBm1wsANG/eHK+99hoCAgLueO3Zs2fvuJzhypUrjgmSXFptOQjcPI+amJiIbdu2oVOn\nTvjtt98wdOhQ8+tDQkIQEhKCq1evIiEhARs2bMCbb77p2BUgp1Zb/hUXF5uXa7VaBAYG3jORS1lZ\nGSRJgtFoRJMmTVBVVdWo2kVOnWljFRUV2LdvHwAgKysLXbt2xfDhw7Ft2zaYTCZIkoTVq1fjH//4\nB9q1aweFQgG9Xg/g5kAuNzc3OcMnF1BTDt7qnbly5Qo8PT3x6KOPoqqqChkZGQBuNoTbt29Hamoq\nAKBFixZ49NFH5VkBcmp15d8tQUFB2LlzJ4xGIwBg69at+Oqrr9C0aVM89thj2L17NwAgIyOjUbWJ\nPEK2sTZt2uDYsWNYsmQJbty4gRUrVqBjx444f/48RowYYR6gMH78eHh4eGDhwoVISEiASqXC6NGj\nzdcAEjVUTTn4+eefA7g5In7AgAEYOnQofH19ER8fj7y8PIwdOxbp6elISEhAcHAwFAoF2rdvj6Sk\nJJnXhpxNTfl36NChO14zePBgnD59GqNGjQIAtGvXztydPW/ePPznf/4nNmzYgNDQULRs2bLRFGWe\nQ7YhvV6PxMRE7N27V+5QqJFiDpKcbJV/kiSZi/Dzzz+PjRs3omPHjrYIUWjssiYiImG88cYbSEtL\nA3Bz+kpJkvDII4/IG5SDsMuaiIiEMXPmTMydOxfbt2+Hh4cHUlJSap0e1dWwy5qIiEgA7LImIiIS\nAAsyERGRAGQ9h1xUVAoA8Pb2RHFxuZyh3BfGXz8ajZfdv8Nat3Lwbs6+T+vD1dexpvVjDlqHMdg+\njrpyUIgjZKVSIXcI94Xxu57GsE1cfR2dff1EiJ8x/Jsj4hCiIBMRETV2LMhEREQCEPI65ElJ++v9\n2vT4QDtGQqJISUnBsWPHUFVVhalTp8LPzw+zZ8+GyWSCRqPBkiVLoFKpkJmZiU2bNsHd3R0REREI\nDw+XO3QhWfM/BvD/TA4vxu2s92u5f1yDxYJsNBoRHx+Py5cv41//+hdiY2PRsWNHNobkMEeOHMHp\n06eRkZGB4uJijBo1Cv7+/oiOjsawYcOwfPly6HQ6hIaGIjU1FTqdDh4eHhgzZgyGDBlivtE6EZHI\nLHZZHzhwAF27dsWWLVuwYsUKJCUlYdWqVYiOjsbnn3+O9u3bQ6fToby8HKmpqdi4cSM2b96MTZs2\n3XEbLqKG6t27N1auXAkAaNasGYxGI/R6PYKCggAAAQEByM3NxfHjx+Hn5wcvLy+o1Wr07NkTeXl5\ncoZORFRvFgvy8OHD8eqrrwIACgoK0LJlSzaG5FAKhcJ8FyydTocBAwbAaDRCpVIBAHx9fVFUVASD\nwQAfHx/z+3x8fFBUVCRLzERE1qr3OeSoqChcvHgRa9euxcSJE23SGHp7e5qHkjf0+kBRrisUJY6G\ncob49+3bB51Oh/T0dAQHB5uX1zb7a31mhb09B+/mDNvEUZx1Wzhr3NQ41bsgb926FT/99BPefvvt\nOxq6+2kMb11krdF41XpxvCUNfZ8t3U/8InBU/PfTOObk5GDt2rVYv349vLy84OnpiYqKCqjVahQW\nFkKr1UKr1cJgMJjfc+nSJfTo0aPOz63tQn9n36e25ozboqZ9yAJNIrPYZZ2fn4+CggIAQKdOnWAy\nmdC0aVNUVFQAQJ2NoVartVPY1JiUlpYiJSUF69atMw/Q6tOnD7KysgAA2dnZ6N+/P7p3744TJ06g\npKQEZWVlyMvLQ69eveQMnYio3iwW5KNHjyI9PR0AYDAYUF5ezsaQHGrXrl0oLi7GrFmzMHbsWIwd\nOxbTpk3Djh07EB0djatXryI0NBRqtRpxcXGYPHkyJk6ciOnTp8PLi0dEROQcLHZZR0VF4Z133kF0\ndDQqKirw7rvvomvXrpgzZw4yMjLQunVrhIaGwsPDw9wYurm5sTEkm4mMjERkZOQ9yz/55JN7loWE\nhCAkJMQRYRER2ZTFgqxWq7Fs2bJ7lrMxJCIish1OnUlERCQAIafOJCISDadvJXtjQSYisoDTt5Ij\nsMuaiMgCTt9KjsCCTERkAadvJUdglzURUT05evrW+rLnDGQizG4mQgyA/eNgQSYiqgdHT99qDXtN\nbSrCFLIixGDLOOoq6uyyJiKygNO3kiPwCJmILJqUtL/er02PD7RjJPK4ffrWW5KSkpCYmMgZC8lm\nWJCJiCzg9K3kCCzIRC7CmqNYIhIPzyETEREJgEfIREROztreEVc8z+8KeIRMREQkABZkIiIiAdSr\ny5p3OSE6NZTaAAAcM0lEQVQiIrIviwWZdzkhIiKyP4td1rzLCRERkf1ZPEKu6S4nhw8fFuYuJ419\nBiEiInIN9b7syd53OXHE3Txc/Y4o98PZ4ycicnb1Ksj2vsuJo+7m4cp3RLkfjoqfRZ+IqHYWzyHz\nLidERET2Z/EImXc5ISIisj+LBZl3OSEiIrI/ztRFREQkABZkIiIiAbAgExERCYAFmYiISAAsyOQU\nTp06hcGDB2PLli0AgIKCAowdOxbR0dGYOXMmKisrAQCZmZkICwtDeHg4tm3bJmfIRERWqfdMXURy\nKS8vx8KFC+Hv729etmrVKt7ghKiBOOWwmHiETMJTqVRIS0uDVqs1L+MNTojI1fAImYSnVCqhVN6Z\nqkaj0SY3OLl9PvW7carPhhFpu4kUC5ElLMjk9O7nBie35lO/m7PPTy4nUbZbTfuQBZpExoJMTslW\nNzgh23PV85OnTp1CbGwsJkyYgJiYGBQUFGD27NkwmUzQaDRYsmQJVCoVMjMzsWnTJri7uyMiIgLh\n4eFyh05OgueQySnxBifkSHUNLPz888/Rvn176HQ6lJeXIzU1FRs3bsTmzZuxadMmXL16VcbIyZk0\nqiNka365A871692V5efnIzk5GRcuXIBSqURWVhaWLl2K+Ph43uCEHOLWwMK0tDTzMr1ejwULFgC4\nObAwPT0dHTp0MA8sBGAeWBgYyLaELGtUBZmcU9euXbF58+Z7lvMGJ+Qo9hxYSHQLCzIR0X26n4GF\ndY30F4EIA+FEiAGwfxwsyEREDWCrgYW1jfQXhdyj5kW54sFWcdRV1Dmoi4ioATiwkGytXkfIHO5P\nRI0ZBxaSI1gsyJxHmIgaOw4sJEewWJA53J+IiOrDVSeFcRSLBZnD/YmIiOzvvkdZ22q4vyjD2m9n\nTUwixm8NZ4+fiMjZNagg23q4vyjD2u9W35hEjb++HBU/iz4RUe0adNkTh/sTERHZlsUjZA73JyIi\nsj+LBZnD/YnIXjgql+jfOFMXERGRAFiQiYiIBMCCTEREJADe7YmInII155sB4OtlI+0UCdmCtfuz\nMYwhYEEmIqJaWVs4qeFYkOvAEaBEROQoPIdMREQkABZkIiIiAbDLmkhgPH9H1HjwCJmIiEgALMhE\nREQCYEEmIiISAAsyERGRAFiQiYiIBMBR1jbCSUSIiMTgrO0xj5CJiIgEYPMj5EWLFuH48eNwc3ND\nQkICunXrZuuvcHrO+uvNWTAHSW7MQdtrDNfk27Qgf//99/j999+RkZGBs2fPIiEhARkZGbb8CqI6\nMQdJbsxBaiibFuTc3FwMHjwYAPDYY4/h2rVruH79Oh588EFbfg1RrWyVgy/G7az3a63pxWgMv/Ib\nO7aD1FA2LcgGgwFdunQxP/bx8UFRURET8T7YswG3VyGRs5udOUhyYw46F5Huy2zXUdaSJNX5vEbj\nVePfvLG4492+/WvirPvEmhy8nb3W11m3o7OylNeOIFoOkrhsOspaq9XCYDCYH1+6dAkajcaWX0FU\nJ+YgyY05SA1l04Lct29fZGVlAQBOnjwJrVbLbhpyKOYgyY05SA1l0y7rnj17okuXLoiKioKbmxvm\nzZtny48nsog5SHJjDlJDuUmWTnAQERGR3XGmLiIiIgGwIBMREQlA9oK8aNEiREZGIioqCv/85z/l\nDsdqp06dwuDBg7Flyxa5Q2mQlJQUREZGIiwsDNnZ2XKHIztnz8e73Z2fBQUFGDt2LKKjozFz5kxU\nVlYCADIzMxEWFobw8HBs27ZNzpCtcnf+usr6yZ2Her0ezz//PMaOHYuxY8di4cKFDv3++uatI2OI\nj4/Hiy++aN4mBw8etP2XSjLS6/XSlClTJEmSpDNnzkgRERFyhmO1srIyKSYmRkpMTJQ2b94sdzhW\ny83NlV555RVJkiTpypUr0sCBA+UNSGbOno93qyk/4+PjpV27dkmSJEnLli2TPvvsM6msrEwKDg6W\nSkpKJKPRKI0YMUIqLi6WM/R6qSl/XWH9RMjDI0eOSDNmzHD490pS/fPW0THMmTNH2r9/v12/V9Yj\n5NqmmHMWKpUKaWlp0Gq1cofSIL1798bKlSsBAM2aNYPRaITJZJI5Kvk4ez7erab81Ov1CAoKAgAE\nBAQgNzcXx48fh5+fH7y8vKBWq9GzZ0/k5eXJFXa91ZS/rrB+rpaH1qpv3jo6BkeQtSAbDAZ4e3ub\nH9+aYs5ZKJVKqNVqucNoMIVCAU9PTwCATqfDgAEDoFAoZI5KPs6ej3e7lZ/nz5/HRx99BAAwGo1Q\nqVQAgKKiIuTm5sJgMMDHx8f8PmdZ75ry9/b18/X1RVFRkdOtnyh5eObMGUybNg0vv/wyvv32W6vf\n/8UXXzToe5VKJTIzM+9YVtN+tafa2vYtW7Zg3LhxePPNN3HlyhWbf6/s55BvJ/EKLFns27cPOp0O\n7777rtyhCMXV8/Gpp57C448/fs9yZ1vv2vK3tvVwtvWTI95HHnkEr7/+OtasWYPk5GS88847Vp23\nNZlMSElJadB3W3qvXPtv5MiReOutt/Dpp5+iU6dO5h+5tmTXuawtcaUp5n777Te8+OKL6Nu3Lw4c\nOIAbN25g+fLlyMnJQWFhIX7++We88MILGD9+PFJTU/H111+jsrISQUFBmDt3LhQKBXbv3o3U1FSY\nTCYolUokJibiueees2vcOTk5WLt2LdavXw8vL/nn/ZWTK+RjVVUV5s2bh6NHj6K6uhpPPfWU+cgC\nAJo0aYK//vWvCAoKgkKhwM8//wytVovvvvsO7733HgoLC/Hdd99Bo9EgICBA+NMxd+evp6cnKioq\noFarUVhYCK1WW+N+7dGjh4xR102EPGzZsiWGDx8OAGjXrh0eeughFBYWom3btvV6/8SJE1FaWoqQ\nkBC8//77WL9+PX799VcAQEJCAgYOHFhjriYlJSE2NhalpaXYsmULYmJiAKDG/epo/v7+5r8DAwMx\nf/58m3+HrEfIrjbF3NmzZ9GtWzdkZWXhtddeM++wQ4cO4eOPP8aECROwc+dO7NmzBzqdDnv37sW5\nc+fw97//HQCwYMECrFu3Drt378a8efOwf799b9VXWlqKlJQUrFu3Di1atLDrdzkDV8jHw4cP4/z5\n89izZw+ys7Px+OOPw9393//marUaCoUCkyZNwg8//IAmTZqge/fuKCwsxO7duzFr1ixoNBq0bdsW\n27dvl3FNLKspf/v06WPeh9nZ2ejfvz+6d++OEydOoKSkBGVlZcjLy0OvXr3kDL1OIuRhZmYmNmzY\nAODmqY3Lly+jZcuW9X7/okWLoFAosGfPHnz44Yfo2LEjsrKy8PHHH2P27NkoLi6uMVd//PFH83tj\nYmLMXfc17VdHmzFjBs6dOwfg5jntJ554wubfIesRsrNPMZefn4/k5GRcuHABVVVVAP79Kyo4OBiJ\niYno06cPunfvbj6HdeDAAYSFhZmPRsPDw/Hpp58iJiYGvr6+2Lp1K6KiotCrVy+7Nxq7du1CcXEx\nZs2aZV6WnJyM1q1b2/V7ReXs+QjcPN949uxZ7N27Fz4+Pjh27Bh++eUXlJaWIjg4GBqNBu7u7oiO\njoZKpULTpk2hVqvRp08f5Obm4p133sHrr7+OX375BQUFBXKvTp1qyt+kpCQkJiYiIyMDrVu3Rmho\nKDw8PBAXF4fJkyfDzc0N06dPF7o3SIQ8DAwMxFtvvYVvvvkGN27cwPz58+/oaamv8vJy6PV68+C7\n9u3b45lnnsGhQ4fw6KOPmnO1X79+5v24f/9+mEwmfPXVV1AqlcjKysLSpUsRHx9/x361p9vb9lsx\nxMTEYNasWWjSpAk8PT2xePFim38vp860Eb1ej7lz55qPaiVJQseOHREREYHq6mp88MEHAIBJkybh\n1KlT5l+8JpMJPj4+yMjIwB9//IE1a9Zg//79aNWqFRISEvDss8/Ktk7knPbs2YPPPvsM+fn5CAwM\nxLBhw/D222/D3d0dgYGBWLJkCYCbOZuYmIi9e/fiww8/xMWLF815evdjImucP38ewcHBOHDgAAYM\nGIAOHTqYnysvL8crr7yCcePG3ZOr8+bNQ0lJCYKDg/G///u/Mq6BPGQ9QnY1V69eNf997do1AEDz\n5s1RXFxsXq7VahEYGGg+N3K7du3aYfHixaiursaOHTsQFxeHnJwc+wdOLiUkJAQhISG4evUqEhIS\nsH79eqhUKnz11VcYP3489u7diyFDhsgdJjUCvr6+UCgU2L59O5o2bXrP83fn6oYNGxAeHi5DpGIQ\napS1s6uoqMC+ffsAAFlZWejatSseeOCBO14TFBSEnTt3wmg0AgC2bt2Kr776CleuXMHEiRNx/fp1\nuLu7o3v37nBzc3P4OpBz2759O1JTUwEALVq0wKOPPgo3Nzc0a9YMrVu3xuLFi7FgwQK7XLJBdIuH\nhweqq6tRUVGBgQMHYuvWrQBuXr40d+5cFBQU1Jirt7+3MV17fQsLsg21adMGx44dw9ChQ7Fu3boa\nz/0MHjwYAQEBGDVqFEJCQrB//37069cPPj4+6N+/P8LCwjB8+HD8x3/8B7sLyWpBQUE4efIkgoOD\nMWzYMJw5cwYTJ040P9+rVy+MGDHCLiNEiW7RaDR45plnEBAQgFdffRU//PADQkJCMGrUKLRt2xat\nWrWqNVdvf6/IE7jYA88h28jt5+OIiIisxSNkIiIiAbAgExERCYBd1kRERALgETIREZEAWJCJiIgE\nIOvEIEVFpTUu9/b2RHFxuYOjuX+Mu24ajXjTFbpaDlqjMa4jc9A6IsQgShy2iqGuHBTyCFmpdM57\n8jJu19EYtgnXUWwixC5CDIAYcTgiBiELMhERUWPDgkxERCQA3lxCBpOS6n+f4/T4QDtGQrV5MW5n\nvV/LfUT2wBxsfHiETEREJAAWZCIiIgGwIBMREQmABZmIiEgAHNQlOGsGgAEc3EFE5Kx4hExERCQA\nHiGTU0hJScGxY8dQVVWFqVOnws/PD7Nnz4bJZIJGo8GSJUugUqmQmZmJTZs2wd3dHREREQgPD5c7\ndCKiemFBJuEdOXIEp0+fRkZGBoqLizFq1Cj4+/sjOjoaw4YNw/Lly6HT6RAaGorU1FTodDp4eHhg\nzJgxGDJkCFq0aCH3KhARWcQuaxJe7969sXLlSgBAs2bNYDQaodfrERQUBAAICAhAbm4ujh8/Dj8/\nP3h5eUGtVqNnz57Iy8uTM3QionrjETIJT6FQwNPTEwCg0+kwYMAAHD58GCqVCgDg6+uLoqIiGAwG\n+Pj4mN/n4+ODoqKiOj/b29vzvieNF/EOQvXlzLHXly3XsaKiAi+88AJiY2Ph7+/P0yZkUyzI5DT2\n7dsHnU6H9PR0BAcHm5dLklTj62tbfjtb3E6tttvniU6j8XLa2Ovr7nW83+K8Zs0aNG/eHACwatUq\nnjYhm2KXNTmFnJwcrF27FmlpafDy8oKnpycqKioAAIWFhdBqtdBqtTAYDOb3XLp0CVqtVq6QycWc\nPXsWZ86cwaBBgwCAp03I5liQSXilpaVISUnBunXrzEcaffr0QVZWFgAgOzsb/fv3R/fu3XHixAmU\nlJSgrKwMeXl56NWrl5yhkwtJTk5GfHy8+bHRaLTJaROiW9hlTcLbtWsXiouLMWvWLPOypKQkJCYm\nIiMjA61bt0ZoaCg8PDwQFxeHyZMnw83NDdOnT4eXl+ufIyX727FjB3r06IG2bdvW+Pz9nDYRfRyD\nKOMMRIjD3jHUqyDzGlCSU2RkJCIjI+9Z/sknn9yzLCQkBCEhIY4IixqRgwcP4ty5czh48CAuXrwI\nlUplPm2iVqvrPG3So0ePOj9b5HEMoowzECEOW8VQV1G3WJB5DSgRNXYrVqww//3hhx+iTZs2+PHH\nH5GVlYWRI0fecdokMTERJSUlUCgUyMvLQ0JCgoyRkzOxeA6Z14ASEd1rxowZ2LFjB6Kjo3H16lWE\nhoZCrVabT5tMnDiRp03IKhaPkOW6BlSE8wUNIXfcDf1+ueMmchYzZsww/83TJmRL9R7U5chrQEU4\nX9AQIsTdkO93VNws+taz5m5fvNMXkXOr12VPvAaUiIjIviwWZF4DSkREZH8Wu6x5DSiR7VjTBU1E\njYvFgsxrQImIiOyPU2cSEREJgAWZiIhIACzIREREAmBBJiIiEgALMhERkQBYkImIiATAgkxERCQA\nFmQiIiIBsCATEREJgAWZiIhIACzIREREAmBBJiIiEgALMhERkQBYkImIiATAgkxERCQAFmQiIiIB\nsCATEREJgAWZnMKpU6cwePBgbNmyBQBQUFCAsWPHIjo6GjNnzkRlZSUAIDMzE2FhYQgPD8e2bdvk\nDJmIyCpKuQMgsqS8vBwLFy6Ev7+/edmqVasQHR2NYcOGYfny5dDpdAgNDUVqaip0Oh08PDwwZswY\nDBkyBC1atLBrfJOS9tv184moceARMglPpVIhLS0NWq3WvEyv1yMoKAgAEBAQgNzcXBw/fhx+fn7w\n8vKCWq1Gz549kZeXJ1fY5GJSUlIQGRmJsLAwZGdns5eGbI5HyCQ8pVIJpfLOVDUajVCpVAAAX19f\nFBUVwWAwwMfHx/waHx8fFBUVOTRWck1HjhzB6dOnkZGRgeLiYowaNQr+/v7C9NKQa6hXQT516hRi\nY2MxYcIExMTEoKCgALNnz4bJZIJGo8GSJUugUqmQmZmJTZs2wd3dHREREQgPD7d3/ESQJMmq5bfz\n9vaEUqmwdUiy0Gi8HPIeZ2OLdezduze6desGAGjWrBmMRiP0ej0WLFgA4GYvTXp6Ojp06GDupQFg\n7qUJDAy87xjI9VksyKKfvxMFzyM6lqenJyoqKqBWq1FYWAitVgutVguDwWB+zaVLl9CjR486P6e4\nuNzeoTpMUVGpVa/XaLysfo+zuXsdG1qcFQoFPD09AQA6nQ4DBgzA4cOHbdJLY4sfhfb8YSXKjzYR\n4rB3DBYL8q3zd2lpaeZl/GVIcuvTpw+ysrIwcuRIZGdno3///ujevTsSExNRUlIChUKBvLw8JCQk\nyB0quZB9+/ZBp9MhPT0dwcHB5uX300tjix+F9vphJcqPNhHisFUMdRV1iwXZnufv6vplKMKvIWfU\n0O0m8vbOz89HcnIyLly4AKVSiaysLCxduhTx8fHIyMhA69atERoaCg8PD8TFxWHy5Mlwc3PD9OnT\nzT8Qie5XTk4O1q5di/Xr18PLy8tmvTREt9z3oC57/DIU4deQs2rIdnPU9m5o0e/atSs2b958z/JP\nPvnknmUhISEICQlp0PcQ1aa0tBQpKSnYuHGj+TQce2nI1hpUkPnLkIgak127dqG4uBizZs0yL0tK\nSkJiYiJ7achmGlSQ+cuQiBqTyMhIREZG3rOcvTRkSxYLMs/fORdrRnunx3PAnSvhvidybhYLMs/f\nERER2R9n6iIicnLWzoPAHhIxcS5rIiIiAbAgExERCYAFmYiISAAsyERERAJgQSYiIhIACzIREZEA\nWJCJiIgEwIJMREQkABZkIiIiAbAgExERCYAFmYiISAAsyERERALgzSWIiBoZ3qpTTEIW5Bfjdtb7\ntUwWIiJyBeyyJiIiEgALMhERkQBYkImIiATAgkxERCQAFmQiIiIBCDnKmoiIxMBLpBzH5gV50aJF\nOH78ONzc3JCQkIBu3brZ+iscxppEdEau+o/mSjlIzok5SA1h04L8/fff4/fff0dGRgbOnj2LhIQE\nZGRk2PIriOrEHKwfa39sOtMPMrkxB6mhbFqQc3NzMXjwYADAY489hmvXruH69et48MEHbfk1JANn\nOZpmDpLcmIP1wx+F97JpQTYYDOjSpYv5sY+PD4qKiuyaiK7erUzWkSMHGwMRfpA5SwPemHPQnu2x\nvXJQpLyy66AuSZLqfF6j8apx+dfLRtojHGqEmIOuo6H7pLZ97CjMQbGJtJ1tetmTVquFwWAwP750\n6RI0Go0tv4KoTsxBkhtzkBrKpgW5b9++yMrKAgCcPHkSWq22UXTTkDiYgyQ35iA1lE27rHv27Iku\nXbogKioKbm5umDdvni0/nsgi5iDJjTlIDeUmWTrBQURERHbHqTOJiIgEwIJMREQkAKHmsnam6eb0\nej1mzpyJJ554AgDw5JNP4pVXXsHs2bNhMpmg0WiwZMkSqFQqmSO96dSpU4iNjcWECRMQExODgoKC\nGmPNzMzEpk2b4O7ujoiICISHh8sdukM5Uw5aw9ny1RqumNuOzsOUlBQcO3YMVVVVmDp1Kvbv34+T\nJ0+iRYsWAIDJkydj0KBBdtuG1uSnvWLYtm0bMjMzzY/z8/MxdOhQh24HSILQ6/XSlClTJEmSpDNn\nzkgREREyR1S3I0eOSDNmzLhjWXx8vLRr1y5JkiRp2bJl0meffSZHaPcoKyuTYmJipMTERGnz5s2S\nJNUca1lZmRQcHCyVlJRIRqNRGjFihFRcXCxn6A7lbDloDWfKV2u4Ym47Og9zc3OlV155RZIkSbpy\n5Yo0cOBAac6cOdL+/fvveJ09t2F989NR+1Gv10vz5893+HYQpsu6tunmnIler0dQUBAAICAgALm5\nuTJHdJNKpUJaWhq0Wq15WU2xHj9+HH5+fvDy8oJarUbPnj2Rl5cnV9gO5wo5aA1R89Uarpjbjs7D\n3r17Y+XKlQCAZs2awWg0wmQy3fM6R29DOfdjamoqYmNja3zOnjEIU5ANBgO8vb3Nj29NNyeyM2fO\nYNq0aXj55Zfx7bffwmg0mrv8fH19hYlfqVRCrVbfsaymWA0GA3x8fMyvcYZ9YEvOmIPWcJZ8tYYr\n5raj81ChUMDT0xMAoNPpMGDAACgUCmzZsgXjxo3Dm2++iStXrth9G9YnPx2xH//5z3+iVatW5slc\nHLkdhDqHfDtJ8KuxHnnkEbz++usYNmwYzp07h3Hjxt3xq1L0+G9XW6zOtA724Err70r5ag1XyG1H\nxbpv3z7odDqkp6cjPz8fLVq0QKdOnfDxxx/jo48+wtNPP223uBqan/bYNjqdDqNGjQIAjBw50qHb\nQZgjZGebbq5ly5YYPnw43Nzc0K5dOzz00EO4du0aKioqAACFhYV3dKOJxtPT855Ya9oHIq+DrTlb\nDlrD2fPVGs6e23LkYU5ODtauXYu0tDR4eXnB398fnTp1AgAEBgbi1KlTdt2G9c1PR+xHvV5vLrqO\n3g7CFGRnm24uMzMTGzZsAAAUFRXh8uXLGD16tHkdsrOz0b9/fzlDrFOfPn3uibV79+44ceIESkpK\nUFZWhry8PPTq1UvmSB3H2XLQGs6er9Zw9tx2dB6WlpYiJSUF69atM48mnjFjBs6dOwfgZoF64okn\n7LoN65uf9t6PhYWFaNq0qbmr3NHbQaiZupYuXYqjR4+ap5vr2LGj3CHV6vr163jrrbdQUlKCGzdu\n4PXXX0enTp0wZ84c/Otf/0Lr1q2xePFieHh4yB0q8vPzkZycjAsXLkCpVKJly5ZYunQp4uPj74l1\nz5492LBhA9zc3BATE4OXXnpJ7vAdyply0BrOlK/WcNXcdmQeZmRk4MMPP0SHDh3My0aPHo0tW7ag\nSZMm8PT0xOLFi+Hr62u3bWhNftpzP+bn52PFihVYv349AODIkSNYsmSJw7aDUAWZiIiosRKmy5qI\niKgxY0EmIiISAAsyERGRAFiQiYiIBMCCTEREJAAWZCIiIgGwIBMREQmABZmIiEgA/w+Ahb6c5CwR\nsQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f560a742828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#histograms\n",
"mydf.hist()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f903f3cd-298e-cb1b-3392-39a67379df1b"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "b40b4516-b3db-7905-12d5-4e1268b10f9c"
},
"outputs": [
{
"data": {
"image/png": 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ViLrofdRsg2vtmjuAJS/XpTK1B5wy7larlS+//NLdsrRLava4u2paHhzTl3+c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\nYGDuDjk71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hmIL/rV2tra5zN9caXOAUxXTWKuaRZu0dOlQ936IiBDgK733s+Fr14BQZBd\nca9XD5O0CUzNyGViSmQefwu0vqelGZgyNgeP+AD1nY2c66inw2bC4XZ4Z+NdP0aXx43L48LpduL0\nuHC6XTjcDu+rxzmoBDZd63MD3dSDTCZjxphc0pID1+b+9LU3etM75+23Yjp5CrlGg0yjQa7RoE5L\nRZOVhT5nPHKNJmDy9kZAdSDNQNb3vo346MPY6uux1NRha2jA1dmJy2TC43J3LaF6ED0iMrUauVaD\nXKtFmZCAdkQ2utGjUSUGx8E5YG1NM5A18WF49GHsRiOW2jqsF+q72mnGbbXgcbkR3S7Uqamkj0jx\naWAWnqTQXQRiR6C19erjBXfMDmwav75ouWQ5L5KW5YeKDBiZrGVksneU229b3G7sFm/nmapXkqpP\nAEIfMW0w37Uv+jWYZ3rTOQ0GHpw48DGbIeuJZ3BtDTXB1ncVcYxXT4DAZlG+iiG1w9373yJQnf5A\nuteb3ilvWEjSDQuv+twBtHQ6oTN4TrVB1QGlASZci2rCtagG+YgTaHcCQZApeG3VwOiJKEdPpC9X\n2aamq7eNIm5ZPj09naampp73jY2NpKWl9XqtoaGB9PT0UIonEeX4ol9KpbLPZyQkgkl/+ioh4S8h\nPQo3b948tm/fDsCRI0dIT0/vWYIaOXIkJpOJc+fO4XK5+Oyzz5g3b14oxZOIcnzRr/6ekZAIJpLu\nSQSTkHrLA7z00kuUlpYiCAKrVq3i6NGjGAwGbrvtNvbv389LL70EwOLFi3nkkUdCKZpEDOCLfl35\nTG5ubjibIDGMkHRPIliE3LhLSEhISEhIBBcpQp2EhISEhESMIRl3CQkJCQmJGEMy7hISEhISEjGG\nZNwlJCQkJCRiDMm4S0hISEhIxBiScZeQkJCQkIgxJOPuJ6tXr6awsJDly5dTWVkZbnH8Yu3atRQW\nFrJ06VI++eSTcIsTc0Srruzbt48bbriBoqIiioqK+NnPfkZ9fT1FRUU88MAD/Md//AcOR+SmCK6u\nrubWW2/lz3/+M0Cfsr/33nssXbqUZcuW8de//jWcIkc00arHvnKl/kQNooTP7Nu3T3z00UdFURTF\nkydPivfff3+YJfKd4uJi8bvf/a4oiqLY0tIiLly4MLwCxRjRrCt79+4Vn3jiics+W7lypfjhhx+K\noiiKL7/8srhly5ZwiDYgZrNZXLFihfiTn/xE3Lx5syiKvctuNpvFxYsXix0dHaLVahXvuususbW1\nNZyiRyTRrMe+0Jv+RAvSzN0P+krZGI3Mnj2bV199FYD4+HisVitu98DZtSQGRyzpCnhn87fccgsA\nixYtori4OMwS9Y5KpWLDhg2X5anoTfaKigqmTp2KwWBAo9Ewc+ZMDh48GC6xI5ZY0+OB6E1/ogXJ\nuPtBU1MTSUlJPe+7UzZGI3K5HJ1OB8C2bdtYsGABcrl8gKckBku068rJkyf5/ve/zze/+U2+/PJL\nrFYrKpU3X1dKSkrEtkWhUKC5Iu1pb7I3NTVdlRI4UtsUTqJdj4dKb/oTLYQ15WusIcZAJN+dO3ey\nbds2Nm3aFG5RYppo0pWxY8fy+OOPc8cdd1BXV8e3vvWty1Z1oqktV9KX7NHcplAifU+RizRz94NY\nS9m4a9cu1q1bx4YNGzAYApOjWsJLNOtKRkYGd955J4IgMHr0aFJTU2lvb8dmswHRl55Zp9NdJXtv\nf59oalOoiGY9Hm5Ixt0PYillY2dnJ2vXrmX9+vUkJiaGW5yYI5p15b333mPjxo0AGI1Gmpub+frX\nv97Tnk8++YT58+eHU8QhMXfu3Ktknz59OocOHaKjowOz2czBgwe57rrrwixp5BHNejzckLLC+Ums\npGzcunUrr732GuPGjev5bM2aNWRnZ4dRqtgiWnXFZDLxzDPP0NHRgdPp5PHHH2fy5Mn8+Mc/xm63\nk52dzS9+8QuUSmW4Rb2Kw4cPs2bNGs6fP49CoSAjI4OXXnqJlStXXiX7xx9/zMaNGxEEgRUrVnDP\nPfeEW/yIJFr12Bd605/XXnstKiZAknGXkJCQkJCIMaRleQkJCQkJiRhDMu4SEhISEhIxhmTcJSQk\nJCQkYgzJuEtISEhISMQYknGXkJCQkJCIMSTjLiEhISEhEWNIxl1CQkJCQiLG+P/0BZgtsu8HqQAA\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f55bf3417f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#density\n",
"mydf.plot(kind='density', subplots=True, layout=(3,3), sharex=False) \n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e5ba127d-0264-9dd3-a16f-740140baf839"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "3daf0c16-ba9c-3edf-98e6-cc781cdefc7a"
},
"outputs": [
{
"data": {
"image/png": 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zgPXnHXPO/Hiu6z3mU+fMOpf1rVu3unxer28059uLrsOmw4SSdptMbT4EYPJ+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\nkImIiCSABZmIiEgC/h/AgdGZBawv0wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f55bbdb3a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#box and whisker plot\n",
"mydf.plot(kind='box', subplots=True, layout=(3,3), sharex=False, sharey=False) \n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "42fa20b4-4411-86d5-7ff7-c7448611c16c"
},
"outputs": [
{
"data": {
"text/plain": [
"537.5999999999999"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"768*0.7"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "a90c91d2-1e21-5721-f84b-997428758190"
},
"outputs": [],
"source": [
"x,y = np.hsplit(mydf,[8])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "65e130e3-59fc-ec12-0ddb-0ab12d99426d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>preg</th>\n",
" <th>plas</th>\n",
" <th>pres</th>\n",
" <th>skin</th>\n",
" <th>test</th>\n",
" <th>mass</th>\n",
" <th>pedi</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6.0</td>\n",
" <td>148.0</td>\n",
" <td>72.0</td>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" <td>33.6</td>\n",
" <td>0.627</td>\n",
" <td>50.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>85.0</td>\n",
" <td>66.0</td>\n",
" <td>29.0</td>\n",
" <td>0.0</td>\n",
" <td>26.6</td>\n",
" <td>0.351</td>\n",
" <td>31.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>8.0</td>\n",
" <td>183.0</td>\n",
" <td>64.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>23.3</td>\n",
" <td>0.672</td>\n",
" <td>32.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>89.0</td>\n",
" <td>66.0</td>\n",
" <td>23.0</td>\n",
" <td>94.0</td>\n",
" <td>28.1</td>\n",
" <td>0.167</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.0</td>\n",
" <td>137.0</td>\n",
" <td>40.0</td>\n",
" <td>35.0</td>\n",
" <td>168.0</td>\n",
" <td>43.1</td>\n",
" <td>2.288</td>\n",
" <td>33.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" preg plas pres skin test mass pedi age\n",
"0 6.0 148.0 72.0 35.0 0.0 33.6 0.627 50.0\n",
"1 1.0 85.0 66.0 29.0 0.0 26.6 0.351 31.0\n",
"2 8.0 183.0 64.0 0.0 0.0 23.3 0.672 32.0\n",
"3 1.0 89.0 66.0 23.0 94.0 28.1 0.167 21.0\n",
"4 0.0 137.0 40.0 35.0 168.0 43.1 2.288 33.0"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.head()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "57d06783-ac09-59d1-0c7a-465c72485c4b"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" class\n",
"0 1.0\n",
"1 0.0\n",
"2 1.0\n",
"3 0.0\n",
"4 1.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3f3a6d98-ef8a-11c9-0130-a762b2abe924"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "290154c6-3007-a514-f558-fad133f754b5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(768, 8) (537, 8) (231, 8) (537, 1) (231, 1) (231, 8)\n"
]
}
],
"source": [
"train_valid_X = x[ 0:537 ]\n",
"train_valid_y = y\n",
"test_X = x[ 537: ]\n",
"train_X , valid_X , train_y , valid_y = train_test_split( x , y , train_size = .7 )\n",
"\n",
"print (x.shape , train_X.shape , valid_X.shape , train_y.shape , valid_y.shape , test_X.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4a71e54e-f70b-217f-6cf1-b7e6d7bace0a"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "cc260a16-dea4-e7a0-d9ff-58f74b4ed6c6"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "1557264e-5c90-dce6-d186-74af9ba08df5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0 0.74025974026\n"
]
}
],
"source": [
"model = RandomForestClassifier(n_estimators=100)\n",
"# Score the model\n",
"score = model.fit( train_X , train_y )\n",
"print (model.score( train_X , train_y ) , model.score( valid_X , valid_y ))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7a18c0b3-4395-996e-6f5e-378658f4b435"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "da899a6a-909a-afcd-3a6b-9cf701889154"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0 0.670995670996\n"
]
}
],
"source": [
"model = SVC()\n",
"# Score the model\n",
"score = model.fit( train_X , train_y )\n",
"print (model.score( train_X , train_y ) , model.score( valid_X , valid_y ))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f88498b8-9bd6-d3c6-0f90-f7add2b60211"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "10cf4e64-fc51-b5c9-3546-094477781508"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.940409683426 0.761904761905\n"
]
}
],
"source": [
"model = GradientBoostingClassifier()\n",
"# Score the model\n",
"score = model.fit( train_X , train_y )\n",
"print (model.score( train_X , train_y ) , model.score( valid_X , valid_y ))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ab344cc6-a253-27d0-4aed-bc041336c2de"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "beb65bd1-e30c-c093-2348-9c9cf23ec0f5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.839851024209 0.692640692641\n"
]
}
],
"source": [
"model = KNeighborsClassifier(n_neighbors = 3)\n",
"# Score the model\n",
"score = model.fit( train_X , train_y )\n",
"print (model.score( train_X , train_y ) , model.score( valid_X , valid_y ))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2d9dfa54-b0f3-4d83-efea-531963bc669d"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "8e2e54b2-0241-9700-b6c7-8db7dbf574f4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.763500931099 0.744588744589\n"
]
}
],
"source": [
"model = GaussianNB()\n",
"# Score the model\n",
"score = model.fit( train_X , train_y )\n",
"print (model.score( train_X , train_y ) , model.score( valid_X , valid_y ))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d0ca99a5-4ede-6545-5742-84884149f475"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "4f8ec8eb-bb6b-2830-3118-4ce3b3b02f6b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.767225325885 0.757575757576\n"
]
}
],
"source": [
"model = LogisticRegression()\n",
"# Score the model\n",
"score = model.fit( train_X , train_y )\n",
"print (model.score( train_X , train_y ) , model.score( valid_X , valid_y ))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "3ebfe9ea-c4f4-d3d1-a868-7580b7da7bdc"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "1b02b223-ff39-45ef-61d1-50e8819ae962"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.0 0.675324675325\n"
]
}
],
"source": [
"model = DecisionTreeClassifier()\n",
"# Score the model\n",
"score = model.fit( train_X , train_y )\n",
"print (model.score( train_X , train_y ) , model.score( valid_X , valid_y ))"
]
}
],
"metadata": {
"_change_revision": 482,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162246.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "0da60419-bfe4-37fe-d68b-d259f39b4b37"
},
"outputs": [
{
"ename": "CalledProcessError",
"evalue": "Command '['ls', '../input']' returned non-zero exit status 2.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-fde84f249a2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msubprocess\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcheck_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheck_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ls\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"../input\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# Any results you write to the current directory are saved as output.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/subprocess.py\u001b[0m in \u001b[0;36mcheck_output\u001b[0;34m(timeout, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 334\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 335\u001b[0m return run(*popenargs, stdout=PIPE, timeout=timeout, check=True,\n\u001b[0;32m--> 336\u001b[0;31m **kwargs).stdout\n\u001b[0m\u001b[1;32m 337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 338\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcheck\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mretcode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 417\u001b[0m raise CalledProcessError(retcode, process.args,\n\u001b[0;32m--> 418\u001b[0;31m output=stdout, stderr=stderr)\n\u001b[0m\u001b[1;32m 419\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mCompletedProcess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mCalledProcessError\u001b[0m: Command '['ls', '../input']' returned non-zero exit status 2."
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3b149ca7-0a75-573b-de89-bbdc4648ce8c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hello\n"
]
}
],
"source": [
"print(\"hello\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "b2c2c162-640c-997f-b9ee-2b03155536aa"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"31\n"
]
}
],
"source": [
"numbers = [1,2,3,4,5,10,6]\n",
"sum = 0\n",
"for number in numbers:\n",
" sum = sum+number\n",
"print (sum) "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "ca649de0-71f5-52ba-51f2-52d9ff2df53b"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'list' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-95334aa9c198>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'list' object is not callable"
]
}
],
"source": [
"numbers(0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "dac71663-6ec9-2123-9bed-1425530a8583"
},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4, 5, 10, 6]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "e63756f8-3908-de39-f860-67ac494799a7"
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers[2]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "98b09d22-037b-f968-f5aa-ad0a06c7ca72"
},
"outputs": [
{
"data": {
"text/plain": [
"[3, 4, 5]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers[2:5]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "1314d8a2-c4b3-fbce-9fef-8bd72ef374cb"
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers[-3]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "ea34e86b-0d64-cc5d-4e01-849eddbff68a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello world \n",
"e\n",
"12\n",
"Hello world How are you\n"
]
}
],
"source": [
"stmt = \"Hello world \"\n",
"print(stmt)\n",
"print(stmt[1])\n",
"print(len(stmt))\n",
"print (stmt + \"How are you\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "e14cd773-c776-5332-9f3f-c8a7f54373a5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0, 2, 4, 6, 8, 10)\n"
]
}
],
"source": [
"tup_ex = 0,2,4,6,8,10\n",
"print(tup_ex)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "8cd282bd-5738-cb63-34e9-627738b8a6ff"
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tup_ex[2]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "b0273964-c60d-c001-cc7a-9bd278fa6039"
},
"outputs": [
{
"data": {
"text/plain": [
"{'ash': 669, 'dam': 369, 'jay': 789, 'joe': 456, 'raj': 345, 'sat': 999}"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dic_ex = {'raj':345,'joe':456,'jay':789,'dam':369,'ash':669,'sat':999}\n",
"dic_ex"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "f0fbb09e-a2f7-a4c7-d105-f25ccd51e1e0"
},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['raj', 'joe', 'jay', 'dam', 'ash', 'sat'])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dic_ex.keys()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "66fc4b87-e5dc-c6dc-b941-49808318c91b"
},
"outputs": [
{
"data": {
"text/plain": [
"345"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dic_ex['raj']"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "05dc0bea-d177-61c4-3651-9285540137c1"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "d3fd73f0-88a5-618d-a7cc-85e48571c7c6"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 164,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162374.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f3337165-304d-aec5-78c3-5766bcd5b0f4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id qid1 qid2 question1 \\\n",
"0 0 1 2 What is the step by step guide to invest in sh... \n",
"1 1 3 4 What is the story of Kohinoor (Koh-i-Noor) Dia... \n",
"\n",
" question2 is_duplicate \n",
"0 What is the step by step guide to invest in sh... 0 \n",
"1 What would happen if the Indian government sto... 0 \n",
"open: 1.060501\n"
]
}
],
"source": [
"import pandas as pd\n",
"# timing function\n",
"import time \n",
"start = time.clock() #_________________ measure efficiency timing\n",
"\n",
"# read data\n",
"#test = pd.read_csv('../input/test.csv',encoding='utf8')[:100]\n",
"#test.fillna(value='leeg',inplace=True)\n",
"train = pd.read_csv('../input/train.csv',encoding='utf8')[:100]\n",
"print(train.head(2))\n",
"train.fillna(value='leeg',inplace=True)\n",
"\n",
"\n",
"end = time.clock()\n",
"print('open:',end-start)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "8261aba5-2701-1aa5-a20a-a346da3fb18c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 (0.7692307692307693, 0.9230769230769231)\n",
"0 (0.14285714285714285, 0.3684210526315789)\n",
"0 (0.0, 0.18181818181818182)\n",
"0 (0.0, 0.037037037037037035)\n",
"0 (0.0, 0.2777777777777778)\n",
"1 (0.15151515151515152, 0.3333333333333333)\n",
"0 (0.0, 0.06666666666666667)\n",
"1 (0.14285714285714285, 0.38461538461538464)\n",
"0 (0.125, 0.5384615384615384)\n",
"0 (0.0, 0.2777777777777778)\n",
"0 (0.0, 0.08)\n",
"1 (0.0625, 0.35714285714285715)\n",
"1 (0.15384615384615385, 0.5454545454545454)\n",
"1 (0.625, 0.875)\n",
"0 (0.6071428571428571, 0.8076923076923077)\n",
"1 (0.034482758620689655, 0.18518518518518517)\n",
"1 (0.14285714285714285, 0.6666666666666666)\n",
"0 (0.0, 0.1)\n",
"1 (0.0, 0.2727272727272727)\n",
"0 (0.2, 0.6666666666666666)\n",
"1 (0.09090909090909091, 0.4)\n",
"0 (0.0625, 0.35714285714285715)\n",
"0 (0.06666666666666667, 0.38461538461538464)\n",
"0 (0.0, 0.05263157894736842)\n",
"0 (0.0, 0.04)\n",
"0 (0.7058823529411765, 0.8235294117647058)\n",
"0 (0.42857142857142855, 0.7142857142857143)\n",
"0 (0.0, 0.21428571428571427)\n",
"0 (0.4166666666666667, 0.7272727272727273)\n",
"1 (0.1111111111111111, 0.4666666666666667)\n",
"0 (0.26666666666666666, 0.6153846153846154)\n",
"1 (0.0, 0.16)\n",
"1 (0.2727272727272727, 0.7647058823529411)\n",
"0 (0.0, 0.07142857142857142)\n",
"0 (0.5555555555555556, 0.7777777777777778)\n",
"0 (0.17391304347826086, 0.2608695652173913)\n",
"0 (0.02564102564102564, 0.3548387096774194)\n",
"0 (0.0, 0.11764705882352941)\n",
"1 (0.3, 0.5)\n",
"0 (0.0, 0.1111111111111111)\n",
"0 (0.0, 0.09090909090909091)\n",
"0 (0.36363636363636365, 0.8888888888888888)\n",
"0 (0.38461538461538464, 0.8181818181818182)\n",
"0 (0.05, 0.35294117647058826)\n",
"0 (0.2608695652173913, 0.7222222222222222)\n",
"0 (0.0, 0.25)\n",
"0 (0.0, 0.05)\n",
"0 (0.030303030303030304, 0.16129032258064516)\n",
"1 (0.09523809523809523, 0.5625)\n",
"1 (0.0, 0.375)\n",
"1 (0.11764705882352941, 0.3125)\n",
"1 (0.09090909090909091, 0.5294117647058824)\n",
"0 (0.0, 0.08333333333333333)\n",
"1 (0.4117647058823529, 0.625)\n",
"0 (0.0, 0.0625)\n",
"0 (0.0, 0.10526315789473684)\n",
"0 (0.0, 0.15)\n",
"0 (0.23076923076923078, 0.5)\n",
"1 (0.02564102564102564, 0.4)\n",
"0 (0.0, 0.14285714285714285)\n",
"0 (0.0, 0.0625)\n",
"0 (0.08, 0.38095238095238093)\n",
"1 (0.16666666666666666, 0.35294117647058826)\n",
"0 (0.36363636363636365, 0.7)\n",
"0 (0.2857142857142857, 0.6666666666666666)\n",
"1 (0.0, 0.1111111111111111)\n",
"1 (0.13333333333333333, 0.46153846153846156)\n",
"1 (0.13333333333333333, 0.26666666666666666)\n",
"0 (0.0, 0.3)\n",
"0 (0.0, 0.034482758620689655)\n",
"0 (0.45454545454545453, 0.6363636363636364)\n",
"1 (0.8333333333333334, 0.8571428571428571)\n",
"1 (0.5555555555555556, 0.7777777777777778)\n",
"1 (0.0, 0.22727272727272727)\n",
"1 (0.8823529411764706, 0.8888888888888888)\n",
"0 (0.0, 0.175)\n",
"0 (0.1111111111111111, 0.3333333333333333)\n",
"0 (0.0, 0.2)\n",
"0 (0.0, 0.15)\n",
"1 (0.16666666666666666, 0.6)\n",
"0 (0.0, 0.05714285714285714)\n",
"0 (0.04, 0.2727272727272727)\n",
"0 (0.021739130434782608, 0.225)\n",
"0 (0.07692307692307693, 0.3333333333333333)\n",
"1 (0.04, 0.4)\n",
"1 (0.0, 0.3333333333333333)\n",
"1 (0.6086956521739131, 0.7727272727272727)\n",
"0 (0.0, 0.5)\n",
"1 (0.2857142857142857, 0.6666666666666666)\n",
"0 (0.75, 0.875)\n",
"0 (0.08, 0.2608695652173913)\n",
"0 (0.05128205128205128, 0.30303030303030304)\n",
"1 (0.0, 0.29411764705882354)\n",
"1 (0.1111111111111111, 0.3333333333333333)\n",
"0 (0.0, 0.3076923076923077)\n",
"1 (0.42857142857142855, 0.7142857142857143)\n",
"0 (0.0, 0.1111111111111111)\n",
"0 (0.0, 0.2)\n",
"0 (0.0, 0.23684210526315788)\n",
"0 (0.0, 0.09090909090909091)\n"
]
}
],
"source": [
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from collections import Counter\n",
"\n",
"def BiJaccard(str1, str2):\n",
" str1 = set(nltk.word_tokenize(str1))\n",
" bigram1 = set(nltk.bigrams( str1 ))\n",
" #print(set(bigram1))\n",
" str2 = set(nltk.word_tokenize(str2))\n",
" bigram2 = set(nltk.bigrams(str2) )\n",
" #print(bigram1 & bigram2)\n",
" return float(len(bigram1 & bigram2)) / len(bigram1 | bigram2),float(len(str1 & str2)/len(str1 |str2))\n",
"\n",
"for xi in range (0,100):\n",
" q1=train.iloc[xi]['question1']\n",
" q2=train.iloc[xi]['question2']\n",
" print(train.iloc[xi]['is_duplicate'],BiJaccard(q1,q2))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "9445ea3c-ec74-b5ef-fb6a-00c738a1ec68"
},
"outputs": [
{
"ename": "ModuleNotFoundError",
"evalue": "No module named '__main__.compat'; '__main__' is not a package",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-cdbca374cec4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0municodedata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcollections\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdefaultdict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_range\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_zip_longest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIS_PY3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mporter\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStemmer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named '__main__.compat'; '__main__' is not a package"
]
}
],
"source": [
"import unicodedata\n",
"from collections import defaultdict\n",
"from .compat import _range, _zip_longest, IS_PY3\n",
"from .porter import Stemmer\n",
"\n",
"\n",
"def _normalize(s):\n",
" return unicodedata.normalize('NFKD', s)\n",
"\n",
"\n",
"def _check_type(s):\n",
" if IS_PY3 and not isinstance(s, str):\n",
" raise TypeError('expected str or unicode, got %s' % type(s).__name__)\n",
" elif not IS_PY3 and not isinstance(s, unicode):\n",
" raise TypeError('expected unicode, got %s' % type(s).__name__)\n",
"\n",
"\n",
"def levenshtein_distance(s1, s2):\n",
" _check_type(s1)\n",
" _check_type(s2)\n",
"\n",
" if s1 == s2:\n",
" return 0\n",
" rows = len(s1)+1\n",
" cols = len(s2)+1\n",
"\n",
" if not s1:\n",
" return cols-1\n",
" if not s2:\n",
" return rows-1\n",
"\n",
" prev = None\n",
" cur = range(cols)\n",
" for r in _range(1, rows):\n",
" prev, cur = cur, [r] + [0]*(cols-1)\n",
" for c in _range(1, cols):\n",
" deletion = prev[c] + 1\n",
" insertion = cur[c-1] + 1\n",
" edit = prev[c-1] + (0 if s1[r-1] == s2[c-1] else 1)\n",
" cur[c] = min(edit, deletion, insertion)\n",
"\n",
" return cur[-1]\n",
"\n",
"\n",
"def _jaro_winkler(ying, yang, long_tolerance, winklerize):\n",
" _check_type(ying)\n",
" _check_type(yang)\n",
"\n",
" ying_len = len(ying)\n",
" yang_len = len(yang)\n",
"\n",
" if not ying_len or not yang_len:\n",
" return 0.0\n",
"\n",
" min_len = max(ying_len, yang_len)\n",
" search_range = (min_len // 2) - 1\n",
" if search_range < 0:\n",
" search_range = 0\n",
"\n",
" ying_flags = [False]*ying_len\n",
" yang_flags = [False]*yang_len\n",
"\n",
" # looking only within search range, count & flag matched pairs\n",
" common_chars = 0\n",
" for i, ying_ch in enumerate(ying):\n",
" low = i - search_range if i > search_range else 0\n",
" hi = i + search_range if i + search_range < yang_len else yang_len - 1\n",
" for j in _range(low, hi+1):\n",
" if not yang_flags[j] and yang[j] == ying_ch:\n",
" ying_flags[i] = yang_flags[j] = True\n",
" common_chars += 1\n",
" break\n",
"\n",
" # short circuit if no characters match\n",
" if not common_chars:\n",
" return 0.0\n",
"\n",
" # count transpositions\n",
" k = trans_count = 0\n",
" for i, ying_f in enumerate(ying_flags):\n",
" if ying_f:\n",
" for j in _range(k, yang_len):\n",
" if yang_flags[j]:\n",
" k = j + 1\n",
" break\n",
" if ying[i] != yang[j]:\n",
" trans_count += 1\n",
" trans_count /= 2\n",
"\n",
" # adjust for similarities in nonmatched characters\n",
" common_chars = float(common_chars)\n",
" weight = ((common_chars/ying_len + common_chars/yang_len +\n",
" (common_chars-trans_count) / common_chars)) / 3\n",
"\n",
" # winkler modification: continue to boost if strings are similar\n",
" if winklerize and weight > 0.7 and ying_len > 3 and yang_len > 3:\n",
" # adjust for up to first 4 chars in common\n",
" j = min(min_len, 4)\n",
" i = 0\n",
" while i < j and ying[i] == yang[i] and ying[i]:\n",
" i += 1\n",
" if i:\n",
" weight += i * 0.1 * (1.0 - weight)\n",
"\n",
" # optionally adjust for long strings\n",
" # after agreeing beginning chars, at least two or more must agree and\n",
" # agreed characters must be > half of remaining characters\n",
" if (long_tolerance and min_len > 4 and common_chars > i+1 and\n",
" 2 * common_chars >= min_len + i):\n",
" weight += ((1.0 - weight) * (float(common_chars-i-1) / float(ying_len+yang_len-i*2+2)))\n",
"\n",
" return weight\n",
"\n",
"\n",
"def damerau_levenshtein_distance(s1, s2):\n",
" _check_type(s1)\n",
" _check_type(s2)\n",
"\n",
" len1 = len(s1)\n",
" len2 = len(s2)\n",
" infinite = len1 + len2\n",
"\n",
" # character array\n",
" da = defaultdict(int)\n",
"\n",
" # distance matrix\n",
" score = [[0]*(len2+2) for x in _range(len1+2)]\n",
"\n",
" score[0][0] = infinite\n",
" for i in _range(0, len1+1):\n",
" score[i+1][0] = infinite\n",
" score[i+1][1] = i\n",
" for i in _range(0, len2+1):\n",
" score[0][i+1] = infinite\n",
" score[1][i+1] = i\n",
"\n",
" for i in _range(1, len1+1):\n",
" db = 0\n",
" for j in _range(1, len2+1):\n",
" i1 = da[s2[j-1]]\n",
" j1 = db\n",
" cost = 1\n",
" if s1[i-1] == s2[j-1]:\n",
" cost = 0\n",
" db = j\n",
"\n",
" score[i+1][j+1] = min(score[i][j] + cost,\n",
" score[i+1][j] + 1,\n",
" score[i][j+1] + 1,\n",
" score[i1][j1] + (i-i1-1) + 1 + (j-j1-1))\n",
" da[s1[i-1]] = i\n",
"\n",
" return score[len1+1][len2+1]\n",
"\n",
"\n",
"def jaro_distance(s1, s2):\n",
" return _jaro_winkler(s1, s2, False, False)\n",
"\n",
"\n",
"def jaro_winkler(s1, s2, long_tolerance=False):\n",
" return _jaro_winkler(s1, s2, long_tolerance, True)\n",
"\n",
"\n",
"def soundex(s):\n",
"\n",
" _check_type(s)\n",
"\n",
" if not s:\n",
" return ''\n",
"\n",
" s = _normalize(s)\n",
" s = s.upper()\n",
"\n",
" replacements = (('BFPV', '1'),\n",
" ('CGJKQSXZ', '2'),\n",
" ('DT', '3'),\n",
" ('L', '4'),\n",
" ('MN', '5'),\n",
" ('R', '6'))\n",
" result = [s[0]]\n",
" count = 1\n",
"\n",
" # find would-be replacment for first character\n",
" for lset, sub in replacements:\n",
" if s[0] in lset:\n",
" last = sub\n",
" break\n",
" else:\n",
" last = None\n",
"\n",
" for letter in s[1:]:\n",
" for lset, sub in replacements:\n",
" if letter in lset:\n",
" if sub != last:\n",
" result.append(sub)\n",
" count += 1\n",
" last = sub\n",
" break\n",
" else:\n",
" last = None\n",
" if count == 4:\n",
" break\n",
"\n",
" result += '0'*(4-count)\n",
" return ''.join(result)\n",
"\n",
"\n",
"def hamming_distance(s1, s2):\n",
" _check_type(s1)\n",
" _check_type(s2)\n",
"\n",
" # ensure length of s1 >= s2\n",
" if len(s2) > len(s1):\n",
" s1, s2 = s2, s1\n",
"\n",
" # distance is difference in length + differing chars\n",
" distance = len(s1) - len(s2)\n",
" for i, c in enumerate(s2):\n",
" if c != s1[i]:\n",
" distance += 1\n",
"\n",
" return distance\n",
"\n",
"\n",
"def nysiis(s):\n",
"\n",
" _check_type(s)\n",
"\n",
" if not s:\n",
" return ''\n",
"\n",
" s = s.upper()\n",
" key = []\n",
"\n",
" # step 1 - prefixes\n",
" if s.startswith('MAC'):\n",
" s = 'MCC' + s[3:]\n",
" elif s.startswith('KN'):\n",
" s = s[1:]\n",
" elif s.startswith('K'):\n",
" s = 'C' + s[1:]\n",
" elif s.startswith(('PH', 'PF')):\n",
" s = 'FF' + s[2:]\n",
" elif s.startswith('SCH'):\n",
" s = 'SSS' + s[3:]\n",
"\n",
" # step 2 - suffixes\n",
" if s.endswith(('IE', 'EE')):\n",
" s = s[:-2] + 'Y'\n",
" elif s.endswith(('DT', 'RT', 'RD', 'NT', 'ND')):\n",
" s = s[:-2] + 'D'\n",
"\n",
" # step 3 - first character of key comes from name\n",
" key.append(s[0])\n",
"\n",
" # step 4 - translate remaining chars\n",
" i = 1\n",
" len_s = len(s)\n",
" while i < len_s:\n",
" ch = s[i]\n",
" if ch == 'E' and i+1 < len_s and s[i+1] == 'V':\n",
" ch = 'AF'\n",
" i += 1\n",
" elif ch in 'AEIOU':\n",
" ch = 'A'\n",
" elif ch == 'Q':\n",
" ch = 'G'\n",
" elif ch == 'Z':\n",
" ch = 'S'\n",
" elif ch == 'M':\n",
" ch = 'N'\n",
" elif ch == 'K':\n",
" if i+1 < len(s) and s[i+1] == 'N':\n",
" ch = 'N'\n",
" else:\n",
" ch = 'C'\n",
" elif ch == 'S' and s[i+1:i+3] == 'CH':\n",
" ch = 'SS'\n",
" i += 2\n",
" elif ch == 'P' and i+1 < len(s) and s[i+1] == 'H':\n",
" ch = 'F'\n",
" i += 1\n",
" elif ch == 'H' and (s[i-1] not in 'AEIOU' or (i+1 < len(s) and s[i+1] not in 'AEIOU')):\n",
" if s[i-1] in 'AEIOU':\n",
" ch = 'A'\n",
" else:\n",
" ch = s[i-1]\n",
" elif ch == 'W' and s[i-1] in 'AEIOU':\n",
" ch = s[i-1]\n",
"\n",
" if ch[-1] != key[-1][-1]:\n",
" key.append(ch)\n",
"\n",
" i += 1\n",
"\n",
" key = ''.join(key)\n",
"\n",
" # step 5 - remove trailing S\n",
" if key.endswith('S') and key != 'S':\n",
" key = key[:-1]\n",
"\n",
" # step 6 - replace AY w/ Y\n",
" if key.endswith('AY'):\n",
" key = key[:-2] + 'Y'\n",
"\n",
" # step 7 - remove trailing A\n",
" if key.endswith('A') and key != 'A':\n",
" key = key[:-1]\n",
"\n",
" # step 8 was already done\n",
"\n",
" return key\n",
"\n",
"\n",
"def match_rating_codex(s):\n",
" _check_type(s)\n",
"\n",
" s = s.upper()\n",
" codex = []\n",
"\n",
" prev = None\n",
" for i, c in enumerate(s):\n",
" # not a space OR\n",
" # starting character & vowel\n",
" # or consonant not preceded by same consonant\n",
" if (c != ' ' and (i == 0 and c in 'AEIOU') or (c not in 'AEIOU' and c != prev)):\n",
" codex.append(c)\n",
"\n",
" prev = c\n",
"\n",
" # just use first/last 3\n",
" if len(codex) > 6:\n",
" return ''.join(codex[:3]+codex[-3:])\n",
" else:\n",
" return ''.join(codex)\n",
"\n",
"\n",
"def match_rating_comparison(s1, s2):\n",
" codex1 = match_rating_codex(s1)\n",
" codex2 = match_rating_codex(s2)\n",
" len1 = len(codex1)\n",
" len2 = len(codex2)\n",
" res1 = []\n",
" res2 = []\n",
"\n",
" # length differs by 3 or more, no result\n",
" if abs(len1-len2) >= 3:\n",
" return None\n",
"\n",
" # get minimum rating based on sums of codexes\n",
" lensum = len1 + len2\n",
" if lensum <= 4:\n",
" min_rating = 5\n",
" elif lensum <= 7:\n",
" min_rating = 4\n",
" elif lensum <= 11:\n",
" min_rating = 3\n",
" else:\n",
" min_rating = 2\n",
"\n",
" # strip off common prefixes\n",
" for c1, c2 in _zip_longest(codex1, codex2):\n",
" if c1 != c2:\n",
" if c1:\n",
" res1.append(c1)\n",
" if c2:\n",
" res2.append(c2)\n",
"\n",
" unmatched_count1 = unmatched_count2 = 0\n",
" for c1, c2 in _zip_longest(reversed(res1), reversed(res2)):\n",
" if c1 != c2:\n",
" if c1:\n",
" unmatched_count1 += 1\n",
" if c2:\n",
" unmatched_count2 += 1\n",
"\n",
" return (6 - max(unmatched_count1, unmatched_count2)) >= min_rating\n",
"\n",
"\n",
"def metaphone(s):\n",
" _check_type(s)\n",
"\n",
" result = []\n",
"\n",
" s = _normalize(s.lower())\n",
"\n",
" # skip first character if s starts with these\n",
" if s.startswith(('kn', 'gn', 'pn', 'ac', 'wr', 'ae')):\n",
" s = s[1:]\n",
"\n",
" i = 0\n",
"\n",
" while i < len(s):\n",
" c = s[i]\n",
" next = s[i+1] if i < len(s)-1 else '*****'\n",
" nextnext = s[i+2] if i < len(s)-2 else '*****'\n",
"\n",
" # skip doubles except for cc\n",
" if c == next and c != 'c':\n",
" i += 1\n",
" continue\n",
"\n",
" if c in 'aeiou':\n",
" if i == 0 or s[i-1] == ' ':\n",
" result.append(c)\n",
" elif c == 'b':\n",
" if (not (i != 0 and s[i-1] == 'm')) or next:\n",
" result.append('b')\n",
" elif c == 'c':\n",
" if next == 'i' and nextnext == 'a' or next == 'h':\n",
" result.append('x')\n",
" i += 1\n",
" elif next in 'iey':\n",
" result.append('s')\n",
" i += 1\n",
" else:\n",
" result.append('k')\n",
" elif c == 'd':\n",
" if next == 'g' and nextnext in 'iey':\n",
" result.append('j')\n",
" i += 2\n",
" else:\n",
" result.append('t')\n",
" elif c in 'fjlmnr':\n",
" result.append(c)\n",
" elif c == 'g':\n",
" if next in 'iey':\n",
" result.append('j')\n",
" elif next not in 'hn':\n",
" result.append('k')\n",
" elif next == 'h' and nextnext and nextnext not in 'aeiou':\n",
" i += 1\n",
" elif c == 'h':\n",
" if i == 0 or next in 'aeiou' or s[i-1] not in 'aeiou':\n",
" result.append('h')\n",
" elif c == 'k':\n",
" if i == 0 or s[i-1] != 'c':\n",
" result.append('k')\n",
" elif c == 'p':\n",
" if next == 'h':\n",
" result.append('f')\n",
" i += 1\n",
" else:\n",
" result.append('p')\n",
" elif c == 'q':\n",
" result.append('k')\n",
" elif c == 's':\n",
" if next == 'h':\n",
" result.append('x')\n",
" i += 1\n",
" elif next == 'i' and nextnext in 'oa':\n",
" result.append('x')\n",
" i += 2\n",
" else:\n",
" result.append('s')\n",
" elif c == 't':\n",
" if next == 'i' and nextnext in 'oa':\n",
" result.append('x')\n",
" elif next == 'h':\n",
" result.append('0')\n",
" i += 1\n",
" elif next != 'c' or nextnext != 'h':\n",
" result.append('t')\n",
" elif c == 'v':\n",
" result.append('f')\n",
" elif c == 'w':\n",
" if i == 0 and next == 'h':\n",
" i += 1\n",
" if nextnext in 'aeiou' or nextnext == '*****':\n",
" result.append('w')\n",
" elif c == 'x':\n",
" if i == 0:\n",
" if next == 'h' or (next == 'i' and nextnext in 'oa'):\n",
" result.append('x')\n",
" else:\n",
" result.append('s')\n",
" else:\n",
" result.append('k')\n",
" result.append('s')\n",
" elif c == 'y':\n",
" if next in 'aeiou':\n",
" result.append('y')\n",
" elif c == 'z':\n",
" result.append('s')\n",
" elif c == ' ':\n",
" if len(result) > 0 and result[-1] != ' ':\n",
" result.append(' ')\n",
"\n",
" i += 1\n",
"\n",
" return ''.join(result).upper()\n",
"\n",
"\n",
"def porter_stem(s):\n",
" _check_type(s)\n",
"\n",
" return Stemmer(s).stem()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "aa0d6baf-1e5c-3bdc-aa55-c8b4164cb259"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'hamming_distance' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-110f675e1c98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mq1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mq2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mxi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'is_duplicate'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhamming_distance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mq2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'hamming_distance' is not defined"
]
}
],
"source": [
"for xi in range (0,100):\n",
" q1=train.iloc[xi]['question1']\n",
" q2=train.iloc[xi]['question2']\n",
" print(train.iloc[xi]['is_duplicate'],hamming_distance(q1,q2))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "fc27e7f4-95dc-e103-1d66-9618f703b003"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
}
],
"source": [
"import unicodedata\n",
"from collections import defaultdict\n",
"\n",
"def levenshtein_distance(s1, s2):\n",
"\n",
" if s1 == s2:\n",
" return 0\n",
" rows = len(s1)+1\n",
" cols = len(s2)+1\n",
"\n",
" if not s1:\n",
" return cols-1\n",
" if not s2:\n",
" return rows-1\n",
"\n",
" prev = None\n",
" cur = range(cols)\n",
" for r in range(1, rows):\n",
" prev, cur = cur, [r] + [0]*(cols-1)\n",
" for c in range(1, cols):\n",
" deletion = prev[c] + 1\n",
" insertion = cur[c-1] + 1\n",
" edit = prev[c-1] + (0 if s1[r-1] == s2[c-1] else 1)\n",
" cur[c] = min(edit, deletion, insertion)\n",
"\n",
" return cur[-1]\n",
"\n",
"\n",
"for xi in range (0,100):\n",
" q1=train.iloc[xi]['question1']\n",
" q2=train.iloc[xi]['question2']\n",
" print(train.iloc[xi]['is_duplicate'],levenshtein_distance(q1,q2))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "7dd1936f-4fdb-e653-dfc7-351147b1d247"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 57,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162471.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f3337165-304d-aec5-78c3-5766bcd5b0f4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id qid1 qid2 question1 \\\n",
"0 0 1 2 What is the step by step guide to invest in sh... \n",
"1 1 3 4 What is the story of Kohinoor (Koh-i-Noor) Dia... \n",
"\n",
" question2 is_duplicate \n",
"0 What is the step by step guide to invest in sh... 0 \n",
"1 What would happen if the Indian government sto... 0 \n",
"open: 1.3449280000000003\n"
]
}
],
"source": [
"import pandas as pd\n",
"# timing function\n",
"import time \n",
"start = time.clock() #_________________ measure efficiency timing\n",
"\n",
"# read data\n",
"#test = pd.read_csv('../input/test.csv',encoding='utf8')[:100]\n",
"#test.fillna(value='leeg',inplace=True)\n",
"train = pd.read_csv('../input/train.csv',encoding='utf8')[:100]\n",
"print(train.head(2))\n",
"train.fillna(value='leeg',inplace=True)\n",
"\n",
"\n",
"end = time.clock()\n",
"print('open:',end-start)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "8261aba5-2701-1aa5-a20a-a346da3fb18c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 (0.7692307692307693, 0.9230769230769231)\n",
"0 (0.09090909090909091, 0.3684210526315789)\n",
"0 (0.043478260869565216, 0.18181818181818182)\n",
"0 (0.0, 0.037037037037037035)\n",
"0 (0.0, 0.2777777777777778)\n",
"1 (0.05555555555555555, 0.3333333333333333)\n",
"0 (0.0, 0.06666666666666667)\n",
"1 (0.14285714285714285, 0.38461538461538464)\n",
"0 (0.2, 0.5384615384615384)\n",
"0 (0.0, 0.2777777777777778)\n",
"0 (0.0, 0.08)\n",
"1 (0.13333333333333333, 0.35714285714285715)\n",
"1 (0.15384615384615385, 0.5454545454545454)\n",
"1 (0.625, 0.875)\n",
"0 (0.6071428571428571, 0.8076923076923077)\n",
"1 (0.0, 0.18518518518518517)\n",
"1 (0.3333333333333333, 0.6666666666666666)\n",
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"1 (0.3333333333333333, 0.4)\n",
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"0 (0.0, 0.05263157894736842)\n",
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"1 (0.4, 0.7777777777777778)\n",
"1 (0.13636363636363635, 0.22727272727272727)\n",
"1 (0.6842105263157895, 0.8888888888888888)\n",
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"0 (0.0, 0.05714285714285714)\n",
"0 (0.13043478260869565, 0.2727272727272727)\n",
"0 (0.06818181818181818, 0.225)\n",
"0 (0.07692307692307693, 0.3333333333333333)\n",
"1 (0.08333333333333333, 0.4)\n",
"1 (0.16666666666666666, 0.3333333333333333)\n",
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"0 (0.14285714285714285, 0.5)\n",
"1 (0.38461538461538464, 0.6666666666666666)\n",
"0 (0.75, 0.875)\n",
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"0 (0.0, 0.30303030303030304)\n",
"1 (0.1111111111111111, 0.29411764705882354)\n",
"1 (0.0, 0.3333333333333333)\n",
"0 (0.0, 0.3076923076923077)\n",
"1 (0.6666666666666666, 0.7142857142857143)\n",
"0 (0.0, 0.1111111111111111)\n",
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"0 (0.0, 0.23684210526315788)\n",
"0 (0.0, 0.09090909090909091)\n"
]
}
],
"source": [
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from collections import Counter\n",
"\n",
"def BiJaccard(str1, str2):\n",
" str1 = set(nltk.word_tokenize(str1))\n",
" bigram1 = set(nltk.bigrams( str1 ))\n",
" #print(set(bigram1))\n",
" str2 = set(nltk.word_tokenize(str2))\n",
" bigram2 = set(nltk.bigrams(str2) )\n",
" #print(bigram1 & bigram2)\n",
" return float(len(bigram1 & bigram2)) / len(bigram1 | bigram2),float(len(str1 & str2)/len(str1 |str2))\n",
"\n",
"for xi in range (0,100):\n",
" q1=train.iloc[xi]['question1']\n",
" q2=train.iloc[xi]['question2']\n",
" print(train.iloc[xi]['is_duplicate'],BiJaccard(q1,q2))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "fc27e7f4-95dc-e103-1d66-9618f703b003"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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]
}
],
"source": [
"import unicodedata\n",
"from collections import defaultdict\n",
"\n",
"def levenshtein_distance(s1, s2):\n",
"\n",
" if s1 == s2:\n",
" return 0\n",
" rows = len(s1)+1\n",
" cols = len(s2)+1\n",
"\n",
" if not s1:\n",
" return cols-1\n",
" if not s2:\n",
" return rows-1\n",
"\n",
" prev = None\n",
" cur = range(cols)\n",
" for r in range(1, rows):\n",
" prev, cur = cur, [r] + [0]*(cols-1)\n",
" for c in range(1, cols):\n",
" deletion = prev[c] + 1\n",
" insertion = cur[c-1] + 1\n",
" edit = prev[c-1] + (0 if s1[r-1] == s2[c-1] else 1)\n",
" cur[c] = min(edit, deletion, insertion)\n",
"\n",
" return cur[-1]\n",
"\n",
"\n",
"for xi in range (0,100):\n",
" q1=train.iloc[xi]['question1']\n",
" q2=train.iloc[xi]['question2']\n",
" print(train.iloc[xi]['is_duplicate'],levenshtein_distance(q1,q2))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "7dd1936f-4fdb-e653-dfc7-351147b1d247"
},
"outputs": [],
"source": [
"def jaro_winkler(ying, yang, long_tolerance, winklerize):\n",
" ying_len = len(ying)\n",
" yang_len = len(yang)\n",
"\n",
" if not ying_len or not yang_len:\n",
" return 0.0\n",
"\n",
" min_len = max(ying_len, yang_len)\n",
" search_range = (min_len // 2) - 1\n",
" if search_range < 0:\n",
" search_range = 0\n",
"\n",
" ying_flags = [False]*ying_len\n",
" yang_flags = [False]*yang_len\n",
"\n",
" # looking only within search range, count & flag matched pairs\n",
" common_chars = 0\n",
" for i, ying_ch in enumerate(ying):\n",
" low = i - search_range if i > search_range else 0\n",
" hi = i + search_range if i + search_range < yang_len else yang_len - 1\n",
" for j in _range(low, hi+1):\n",
" if not yang_flags[j] and yang[j] == ying_ch:\n",
" ying_flags[i] = yang_flags[j] = True\n",
" common_chars += 1\n",
" break\n",
"\n",
" # short circuit if no characters match\n",
" if not common_chars:\n",
" return 0.0\n",
"\n",
" # count transpositions\n",
" k = trans_count = 0\n",
" for i, ying_f in enumerate(ying_flags):\n",
" if ying_f:\n",
" for j in _range(k, yang_len):\n",
" if yang_flags[j]:\n",
" k = j + 1\n",
" break\n",
" if ying[i] != yang[j]:\n",
" trans_count += 1\n",
" trans_count /= 2\n",
"\n",
" # adjust for similarities in nonmatched characters\n",
" common_chars = float(common_chars)\n",
" weight = ((common_chars/ying_len + common_chars/yang_len +\n",
" (common_chars-trans_count) / common_chars)) / 3\n",
"\n",
" # winkler modification: continue to boost if strings are similar\n",
" if winklerize and weight > 0.7 and ying_len > 3 and yang_len > 3:\n",
" # adjust for up to first 4 chars in common\n",
" j = min(min_len, 4)\n",
" i = 0\n",
" while i < j and ying[i] == yang[i] and ying[i]:\n",
" i += 1\n",
" if i:\n",
" weight += i * 0.1 * (1.0 - weight)\n",
"\n",
" # optionally adjust for long strings\n",
" # after agreeing beginning chars, at least two or more must agree and\n",
" # agreed characters must be > half of remaining characters\n",
" if (long_tolerance and min_len > 4 and common_chars > i+1 and\n",
" 2 * common_chars >= min_len + i):\n",
" weight += ((1.0 - weight) * (float(common_chars-i-1) / float(ying_len+yang_len-i*2+2)))\n",
"\n",
" return weight"
]
}
],
"metadata": {
"_change_revision": 6,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162502.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "ac7edcb4-b4c1-1e17-ff97-1838784052c2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"titanic_data.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "5461d7f2-f17f-66be-73ef-fda0684cd24f"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/titanic_data.csv')\n",
"df.shape\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "cf3fc31a-2825-66d4-9aba-8d4aeadc8a21"
},
"outputs": [
{
"data": {
"text/plain": [
"array([['Woolner, Mr. Hugh', 1, nan],\n",
" ['Rugg, Miss. Emily', 1, 21.0],\n",
" ['Novel, Mr. Mansouer', 0, 28.5],\n",
" ['West, Miss. Constance Mirium', 1, 5.0],\n",
" ['Goodwin, Master. William Frederick', 0, 11.0]], dtype=object)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['Name', 'Survived', 'Age']].values[55:60]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "91c497e8-f933-9ee6-900e-1f2f3d9a2d61"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Sex Age SibSp Parch Fare\n",
"0 1 0 3 1 22.0 1 0 7.2500\n",
"1 2 1 1 2 38.0 1 0 71.2833\n",
"2 3 1 3 2 26.0 0 0 7.9250\n",
"3 4 1 1 2 35.0 1 0 53.1000\n",
"4 5 0 3 1 35.0 0 0 8.0500"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import tree\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"df2 = df\n",
"df2 = df2.drop(['Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)\n",
"\n",
"df2 = df2.dropna()\n",
"\n",
"sexConvDict = {\"male\":1 ,\"female\" :2}\n",
"df2['Sex'] = df2['Sex'].apply(sexConvDict.get).astype(int)\n",
"\n",
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "0d4ff24e-9dbf-17b4-8496-df35b0e87925"
},
"outputs": [],
"source": [
"#features = ['Sex']\n",
"#features = ['Sex', 'Parch']\n",
"#features = ['Sex', 'Parch', 'Pclass']\n",
"#features = ['Sex', 'Parch', 'Pclass', 'Age']\n",
"#features = ['Sex', 'Parch', 'Pclass', 'Age', 'Fare']\n",
"features = ['Sex', 'Parch', 'Pclass', 'Age', 'Fare', 'SibSp']\n",
"\n",
"X = df2[features].values\n",
"y = df2['Survived'].values\n",
"scaler = StandardScaler()\n",
"X_standard = scaler.fit_transform(df2[features].values)\n",
"X_train, X_test, X_std_train, X_std_test, y_train, y_test = train_test_split(X, X_standard, y, test_size=0.50, random_state=1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "c6cde684-fe7d-5c59-3aa4-b38f23c1754f"
},
"outputs": [],
"source": [
"clf = tree.DecisionTreeClassifier()\n",
"clf = clf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "86629e61-b13a-a32a-6d77-d1aed89e84ad"
},
"outputs": [],
"source": [
"y_predicted = clf.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "4d96f030-6006-c474-6817-9c227d0677c9"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[162, 49],\n",
" [ 38, 108]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"\n",
"confusion_matrix(y_test, y_predicted)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "13c29145-db8b-88bf-8d9a-62033712d546"
},
"outputs": [
{
"data": {
"text/plain": [
"0.75630252100840334"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"accuracy_score(y_test, y_predicted)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "250326cc-3ce7-8207-06b0-b748410c5df6"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[191, 20],\n",
" [ 43, 103]])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
"\n",
"clf = MLPClassifier(solver='lbfgs', alpha=1e-7, hidden_layer_sizes=(3, 5), random_state=0)\n",
"clf = clf.fit(X_std_train, y_train)\n",
"y_predicted = clf.predict(X_std_test)\n",
"\n",
"confusion_matrix(y_test, y_predicted)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "e19ae612-a7e0-d767-fa56-26213c192525"
},
"outputs": [
{
"data": {
"text/plain": [
"0.82352941176470584"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accuracy_score(y_test, y_predicted)"
]
}
],
"metadata": {
"_change_revision": 1757,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162508.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "ac7edcb4-b4c1-1e17-ff97-1838784052c2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"titanic_data.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "5d8a2c60-7659-994d-e600-58fce9c45cfa"
},
"outputs": [
{
"data": {
"text/plain": [
"(891, 12)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/titanic_data.csv')\n",
"df.shape\n",
"\n",
"# 891 rows, 12 features"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "db6efdaa-4b0c-a35c-b891-88fb8541e048"
},
"outputs": [
{
"data": {
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" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
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" <td>female</td>\n",
" <td>38.0</td>\n",
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" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "5eb48435-d590-696d-6c92-9130415c0e0e"
},
"outputs": [
{
"data": {
"text/plain": [
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" 8. , 38. , nan, 19. , nan, nan, 40. , nan,\n",
" nan, 66. , 28. , 42. , nan, 21. , 18. , 14. ,\n",
" 40. , 27. , nan, 3. , 19. , nan, nan, nan,\n",
" nan, 18. , 7. , 21. , 49. , 29. , 65. , nan,\n",
" 21. , 28.5 , 5. , 11. , 22. , 38. , 45. , 4. ,\n",
" nan, nan, 29. , 19. , 17. , 26. , 32. , 16. ,\n",
" 21. , 26. , 32. , 25. , nan, nan, 0.83, 30. ,\n",
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" 19. , 37. , 16. , 24. , nan, 22. , 24. , 19. ,\n",
" 18. , 19. , 27. , 9. , 36.5 , 42. , 51. , 22. ,\n",
" 55.5 , 40.5 , nan, 51. , 16. , 30. , nan, nan,\n",
" 44. , 40. , 26. , 17. , 1. , 9. , nan, 45. ,\n",
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" 16. , 40. , 24. , 35. , 22. , 30. , nan, 31. ,\n",
" 27. , 42. , 32. , 30. , 16. , 27. , 51. , nan,\n",
" 38. , 22. , 19. , 20.5 , 18. , nan, 35. , 29. ,\n",
" 59. , 5. , 24. , nan, 44. , 8. , 19. , 33. ,\n",
" nan, nan, 29. , 22. , 30. , 44. , 25. , 24. ,\n",
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" 42. , 22. , 26. , 19. , 36. , 24. , 24. , nan,\n",
" 23.5 , 2. , nan, 50. , nan, nan, 19. , nan,\n",
" nan, 0.92, nan, 17. , 30. , 30. , 24. , 18. ,\n",
" 26. , 28. , 43. , 26. , 24. , 54. , 31. , 40. ,\n",
" 22. , 27. , 30. , 22. , nan, 36. , 61. , 36. ,\n",
" 31. , 16. , nan, 45.5 , 38. , 16. , nan, nan,\n",
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" 15. , 25. , nan, 28. , 22. , 38. , nan, nan,\n",
" 40. , 29. , 45. , 35. , nan, 30. , 60. , nan,\n",
" nan, 24. , 25. , 18. , 19. , 22. , 3. , nan,\n",
" 22. , 27. , 20. , 19. , 42. , 1. , 32. , 35. ,\n",
" nan, 18. , 1. , 36. , nan, 17. , 36. , 21. ,\n",
" 28. , 23. , 24. , 22. , 31. , 46. , 23. , 28. ,\n",
" 39. , 26. , 21. , 28. , 20. , 34. , 51. , 3. ,\n",
" 21. , nan, nan, nan, 33. , nan, 44. , nan,\n",
" 34. , 18. , 30. , 10. , nan, 21. , 29. , 28. ,\n",
" 18. , nan, 28. , 19. , nan, 32. , 28. , nan,\n",
" 42. , 17. , 50. , 14. , 21. , 24. , 64. , 31. ,\n",
" 45. , 20. , 25. , 28. , nan, 4. , 13. , 34. ,\n",
" 5. , 52. , 36. , nan, 30. , 49. , nan, 29. ,\n",
" 65. , nan, 50. , nan, 48. , 34. , 47. , 48. ,\n",
" nan, 38. , nan, 56. , nan, 0.75, nan, 38. ,\n",
" 33. , 23. , 22. , nan, 34. , 29. , 22. , 2. ,\n",
" 9. , nan, 50. , 63. , 25. , nan, 35. , 58. ,\n",
" 30. , 9. , nan, 21. , 55. , 71. , 21. , nan,\n",
" 54. , nan, 25. , 24. , 17. , 21. , nan, 37. ,\n",
" 16. , 18. , 33. , nan, 28. , 26. , 29. , nan,\n",
" 36. , 54. , 24. , 47. , 34. , nan, 36. , 32. ,\n",
" 30. , 22. , nan, 44. , nan, 40.5 , 50. , nan,\n",
" 39. , 23. , 2. , nan, 17. , nan, 30. , 7. ,\n",
" 45. , 30. , nan, 22. , 36. , 9. , 11. , 32. ,\n",
" 50. , 64. , 19. , nan, 33. , 8. , 17. , 27. ,\n",
" nan, 22. , 22. , 62. , 48. , nan, 39. , 36. ,\n",
" nan, 40. , 28. , nan, nan, 24. , 19. , 29. ,\n",
" nan, 32. , 62. , 53. , 36. , nan, 16. , 19. ,\n",
" 34. , 39. , nan, 32. , 25. , 39. , 54. , 36. ,\n",
" nan, 18. , 47. , 60. , 22. , nan, 35. , 52. ,\n",
" 47. , nan, 37. , 36. , nan, 49. , nan, 49. ,\n",
" 24. , nan, nan, 44. , 35. , 36. , 30. , 27. ,\n",
" 22. , 40. , 39. , nan, nan, nan, 35. , 24. ,\n",
" 34. , 26. , 4. , 26. , 27. , 42. , 20. , 21. ,\n",
" 21. , 61. , 57. , 21. , 26. , nan, 80. , 51. ,\n",
" 32. , nan, 9. , 28. , 32. , 31. , 41. , nan,\n",
" 20. , 24. , 2. , nan, 0.75, 48. , 19. , 56. ,\n",
" nan, 23. , nan, 18. , 21. , nan, 18. , 24. ,\n",
" nan, 32. , 23. , 58. , 50. , 40. , 47. , 36. ,\n",
" 20. , 32. , 25. , nan, 43. , nan, 40. , 31. ,\n",
" 70. , 31. , nan, 18. , 24.5 , 18. , 43. , 36. ,\n",
" nan, 27. , 20. , 14. , 60. , 25. , 14. , 19. ,\n",
" 18. , 15. , 31. , 4. , nan, 25. , 60. , 52. ,\n",
" 44. , nan, 49. , 42. , 18. , 35. , 18. , 25. ,\n",
" 26. , 39. , 45. , 42. , 22. , nan, 24. , nan,\n",
" 48. , 29. , 52. , 19. , 38. , 27. , nan, 33. ,\n",
" 6. , 17. , 34. , 50. , 27. , 20. , 30. , nan,\n",
" 25. , 25. , 29. , 11. , nan, 23. , 23. , 28.5 ,\n",
" 48. , 35. , nan, nan, nan, 36. , 21. , 24. ,\n",
" 31. , 70. , 16. , 30. , 19. , 31. , 4. , 6. ,\n",
" 33. , 23. , 48. , 0.67, 28. , 18. , 34. , 33. ,\n",
" nan, 41. , 20. , 36. , 16. , 51. , nan, 30.5 ,\n",
" nan, 32. , 24. , 48. , 57. , nan, 54. , 18. ,\n",
" nan, 5. , nan, 43. , 13. , 17. , 29. , nan,\n",
" 25. , 25. , 18. , 8. , 1. , 46. , nan, 16. ,\n",
" nan, nan, 25. , 39. , 49. , 31. , 30. , 30. ,\n",
" 34. , 31. , 11. , 0.42, 27. , 31. , 39. , 18. ,\n",
" 39. , 33. , 26. , 39. , 35. , 6. , 30.5 , nan,\n",
" 23. , 31. , 43. , 10. , 52. , 27. , 38. , 27. ,\n",
" 2. , nan, nan, 1. , nan, 62. , 15. , 0.83,\n",
" nan, 23. , 18. , 39. , 21. , nan, 32. , nan,\n",
" 20. , 16. , 30. , 34.5 , 17. , 42. , nan, 35. ,\n",
" 28. , nan, 4. , 74. , 9. , 16. , 44. , 18. ,\n",
" 45. , 51. , 24. , nan, 41. , 21. , 48. , nan,\n",
" 24. , 42. , 27. , 31. , nan, 4. , 26. , 47. ,\n",
" 33. , 47. , 28. , 15. , 20. , 19. , nan, 56. ,\n",
" 25. , 33. , 22. , 28. , 25. , 39. , 27. , 19. ,\n",
" nan, 26. , 32. ])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Age'].values"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "a02250e6-8fd4-3489-0fa2-870d35116cef"
},
"outputs": [
{
"data": {
"text/plain": [
"array([56, 1, 1, 'Woolner, Mr. Hugh', 'male', nan, 0, 0, '19947', 35.5,\n",
" 'C52', 'S'], dtype=object)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# to see data in row 55\n",
"df.values[55]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "274736bd-22e3-32c7-274c-24c9e188ce4e"
},
"outputs": [
{
"data": {
"text/plain": [
"array([['Woolner, Mr. Hugh', 1, nan],\n",
" ['Rugg, Miss. Emily', 1, 21.0],\n",
" ['Novel, Mr. Mansouer', 0, 28.5],\n",
" ['West, Miss. Constance Mirium', 1, 5.0],\n",
" ['Goodwin, Master. William Frederick', 0, 11.0]], dtype=object)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make column name to array of column names\n",
"df[['Name', 'Survived','Age']].values[55:60]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "a89ebd03-a66a-3998-b6ce-aa1657554b26"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>1</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>2</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>2</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>2</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp Parch \\\n",
"0 Braund, Mr. Owen Harris 1 22.0 1 0 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... 2 38.0 1 0 \n",
"2 Heikkinen, Miss. Laina 2 26.0 0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 2 35.0 1 0 \n",
"4 Allen, Mr. William Henry 1 35.0 0 0 \n",
"\n",
" Ticket Fare Cabin Embarked \n",
"0 A/5 21171 7.2500 NaN S \n",
"1 PC 17599 71.2833 C85 C \n",
"2 STON/O2. 3101282 7.9250 NaN S \n",
"3 113803 53.1000 C123 S \n",
"4 373450 8.0500 NaN S "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# df = df.drop(['Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)\n",
"df.head()\n",
"# df = df.dropna()\n",
"df.shape\n",
"\n",
"sexConvDict = {'male':1, 'female':2}\n",
"df['Sex'] = df['Sex'].apply(sexConvDict.get).astype(int)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "9d5fdec2-9db3-91c2-a0f7-93867025f2c5"
},
"outputs": [],
"source": [
"# decission tree\n",
"from sklearn import tree\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"features = ['Sex']\n",
"X = df[features].values\n",
"y = df['Survived'].values\n",
"\n",
"# native array python\n",
"# feature_train, feature_test, label_train, label_test\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.50, random_state=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "4cf4263e-a8c1-374e-3872-c2b6201ef81a"
},
"outputs": [],
"source": [
"clf = tree.DecisionTreeClassifier()\n",
"clf = clf.fit(X_train, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "f527c3c0-aafc-be24-a9aa-98c21abc95a4"
},
"outputs": [],
"source": [
"#test\n",
"y_predicted = clf.predict(X_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "f322a9c3-5cd3-de03-ab66-897e4f62a774"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[223, 40],\n",
" [ 66, 117]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"confusion_matrix(y_test, y_predicted)\n",
"\n",
"# ( [TN, FP],\n",
"# [FN, TP])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "fd80f856-9a1f-6c71-bd6a-c13314f47c3f"
},
"outputs": [
{
"data": {
"text/plain": [
"0.7623318385650224"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_test, y_predicted)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "5b82f34e-c670-4ea1-13ef-c68ffc028292"
},
"outputs": [],
"source": [
"# now we know that sex is one feature that significant"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "21aa19a7-ff17-13cf-13d4-b14264552fcd"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-abbc192b73da>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 33\u001b[0m \u001b[0my_predicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_predicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m 773\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 774\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 775\u001b[0;31m X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m 776\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 777\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_random_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 117\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 407\u001b[0m % (array.ndim, estimator_name))\n\u001b[1;32m 408\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m 38\u001b[0m and not np.isfinite(X).all()):\n\u001b[1;32m 39\u001b[0m raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 40\u001b[0;31m \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
]
}
],
"source": [
"# 0.77\n",
"features = ['Sex']\n",
"\n",
"# 0.61\n",
"features = ['Parch']\n",
"\n",
"#0.66\n",
"features = ['Pclass']\n",
"\n",
"#0.58\n",
"features = ['Age']\n",
"\n",
"#0.65\n",
"features = ['Fare']\n",
"\n",
"#0.57\n",
"features = ['SibSp']\n",
"\n",
"# 0.78\n",
"features = ['Sex', 'Pclass']\n",
"\n",
"# 0.80\n",
"features = ['Sex', 'Pclass', 'Age']\n",
"\n",
"X = df[features].values\n",
"y = df['Survived'].values\n",
"\n",
"# native array python\n",
"# feature_train, feature_test, label_train, label_test\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.50, random_state=1)\n",
"clf = tree.DecisionTreeClassifier()\n",
"clf = clf.fit(X_train, y_train)\n",
"y_predicted = clf.predict(X_test)\n",
"accuracy_score(y_test, y_predicted)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "4a0ceb9a-3ce9-f7f8-5379-c744648ac64f"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-a6c44d7ea5d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0mscaler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mX_standard\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;31m# native array python\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36mfit_transform\u001b[0;34m(self, X, y, **fit_params)\u001b[0m\n\u001b[1;32m 493\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 494\u001b[0m \u001b[0;31m# fit method of arity 1 (unsupervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 495\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 496\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 497\u001b[0m \u001b[0;31m# fit method of arity 2 (supervised transformation)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/data.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 519\u001b[0m \u001b[0;31m# Reset internal state before fitting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 520\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mpartial_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/data.py\u001b[0m in \u001b[0;36mpartial_fit\u001b[0;34m(self, X, y)\u001b[0m\n\u001b[1;32m 541\u001b[0m \"\"\"\n\u001b[1;32m 542\u001b[0m X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,\n\u001b[0;32m--> 543\u001b[0;31m warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)\n\u001b[0m\u001b[1;32m 544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 545\u001b[0m \u001b[0;31m# Even in the case of `with_mean=False`, we update the mean anyway\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m 407\u001b[0m % (array.ndim, estimator_name))\n\u001b[1;32m 408\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 409\u001b[0;31m \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m 38\u001b[0m and not np.isfinite(X).all()):\n\u001b[1;32m 39\u001b[0m raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 40\u001b[0;31m \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
]
}
],
"source": [
"#neuron network\n",
"from sklearn.neural_network import MLPClassifier\n",
"\n",
"# 0.77\n",
"features = ['Sex']\n",
"\n",
"# 0.61\n",
"features = ['Parch']\n",
"\n",
"#0.66\n",
"features = ['Pclass']\n",
"\n",
"#0.58\n",
"features = ['Age']\n",
"\n",
"#0.65\n",
"features = ['Fare']\n",
"\n",
"#0.57\n",
"features = ['SibSp']\n",
"\n",
"# 0.78\n",
"features = ['Sex', 'Pclass']\n",
"\n",
"# 0.80\n",
"features = ['Sex', 'Pclass', 'Age']\n",
"X = df[features].values\n",
"y = df['Survived'].values\n",
"\n",
"# pre-processing data for neuron network\n",
"\n",
"scaler = StandardScaler()\n",
"X_standard = scaler.fit_transform(df[features].values)\n",
"\n",
"# native array python\n",
"# feature_train, feature_test, label_train, label_test\n",
"X_train, X_test, X_std_train, X_std_test, y_train, y_test = train_test_split(X, X_standard, y, test_size=0.50, random_state=1)\n",
"clf = MLPClassifier(solver='lbfgs', alpha=1e-7, hidden_layer_sizes=(10,15), random_state=0)\n",
"# clf = tree.DecisionTreeClassifier()\n",
"clf = clf.fit(X_train, y_train)\n",
"y_predicted = clf.predict(X_test)\n",
"accuracy_score(y_test, y_predicted)\n",
"\n",
"# non-std 0.60784313725490191\n",
"# std 0.79271708683473385"
]
}
],
"metadata": {
"_change_revision": 700,
"_is_fork": false,
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| 0001/162/1162509.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f0fbdf4a-1626-c96c-9d68-8b26bf2de75b"
},
"source": [
"TR Workshop \n",
"\n",
" 1. Data set\n",
" 2. Cleaning data (ex. Drop some empty record )\n",
" 4. Define Training data and Test data (75/25)%\n",
"\n",
"https://docs.google.com/document/d/1p9ByI2w1VqIRJJ_sPtWZqge3b9eDqMBVfJfpN20-TQc/edit"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "ac7edcb4-b4c1-1e17-ff97-1838784052c2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"titanic_data.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "96d763e2-4fca-1af5-1c0f-44a3e6574db6"
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
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" <tr>\n",
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" <td>3</td>\n",
" <td>1</td>\n",
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" <td>female</td>\n",
" <td>35.0</td>\n",
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" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
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" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(\"../input/titanic_data.csv\")\n",
"df.shape\n",
"df.head()\n",
"\n",
"#df[['Name','Age','Survived']].values[54:60]\n",
"#df.values[54:60]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "f064a389-2f80-e72c-9970-b35e1a10129b"
},
"outputs": [],
"source": [
"# Cleaning data drop empty data\n",
"df2 = df\n",
"df2 = df2.drop(['Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)\n",
"df2 = df2.dropna()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "ef90476a-a1e3-dc53-637f-db5f06c2e585"
},
"outputs": [],
"source": [
"# Convert data to number\n",
"# print(df3)\n",
"# df = pd.read_csv(\"../input/titanic_data.csv\")\n",
"# df2 = df.drop(['Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)\n",
"# df3 = df2.dropna()\n",
"sexConvDict = {\"male\":1 ,\"female\" :2}\n",
"df2['Sex'] = df2['Sex'].apply(sexConvDict.get).astype(int)\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "492fa4fd-3fa5-af53-c38f-d51cb2932863"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'df3' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-f80d374c0b6f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Sex'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Parch'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Pclass'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Age'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Fare'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'SibSp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Survived'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'df3' is not defined"
]
}
],
"source": [
"# Random to pick data for training\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn import tree\n",
"# Decision Tree\n",
"# Cons: ไม่ดีตรงมัน ไวต่อค่า\n",
"\n",
"features = ['Sex','Parch','Pclass','Age','Fare', 'SibSp']\n",
"X = df3[features].values\n",
"y = df3['Survived'].values\n",
"\n",
"# Neural network\n",
"scaler = StandardScaler()\n",
"X_standard = scaler.fit_transform(df2[features].values) #Convert range between -1 to 1\n",
"\n",
"# Split test and train data\n",
"X_train, X_test, X_std_train, X_std_test, y_train, y_test = train_test_split(X, X_standard, y, test_size=0.50, random_state=1)\n",
"\n",
"\n",
"# Select algorithm Decistion Tree\n",
"clf = tree.DecisionTreeClassifier()\n",
"clf = clf.fit(X_train, y_train) # Train data and Result = Model\n",
"y_predicted = clf.predict(X_test) # Model predict test data\n",
"\n",
"# Results TN TP FN FP\n",
"from sklearn.metrics import confusion_matrix\n",
"confusion_matrix(y_test, y_predicted) # comparing model predict VS testing result"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "9a9a1490-05ad-e42f-2e6e-42c6a4bbb096"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'y_test' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-5a402375f96e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Accuracy score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_predicted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'y_test' is not defined"
]
}
],
"source": [
"# Accuracy score\n",
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_test, y_predicted)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "cfc0cd14-c361-d042-3e11-42e4ed7d1521"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X_std_train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-228611e8f7ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# clf = MLPClassifier() #Default Neural Network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMLPClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msolver\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'lbfgs'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhidden_layer_sizes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_std_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0my_std_predicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_std_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'X_std_train' is not defined"
]
}
],
"source": [
"# Neural network predicted\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import accuracy_score\n",
"\n",
"# clf = MLPClassifier() #Default Neural Network\n",
"clf = MLPClassifier(solver='lbfgs', alpha=1e-7, hidden_layer_sizes=(3, 2), random_state=0)\n",
"clf = clf.fit(X_std_train, y_train)\n",
"\n",
"y_std_predicted = clf.predict(X_std_test)\n",
"\n",
"confusion_matrix(y_test, y_std_predicted)\n",
"accuracy_score(y_test, y_std_predicted)"
]
}
],
"metadata": {
"_change_revision": 1102,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
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"name": "python3"
},
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}
},
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"nbformat_minor": 0
}
| 0001/162/1162517.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "ac7edcb4-b4c1-1e17-ff97-1838784052c2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"titanic_data.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "b7b8687b-f1aa-3b67-310d-51c496d43459"
},
"outputs": [
{
"data": {
"text/plain": [
"array([56, 1, 1, 'Woolner, Mr. Hugh', 'male', nan, 0, 0, '19947', 35.5,\n",
" 'C52', 'S'], dtype=object)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/titanic_data.csv')\n",
"df.shape\n",
"df.head()\n",
"\n",
"df.values[55]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "979aca63-fa71-2b86-486a-32915ecc7c6f"
},
"outputs": [
{
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" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Sex Age SibSp Parch Fare\n",
"0 1 0 3 1 22.0 1 0 7.2500\n",
"1 2 1 1 2 38.0 1 0 71.2833\n",
"2 3 1 3 2 26.0 0 0 7.9250\n",
"3 4 1 1 2 35.0 1 0 53.1000\n",
"4 5 0 3 1 35.0 0 0 8.0500"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import tree\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"df = df.drop(['Name', 'Ticket', 'Cabin', 'Embarked'], axis=1)\n",
"\n",
"df = df.dropna()\n",
"\n",
"sexConvDict = {\"male\":1 ,\"female\" :2}\n",
"df['Sex'] = df['Sex'].apply(sexConvDict.get).astype(int)\n",
"\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "8a8c3863-7439-8b3c-7860-a008fa8c31f9"
},
"outputs": [],
"source": [
"#features = ['Sex']\n",
"#features = ['Sex', 'Parch']\n",
"#features = ['Sex', 'Parch', 'Pclass']\n",
"features = ['Sex', 'Parch', 'Pclass', 'Age']\n",
"#features = ['Sex', 'Parch', 'Pclass', 'Age', 'Fare']\n",
"#features = ['Sex', 'Parch', 'Pclass', 'Age', 'Fare', 'SibSp']\n",
"\n",
"X = df[features].values\n",
"y = df['Survived'].values\n",
"scaler = StandardScaler()\n",
"X_standard = scaler.fit_transform(df[features].values)\n",
"X_train, X_test, X_std_train, X_std_test, y_train, y_test = train_test_split(X, X_standard, y, test_size=0.50, random_state=1)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "45df1dfe-7eb2-aabe-8e70-b6268a306fa1"
},
"outputs": [],
"source": [
"clf = tree.DecisionTreeClassifier()\n",
"clf = clf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "f08dfda4-e9f9-046c-415c-c35998a78317"
},
"outputs": [],
"source": [
"y_predicted = clf.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "8a4cf78c-fcf7-8bcb-0955-10f10f23497a"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[185, 26],\n",
" [ 47, 99]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import confusion_matrix\n",
"\n",
"confusion_matrix(y_test, y_predicted)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "5e377ba1-fce3-cbe4-5259-6c58cc189091"
},
"outputs": [
{
"data": {
"text/plain": [
"0.79551820728291311"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_test, y_predicted)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "333bb31a-c428-4e18-b6c7-c0ebc86edc19"
},
"outputs": [],
"source": [
"from sklearn.neural_network import MLPClassifier\n",
"clf = MLPClassifier(solver='lbfgs', alpha=1e-7, hidden_layer_sizes=(10, 15), random_state=None)\n",
"clf = clf.fit(X_std_train, y_train)\n",
"y_std_predicted= clf.predict(X_std_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "b7ed9fe7-45bc-baf4-e051-ce32a77687bf"
},
"outputs": [
{
"data": {
"text/plain": [
"0.79271708683473385"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"confusion_matrix(y_test, y_std_predicted)\n",
"accuracy_score(y_test, y_std_predicted)"
]
}
],
"metadata": {
"_change_revision": 1086,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162523.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "ac7edcb4-b4c1-1e17-ff97-1838784052c2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"titanic_data.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output.\n",
"\n",
"#[Neural]for neural network using\n",
"from sklearn.preprocessing import StandardScaler"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "05fb8f3f-6456-4e82-1720-afa10dfec89e"
},
"outputs": [
{
"data": {
"text/plain": [
"array([['Woolner, Mr. Hugh', nan, 1],\n",
" ['Rugg, Miss. Emily', 21.0, 1],\n",
" ['Novel, Mr. Mansouer', 28.5, 0],\n",
" ['West, Miss. Constance Mirium', 5.0, 1],\n",
" ['Goodwin, Master. William Frederick', 11.0, 0]], dtype=object)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#### Load dataframe ####\n",
"\n",
"# csv file which we upload will be in input folder\n",
"df=pd.read_csv('../input/titanic_data.csv')\n",
"#show dimension of dataframe (here is row,feature)\n",
"df.shape\n",
"#get some example of dataframe\n",
"df.head()\n",
"#choose feature what u need\n",
"df['Sex'].values\n",
"#choose row u need\n",
"df.values[10]\n",
"\n",
"#test need row55-60 only Name,Survived,Age\n",
"df[['Name','Age','Survived']].values[55:60]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "0e834ea5-172d-f69d-55ee-de7f85bb71c4"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.2500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>71.2833</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7.9250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>53.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8.0500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Sex Age SibSp Parch Fare\n",
"0 1 0 3 1 22.0 1 0 7.2500\n",
"1 2 1 1 2 38.0 1 0 71.2833\n",
"2 3 1 3 2 26.0 0 0 7.9250\n",
"3 4 1 1 2 35.0 1 0 53.1000\n",
"4 5 0 3 1 35.0 0 0 8.0500"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"##### Filter df to have only neccesary columns and \n",
"# convert value to be integer using in algorithm #####\n",
"\n",
"#import lib for ML first\n",
"from sklearn import tree\n",
"#split test and train data\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"#drop unused columns to not use as feature\n",
"df = df.drop(['Name','Ticket','Cabin','Embarked'],axis=1)\n",
"#drop row which has no value in some columns\n",
"df = df.dropna()\n",
"\n",
"#convert sex from Male,Female to 1,2\n",
"sexConverter = {\"male\":1,\"female\":2}\n",
"df['Sex'] = df['Sex'].apply(sexConverter.get).astype(int)\n",
"df.head()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "60fcf94e-d5b7-8c9f-147a-214c2333fbef"
},
"outputs": [],
"source": [
"##### Define which is test or train data and fit a model for decision tree to calculate result#####\n",
"\n",
"#define which columns to be as feature to train\n",
"#features = ['Sex']\n",
"#features = ['Sex','Pclass']\n",
"#features = ['Sex','Pclass','Age']\n",
"#features = ['Sex','Pclass','Age','SibSp']\n",
"#features = ['Sex','Pclass','Age','SibSp','Parch']\n",
"features = ['Sex','Pclass','Age','SibSp','Parch','Fare'] \n",
"\n",
"#initiate X as all features for calculation\n",
"X = df[features].values\n",
"#initiate y as the result of calculation\n",
"y = df['Survived'].values\n",
"\n",
"#select data to train\n",
"#use 50% train and select the 50% train randomly\n",
"#[DCT]\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.50, random_state=1)\n",
"\n",
"#[Neural] have do standardization.\n",
"scaler = StandardScaler()\n",
"X_standard = scaler.fit_transform(df[features].values)\n",
"X_train, X_test, X_std_train,X_std_test, y_train, y_test = train_test_split(X,X_standard,y,test_size=0.50, random_state=1)\n",
"X_standard[0]\n",
"#[End Neural]\n",
"\n",
"#create empty decision tree\n",
"clf = tree.DecisionTreeClassifier()\n",
"#fit model and get finally decision tree using train data above\n",
"clf = clf.fit(X_train,y_train)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "79e8c3dc-d4a0-ae96-7816-3fa426928dd7"
},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
" 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0,\n",
" 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1,\n",
" 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
" 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1,\n",
" 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0,\n",
" 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
" 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1,\n",
" 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0,\n",
" 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1,\n",
" 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
" 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1,\n",
" 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,\n",
" 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0,\n",
" 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
" 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"##### Predict the test data #####\n",
"\n",
"y_predicted= clf.predict(X_test)\n",
"y_predicted"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "85d407c8-2479-0900-bfe7-18492d9a1352"
},
"outputs": [
{
"data": {
"text/plain": [
"0.77871148459383754"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"##### Performance measure #####\n",
"\n",
"#use confusion matrix and accuracy score.\n",
"from sklearn.metrics import confusion_matrix\n",
"confusion_matrix(y_test,y_predicted)\n",
"\n",
"#check accuracy of prediction\n",
"from sklearn.metrics import accuracy_score\n",
"accuracy_score(y_test,y_predicted)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "fdb8502f-b662-3051-075e-94dded40dad1"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/sklearn/neural_network/multilayer_perceptron.py:563: ConvergenceWarning: Stochastic Optimizer: Maximum iterations reached and the optimization hasn't converged yet.\n",
" % (), ConvergenceWarning)\n"
]
},
{
"data": {
"text/plain": [
"0.83193277310924374"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"##### Using Neural Network algorithm #####\n",
"from sklearn.neural_network import MLPClassifier\n",
"\n",
"#NOTE: adjust parameters to improve algorithm (add/remove features is also improve algorithm)\n",
"clf = MLPClassifier()\n",
"clf = clf.fit(X_std_train,y_train)\n",
"y_predicted = clf.predict(X_std_test)\n",
"\n",
"# HW: check different between standard and no standard.\n",
"#clf_standard = clf.fit(X_std_train,y_train)\n",
"#clf_no_standard = clf.fit(X_train,y_train)\n",
"#y_standard_predicted = clf_standard.predict(X_std_test)\n",
"#y_no_standard_predicted = clf_no_standard.predict(X_test)\n",
"\n",
"#test neural performance\n",
"confusion_matrix(y_test,y_predicted)\n",
"accuracy_score(y_test,y_predicted)"
]
}
],
"metadata": {
"_change_revision": 1036,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162528.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "1784fbf7-78e8-591b-5d96-80325c1be182"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"macro.csv\n",
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "8a0a9c64-7264-ba5e-444e-14dbd55c366c"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn import model_selection, preprocessing\n",
"import xgboost as xgb\n",
"%matplotlib inline\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "bb311107-8b64-6b99-5d43-fd0be141323f"
},
"outputs": [
{
"data": {
"text/plain": [
"(30471, 292)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train = pd.read_csv(\"../input/train.csv\")\n",
"df_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "52f4ee49-bc2d-ba4a-c07f-8bbb81020caa"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>...</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-08-20</td>\n",
" <td>43</td>\n",
" <td>27.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>4</td>\n",
" <td>5850000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-08-23</td>\n",
" <td>34</td>\n",
" <td>19.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>66</td>\n",
" <td>14</td>\n",
" <td>6000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-08-27</td>\n",
" <td>43</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>5700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-09-01</td>\n",
" <td>89</td>\n",
" <td>50.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>13100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-09-05</td>\n",
" <td>77</td>\n",
" <td>77.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>319</td>\n",
" <td>108</td>\n",
" <td>17</td>\n",
" <td>135</td>\n",
" <td>236</td>\n",
" <td>2</td>\n",
" <td>91</td>\n",
" <td>195</td>\n",
" <td>14</td>\n",
" <td>16331452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 292 columns</p>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material build_year \\\n",
"0 1 2011-08-20 43 27.0 4.0 NaN NaN NaN \n",
"1 2 2011-08-23 34 19.0 3.0 NaN NaN NaN \n",
"2 3 2011-08-27 43 29.0 2.0 NaN NaN NaN \n",
"3 4 2011-09-01 89 50.0 9.0 NaN NaN NaN \n",
"4 5 2011-09-05 77 77.0 4.0 NaN NaN NaN \n",
"\n",
" num_room kitch_sq ... cafe_count_5000_price_2500 \\\n",
"0 NaN NaN ... 9 \n",
"1 NaN NaN ... 15 \n",
"2 NaN NaN ... 10 \n",
"3 NaN NaN ... 11 \n",
"4 NaN NaN ... 319 \n",
"\n",
" cafe_count_5000_price_4000 cafe_count_5000_price_high \\\n",
"0 4 0 \n",
"1 3 0 \n",
"2 3 0 \n",
"3 2 1 \n",
"4 108 17 \n",
"\n",
" big_church_count_5000 church_count_5000 mosque_count_5000 \\\n",
"0 13 22 1 \n",
"1 15 29 1 \n",
"2 11 27 0 \n",
"3 4 4 0 \n",
"4 135 236 2 \n",
"\n",
" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
"0 0 52 4 5850000 \n",
"1 10 66 14 6000000 \n",
"2 4 67 10 5700000 \n",
"3 0 26 3 13100000 \n",
"4 91 195 14 16331452 \n",
"\n",
"[5 rows x 292 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "746dea9f-a1e1-d91b-51bd-3a0b5639d0f5"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>state</th>\n",
" <th>product_type</th>\n",
" <th>sub_area</th>\n",
" <th>area_m</th>\n",
" <th>raion_popul</th>\n",
" <th>green_zone_part</th>\n",
" <th>indust_part</th>\n",
" <th>children_preschool</th>\n",
" <th>preschool_quota</th>\n",
" <th>preschool_education_centers_raion</th>\n",
" <th>children_school</th>\n",
" <th>school_quota</th>\n",
" <th>school_education_centers_raion</th>\n",
" <th>school_education_centers_top_20_raion</th>\n",
" <th>hospital_beds_raion</th>\n",
" <th>healthcare_centers_raion</th>\n",
" <th>university_top_20_raion</th>\n",
" <th>sport_objects_raion</th>\n",
" <th>additional_education_raion</th>\n",
" <th>culture_objects_top_25</th>\n",
" <th>culture_objects_top_25_raion</th>\n",
" <th>shopping_centers_raion</th>\n",
" <th>office_raion</th>\n",
" <th>thermal_power_plant_raion</th>\n",
" <th>incineration_raion</th>\n",
" <th>oil_chemistry_raion</th>\n",
" <th>radiation_raion</th>\n",
" <th>railroad_terminal_raion</th>\n",
" <th>big_market_raion</th>\n",
" <th>nuclear_reactor_raion</th>\n",
" <th>detention_facility_raion</th>\n",
" <th>full_all</th>\n",
" <th>male_f</th>\n",
" <th>female_f</th>\n",
" <th>young_all</th>\n",
" <th>young_male</th>\n",
" <th>young_female</th>\n",
" <th>work_all</th>\n",
" <th>work_male</th>\n",
" <th>work_female</th>\n",
" <th>ekder_all</th>\n",
" <th>ekder_male</th>\n",
" <th>ekder_female</th>\n",
" <th>0_6_all</th>\n",
" <th>0_6_male</th>\n",
" <th>0_6_female</th>\n",
" <th>7_14_all</th>\n",
" <th>7_14_male</th>\n",
" <th>7_14_female</th>\n",
" <th>0_17_all</th>\n",
" <th>0_17_male</th>\n",
" <th>0_17_female</th>\n",
" <th>16_29_all</th>\n",
" <th>16_29_male</th>\n",
" <th>16_29_female</th>\n",
" <th>0_13_all</th>\n",
" <th>0_13_male</th>\n",
" <th>0_13_female</th>\n",
" <th>raion_build_count_with_material_info</th>\n",
" <th>build_count_block</th>\n",
" <th>build_count_wood</th>\n",
" <th>build_count_frame</th>\n",
" <th>build_count_brick</th>\n",
" <th>build_count_monolith</th>\n",
" <th>build_count_panel</th>\n",
" <th>build_count_foam</th>\n",
" <th>build_count_slag</th>\n",
" <th>build_count_mix</th>\n",
" <th>raion_build_count_with_builddate_info</th>\n",
" <th>build_count_before_1920</th>\n",
" <th>build_count_1921-1945</th>\n",
" <th>build_count_1946-1970</th>\n",
" <th>build_count_1971-1995</th>\n",
" <th>build_count_after_1995</th>\n",
" <th>ID_metro</th>\n",
" <th>metro_min_avto</th>\n",
" <th>metro_km_avto</th>\n",
" <th>metro_min_walk</th>\n",
" <th>metro_km_walk</th>\n",
" <th>kindergarten_km</th>\n",
" <th>school_km</th>\n",
" <th>park_km</th>\n",
" <th>green_zone_km</th>\n",
" <th>industrial_km</th>\n",
" <th>water_treatment_km</th>\n",
" <th>cemetery_km</th>\n",
" <th>incineration_km</th>\n",
" <th>railroad_station_walk_km</th>\n",
" <th>railroad_station_walk_min</th>\n",
" <th>ID_railroad_station_walk</th>\n",
" <th>railroad_station_avto_km</th>\n",
" <th>railroad_station_avto_min</th>\n",
" <th>ID_railroad_station_avto</th>\n",
" <th>public_transport_station_km</th>\n",
" <th>public_transport_station_min_walk</th>\n",
" <th>water_km</th>\n",
" <th>water_1line</th>\n",
" <th>mkad_km</th>\n",
" <th>ttk_km</th>\n",
" <th>sadovoe_km</th>\n",
" <th>bulvar_ring_km</th>\n",
" <th>kremlin_km</th>\n",
" <th>big_road1_km</th>\n",
" <th>ID_big_road1</th>\n",
" <th>big_road1_1line</th>\n",
" <th>big_road2_km</th>\n",
" <th>ID_big_road2</th>\n",
" <th>railroad_km</th>\n",
" <th>railroad_1line</th>\n",
" <th>zd_vokzaly_avto_km</th>\n",
" <th>ID_railroad_terminal</th>\n",
" <th>bus_terminal_avto_km</th>\n",
" <th>ID_bus_terminal</th>\n",
" <th>oil_chemistry_km</th>\n",
" <th>nuclear_reactor_km</th>\n",
" <th>radiation_km</th>\n",
" <th>power_transmission_line_km</th>\n",
" <th>thermal_power_plant_km</th>\n",
" <th>ts_km</th>\n",
" <th>big_market_km</th>\n",
" <th>market_shop_km</th>\n",
" <th>fitness_km</th>\n",
" <th>swim_pool_km</th>\n",
" <th>ice_rink_km</th>\n",
" <th>stadium_km</th>\n",
" <th>basketball_km</th>\n",
" <th>hospice_morgue_km</th>\n",
" <th>detention_facility_km</th>\n",
" <th>public_healthcare_km</th>\n",
" <th>university_km</th>\n",
" <th>workplaces_km</th>\n",
" <th>shopping_centers_km</th>\n",
" <th>office_km</th>\n",
" <th>additional_education_km</th>\n",
" <th>preschool_km</th>\n",
" <th>big_church_km</th>\n",
" <th>church_synagogue_km</th>\n",
" <th>mosque_km</th>\n",
" <th>theater_km</th>\n",
" <th>museum_km</th>\n",
" <th>exhibition_km</th>\n",
" <th>catering_km</th>\n",
" <th>ecology</th>\n",
" <th>green_part_500</th>\n",
" <th>prom_part_500</th>\n",
" <th>office_count_500</th>\n",
" <th>office_sqm_500</th>\n",
" <th>trc_count_500</th>\n",
" <th>trc_sqm_500</th>\n",
" <th>cafe_count_500</th>\n",
" <th>cafe_sum_500_min_price_avg</th>\n",
" <th>cafe_sum_500_max_price_avg</th>\n",
" <th>cafe_avg_price_500</th>\n",
" <th>cafe_count_500_na_price</th>\n",
" <th>cafe_count_500_price_500</th>\n",
" <th>cafe_count_500_price_1000</th>\n",
" <th>cafe_count_500_price_1500</th>\n",
" <th>cafe_count_500_price_2500</th>\n",
" <th>cafe_count_500_price_4000</th>\n",
" <th>cafe_count_500_price_high</th>\n",
" <th>big_church_count_500</th>\n",
" <th>church_count_500</th>\n",
" <th>mosque_count_500</th>\n",
" <th>leisure_count_500</th>\n",
" <th>sport_count_500</th>\n",
" <th>market_count_500</th>\n",
" <th>green_part_1000</th>\n",
" <th>prom_part_1000</th>\n",
" <th>office_count_1000</th>\n",
" <th>office_sqm_1000</th>\n",
" <th>trc_count_1000</th>\n",
" <th>trc_sqm_1000</th>\n",
" <th>cafe_count_1000</th>\n",
" <th>cafe_sum_1000_min_price_avg</th>\n",
" <th>cafe_sum_1000_max_price_avg</th>\n",
" <th>cafe_avg_price_1000</th>\n",
" <th>cafe_count_1000_na_price</th>\n",
" <th>cafe_count_1000_price_500</th>\n",
" <th>cafe_count_1000_price_1000</th>\n",
" <th>cafe_count_1000_price_1500</th>\n",
" <th>cafe_count_1000_price_2500</th>\n",
" <th>cafe_count_1000_price_4000</th>\n",
" <th>cafe_count_1000_price_high</th>\n",
" <th>big_church_count_1000</th>\n",
" <th>church_count_1000</th>\n",
" <th>mosque_count_1000</th>\n",
" <th>leisure_count_1000</th>\n",
" <th>sport_count_1000</th>\n",
" <th>market_count_1000</th>\n",
" <th>green_part_1500</th>\n",
" <th>prom_part_1500</th>\n",
" <th>office_count_1500</th>\n",
" <th>office_sqm_1500</th>\n",
" <th>trc_count_1500</th>\n",
" <th>trc_sqm_1500</th>\n",
" <th>cafe_count_1500</th>\n",
" <th>cafe_sum_1500_min_price_avg</th>\n",
" <th>cafe_sum_1500_max_price_avg</th>\n",
" <th>cafe_avg_price_1500</th>\n",
" <th>cafe_count_1500_na_price</th>\n",
" <th>cafe_count_1500_price_500</th>\n",
" <th>cafe_count_1500_price_1000</th>\n",
" <th>cafe_count_1500_price_1500</th>\n",
" <th>cafe_count_1500_price_2500</th>\n",
" <th>cafe_count_1500_price_4000</th>\n",
" <th>cafe_count_1500_price_high</th>\n",
" <th>big_church_count_1500</th>\n",
" <th>church_count_1500</th>\n",
" <th>mosque_count_1500</th>\n",
" <th>leisure_count_1500</th>\n",
" <th>sport_count_1500</th>\n",
" <th>market_count_1500</th>\n",
" <th>green_part_2000</th>\n",
" <th>prom_part_2000</th>\n",
" <th>office_count_2000</th>\n",
" <th>office_sqm_2000</th>\n",
" <th>trc_count_2000</th>\n",
" <th>trc_sqm_2000</th>\n",
" <th>cafe_count_2000</th>\n",
" <th>cafe_sum_2000_min_price_avg</th>\n",
" <th>cafe_sum_2000_max_price_avg</th>\n",
" <th>cafe_avg_price_2000</th>\n",
" <th>cafe_count_2000_na_price</th>\n",
" <th>cafe_count_2000_price_500</th>\n",
" <th>cafe_count_2000_price_1000</th>\n",
" <th>cafe_count_2000_price_1500</th>\n",
" <th>cafe_count_2000_price_2500</th>\n",
" <th>cafe_count_2000_price_4000</th>\n",
" <th>cafe_count_2000_price_high</th>\n",
" <th>big_church_count_2000</th>\n",
" <th>church_count_2000</th>\n",
" <th>mosque_count_2000</th>\n",
" <th>leisure_count_2000</th>\n",
" <th>sport_count_2000</th>\n",
" <th>market_count_2000</th>\n",
" <th>green_part_3000</th>\n",
" <th>prom_part_3000</th>\n",
" <th>office_count_3000</th>\n",
" <th>office_sqm_3000</th>\n",
" <th>trc_count_3000</th>\n",
" <th>trc_sqm_3000</th>\n",
" <th>cafe_count_3000</th>\n",
" <th>cafe_sum_3000_min_price_avg</th>\n",
" <th>cafe_sum_3000_max_price_avg</th>\n",
" <th>cafe_avg_price_3000</th>\n",
" <th>cafe_count_3000_na_price</th>\n",
" <th>cafe_count_3000_price_500</th>\n",
" <th>cafe_count_3000_price_1000</th>\n",
" <th>cafe_count_3000_price_1500</th>\n",
" <th>cafe_count_3000_price_2500</th>\n",
" <th>cafe_count_3000_price_4000</th>\n",
" <th>cafe_count_3000_price_high</th>\n",
" <th>big_church_count_3000</th>\n",
" <th>church_count_3000</th>\n",
" <th>mosque_count_3000</th>\n",
" <th>leisure_count_3000</th>\n",
" <th>sport_count_3000</th>\n",
" <th>market_count_3000</th>\n",
" <th>green_part_5000</th>\n",
" <th>prom_part_5000</th>\n",
" <th>office_count_5000</th>\n",
" <th>office_sqm_5000</th>\n",
" <th>trc_count_5000</th>\n",
" <th>trc_sqm_5000</th>\n",
" <th>cafe_count_5000</th>\n",
" <th>cafe_sum_5000_min_price_avg</th>\n",
" <th>cafe_sum_5000_max_price_avg</th>\n",
" <th>cafe_avg_price_5000</th>\n",
" <th>cafe_count_5000_na_price</th>\n",
" <th>cafe_count_5000_price_500</th>\n",
" <th>cafe_count_5000_price_1000</th>\n",
" <th>cafe_count_5000_price_1500</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-08-20</td>\n",
" <td>43</td>\n",
" <td>27.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Bibirevo</td>\n",
" <td>6.407578e+06</td>\n",
" <td>155572</td>\n",
" <td>0.189727</td>\n",
" <td>0.000070</td>\n",
" <td>9576</td>\n",
" <td>5001.0</td>\n",
" <td>5</td>\n",
" <td>10309</td>\n",
" <td>11065.0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>240.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
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" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>86206</td>\n",
" <td>40477</td>\n",
" <td>45729</td>\n",
" <td>21154</td>\n",
" <td>11007</td>\n",
" <td>10147</td>\n",
" <td>98207</td>\n",
" <td>52277</td>\n",
" <td>45930</td>\n",
" <td>36211</td>\n",
" <td>10580</td>\n",
" <td>25631</td>\n",
" <td>9576</td>\n",
" <td>4899</td>\n",
" <td>4677</td>\n",
" <td>10309</td>\n",
" <td>5463</td>\n",
" <td>4846</td>\n",
" <td>23603</td>\n",
" <td>12286</td>\n",
" <td>11317</td>\n",
" <td>17508</td>\n",
" <td>9425</td>\n",
" <td>8083</td>\n",
" <td>18654</td>\n",
" <td>9709</td>\n",
" <td>8945</td>\n",
" <td>211.0</td>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>184.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>211.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>206.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" <td>2.590241</td>\n",
" <td>1.131260</td>\n",
" <td>13.575119</td>\n",
" <td>1.131260</td>\n",
" <td>0.145700</td>\n",
" <td>0.177975</td>\n",
" <td>2.158587</td>\n",
" <td>0.600973</td>\n",
" <td>1.080934</td>\n",
" <td>23.683460</td>\n",
" <td>1.804127</td>\n",
" <td>3.633334</td>\n",
" <td>5.419893</td>\n",
" <td>65.038716</td>\n",
" <td>1.0</td>\n",
" <td>5.419893</td>\n",
" <td>6.905893</td>\n",
" <td>1</td>\n",
" <td>0.274985</td>\n",
" <td>3.299822</td>\n",
" <td>0.992631</td>\n",
" <td>no</td>\n",
" <td>1.422391</td>\n",
" <td>10.918587</td>\n",
" <td>13.100618</td>\n",
" <td>13.675657</td>\n",
" <td>15.156211</td>\n",
" <td>1.422391</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>3.830951</td>\n",
" <td>5</td>\n",
" <td>1.305159</td>\n",
" <td>no</td>\n",
" <td>14.231961</td>\n",
" <td>101</td>\n",
" <td>24.292406</td>\n",
" <td>1</td>\n",
" <td>18.152338</td>\n",
" <td>5.718519</td>\n",
" <td>1.210027</td>\n",
" <td>1.062513</td>\n",
" <td>5.814135</td>\n",
" <td>4.308127</td>\n",
" <td>10.814172</td>\n",
" <td>1.676258</td>\n",
" <td>0.485841</td>\n",
" <td>3.065047</td>\n",
" <td>1.107594</td>\n",
" <td>8.148591</td>\n",
" <td>3.516513</td>\n",
" <td>2.392353</td>\n",
" <td>4.248036</td>\n",
" <td>0.974743</td>\n",
" <td>6.715026</td>\n",
" <td>0.884350</td>\n",
" <td>0.648488</td>\n",
" <td>0.637189</td>\n",
" <td>0.947962</td>\n",
" <td>0.177975</td>\n",
" <td>0.625783</td>\n",
" <td>0.628187</td>\n",
" <td>3.932040</td>\n",
" <td>14.053047</td>\n",
" <td>7.389498</td>\n",
" <td>7.023705</td>\n",
" <td>0.516838</td>\n",
" <td>good</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.36</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>30500</td>\n",
" <td>3</td>\n",
" <td>55600</td>\n",
" <td>19</td>\n",
" <td>527.78</td>\n",
" <td>888.89</td>\n",
" <td>708.33</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>14.27</td>\n",
" <td>6.92</td>\n",
" <td>3</td>\n",
" <td>39554</td>\n",
" <td>9</td>\n",
" <td>171420</td>\n",
" <td>34</td>\n",
" <td>566.67</td>\n",
" <td>969.70</td>\n",
" <td>768.18</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>11.77</td>\n",
" <td>15.97</td>\n",
" <td>9</td>\n",
" <td>188854</td>\n",
" <td>19</td>\n",
" <td>1244891</td>\n",
" <td>36</td>\n",
" <td>614.29</td>\n",
" <td>1042.86</td>\n",
" <td>828.57</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>11.98</td>\n",
" <td>13.55</td>\n",
" <td>12</td>\n",
" <td>251554</td>\n",
" <td>23</td>\n",
" <td>1419204</td>\n",
" <td>68</td>\n",
" <td>639.68</td>\n",
" <td>1079.37</td>\n",
" <td>859.52</td>\n",
" <td>5</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>16</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>13.09</td>\n",
" <td>13.31</td>\n",
" <td>29</td>\n",
" <td>807385</td>\n",
" <td>52</td>\n",
" <td>4036616</td>\n",
" <td>152</td>\n",
" <td>708.57</td>\n",
" <td>1185.71</td>\n",
" <td>947.14</td>\n",
" <td>12</td>\n",
" <td>39</td>\n",
" <td>48</td>\n",
" <td>40</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>4</td>\n",
" <td>5850000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-08-23</td>\n",
" <td>34</td>\n",
" <td>19.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Nagatinskij Zaton</td>\n",
" <td>9.589337e+06</td>\n",
" <td>115352</td>\n",
" <td>0.372602</td>\n",
" <td>0.049637</td>\n",
" <td>6880</td>\n",
" <td>3119.0</td>\n",
" <td>5</td>\n",
" <td>7759</td>\n",
" <td>6237.0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>229.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>yes</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>76284</td>\n",
" <td>34200</td>\n",
" <td>42084</td>\n",
" <td>15727</td>\n",
" <td>7925</td>\n",
" <td>7802</td>\n",
" <td>70194</td>\n",
" <td>35622</td>\n",
" <td>34572</td>\n",
" <td>29431</td>\n",
" <td>9266</td>\n",
" <td>20165</td>\n",
" <td>6880</td>\n",
" <td>3466</td>\n",
" <td>3414</td>\n",
" <td>7759</td>\n",
" <td>3909</td>\n",
" <td>3850</td>\n",
" <td>17700</td>\n",
" <td>8998</td>\n",
" <td>8702</td>\n",
" <td>15164</td>\n",
" <td>7571</td>\n",
" <td>7593</td>\n",
" <td>13729</td>\n",
" <td>6929</td>\n",
" <td>6800</td>\n",
" <td>245.0</td>\n",
" <td>83.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>67.0</td>\n",
" <td>4.0</td>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>244.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>143.0</td>\n",
" <td>84.0</td>\n",
" <td>15.0</td>\n",
" <td>2</td>\n",
" <td>0.936700</td>\n",
" <td>0.647337</td>\n",
" <td>7.620630</td>\n",
" <td>0.635053</td>\n",
" <td>0.147754</td>\n",
" <td>0.273345</td>\n",
" <td>0.550690</td>\n",
" <td>0.065321</td>\n",
" <td>0.966479</td>\n",
" <td>1.317476</td>\n",
" <td>4.655004</td>\n",
" <td>8.648587</td>\n",
" <td>3.411993</td>\n",
" <td>40.943917</td>\n",
" <td>2.0</td>\n",
" <td>3.641773</td>\n",
" <td>4.679745</td>\n",
" <td>2</td>\n",
" <td>0.065263</td>\n",
" <td>0.783160</td>\n",
" <td>0.698081</td>\n",
" <td>no</td>\n",
" <td>9.503405</td>\n",
" <td>3.103996</td>\n",
" <td>6.444333</td>\n",
" <td>8.132640</td>\n",
" <td>8.698054</td>\n",
" <td>2.887377</td>\n",
" <td>2</td>\n",
" <td>no</td>\n",
" <td>3.103996</td>\n",
" <td>4</td>\n",
" <td>0.694536</td>\n",
" <td>no</td>\n",
" <td>9.242586</td>\n",
" <td>32</td>\n",
" <td>5.706113</td>\n",
" <td>2</td>\n",
" <td>9.034642</td>\n",
" <td>3.489954</td>\n",
" <td>2.724295</td>\n",
" <td>1.246149</td>\n",
" <td>3.419574</td>\n",
" <td>0.725560</td>\n",
" <td>6.910568</td>\n",
" <td>3.424716</td>\n",
" <td>0.668364</td>\n",
" <td>2.000154</td>\n",
" <td>8.972823</td>\n",
" <td>6.127073</td>\n",
" <td>1.161579</td>\n",
" <td>2.543747</td>\n",
" <td>12.649879</td>\n",
" <td>1.477723</td>\n",
" <td>1.852560</td>\n",
" <td>0.686252</td>\n",
" <td>0.519311</td>\n",
" <td>0.688796</td>\n",
" <td>1.072315</td>\n",
" <td>0.273345</td>\n",
" <td>0.967821</td>\n",
" <td>0.471447</td>\n",
" <td>4.841544</td>\n",
" <td>6.829889</td>\n",
" <td>0.709260</td>\n",
" <td>2.358840</td>\n",
" <td>0.230287</td>\n",
" <td>excellent</td>\n",
" <td>25.14</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>860.00</td>\n",
" <td>1500.00</td>\n",
" <td>1180.00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.66</td>\n",
" <td>0.07</td>\n",
" <td>2</td>\n",
" <td>86600</td>\n",
" <td>5</td>\n",
" <td>94065</td>\n",
" <td>13</td>\n",
" <td>615.38</td>\n",
" <td>1076.92</td>\n",
" <td>846.15</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>21.53</td>\n",
" <td>7.71</td>\n",
" <td>3</td>\n",
" <td>102910</td>\n",
" <td>7</td>\n",
" <td>127065</td>\n",
" <td>17</td>\n",
" <td>694.12</td>\n",
" <td>1205.88</td>\n",
" <td>950.00</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>22.37</td>\n",
" <td>19.25</td>\n",
" <td>4</td>\n",
" <td>165510</td>\n",
" <td>8</td>\n",
" <td>179065</td>\n",
" <td>21</td>\n",
" <td>695.24</td>\n",
" <td>1190.48</td>\n",
" <td>942.86</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>18.07</td>\n",
" <td>27.32</td>\n",
" <td>12</td>\n",
" <td>821986</td>\n",
" <td>14</td>\n",
" <td>491565</td>\n",
" <td>30</td>\n",
" <td>631.03</td>\n",
" <td>1086.21</td>\n",
" <td>858.62</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>10.26</td>\n",
" <td>27.47</td>\n",
" <td>66</td>\n",
" <td>2690465</td>\n",
" <td>40</td>\n",
" <td>2034942</td>\n",
" <td>177</td>\n",
" <td>673.81</td>\n",
" <td>1148.81</td>\n",
" <td>911.31</td>\n",
" <td>9</td>\n",
" <td>49</td>\n",
" <td>65</td>\n",
" <td>36</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>66</td>\n",
" <td>14</td>\n",
" <td>6000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-08-27</td>\n",
" <td>43</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Tekstil'shhiki</td>\n",
" <td>4.808270e+06</td>\n",
" <td>101708</td>\n",
" <td>0.112560</td>\n",
" <td>0.118537</td>\n",
" <td>5879</td>\n",
" <td>1463.0</td>\n",
" <td>4</td>\n",
" <td>6207</td>\n",
" <td>5580.0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1183.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>101982</td>\n",
" <td>46076</td>\n",
" <td>55906</td>\n",
" <td>13028</td>\n",
" <td>6835</td>\n",
" <td>6193</td>\n",
" <td>63388</td>\n",
" <td>31813</td>\n",
" <td>31575</td>\n",
" <td>25292</td>\n",
" <td>7609</td>\n",
" <td>17683</td>\n",
" <td>5879</td>\n",
" <td>3095</td>\n",
" <td>2784</td>\n",
" <td>6207</td>\n",
" <td>3269</td>\n",
" <td>2938</td>\n",
" <td>14884</td>\n",
" <td>7821</td>\n",
" <td>7063</td>\n",
" <td>19401</td>\n",
" <td>9045</td>\n",
" <td>10356</td>\n",
" <td>11252</td>\n",
" <td>5916</td>\n",
" <td>5336</td>\n",
" <td>330.0</td>\n",
" <td>59.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>206.0</td>\n",
" <td>4.0</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>330.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>246.0</td>\n",
" <td>63.0</td>\n",
" <td>20.0</td>\n",
" <td>3</td>\n",
" <td>2.120999</td>\n",
" <td>1.637996</td>\n",
" <td>17.351515</td>\n",
" <td>1.445960</td>\n",
" <td>0.049102</td>\n",
" <td>0.158072</td>\n",
" <td>0.374848</td>\n",
" <td>0.453172</td>\n",
" <td>0.939275</td>\n",
" <td>4.912660</td>\n",
" <td>3.381083</td>\n",
" <td>11.996480</td>\n",
" <td>1.277658</td>\n",
" <td>15.331896</td>\n",
" <td>3.0</td>\n",
" <td>1.277658</td>\n",
" <td>1.701420</td>\n",
" <td>3</td>\n",
" <td>0.328756</td>\n",
" <td>3.945073</td>\n",
" <td>0.468265</td>\n",
" <td>no</td>\n",
" <td>5.604800</td>\n",
" <td>2.927487</td>\n",
" <td>6.963403</td>\n",
" <td>8.054252</td>\n",
" <td>9.067885</td>\n",
" <td>0.647250</td>\n",
" <td>3</td>\n",
" <td>no</td>\n",
" <td>2.927487</td>\n",
" <td>4</td>\n",
" <td>0.700691</td>\n",
" <td>no</td>\n",
" <td>9.540544</td>\n",
" <td>5</td>\n",
" <td>6.710302</td>\n",
" <td>3</td>\n",
" <td>5.777394</td>\n",
" <td>7.506612</td>\n",
" <td>0.772216</td>\n",
" <td>1.602183</td>\n",
" <td>3.682455</td>\n",
" <td>3.562188</td>\n",
" <td>5.752368</td>\n",
" <td>1.375443</td>\n",
" <td>0.733101</td>\n",
" <td>1.239304</td>\n",
" <td>1.978517</td>\n",
" <td>0.767569</td>\n",
" <td>1.952771</td>\n",
" <td>0.621357</td>\n",
" <td>7.682303</td>\n",
" <td>0.097144</td>\n",
" <td>0.841254</td>\n",
" <td>1.510089</td>\n",
" <td>1.486533</td>\n",
" <td>1.543049</td>\n",
" <td>0.391957</td>\n",
" <td>0.158072</td>\n",
" <td>3.178751</td>\n",
" <td>0.755946</td>\n",
" <td>7.922152</td>\n",
" <td>4.273200</td>\n",
" <td>3.156423</td>\n",
" <td>4.958214</td>\n",
" <td>0.190462</td>\n",
" <td>poor</td>\n",
" <td>1.67</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>666.67</td>\n",
" <td>1166.67</td>\n",
" <td>916.67</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4.99</td>\n",
" <td>0.29</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>642.86</td>\n",
" <td>1142.86</td>\n",
" <td>892.86</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>9.92</td>\n",
" <td>6.73</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2600</td>\n",
" <td>14</td>\n",
" <td>516.67</td>\n",
" <td>916.67</td>\n",
" <td>716.67</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>12.99</td>\n",
" <td>12.75</td>\n",
" <td>4</td>\n",
" <td>100200</td>\n",
" <td>7</td>\n",
" <td>52550</td>\n",
" <td>24</td>\n",
" <td>563.64</td>\n",
" <td>977.27</td>\n",
" <td>770.45</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>12.14</td>\n",
" <td>26.46</td>\n",
" <td>8</td>\n",
" <td>110856</td>\n",
" <td>7</td>\n",
" <td>52550</td>\n",
" <td>41</td>\n",
" <td>697.44</td>\n",
" <td>1192.31</td>\n",
" <td>944.87</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>17</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>13.69</td>\n",
" <td>21.58</td>\n",
" <td>43</td>\n",
" <td>1478160</td>\n",
" <td>35</td>\n",
" <td>1572990</td>\n",
" <td>122</td>\n",
" <td>702.68</td>\n",
" <td>1196.43</td>\n",
" <td>949.55</td>\n",
" <td>10</td>\n",
" <td>29</td>\n",
" <td>45</td>\n",
" <td>25</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>5700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-09-01</td>\n",
" <td>89</td>\n",
" <td>50.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Mitino</td>\n",
" <td>1.258354e+07</td>\n",
" <td>178473</td>\n",
" <td>0.194703</td>\n",
" <td>0.069753</td>\n",
" <td>13087</td>\n",
" <td>6839.0</td>\n",
" <td>9</td>\n",
" <td>13670</td>\n",
" <td>17063.0</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>6</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>21155</td>\n",
" <td>9828</td>\n",
" <td>11327</td>\n",
" <td>28563</td>\n",
" <td>14680</td>\n",
" <td>13883</td>\n",
" <td>120381</td>\n",
" <td>60040</td>\n",
" <td>60341</td>\n",
" <td>29529</td>\n",
" <td>9083</td>\n",
" <td>20446</td>\n",
" <td>13087</td>\n",
" <td>6645</td>\n",
" <td>6442</td>\n",
" <td>13670</td>\n",
" <td>7126</td>\n",
" <td>6544</td>\n",
" <td>32063</td>\n",
" <td>16513</td>\n",
" <td>15550</td>\n",
" <td>3292</td>\n",
" <td>1450</td>\n",
" <td>1842</td>\n",
" <td>24934</td>\n",
" <td>12782</td>\n",
" <td>12152</td>\n",
" <td>458.0</td>\n",
" <td>9.0</td>\n",
" <td>51.0</td>\n",
" <td>12.0</td>\n",
" <td>124.0</td>\n",
" <td>50.0</td>\n",
" <td>201.0</td>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>2.0</td>\n",
" <td>459.0</td>\n",
" <td>13.0</td>\n",
" <td>24.0</td>\n",
" <td>40.0</td>\n",
" <td>130.0</td>\n",
" <td>252.0</td>\n",
" <td>4</td>\n",
" <td>1.489049</td>\n",
" <td>0.984537</td>\n",
" <td>11.565624</td>\n",
" <td>0.963802</td>\n",
" <td>0.179441</td>\n",
" <td>0.236455</td>\n",
" <td>0.078090</td>\n",
" <td>0.106125</td>\n",
" <td>0.451173</td>\n",
" <td>15.623710</td>\n",
" <td>2.017080</td>\n",
" <td>14.317640</td>\n",
" <td>4.291432</td>\n",
" <td>51.497190</td>\n",
" <td>4.0</td>\n",
" <td>3.816045</td>\n",
" <td>5.271136</td>\n",
" <td>4</td>\n",
" <td>0.131597</td>\n",
" <td>1.579164</td>\n",
" <td>1.200336</td>\n",
" <td>no</td>\n",
" <td>2.677824</td>\n",
" <td>14.606501</td>\n",
" <td>17.457198</td>\n",
" <td>18.309433</td>\n",
" <td>19.487005</td>\n",
" <td>2.677824</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>2.780449</td>\n",
" <td>17</td>\n",
" <td>1.999265</td>\n",
" <td>no</td>\n",
" <td>17.478380</td>\n",
" <td>83</td>\n",
" <td>6.734618</td>\n",
" <td>1</td>\n",
" <td>27.667863</td>\n",
" <td>9.522538</td>\n",
" <td>6.348716</td>\n",
" <td>1.767612</td>\n",
" <td>11.178333</td>\n",
" <td>0.583025</td>\n",
" <td>27.892717</td>\n",
" <td>0.811275</td>\n",
" <td>0.623484</td>\n",
" <td>1.950317</td>\n",
" <td>6.483172</td>\n",
" <td>7.385521</td>\n",
" <td>4.923843</td>\n",
" <td>3.549558</td>\n",
" <td>8.789894</td>\n",
" <td>2.163735</td>\n",
" <td>10.903161</td>\n",
" <td>0.622272</td>\n",
" <td>0.599914</td>\n",
" <td>0.934273</td>\n",
" <td>0.892674</td>\n",
" <td>0.236455</td>\n",
" <td>1.031777</td>\n",
" <td>1.561505</td>\n",
" <td>15.300449</td>\n",
" <td>16.990677</td>\n",
" <td>16.041521</td>\n",
" <td>5.029696</td>\n",
" <td>0.465820</td>\n",
" <td>good</td>\n",
" <td>17.36</td>\n",
" <td>0.57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1000.00</td>\n",
" <td>1500.00</td>\n",
" <td>1250.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19.25</td>\n",
" <td>10.35</td>\n",
" <td>1</td>\n",
" <td>11000</td>\n",
" <td>6</td>\n",
" <td>80780</td>\n",
" <td>12</td>\n",
" <td>658.33</td>\n",
" <td>1083.33</td>\n",
" <td>870.83</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>28.38</td>\n",
" <td>6.57</td>\n",
" <td>2</td>\n",
" <td>11000</td>\n",
" <td>7</td>\n",
" <td>89492</td>\n",
" <td>23</td>\n",
" <td>673.91</td>\n",
" <td>1130.43</td>\n",
" <td>902.17</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>32.29</td>\n",
" <td>5.73</td>\n",
" <td>2</td>\n",
" <td>11000</td>\n",
" <td>7</td>\n",
" <td>89492</td>\n",
" <td>25</td>\n",
" <td>660.00</td>\n",
" <td>1120.00</td>\n",
" <td>890.00</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>20.79</td>\n",
" <td>3.57</td>\n",
" <td>4</td>\n",
" <td>167000</td>\n",
" <td>12</td>\n",
" <td>205756</td>\n",
" <td>32</td>\n",
" <td>718.75</td>\n",
" <td>1218.75</td>\n",
" <td>968.75</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14.18</td>\n",
" <td>3.89</td>\n",
" <td>8</td>\n",
" <td>244166</td>\n",
" <td>22</td>\n",
" <td>942180</td>\n",
" <td>61</td>\n",
" <td>931.58</td>\n",
" <td>1552.63</td>\n",
" <td>1242.11</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>13100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-09-05</td>\n",
" <td>77</td>\n",
" <td>77.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Basmannoe</td>\n",
" <td>8.398461e+06</td>\n",
" <td>108171</td>\n",
" <td>0.015234</td>\n",
" <td>0.037316</td>\n",
" <td>5706</td>\n",
" <td>3240.0</td>\n",
" <td>7</td>\n",
" <td>6748</td>\n",
" <td>7770.0</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>562.0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>93</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>28179</td>\n",
" <td>13522</td>\n",
" <td>14657</td>\n",
" <td>13368</td>\n",
" <td>7159</td>\n",
" <td>6209</td>\n",
" <td>68043</td>\n",
" <td>34236</td>\n",
" <td>33807</td>\n",
" <td>26760</td>\n",
" <td>8563</td>\n",
" <td>18197</td>\n",
" <td>5706</td>\n",
" <td>2982</td>\n",
" <td>2724</td>\n",
" <td>6748</td>\n",
" <td>3664</td>\n",
" <td>3084</td>\n",
" <td>15237</td>\n",
" <td>8113</td>\n",
" <td>7124</td>\n",
" <td>5164</td>\n",
" <td>2583</td>\n",
" <td>2581</td>\n",
" <td>11631</td>\n",
" <td>6223</td>\n",
" <td>5408</td>\n",
" <td>746.0</td>\n",
" <td>48.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>643.0</td>\n",
" <td>16.0</td>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>746.0</td>\n",
" <td>371.0</td>\n",
" <td>114.0</td>\n",
" <td>146.0</td>\n",
" <td>62.0</td>\n",
" <td>53.0</td>\n",
" <td>5</td>\n",
" <td>1.257186</td>\n",
" <td>0.876620</td>\n",
" <td>8.266305</td>\n",
" <td>0.688859</td>\n",
" <td>0.247901</td>\n",
" <td>0.376838</td>\n",
" <td>0.258289</td>\n",
" <td>0.236214</td>\n",
" <td>0.392871</td>\n",
" <td>10.683540</td>\n",
" <td>2.936581</td>\n",
" <td>11.903910</td>\n",
" <td>0.853960</td>\n",
" <td>10.247521</td>\n",
" <td>5.0</td>\n",
" <td>1.595898</td>\n",
" <td>2.156284</td>\n",
" <td>113</td>\n",
" <td>0.071480</td>\n",
" <td>0.857764</td>\n",
" <td>0.820294</td>\n",
" <td>no</td>\n",
" <td>11.616653</td>\n",
" <td>1.721834</td>\n",
" <td>0.046810</td>\n",
" <td>0.787593</td>\n",
" <td>2.578671</td>\n",
" <td>1.721834</td>\n",
" <td>4</td>\n",
" <td>no</td>\n",
" <td>3.133531</td>\n",
" <td>10</td>\n",
" <td>0.084113</td>\n",
" <td>yes</td>\n",
" <td>1.595898</td>\n",
" <td>113</td>\n",
" <td>1.423428</td>\n",
" <td>4</td>\n",
" <td>6.515857</td>\n",
" <td>8.671016</td>\n",
" <td>1.638318</td>\n",
" <td>3.632640</td>\n",
" <td>4.587917</td>\n",
" <td>2.609420</td>\n",
" <td>9.155057</td>\n",
" <td>1.969738</td>\n",
" <td>0.220288</td>\n",
" <td>2.544696</td>\n",
" <td>3.975401</td>\n",
" <td>3.610754</td>\n",
" <td>0.307915</td>\n",
" <td>1.864637</td>\n",
" <td>3.779781</td>\n",
" <td>1.121703</td>\n",
" <td>0.991683</td>\n",
" <td>0.892668</td>\n",
" <td>0.429052</td>\n",
" <td>0.077901</td>\n",
" <td>0.810801</td>\n",
" <td>0.376838</td>\n",
" <td>0.378756</td>\n",
" <td>0.121681</td>\n",
" <td>2.584370</td>\n",
" <td>1.112486</td>\n",
" <td>1.800125</td>\n",
" <td>1.339652</td>\n",
" <td>0.026102</td>\n",
" <td>excellent</td>\n",
" <td>3.56</td>\n",
" <td>4.44</td>\n",
" <td>15</td>\n",
" <td>293699</td>\n",
" <td>1</td>\n",
" <td>45000</td>\n",
" <td>48</td>\n",
" <td>702.22</td>\n",
" <td>1166.67</td>\n",
" <td>934.44</td>\n",
" <td>3</td>\n",
" <td>17</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3.34</td>\n",
" <td>8.29</td>\n",
" <td>46</td>\n",
" <td>420952</td>\n",
" <td>3</td>\n",
" <td>158200</td>\n",
" <td>153</td>\n",
" <td>763.45</td>\n",
" <td>1272.41</td>\n",
" <td>1017.93</td>\n",
" <td>8</td>\n",
" <td>39</td>\n",
" <td>45</td>\n",
" <td>39</td>\n",
" <td>19</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>4.12</td>\n",
" <td>4.83</td>\n",
" <td>93</td>\n",
" <td>1195735</td>\n",
" <td>9</td>\n",
" <td>445900</td>\n",
" <td>272</td>\n",
" <td>766.80</td>\n",
" <td>1272.73</td>\n",
" <td>1019.76</td>\n",
" <td>19</td>\n",
" <td>70</td>\n",
" <td>74</td>\n",
" <td>72</td>\n",
" <td>30</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>4.53</td>\n",
" <td>5.02</td>\n",
" <td>149</td>\n",
" <td>1625130</td>\n",
" <td>17</td>\n",
" <td>564843</td>\n",
" <td>483</td>\n",
" <td>765.93</td>\n",
" <td>1269.23</td>\n",
" <td>1017.58</td>\n",
" <td>28</td>\n",
" <td>130</td>\n",
" <td>129</td>\n",
" <td>131</td>\n",
" <td>50</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>35</td>\n",
" <td>61</td>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>3</td>\n",
" <td>5.06</td>\n",
" <td>8.62</td>\n",
" <td>305</td>\n",
" <td>3420907</td>\n",
" <td>60</td>\n",
" <td>2296870</td>\n",
" <td>1068</td>\n",
" <td>853.03</td>\n",
" <td>1410.45</td>\n",
" <td>1131.74</td>\n",
" <td>63</td>\n",
" <td>266</td>\n",
" <td>267</td>\n",
" <td>262</td>\n",
" <td>149</td>\n",
" <td>57</td>\n",
" <td>4</td>\n",
" <td>70</td>\n",
" <td>121</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>77</td>\n",
" <td>5</td>\n",
" <td>8.38</td>\n",
" <td>10.92</td>\n",
" <td>689</td>\n",
" <td>8404624</td>\n",
" <td>114</td>\n",
" <td>3503058</td>\n",
" <td>2283</td>\n",
" <td>853.88</td>\n",
" <td>1411.45</td>\n",
" <td>1132.66</td>\n",
" <td>143</td>\n",
" <td>566</td>\n",
" <td>578</td>\n",
" <td>552</td>\n",
" <td>319</td>\n",
" <td>108</td>\n",
" <td>17</td>\n",
" <td>135</td>\n",
" <td>236</td>\n",
" <td>2</td>\n",
" <td>91</td>\n",
" <td>195</td>\n",
" <td>14</td>\n",
" <td>16331452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material build_year \\\n",
"0 1 2011-08-20 43 27.0 4.0 NaN NaN NaN \n",
"1 2 2011-08-23 34 19.0 3.0 NaN NaN NaN \n",
"2 3 2011-08-27 43 29.0 2.0 NaN NaN NaN \n",
"3 4 2011-09-01 89 50.0 9.0 NaN NaN NaN \n",
"4 5 2011-09-05 77 77.0 4.0 NaN NaN NaN \n",
"\n",
" num_room kitch_sq state product_type sub_area area_m \\\n",
"0 NaN NaN NaN Investment Bibirevo 6.407578e+06 \n",
"1 NaN NaN NaN Investment Nagatinskij Zaton 9.589337e+06 \n",
"2 NaN NaN NaN Investment Tekstil'shhiki 4.808270e+06 \n",
"3 NaN NaN NaN Investment Mitino 1.258354e+07 \n",
"4 NaN NaN NaN Investment Basmannoe 8.398461e+06 \n",
"\n",
" raion_popul green_zone_part indust_part children_preschool \\\n",
"0 155572 0.189727 0.000070 9576 \n",
"1 115352 0.372602 0.049637 6880 \n",
"2 101708 0.112560 0.118537 5879 \n",
"3 178473 0.194703 0.069753 13087 \n",
"4 108171 0.015234 0.037316 5706 \n",
"\n",
" preschool_quota preschool_education_centers_raion children_school \\\n",
"0 5001.0 5 10309 \n",
"1 3119.0 5 7759 \n",
"2 1463.0 4 6207 \n",
"3 6839.0 9 13670 \n",
"4 3240.0 7 6748 \n",
"\n",
" school_quota school_education_centers_raion \\\n",
"0 11065.0 5 \n",
"1 6237.0 8 \n",
"2 5580.0 7 \n",
"3 17063.0 10 \n",
"4 7770.0 9 \n",
"\n",
" school_education_centers_top_20_raion hospital_beds_raion \\\n",
"0 0 240.0 \n",
"1 0 229.0 \n",
"2 0 1183.0 \n",
"3 0 NaN \n",
"4 0 562.0 \n",
"\n",
" healthcare_centers_raion university_top_20_raion sport_objects_raion \\\n",
"0 1 0 7 \n",
"1 1 0 6 \n",
"2 1 0 5 \n",
"3 1 0 17 \n",
"4 4 2 25 \n",
"\n",
" additional_education_raion culture_objects_top_25 \\\n",
"0 3 no \n",
"1 1 yes \n",
"2 1 no \n",
"3 6 no \n",
"4 2 no \n",
"\n",
" culture_objects_top_25_raion shopping_centers_raion office_raion \\\n",
"0 0 16 1 \n",
"1 1 3 0 \n",
"2 0 0 1 \n",
"3 0 11 4 \n",
"4 0 10 93 \n",
"\n",
" thermal_power_plant_raion incineration_raion oil_chemistry_raion \\\n",
"0 no no no \n",
"1 no no no \n",
"2 no no no \n",
"3 no no no \n",
"4 no no no \n",
"\n",
" radiation_raion railroad_terminal_raion big_market_raion \\\n",
"0 no no no \n",
"1 no no no \n",
"2 yes no no \n",
"3 no no no \n",
"4 yes yes no \n",
"\n",
" nuclear_reactor_raion detention_facility_raion full_all male_f female_f \\\n",
"0 no no 86206 40477 45729 \n",
"1 no no 76284 34200 42084 \n",
"2 no no 101982 46076 55906 \n",
"3 no no 21155 9828 11327 \n",
"4 no no 28179 13522 14657 \n",
"\n",
" young_all young_male young_female work_all work_male work_female \\\n",
"0 21154 11007 10147 98207 52277 45930 \n",
"1 15727 7925 7802 70194 35622 34572 \n",
"2 13028 6835 6193 63388 31813 31575 \n",
"3 28563 14680 13883 120381 60040 60341 \n",
"4 13368 7159 6209 68043 34236 33807 \n",
"\n",
" ekder_all ekder_male ekder_female 0_6_all 0_6_male 0_6_female \\\n",
"0 36211 10580 25631 9576 4899 4677 \n",
"1 29431 9266 20165 6880 3466 3414 \n",
"2 25292 7609 17683 5879 3095 2784 \n",
"3 29529 9083 20446 13087 6645 6442 \n",
"4 26760 8563 18197 5706 2982 2724 \n",
"\n",
" 7_14_all 7_14_male 7_14_female 0_17_all 0_17_male 0_17_female \\\n",
"0 10309 5463 4846 23603 12286 11317 \n",
"1 7759 3909 3850 17700 8998 8702 \n",
"2 6207 3269 2938 14884 7821 7063 \n",
"3 13670 7126 6544 32063 16513 15550 \n",
"4 6748 3664 3084 15237 8113 7124 \n",
"\n",
" 16_29_all 16_29_male 16_29_female 0_13_all 0_13_male 0_13_female \\\n",
"0 17508 9425 8083 18654 9709 8945 \n",
"1 15164 7571 7593 13729 6929 6800 \n",
"2 19401 9045 10356 11252 5916 5336 \n",
"3 3292 1450 1842 24934 12782 12152 \n",
"4 5164 2583 2581 11631 6223 5408 \n",
"\n",
" raion_build_count_with_material_info build_count_block build_count_wood \\\n",
"0 211.0 25.0 0.0 \n",
"1 245.0 83.0 1.0 \n",
"2 330.0 59.0 0.0 \n",
"3 458.0 9.0 51.0 \n",
"4 746.0 48.0 0.0 \n",
"\n",
" build_count_frame build_count_brick build_count_monolith \\\n",
"0 0.0 0.0 2.0 \n",
"1 0.0 67.0 4.0 \n",
"2 0.0 206.0 4.0 \n",
"3 12.0 124.0 50.0 \n",
"4 0.0 643.0 16.0 \n",
"\n",
" build_count_panel build_count_foam build_count_slag build_count_mix \\\n",
"0 184.0 0.0 0.0 0.0 \n",
"1 90.0 0.0 0.0 0.0 \n",
"2 60.0 0.0 1.0 0.0 \n",
"3 201.0 0.0 9.0 2.0 \n",
"4 35.0 0.0 3.0 1.0 \n",
"\n",
" raion_build_count_with_builddate_info build_count_before_1920 \\\n",
"0 211.0 0.0 \n",
"1 244.0 1.0 \n",
"2 330.0 1.0 \n",
"3 459.0 13.0 \n",
"4 746.0 371.0 \n",
"\n",
" build_count_1921-1945 build_count_1946-1970 build_count_1971-1995 \\\n",
"0 0.0 0.0 206.0 \n",
"1 1.0 143.0 84.0 \n",
"2 0.0 246.0 63.0 \n",
"3 24.0 40.0 130.0 \n",
"4 114.0 146.0 62.0 \n",
"\n",
" build_count_after_1995 ID_metro metro_min_avto metro_km_avto \\\n",
"0 5.0 1 2.590241 1.131260 \n",
"1 15.0 2 0.936700 0.647337 \n",
"2 20.0 3 2.120999 1.637996 \n",
"3 252.0 4 1.489049 0.984537 \n",
"4 53.0 5 1.257186 0.876620 \n",
"\n",
" metro_min_walk metro_km_walk kindergarten_km school_km park_km \\\n",
"0 13.575119 1.131260 0.145700 0.177975 2.158587 \n",
"1 7.620630 0.635053 0.147754 0.273345 0.550690 \n",
"2 17.351515 1.445960 0.049102 0.158072 0.374848 \n",
"3 11.565624 0.963802 0.179441 0.236455 0.078090 \n",
"4 8.266305 0.688859 0.247901 0.376838 0.258289 \n",
"\n",
" green_zone_km industrial_km water_treatment_km cemetery_km \\\n",
"0 0.600973 1.080934 23.683460 1.804127 \n",
"1 0.065321 0.966479 1.317476 4.655004 \n",
"2 0.453172 0.939275 4.912660 3.381083 \n",
"3 0.106125 0.451173 15.623710 2.017080 \n",
"4 0.236214 0.392871 10.683540 2.936581 \n",
"\n",
" incineration_km railroad_station_walk_km railroad_station_walk_min \\\n",
"0 3.633334 5.419893 65.038716 \n",
"1 8.648587 3.411993 40.943917 \n",
"2 11.996480 1.277658 15.331896 \n",
"3 14.317640 4.291432 51.497190 \n",
"4 11.903910 0.853960 10.247521 \n",
"\n",
" ID_railroad_station_walk railroad_station_avto_km \\\n",
"0 1.0 5.419893 \n",
"1 2.0 3.641773 \n",
"2 3.0 1.277658 \n",
"3 4.0 3.816045 \n",
"4 5.0 1.595898 \n",
"\n",
" railroad_station_avto_min ID_railroad_station_avto \\\n",
"0 6.905893 1 \n",
"1 4.679745 2 \n",
"2 1.701420 3 \n",
"3 5.271136 4 \n",
"4 2.156284 113 \n",
"\n",
" public_transport_station_km public_transport_station_min_walk water_km \\\n",
"0 0.274985 3.299822 0.992631 \n",
"1 0.065263 0.783160 0.698081 \n",
"2 0.328756 3.945073 0.468265 \n",
"3 0.131597 1.579164 1.200336 \n",
"4 0.071480 0.857764 0.820294 \n",
"\n",
" water_1line mkad_km ttk_km sadovoe_km bulvar_ring_km kremlin_km \\\n",
"0 no 1.422391 10.918587 13.100618 13.675657 15.156211 \n",
"1 no 9.503405 3.103996 6.444333 8.132640 8.698054 \n",
"2 no 5.604800 2.927487 6.963403 8.054252 9.067885 \n",
"3 no 2.677824 14.606501 17.457198 18.309433 19.487005 \n",
"4 no 11.616653 1.721834 0.046810 0.787593 2.578671 \n",
"\n",
" big_road1_km ID_big_road1 big_road1_1line big_road2_km ID_big_road2 \\\n",
"0 1.422391 1 no 3.830951 5 \n",
"1 2.887377 2 no 3.103996 4 \n",
"2 0.647250 3 no 2.927487 4 \n",
"3 2.677824 1 no 2.780449 17 \n",
"4 1.721834 4 no 3.133531 10 \n",
"\n",
" railroad_km railroad_1line zd_vokzaly_avto_km ID_railroad_terminal \\\n",
"0 1.305159 no 14.231961 101 \n",
"1 0.694536 no 9.242586 32 \n",
"2 0.700691 no 9.540544 5 \n",
"3 1.999265 no 17.478380 83 \n",
"4 0.084113 yes 1.595898 113 \n",
"\n",
" bus_terminal_avto_km ID_bus_terminal oil_chemistry_km \\\n",
"0 24.292406 1 18.152338 \n",
"1 5.706113 2 9.034642 \n",
"2 6.710302 3 5.777394 \n",
"3 6.734618 1 27.667863 \n",
"4 1.423428 4 6.515857 \n",
"\n",
" nuclear_reactor_km radiation_km power_transmission_line_km \\\n",
"0 5.718519 1.210027 1.062513 \n",
"1 3.489954 2.724295 1.246149 \n",
"2 7.506612 0.772216 1.602183 \n",
"3 9.522538 6.348716 1.767612 \n",
"4 8.671016 1.638318 3.632640 \n",
"\n",
" thermal_power_plant_km ts_km big_market_km market_shop_km \\\n",
"0 5.814135 4.308127 10.814172 1.676258 \n",
"1 3.419574 0.725560 6.910568 3.424716 \n",
"2 3.682455 3.562188 5.752368 1.375443 \n",
"3 11.178333 0.583025 27.892717 0.811275 \n",
"4 4.587917 2.609420 9.155057 1.969738 \n",
"\n",
" fitness_km swim_pool_km ice_rink_km stadium_km basketball_km \\\n",
"0 0.485841 3.065047 1.107594 8.148591 3.516513 \n",
"1 0.668364 2.000154 8.972823 6.127073 1.161579 \n",
"2 0.733101 1.239304 1.978517 0.767569 1.952771 \n",
"3 0.623484 1.950317 6.483172 7.385521 4.923843 \n",
"4 0.220288 2.544696 3.975401 3.610754 0.307915 \n",
"\n",
" hospice_morgue_km detention_facility_km public_healthcare_km \\\n",
"0 2.392353 4.248036 0.974743 \n",
"1 2.543747 12.649879 1.477723 \n",
"2 0.621357 7.682303 0.097144 \n",
"3 3.549558 8.789894 2.163735 \n",
"4 1.864637 3.779781 1.121703 \n",
"\n",
" university_km workplaces_km shopping_centers_km office_km \\\n",
"0 6.715026 0.884350 0.648488 0.637189 \n",
"1 1.852560 0.686252 0.519311 0.688796 \n",
"2 0.841254 1.510089 1.486533 1.543049 \n",
"3 10.903161 0.622272 0.599914 0.934273 \n",
"4 0.991683 0.892668 0.429052 0.077901 \n",
"\n",
" additional_education_km preschool_km big_church_km church_synagogue_km \\\n",
"0 0.947962 0.177975 0.625783 0.628187 \n",
"1 1.072315 0.273345 0.967821 0.471447 \n",
"2 0.391957 0.158072 3.178751 0.755946 \n",
"3 0.892674 0.236455 1.031777 1.561505 \n",
"4 0.810801 0.376838 0.378756 0.121681 \n",
"\n",
" mosque_km theater_km museum_km exhibition_km catering_km ecology \\\n",
"0 3.932040 14.053047 7.389498 7.023705 0.516838 good \n",
"1 4.841544 6.829889 0.709260 2.358840 0.230287 excellent \n",
"2 7.922152 4.273200 3.156423 4.958214 0.190462 poor \n",
"3 15.300449 16.990677 16.041521 5.029696 0.465820 good \n",
"4 2.584370 1.112486 1.800125 1.339652 0.026102 excellent \n",
"\n",
" green_part_500 prom_part_500 office_count_500 office_sqm_500 \\\n",
"0 0.00 0.00 0 0 \n",
"1 25.14 0.00 0 0 \n",
"2 1.67 0.00 0 0 \n",
"3 17.36 0.57 0 0 \n",
"4 3.56 4.44 15 293699 \n",
"\n",
" trc_count_500 trc_sqm_500 cafe_count_500 cafe_sum_500_min_price_avg \\\n",
"0 0 0 0 NaN \n",
"1 0 0 5 860.00 \n",
"2 0 0 3 666.67 \n",
"3 0 0 2 1000.00 \n",
"4 1 45000 48 702.22 \n",
"\n",
" cafe_sum_500_max_price_avg cafe_avg_price_500 cafe_count_500_na_price \\\n",
"0 NaN NaN 0 \n",
"1 1500.00 1180.00 0 \n",
"2 1166.67 916.67 0 \n",
"3 1500.00 1250.00 0 \n",
"4 1166.67 934.44 3 \n",
"\n",
" cafe_count_500_price_500 cafe_count_500_price_1000 \\\n",
"0 0 0 \n",
"1 1 3 \n",
"2 0 2 \n",
"3 0 0 \n",
"4 17 10 \n",
"\n",
" cafe_count_500_price_1500 cafe_count_500_price_2500 \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 1 0 \n",
"3 2 0 \n",
"4 11 7 \n",
"\n",
" cafe_count_500_price_4000 cafe_count_500_price_high big_church_count_500 \\\n",
"0 0 0 0 \n",
"1 1 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 1 \n",
"\n",
" church_count_500 mosque_count_500 leisure_count_500 sport_count_500 \\\n",
"0 0 0 0 1 \n",
"1 1 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 4 0 2 3 \n",
"\n",
" market_count_500 green_part_1000 prom_part_1000 office_count_1000 \\\n",
"0 0 7.36 0.00 1 \n",
"1 0 26.66 0.07 2 \n",
"2 0 4.99 0.29 0 \n",
"3 0 19.25 10.35 1 \n",
"4 0 3.34 8.29 46 \n",
"\n",
" office_sqm_1000 trc_count_1000 trc_sqm_1000 cafe_count_1000 \\\n",
"0 30500 3 55600 19 \n",
"1 86600 5 94065 13 \n",
"2 0 0 0 9 \n",
"3 11000 6 80780 12 \n",
"4 420952 3 158200 153 \n",
"\n",
" cafe_sum_1000_min_price_avg cafe_sum_1000_max_price_avg \\\n",
"0 527.78 888.89 \n",
"1 615.38 1076.92 \n",
"2 642.86 1142.86 \n",
"3 658.33 1083.33 \n",
"4 763.45 1272.41 \n",
"\n",
" cafe_avg_price_1000 cafe_count_1000_na_price cafe_count_1000_price_500 \\\n",
"0 708.33 1 10 \n",
"1 846.15 0 5 \n",
"2 892.86 2 0 \n",
"3 870.83 0 3 \n",
"4 1017.93 8 39 \n",
"\n",
" cafe_count_1000_price_1000 cafe_count_1000_price_1500 \\\n",
"0 4 3 \n",
"1 6 1 \n",
"2 5 2 \n",
"3 4 5 \n",
"4 45 39 \n",
"\n",
" cafe_count_1000_price_2500 cafe_count_1000_price_4000 \\\n",
"0 1 0 \n",
"1 0 1 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 19 2 \n",
"\n",
" cafe_count_1000_price_high big_church_count_1000 church_count_1000 \\\n",
"0 0 1 2 \n",
"1 0 1 2 \n",
"2 0 0 1 \n",
"3 0 0 0 \n",
"4 1 7 12 \n",
"\n",
" mosque_count_1000 leisure_count_1000 sport_count_1000 market_count_1000 \\\n",
"0 0 0 6 1 \n",
"1 0 4 2 0 \n",
"2 0 0 5 3 \n",
"3 0 0 3 1 \n",
"4 0 6 7 0 \n",
"\n",
" green_part_1500 prom_part_1500 office_count_1500 office_sqm_1500 \\\n",
"0 14.27 6.92 3 39554 \n",
"1 21.53 7.71 3 102910 \n",
"2 9.92 6.73 0 0 \n",
"3 28.38 6.57 2 11000 \n",
"4 4.12 4.83 93 1195735 \n",
"\n",
" trc_count_1500 trc_sqm_1500 cafe_count_1500 cafe_sum_1500_min_price_avg \\\n",
"0 9 171420 34 566.67 \n",
"1 7 127065 17 694.12 \n",
"2 1 2600 14 516.67 \n",
"3 7 89492 23 673.91 \n",
"4 9 445900 272 766.80 \n",
"\n",
" cafe_sum_1500_max_price_avg cafe_avg_price_1500 cafe_count_1500_na_price \\\n",
"0 969.70 768.18 1 \n",
"1 1205.88 950.00 0 \n",
"2 916.67 716.67 2 \n",
"3 1130.43 902.17 0 \n",
"4 1272.73 1019.76 19 \n",
"\n",
" cafe_count_1500_price_500 cafe_count_1500_price_1000 \\\n",
"0 14 11 \n",
"1 6 7 \n",
"2 4 6 \n",
"3 5 9 \n",
"4 70 74 \n",
"\n",
" cafe_count_1500_price_1500 cafe_count_1500_price_2500 \\\n",
"0 6 2 \n",
"1 1 2 \n",
"2 2 0 \n",
"3 8 1 \n",
"4 72 30 \n",
"\n",
" cafe_count_1500_price_4000 cafe_count_1500_price_high \\\n",
"0 0 0 \n",
"1 1 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 6 1 \n",
"\n",
" big_church_count_1500 church_count_1500 mosque_count_1500 \\\n",
"0 1 2 0 \n",
"1 1 5 0 \n",
"2 0 4 0 \n",
"3 1 0 0 \n",
"4 18 30 0 \n",
"\n",
" leisure_count_1500 sport_count_1500 market_count_1500 green_part_2000 \\\n",
"0 0 7 1 11.77 \n",
"1 4 9 0 22.37 \n",
"2 0 6 5 12.99 \n",
"3 0 9 2 32.29 \n",
"4 10 14 2 4.53 \n",
"\n",
" prom_part_2000 office_count_2000 office_sqm_2000 trc_count_2000 \\\n",
"0 15.97 9 188854 19 \n",
"1 19.25 4 165510 8 \n",
"2 12.75 4 100200 7 \n",
"3 5.73 2 11000 7 \n",
"4 5.02 149 1625130 17 \n",
"\n",
" trc_sqm_2000 cafe_count_2000 cafe_sum_2000_min_price_avg \\\n",
"0 1244891 36 614.29 \n",
"1 179065 21 695.24 \n",
"2 52550 24 563.64 \n",
"3 89492 25 660.00 \n",
"4 564843 483 765.93 \n",
"\n",
" cafe_sum_2000_max_price_avg cafe_avg_price_2000 cafe_count_2000_na_price \\\n",
"0 1042.86 828.57 1 \n",
"1 1190.48 942.86 0 \n",
"2 977.27 770.45 2 \n",
"3 1120.00 890.00 0 \n",
"4 1269.23 1017.58 28 \n",
"\n",
" cafe_count_2000_price_500 cafe_count_2000_price_1000 \\\n",
"0 15 11 \n",
"1 7 8 \n",
"2 8 9 \n",
"3 5 11 \n",
"4 130 129 \n",
"\n",
" cafe_count_2000_price_1500 cafe_count_2000_price_2500 \\\n",
"0 6 2 \n",
"1 3 2 \n",
"2 4 1 \n",
"3 8 1 \n",
"4 131 50 \n",
"\n",
" cafe_count_2000_price_4000 cafe_count_2000_price_high \\\n",
"0 1 0 \n",
"1 1 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 14 1 \n",
"\n",
" big_church_count_2000 church_count_2000 mosque_count_2000 \\\n",
"0 1 2 0 \n",
"1 1 5 0 \n",
"2 0 4 0 \n",
"3 1 1 0 \n",
"4 35 61 0 \n",
"\n",
" leisure_count_2000 sport_count_2000 market_count_2000 green_part_3000 \\\n",
"0 0 10 1 11.98 \n",
"1 4 11 0 18.07 \n",
"2 0 8 5 12.14 \n",
"3 0 13 2 20.79 \n",
"4 17 21 3 5.06 \n",
"\n",
" prom_part_3000 office_count_3000 office_sqm_3000 trc_count_3000 \\\n",
"0 13.55 12 251554 23 \n",
"1 27.32 12 821986 14 \n",
"2 26.46 8 110856 7 \n",
"3 3.57 4 167000 12 \n",
"4 8.62 305 3420907 60 \n",
"\n",
" trc_sqm_3000 cafe_count_3000 cafe_sum_3000_min_price_avg \\\n",
"0 1419204 68 639.68 \n",
"1 491565 30 631.03 \n",
"2 52550 41 697.44 \n",
"3 205756 32 718.75 \n",
"4 2296870 1068 853.03 \n",
"\n",
" cafe_sum_3000_max_price_avg cafe_avg_price_3000 cafe_count_3000_na_price \\\n",
"0 1079.37 859.52 5 \n",
"1 1086.21 858.62 1 \n",
"2 1192.31 944.87 2 \n",
"3 1218.75 968.75 0 \n",
"4 1410.45 1131.74 63 \n",
"\n",
" cafe_count_3000_price_500 cafe_count_3000_price_1000 \\\n",
"0 21 22 \n",
"1 11 11 \n",
"2 9 17 \n",
"3 5 14 \n",
"4 266 267 \n",
"\n",
" cafe_count_3000_price_1500 cafe_count_3000_price_2500 \\\n",
"0 16 3 \n",
"1 4 2 \n",
"2 9 3 \n",
"3 10 3 \n",
"4 262 149 \n",
"\n",
" cafe_count_3000_price_4000 cafe_count_3000_price_high \\\n",
"0 1 0 \n",
"1 1 0 \n",
"2 1 0 \n",
"3 0 0 \n",
"4 57 4 \n",
"\n",
" big_church_count_3000 church_count_3000 mosque_count_3000 \\\n",
"0 2 4 0 \n",
"1 1 7 0 \n",
"2 0 11 0 \n",
"3 1 2 0 \n",
"4 70 121 1 \n",
"\n",
" leisure_count_3000 sport_count_3000 market_count_3000 green_part_5000 \\\n",
"0 0 21 1 13.09 \n",
"1 6 19 1 10.26 \n",
"2 0 20 6 13.69 \n",
"3 0 18 3 14.18 \n",
"4 40 77 5 8.38 \n",
"\n",
" prom_part_5000 office_count_5000 office_sqm_5000 trc_count_5000 \\\n",
"0 13.31 29 807385 52 \n",
"1 27.47 66 2690465 40 \n",
"2 21.58 43 1478160 35 \n",
"3 3.89 8 244166 22 \n",
"4 10.92 689 8404624 114 \n",
"\n",
" trc_sqm_5000 cafe_count_5000 cafe_sum_5000_min_price_avg \\\n",
"0 4036616 152 708.57 \n",
"1 2034942 177 673.81 \n",
"2 1572990 122 702.68 \n",
"3 942180 61 931.58 \n",
"4 3503058 2283 853.88 \n",
"\n",
" cafe_sum_5000_max_price_avg cafe_avg_price_5000 cafe_count_5000_na_price \\\n",
"0 1185.71 947.14 12 \n",
"1 1148.81 911.31 9 \n",
"2 1196.43 949.55 10 \n",
"3 1552.63 1242.11 4 \n",
"4 1411.45 1132.66 143 \n",
"\n",
" cafe_count_5000_price_500 cafe_count_5000_price_1000 \\\n",
"0 39 48 \n",
"1 49 65 \n",
"2 29 45 \n",
"3 7 21 \n",
"4 566 578 \n",
"\n",
" cafe_count_5000_price_1500 cafe_count_5000_price_2500 \\\n",
"0 40 9 \n",
"1 36 15 \n",
"2 25 10 \n",
"3 15 11 \n",
"4 552 319 \n",
"\n",
" cafe_count_5000_price_4000 cafe_count_5000_price_high \\\n",
"0 4 0 \n",
"1 3 0 \n",
"2 3 0 \n",
"3 2 1 \n",
"4 108 17 \n",
"\n",
" big_church_count_5000 church_count_5000 mosque_count_5000 \\\n",
"0 13 22 1 \n",
"1 15 29 1 \n",
"2 11 27 0 \n",
"3 4 4 0 \n",
"4 135 236 2 \n",
"\n",
" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
"0 0 52 4 5850000 \n",
"1 10 66 14 6000000 \n",
"2 4 67 10 5700000 \n",
"3 0 26 3 13100000 \n",
"4 91 195 14 16331452 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# for taking a look at all the columns\n",
"pd.set_option('display.max_columns',300)\n",
"df_train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1f1f8ecf-a4c7-e851-3c88-0cbc66f64162"
},
"source": [
"Wow, this is better!"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a9b8ccca-a356-883e-d503-b324b8f714d1"
},
"source": [
"Now, what matters to us is the price_doc column because that's what we have to predict. So let's see a simple scatter plot and find out if there are any outliers. A wise thing to do, right!?"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "f0fb9fb1-0d7d-587a-c653-8172f0d98d44"
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f3ed6cc0860>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8,6))\n",
"plt.scatter(range(df_train.shape[0]), np.sort(df_train.price_doc.values))\n",
"plt.xlabel(\"Houses\")\n",
"plt.ylabel(\"Price\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6e60921b-5383-1a6b-fd02-36d964a11799"
},
"source": [
"Whoa, The graph looks pretty consistent to me. Even though there seem like a couple of outliers at higher price points but there might be something behind such high prices that we don't know as of. So, we'll let it be."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "c3d8979d-167b-1777-2253-c2f726024eb0"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>state</th>\n",
" <th>product_type</th>\n",
" <th>sub_area</th>\n",
" <th>area_m</th>\n",
" <th>raion_popul</th>\n",
" <th>green_zone_part</th>\n",
" <th>indust_part</th>\n",
" <th>children_preschool</th>\n",
" <th>preschool_quota</th>\n",
" <th>preschool_education_centers_raion</th>\n",
" <th>children_school</th>\n",
" <th>school_quota</th>\n",
" <th>school_education_centers_raion</th>\n",
" <th>school_education_centers_top_20_raion</th>\n",
" <th>hospital_beds_raion</th>\n",
" <th>healthcare_centers_raion</th>\n",
" <th>university_top_20_raion</th>\n",
" <th>sport_objects_raion</th>\n",
" <th>additional_education_raion</th>\n",
" <th>culture_objects_top_25</th>\n",
" <th>culture_objects_top_25_raion</th>\n",
" <th>shopping_centers_raion</th>\n",
" <th>office_raion</th>\n",
" <th>thermal_power_plant_raion</th>\n",
" <th>incineration_raion</th>\n",
" <th>oil_chemistry_raion</th>\n",
" <th>radiation_raion</th>\n",
" <th>railroad_terminal_raion</th>\n",
" <th>big_market_raion</th>\n",
" <th>nuclear_reactor_raion</th>\n",
" <th>detention_facility_raion</th>\n",
" <th>full_all</th>\n",
" <th>male_f</th>\n",
" <th>female_f</th>\n",
" <th>young_all</th>\n",
" <th>young_male</th>\n",
" <th>young_female</th>\n",
" <th>work_all</th>\n",
" <th>work_male</th>\n",
" <th>work_female</th>\n",
" <th>ekder_all</th>\n",
" <th>ekder_male</th>\n",
" <th>ekder_female</th>\n",
" <th>0_6_all</th>\n",
" <th>0_6_male</th>\n",
" <th>0_6_female</th>\n",
" <th>7_14_all</th>\n",
" <th>7_14_male</th>\n",
" <th>7_14_female</th>\n",
" <th>0_17_all</th>\n",
" <th>0_17_male</th>\n",
" <th>0_17_female</th>\n",
" <th>16_29_all</th>\n",
" <th>16_29_male</th>\n",
" <th>16_29_female</th>\n",
" <th>0_13_all</th>\n",
" <th>0_13_male</th>\n",
" <th>0_13_female</th>\n",
" <th>raion_build_count_with_material_info</th>\n",
" <th>build_count_block</th>\n",
" <th>build_count_wood</th>\n",
" <th>build_count_frame</th>\n",
" <th>build_count_brick</th>\n",
" <th>build_count_monolith</th>\n",
" <th>build_count_panel</th>\n",
" <th>build_count_foam</th>\n",
" <th>build_count_slag</th>\n",
" <th>build_count_mix</th>\n",
" <th>raion_build_count_with_builddate_info</th>\n",
" <th>build_count_before_1920</th>\n",
" <th>build_count_1921-1945</th>\n",
" <th>build_count_1946-1970</th>\n",
" <th>build_count_1971-1995</th>\n",
" <th>build_count_after_1995</th>\n",
" <th>ID_metro</th>\n",
" <th>metro_min_avto</th>\n",
" <th>metro_km_avto</th>\n",
" <th>metro_min_walk</th>\n",
" <th>metro_km_walk</th>\n",
" <th>kindergarten_km</th>\n",
" <th>school_km</th>\n",
" <th>park_km</th>\n",
" <th>green_zone_km</th>\n",
" <th>industrial_km</th>\n",
" <th>water_treatment_km</th>\n",
" <th>cemetery_km</th>\n",
" <th>incineration_km</th>\n",
" <th>railroad_station_walk_km</th>\n",
" <th>railroad_station_walk_min</th>\n",
" <th>ID_railroad_station_walk</th>\n",
" <th>railroad_station_avto_km</th>\n",
" <th>railroad_station_avto_min</th>\n",
" <th>ID_railroad_station_avto</th>\n",
" <th>public_transport_station_km</th>\n",
" <th>public_transport_station_min_walk</th>\n",
" <th>water_km</th>\n",
" <th>water_1line</th>\n",
" <th>mkad_km</th>\n",
" <th>ttk_km</th>\n",
" <th>sadovoe_km</th>\n",
" <th>bulvar_ring_km</th>\n",
" <th>kremlin_km</th>\n",
" <th>big_road1_km</th>\n",
" <th>ID_big_road1</th>\n",
" <th>big_road1_1line</th>\n",
" <th>big_road2_km</th>\n",
" <th>ID_big_road2</th>\n",
" <th>railroad_km</th>\n",
" <th>railroad_1line</th>\n",
" <th>zd_vokzaly_avto_km</th>\n",
" <th>ID_railroad_terminal</th>\n",
" <th>bus_terminal_avto_km</th>\n",
" <th>ID_bus_terminal</th>\n",
" <th>oil_chemistry_km</th>\n",
" <th>nuclear_reactor_km</th>\n",
" <th>radiation_km</th>\n",
" <th>power_transmission_line_km</th>\n",
" <th>thermal_power_plant_km</th>\n",
" <th>ts_km</th>\n",
" <th>big_market_km</th>\n",
" <th>market_shop_km</th>\n",
" <th>fitness_km</th>\n",
" <th>swim_pool_km</th>\n",
" <th>ice_rink_km</th>\n",
" <th>stadium_km</th>\n",
" <th>basketball_km</th>\n",
" <th>hospice_morgue_km</th>\n",
" <th>detention_facility_km</th>\n",
" <th>public_healthcare_km</th>\n",
" <th>university_km</th>\n",
" <th>workplaces_km</th>\n",
" <th>shopping_centers_km</th>\n",
" <th>office_km</th>\n",
" <th>additional_education_km</th>\n",
" <th>preschool_km</th>\n",
" <th>big_church_km</th>\n",
" <th>church_synagogue_km</th>\n",
" <th>mosque_km</th>\n",
" <th>theater_km</th>\n",
" <th>museum_km</th>\n",
" <th>exhibition_km</th>\n",
" <th>catering_km</th>\n",
" <th>ecology</th>\n",
" <th>green_part_500</th>\n",
" <th>prom_part_500</th>\n",
" <th>office_count_500</th>\n",
" <th>office_sqm_500</th>\n",
" <th>trc_count_500</th>\n",
" <th>trc_sqm_500</th>\n",
" <th>cafe_count_500</th>\n",
" <th>cafe_sum_500_min_price_avg</th>\n",
" <th>cafe_sum_500_max_price_avg</th>\n",
" <th>cafe_avg_price_500</th>\n",
" <th>cafe_count_500_na_price</th>\n",
" <th>cafe_count_500_price_500</th>\n",
" <th>cafe_count_500_price_1000</th>\n",
" <th>cafe_count_500_price_1500</th>\n",
" <th>cafe_count_500_price_2500</th>\n",
" <th>cafe_count_500_price_4000</th>\n",
" <th>cafe_count_500_price_high</th>\n",
" <th>big_church_count_500</th>\n",
" <th>church_count_500</th>\n",
" <th>mosque_count_500</th>\n",
" <th>leisure_count_500</th>\n",
" <th>sport_count_500</th>\n",
" <th>market_count_500</th>\n",
" <th>green_part_1000</th>\n",
" <th>prom_part_1000</th>\n",
" <th>office_count_1000</th>\n",
" <th>office_sqm_1000</th>\n",
" <th>trc_count_1000</th>\n",
" <th>trc_sqm_1000</th>\n",
" <th>cafe_count_1000</th>\n",
" <th>cafe_sum_1000_min_price_avg</th>\n",
" <th>cafe_sum_1000_max_price_avg</th>\n",
" <th>cafe_avg_price_1000</th>\n",
" <th>cafe_count_1000_na_price</th>\n",
" <th>cafe_count_1000_price_500</th>\n",
" <th>cafe_count_1000_price_1000</th>\n",
" <th>cafe_count_1000_price_1500</th>\n",
" <th>cafe_count_1000_price_2500</th>\n",
" <th>cafe_count_1000_price_4000</th>\n",
" <th>cafe_count_1000_price_high</th>\n",
" <th>big_church_count_1000</th>\n",
" <th>church_count_1000</th>\n",
" <th>mosque_count_1000</th>\n",
" <th>leisure_count_1000</th>\n",
" <th>sport_count_1000</th>\n",
" <th>market_count_1000</th>\n",
" <th>green_part_1500</th>\n",
" <th>prom_part_1500</th>\n",
" <th>office_count_1500</th>\n",
" <th>office_sqm_1500</th>\n",
" <th>trc_count_1500</th>\n",
" <th>trc_sqm_1500</th>\n",
" <th>cafe_count_1500</th>\n",
" <th>cafe_sum_1500_min_price_avg</th>\n",
" <th>cafe_sum_1500_max_price_avg</th>\n",
" <th>cafe_avg_price_1500</th>\n",
" <th>cafe_count_1500_na_price</th>\n",
" <th>cafe_count_1500_price_500</th>\n",
" <th>cafe_count_1500_price_1000</th>\n",
" <th>cafe_count_1500_price_1500</th>\n",
" <th>cafe_count_1500_price_2500</th>\n",
" <th>cafe_count_1500_price_4000</th>\n",
" <th>cafe_count_1500_price_high</th>\n",
" <th>big_church_count_1500</th>\n",
" <th>church_count_1500</th>\n",
" <th>mosque_count_1500</th>\n",
" <th>leisure_count_1500</th>\n",
" <th>sport_count_1500</th>\n",
" <th>market_count_1500</th>\n",
" <th>green_part_2000</th>\n",
" <th>prom_part_2000</th>\n",
" <th>office_count_2000</th>\n",
" <th>office_sqm_2000</th>\n",
" <th>trc_count_2000</th>\n",
" <th>trc_sqm_2000</th>\n",
" <th>cafe_count_2000</th>\n",
" <th>cafe_sum_2000_min_price_avg</th>\n",
" <th>cafe_sum_2000_max_price_avg</th>\n",
" <th>cafe_avg_price_2000</th>\n",
" <th>cafe_count_2000_na_price</th>\n",
" <th>cafe_count_2000_price_500</th>\n",
" <th>cafe_count_2000_price_1000</th>\n",
" <th>cafe_count_2000_price_1500</th>\n",
" <th>cafe_count_2000_price_2500</th>\n",
" <th>cafe_count_2000_price_4000</th>\n",
" <th>cafe_count_2000_price_high</th>\n",
" <th>big_church_count_2000</th>\n",
" <th>church_count_2000</th>\n",
" <th>mosque_count_2000</th>\n",
" <th>leisure_count_2000</th>\n",
" <th>sport_count_2000</th>\n",
" <th>market_count_2000</th>\n",
" <th>green_part_3000</th>\n",
" <th>prom_part_3000</th>\n",
" <th>office_count_3000</th>\n",
" <th>office_sqm_3000</th>\n",
" <th>trc_count_3000</th>\n",
" <th>trc_sqm_3000</th>\n",
" <th>cafe_count_3000</th>\n",
" <th>cafe_sum_3000_min_price_avg</th>\n",
" <th>cafe_sum_3000_max_price_avg</th>\n",
" <th>cafe_avg_price_3000</th>\n",
" <th>cafe_count_3000_na_price</th>\n",
" <th>cafe_count_3000_price_500</th>\n",
" <th>cafe_count_3000_price_1000</th>\n",
" <th>cafe_count_3000_price_1500</th>\n",
" <th>cafe_count_3000_price_2500</th>\n",
" <th>cafe_count_3000_price_4000</th>\n",
" <th>cafe_count_3000_price_high</th>\n",
" <th>big_church_count_3000</th>\n",
" <th>church_count_3000</th>\n",
" <th>mosque_count_3000</th>\n",
" <th>leisure_count_3000</th>\n",
" <th>sport_count_3000</th>\n",
" <th>market_count_3000</th>\n",
" <th>green_part_5000</th>\n",
" <th>prom_part_5000</th>\n",
" <th>office_count_5000</th>\n",
" <th>office_sqm_5000</th>\n",
" <th>trc_count_5000</th>\n",
" <th>trc_sqm_5000</th>\n",
" <th>cafe_count_5000</th>\n",
" <th>cafe_sum_5000_min_price_avg</th>\n",
" <th>cafe_sum_5000_max_price_avg</th>\n",
" <th>cafe_avg_price_5000</th>\n",
" <th>cafe_count_5000_na_price</th>\n",
" <th>cafe_count_5000_price_500</th>\n",
" <th>cafe_count_5000_price_1000</th>\n",
" <th>cafe_count_5000_price_1500</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>201108</td>\n",
" <td>43</td>\n",
" <td>27.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Bibirevo</td>\n",
" <td>6.407578e+06</td>\n",
" <td>155572</td>\n",
" <td>0.189727</td>\n",
" <td>0.000070</td>\n",
" <td>9576</td>\n",
" <td>5001.0</td>\n",
" <td>5</td>\n",
" <td>10309</td>\n",
" <td>11065.0</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>240.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>86206</td>\n",
" <td>40477</td>\n",
" <td>45729</td>\n",
" <td>21154</td>\n",
" <td>11007</td>\n",
" <td>10147</td>\n",
" <td>98207</td>\n",
" <td>52277</td>\n",
" <td>45930</td>\n",
" <td>36211</td>\n",
" <td>10580</td>\n",
" <td>25631</td>\n",
" <td>9576</td>\n",
" <td>4899</td>\n",
" <td>4677</td>\n",
" <td>10309</td>\n",
" <td>5463</td>\n",
" <td>4846</td>\n",
" <td>23603</td>\n",
" <td>12286</td>\n",
" <td>11317</td>\n",
" <td>17508</td>\n",
" <td>9425</td>\n",
" <td>8083</td>\n",
" <td>18654</td>\n",
" <td>9709</td>\n",
" <td>8945</td>\n",
" <td>211.0</td>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>184.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>211.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>206.0</td>\n",
" <td>5.0</td>\n",
" <td>1</td>\n",
" <td>2.590241</td>\n",
" <td>1.131260</td>\n",
" <td>13.575119</td>\n",
" <td>1.131260</td>\n",
" <td>0.145700</td>\n",
" <td>0.177975</td>\n",
" <td>2.158587</td>\n",
" <td>0.600973</td>\n",
" <td>1.080934</td>\n",
" <td>23.683460</td>\n",
" <td>1.804127</td>\n",
" <td>3.633334</td>\n",
" <td>5.419893</td>\n",
" <td>65.038716</td>\n",
" <td>1.0</td>\n",
" <td>5.419893</td>\n",
" <td>6.905893</td>\n",
" <td>1</td>\n",
" <td>0.274985</td>\n",
" <td>3.299822</td>\n",
" <td>0.992631</td>\n",
" <td>no</td>\n",
" <td>1.422391</td>\n",
" <td>10.918587</td>\n",
" <td>13.100618</td>\n",
" <td>13.675657</td>\n",
" <td>15.156211</td>\n",
" <td>1.422391</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>3.830951</td>\n",
" <td>5</td>\n",
" <td>1.305159</td>\n",
" <td>no</td>\n",
" <td>14.231961</td>\n",
" <td>101</td>\n",
" <td>24.292406</td>\n",
" <td>1</td>\n",
" <td>18.152338</td>\n",
" <td>5.718519</td>\n",
" <td>1.210027</td>\n",
" <td>1.062513</td>\n",
" <td>5.814135</td>\n",
" <td>4.308127</td>\n",
" <td>10.814172</td>\n",
" <td>1.676258</td>\n",
" <td>0.485841</td>\n",
" <td>3.065047</td>\n",
" <td>1.107594</td>\n",
" <td>8.148591</td>\n",
" <td>3.516513</td>\n",
" <td>2.392353</td>\n",
" <td>4.248036</td>\n",
" <td>0.974743</td>\n",
" <td>6.715026</td>\n",
" <td>0.884350</td>\n",
" <td>0.648488</td>\n",
" <td>0.637189</td>\n",
" <td>0.947962</td>\n",
" <td>0.177975</td>\n",
" <td>0.625783</td>\n",
" <td>0.628187</td>\n",
" <td>3.932040</td>\n",
" <td>14.053047</td>\n",
" <td>7.389498</td>\n",
" <td>7.023705</td>\n",
" <td>0.516838</td>\n",
" <td>good</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7.36</td>\n",
" <td>0.00</td>\n",
" <td>1</td>\n",
" <td>30500</td>\n",
" <td>3</td>\n",
" <td>55600</td>\n",
" <td>19</td>\n",
" <td>527.78</td>\n",
" <td>888.89</td>\n",
" <td>708.33</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>14.27</td>\n",
" <td>6.92</td>\n",
" <td>3</td>\n",
" <td>39554</td>\n",
" <td>9</td>\n",
" <td>171420</td>\n",
" <td>34</td>\n",
" <td>566.67</td>\n",
" <td>969.70</td>\n",
" <td>768.18</td>\n",
" <td>1</td>\n",
" <td>14</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>11.77</td>\n",
" <td>15.97</td>\n",
" <td>9</td>\n",
" <td>188854</td>\n",
" <td>19</td>\n",
" <td>1244891</td>\n",
" <td>36</td>\n",
" <td>614.29</td>\n",
" <td>1042.86</td>\n",
" <td>828.57</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>11.98</td>\n",
" <td>13.55</td>\n",
" <td>12</td>\n",
" <td>251554</td>\n",
" <td>23</td>\n",
" <td>1419204</td>\n",
" <td>68</td>\n",
" <td>639.68</td>\n",
" <td>1079.37</td>\n",
" <td>859.52</td>\n",
" <td>5</td>\n",
" <td>21</td>\n",
" <td>22</td>\n",
" <td>16</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>1</td>\n",
" <td>13.09</td>\n",
" <td>13.31</td>\n",
" <td>29</td>\n",
" <td>807385</td>\n",
" <td>52</td>\n",
" <td>4036616</td>\n",
" <td>152</td>\n",
" <td>708.57</td>\n",
" <td>1185.71</td>\n",
" <td>947.14</td>\n",
" <td>12</td>\n",
" <td>39</td>\n",
" <td>48</td>\n",
" <td>40</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>4</td>\n",
" <td>5850000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>201108</td>\n",
" <td>34</td>\n",
" <td>19.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Nagatinskij Zaton</td>\n",
" <td>9.589337e+06</td>\n",
" <td>115352</td>\n",
" <td>0.372602</td>\n",
" <td>0.049637</td>\n",
" <td>6880</td>\n",
" <td>3119.0</td>\n",
" <td>5</td>\n",
" <td>7759</td>\n",
" <td>6237.0</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>229.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>yes</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>76284</td>\n",
" <td>34200</td>\n",
" <td>42084</td>\n",
" <td>15727</td>\n",
" <td>7925</td>\n",
" <td>7802</td>\n",
" <td>70194</td>\n",
" <td>35622</td>\n",
" <td>34572</td>\n",
" <td>29431</td>\n",
" <td>9266</td>\n",
" <td>20165</td>\n",
" <td>6880</td>\n",
" <td>3466</td>\n",
" <td>3414</td>\n",
" <td>7759</td>\n",
" <td>3909</td>\n",
" <td>3850</td>\n",
" <td>17700</td>\n",
" <td>8998</td>\n",
" <td>8702</td>\n",
" <td>15164</td>\n",
" <td>7571</td>\n",
" <td>7593</td>\n",
" <td>13729</td>\n",
" <td>6929</td>\n",
" <td>6800</td>\n",
" <td>245.0</td>\n",
" <td>83.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>67.0</td>\n",
" <td>4.0</td>\n",
" <td>90.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>244.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>143.0</td>\n",
" <td>84.0</td>\n",
" <td>15.0</td>\n",
" <td>2</td>\n",
" <td>0.936700</td>\n",
" <td>0.647337</td>\n",
" <td>7.620630</td>\n",
" <td>0.635053</td>\n",
" <td>0.147754</td>\n",
" <td>0.273345</td>\n",
" <td>0.550690</td>\n",
" <td>0.065321</td>\n",
" <td>0.966479</td>\n",
" <td>1.317476</td>\n",
" <td>4.655004</td>\n",
" <td>8.648587</td>\n",
" <td>3.411993</td>\n",
" <td>40.943917</td>\n",
" <td>2.0</td>\n",
" <td>3.641773</td>\n",
" <td>4.679745</td>\n",
" <td>2</td>\n",
" <td>0.065263</td>\n",
" <td>0.783160</td>\n",
" <td>0.698081</td>\n",
" <td>no</td>\n",
" <td>9.503405</td>\n",
" <td>3.103996</td>\n",
" <td>6.444333</td>\n",
" <td>8.132640</td>\n",
" <td>8.698054</td>\n",
" <td>2.887377</td>\n",
" <td>2</td>\n",
" <td>no</td>\n",
" <td>3.103996</td>\n",
" <td>4</td>\n",
" <td>0.694536</td>\n",
" <td>no</td>\n",
" <td>9.242586</td>\n",
" <td>32</td>\n",
" <td>5.706113</td>\n",
" <td>2</td>\n",
" <td>9.034642</td>\n",
" <td>3.489954</td>\n",
" <td>2.724295</td>\n",
" <td>1.246149</td>\n",
" <td>3.419574</td>\n",
" <td>0.725560</td>\n",
" <td>6.910568</td>\n",
" <td>3.424716</td>\n",
" <td>0.668364</td>\n",
" <td>2.000154</td>\n",
" <td>8.972823</td>\n",
" <td>6.127073</td>\n",
" <td>1.161579</td>\n",
" <td>2.543747</td>\n",
" <td>12.649879</td>\n",
" <td>1.477723</td>\n",
" <td>1.852560</td>\n",
" <td>0.686252</td>\n",
" <td>0.519311</td>\n",
" <td>0.688796</td>\n",
" <td>1.072315</td>\n",
" <td>0.273345</td>\n",
" <td>0.967821</td>\n",
" <td>0.471447</td>\n",
" <td>4.841544</td>\n",
" <td>6.829889</td>\n",
" <td>0.709260</td>\n",
" <td>2.358840</td>\n",
" <td>0.230287</td>\n",
" <td>excellent</td>\n",
" <td>25.14</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>860.00</td>\n",
" <td>1500.00</td>\n",
" <td>1180.00</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26.66</td>\n",
" <td>0.07</td>\n",
" <td>2</td>\n",
" <td>86600</td>\n",
" <td>5</td>\n",
" <td>94065</td>\n",
" <td>13</td>\n",
" <td>615.38</td>\n",
" <td>1076.92</td>\n",
" <td>846.15</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>21.53</td>\n",
" <td>7.71</td>\n",
" <td>3</td>\n",
" <td>102910</td>\n",
" <td>7</td>\n",
" <td>127065</td>\n",
" <td>17</td>\n",
" <td>694.12</td>\n",
" <td>1205.88</td>\n",
" <td>950.00</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>22.37</td>\n",
" <td>19.25</td>\n",
" <td>4</td>\n",
" <td>165510</td>\n",
" <td>8</td>\n",
" <td>179065</td>\n",
" <td>21</td>\n",
" <td>695.24</td>\n",
" <td>1190.48</td>\n",
" <td>942.86</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>18.07</td>\n",
" <td>27.32</td>\n",
" <td>12</td>\n",
" <td>821986</td>\n",
" <td>14</td>\n",
" <td>491565</td>\n",
" <td>30</td>\n",
" <td>631.03</td>\n",
" <td>1086.21</td>\n",
" <td>858.62</td>\n",
" <td>1</td>\n",
" <td>11</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>19</td>\n",
" <td>1</td>\n",
" <td>10.26</td>\n",
" <td>27.47</td>\n",
" <td>66</td>\n",
" <td>2690465</td>\n",
" <td>40</td>\n",
" <td>2034942</td>\n",
" <td>177</td>\n",
" <td>673.81</td>\n",
" <td>1148.81</td>\n",
" <td>911.31</td>\n",
" <td>9</td>\n",
" <td>49</td>\n",
" <td>65</td>\n",
" <td>36</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>66</td>\n",
" <td>14</td>\n",
" <td>6000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>201108</td>\n",
" <td>43</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Tekstil'shhiki</td>\n",
" <td>4.808270e+06</td>\n",
" <td>101708</td>\n",
" <td>0.112560</td>\n",
" <td>0.118537</td>\n",
" <td>5879</td>\n",
" <td>1463.0</td>\n",
" <td>4</td>\n",
" <td>6207</td>\n",
" <td>5580.0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>1183.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>101982</td>\n",
" <td>46076</td>\n",
" <td>55906</td>\n",
" <td>13028</td>\n",
" <td>6835</td>\n",
" <td>6193</td>\n",
" <td>63388</td>\n",
" <td>31813</td>\n",
" <td>31575</td>\n",
" <td>25292</td>\n",
" <td>7609</td>\n",
" <td>17683</td>\n",
" <td>5879</td>\n",
" <td>3095</td>\n",
" <td>2784</td>\n",
" <td>6207</td>\n",
" <td>3269</td>\n",
" <td>2938</td>\n",
" <td>14884</td>\n",
" <td>7821</td>\n",
" <td>7063</td>\n",
" <td>19401</td>\n",
" <td>9045</td>\n",
" <td>10356</td>\n",
" <td>11252</td>\n",
" <td>5916</td>\n",
" <td>5336</td>\n",
" <td>330.0</td>\n",
" <td>59.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>206.0</td>\n",
" <td>4.0</td>\n",
" <td>60.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>330.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>246.0</td>\n",
" <td>63.0</td>\n",
" <td>20.0</td>\n",
" <td>3</td>\n",
" <td>2.120999</td>\n",
" <td>1.637996</td>\n",
" <td>17.351515</td>\n",
" <td>1.445960</td>\n",
" <td>0.049102</td>\n",
" <td>0.158072</td>\n",
" <td>0.374848</td>\n",
" <td>0.453172</td>\n",
" <td>0.939275</td>\n",
" <td>4.912660</td>\n",
" <td>3.381083</td>\n",
" <td>11.996480</td>\n",
" <td>1.277658</td>\n",
" <td>15.331896</td>\n",
" <td>3.0</td>\n",
" <td>1.277658</td>\n",
" <td>1.701420</td>\n",
" <td>3</td>\n",
" <td>0.328756</td>\n",
" <td>3.945073</td>\n",
" <td>0.468265</td>\n",
" <td>no</td>\n",
" <td>5.604800</td>\n",
" <td>2.927487</td>\n",
" <td>6.963403</td>\n",
" <td>8.054252</td>\n",
" <td>9.067885</td>\n",
" <td>0.647250</td>\n",
" <td>3</td>\n",
" <td>no</td>\n",
" <td>2.927487</td>\n",
" <td>4</td>\n",
" <td>0.700691</td>\n",
" <td>no</td>\n",
" <td>9.540544</td>\n",
" <td>5</td>\n",
" <td>6.710302</td>\n",
" <td>3</td>\n",
" <td>5.777394</td>\n",
" <td>7.506612</td>\n",
" <td>0.772216</td>\n",
" <td>1.602183</td>\n",
" <td>3.682455</td>\n",
" <td>3.562188</td>\n",
" <td>5.752368</td>\n",
" <td>1.375443</td>\n",
" <td>0.733101</td>\n",
" <td>1.239304</td>\n",
" <td>1.978517</td>\n",
" <td>0.767569</td>\n",
" <td>1.952771</td>\n",
" <td>0.621357</td>\n",
" <td>7.682303</td>\n",
" <td>0.097144</td>\n",
" <td>0.841254</td>\n",
" <td>1.510089</td>\n",
" <td>1.486533</td>\n",
" <td>1.543049</td>\n",
" <td>0.391957</td>\n",
" <td>0.158072</td>\n",
" <td>3.178751</td>\n",
" <td>0.755946</td>\n",
" <td>7.922152</td>\n",
" <td>4.273200</td>\n",
" <td>3.156423</td>\n",
" <td>4.958214</td>\n",
" <td>0.190462</td>\n",
" <td>poor</td>\n",
" <td>1.67</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>666.67</td>\n",
" <td>1166.67</td>\n",
" <td>916.67</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4.99</td>\n",
" <td>0.29</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>642.86</td>\n",
" <td>1142.86</td>\n",
" <td>892.86</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>9.92</td>\n",
" <td>6.73</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2600</td>\n",
" <td>14</td>\n",
" <td>516.67</td>\n",
" <td>916.67</td>\n",
" <td>716.67</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>5</td>\n",
" <td>12.99</td>\n",
" <td>12.75</td>\n",
" <td>4</td>\n",
" <td>100200</td>\n",
" <td>7</td>\n",
" <td>52550</td>\n",
" <td>24</td>\n",
" <td>563.64</td>\n",
" <td>977.27</td>\n",
" <td>770.45</td>\n",
" <td>2</td>\n",
" <td>8</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>12.14</td>\n",
" <td>26.46</td>\n",
" <td>8</td>\n",
" <td>110856</td>\n",
" <td>7</td>\n",
" <td>52550</td>\n",
" <td>41</td>\n",
" <td>697.44</td>\n",
" <td>1192.31</td>\n",
" <td>944.87</td>\n",
" <td>2</td>\n",
" <td>9</td>\n",
" <td>17</td>\n",
" <td>9</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" <td>6</td>\n",
" <td>13.69</td>\n",
" <td>21.58</td>\n",
" <td>43</td>\n",
" <td>1478160</td>\n",
" <td>35</td>\n",
" <td>1572990</td>\n",
" <td>122</td>\n",
" <td>702.68</td>\n",
" <td>1196.43</td>\n",
" <td>949.55</td>\n",
" <td>10</td>\n",
" <td>29</td>\n",
" <td>45</td>\n",
" <td>25</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>5700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>201109</td>\n",
" <td>89</td>\n",
" <td>50.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Mitino</td>\n",
" <td>1.258354e+07</td>\n",
" <td>178473</td>\n",
" <td>0.194703</td>\n",
" <td>0.069753</td>\n",
" <td>13087</td>\n",
" <td>6839.0</td>\n",
" <td>9</td>\n",
" <td>13670</td>\n",
" <td>17063.0</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>6</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>21155</td>\n",
" <td>9828</td>\n",
" <td>11327</td>\n",
" <td>28563</td>\n",
" <td>14680</td>\n",
" <td>13883</td>\n",
" <td>120381</td>\n",
" <td>60040</td>\n",
" <td>60341</td>\n",
" <td>29529</td>\n",
" <td>9083</td>\n",
" <td>20446</td>\n",
" <td>13087</td>\n",
" <td>6645</td>\n",
" <td>6442</td>\n",
" <td>13670</td>\n",
" <td>7126</td>\n",
" <td>6544</td>\n",
" <td>32063</td>\n",
" <td>16513</td>\n",
" <td>15550</td>\n",
" <td>3292</td>\n",
" <td>1450</td>\n",
" <td>1842</td>\n",
" <td>24934</td>\n",
" <td>12782</td>\n",
" <td>12152</td>\n",
" <td>458.0</td>\n",
" <td>9.0</td>\n",
" <td>51.0</td>\n",
" <td>12.0</td>\n",
" <td>124.0</td>\n",
" <td>50.0</td>\n",
" <td>201.0</td>\n",
" <td>0.0</td>\n",
" <td>9.0</td>\n",
" <td>2.0</td>\n",
" <td>459.0</td>\n",
" <td>13.0</td>\n",
" <td>24.0</td>\n",
" <td>40.0</td>\n",
" <td>130.0</td>\n",
" <td>252.0</td>\n",
" <td>4</td>\n",
" <td>1.489049</td>\n",
" <td>0.984537</td>\n",
" <td>11.565624</td>\n",
" <td>0.963802</td>\n",
" <td>0.179441</td>\n",
" <td>0.236455</td>\n",
" <td>0.078090</td>\n",
" <td>0.106125</td>\n",
" <td>0.451173</td>\n",
" <td>15.623710</td>\n",
" <td>2.017080</td>\n",
" <td>14.317640</td>\n",
" <td>4.291432</td>\n",
" <td>51.497190</td>\n",
" <td>4.0</td>\n",
" <td>3.816045</td>\n",
" <td>5.271136</td>\n",
" <td>4</td>\n",
" <td>0.131597</td>\n",
" <td>1.579164</td>\n",
" <td>1.200336</td>\n",
" <td>no</td>\n",
" <td>2.677824</td>\n",
" <td>14.606501</td>\n",
" <td>17.457198</td>\n",
" <td>18.309433</td>\n",
" <td>19.487005</td>\n",
" <td>2.677824</td>\n",
" <td>1</td>\n",
" <td>no</td>\n",
" <td>2.780449</td>\n",
" <td>17</td>\n",
" <td>1.999265</td>\n",
" <td>no</td>\n",
" <td>17.478380</td>\n",
" <td>83</td>\n",
" <td>6.734618</td>\n",
" <td>1</td>\n",
" <td>27.667863</td>\n",
" <td>9.522538</td>\n",
" <td>6.348716</td>\n",
" <td>1.767612</td>\n",
" <td>11.178333</td>\n",
" <td>0.583025</td>\n",
" <td>27.892717</td>\n",
" <td>0.811275</td>\n",
" <td>0.623484</td>\n",
" <td>1.950317</td>\n",
" <td>6.483172</td>\n",
" <td>7.385521</td>\n",
" <td>4.923843</td>\n",
" <td>3.549558</td>\n",
" <td>8.789894</td>\n",
" <td>2.163735</td>\n",
" <td>10.903161</td>\n",
" <td>0.622272</td>\n",
" <td>0.599914</td>\n",
" <td>0.934273</td>\n",
" <td>0.892674</td>\n",
" <td>0.236455</td>\n",
" <td>1.031777</td>\n",
" <td>1.561505</td>\n",
" <td>15.300449</td>\n",
" <td>16.990677</td>\n",
" <td>16.041521</td>\n",
" <td>5.029696</td>\n",
" <td>0.465820</td>\n",
" <td>good</td>\n",
" <td>17.36</td>\n",
" <td>0.57</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1000.00</td>\n",
" <td>1500.00</td>\n",
" <td>1250.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19.25</td>\n",
" <td>10.35</td>\n",
" <td>1</td>\n",
" <td>11000</td>\n",
" <td>6</td>\n",
" <td>80780</td>\n",
" <td>12</td>\n",
" <td>658.33</td>\n",
" <td>1083.33</td>\n",
" <td>870.83</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>28.38</td>\n",
" <td>6.57</td>\n",
" <td>2</td>\n",
" <td>11000</td>\n",
" <td>7</td>\n",
" <td>89492</td>\n",
" <td>23</td>\n",
" <td>673.91</td>\n",
" <td>1130.43</td>\n",
" <td>902.17</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>32.29</td>\n",
" <td>5.73</td>\n",
" <td>2</td>\n",
" <td>11000</td>\n",
" <td>7</td>\n",
" <td>89492</td>\n",
" <td>25</td>\n",
" <td>660.00</td>\n",
" <td>1120.00</td>\n",
" <td>890.00</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>11</td>\n",
" <td>8</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>2</td>\n",
" <td>20.79</td>\n",
" <td>3.57</td>\n",
" <td>4</td>\n",
" <td>167000</td>\n",
" <td>12</td>\n",
" <td>205756</td>\n",
" <td>32</td>\n",
" <td>718.75</td>\n",
" <td>1218.75</td>\n",
" <td>968.75</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>14</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>3</td>\n",
" <td>14.18</td>\n",
" <td>3.89</td>\n",
" <td>8</td>\n",
" <td>244166</td>\n",
" <td>22</td>\n",
" <td>942180</td>\n",
" <td>61</td>\n",
" <td>931.58</td>\n",
" <td>1552.63</td>\n",
" <td>1242.11</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>21</td>\n",
" <td>15</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>13100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>201109</td>\n",
" <td>77</td>\n",
" <td>77.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Investment</td>\n",
" <td>Basmannoe</td>\n",
" <td>8.398461e+06</td>\n",
" <td>108171</td>\n",
" <td>0.015234</td>\n",
" <td>0.037316</td>\n",
" <td>5706</td>\n",
" <td>3240.0</td>\n",
" <td>7</td>\n",
" <td>6748</td>\n",
" <td>7770.0</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>562.0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>25</td>\n",
" <td>2</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>93</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>28179</td>\n",
" <td>13522</td>\n",
" <td>14657</td>\n",
" <td>13368</td>\n",
" <td>7159</td>\n",
" <td>6209</td>\n",
" <td>68043</td>\n",
" <td>34236</td>\n",
" <td>33807</td>\n",
" <td>26760</td>\n",
" <td>8563</td>\n",
" <td>18197</td>\n",
" <td>5706</td>\n",
" <td>2982</td>\n",
" <td>2724</td>\n",
" <td>6748</td>\n",
" <td>3664</td>\n",
" <td>3084</td>\n",
" <td>15237</td>\n",
" <td>8113</td>\n",
" <td>7124</td>\n",
" <td>5164</td>\n",
" <td>2583</td>\n",
" <td>2581</td>\n",
" <td>11631</td>\n",
" <td>6223</td>\n",
" <td>5408</td>\n",
" <td>746.0</td>\n",
" <td>48.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>643.0</td>\n",
" <td>16.0</td>\n",
" <td>35.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>746.0</td>\n",
" <td>371.0</td>\n",
" <td>114.0</td>\n",
" <td>146.0</td>\n",
" <td>62.0</td>\n",
" <td>53.0</td>\n",
" <td>5</td>\n",
" <td>1.257186</td>\n",
" <td>0.876620</td>\n",
" <td>8.266305</td>\n",
" <td>0.688859</td>\n",
" <td>0.247901</td>\n",
" <td>0.376838</td>\n",
" <td>0.258289</td>\n",
" <td>0.236214</td>\n",
" <td>0.392871</td>\n",
" <td>10.683540</td>\n",
" <td>2.936581</td>\n",
" <td>11.903910</td>\n",
" <td>0.853960</td>\n",
" <td>10.247521</td>\n",
" <td>5.0</td>\n",
" <td>1.595898</td>\n",
" <td>2.156284</td>\n",
" <td>113</td>\n",
" <td>0.071480</td>\n",
" <td>0.857764</td>\n",
" <td>0.820294</td>\n",
" <td>no</td>\n",
" <td>11.616653</td>\n",
" <td>1.721834</td>\n",
" <td>0.046810</td>\n",
" <td>0.787593</td>\n",
" <td>2.578671</td>\n",
" <td>1.721834</td>\n",
" <td>4</td>\n",
" <td>no</td>\n",
" <td>3.133531</td>\n",
" <td>10</td>\n",
" <td>0.084113</td>\n",
" <td>yes</td>\n",
" <td>1.595898</td>\n",
" <td>113</td>\n",
" <td>1.423428</td>\n",
" <td>4</td>\n",
" <td>6.515857</td>\n",
" <td>8.671016</td>\n",
" <td>1.638318</td>\n",
" <td>3.632640</td>\n",
" <td>4.587917</td>\n",
" <td>2.609420</td>\n",
" <td>9.155057</td>\n",
" <td>1.969738</td>\n",
" <td>0.220288</td>\n",
" <td>2.544696</td>\n",
" <td>3.975401</td>\n",
" <td>3.610754</td>\n",
" <td>0.307915</td>\n",
" <td>1.864637</td>\n",
" <td>3.779781</td>\n",
" <td>1.121703</td>\n",
" <td>0.991683</td>\n",
" <td>0.892668</td>\n",
" <td>0.429052</td>\n",
" <td>0.077901</td>\n",
" <td>0.810801</td>\n",
" <td>0.376838</td>\n",
" <td>0.378756</td>\n",
" <td>0.121681</td>\n",
" <td>2.584370</td>\n",
" <td>1.112486</td>\n",
" <td>1.800125</td>\n",
" <td>1.339652</td>\n",
" <td>0.026102</td>\n",
" <td>excellent</td>\n",
" <td>3.56</td>\n",
" <td>4.44</td>\n",
" <td>15</td>\n",
" <td>293699</td>\n",
" <td>1</td>\n",
" <td>45000</td>\n",
" <td>48</td>\n",
" <td>702.22</td>\n",
" <td>1166.67</td>\n",
" <td>934.44</td>\n",
" <td>3</td>\n",
" <td>17</td>\n",
" <td>10</td>\n",
" <td>11</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>3.34</td>\n",
" <td>8.29</td>\n",
" <td>46</td>\n",
" <td>420952</td>\n",
" <td>3</td>\n",
" <td>158200</td>\n",
" <td>153</td>\n",
" <td>763.45</td>\n",
" <td>1272.41</td>\n",
" <td>1017.93</td>\n",
" <td>8</td>\n",
" <td>39</td>\n",
" <td>45</td>\n",
" <td>39</td>\n",
" <td>19</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>4.12</td>\n",
" <td>4.83</td>\n",
" <td>93</td>\n",
" <td>1195735</td>\n",
" <td>9</td>\n",
" <td>445900</td>\n",
" <td>272</td>\n",
" <td>766.80</td>\n",
" <td>1272.73</td>\n",
" <td>1019.76</td>\n",
" <td>19</td>\n",
" <td>70</td>\n",
" <td>74</td>\n",
" <td>72</td>\n",
" <td>30</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>30</td>\n",
" <td>0</td>\n",
" <td>10</td>\n",
" <td>14</td>\n",
" <td>2</td>\n",
" <td>4.53</td>\n",
" <td>5.02</td>\n",
" <td>149</td>\n",
" <td>1625130</td>\n",
" <td>17</td>\n",
" <td>564843</td>\n",
" <td>483</td>\n",
" <td>765.93</td>\n",
" <td>1269.23</td>\n",
" <td>1017.58</td>\n",
" <td>28</td>\n",
" <td>130</td>\n",
" <td>129</td>\n",
" <td>131</td>\n",
" <td>50</td>\n",
" <td>14</td>\n",
" <td>1</td>\n",
" <td>35</td>\n",
" <td>61</td>\n",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>21</td>\n",
" <td>3</td>\n",
" <td>5.06</td>\n",
" <td>8.62</td>\n",
" <td>305</td>\n",
" <td>3420907</td>\n",
" <td>60</td>\n",
" <td>2296870</td>\n",
" <td>1068</td>\n",
" <td>853.03</td>\n",
" <td>1410.45</td>\n",
" <td>1131.74</td>\n",
" <td>63</td>\n",
" <td>266</td>\n",
" <td>267</td>\n",
" <td>262</td>\n",
" <td>149</td>\n",
" <td>57</td>\n",
" <td>4</td>\n",
" <td>70</td>\n",
" <td>121</td>\n",
" <td>1</td>\n",
" <td>40</td>\n",
" <td>77</td>\n",
" <td>5</td>\n",
" <td>8.38</td>\n",
" <td>10.92</td>\n",
" <td>689</td>\n",
" <td>8404624</td>\n",
" <td>114</td>\n",
" <td>3503058</td>\n",
" <td>2283</td>\n",
" <td>853.88</td>\n",
" <td>1411.45</td>\n",
" <td>1132.66</td>\n",
" <td>143</td>\n",
" <td>566</td>\n",
" <td>578</td>\n",
" <td>552</td>\n",
" <td>319</td>\n",
" <td>108</td>\n",
" <td>17</td>\n",
" <td>135</td>\n",
" <td>236</td>\n",
" <td>2</td>\n",
" <td>91</td>\n",
" <td>195</td>\n",
" <td>14</td>\n",
" <td>16331452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material build_year \\\n",
"0 1 201108 43 27.0 4.0 NaN NaN NaN \n",
"1 2 201108 34 19.0 3.0 NaN NaN NaN \n",
"2 3 201108 43 29.0 2.0 NaN NaN NaN \n",
"3 4 201109 89 50.0 9.0 NaN NaN NaN \n",
"4 5 201109 77 77.0 4.0 NaN NaN NaN \n",
"\n",
" num_room kitch_sq state product_type sub_area area_m \\\n",
"0 NaN NaN NaN Investment Bibirevo 6.407578e+06 \n",
"1 NaN NaN NaN Investment Nagatinskij Zaton 9.589337e+06 \n",
"2 NaN NaN NaN Investment Tekstil'shhiki 4.808270e+06 \n",
"3 NaN NaN NaN Investment Mitino 1.258354e+07 \n",
"4 NaN NaN NaN Investment Basmannoe 8.398461e+06 \n",
"\n",
" raion_popul green_zone_part indust_part children_preschool \\\n",
"0 155572 0.189727 0.000070 9576 \n",
"1 115352 0.372602 0.049637 6880 \n",
"2 101708 0.112560 0.118537 5879 \n",
"3 178473 0.194703 0.069753 13087 \n",
"4 108171 0.015234 0.037316 5706 \n",
"\n",
" preschool_quota preschool_education_centers_raion children_school \\\n",
"0 5001.0 5 10309 \n",
"1 3119.0 5 7759 \n",
"2 1463.0 4 6207 \n",
"3 6839.0 9 13670 \n",
"4 3240.0 7 6748 \n",
"\n",
" school_quota school_education_centers_raion \\\n",
"0 11065.0 5 \n",
"1 6237.0 8 \n",
"2 5580.0 7 \n",
"3 17063.0 10 \n",
"4 7770.0 9 \n",
"\n",
" school_education_centers_top_20_raion hospital_beds_raion \\\n",
"0 0 240.0 \n",
"1 0 229.0 \n",
"2 0 1183.0 \n",
"3 0 NaN \n",
"4 0 562.0 \n",
"\n",
" healthcare_centers_raion university_top_20_raion sport_objects_raion \\\n",
"0 1 0 7 \n",
"1 1 0 6 \n",
"2 1 0 5 \n",
"3 1 0 17 \n",
"4 4 2 25 \n",
"\n",
" additional_education_raion culture_objects_top_25 \\\n",
"0 3 no \n",
"1 1 yes \n",
"2 1 no \n",
"3 6 no \n",
"4 2 no \n",
"\n",
" culture_objects_top_25_raion shopping_centers_raion office_raion \\\n",
"0 0 16 1 \n",
"1 1 3 0 \n",
"2 0 0 1 \n",
"3 0 11 4 \n",
"4 0 10 93 \n",
"\n",
" thermal_power_plant_raion incineration_raion oil_chemistry_raion \\\n",
"0 no no no \n",
"1 no no no \n",
"2 no no no \n",
"3 no no no \n",
"4 no no no \n",
"\n",
" radiation_raion railroad_terminal_raion big_market_raion \\\n",
"0 no no no \n",
"1 no no no \n",
"2 yes no no \n",
"3 no no no \n",
"4 yes yes no \n",
"\n",
" nuclear_reactor_raion detention_facility_raion full_all male_f female_f \\\n",
"0 no no 86206 40477 45729 \n",
"1 no no 76284 34200 42084 \n",
"2 no no 101982 46076 55906 \n",
"3 no no 21155 9828 11327 \n",
"4 no no 28179 13522 14657 \n",
"\n",
" young_all young_male young_female work_all work_male work_female \\\n",
"0 21154 11007 10147 98207 52277 45930 \n",
"1 15727 7925 7802 70194 35622 34572 \n",
"2 13028 6835 6193 63388 31813 31575 \n",
"3 28563 14680 13883 120381 60040 60341 \n",
"4 13368 7159 6209 68043 34236 33807 \n",
"\n",
" ekder_all ekder_male ekder_female 0_6_all 0_6_male 0_6_female \\\n",
"0 36211 10580 25631 9576 4899 4677 \n",
"1 29431 9266 20165 6880 3466 3414 \n",
"2 25292 7609 17683 5879 3095 2784 \n",
"3 29529 9083 20446 13087 6645 6442 \n",
"4 26760 8563 18197 5706 2982 2724 \n",
"\n",
" 7_14_all 7_14_male 7_14_female 0_17_all 0_17_male 0_17_female \\\n",
"0 10309 5463 4846 23603 12286 11317 \n",
"1 7759 3909 3850 17700 8998 8702 \n",
"2 6207 3269 2938 14884 7821 7063 \n",
"3 13670 7126 6544 32063 16513 15550 \n",
"4 6748 3664 3084 15237 8113 7124 \n",
"\n",
" 16_29_all 16_29_male 16_29_female 0_13_all 0_13_male 0_13_female \\\n",
"0 17508 9425 8083 18654 9709 8945 \n",
"1 15164 7571 7593 13729 6929 6800 \n",
"2 19401 9045 10356 11252 5916 5336 \n",
"3 3292 1450 1842 24934 12782 12152 \n",
"4 5164 2583 2581 11631 6223 5408 \n",
"\n",
" raion_build_count_with_material_info build_count_block build_count_wood \\\n",
"0 211.0 25.0 0.0 \n",
"1 245.0 83.0 1.0 \n",
"2 330.0 59.0 0.0 \n",
"3 458.0 9.0 51.0 \n",
"4 746.0 48.0 0.0 \n",
"\n",
" build_count_frame build_count_brick build_count_monolith \\\n",
"0 0.0 0.0 2.0 \n",
"1 0.0 67.0 4.0 \n",
"2 0.0 206.0 4.0 \n",
"3 12.0 124.0 50.0 \n",
"4 0.0 643.0 16.0 \n",
"\n",
" build_count_panel build_count_foam build_count_slag build_count_mix \\\n",
"0 184.0 0.0 0.0 0.0 \n",
"1 90.0 0.0 0.0 0.0 \n",
"2 60.0 0.0 1.0 0.0 \n",
"3 201.0 0.0 9.0 2.0 \n",
"4 35.0 0.0 3.0 1.0 \n",
"\n",
" raion_build_count_with_builddate_info build_count_before_1920 \\\n",
"0 211.0 0.0 \n",
"1 244.0 1.0 \n",
"2 330.0 1.0 \n",
"3 459.0 13.0 \n",
"4 746.0 371.0 \n",
"\n",
" build_count_1921-1945 build_count_1946-1970 build_count_1971-1995 \\\n",
"0 0.0 0.0 206.0 \n",
"1 1.0 143.0 84.0 \n",
"2 0.0 246.0 63.0 \n",
"3 24.0 40.0 130.0 \n",
"4 114.0 146.0 62.0 \n",
"\n",
" build_count_after_1995 ID_metro metro_min_avto metro_km_avto \\\n",
"0 5.0 1 2.590241 1.131260 \n",
"1 15.0 2 0.936700 0.647337 \n",
"2 20.0 3 2.120999 1.637996 \n",
"3 252.0 4 1.489049 0.984537 \n",
"4 53.0 5 1.257186 0.876620 \n",
"\n",
" metro_min_walk metro_km_walk kindergarten_km school_km park_km \\\n",
"0 13.575119 1.131260 0.145700 0.177975 2.158587 \n",
"1 7.620630 0.635053 0.147754 0.273345 0.550690 \n",
"2 17.351515 1.445960 0.049102 0.158072 0.374848 \n",
"3 11.565624 0.963802 0.179441 0.236455 0.078090 \n",
"4 8.266305 0.688859 0.247901 0.376838 0.258289 \n",
"\n",
" green_zone_km industrial_km water_treatment_km cemetery_km \\\n",
"0 0.600973 1.080934 23.683460 1.804127 \n",
"1 0.065321 0.966479 1.317476 4.655004 \n",
"2 0.453172 0.939275 4.912660 3.381083 \n",
"3 0.106125 0.451173 15.623710 2.017080 \n",
"4 0.236214 0.392871 10.683540 2.936581 \n",
"\n",
" incineration_km railroad_station_walk_km railroad_station_walk_min \\\n",
"0 3.633334 5.419893 65.038716 \n",
"1 8.648587 3.411993 40.943917 \n",
"2 11.996480 1.277658 15.331896 \n",
"3 14.317640 4.291432 51.497190 \n",
"4 11.903910 0.853960 10.247521 \n",
"\n",
" ID_railroad_station_walk railroad_station_avto_km \\\n",
"0 1.0 5.419893 \n",
"1 2.0 3.641773 \n",
"2 3.0 1.277658 \n",
"3 4.0 3.816045 \n",
"4 5.0 1.595898 \n",
"\n",
" railroad_station_avto_min ID_railroad_station_avto \\\n",
"0 6.905893 1 \n",
"1 4.679745 2 \n",
"2 1.701420 3 \n",
"3 5.271136 4 \n",
"4 2.156284 113 \n",
"\n",
" public_transport_station_km public_transport_station_min_walk water_km \\\n",
"0 0.274985 3.299822 0.992631 \n",
"1 0.065263 0.783160 0.698081 \n",
"2 0.328756 3.945073 0.468265 \n",
"3 0.131597 1.579164 1.200336 \n",
"4 0.071480 0.857764 0.820294 \n",
"\n",
" water_1line mkad_km ttk_km sadovoe_km bulvar_ring_km kremlin_km \\\n",
"0 no 1.422391 10.918587 13.100618 13.675657 15.156211 \n",
"1 no 9.503405 3.103996 6.444333 8.132640 8.698054 \n",
"2 no 5.604800 2.927487 6.963403 8.054252 9.067885 \n",
"3 no 2.677824 14.606501 17.457198 18.309433 19.487005 \n",
"4 no 11.616653 1.721834 0.046810 0.787593 2.578671 \n",
"\n",
" big_road1_km ID_big_road1 big_road1_1line big_road2_km ID_big_road2 \\\n",
"0 1.422391 1 no 3.830951 5 \n",
"1 2.887377 2 no 3.103996 4 \n",
"2 0.647250 3 no 2.927487 4 \n",
"3 2.677824 1 no 2.780449 17 \n",
"4 1.721834 4 no 3.133531 10 \n",
"\n",
" railroad_km railroad_1line zd_vokzaly_avto_km ID_railroad_terminal \\\n",
"0 1.305159 no 14.231961 101 \n",
"1 0.694536 no 9.242586 32 \n",
"2 0.700691 no 9.540544 5 \n",
"3 1.999265 no 17.478380 83 \n",
"4 0.084113 yes 1.595898 113 \n",
"\n",
" bus_terminal_avto_km ID_bus_terminal oil_chemistry_km \\\n",
"0 24.292406 1 18.152338 \n",
"1 5.706113 2 9.034642 \n",
"2 6.710302 3 5.777394 \n",
"3 6.734618 1 27.667863 \n",
"4 1.423428 4 6.515857 \n",
"\n",
" nuclear_reactor_km radiation_km power_transmission_line_km \\\n",
"0 5.718519 1.210027 1.062513 \n",
"1 3.489954 2.724295 1.246149 \n",
"2 7.506612 0.772216 1.602183 \n",
"3 9.522538 6.348716 1.767612 \n",
"4 8.671016 1.638318 3.632640 \n",
"\n",
" thermal_power_plant_km ts_km big_market_km market_shop_km \\\n",
"0 5.814135 4.308127 10.814172 1.676258 \n",
"1 3.419574 0.725560 6.910568 3.424716 \n",
"2 3.682455 3.562188 5.752368 1.375443 \n",
"3 11.178333 0.583025 27.892717 0.811275 \n",
"4 4.587917 2.609420 9.155057 1.969738 \n",
"\n",
" fitness_km swim_pool_km ice_rink_km stadium_km basketball_km \\\n",
"0 0.485841 3.065047 1.107594 8.148591 3.516513 \n",
"1 0.668364 2.000154 8.972823 6.127073 1.161579 \n",
"2 0.733101 1.239304 1.978517 0.767569 1.952771 \n",
"3 0.623484 1.950317 6.483172 7.385521 4.923843 \n",
"4 0.220288 2.544696 3.975401 3.610754 0.307915 \n",
"\n",
" hospice_morgue_km detention_facility_km public_healthcare_km \\\n",
"0 2.392353 4.248036 0.974743 \n",
"1 2.543747 12.649879 1.477723 \n",
"2 0.621357 7.682303 0.097144 \n",
"3 3.549558 8.789894 2.163735 \n",
"4 1.864637 3.779781 1.121703 \n",
"\n",
" university_km workplaces_km shopping_centers_km office_km \\\n",
"0 6.715026 0.884350 0.648488 0.637189 \n",
"1 1.852560 0.686252 0.519311 0.688796 \n",
"2 0.841254 1.510089 1.486533 1.543049 \n",
"3 10.903161 0.622272 0.599914 0.934273 \n",
"4 0.991683 0.892668 0.429052 0.077901 \n",
"\n",
" additional_education_km preschool_km big_church_km church_synagogue_km \\\n",
"0 0.947962 0.177975 0.625783 0.628187 \n",
"1 1.072315 0.273345 0.967821 0.471447 \n",
"2 0.391957 0.158072 3.178751 0.755946 \n",
"3 0.892674 0.236455 1.031777 1.561505 \n",
"4 0.810801 0.376838 0.378756 0.121681 \n",
"\n",
" mosque_km theater_km museum_km exhibition_km catering_km ecology \\\n",
"0 3.932040 14.053047 7.389498 7.023705 0.516838 good \n",
"1 4.841544 6.829889 0.709260 2.358840 0.230287 excellent \n",
"2 7.922152 4.273200 3.156423 4.958214 0.190462 poor \n",
"3 15.300449 16.990677 16.041521 5.029696 0.465820 good \n",
"4 2.584370 1.112486 1.800125 1.339652 0.026102 excellent \n",
"\n",
" green_part_500 prom_part_500 office_count_500 office_sqm_500 \\\n",
"0 0.00 0.00 0 0 \n",
"1 25.14 0.00 0 0 \n",
"2 1.67 0.00 0 0 \n",
"3 17.36 0.57 0 0 \n",
"4 3.56 4.44 15 293699 \n",
"\n",
" trc_count_500 trc_sqm_500 cafe_count_500 cafe_sum_500_min_price_avg \\\n",
"0 0 0 0 NaN \n",
"1 0 0 5 860.00 \n",
"2 0 0 3 666.67 \n",
"3 0 0 2 1000.00 \n",
"4 1 45000 48 702.22 \n",
"\n",
" cafe_sum_500_max_price_avg cafe_avg_price_500 cafe_count_500_na_price \\\n",
"0 NaN NaN 0 \n",
"1 1500.00 1180.00 0 \n",
"2 1166.67 916.67 0 \n",
"3 1500.00 1250.00 0 \n",
"4 1166.67 934.44 3 \n",
"\n",
" cafe_count_500_price_500 cafe_count_500_price_1000 \\\n",
"0 0 0 \n",
"1 1 3 \n",
"2 0 2 \n",
"3 0 0 \n",
"4 17 10 \n",
"\n",
" cafe_count_500_price_1500 cafe_count_500_price_2500 \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 1 0 \n",
"3 2 0 \n",
"4 11 7 \n",
"\n",
" cafe_count_500_price_4000 cafe_count_500_price_high big_church_count_500 \\\n",
"0 0 0 0 \n",
"1 1 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 1 \n",
"\n",
" church_count_500 mosque_count_500 leisure_count_500 sport_count_500 \\\n",
"0 0 0 0 1 \n",
"1 1 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 4 0 2 3 \n",
"\n",
" market_count_500 green_part_1000 prom_part_1000 office_count_1000 \\\n",
"0 0 7.36 0.00 1 \n",
"1 0 26.66 0.07 2 \n",
"2 0 4.99 0.29 0 \n",
"3 0 19.25 10.35 1 \n",
"4 0 3.34 8.29 46 \n",
"\n",
" office_sqm_1000 trc_count_1000 trc_sqm_1000 cafe_count_1000 \\\n",
"0 30500 3 55600 19 \n",
"1 86600 5 94065 13 \n",
"2 0 0 0 9 \n",
"3 11000 6 80780 12 \n",
"4 420952 3 158200 153 \n",
"\n",
" cafe_sum_1000_min_price_avg cafe_sum_1000_max_price_avg \\\n",
"0 527.78 888.89 \n",
"1 615.38 1076.92 \n",
"2 642.86 1142.86 \n",
"3 658.33 1083.33 \n",
"4 763.45 1272.41 \n",
"\n",
" cafe_avg_price_1000 cafe_count_1000_na_price cafe_count_1000_price_500 \\\n",
"0 708.33 1 10 \n",
"1 846.15 0 5 \n",
"2 892.86 2 0 \n",
"3 870.83 0 3 \n",
"4 1017.93 8 39 \n",
"\n",
" cafe_count_1000_price_1000 cafe_count_1000_price_1500 \\\n",
"0 4 3 \n",
"1 6 1 \n",
"2 5 2 \n",
"3 4 5 \n",
"4 45 39 \n",
"\n",
" cafe_count_1000_price_2500 cafe_count_1000_price_4000 \\\n",
"0 1 0 \n",
"1 0 1 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 19 2 \n",
"\n",
" cafe_count_1000_price_high big_church_count_1000 church_count_1000 \\\n",
"0 0 1 2 \n",
"1 0 1 2 \n",
"2 0 0 1 \n",
"3 0 0 0 \n",
"4 1 7 12 \n",
"\n",
" mosque_count_1000 leisure_count_1000 sport_count_1000 market_count_1000 \\\n",
"0 0 0 6 1 \n",
"1 0 4 2 0 \n",
"2 0 0 5 3 \n",
"3 0 0 3 1 \n",
"4 0 6 7 0 \n",
"\n",
" green_part_1500 prom_part_1500 office_count_1500 office_sqm_1500 \\\n",
"0 14.27 6.92 3 39554 \n",
"1 21.53 7.71 3 102910 \n",
"2 9.92 6.73 0 0 \n",
"3 28.38 6.57 2 11000 \n",
"4 4.12 4.83 93 1195735 \n",
"\n",
" trc_count_1500 trc_sqm_1500 cafe_count_1500 cafe_sum_1500_min_price_avg \\\n",
"0 9 171420 34 566.67 \n",
"1 7 127065 17 694.12 \n",
"2 1 2600 14 516.67 \n",
"3 7 89492 23 673.91 \n",
"4 9 445900 272 766.80 \n",
"\n",
" cafe_sum_1500_max_price_avg cafe_avg_price_1500 cafe_count_1500_na_price \\\n",
"0 969.70 768.18 1 \n",
"1 1205.88 950.00 0 \n",
"2 916.67 716.67 2 \n",
"3 1130.43 902.17 0 \n",
"4 1272.73 1019.76 19 \n",
"\n",
" cafe_count_1500_price_500 cafe_count_1500_price_1000 \\\n",
"0 14 11 \n",
"1 6 7 \n",
"2 4 6 \n",
"3 5 9 \n",
"4 70 74 \n",
"\n",
" cafe_count_1500_price_1500 cafe_count_1500_price_2500 \\\n",
"0 6 2 \n",
"1 1 2 \n",
"2 2 0 \n",
"3 8 1 \n",
"4 72 30 \n",
"\n",
" cafe_count_1500_price_4000 cafe_count_1500_price_high \\\n",
"0 0 0 \n",
"1 1 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 6 1 \n",
"\n",
" big_church_count_1500 church_count_1500 mosque_count_1500 \\\n",
"0 1 2 0 \n",
"1 1 5 0 \n",
"2 0 4 0 \n",
"3 1 0 0 \n",
"4 18 30 0 \n",
"\n",
" leisure_count_1500 sport_count_1500 market_count_1500 green_part_2000 \\\n",
"0 0 7 1 11.77 \n",
"1 4 9 0 22.37 \n",
"2 0 6 5 12.99 \n",
"3 0 9 2 32.29 \n",
"4 10 14 2 4.53 \n",
"\n",
" prom_part_2000 office_count_2000 office_sqm_2000 trc_count_2000 \\\n",
"0 15.97 9 188854 19 \n",
"1 19.25 4 165510 8 \n",
"2 12.75 4 100200 7 \n",
"3 5.73 2 11000 7 \n",
"4 5.02 149 1625130 17 \n",
"\n",
" trc_sqm_2000 cafe_count_2000 cafe_sum_2000_min_price_avg \\\n",
"0 1244891 36 614.29 \n",
"1 179065 21 695.24 \n",
"2 52550 24 563.64 \n",
"3 89492 25 660.00 \n",
"4 564843 483 765.93 \n",
"\n",
" cafe_sum_2000_max_price_avg cafe_avg_price_2000 cafe_count_2000_na_price \\\n",
"0 1042.86 828.57 1 \n",
"1 1190.48 942.86 0 \n",
"2 977.27 770.45 2 \n",
"3 1120.00 890.00 0 \n",
"4 1269.23 1017.58 28 \n",
"\n",
" cafe_count_2000_price_500 cafe_count_2000_price_1000 \\\n",
"0 15 11 \n",
"1 7 8 \n",
"2 8 9 \n",
"3 5 11 \n",
"4 130 129 \n",
"\n",
" cafe_count_2000_price_1500 cafe_count_2000_price_2500 \\\n",
"0 6 2 \n",
"1 3 2 \n",
"2 4 1 \n",
"3 8 1 \n",
"4 131 50 \n",
"\n",
" cafe_count_2000_price_4000 cafe_count_2000_price_high \\\n",
"0 1 0 \n",
"1 1 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 14 1 \n",
"\n",
" big_church_count_2000 church_count_2000 mosque_count_2000 \\\n",
"0 1 2 0 \n",
"1 1 5 0 \n",
"2 0 4 0 \n",
"3 1 1 0 \n",
"4 35 61 0 \n",
"\n",
" leisure_count_2000 sport_count_2000 market_count_2000 green_part_3000 \\\n",
"0 0 10 1 11.98 \n",
"1 4 11 0 18.07 \n",
"2 0 8 5 12.14 \n",
"3 0 13 2 20.79 \n",
"4 17 21 3 5.06 \n",
"\n",
" prom_part_3000 office_count_3000 office_sqm_3000 trc_count_3000 \\\n",
"0 13.55 12 251554 23 \n",
"1 27.32 12 821986 14 \n",
"2 26.46 8 110856 7 \n",
"3 3.57 4 167000 12 \n",
"4 8.62 305 3420907 60 \n",
"\n",
" trc_sqm_3000 cafe_count_3000 cafe_sum_3000_min_price_avg \\\n",
"0 1419204 68 639.68 \n",
"1 491565 30 631.03 \n",
"2 52550 41 697.44 \n",
"3 205756 32 718.75 \n",
"4 2296870 1068 853.03 \n",
"\n",
" cafe_sum_3000_max_price_avg cafe_avg_price_3000 cafe_count_3000_na_price \\\n",
"0 1079.37 859.52 5 \n",
"1 1086.21 858.62 1 \n",
"2 1192.31 944.87 2 \n",
"3 1218.75 968.75 0 \n",
"4 1410.45 1131.74 63 \n",
"\n",
" cafe_count_3000_price_500 cafe_count_3000_price_1000 \\\n",
"0 21 22 \n",
"1 11 11 \n",
"2 9 17 \n",
"3 5 14 \n",
"4 266 267 \n",
"\n",
" cafe_count_3000_price_1500 cafe_count_3000_price_2500 \\\n",
"0 16 3 \n",
"1 4 2 \n",
"2 9 3 \n",
"3 10 3 \n",
"4 262 149 \n",
"\n",
" cafe_count_3000_price_4000 cafe_count_3000_price_high \\\n",
"0 1 0 \n",
"1 1 0 \n",
"2 1 0 \n",
"3 0 0 \n",
"4 57 4 \n",
"\n",
" big_church_count_3000 church_count_3000 mosque_count_3000 \\\n",
"0 2 4 0 \n",
"1 1 7 0 \n",
"2 0 11 0 \n",
"3 1 2 0 \n",
"4 70 121 1 \n",
"\n",
" leisure_count_3000 sport_count_3000 market_count_3000 green_part_5000 \\\n",
"0 0 21 1 13.09 \n",
"1 6 19 1 10.26 \n",
"2 0 20 6 13.69 \n",
"3 0 18 3 14.18 \n",
"4 40 77 5 8.38 \n",
"\n",
" prom_part_5000 office_count_5000 office_sqm_5000 trc_count_5000 \\\n",
"0 13.31 29 807385 52 \n",
"1 27.47 66 2690465 40 \n",
"2 21.58 43 1478160 35 \n",
"3 3.89 8 244166 22 \n",
"4 10.92 689 8404624 114 \n",
"\n",
" trc_sqm_5000 cafe_count_5000 cafe_sum_5000_min_price_avg \\\n",
"0 4036616 152 708.57 \n",
"1 2034942 177 673.81 \n",
"2 1572990 122 702.68 \n",
"3 942180 61 931.58 \n",
"4 3503058 2283 853.88 \n",
"\n",
" cafe_sum_5000_max_price_avg cafe_avg_price_5000 cafe_count_5000_na_price \\\n",
"0 1185.71 947.14 12 \n",
"1 1148.81 911.31 9 \n",
"2 1196.43 949.55 10 \n",
"3 1552.63 1242.11 4 \n",
"4 1411.45 1132.66 143 \n",
"\n",
" cafe_count_5000_price_500 cafe_count_5000_price_1000 \\\n",
"0 39 48 \n",
"1 49 65 \n",
"2 29 45 \n",
"3 7 21 \n",
"4 566 578 \n",
"\n",
" cafe_count_5000_price_1500 cafe_count_5000_price_2500 \\\n",
"0 40 9 \n",
"1 36 15 \n",
"2 25 10 \n",
"3 15 11 \n",
"4 552 319 \n",
"\n",
" cafe_count_5000_price_4000 cafe_count_5000_price_high \\\n",
"0 4 0 \n",
"1 3 0 \n",
"2 3 0 \n",
"3 2 1 \n",
"4 108 17 \n",
"\n",
" big_church_count_5000 church_count_5000 mosque_count_5000 \\\n",
"0 13 22 1 \n",
"1 15 29 1 \n",
"2 11 27 0 \n",
"3 4 4 0 \n",
"4 135 236 2 \n",
"\n",
" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
"0 0 52 4 5850000 \n",
"1 10 66 14 6000000 \n",
"2 4 67 10 5700000 \n",
"3 0 26 3 13100000 \n",
"4 91 195 14 16331452 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Let's make the timestamp containing only month and year\n",
"df_train['timestamp'] = df_train['timestamp'].apply(lambda x: x[:4]+x[5:7])\n",
"df_group = df_train.groupby('timestamp')['price_doc'].aggregate(np.median).reset_index()\n",
"df_train.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "89f7fde3-613a-efbb-c35b-fec54027fa7b"
},
"source": [
"Now let's see the change in median price of houses over the months"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "ea7c47bd-9a20-9136-8428-2b8dbe515308"
},
"outputs": [
{
"data": {
"image/png": 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FwAXAs4GtwAmZeWdEHAR8BpgEbsvMk6rx3g28oWo/IzMvi4ilwBeApcBDwHGZuWFQa5Yk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\n/DKDlgEaHAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3ed5402630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,9))\n",
"sns.barplot(df_group.timestamp.values, df_group.price_doc.values, alpha=0.8)\n",
"plt.xlabel(\"Months\")\n",
"plt.ylabel(\"Price\")\n",
"plt.xticks(rotation='vertical')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "61f2188f-2029-9690-4383-96db7550d789"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 36,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162537.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0bf669ae-359e-9db0-fa2a-4bf21699e39f"
},
"source": [
"I manually manipulated the Titanic competition dataset to be better suited for binary logistic regression."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "94b736ec-22fe-4fdd-609e-fa52ee6180fc"
},
"outputs": [],
"source": [
"# Importing the required libraries (plus no heavy use of scikit-learn):\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"from sklearn.utils import shuffle"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "4ffa6327-3ca6-77e0-4bea-46abcd9b6457"
},
"outputs": [],
"source": [
"# Processing the data:\n",
"\n",
"def get_data():\n",
"\ttitanic = pd.read_csv(\"../input/train_and_test2.csv\")\n",
"\tdata = titanic.as_matrix()\n",
"\tX = data[:,1:-1]\n",
"\tY = data[:,-1]\n",
"\tX[:,1] = (X[:,1]-X[:,1].mean())/X[:,1].std()\n",
"\tX[:,0] = (X[:,0]-X[:,0].mean())/X[:,0].std()\n",
"\tN, D = X.shape\n",
"\tX2 = np.zeros((N,D))\n",
"\tX2[:,0:3] = X[:,0:3]\n",
" \n",
"\t# One-Hot-Encoding:\n",
"\tfor n in range(N):\n",
"\t\tt = int(X[n,D-3]) #Embarked\n",
"\t\tt2 = int(X[n,D-6]) #pclass\n",
"\t\tt3 = int(X[n,D-15]) #parch\n",
"\t\tt4 = int(X[n,D-23]) #sibsp\n",
"\t\tX2[n,t+D-3] = 1\n",
"\t\tX2[n,t2+D-6] = 1\n",
"\t\tX2[n,t3+D-15] = 1\n",
"\t\tX2[n,t4+D-23] = 1\n",
"\n",
"\t\treturn X2, Y\n",
"\n",
"def get_binary_data():\n",
"\tX, Y = get_data()\n",
"\tX2 = X[Y <= 1]\n",
"\tY2 = Y[Y <= 1]\n",
"\treturn X2, Y2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "5f9759f6-f086-9ce1-4a69-1839408e1f89"
},
"outputs": [],
"source": [
"X, Y = get_binary_data()\n",
"X, Y = shuffle(X,Y)\n",
"\n",
"Xtrain = X[0:891,:]\n",
"Ytrain = Y[0:891]\n",
"Xtest = X[-418:]\n",
"Ytest = Y[-418:] # Which I manually put to be zero in every row!\n",
"\n",
"D = X.shape[1]\n",
"W = np.random.randn(D)\n",
"b = 0"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "daa7e2d2-2887-85f6-0b32-17f2d2f83eff"
},
"outputs": [],
"source": [
"# Making some necessary functions:\n",
"\n",
"def sigmoid(z):\n",
"\treturn 1/(1+np.exp(-z))\n",
"\n",
"def forward(X,W,b):\n",
"\treturn sigmoid(X.dot(W)+b)\n",
"\n",
"def classification_rate(targets,predictions):\n",
"\treturn np.mean(targets == predictions)\n",
"\n",
"def cross_entropy(T,pY):\n",
"\treturn -np.mean(T*np.log(pY) + (1-T)*np.log(1-pY))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "90cb5dbb-c2ce-f479-893b-d4c41f5e733a"
},
"outputs": [],
"source": [
"# Logistic regression via gradient descent plus L2 regularization:\n",
"\n",
"train_costs = []\n",
"test_costs = []\n",
"learning_rate = 0.001\n",
"\n",
"for i in range(10000):\n",
"\tpYtrain = forward(Xtrain,W,b)\n",
"\tpYtest = forward(Xtest,W,b)\n",
"\n",
"\tctrain = cross_entropy(Ytrain,pYtrain)\n",
"\tctest = cross_entropy(Ytest,pYtest)\n",
"\ttrain_costs.append(ctrain)\n",
"\ttest_costs.append(ctest)\n",
"\n",
"\tW -= learning_rate*(Xtrain.T.dot(pYtrain-Ytrain)-0.1*W)\n",
"\tb -= learning_rate*(pYtrain-Ytrain).sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "144e3986-04b1-209b-f7b6-363a7cc04fd8"
},
"outputs": [
{
"data": {
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Bgwdr8ODBDVtVmDg5GgBgIqOvmGVxejQAwGBGh/BxDIUBAOYxPIQZCQMAzGV0CNdEMONg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f6e680a93c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Final Train Classification Rate: 0.769921436588\n",
"Final Test Classification Rate: 0.755980861244\n"
]
}
],
"source": [
"# Displaying my model:\n",
"\n",
"legend1, = plt.plot(train_costs,label='Train Cost')\n",
"legend2, = plt.plot(test_costs,label='Test Cost')\n",
"plt.legend([legend1,legend2])\n",
"plt.show()\n",
"\n",
"print(\"Final Train Classification Rate: \",classification_rate(Ytrain,np.round(pYtrain)))\n",
"print(\"Final Test Classification Rate: \",classification_rate(Ytest,np.round(pYtest)))"
]
}
],
"metadata": {
"_change_revision": 2,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162657.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "50cfce73-0cd8-092a-fe57-9c3ac000e875"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# visualization\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.metrics import mean_squared_error\n",
"import xgboost as xgb\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn import model_selection, preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "912d98a7-bb10-cab8-0fa8-d38f4b341d7c"
},
"outputs": [],
"source": [
"train_df = pd.read_csv('../input/train.csv',parse_dates=['timestamp'])\n",
"result_df = pd.read_csv('../input/test.csv',parse_dates=['timestamp'])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c32209e5-5655-1787-2d44-2bb952ca708f"
},
"outputs": [],
"source": [
"dummies = pd.get_dummies(train_df['sub_area']).rename(columns=lambda x: 'sub_area_' + str(x))\n",
"train_df = pd.concat([train_df, dummies], axis=1)\n",
"train_df = train_df.drop('sub_area',1)\n",
"train_df = train_df.drop('sub_area_Poselenie Klenovskoe',1)\n",
"\n",
"dummies = pd.get_dummies(result_df['sub_area']).rename(columns=lambda x: 'sub_area_' + str(x))\n",
"result_df = pd.concat([result_df, dummies], axis=1)\n",
"result_df = result_df.drop('sub_area',1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "c9be57ef-bcb3-c3ff-338f-d6f433c645cc"
},
"outputs": [],
"source": [
"for c in train_df.columns:\n",
" if train_df[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(train_df[c].values)) \n",
" train_df[c] = lbl.transform(list(train_df[c].values))\n",
" #x_train.drop(c,axis=1,inplace=True)\n",
" \n",
"for c in result_df.columns:\n",
" if result_df[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(result_df[c].values)) \n",
" result_df[c] = lbl.transform(list(result_df[c].values))\n",
" #x_test.drop(c,axis=1,inplace=True) \n",
" \n",
"all_df_values = train_df\n",
"result_df_values = result_df"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "d81e968c-2c8c-3a42-16d4-cdf064020037"
},
"outputs": [
{
"data": {
"text/plain": [
"(30471, 433)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_all = all_df_values.drop(['price_doc','id','timestamp'],axis=1)\n",
"Y_all = all_df_values['price_doc'].values.reshape(-1,1)\n",
"X_all.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "8b7b7718-d741-ab52-06bc-f83f946bcfbe"
},
"outputs": [
{
"data": {
"text/plain": [
"(7662, 433)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_result = result_df_values.drop(['id','timestamp'],axis=1)\n",
"id_test = result_df_values['id']\n",
"X_result.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "46e17dae-f980-cc0f-f8ac-1012251e4d21"
},
"outputs": [],
"source": [
"#dtrain = xgb.DMatrix(X_train[:], Y_train[:])\n",
"#dtest = xgb.DMatrix(X_test, Y_test)\n",
"dall = xgb.DMatrix(X_all,Y_all)\n",
"dresult=xgb.DMatrix(X_result)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "59b9c470-d047-916c-7af0-608ea01d4c58"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\tall-rmse:8.20853e+06\n",
"[20]\tall-rmse:3.99548e+06\n",
"[40]\tall-rmse:2.80664e+06\n",
"[60]\tall-rmse:2.45841e+06\n",
"[80]\tall-rmse:2.33156e+06\n",
"[100]\tall-rmse:2.27011e+06\n",
"[120]\tall-rmse:2.21791e+06\n",
"[140]\tall-rmse:2.17655e+06\n",
"[160]\tall-rmse:2.1493e+06\n",
"[180]\tall-rmse:2.127e+06\n",
"[200]\tall-rmse:2.10054e+06\n",
"[220]\tall-rmse:2.06923e+06\n",
"[240]\tall-rmse:2.0409e+06\n",
"[260]\tall-rmse:2.02054e+06\n",
"[280]\tall-rmse:1.9999e+06\n",
"[300]\tall-rmse:1.98e+06\n",
"[320]\tall-rmse:1.95685e+06\n",
"[340]\tall-rmse:1.93656e+06\n",
"[360]\tall-rmse:1.91804e+06\n",
"[380]\tall-rmse:1.90037e+06\n",
"[399]\tall-rmse:1.88703e+06\n"
]
}
],
"source": [
"xgb_params = {\n",
" 'eta': 0.05,\n",
" 'max_depth': 5,\n",
" 'subsample': 0.7,\n",
" 'colsample_bytree': 0.7,\n",
" 'objective': 'reg:linear',\n",
" 'eval_metric': 'rmse',\n",
" 'silent': 1\n",
"}\n",
"# Uncomment to tune XGB `num_boost_rounds`\n",
"#model = xgb.cv(xgb_params, dtrain, num_boost_round=200,\n",
" #early_stopping_rounds=30, verbose_eval=10)\n",
"model = xgb.train(xgb_params, dall, num_boost_round=400,verbose_eval=20,evals=[(dall,'all')])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "e5520862-a82a-495c-9f44-cb5db3d4f5d1"
},
"outputs": [],
"source": [
"y_pred=model.predict(dresult)\n",
"output=pd.DataFrame(data={'price_doc':y_pred},index=id_test)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "5beb30f0-e48f-6415-44e7-6766b0c343c5"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>30474</th>\n",
" <td>5615906.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30475</th>\n",
" <td>8441718.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30476</th>\n",
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" <td>5784106.0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>5211336.5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
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"text/plain": [
" price_doc\n",
"id \n",
"30474 5615906.5\n",
"30475 8441718.0\n",
"30476 5429942.0\n",
"30477 5784106.0\n",
"30478 5211336.5"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "c7369d2d-f24c-ae51-7261-9bcd9ce88501"
},
"outputs": [],
"source": [
"output.to_csv('output.csv',header=True)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "b749a59f-6954-cc76-fb16-76e3392bb216"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 95,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162712.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d2a6d819-0b1a-d7c0-7d96-0f88c99d896c"
},
"source": [
"# This notebook follows the blog post from Dataquest.io for the older House Prices Competition, adapted for the new data set.\n",
"\n",
"[https://www.dataquest.io/blog/kaggle-getting-started/?utm_campaign=Data%2BElixir&utm_medium=email&utm_source=Data_Elixir_131][1]\n",
"\n",
"\n",
" [1]: https://www.dataquest.io/blog/kaggle-getting-started/?utm_campaign=Data%2BElixir&utm_medium=email&utm_source=Data_Elixir_131"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "b4c99295-0611-0812-a83b-e7e80b5dcbf7"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"macro.csv\n",
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "43f3134c-894e-ff45-465f-775a191e0c98"
},
"source": [
"# Step 1: Acquire the data and create our environment"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3e7ddac1-b48c-3c2a-52c6-044727cca40f"
},
"outputs": [],
"source": [
"# import packages\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c1d3fec9-fef4-b5f6-8cda-2a204d9853fa"
},
"outputs": [],
"source": [
"# import data\n",
"train = pd.read_csv('../input/train.csv')\n",
"test = pd.read_csv('../input/test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "47c8bca3-e05e-bd2c-fa46-6d032eea7d0f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train data shape: (30471, 292)\n",
"Test data shape: (7662, 291)\n"
]
}
],
"source": [
"# check out size of data\n",
"print (\"Train data shape:\", train.shape)\n",
"print (\"Test data shape:\", test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "8321ffdd-acf9-9a6b-2c63-84156e0d0d44"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>timestamp</th>\n",
" <th>full_sq</th>\n",
" <th>life_sq</th>\n",
" <th>floor</th>\n",
" <th>max_floor</th>\n",
" <th>material</th>\n",
" <th>build_year</th>\n",
" <th>num_room</th>\n",
" <th>kitch_sq</th>\n",
" <th>...</th>\n",
" <th>cafe_count_5000_price_2500</th>\n",
" <th>cafe_count_5000_price_4000</th>\n",
" <th>cafe_count_5000_price_high</th>\n",
" <th>big_church_count_5000</th>\n",
" <th>church_count_5000</th>\n",
" <th>mosque_count_5000</th>\n",
" <th>leisure_count_5000</th>\n",
" <th>sport_count_5000</th>\n",
" <th>market_count_5000</th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-08-20</td>\n",
" <td>43</td>\n",
" <td>27.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>22</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>4</td>\n",
" <td>5850000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-08-23</td>\n",
" <td>34</td>\n",
" <td>19.0</td>\n",
" <td>3.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>29</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>66</td>\n",
" <td>14</td>\n",
" <td>6000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-08-27</td>\n",
" <td>43</td>\n",
" <td>29.0</td>\n",
" <td>2.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>67</td>\n",
" <td>10</td>\n",
" <td>5700000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-09-01</td>\n",
" <td>89</td>\n",
" <td>50.0</td>\n",
" <td>9.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>11</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" <td>3</td>\n",
" <td>13100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-09-05</td>\n",
" <td>77</td>\n",
" <td>77.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>...</td>\n",
" <td>319</td>\n",
" <td>108</td>\n",
" <td>17</td>\n",
" <td>135</td>\n",
" <td>236</td>\n",
" <td>2</td>\n",
" <td>91</td>\n",
" <td>195</td>\n",
" <td>14</td>\n",
" <td>16331452</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 292 columns</p>\n",
"</div>"
],
"text/plain": [
" id timestamp full_sq life_sq floor max_floor material build_year \\\n",
"0 1 2011-08-20 43 27.0 4.0 NaN NaN NaN \n",
"1 2 2011-08-23 34 19.0 3.0 NaN NaN NaN \n",
"2 3 2011-08-27 43 29.0 2.0 NaN NaN NaN \n",
"3 4 2011-09-01 89 50.0 9.0 NaN NaN NaN \n",
"4 5 2011-09-05 77 77.0 4.0 NaN NaN NaN \n",
"\n",
" num_room kitch_sq ... cafe_count_5000_price_2500 \\\n",
"0 NaN NaN ... 9 \n",
"1 NaN NaN ... 15 \n",
"2 NaN NaN ... 10 \n",
"3 NaN NaN ... 11 \n",
"4 NaN NaN ... 319 \n",
"\n",
" cafe_count_5000_price_4000 cafe_count_5000_price_high \\\n",
"0 4 0 \n",
"1 3 0 \n",
"2 3 0 \n",
"3 2 1 \n",
"4 108 17 \n",
"\n",
" big_church_count_5000 church_count_5000 mosque_count_5000 \\\n",
"0 13 22 1 \n",
"1 15 29 1 \n",
"2 11 27 0 \n",
"3 4 4 0 \n",
"4 135 236 2 \n",
"\n",
" leisure_count_5000 sport_count_5000 market_count_5000 price_doc \n",
"0 0 52 4 5850000 \n",
"1 10 66 14 6000000 \n",
"2 4 67 10 5700000 \n",
"3 0 26 3 13100000 \n",
"4 91 195 14 16331452 \n",
"\n",
"[5 rows x 292 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# first look\n",
"train.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "e3ac9d12-9828-f753-3c98-6bfa4fd2e539"
},
"outputs": [],
"source": [
"# import matplotlib for visualization\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use(style='ggplot')\n",
"plt.rcParams['figure.figsize'] = (10, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f45478df-9a46-0c45-0c1f-dc3562aa7010"
},
"source": [
"# Step 2: Explore the data and engineer Features"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "e18c6fa5-1580-6654-55ab-bff9147370b8"
},
"outputs": [
{
"data": {
"text/plain": [
"count 3.047100e+04\n",
"mean 7.123035e+06\n",
"std 4.780111e+06\n",
"min 1.000000e+05\n",
"25% 4.740002e+06\n",
"50% 6.274411e+06\n",
"75% 8.300000e+06\n",
"max 1.111111e+08\n",
"Name: price_doc, dtype: float64"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check out target variable\n",
"train['price_doc'].describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "16433716-cdd3-bca7-e028-7327d4b1b540"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skew is: 4.47474487357\n"
]
},
{
"data": {
"image/png": 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ubq7y8vLU09OjaDSqEydOqLi4WJZlqby8PP4cAACA80nSlzI9Ho9uvvlm3XHHHbrgggu0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\nJH9zAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0c408a630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print (\"Skew is:\", train['price_doc'].skew())\n",
"plt.hist(train['price_doc'], color='blue', bins=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "879f56d3-69b2-b149-1808-13c89acd92fe"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skew is: -0.686715679719\n"
]
},
{
"data": {
"image/png": 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Jcvz48fT19WViYiJdXV3ZuXNnKpXK5Y4GALCgXPYZs3/913/NunXrpr7u7+/P5s2bs3//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\nMwCAQggzAIBCCDMAgEL8P64V84vfsjT+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0ee985438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"target = np.log(train['price_doc'])\n",
"print (\"Skew is:\", target.skew())\n",
"plt.hist(target, color='blue', bins=20)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "d1c70cc7-1757-36ff-0e5c-e073c254896c"
},
"outputs": [
{
"data": {
"text/plain": [
"id int64\n",
"full_sq int64\n",
"life_sq float64\n",
"floor float64\n",
"max_floor float64\n",
"material float64\n",
"build_year float64\n",
"num_room float64\n",
"kitch_sq float64\n",
"state float64\n",
"area_m float64\n",
"raion_popul int64\n",
"green_zone_part float64\n",
"indust_part float64\n",
"children_preschool int64\n",
"preschool_quota float64\n",
"preschool_education_centers_raion int64\n",
"children_school int64\n",
"school_quota float64\n",
"school_education_centers_raion int64\n",
"school_education_centers_top_20_raion int64\n",
"hospital_beds_raion float64\n",
"healthcare_centers_raion int64\n",
"university_top_20_raion int64\n",
"sport_objects_raion int64\n",
"additional_education_raion int64\n",
"culture_objects_top_25_raion int64\n",
"shopping_centers_raion int64\n",
"office_raion int64\n",
"full_all int64\n",
" ... \n",
"big_church_count_3000 int64\n",
"church_count_3000 int64\n",
"mosque_count_3000 int64\n",
"leisure_count_3000 int64\n",
"sport_count_3000 int64\n",
"market_count_3000 int64\n",
"green_part_5000 float64\n",
"prom_part_5000 float64\n",
"office_count_5000 int64\n",
"office_sqm_5000 int64\n",
"trc_count_5000 int64\n",
"trc_sqm_5000 int64\n",
"cafe_count_5000 int64\n",
"cafe_sum_5000_min_price_avg float64\n",
"cafe_sum_5000_max_price_avg float64\n",
"cafe_avg_price_5000 float64\n",
"cafe_count_5000_na_price int64\n",
"cafe_count_5000_price_500 int64\n",
"cafe_count_5000_price_1000 int64\n",
"cafe_count_5000_price_1500 int64\n",
"cafe_count_5000_price_2500 int64\n",
"cafe_count_5000_price_4000 int64\n",
"cafe_count_5000_price_high int64\n",
"big_church_count_5000 int64\n",
"church_count_5000 int64\n",
"mosque_count_5000 int64\n",
"leisure_count_5000 int64\n",
"sport_count_5000 int64\n",
"market_count_5000 int64\n",
"price_doc int64\n",
"dtype: object"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numeric_features = train.select_dtypes(include=[np.number])\n",
"numeric_features.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "8203e428-8474-6238-380f-e972da353f46"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"price_doc 1.000000\n",
"num_room 0.476337\n",
"full_sq 0.341840\n",
"sport_count_5000 0.294864\n",
"sport_count_3000 0.290651\n",
"Name: price_doc, dtype: float64 \n",
"\n",
"ttk_km -0.272620\n",
"bulvar_ring_km -0.279158\n",
"kremlin_km -0.279249\n",
"sadovoe_km -0.283622\n",
"zd_vokzaly_avto_km -0.284069\n",
"Name: price_doc, dtype: float64\n"
]
}
],
"source": [
"corr = numeric_features.corr()\n",
"\n",
"print (corr['price_doc'].sort_values(ascending=False)[:5], '\\n')\n",
"print (corr['price_doc'].sort_values(ascending=False)[-5:])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "1fc79cd8-eca0-1e7d-3321-c444f16ff1be"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ nan, 2., 1., 3., 4., 5., 6., 0., 19., 10., 8.,\n",
" 7., 17., 9.])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['num_room'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "2d086388-9788-30a6-ba60-52c1c46ed776"
},
"outputs": [],
"source": [
"numroom_pivot = train.pivot_table(index='num_room',\n",
" values='price_doc', aggfunc=np.median)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "60ee27ab-f725-b2aa-9a87-de0a6b438370"
},
"outputs": [
{
"data": {
"text/plain": [
"num_room\n",
"0.0 7590001\n",
"1.0 5250000\n",
"2.0 6824493\n",
"3.0 9205505\n",
"4.0 14400000\n",
"5.0 16850000\n",
"6.0 23000000\n",
"7.0 25500000\n",
"8.0 35000000\n",
"9.0 95122496\n",
"10.0 8500000\n",
"17.0 13150000\n",
"19.0 2630000\n",
"Name: price_doc, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numroom_pivot"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "2407e8c8-0b72-7265-6cc0-8712c73f7272"
},
"outputs": [
{
"data": {
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AACDUXbScjR07Npg5AAAAoCrKWceOHYOZAwAAAKrGMWcHDhzQrl27dPz4cVmW5V1+7733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\nAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0b79e4080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"numroom_pivot.plot(kind='bar', color='blue')\n",
"plt.xlabel('Number of Rooms')\n",
"plt.ylabel('Median Sale Price')\n",
"plt.xticks(rotation=0)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "4b023a85-925d-1e6b-65d1-d9e80efff67e"
},
"outputs": [
{
"data": {
"image/png": 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77jnl5eXp5MmTXnRlUFZjfXQ/aOGAdQAAEBtPAtmiRYt077339rteW1ur/fv3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\nz/4/Fv7MuTVCwqkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0b7a24278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=train['full_sq'], y=target)\n",
"plt.ylabel('Sale Price')\n",
"plt.xlabel('Full Sq')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "72e74098-dd9d-f0d0-82a9-a75f9d3cbd93"
},
"outputs": [
{
"data": {
"text/plain": [
"(30455, 292)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Outlier handling. <1000 revealed there were other outliers >250 & <1000\n",
"train = train[train['full_sq'] < 250]\n",
"train.shape"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "565c85e0-33d1-a632-bf28-9b2b0644d6fe"
},
"outputs": [
{
"data": {
"image/png": 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/I8LlgsvXhOoVj2DkP17F+Dt7ktcJh6Hs3Q3p+GF+5WMuMdjvXIujCC5/E9xN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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0c407d518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=train['full_sq'], y=np.log(train['price_doc']))\n",
"# plt.xlim(-200,1600) # This forces the same scale as before\n",
"plt.ylabel('Sale Price')\n",
"plt.xlabel('Full Sq')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "98fa451d-4e9c-03a3-af44-794a1d7eca83"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "x and y must be the same size",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-70df460142b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sport_count_5000'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Sale Price'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sport_count_5000'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 3433\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3434\u001b[0m \u001b[0mlinewidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlinewidths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverts\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3435\u001b[0;31m edgecolors=edgecolors, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 3436\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3437\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1890\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1891\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1892\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1893\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1894\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mscatter\u001b[0;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, verts, edgecolors, **kwargs)\u001b[0m\n\u001b[1;32m 3956\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3957\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3958\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"x and y must be the same size\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3960\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: x and y must be the same size"
]
},
{
"data": {
"image/png": 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IbAEAJBJbAACJxBYAQCKxBQCQSGwBACQSWwAAicQWAEAisQUAkEhsAQAkElsAAInEFgBA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\nSUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0a3808ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=train['sport_count_5000'], y=target)\n",
"plt.ylabel('Sale Price')\n",
"plt.xlabel('sport_count_5000')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "59b574db-6ebf-8f61-452f-896a4d8e2a7d"
},
"source": [
"# Handling Null Values"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "e399c03c-a571-ad48-c26f-ff08a66f3331"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Null Count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Feature</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>hospital_beds_raion</th>\n",
" <td>14438</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_year</th>\n",
" <td>13592</td>\n",
" </tr>\n",
" <tr>\n",
" <th>state</th>\n",
" <td>13546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cafe_avg_price_500</th>\n",
" <td>13272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cafe_sum_500_max_price_avg</th>\n",
" <td>13272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cafe_sum_500_min_price_avg</th>\n",
" <td>13272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max_floor</th>\n",
" <td>9562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>material</th>\n",
" <td>9562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>num_room</th>\n",
" <td>9562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>kitch_sq</th>\n",
" <td>9562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>preschool_quota</th>\n",
" <td>6688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>school_quota</th>\n",
" <td>6685</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cafe_sum_1000_min_price_avg</th>\n",
" <td>6521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cafe_sum_1000_max_price_avg</th>\n",
" <td>6521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cafe_avg_price_1000</th>\n",
" <td>6521</td>\n",
" </tr>\n",
" <tr>\n",
" <th>life_sq</th>\n",
" <td>6379</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_count_frame</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_count_1971-1995</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_count_block</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>raion_build_count_with_material_info</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_count_after_1995</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_count_brick</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_count_wood</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_count_mix</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>build_count_1921-1945</th>\n",
" <td>4991</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Null Count\n",
"Feature \n",
"hospital_beds_raion 14438\n",
"build_year 13592\n",
"state 13546\n",
"cafe_avg_price_500 13272\n",
"cafe_sum_500_max_price_avg 13272\n",
"cafe_sum_500_min_price_avg 13272\n",
"max_floor 9562\n",
"material 9562\n",
"num_room 9562\n",
"kitch_sq 9562\n",
"preschool_quota 6688\n",
"school_quota 6685\n",
"cafe_sum_1000_min_price_avg 6521\n",
"cafe_sum_1000_max_price_avg 6521\n",
"cafe_avg_price_1000 6521\n",
"life_sq 6379\n",
"build_count_frame 4991\n",
"build_count_1971-1995 4991\n",
"build_count_block 4991\n",
"raion_build_count_with_material_info 4991\n",
"build_count_after_1995 4991\n",
"build_count_brick 4991\n",
"build_count_wood 4991\n",
"build_count_mix 4991\n",
"build_count_1921-1945 4991"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nulls = pd.DataFrame(train.isnull().sum().sort_values(ascending=False)[:25])\n",
"nulls.columns = ['Null Count']\n",
"nulls.index.name = 'Feature'\n",
"nulls"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a1b71aec-73fa-e8e1-8ad2-630c98b03d65"
},
"source": [
"# Wrangling the non-numeric Features"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "881cad27-eda8-de58-9891-a87bab51ff60"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>timestamp</th>\n",
" <th>product_type</th>\n",
" <th>sub_area</th>\n",
" <th>culture_objects_top_25</th>\n",
" <th>thermal_power_plant_raion</th>\n",
" <th>incineration_raion</th>\n",
" <th>oil_chemistry_raion</th>\n",
" <th>radiation_raion</th>\n",
" <th>railroad_terminal_raion</th>\n",
" <th>big_market_raion</th>\n",
" <th>nuclear_reactor_raion</th>\n",
" <th>detention_facility_raion</th>\n",
" <th>water_1line</th>\n",
" <th>big_road1_1line</th>\n",
" <th>railroad_1line</th>\n",
" <th>ecology</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" <td>30455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>1161</td>\n",
" <td>2</td>\n",
" <td>146</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>2014-12-16</td>\n",
" <td>Investment</td>\n",
" <td>Poselenie Sosenskoe</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>poor</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>160</td>\n",
" <td>19439</td>\n",
" <td>1776</td>\n",
" <td>28528</td>\n",
" <td>28801</td>\n",
" <td>28141</td>\n",
" <td>30159</td>\n",
" <td>19595</td>\n",
" <td>29320</td>\n",
" <td>27634</td>\n",
" <td>29593</td>\n",
" <td>27413</td>\n",
" <td>28119</td>\n",
" <td>29675</td>\n",
" <td>29563</td>\n",
" <td>8014</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" timestamp product_type sub_area culture_objects_top_25 \\\n",
"count 30455 30455 30455 30455 \n",
"unique 1161 2 146 2 \n",
"top 2014-12-16 Investment Poselenie Sosenskoe no \n",
"freq 160 19439 1776 28528 \n",
"\n",
" thermal_power_plant_raion incineration_raion oil_chemistry_raion \\\n",
"count 30455 30455 30455 \n",
"unique 2 2 2 \n",
"top no no no \n",
"freq 28801 28141 30159 \n",
"\n",
" radiation_raion railroad_terminal_raion big_market_raion \\\n",
"count 30455 30455 30455 \n",
"unique 2 2 2 \n",
"top no no no \n",
"freq 19595 29320 27634 \n",
"\n",
" nuclear_reactor_raion detention_facility_raion water_1line \\\n",
"count 30455 30455 30455 \n",
"unique 2 2 2 \n",
"top no no no \n",
"freq 29593 27413 28119 \n",
"\n",
" big_road1_1line railroad_1line ecology \n",
"count 30455 30455 30455 \n",
"unique 2 2 5 \n",
"top no no poor \n",
"freq 29675 29563 8014 "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"categoricals = train.select_dtypes(exclude=[np.number])\n",
"categoricals.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "52be1499-e496-bda9-80c8-49377f3a8572"
},
"source": [
"# Transforming and engineering features"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "258a6428-27fa-8f9a-bbc8-ca91c4cae6e2"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original: \n",
"\n",
"Investment 19439\n",
"OwnerOccupier 11016\n",
"Name: product_type, dtype: int64 \n",
"\n"
]
}
],
"source": [
"print (\"Original: \\n\") \n",
"print (train['product_type'].value_counts(), \"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "f7f12de3-c99a-e943-9a4b-98b745f42edc"
},
"outputs": [],
"source": [
"train['enc_product_type'] = pd.get_dummies(train['product_type'], drop_first=True)\n",
"test['enc_product_type'] = pd.get_dummies(train['product_type'], drop_first=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "f5afc59d-52cd-d242-9e86-6a09596c422a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Encoded: \n",
"\n",
"0 19439\n",
"1 11016\n",
"Name: enc_product_type, dtype: int64\n"
]
}
],
"source": [
"print ('Encoded: \\n') \n",
"print (train['enc_product_type'].value_counts())"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "377f698c-9166-e49f-40f7-2c470cb391f3"
},
"outputs": [
{
"data": {
"image/png": 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WlgKQk5NDVFRUP/do8NBY+q/BXrsLLhjSD3uN+fJFesmJE+0+25f0rsH+szfYDdT6eRUI\nOzo6KC4uZvPmzbS0tPDCCy9QW1vLwYMH+fa3v+3Vjux2OwDDhw9n6tSp7Nq1i+HDh9PU1ERkZCRN\nTU2Eh4d7lm1sbPSs63K5sNvtp7U3NjZ6tnvqPYfDQVdXF62trYSFhWG329mxY0ePbU2ePJmwsDBa\nW1vp6uoiMDDQs48zSUtLIy0tzfO6oaHBq8/cO3z3C6I/+HYsfU2182+qn38bvPVT7fybr+v3rxNw\nZ+PVOYQvvPACn332GYsWLfLM4o0dO5Y33njDq520tbVx/Phxz9cffPAB48aNIykpiYqKCgAqKiqY\nOnUqAElJSVRWVtLR0UF9fT0HDx5kwoQJREZGMmzYMHbu3IlpmmzatImkpCQApkyZQnl5OQBVVVXE\nxcVhGAZOp5Pa2lrcbjdut5va2lqcTieGYRAXF0dVVRVw8krkU9sSERERsRKvZgjfeecd8vPzGTp0\nqCcQns85d83NzTz++OMAdHV1cdVVV+F0Orn44ovJzc2lrKzMc9sZOBk2r7zySu6++24CAgL4yU9+\nQkDAyex6yy23sGLFCtrb23E6nSQmJgIwa9YsCgsLyczMxGazkZWVBYDNZuO6667jF7/4BQDXX3+9\n59Y2P/rRj8jLy2PdunWMHz+eWbNmefV5RERERAYTwzRN88sWWrBgAY8//jghISFkZGTw/PPPc/To\nUe6//34KCgp80c8B5cCBAz7b1+jRg3vqfP9+342lr6l2/k3182+DuX6qnX/zdf169ZBxcnIyhYWF\n1NfXA9DU1MTq1auZNm3aV++hiIiIiAwIXgXCOXPmEB0dzT333ENrayuLFi0iMjKS66+/vq/7JyIi\nIiJ9zKtzCIOCgpg7dy5z587l6NGjhIWFDdhbxIiIiIjI+fFqhrCiooJPPvkEgPDwcAzDYO/evWza\ntKlPOyciIiIifc+rQPjSSy/1eNoHnLyx4rp16/qkUyIiIiLiO14FwuPHjxMSEtKjLSQkhGPHjvVJ\np0RERETEd7wKhGPGjPHcwPmUd955hzFjxvRJp0RERETEd7y6qORHP/oRS5YsobKyklGjRnHo0CG2\nbt3qudmziIiIiPgvrwLh1772NR5//HE2b95MQ0MDEyZMYO7cuQP2Ac0iIiIi4j2vAiHAiBEjuPba\na/uyLyIiIiLSD84aCJ9++mnmz58PQEFBwVnvO3jnnXf2Tc9ERERExCfOGgijo6M9X48aNconnRER\nERER3ztrIPz+978PQHd3Nw6Hg6uuuoohQ4b4rGMiIiIi4htfetuZgIAAfve73ykMioiIiAxSXt2H\ncMqUKWzZsqWv+yIiIiIi/cCrq4w7OjpYvnw5kyZNwuFw9LjARBeViIiIiPg3rwLh2LFjGTt2bF/3\nRURERET6gVeB8IYbbujrfoiIiIhIPzlnIDxw4AArVqzgs88+Y/z48SxYsKDH7WhERERExP+d86KS\n5557jujoaO666y7sdju//e1vfdQtEREREfGVc84Q7tmzh5UrVzJkyBAmT57MXXfd5at+iYiIiIiP\nnHOGsLOz03P/waFDh9Le3u6TTomIiIiI75xzhrCjo4OXXnrJ87q9vb3Ha4Af/OAHfdMzEREREfGJ\ncwbCq666isbGRs/r6dOn93gtIiIiIv7vnIFwwYIFvuqHiIiIiPQTrx5dJyIiIiKDlwKhiIiIiMUp\nEIqIiIhYnAKhiIiIiMV59SxjgA8++IDNmzfT3NzMfffdx+7duzl+/DiXXXZZX/ZPRERERPqYVzOE\nr732GqtWreLCCy/kww8/BGDIkCGsW7euTzsnIiIiIn3Pq0D4l7/8hV/96ldce+21BAScXGX06NEc\nOHCgTzsnIiIiIn3Pq0B4/PhxoqKierR1dnYSFOT1EWcRERERGaC8CoSXXnopJSUlPdpee+014uLi\n+qRTIiIiIuI7Xk3xzZs3j6VLl/Lmm2/S1tbGXXfdxbBhw7jvvvvOa2fd3d3cd9992O127rvvPtxu\nN7m5uRw5coQRI0awePFibDYbABs2bKCsrIyAgAAyMjJwOp0A1NXVUVRURHt7O4mJiWRkZGAYBh0d\nHRQWFlJXV0dYWBhZWVlER0cDUF5ezvr16wGYPXs2qampANTX15OXl0dLSwuxsbFkZmZq1lNEREQs\nx6sZwsjISJYsWUJWVhaLFi1i4cKFZGdnExERcV47+8tf/sLo0aM9r0tKSoiPjyc/P5/4+HjPLOS+\nffuorKxk+fLl3H///axevZru7m4AVq1axfz588nPz+fQoUPU1NQAUFZWRmhoKAUFBaSnp7N27VoA\n3G43xcXFZGdnk52dTXFxMW63G4A1a9aQnp5OQUEBoaGhlJWVndfnERERERkMvL4PoWEYTJw4kSuv\nvJJJkyZ5Li7xVmNjI++99x7f/OY3PW3V1dWkpKQAkJKSQnV1tad92rRpBAcHEx0dzahRo9i1axdN\nTU0cP36cSZMmYRgGM2bM8KyzZcsWz8xfcnIy27ZtwzRNampqSEhIwGazYbPZSEhIoKamBtM02b59\nO8nJyQCkpqZ6tiUiIiJiJWc9PnrHHXd4tYGVK1d6tdxvf/tbbrrpJo4fP+5pa25uJjIyEoCIiAia\nm5sBcLlcTJw40bOc3W7H5XIRGBiIw+HwtDscDlwul2edU+8FBgYSEhJCS0tLj/YvbqulpYWQkBAC\nAwN7tIuIiIhYzVkDYWZmZq/t5N1332X48OHExsayffv2My5jGAaGYfTaPntTaWkppaWlAOTk5Jx2\nxbV8dRpL/6Xa+TfVz3+pdv5toNbvrIFw8uTJvbaTjz76iC1btvD+++/T3t7O8ePHyc/PZ/jw4TQ1\nNREZGUlTUxPh4eHAydm6xsZGz/oulwu73X5ae2NjI3a7vcc6DoeDrq4uWltbCQsLw263s2PHjh7b\nmjx5MmFhYbS2ttLV1UVgYKBnH2eSlpZGWlqa53VDQ0Ovjc2Xi/HhvnzPt2Ppa6qdf1P9/NvgrZ9q\n5998Xb+YGO/G0+sTAffu3ctrr73Gyy+/zEsvveT55405c+bw1FNPUVRURFZWFpdddhmLFi0iKSmJ\niooKACoqKpg6dSoASUlJVFZW0tHRQX19PQcPHmTChAlERkYybNgwdu7ciWmabNq0iaSkJACmTJlC\neXk5AFVVVcTFxWEYBk6nk9raWtxuN263m9raWpxOJ4ZhEBcXR1VVFXDySuRT2xIRERGxEq/usVJa\nWsoLL7zguSDD6XTywQcf/NsB6tprryU3N5eysjLPbWcAxo4dy5VXXsndd99NQEAAP/nJTzwXsdxy\nyy2sWLGC9vZ2nE4niYmJAMyaNYvCwkIyMzOx2WxkZWUBYLPZuO666/jFL34BwPXXX++5tc2PfvQj\n8vLyWLduHePHj2fWrFn/1ucRERER8UeGaZrmly2UmZnJggULuPTSS8nIyOD555/n/fffZ/Pmzdx5\n552+6OeA4stH9o0ePbinzvfvH7yPP1Tt/Jvq598Gc/1UO//m6/r16iHjo0ePcumllwInL/7o7u4m\nMTGRd99996v3UEREREQGBK8OGdvtdurr64mOjubCCy9ky5YthIWF6akeIiIiIoOAV4nummuuYf/+\n/URHR3P99dezfPlyOjs7ycjI6Ov+iYiIiEgf8yoQnnoCCEBiYiLPP/88nZ2dDB06tK/6JSIiIiI+\ncn7PnwM++OAD/vrXv/Lpp5/2RX9ERERExMfOGQjz8vJ48803Pa9feeUVcnJy2Lx5Mw8//DCbNm3q\n8w6KiIiISN865yHjjz76yHOeYHd3N6+++iqLFi0iOTmZ999/nz/84Q/MmDHDJx0VERERkb5xzhnC\n1tZWhg8fDpx8UklHRwdf//rXAXA6nRw5cqTveygiIiIifeqcgTAsLIz6+noAtm3bxqRJkzxPDDlx\n4oTnaxERERHxX+c8ZDxr1ixycnK4/PLL2bRpU4/bzOzYsYPRo0f3eQdFREREpG+dMxDOnj0bu91O\nXV0dc+fO5aqrrvK8d/ToUf77v/+7zzsoIiIiIn3rS+9DmJqa2uM+hF9sFxERERH/p5MARURERCxO\ngVBERETE4hQIRURERCxOgVBERETE4r70ohIAt9vNq6++yieffEJbW1uP9x566KE+6ZiIiIiI+IZX\ngfDJJ5+ks7OTK6+8kiFDhvR1n0RERETEh7wKhDt37uTZZ58lODi4r/sjIiIiIj7m1TmE48aNo7Gx\nsa/7IiIiIiL9wKsZwssuu4zs7GxSU1OJiIjo8d6sWbP6pGMiIiIi4hteBcJ//vOfOBwOtm7detp7\nCoQiIiIi/s2rQPjAAw/0dT9EREREpJ94FQi/yDRNTNP0vA4I0K0MRURERPyZV4HQ5XKxevVqPvzw\nQ44dO9bjvZdeeqlPOiYiIiIivuHV9N4zzzxDUFAQv/71rxk6dChLly4lKSmJW2+9ta/7JyIiIiJ9\nzKtAuHPnTu644w4uuugiDMPgoosu4o477uBPf/pTX/dPRERERPqYV4EwICCAwMBAAEJDQzl69CgX\nXHABLperTzsnIiIiIn3Pq3MIJ0yYwPvvv8/Xv/51Lr/8cnJzcxkyZAgXX3xxX/dPRERERPqYV4Ew\nMzPTc2Xx3LlzefXVV2lrayM9Pb1POyciIiIifc+rQBgaGur5esiQIVx//fV91iERERER8a2zBsL1\n69cze/Zs4Ny3lvnBD37Q+70SEREREZ85ayBsbGw849ciIiIiMricNRB+8R6DCxYs8ElnRERERMT3\nzhoIDx8+7NUGRo4c+aXLtLe388ADD9DZ2UlXVxfJycnceOONuN1ucnNzOXLkCCNGjGDx4sXYbDYA\nNmzYQFlZGQEBAWRkZOB0OgGoq6ujqKiI9vZ2EhMTycjIwDAMOjo6KCwspK6ujrCwMLKysoiOjgag\nvLyc9evXAzB79mxSU1MBqK+vJy8vj5aWFmJjY8nMzCQo6Lyf5iciIiLi186afhYtWuTVBrx5dF1w\ncDAPPPAAQ4cOpbOzk1//+tc4nU7eeecd4uPjufbaaykpKaGkpISbbrqJffv2UVlZyfLly2lqauLh\nhx/mySefJCAggFWrVjF//nwmTpzIkiVLqKmpITExkbKyMkJDQykoKGDz5s2sXbuWxYsX43a7KS4u\nJicnB4D77ruPpKQkbDYba9asIT09nenTp/PMM89QVlbGf/7nf3o5dCIiIiKDw1lvTP3SSy95/t1+\n++1Mnz6dvLw81q5dS15eHldddRV33HGHVzsxDIOhQ4cC0NXVRVdXF4ZhUF1dTUpKCgApKSlUV1cD\nUF1dzbRp0wgODiY6OppRo0axa9cumpqaOH78OJMmTcIwDGbMmOFZZ8uWLZ6Zv+TkZLZt24ZpmtTU\n1JCQkIDNZsNms5GQkEBNTQ2mabJ9+3aSk5MBSE1N9WxLRERExEq8elLJqVB44YUXEhQUxIUXXsht\nt93GunXrvN5Rd3c39957L7fccgvx8fFMnDiR5uZmIiMjAYiIiKC5uRkAl8uFw+HwrGu323G5XKe1\nOxwOz9NSvvheYGAgISEhtLS0nHVbLS0thISEeJ7AcqpdRERExGq8OmHONE3q6+sZM2aMp+3IkSN0\nd3d7vaOAgACWLVvGsWPHePzxx/n00097vG8YBoZheL09XyotLaW0tBSAnJwcoqKi+rlHg4fG0n+p\ndv5N9fNfqp1/G6j18yoQpqen85vf/IbU1FSioqJoaGigoqLiKz2pJDQ0lLi4OGpqahg+fDhNTU1E\nRkbS1NREeHg4cHK27ou3unG5XNjt9tPaGxsbsdvtPdZxOBx0dXXR2tpKWFgYdrudHTt29NjW5MmT\nCQsLo7W1la6uLgIDAz37OJO0tDTS0tI8rxsaGs77c391MT7cl+/5dix9TbXzb6qffxu89VPt/Juv\n6xcT4914enXI+Hvf+x4LFiygubmZLVu28Pnnn3PHHXdwzTXXeLWTo0ePcuzYMeDkFccffPABo0eP\nJikpiYqKCgAqKiqYOnUqAElJSVRWVtLR0UF9fT0HDx5kwoQJREZGMmzYMHbu3IlpmmzatImkpCQA\npkyZQnl5OQBVVVXExcVhGAZOp5Pa2lrcbjdut5va2lqcTieGYRAXF0dVVRVw8krkU9sSERERsRLD\nPPWQ4j50szMeAAAd5klEQVT0ySefUFRURHd3N6ZpcuWVV3L99dfT0tJCbm4uDQ0Np912Zv369Wzc\nuJGAgADmzp1LYmIiALt372bFihW0t7fjdDqZN28ehmHQ3t5OYWEhe/bswWazkZWV5bklTllZGRs2\nbABO3nZm5syZwMlb6+Tl5eF2uxk/fjyZmZkEBwd/6ec5cOBAXwzTGY0ePbj/Utq/33dj6WuqnX9T\n/fzbYK6faufffF0/b2cIvQqEHR0dFBcXs3nzZlpaWnjhhReora3l4MGDfPvb3/63O+tvFAh7z2D+\nj02182+qn38bzPVT7fzbQA2EXh0yfuGFF/jss89YtGiR58KPsWPH8sYbb3z1HoqIiIjIgODVRSXv\nvPMO+fn5DB061BMIdZsWERERkcHBqxnCoKCg024xc/ToUcLCwvqkUyIiIiLiO14FwuTkZAoLC6mv\nrwegqamJ1atXM23atD7tnIiIiIj0Pa8C4Zw5c4iOjuaee+6htbWVRYsWERkZyQ033NDX/RMRERGR\nPubVOYRBQUHMnTuXuXPneg4VD9SnioiIiIjI+TlnIDzb3bS/+LSQgfoIFhERERHxzjkD4cKFC790\nAy+99FKvdUZEREREfO+cgfA//uM/aG9vJyUlhauvvvqsz/oVEREREf91zkD42GOP8emnn1JRUcGv\nfvUrxowZw4wZM/jGN77BkCFDfNVHEREREelDX3qV8bhx47j55pspKioiPT2dd999l9tuu426ujpf\n9E9ERERE+phXt50BOHToEDt27ODjjz9m/Pjx2Gy2vuyXiIiIiPjIOQ8Zu91u3n77bSoqKmhra+Pq\nq6/moYce0pXFIiIiIoPIOQPh/PnziY6O5uqrr2bSpEnAyZnCQ4cOeZa57LLL+raHIiIiItKnzhkI\nIyIiaG9v58033+TNN9887X3DMCgsLOyzzomIiIhI3ztnICwqKvJVP0RERESkn3h9UYmIiIiIDE4K\nhCIiIiIWp0AoIiIiYnEKhCIiIiIWp0AoIiIiYnEKhCIiIiIWp0AoIiIiYnEKhCIiIiIWp0AoIiIi\nYnEKhCIiIiIWp0AoIiIiYnEKhCIiIiIWp0AoIiIiYnEKhCIiIiIWp0AoIiIiYnEKhCIiIiIWp0Ao\nIiIiYnEKhCIiIiIWp0AoIiIiYnFBvthJQ0MDRUVFfP755xiGQVpaGt/5zndwu93k5uZy5MgRRowY\nweLFi7HZbABs2LCBsrIyAgICyMjIwOl0AlBXV0dRURHt7e0kJiaSkZGBYRh0dHRQWFhIXV0dYWFh\nZGVlER0dDUB5eTnr168HYPbs2aSmpgJQX19PXl4eLS0txMbGkpmZSVCQT4ZEREREZMDwyQxhYGAg\nN998M7m5uTz66KO8/vrr7Nu3j5KSEuLj48nPzyc+Pp6SkhIA9u3bR2VlJcuXL+f+++9n9erVdHd3\nA7Bq1Srmz59Pfn4+hw4doqamBoCysjJCQ0MpKCggPT2dtWvXAuB2uykuLiY7O5vs7GyKi4txu90A\nrFmzhvT0dAoKCggNDaWsrMwXwyEiIiIyoPgkEEZGRhIbGwvAsGHDGD16NC6Xi+rqalJSUgBISUmh\nuroagOrqaqZNm0ZwcDDR0dGMGjWKXbt20dTUxPHjx5k0aRKGYTBjxgzPOlu2bPHM/CUnJ7Nt2zZM\n06SmpoaEhARsNhs2m42EhARqamowTZPt27eTnJwMQGpqqmdbIiIiIlbi83MI6+vr2bNnDxMmTKC5\nuZnIyEgAIiIiaG5uBsDlcuFwODzr2O12XC7Xae0OhwOXy3XaOoGBgYSEhNDS0nLWbbW0tBASEkJg\nYGCPdhERERGr8ekJc21tbTzxxBPMnTuXkJCQHu8ZhoFhGL7sjtdKS0spLS0FICcnh6ioqH7u0eCh\nsfRfqp1/U/38l2rn3wZq/XwWCDs7O3niiSe4+uqr+cY3vgHA8OHDaWpqIjIykqamJsLDw4GTs3WN\njY2edV0uF3a7/bT2xsZG7HZ7j3UcDgddXV20trYSFhaG3W5nx44dPbY1efJkwsLCaG1tpauri8DA\nQM8+ziQtLY20tDTP64aGht4bmC8V48N9+Z5vx9LXVDv/pvr5t8FbP9XOv/m6fjEx3o2nTw4Zm6bJ\nU089xejRo/nud7/raU9KSqKiogKAiooKpk6d6mmvrKyko6OD+vp6Dh48yIQJE4iMjGTYsGHs3LkT\n0zTZtGkTSUlJAEyZMoXy8nIAqqqqiIuLwzAMnE4ntbW1uN1u3G43tbW1OJ1ODMMgLi6Oqqoq4OSV\nyKe2JSIiImIlhmmaZl/v5J///Ce//vWvGTdunOew8P/8z/8wceJEcnNzaWhoOO22M+vXr2fjxo0E\nBAQwd+5cEhMTAdi9ezcrVqygvb0dp9PJvHnzMAyD9vZ2CgsL2bNnDzabjaysLEaOHAmcvAJ5w4YN\nwMnbzsycOROAw4cPk5eXh9vtZvz48WRmZhIcHPyln+fAgQO9PkZnM3r04P5Laf9+342lr6l2/k31\n82+DuX6qnX/zdf28nSH0SSAcbBQIe89g/o9NtfNvqp9/G8z1U+3820ANhHpSiYiIiIjFKRCKiIiI\nWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCK\niIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjF\nKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiI\niIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwCoYiIiIjFKRCKiIiIWJwC\noYiIiIjFKRCKiIiIWFyQL3ayYsUK3nvvPYYPH84TTzwBgNvtJjc3lyNHjjBixAgWL16MzWYDYMOG\nDZSVlREQEEBGRgZOpxOAuro6ioqKaG9vJzExkYyMDAzDoKOjg8LCQurq6ggLCyMrK4vo6GgAysvL\nWb9+PQCzZ88mNTUVgPr6evLy8mhpaSE2NpbMzEyCgnwyHCIiIiIDik9mCFNTU/nlL3/Zo62kpIT4\n+Hjy8/OJj4+npKQEgH379lFZWcny5cu5//77Wb16Nd3d3QCsWrWK+fPnk5+fz6FDh6ipqQGgrKyM\n0NBQCgoKSE9PZ+3atcDJ0FlcXEx2djbZ2dkUFxfjdrsBWLNmDenp6RQUFBAaGkpZWZkvhkJERERk\nwPFJIJw8ebJn9u+U6upqUlJSAEhJSaG6utrTPm3aNIKDg4mOjmbUqFHs2rWLpqYmjh8/zqRJkzAM\ngxkzZnjW2bJli2fmLzk5mW3btmGaJjU1NSQkJGCz2bDZbCQkJFBTU4Npmmzfvp3k5GTgZGA9tS0R\nERERq+m3cwibm5uJjIwEICIigubmZgBcLhcOh8OznN1ux+VyndbucDhwuVynrRMYGEhISAgtLS1n\n3VZLSwshISEEBgb2aBcRERGxogFx0pxhGBiG0d/dOKvS0lJKS0sByMnJISoqqp97NHhoLP2Xauff\nVD//pdr5t4Fav34LhMOHD6epqYnIyEiampoIDw8HTs7WNTY2epZzuVzY7fbT2hsbG7Hb7T3WcTgc\ndHV10draSlhYGHa7nR07dvTY1uTJkwkLC6O1tZWuri4CAwM9+zibtLQ00tLSPK8bGhp6bRy+XIwP\n9+V7vh1LX1Pt/Jvq598Gb/1UO//m6/rFxHg3nv12yDgpKYmKigoAKioqmDp1qqe9srKSjo4O6uvr\nOXjwIBMmTCAyMpJhw4axc+dOTNNk06ZNJCUlATBlyhTKy8sBqKqqIi4uDsMwcDqd1NbW4na7cbvd\n1NbW4nQ6MQyDuLg4qqqqgJNXIp/aloiIiIjVGKZpmn29k7y8PHbs2EFLSwvDhw/nxhtvZOrUqeTm\n5tLQ0HDabWfWr1/Pxo0bCQgIYO7cuSQmJgKwe/duVqxYQXt7O06nk3nz5mEYBu3t7RQWFrJnzx5s\nNhtZWVmMHDkSOHkF8oYNG4CTt52ZOXMmAIcPHyYvLw+328348ePJzMwkODjYq89z4MCB3h6isxo9\nenD/pbR/v+/G0tdUO/+m+vm3wVw/1c6/+bp+3s4Q+iQQDjYKhL1nMP/Hptr5N9XPvw3m+ql2/m2g\nBkI9qURERETE4hQIRURERCxOgVBERETE4hQIRURERCxOgVBERETE4hQIRURERCxOgVBERETE4hQI\nRURERCxOgVBERETE4hQIRURERCxOgVBERETE4hQIRURERCxOgVBERETE4hQIRURERCxOgVBERETE\n4hQIRURERCxOgVBERETE4hQIRURERCxOgVBERETE4hQIRURERCxOgVBERETE4hQIRURERCxOgVBE\nRETE4hQIRURERCxOgVBERETE4hQIRURERCxOgVBERETE4hQIRURERCxOgVBERETE4hQIRURERCxO\ngVBERETE4hQIRURERCxOgVBERETE4hQIRURERCwuqL870N9qamp4/vnn6e7u5pvf/CbXXnttf3dJ\nRERExKcsPUPY3d3N6tWr+eUvf0lubi6bN29m3759/d0tEREREZ+ydCDctWsXo0aNYuTIkQQFBTFt\n2jSqq6v7u1siIiIiPmXpQOhyuXA4HJ7XDocDl8vVjz0SERER8T3Ln0PojdLSUkpLSwHIyckhJibG\nZ/s2TZ/tqp/4bix9TbXzb6qffxvc9VPt/NvArJ+lZwjtdjuNjY2e142Njdjt9tOWS0tLIycnh5yc\nHF92r1/cd999/d0F+YpUO/+m+vkv1c6/qX4nWToQXnzxxRw8eJD6+no6OzuprKwkKSmpv7slIiIi\n4lOWPmQcGBjIvHnzePTRR+nu7mbmzJmMHTu2v7slIiIi4lOWDoQAV1xxBVdccUV/d2PASEtL6+8u\nyFek2vk31c9/qXb+TfU7yTDNwX/6poiIiIicnaXPIRQRERERBUL5F+Xl5axevRqAl19+mVdfffUr\nbae+vp633367N7smvaC+vp577rmnv7shZ3HzzTef8/1jx47x+uuv+6g3IgNbeXl5j3sHP/XUU+d8\n2tj+/fu59957+dnPfsahQ4fOa1/bt2/no48++sp99QcKhNInjhw5okAo0suOHTvGG2+80d/dkH9D\nd3d3f3dh0CgvL6epqcnz+vbbb2fMmDFnXb66uprk5GQee+wxRo0adV77+iqBsKur67yW72+Wv6jE\nX23atInXXnuNzs5OJk6cyPe//30efvhhHnnkEWw2Gw8++CDXXXcdl19+ORUVFfzxj3/EMAzGjRtH\nZmYmR48e5ZlnnvHch/HHP/4xX/va1866v0OHDrF69WqOHj3KBRdcwPz58xk9ejRFRUUMGzaMuro6\nPv/8c2666SaSk5P5wx/+wL59+7j33ntJSUnhu9/9rq+GZlApLi7mrbfeIjw8HIfDQWxsLAkJCaxa\ntYoTJ04wcuRI7rjjDmw2G3v37j1je11dHStXrgQgISGhnz/R4FJfX8+SJUu45JJL2LlzJ3a7nZ/9\n7GcMGTLkrPX41/WffPJJ2tramDp1qqe9ra2Nxx57jGPHjtHZ2ckPf/hDpk6dyh/+8AcOHTrEvffe\nS0JCAjfccMMZl5PeUV9fT3Z2NrGxsezZs4cxY8Zw5513snPnTn7/+9/T1dXFxRdfzK233kpwcDBb\nt249Y/vChQu58sor2bp1K9/73veYPn16f3+0AautrY3c3FxcLhfd3d1cd911HDhwgHfffZf29nYm\nTZrEbbfdxj/+8Q92795Nfn4+Q4YM4dFHHyU7O5ubb76Z8ePHs3LlSurq6gCYOXMmMTEx/PnPfyYg\nIIBt27bxwAMP8Nhjj9HY2EhHRwff+c53PBeX1NTU8OKLL9Ld3U1YWBi33347f/vb3wgICOCtt95i\n3rx5OBwOVq5cSUtLC+Hh4SxYsICoqCiKiooIDg5m7969XHLJJbz77rs88sgjhIeH093dzV133cWj\njz5KeHh4fw7zmZnidz777DNzyZIlZkdHh2maprlq1SqzvLzcLC0tNZ944gnzlVdeMZ9++mnTNE3z\n008/NRctWmQ2NzebpmmaLS0tpmmaZl5envnhhx+apmmaR44cMbOyskzTNM2NGzeazz77rGmapvnS\nSy+Zr7zyimmapvnQQw+ZBw4cME3TNHfu3Gk++OCDpmmaZmFhofnEE0+YXV1d5meffWbeeeedpmma\n5rZt28wlS5b0+VgMZh9//LH505/+1Dxx4oTZ2tpqZmZmmq+88op5zz33mNu3bzdN0zTXrVtnPv/8\n86Zpml61/+53vzPvvvtun3+Wwerw4cPmD37wA3PPnj2maZrmE088YVZUVJimefZ6fFFOTo5ZXl5u\nmqZpvvbaa+ZNN91kmqZpdnZ2mseOHTNN0zSbm5vNO++80+zu7jYPHz7co35nW056x+HDh80bbrjB\n839lUVGRWVxcbN5+++3m/v37TdM0zYKCAvNPf/qTeeLEiTO2m6ZpLliwwCwpKemfD+Fn/v73v5sr\nV670vD527Jjn95ZpmmZ+fr5ZXV1tmqZpPvDAA+auXbs87516vXv3bvM3v/mNp93tdpum2fN3mmn+\n/9+HJ06cMO+++27z6NGjZnNzs3n77bebhw8f7rHMv667ZMkSc+PGjaZpmuabb75pLl261DTNk78T\nlyxZYnZ1dZmmaZovv/yy5/ugpqbGXLZs2b8zPH1KM4R+aNu2bezZs4df/OIXALS3txMeHs6NN95I\nVVUVf/vb33jsscc8yyYnJ3v+Gjk1Q7F169Ye51q0trbS1tZ2xv21tbXx0UcfsXz5ck9bZ2en5+up\nU6cSEBDAmDFjaG5u7t0Pa2EfffQRU6dOZciQIQBMmTKFEydOcOzYMSZPngxASkoKubm5tLa2nrH9\n2LFjPdpnzJhBTU1N/3ygQSo6OpqLLroIgNjYWI4cOXLWevyrjz76yHNO54wZM1i7di0Apmny4osv\n8uGHH2IYBi6X64w/W2dbLiIioo8+rfU4HA7P0ZMZM2bwf//3f0RHR3seYZqSksLrr79OXFzcGdvT\n09MBmDZtWv98AD8zbtw4fv/737NmzRqmTJnCpZdeSlVVFa+++ionTpzA7XYzduzYcz5EIjo6mvr6\nep577jmuuOKKsx4Z+ctf/kJ1dTUADQ0NHDx4kKNHj3LppZcSHR0NcNqs/ikff/wxP/3pT4GeP7sA\nycnJBAScPCNv5syZLFu2jPT0dDZu3MjMmTPPf1B8RIHQD5mmSUpKCnPmzOnRfuLECc8h4La2NoYN\nG3bObTz66KOesHEu3d3dhIaGsmzZsjO+Hxwc3GO7Ilbyxe//gIAA2tvbz2t9wzBOa3v77bc5evQo\nOTk5BAUFsXDhwjNu19vl5Kv71/qEhITgdrvPezsXXHBBb3VpUIuJiWHp0qW89957rFu3jvj4eF5/\n/XWWLFlCVFQUL7/88pd+j9tsNpYtW0ZNTQ1vvPEGlZWVLFiwoMcy27dvZ+vWrTzyyCNccMEFPPjg\ng3R0dPTKZxg6dKjn66ioKIYPH862bdvYtWsXixYt6pV99AVdVOKH4uPjqaqq8swYuN1ujhw5wtq1\na7nqqqu48cYbefrppwG47LLLqKqqoqWlxbMsnDyX7K9//atnm3v37j3r/kJCQoiOjubvf/87cDL0\nnWt5gGHDhnH8+PGv+hEFPOeftLe309bWxnvvvccFF1yAzWbjww8/BE6eS3rppZcSEhJyxvbQ0FBC\nQ0P55z//CcBbb73Vb5/HSs5Wj391ySWXsHnzZoAeF2G1trYyfPhwgoKC2LZtG0eOHAFO/7k623LS\nexoaGti5cydwskYXX3wx9fX1nqtUN23axOTJk4mJiTlju5wfl8vFkCFDmDFjBt/73vc85wGGh4fT\n1tbGP/7xD8+yQ4cOPePvmaNHj9Ld3U1ycjI//OEP2bNnz2nLtLa2EhoaygUXXMD+/fv5+OOPAZg0\naRIffvgh9fX1wP//nTls2LAeR9EmTZpEZWUlcPL74lzn4M+aNYuCgoIeM4cDkWYI/dCYMWP44Q9/\nyCOPPIJpmgQGBvLjH/+Y3bt38/DDDxMQEMA//vEPz/T097//fR588EECAgK46KKLWLhwIRkZGaxe\nvZqf/vSndHV1cemll3LbbbeddZ+LFi1i1apVrF+/ns7OTqZPn+45THYm48aNIyAgQBeV/BsmTJjA\nlClTuPfeexk+fDhjx44lJCSEhQsXei5WiI6O9vzle7b2BQsWeC4qufzyy/vt81jN2erxRRkZGTz5\n5JO88sorPS4Gueqqq1i6dCn33HMPF198MaNHjwYgLCyMSy65hHvuuQen08k111xzxuWk98TExPDX\nv/6VlStXMnr0aDIyMpg4cSLLly/3XDzyrW99i+DgYBYsWHBau5yfTz/9lDVr1mAYBkFBQdxyyy1U\nV1dzzz33EBERwcUXX+xZNjU1lVWrVnkuKjnF5XKxcuVKzxXd/3o0DcDpdPK3v/2NxYsXc+GFFzJx\n4kTgZPC87bbbePzxxzFNk/DwcH71q18xZcoUli9fTnV1NfPmzWPevHmsWLGCV1991XNRydkkJSWx\ncuXKAX24GPSkEpEBra2tjaFDh3LixAkeeOABbrvtNmJjY/u7WyKWUF9fz9KlS3niiSf6uyvix3bv\n3s0LL7zAb37zm/7uyjlphlBkAHv66afZt28fHR0dpKSkKAyKiPiRkpIS3njjjQF97uApmiEUERER\nsbiBe3ajiIiIiPiEAqGIiIiIxSkQioiIiFicAqGISD8qKipi3bp1/d0NEbE4XWUsInIeFi5cyOef\nf97jBrOpqan85Cc/6cdeiYj8exQIRUTO089//vOzPh9VRMQfKRCKiPSS0tJS/vznP9PY2IjD4SAz\nM5PY2Fj27dvHs88+y969e7Hb7cyZM4ekpKSzbuOVV17B7Xbzta99jVtvvRW73Q5AbW0tzz33HJ9/\n/jlXX301n332GTNmzCAlJYVbb72Vhx56iHHjxgHQ3NzMwoULWbFiBeHh4T4bAxHxTzqHUESkF/z9\n73/nf//3f1m4cCEvvPACP//5zwkLC6Ozs5OlS5eSkJDAs88+y7x588jPz+fAgQOnbWPbtm28+OKL\nLF68mGeeeYYRI0bw5JNPAiefz7p8+XLmzJnDc889R0xMjOcZu0FBQUyfPp1NmzZ5trV582Yuu+wy\nhUER8YoCoYjIeVq2bBlz5871/CstLaWsrIxrrrmGCRMmYBgGo0aNYsSIEXz88ce0tbVx7bXXEhQU\nxGWXXcYVV1zB22+/fdp233rrLWbOnElsbCzBwcHMmTOHnTt3Ul9fz/vvv8+YMWP4xje+QWBgIP/1\nX/9FRESEZ92UlBQ2b97MqWcNbNq0iRkzZvhsTETEv+mQsYjIebr33ntPO4fwz3/+MyNHjjxt2aam\nJqKionpchDJixAhcLtcZlx0/frzn9dChQ7HZbLhcLpqamnA4HJ73DMPwHEoGmDhxIhdccAHbt28n\nMjKSQ4cOnfWwtIjIv1IgFBHpBVFRURw+fPi09sjISBoaGuju7vaEwoaGBi688MKzLntKW1sbbrcb\nu91OREREjxBpmuZpoTIlJYW33nqLiIgIkpOTGTJkSG99PBEZ5HTIWESkF8yaNYs//vGP1NXVYZom\nhw4d4siRI56Zu1dffZXOzk62b9/Ou+++y/Tp00/bxvTp09m4cSN79+6lo6ODF198kQkTJhAdHc0V\nV1zBp59+yjvvvENXVxevv/46n3/+eY/1r776at555/+1b8coCkNRFIbPbMAiaBqtLBQhZAc27sAq\nbkGIIEJwDRYpFLQSbO1e5QrSpbJ2BWJho6kiZioDw1QWg2Te/5V5EC6vOuTcpEqShLoYwFv4QggA\nb1osFj8qYN/3FUWRbreblsulrterXNdVGIZqNBqaz+fabrcyxshxHIVhqGaz+eu9vu8rCALFcaz7\n/a5ut6vpdCpJqtVqms1m2u12Wq/X6vf75a7hS71eV7vd1vl8Vq/X+/uLAPBvfBWvDWQAQGU8n0+N\nx2NNJhN5nlc+32w2chxHo9Hog9MBqBoqYwCoiOPxqCzLlOe5jDEqikKdTqc8v1wuStNUg8Hgg1MC\nqCIqYwCoiNPppNVqpcfjoVarpSiKyh9H9vu9DoeDhsOhXNf98KQAqobKGAAAwHJUxgAAAJYjEAIA\nAFiOQAgAAGA5AiEAAIDlCIQAAACWIxACAABY7ht6Au5ll57jrgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0a3597d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ecology_pivot = train.pivot_table(index='ecology',\n",
" values='price_doc', aggfunc=np.median)\n",
"ecology_pivot.plot(kind='bar', color='blue')\n",
"plt.xlabel('Ecology')\n",
"plt.ylabel('Median Sale Price')\n",
"plt.xticks(rotation=0)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"_cell_guid": "10a541f6-d7ac-dd5a-8d3d-83624b0ae976"
},
"outputs": [],
"source": [
"def encode(x): return 0 if x == 'no data' else 1\n",
"train['enc_ecology'] = train['ecology'].apply(encode)\n",
"test['enc_ecology'] = test['ecology'].apply(encode)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"_cell_guid": "4f80c2d3-45bc-f07d-0c08-fa66fe0a20f0"
},
"outputs": [
{
"data": {
"image/png": 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DnZ2dks7ciXx2XQAAAGZiBAKBQLg38sYbb+j73/++JGlsbEw33nijioqK5PV6VVFRof7+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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0a78e5630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ecology_pivot = train.pivot_table(index='enc_ecology', values='price_doc', aggfunc=np.median)\n",
"ecology_pivot.plot(kind='bar', color='blue')\n",
"plt.xlabel('Encoded Ecology')\n",
"plt.ylabel('Median Sale Price')\n",
"plt.xticks(rotation=0)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"_cell_guid": "c6716fdb-8873-6240-6b71-39856841f66f"
},
"outputs": [],
"source": [
"data = train.select_dtypes(include=[np.number]).interpolate().dropna() "
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"_cell_guid": "57f711ec-fb80-7272-665c-0bbc91e97694"
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(data.isnull().sum() != 0)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "57f3b668-f356-45e7-f4da-beb71fbf8d84"
},
"source": [
"# Step 3: Build a linear model"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"_cell_guid": "6757f9ca-acbc-2418-ef11-e7b9050a4912"
},
"outputs": [],
"source": [
"y = np.log(data['price_doc'])\n",
"X = data.drop(['price_doc', 'id'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"_cell_guid": "1ca0e433-3f51-4b80-fe6e-c2431ec6e05d"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, random_state=42, test_size=.33)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "11c4cee7-2b02-d63d-0d25-355611498b4f"
},
"source": [
"# Begin modelling"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"_cell_guid": "494d466e-3850-20fe-4b1b-d7657a45fa0f"
},
"outputs": [],
"source": [
"from sklearn import linear_model\n",
"lr = linear_model.LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"_cell_guid": "30b5dd96-a1a8-3c0b-0910-988d4ecae324"
},
"outputs": [],
"source": [
"model = lr.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"_cell_guid": "4ef4f481-af6e-fe15-c648-53477dd99f5e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"R^2 is: \n",
" 0.417872201429\n"
]
}
],
"source": [
"print (\"R^2 is: \\n\", model.score(X_test, y_test))"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"_cell_guid": "32e949e4-6f5d-c373-aeb7-88775c34f6a3"
},
"outputs": [],
"source": [
"predictions = model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"_cell_guid": "f3ba3d22-b879-ec66-5e04-b1bc75a9b6b5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RMSE is: \n",
" 0.20634755511\n"
]
}
],
"source": [
"from sklearn.metrics import mean_squared_error\n",
"print ('RMSE is: \\n', mean_squared_error(y_test, predictions))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"_cell_guid": "f025b43c-a8eb-2dc3-216c-e462614dd6a8"
},
"outputs": [
{
"data": {
"image/png": 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PfexjeP311/GjH/0I999/f8Ln7t69G7t37wYAPProoyguLp7KpcahKEpW959N6Njp2Gcb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\ngcWiTYpPS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"text/plain": [
"<matplotlib.figure.Figure at 0x7fa0962cf198>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"actual_values = y_test\n",
"plt.scatter(predictions, actual_values, alpha=.75,\n",
" color='b') #alpha helps to show overlapping data\n",
"plt.xlabel('Predicted Price')\n",
"plt.ylabel('Actual Price')\n",
"plt.title('Linear Regression Model')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"_cell_guid": "869974c1-26a6-6ba9-6f75-111e30747587"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 239,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162770.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "c4932b18-9940-a8b9-26d9-de8d27d87ec1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iris.csv\n",
"database.sqlite\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "0355282e-71d4-a4bb-a830-c44a29f3bbbd"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"iris_dataset = pd.read_csv('../input/Iris.csv')\n",
"iris_dataset.head(5)\n",
"species_color=iris_dataset['Species'].unique()\n",
"print(species_color)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "92ef2510-2f8a-0127-d69b-fd4f23ddbc6e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>SepalLengthCm</th>\n",
" <th>SepalWidthCm</th>\n",
" <th>PetalLengthCm</th>\n",
" <th>PetalWidthCm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" <td>150.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>75.500000</td>\n",
" <td>5.843333</td>\n",
" <td>3.054000</td>\n",
" <td>3.758667</td>\n",
" <td>1.198667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>43.445368</td>\n",
" <td>0.828066</td>\n",
" <td>0.433594</td>\n",
" <td>1.764420</td>\n",
" <td>0.763161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>4.300000</td>\n",
" <td>2.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.100000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>38.250000</td>\n",
" <td>5.100000</td>\n",
" <td>2.800000</td>\n",
" <td>1.600000</td>\n",
" <td>0.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>75.500000</td>\n",
" <td>5.800000</td>\n",
" <td>3.000000</td>\n",
" <td>4.350000</td>\n",
" <td>1.300000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>112.750000</td>\n",
" <td>6.400000</td>\n",
" <td>3.300000</td>\n",
" <td>5.100000</td>\n",
" <td>1.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>150.000000</td>\n",
" <td>7.900000</td>\n",
" <td>4.400000</td>\n",
" <td>6.900000</td>\n",
" <td>2.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm\n",
"count 150.000000 150.000000 150.000000 150.000000 150.000000\n",
"mean 75.500000 5.843333 3.054000 3.758667 1.198667\n",
"std 43.445368 0.828066 0.433594 1.764420 0.763161\n",
"min 1.000000 4.300000 2.000000 1.000000 0.100000\n",
"25% 38.250000 5.100000 2.800000 1.600000 0.300000\n",
"50% 75.500000 5.800000 3.000000 4.350000 1.300000\n",
"75% 112.750000 6.400000 3.300000 5.100000 1.800000\n",
"max 150.000000 7.900000 4.400000 6.900000 2.500000"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris_dataset.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "e6efd0e0-2839-fa15-81bb-99da382e832e"
},
"outputs": [
{
"data": {
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kKpNdUdAvKQmPDxwIQOZR76WRevf2vn3RK0KKmVyLBX/Jy4NVUWCAvEDZFAVX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\nuvFGooMHA9r4+YNNtLrXLCp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VlkIyR01NjTz/GxqkSiwvr/1tFBcDX30l7+LPOUdW1/PH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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f13ac8e3f98>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>SepalLengthCm</th>\n",
" <th>SepalWidthCm</th>\n",
" <th>PetalLengthCm</th>\n",
" <th>PetalWidthCm</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Id</th>\n",
" <td>1887.500000</td>\n",
" <td>25.782886</td>\n",
" <td>-7.492282</td>\n",
" <td>67.667785</td>\n",
" <td>29.832215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SepalLengthCm</th>\n",
" <td>25.782886</td>\n",
" <td>0.685694</td>\n",
" <td>-0.039268</td>\n",
" <td>1.273682</td>\n",
" <td>0.516904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SepalWidthCm</th>\n",
" <td>-7.492282</td>\n",
" <td>-0.039268</td>\n",
" <td>0.188004</td>\n",
" <td>-0.321713</td>\n",
" <td>-0.117981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PetalLengthCm</th>\n",
" <td>67.667785</td>\n",
" <td>1.273682</td>\n",
" <td>-0.321713</td>\n",
" <td>3.113179</td>\n",
" <td>1.296387</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PetalWidthCm</th>\n",
" <td>29.832215</td>\n",
" <td>0.516904</td>\n",
" <td>-0.117981</td>\n",
" <td>1.296387</td>\n",
" <td>0.582414</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Id SepalLengthCm SepalWidthCm PetalLengthCm \\\n",
"Id 1887.500000 25.782886 -7.492282 67.667785 \n",
"SepalLengthCm 25.782886 0.685694 -0.039268 1.273682 \n",
"SepalWidthCm -7.492282 -0.039268 0.188004 -0.321713 \n",
"PetalLengthCm 67.667785 1.273682 -0.321713 3.113179 \n",
"PetalWidthCm 29.832215 0.516904 -0.117981 1.296387 \n",
"\n",
" PetalWidthCm \n",
"Id 29.832215 \n",
"SepalLengthCm 0.516904 \n",
"SepalWidthCm -0.117981 \n",
"PetalLengthCm 1.296387 \n",
"PetalWidthCm 0.582414 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"iris_dataset = pd.read_csv('../input/Iris.csv')\n",
"fig , ax1 = plt.subplots()\n",
"x1 = iris_dataset['SepalLengthCm']\n",
"y1= iris_dataset['SepalWidthCm']\n",
"x2 = iris_dataset['PetalLengthCm']\n",
"y2= iris_dataset['PetalWidthCm']\n",
"ax1.scatter(x1,y1,c=iris_dataset['Species'].apply(lambda x : 'c' if x =='Iris-setosa' else ( 'b' if x == 'Iris-versicolor' else 'r')))\n",
"#ax1.scatter(x2,y2,c='b')\n",
"plt.show()\n",
"\n",
"iris_dataset.cov()"
]
}
],
"metadata": {
"_change_revision": 152,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162780.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "4717f4ea-0b6e-e952-aff7-557636a3c973"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mnist.pkl.gz\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "bc07a791-35a0-2f2b-cfb6-c3392dd5aaf8"
},
"outputs": [],
"source": [
"import gzip, pickle, sys\n",
"f = gzip.open('../input/mnist.pkl.gz', 'rb')\n",
"(train_X, train_y), (valid_X, valid_y), _ = pickle.load(f, encoding=\"bytes\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "9626dec5-60f9-9c42-10f9-4ec5b8fea0fa"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from sklearn.utils import shuffle\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import f1_score\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"import gzip, pickle, sys\n",
"f = gzip.open('../input/mnist.pkl.gz', 'rb')\n",
"(train_X, train_y), (test_X, test_y), _ = pickle.load(f, encoding=\"bytes\")\n",
"\n",
"oneHot = OneHotEncoder(10, sparse=False)\n",
"train_y = oneHot.fit_transform(train_y[:, np.newaxis])\n",
"test_y = oneHot.fit_transform(test_y[:, np.newaxis])\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "3e540dd6-0b7a-2e74-237f-412509ecea38"
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 1., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" ..., \n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 1., 0.]])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_y"
]
}
],
"metadata": {
"_change_revision": 71,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162790.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ca6ad12b-48ae-a54d-4138-060d7ee040f6"
},
"source": [
"Features Extraction"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "eab5fd01-7bd8-699d-73f5-69d5bd37d8c8"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import nltk\n",
"from nltk.corpus import stopwords\n",
"from nltk.stem import SnowballStemmer\n",
"import re\n",
"from string import punctuation"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "7565f379-9887-7c91-fab8-9a188dd7a519"
},
"outputs": [],
"source": [
"train = pd.read_csv(\"../input/train.csv\")[:100]\n",
"test = pd.read_csv(\"../input/test.csv\")[:100]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "df2ba0b6-d476-c6c1-c375-ef56acca722f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 100 entries, 0 to 99\n",
"Data columns (total 6 columns):\n",
"id 100 non-null int64\n",
"qid1 100 non-null int64\n",
"qid2 100 non-null int64\n",
"question1 100 non-null object\n",
"question2 100 non-null object\n",
"is_duplicate 100 non-null int64\n",
"dtypes: int64(4), object(2)\n",
"memory usage: 4.8+ KB\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 100 entries, 0 to 99\n",
"Data columns (total 3 columns):\n",
"test_id 100 non-null int64\n",
"question1 100 non-null object\n",
"question2 100 non-null object\n",
"dtypes: int64(1), object(2)\n",
"memory usage: 2.4+ KB\n"
]
}
],
"source": [
"train.info()\n",
"test.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "9d025e51-404f-3912-7535-4e20520d216c"
},
"source": [
"Empty vs Null \n",
"\n",
"if yourObject is None: *# if yourObject == None:*\n",
" ...\n",
"\n",
"if yourString == \"\": *# if yourString.len() == 0:*\n",
" ..."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "b51ed280-0bf4-d55c-277d-6e537b1afa66"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"id 0\n",
"qid1 0\n",
"qid2 0\n",
"question1 0\n",
"question2 0\n",
"is_duplicate 0\n",
"dtype: int64\n",
"test_id 0\n",
"question1 0\n",
"question2 0\n",
"dtype: int64\n"
]
}
],
"source": [
"#check for any null values\n",
"print(train.isnull().sum())\n",
"print(test.isnull().sum())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "eee5f961-0bd3-1079-8251-4669362e5491"
},
"outputs": [],
"source": [
"# add the string 'empty' to empty string\n",
"train = train.fillna('empty')\n",
"test = test.fillna('empty')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "277454af-45e2-a018-f77a-138c331cf34e"
},
"outputs": [],
"source": [
"# Preview some of the "
]
}
],
"metadata": {
"_change_revision": 64,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162825.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "49753661-46ec-9519-ae01-d5f284467006"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"database.sqlite\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import seaborn as sns\n",
"import sqlite3\n",
"%matplotlib inline\n",
"\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "cf6c3112-727f-50cf-6505-ed285f6bb54a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>Belgium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1729</td>\n",
" <td>England</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4769</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>7809</td>\n",
" <td>Germany</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10257</td>\n",
" <td>Italy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>13274</td>\n",
" <td>Netherlands</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>15722</td>\n",
" <td>Poland</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>17642</td>\n",
" <td>Portugal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>19694</td>\n",
" <td>Scotland</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>21518</td>\n",
" <td>Spain</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>24558</td>\n",
" <td>Switzerland</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id name\n",
"0 1 Belgium\n",
"1 1729 England\n",
"2 4769 France\n",
"3 7809 Germany\n",
"4 10257 Italy\n",
"5 13274 Netherlands\n",
"6 15722 Poland\n",
"7 17642 Portugal\n",
"8 19694 Scotland\n",
"9 21518 Spain\n",
"10 24558 Switzerland"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with sqlite3.connect('../input/database.sqlite') as con:\n",
" countries = pd.read_sql_query(\"SELECT * from Country\", con)\n",
" matches = pd.read_sql_query(\"SELECT * from Match\", con)\n",
" leagues = pd.read_sql_query(\"SELECT * from League\", con)\n",
" teams = pd.read_sql_query(\"SELECT * from Team\", con)\n",
" players = pd.read_sql_query('SELECT * from Player',con)\n",
"\n",
"countries"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "6451e546-9533-7e87-2763-86d320e5205c"
},
"outputs": [],
"source": [
"selected_countries = ['England','France','Germany','Italy','Spain']\n",
"countries = countries[countries.name.isin(selected_countries)]\n",
"leagues = countries.merge(leagues,on='id',suffixes=('', '_y'))\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "8c34cc17-a018-6c41-f6f9-8ffbdf6cfc88"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>country_id</th>\n",
" <th>league_id</th>\n",
" <th>season</th>\n",
" <th>stage</th>\n",
" <th>date</th>\n",
" <th>match_api_id</th>\n",
" <th>home_team_api_id</th>\n",
" <th>away_team_api_id</th>\n",
" <th>B365H</th>\n",
" <th>B365D</th>\n",
" <th>B365A</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1728</th>\n",
" <td>1729</td>\n",
" <td>1729</td>\n",
" <td>1729</td>\n",
" <td>2008/2009</td>\n",
" <td>1</td>\n",
" <td>2008-08-17 00:00:00</td>\n",
" <td>489042</td>\n",
" <td>10260</td>\n",
" <td>10261</td>\n",
" <td>1.29</td>\n",
" <td>5.5</td>\n",
" <td>11.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1729</th>\n",
" <td>1730</td>\n",
" <td>1729</td>\n",
" <td>1729</td>\n",
" <td>2008/2009</td>\n",
" <td>1</td>\n",
" <td>2008-08-16 00:00:00</td>\n",
" <td>489043</td>\n",
" <td>9825</td>\n",
" <td>8659</td>\n",
" <td>1.20</td>\n",
" <td>6.5</td>\n",
" <td>15.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1730</th>\n",
" <td>1731</td>\n",
" <td>1729</td>\n",
" <td>1729</td>\n",
" <td>2008/2009</td>\n",
" <td>1</td>\n",
" <td>2008-08-16 00:00:00</td>\n",
" <td>489044</td>\n",
" <td>8472</td>\n",
" <td>8650</td>\n",
" <td>5.50</td>\n",
" <td>3.6</td>\n",
" <td>1.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1731</th>\n",
" <td>1732</td>\n",
" <td>1729</td>\n",
" <td>1729</td>\n",
" <td>2008/2009</td>\n",
" <td>1</td>\n",
" <td>2008-08-16 00:00:00</td>\n",
" <td>489045</td>\n",
" <td>8654</td>\n",
" <td>8528</td>\n",
" <td>1.91</td>\n",
" <td>3.4</td>\n",
" <td>4.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1732</th>\n",
" <td>1733</td>\n",
" <td>1729</td>\n",
" <td>1729</td>\n",
" <td>2008/2009</td>\n",
" <td>1</td>\n",
" <td>2008-08-17 00:00:00</td>\n",
" <td>489046</td>\n",
" <td>10252</td>\n",
" <td>8456</td>\n",
" <td>1.91</td>\n",
" <td>3.4</td>\n",
" <td>4.33</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id country_id league_id season stage date \\\n",
"1728 1729 1729 1729 2008/2009 1 2008-08-17 00:00:00 \n",
"1729 1730 1729 1729 2008/2009 1 2008-08-16 00:00:00 \n",
"1730 1731 1729 1729 2008/2009 1 2008-08-16 00:00:00 \n",
"1731 1732 1729 1729 2008/2009 1 2008-08-16 00:00:00 \n",
"1732 1733 1729 1729 2008/2009 1 2008-08-17 00:00:00 \n",
"\n",
" match_api_id home_team_api_id away_team_api_id B365H B365D B365A \n",
"1728 489042 10260 10261 1.29 5.5 11.00 \n",
"1729 489043 9825 8659 1.20 6.5 15.00 \n",
"1730 489044 8472 8650 5.50 3.6 1.67 \n",
"1731 489045 8654 8528 1.91 3.4 4.20 \n",
"1732 489046 10252 8456 1.91 3.4 4.33 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"matches = matches[matches.league_id.isin(leagues.id)]\n",
"matches = matches[['id', 'country_id' ,'league_id', 'season', 'stage', 'date','match_api_id', 'home_team_api_id', 'away_team_api_id','B365H', 'B365D' ,'B365A']]\n",
"\n",
"matches.dropna(inplace=True)\n",
"\n",
"matches.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "7f1f0ddc-9041-9775-fcd8-06bb9e65c039"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 312,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162853.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "50cfce73-0cd8-092a-fe57-9c3ac000e875"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# visualization\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"\n",
"import xgboost as xgb\n",
"from sklearn import model_selection, preprocessing\n",
"\n",
"\n",
"pd.set_option('display.max_columns', 500)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "912d98a7-bb10-cab8-0fa8-d38f4b341d7c"
},
"outputs": [],
"source": [
"train_df = pd.read_csv('../input/train.csv',parse_dates=['timestamp'])\n",
"result_df = pd.read_csv('../input/test.csv',parse_dates=['timestamp'])\n",
"train_df.shape\n",
"combine=[train_df,result_df]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "6ed4b2d7-8f6c-f254-a593-53a03ab07a5e"
},
"outputs": [],
"source": [
"#Cleaning\n",
"for df in combine:\n",
" df.ix[df.life_sq<2, 'life_sq'] = np.nan\n",
" df.ix[df.build_year<1500,'build_year']=np.nan\n",
" df.ix[df.max_floor<df.floor,'max_floor']=np.nan\n",
" df.ix[df.full_sq<2,'full_sq']=np.nan\n",
" df.ix[df.full_sq<df.life_sq,'life_sq']=np.nan\n",
" df.ix[df.kitch_sq>df.life_sq,'kitch_sq']=np.nan\n",
" df.ix[df.kitch_sq<2,'kitch_sq']=np.nan\n",
" df.ix[df.floor==0,'floor']=np.nan\n",
" df.ix[df.max_floor==0,'max_floor']=np.nan\n",
" df.ix[df.max_floor>70,'max_floor']=np.nan\n",
" df.ix[df.num_room==0,'num_room']=np.nan "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "359eb732-f583-474b-ff72-127359585797"
},
"outputs": [],
"source": [
"#New features\n",
"for df in combine:\n",
" df['life_pct']=df['life_sq']/df['full_sq'].astype(float)\n",
" df['rel_kitch']=df['kitch_sq']/df['full_sq'].astype(float)\n",
" df['rel_floor']=df['floor']/df['max_floor'].astype(float)\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "c9be57ef-bcb3-c3ff-338f-d6f433c645cc"
},
"outputs": [],
"source": [
"for c in train_df.columns:\n",
" if train_df[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(train_df[c].values)) \n",
" train_df[c] = lbl.transform(list(train_df[c].values))\n",
" #x_train.drop(c,axis=1,inplace=True)\n",
" \n",
"for c in result_df.columns:\n",
" if result_df[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(result_df[c].values)) \n",
" result_df[c] = lbl.transform(list(result_df[c].values))\n",
" #x_test.drop(c,axis=1,inplace=True) \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "e4063db2-adcf-e873-9247-f752f53c5254"
},
"outputs": [
{
"data": {
"text/plain": [
"(30471, 292)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train = train_df.drop(['price_doc','id','timestamp'],axis=1)\n",
"Y_train = train_df['price_doc'].values.reshape(-1,1)\n",
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "8b7b7718-d741-ab52-06bc-f83f946bcfbe"
},
"outputs": [
{
"data": {
"text/plain": [
"(7662, 292)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_result = result_df.drop(['id','timestamp'],axis=1)\n",
"id_test = result_df['id']\n",
"X_result.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "46e17dae-f980-cc0f-f8ac-1012251e4d21"
},
"outputs": [],
"source": [
"dtrain = xgb.DMatrix(X_train, Y_train)\n",
"dresult=xgb.DMatrix(X_result)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "9f919b86-aa0f-a7af-5ba3-7e43308a216d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-rmse:8.20591e+06\ttest-rmse:8.20837e+06\n",
"[25]\ttrain-rmse:3.48987e+06\ttest-rmse:3.67577e+06\n",
"[50]\ttrain-rmse:2.52311e+06\ttest-rmse:2.88828e+06\n",
"[75]\ttrain-rmse:2.29588e+06\ttest-rmse:2.75627e+06\n",
"[100]\ttrain-rmse:2.19103e+06\ttest-rmse:2.71604e+06\n",
"[125]\ttrain-rmse:2.12724e+06\ttest-rmse:2.69512e+06\n",
"[150]\ttrain-rmse:2.07246e+06\ttest-rmse:2.67635e+06\n",
"[175]\ttrain-rmse:2.02523e+06\ttest-rmse:2.66618e+06\n",
"[200]\ttrain-rmse:1.98523e+06\ttest-rmse:2.65696e+06\n",
"[225]\ttrain-rmse:1.94809e+06\ttest-rmse:2.65115e+06\n",
"[250]\ttrain-rmse:1.91784e+06\ttest-rmse:2.6445e+06\n",
"[275]\ttrain-rmse:1.88668e+06\ttest-rmse:2.64064e+06\n",
"[300]\ttrain-rmse:1.8534e+06\ttest-rmse:2.63574e+06\n",
"[325]\ttrain-rmse:1.82289e+06\ttest-rmse:2.63327e+06\n",
"[350]\ttrain-rmse:1.79478e+06\ttest-rmse:2.63057e+06\n",
"[375]\ttrain-rmse:1.76883e+06\ttest-rmse:2.62791e+06\n",
"[400]\ttrain-rmse:1.74498e+06\ttest-rmse:2.62676e+06\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f535acdd4a8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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4fPp2rMMxxpg+yxJKBC4dmc1bjWPQTzfaLzgaY0wHLKFEYMp5Ofw1OAEJVNp1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\nwhKKMcaYqPj/AVEzf9Xf1ldYAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f535acdde80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"xgb_params = {\n",
" 'eta': 0.05,\n",
" 'max_depth': 5,\n",
" 'subsample': 0.7,\n",
" 'colsample_bytree': 0.7,\n",
" 'objective': 'reg:linear',\n",
" 'eval_metric': 'rmse',\n",
" 'silent': 1\n",
"}\n",
"\n",
"cv_output = xgb.cv(xgb_params, dtrain, num_boost_round=1000, early_stopping_rounds=20,\n",
" verbose_eval=25, show_stdv=False)\n",
"cv_output[['train-rmse-mean', 'test-rmse-mean']].plot()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "e0f5d33b-f020-ba77-12d0-587e57ebdca4"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'model' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-10-a52d875c968b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mxgb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_importance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_num_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
]
},
{
"data": {
"image/png": 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AUE6sAaCcWANAObEGgHJiDQDlxBoAyok1AJQTawAoJ9YAUE6sAaCcWANAObEGgHJiDQDl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\noJxYA0C5/wMsUL1/FNTWuQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f535b408b38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1, figsize=(8, 16))\n",
"xgb.plot_importance(model, max_num_features=50, height=0.5, ax=ax)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "59b9c470-d047-916c-7af0-608ea01d4c58"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-rmse:8.20943e+06\n",
"[25]\ttrain-rmse:3.51412e+06\n",
"[50]\ttrain-rmse:2.57926e+06\n",
"[75]\ttrain-rmse:2.3542e+06\n",
"[100]\ttrain-rmse:2.26321e+06\n",
"[125]\ttrain-rmse:2.20656e+06\n",
"[150]\ttrain-rmse:2.14973e+06\n",
"[175]\ttrain-rmse:2.10675e+06\n",
"[200]\ttrain-rmse:2.07328e+06\n",
"[225]\ttrain-rmse:2.03957e+06\n",
"[250]\ttrain-rmse:2.01038e+06\n",
"[275]\ttrain-rmse:1.9826e+06\n",
"[300]\ttrain-rmse:1.95667e+06\n",
"[325]\ttrain-rmse:1.93225e+06\n",
"[350]\ttrain-rmse:1.91123e+06\n",
"[375]\ttrain-rmse:1.88841e+06\n",
"[399]\ttrain-rmse:1.86702e+06\n"
]
}
],
"source": [
"xgb_params = {\n",
" 'eta': 0.05,\n",
" 'max_depth': 5,\n",
" 'subsample': 0.7,\n",
" 'colsample_bytree': 0.7,\n",
" 'objective': 'reg:linear',\n",
" 'eval_metric': 'rmse',\n",
" 'silent': 1\n",
"}\n",
"# Uncomment to tune XGB `num_boost_rounds`\n",
"#model = xgb.cv(xgb_params, dtrain, num_boost_round=200,\n",
" #early_stopping_rounds=30, verbose_eval=10)\n",
"model = xgb.train(xgb_params, dtrain, num_boost_round=400,verbose_eval=25, evals=[(dtrain,'train')])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "e5520862-a82a-495c-9f44-cb5db3d4f5d1"
},
"outputs": [],
"source": [
"y_pred=model.predict(dresult)\n",
"output=pd.DataFrame(data={'price_doc':y_pred},index=id_test)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "fadcc476-1cd2-ea5a-5e90-77012a2259d1"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: overflow encountered in exp\n",
" from ipykernel import kernelapp as app\n"
]
}
],
"source": [
"logy_pred=model.predict(dresult)\n",
"y_pred = np.exp(logy_pred)-1\n",
"output=pd.DataFrame(data={'price_doc':y_pred},index=id_test)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "cb827739-897b-f670-cdb3-1daaa7265453"
},
"outputs": [
{
"ename": "ValueError",
"evalue": "range parameter must be finite.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-14-a1a4eccadcd2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, hold, data, **kwargs)\u001b[0m\n\u001b[1;32m 3080\u001b[0m \u001b[0mhisttype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhisttype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3081\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3082\u001b[0;31m stacked=stacked, data=data, **kwargs)\n\u001b[0m\u001b[1;32m 3083\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3084\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1890\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1891\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1892\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1893\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1894\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m 6190\u001b[0m \u001b[0;31m# this will automatically overwrite bins,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6191\u001b[0m \u001b[0;31m# so that each histogram uses the same bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6192\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhist_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6193\u001b[0m \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# causes problems later if it's an int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6194\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmlast\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 668\u001b[0m raise ValueError(\n\u001b[0;32m--> 669\u001b[0;31m 'range parameter must be finite.')\n\u001b[0m\u001b[1;32m 670\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 671\u001b[0m \u001b[0mmn\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: range parameter must be finite."
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f535a98fe10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(y_pred,bins=100)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "5beb30f0-e48f-6415-44e7-6766b0c343c5"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>30474</th>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30475</th>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30476</th>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30477</th>\n",
" <td>inf</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30478</th>\n",
" <td>inf</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" price_doc\n",
"id \n",
"30474 inf\n",
"30475 inf\n",
"30476 inf\n",
"30477 inf\n",
"30478 inf"
]
},
"execution_count": 15,
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"metadata": {
"_cell_guid": "c7369d2d-f24c-ae51-7261-9bcd9ce88501"
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"source": [
"output.to_csv('output.csv',header=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
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"_cell_guid": "b749a59f-6954-cc76-fb16-76e3392bb216"
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| 0001/162/1162914.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "238d246f-2485-16d5-51d4-33fe2cd9d85b"
},
"outputs": [],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import os\n",
"import gc\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "1a41402a-f7eb-4d32-7ed2-10892f05114c"
},
"outputs": [],
"source": [
"input = '../input/'\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "19888aba-d433-9ea5-ae24-756c9abc75b2"
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{
"data": {
"text/plain": "'../input/'"
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"input"
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},
{
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"metadata": {
"_cell_guid": "221b71b6-94e7-7092-a3e0-653b9bade8f8"
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"source": [
"df_train = pd.read_csv(input+\"train.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "6c8b4882-57c0-3998-3b03-72cc160dbc19"
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{
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\n27 Does society place too much importance on sports? \n28 What is best way to make money online? \n29 How should I prepare for CA final law? \n... ... \n404260 Which phone is best under 12000? \n404261 Who is the overall most popular Game of Throne... \n404262 How do you troubleshoot a Toshiba laptop? \n404263 How does the burning of fossil fuels contribut... \n404264 Is it safe to store an external battery power ... \n404265 How can I gain weight on my body? \n404266 What is the green dot next to the phone icon o... \n404267 What are the causes of the fall of the Roman E... \n404268 Why don't we still do great music like in the ... \n404269 How do you diagnose antisocial personality dis... \n404270 What is the difference between who and how? \n404271 Does Stalin have any grandchildren that are st... \n404272 What are the best new car products or inventio... \n404273 What happens if you put milk in a coffee maker? \n404274 Will the next generation of parenting change o... \n404275 In accounting, why do we debit expenses and cr... \n404276 What is copilotsearch.com? \n404277 What does analytics do? \n404278 How did you prepare for AIIMS/NEET/AIPMT? \n404279 What is the minimum time required to build a f... \n404280 What are some outfit ideas to wear to a frat p... \n404281 Why is Manaphy childish in Pok\u00e9mon Ranger and ... \n404282 How does a long distance relationship work? \n404283 What do you think of the removal of the MagSaf... \n404284 What does Jainism say about homosexuality? \n404285 How many keywords are there in the Racket prog... \n404286 Do you believe there is life after death? \n404287 What is one coin? \n404288 What is the approx annual cost of living while... \n404289 What is like to have sex with cousin? \n\n question2 is_duplicate \n0 What is the step by step guide to invest in sh... 0 \n1 What would happen if the Indian government sto... 0 \n2 How can Internet speed be increased by hacking... 0 \n3 Find the remainder when [math]23^{24}[/math] i... 0 \n4 Which fish would survive in salt water? 0 \n5 I'm a triple Capricorn (Sun, Moon and ascendan... 1 \n6 What keeps childern active and far from phone ... 0 \n7 What should I do to be a great geologist? 1 \n8 When do you use \"&\" instead of \"and\"? 0 \n9 How do I hack Motorola DCX3400 for free internet? 0 \n10 What are some of the things technicians can te... 0 \n11 How can I see all my Youtube comments? 1 \n12 How can you make physics easy to learn? 1 \n13 What was your first sexual experience? 1 \n14 What are the laws to change your status from a... 0 \n15 How will a Trump presidency affect the student... 1 \n16 What does manipulation means? 1 \n17 How do guys feel after rejecting a girl? 0 \n18 Why do people ask Quora questions which can be... 1 \n19 Which is the best digital marketing institute ... 0 \n20 Why are rockets and boosters painted white? 1 \n21 What can I do to avoid being jealous of someone? 0 \n22 Which question should I ask on Quora? 0 \n23 Where can I find a conversion chart for CC to ... 0 \n24 How many times a day do a clock\u2019s hands overlap? 0 \n25 What are some tips on making it through the jo... 0 \n26 What is the web application framework? 0 \n27 How do sports contribute to the society? 0 \n28 What is best way to ask for money online? 0 \n29 How one should know that he/she completely pre... 1 \n... ... ... \n404260 What is the best phone to buy below 15k? 0 \n404261 Who is the most popular character in the Game ... 1 \n404262 How do I reset a Toshiba laptop? 0 \n404263 Why does CO2 contribute more to global warming... 0 \n404264 How do I make a safe and cheap power bank? 0 \n404265 What should I eat to gain weight? 1 \n404266 My boyfriend says he deleted his Facebook Mess... 0 \n404267 What were the most important causes and effect... 1 \n404268 Should I raise my young child on 80's music? 0 \n404269 What Does It Feel Like to have antisocial pers... 0 \n404270 What is the difference between \"&\" and \"and\"? 0 \n404271 What was Joseph Stalin's 5 year plan? How did ... 0 \n404272 What are some mind-blowing vehicles tools that... 1 \n404273 What would happen if I put milk instead of wat... 1 \n404274 What kind of parents will the next generation ... 1 \n404275 What is a utilities expense in accounting? How... 0 \n404276 What is ContenVania.com? 0 \n404277 What are analytical people like? 0 \n404278 How did you prepare for the AIIMS UG entrance ... 0 \n404279 What is a cheaper and quicker way to build an ... 0 \n404280 What are some outfit ideas wear to a frat them... 1 \n404281 Why is Manaphy annoying in Pokemon ranger and ... 1 \n404282 How are long distance relationships maintained? 1 \n404283 What will the CPU upgrade to the 2016 Apple Ma... 0 \n404284 What does Jainism say about Gays and Homosexua... 1 \n404285 How many keywords are there in PERL Programmin... 0 \n404286 Is it true that there is life after death? 1 \n404287 What's this coin? 0 \n404288 I am having little hairfall problem but I want... 0 \n404289 What is it like to have sex with your cousin? 0 \n\n[404290 rows x 6 columns]"
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "29b1ab03-e31f-d0d1-a252-835decbda380"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>qid1</th>\n <th>qid2</th>\n <th>question1</th>\n <th>question2</th>\n <th>is_duplicate</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>What is the step by step guide to invest in sh...</td>\n <td>What is the step by step guide to invest in sh...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>3</td>\n <td>4</td>\n <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n <td>What would happen if the Indian government sto...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>5</td>\n <td>6</td>\n <td>How can I increase the speed of my internet co...</td>\n <td>How can Internet speed be increased by hacking...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>7</td>\n <td>8</td>\n <td>Why am I mentally very lonely? How can I solve...</td>\n <td>Find the remainder when [math]23^{24}[/math] i...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4</td>\n <td>9</td>\n <td>10</td>\n <td>Which one dissolve in water quikly sugar, salt...</td>\n <td>Which fish would survive in salt water?</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " id qid1 qid2 question1 \\\n0 0 1 2 What is the step by step guide to invest in sh... \n1 1 3 4 What is the story of Kohinoor (Koh-i-Noor) Dia... \n2 2 5 6 How can I increase the speed of my internet co... \n3 3 7 8 Why am I mentally very lonely? How can I solve... \n4 4 9 10 Which one dissolve in water quikly sugar, salt... \n\n question2 is_duplicate \n0 What is the step by step guide to invest in sh... 0 \n1 What would happen if the Indian government sto... 0 \n2 How can Internet speed be increased by hacking... 0 \n3 Find the remainder when [math]23^{24}[/math] i... 0 \n4 Which fish would survive in salt water? 0 "
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "93aa62e3-d225-8ea8-bd3f-f76c6776fb67"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "lenght of train data: 404290\n"
}
],
"source": [
"print(\"lenght of train data: \"+str(len(df_train)))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "fe958fc8-7747-a69e-f0a3-f13de38db885"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "length of train data: 404290\n"
}
],
"source": [
"print(\"length of train data: \"+str(len(df_train)))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "28a3be25-ffb5-0257-673c-65ad6234a5f5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "length of duplicate pairs404290\n"
}
],
"source": [
"print(\"length of duplicate pairs\"+str(len(df_train['is_duplicate'])))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "3ccd3906-0aaa-2f82-3cf5-7d557368a174"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "length of duplicate pairs: 149263\n"
}
],
"source": [
"print(\"length of duplicate pairs: \"+str(df_train['is_duplicate'].sum()\n",
" ))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "307c127f-85d4-238b-2ac4-b72a60f0966d"
},
"outputs": [],
"source": [
"qids = pd.Series(df_train['qid1'].tolist()+df_train['qid2'].tolist())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "e404ff2a-2fe9-a2d5-3e75-4d49d3d4322a"
},
"outputs": [
{
"data": {
"text/plain": "0 1\n1 3\n2 5\n3 7\n4 9\n5 11\n6 13\n7 15\n8 17\n9 19\n10 21\n11 23\n12 25\n13 27\n14 29\n15 31\n16 33\n17 35\n18 37\n19 39\n20 41\n21 43\n22 45\n23 47\n24 49\n25 51\n26 53\n27 55\n28 57\n29 59\n ... \n808550 691\n808551 124172\n808552 466328\n808553 537906\n808554 537908\n808555 16064\n808556 146284\n808557 290649\n808558 537910\n808559 349794\n808560 35364\n808561 537914\n808562 14005\n808563 537916\n808564 87385\n808565 537917\n808566 308365\n808567 537918\n808568 169786\n808569 537921\n808570 537923\n808571 81495\n808572 16773\n808573 537925\n808574 537927\n808575 379845\n808576 155606\n808577 537929\n808578 537931\n808579 537933\ndtype: int64"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qids"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "7bb40bbf-037e-ddec-e4c9-6e4e8fdace3c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "How many unique qids: 537933\n"
}
],
"source": [
"print(\"How many unique qids: \"+ str(len(np.unique(qids))))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "94906c38-c7c4-f8bb-4cbc-386fd94dc4de"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Number of questions that appear multiple times: 111780\n"
}
],
"source": [
"print('Number of questions that appear multiple times: {}'.format(np.sum(qids.value_counts() > 1)))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "39ba0c6d-3350-2930-bdd8-181129f60435"
},
"outputs": [
{
"data": {
"text/plain": "2559 157\n30782 120\n4044 111\n2561 88\n14376 79\n17978 77\n2675 68\n28764 66\n1772 63\n24555 61\n18753 58\n4018 57\n28133 56\n3595 55\n19621 55\n18531 55\n4951 55\n13748 54\n10024 54\n10330 54\n38 54\n18296 53\n11264 52\n6551 52\n20190 52\n691 51\n8461 51\n6749 51\n2322 51\n33412 51\n ... \n95599 1\n87403 1\n259391 1\n89450 1\n1345 1\n7490 1\n13639 1\n19784 1\n17737 1\n23882 1\n32078 1\n30031 1\n34129 1\n40274 1\n38227 1\n42325 1\n48470 1\n56666 1\n54619 1\n60764 1\n64862 1\n62815 1\n66913 1\n73058 1\n77156 1\n75109 1\n81254 1\n85352 1\n83305 1\n168274 1\ndtype: int64"
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qids.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "0f55d953-74e6-2bdc-7708-6ff7273cb6c6"
},
"outputs": [
{
"data": {
"text/plain": "(array([ 5.21491000e+05, 9.55300000e+03, 2.93000000e+03,\n 1.44500000e+03, 7.90000000e+02, 5.32000000e+02,\n 3.70000000e+02, 2.68000000e+02, 2.03000000e+02,\n 1.01000000e+02, 7.70000000e+01, 3.50000000e+01,\n 3.50000000e+01, 3.30000000e+01, 2.10000000e+01,\n 1.70000000e+01, 1.50000000e+01, 6.00000000e+00,\n 1.00000000e+00, 2.00000000e+00, 1.00000000e+00,\n 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 1.00000000e+00, 1.00000000e+00, 0.00000000e+00,\n 1.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 1.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n 0.00000000e+00, 1.00000000e+00]),\n array([ 1. , 4.12, 7.24, 10.36, 13.48, 16.6 , 19.72,\n 22.84, 25.96, 29.08, 32.2 , 35.32, 38.44, 41.56,\n 44.68, 47.8 , 50.92, 54.04, 57.16, 60.28, 63.4 ,\n 66.52, 69.64, 72.76, 75.88, 79. , 82.12, 85.24,\n 88.36, 91.48, 94.6 , 97.72, 100.84, 103.96, 107.08,\n 110.2 , 113.32, 116.44, 119.56, 122.68, 125.8 , 128.92,\n 132.04, 135.16, 138.28, 141.4 , 144.52, 147.64, 150.76,\n 153.88, 157. ]),\n <a list of 50 Patch objects>)"
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AAIanFAMAMDylGACA4SnFAAAMTykGAGB4C/9Eu6p6aZJ/muTLST7d3R9e9HMAAMAizbRT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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f75a5a40908>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 5))\n",
"plt.hist(qids.value_counts(), bins=50,log = 'True')\n",
"plt.title('Normalised histogram of word count in questions', fontsize=15)\n",
"plt.legend()\n",
"plt.xlabel('Number of words', fontsize=15)\n",
"plt.ylabel('Probability', fontsize=15)\n",
"# plt.yscale('log', nonposy='clip')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "617013eb-3d1e-0817-942b-87d6c92fa4c8"
},
"outputs": [
{
"data": {
"text/plain": "2559 157\n30782 120\n4044 111\n2561 88\n14376 79\n17978 77\n2675 68\n28764 66\n1772 63\n24555 61\n18753 58\n4018 57\n28133 56\n3595 55\n19621 55\n18531 55\n4951 55\n13748 54\n10024 54\n10330 54\n38 54\n18296 53\n11264 52\n6551 52\n20190 52\n691 51\n8461 51\n6749 51\n2322 51\n33412 51\n ... \n95599 1\n87403 1\n259391 1\n89450 1\n1345 1\n7490 1\n13639 1\n19784 1\n17737 1\n23882 1\n32078 1\n30031 1\n34129 1\n40274 1\n38227 1\n42325 1\n48470 1\n56666 1\n54619 1\n60764 1\n64862 1\n62815 1\n66913 1\n73058 1\n77156 1\n75109 1\n81254 1\n85352 1\n83305 1\n168274 1\ndtype: int64"
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qids.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "0b2a4947-e9aa-064b-3d13-adb35e36d17e"
},
"outputs": [],
"source": [
"df_test = pd.read_csv(input+\"test.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "79f1ce4c-31a3-2e9d-5eec-01b064c7f90d"
},
"outputs": [],
"source": [
"train_qs = pd.Series(df_train[\"question1\"].tolist()+df_train['question2'].tolist()).astype(str)\n",
"test_qs = pd.Series(df_test[\"question1\"].tolist()+df_test['question2'].tolist()).astype(str)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "4b4956b7-c75b-b7d8-fa6e-ac4a8a814f55"
},
"outputs": [],
"source": [
"pal = sns.color_palette()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "dc950cf2-4335-2e93-6505-6a4d3b804595"
},
"outputs": [
{
"data": {
"text/plain": "(array([ 0.00000000e+00, 1.24070806e-05, 5.17674742e-05,\n 2.03861169e-04, 6.93085192e-04, 6.96507835e-04,\n 2.43863308e-04, 4.85587465e-04, 8.73415691e-04,\n 1.00689876e-03, 9.14273491e-04, 9.95347345e-04,\n 1.44692230e-03, 1.78169956e-03, 1.97507888e-03,\n 2.15155891e-03, 2.53810364e-03, 2.89170544e-03,\n 3.40167923e-03, 3.82608696e-03, 4.35766619e-03,\n 4.94400770e-03, 5.45847372e-03, 6.20931601e-03,\n 6.88764105e-03, 7.59120809e-03, 8.37413765e-03,\n 9.23707150e-03, 9.98748596e-03, 1.08146960e-02,\n 1.16376277e-02, 1.24374565e-02, 1.34212525e-02,\n 1.41374405e-02, 1.47582224e-02, 1.55621156e-02,\n 1.62047168e-02, 1.68909567e-02, 1.74229638e-02,\n 1.80069522e-02, 1.84204503e-02, 1.88112733e-02,\n 1.90771699e-02, 1.94277769e-02, 1.97379539e-02,\n 1.95984812e-02, 1.97796674e-02, 1.96256484e-02,\n 1.94125889e-02, 1.92930103e-02, 1.88497781e-02,\n 1.85838815e-02, 1.82020429e-02, 1.74541954e-02,\n 1.72757901e-02, 1.64391679e-02, 1.60059896e-02,\n 1.55559121e-02, 1.49858281e-02, 1.43894326e-02,\n 1.38858763e-02, 1.33483074e-02, 1.29891438e-02,\n 1.26496604e-02, 1.17426600e-02, 1.13009252e-02,\n 1.09430451e-02, 1.05079416e-02, 1.01261030e-02,\n 9.60778651e-03, 9.33183593e-03, 8.93074496e-03,\n 8.39595700e-03, 8.14610407e-03, 7.79827798e-03,\n 7.59313332e-03, 7.34221081e-03, 6.93769720e-03,\n 6.82196909e-03, 6.56227606e-03, 6.22600139e-03,\n 6.05123269e-03, 5.81442858e-03, 5.59559335e-03,\n 5.45697631e-03, 5.31365314e-03, 5.05802449e-03,\n 4.94186855e-03, 4.73779346e-03, 4.61864271e-03,\n 4.49007968e-03, 4.34140863e-03, 4.16214771e-03,\n 4.04556393e-03, 3.91336435e-03, 3.77068292e-03,\n 3.70009091e-03, 3.55420076e-03, 3.38477993e-03,\n 3.21642869e-03, 3.14690625e-03, 3.02625809e-03,\n 2.90667950e-03, 2.79073747e-03, 2.65361784e-03,\n 2.64484732e-03, 2.55371945e-03, 2.41125194e-03,\n 2.33873469e-03, 2.25637735e-03, 2.18514359e-03,\n 2.14064923e-03, 2.04331782e-03, 2.01379753e-03,\n 1.90641211e-03, 1.91753570e-03, 1.81849297e-03,\n 1.75367667e-03, 1.76223327e-03, 1.76094978e-03,\n 1.74447831e-03, 1.66511578e-03, 1.66896625e-03,\n 1.63880421e-03, 1.58404193e-03, 1.53954757e-03,\n 1.47644259e-03, 1.46938339e-03, 1.42724210e-03,\n 1.39130435e-03, 1.35750575e-03, 1.33953687e-03,\n 1.31301139e-03, 1.32242366e-03, 1.33311942e-03,\n 1.28563025e-03, 1.27044227e-03, 1.29418685e-03,\n 1.29119204e-03, 1.31301139e-03, 1.27108402e-03,\n 1.28669982e-03, 1.30274346e-03, 1.33611423e-03,\n 1.34809348e-03, 1.34295952e-03, 1.34210386e-03,\n 1.36477887e-03, 1.34509867e-03, 1.35237178e-03,\n 1.36520670e-03, 1.01652495e-03, 9.72886251e-04,\n 8.72560030e-04, 7.85282635e-04, 6.83459009e-04,\n 5.54468153e-04, 4.40237446e-04, 3.68148029e-04,\n 3.06326542e-04, 2.61832184e-04, 2.60120862e-04,\n 2.24824857e-04, 2.13487352e-04, 2.08353388e-04,\n 1.81613990e-04, 1.84394887e-04, 1.70918231e-04,\n 1.65570351e-04, 1.64928606e-04, 1.67495588e-04,\n 1.50168458e-04, 1.48243222e-04, 1.52521525e-04,\n 1.34766565e-04, 1.33269159e-04, 1.33055244e-04,\n 1.28990855e-04, 1.37333547e-04, 1.18509011e-04,\n 1.36050056e-04, 1.25140382e-04, 1.25354297e-04,\n 1.35408311e-04, 1.19792502e-04, 1.21503824e-04,\n 1.26423873e-04, 1.26851703e-04, 1.07385422e-04,\n 1.12947216e-04, 1.04390609e-04, 1.00754051e-04,\n 9.86148992e-05, 1.00754051e-04, 1.11021980e-04,\n 1.03962779e-04, 9.26252741e-05, 1.02037542e-04,\n 1.02251457e-04, 1.80544414e-04]),\n array([ 0., 1., 2., 3., 4., 5., 6., 7., 8.,\n 9., 10., 11., 12., 13., 14., 15., 16., 17.,\n 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n 27., 28., 29., 30., 31., 32., 33., 34., 35.,\n 36., 37., 38., 39., 40., 41., 42., 43., 44.,\n 45., 46., 47., 48., 49., 50., 51., 52., 53.,\n 54., 55., 56., 57., 58., 59., 60., 61., 62.,\n 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n 72., 73., 74., 75., 76., 77., 78., 79., 80.,\n 81., 82., 83., 84., 85., 86., 87., 88., 89.,\n 90., 91., 92., 93., 94., 95., 96., 97., 98.,\n 99., 100., 101., 102., 103., 104., 105., 106., 107.,\n 108., 109., 110., 111., 112., 113., 114., 115., 116.,\n 117., 118., 119., 120., 121., 122., 123., 124., 125.,\n 126., 127., 128., 129., 130., 131., 132., 133., 134.,\n 135., 136., 137., 138., 139., 140., 141., 142., 143.,\n 144., 145., 146., 147., 148., 149., 150., 151., 152.,\n 153., 154., 155., 156., 157., 158., 159., 160., 161.,\n 162., 163., 164., 165., 166., 167., 168., 169., 170.,\n 171., 172., 173., 174., 175., 176., 177., 178., 179.,\n 180., 181., 182., 183., 184., 185., 186., 187., 188.,\n 189., 190., 191., 192., 193., 194., 195., 196., 197.,\n 198., 199., 200.]),\n <a list of 200 Patch objects>)"
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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5AACABglzAAAADRLmAAAAGiTMAQAANEiYAwAAaJDVLJlqC/v3dV0CAABsCJ05AACABunM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\nEQAAAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7577da1630>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"dist_train = train_qs.apply(len)\n",
"dist_test = test_qs.apply(len)\n",
"plt.figure(figsize=(15, 10))\n",
"plt.hist(dist_train, bins=200, range=[0, 200], color=pal[2], normed=True, label='train')\n",
"plt.hist(dist_test, bins=200, range=[0, 200], color=pal[1], normed=True, alpha = 0.5 ,label='test')\n",
"plt.title('Normalised histogram of word count in questions', fontsize=15)\n",
"plt.legend()\n",
"plt.xlabel('Number of words', fontsize=15)\n",
"plt.ylabel('Probability', fontsize=15)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e803745e-df06-3538-dd45-2607fbd3899b"
},
"source": [
"if 150 characters' steep down are the limit, how could there be some question got over 150 words?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "38bbf0d8-dafe-4f71-e545-8c80b3b27713"
},
"outputs": [],
"source": [
"import re\n",
"r = re.compile(\"[ ,.?|']\")\n",
"dist_train = train_qs.apply(lambda x: len(r.split(x)))\n",
"dist_test = test_qs.apply(lambda x: len(r.split(x)))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "09da1a9f-637b-bb7d-e546-0872ad674e81"
},
"outputs": [
{
"data": {
"text/plain": "(array([ 0.00000000e+00, 2.62174432e-05, 1.79919868e-03,\n 5.23879934e-03, 1.40926218e-02, 2.83617317e-02,\n 4.73476367e-02, 7.04776029e-02, 9.20699055e-02,\n 1.04061082e-01, 1.03567427e-01, 9.51518406e-02,\n 7.72114360e-02, 6.06921021e-02, 4.85738883e-02,\n 3.93046367e-02, 3.25217791e-02, 2.74046884e-02,\n 2.20439673e-02, 1.80968566e-02, 1.49420243e-02,\n 1.26871110e-02, 1.06419373e-02, 8.98277812e-03,\n 8.04001592e-03, 7.46962667e-03, 6.71656791e-03,\n 6.03853794e-03, 5.71774727e-03, 5.17421491e-03,\n 4.55160392e-03, 3.77659072e-03, 3.05102831e-03,\n 2.38088488e-03, 1.83095803e-03, 1.47180037e-03,\n 1.16017515e-03, 9.48517254e-04, 8.37252983e-04,\n 7.22365163e-04, 5.72520752e-04, 4.93655272e-04,\n 4.62109080e-04, 3.80898951e-04, 3.28890365e-04,\n 2.75389729e-04, 2.39793688e-04, 1.84587852e-04,\n 1.76701304e-04, 1.56452059e-04, 1.28529417e-04,\n 1.13182621e-04, 1.05935523e-04, 1.07427572e-04,\n 9.16544763e-05, 7.95049294e-05, 8.67520275e-05,\n 8.93098269e-05, 8.69651775e-05, 7.33235810e-05,\n 7.28972811e-05, 5.88293848e-05, 5.64847354e-05,\n 6.47975832e-05, 4.98770871e-05, 4.15642392e-05,\n 4.11379393e-05, 3.77275402e-05, 2.34464939e-05,\n 1.81177453e-05, 1.68388456e-05, 1.42810463e-05,\n 1.00180474e-05, 1.17232470e-05, 7.46024807e-06,\n 6.60764829e-06, 7.46024807e-06, 8.73914774e-06,\n 4.04984895e-06, 1.91834950e-06, 5.11559868e-06,\n 2.98409923e-06, 6.39449834e-07, 1.49204961e-06,\n 1.91834950e-06, 1.27889967e-06, 1.49204961e-06,\n 1.91834950e-06, 1.70519956e-06, 0.00000000e+00,\n 6.39449834e-07, 1.06574972e-06, 8.52599779e-07,\n 1.27889967e-06, 1.06574972e-06, 4.26299890e-07,\n 8.52599779e-07, 8.52599779e-07, 4.26299890e-07,\n 1.27889967e-06]),\n array([ 0., 1., 2., 3., 4., 5., 6., 7., 8.,\n 9., 10., 11., 12., 13., 14., 15., 16., 17.,\n 18., 19., 20., 21., 22., 23., 24., 25., 26.,\n 27., 28., 29., 30., 31., 32., 33., 34., 35.,\n 36., 37., 38., 39., 40., 41., 42., 43., 44.,\n 45., 46., 47., 48., 49., 50., 51., 52., 53.,\n 54., 55., 56., 57., 58., 59., 60., 61., 62.,\n 63., 64., 65., 66., 67., 68., 69., 70., 71.,\n 72., 73., 74., 75., 76., 77., 78., 79., 80.,\n 81., 82., 83., 84., 85., 86., 87., 88., 89.,\n 90., 91., 92., 93., 94., 95., 96., 97., 98.,\n 99., 100.]),\n <a list of 100 Patch objects>)"
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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e5OObmh0AAABT3SoJAADAHAk3AACA5oQbAABAc8INAACgOeEGAADQnHADAABoTrgBAAA0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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7577da1208>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15, 10))\n",
"plt.hist(dist_train, bins=100, range=[0, 100], color=pal[2], normed=True, label='train')\n",
"plt.hist(dist_test, bins=100, range=[0, 100], color=pal[1], normed=True, alpha=0.5, label='test')\n",
"plt.title('Normalised histogram of word count in questions', fontsize=15)\n",
"plt.legend()\n",
"plt.xlabel('Number of words', fontsize=15)\n",
"plt.ylabel('Probability', fontsize=15)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "847aa66d-8041-021e-6b43-9fc237130b6c"
},
"source": [
"max word limit may be 70? The two distribution are very similar."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4e475988-37aa-28a2-9f39-9bf6dd56d0b4"
},
"source": [
"By using the feature like number of shared words, we have the following idea."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "bb307674-3392-e5fb-c6dd-633a43b064e0"
},
"outputs": [],
"source": [
"from nltk.corpus import stopwords\n",
"\n",
"stops = set(stopwords.words('english'))"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "5acc4814-baf1-9101-e2ad-2fba1845864f"
},
"outputs": [
{
"data": {
"text/plain": "<matplotlib.text.Text at 0x7f756de66f98>"
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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pzzS6hE5tvtFajS5BktRg9UwScglwSTfrfwts05dFSZIkSdJwVM80+5IkSZKk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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f7570447748>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def word_match(row):\n",
" q1_word = {}\n",
" q2_word = {}\n",
" for word in r.split(str(row[\"question1\"]).lower()):\n",
" if word not in stops:\n",
" q1_word[word] = 1\n",
" for word in r.split(str(row[\"question2\"]).lower()):\n",
" if word not in stops:\n",
" q2_word[word] = 1\n",
" if len(q1_word) == 0 or len(q2_word)==0:\n",
" return 0\n",
" shared_word_in_q1 = [w for w in q1_word.keys() if w in q2_word]\n",
" shared_word_in_q2 = [w for w in q2_word.keys() if w in q1_word]\n",
" R = (len(shared_word_in_q1)+len(shared_word_in_q2))/(len(q1_word)+len(q2_word))\n",
" return R\n",
"\n",
"plt.figure(figsize=(15,5))\n",
"train_word_related = df_train.apply(word_match,axis = 1,raw = 1)\n",
"plt.hist(train_word_related[df_train['is_duplicate'] == 0].fillna(0), bins=20, normed=True, label='Not Duplicate')\n",
"plt.hist(train_word_related[df_train['is_duplicate'] == 1].fillna(0), bins=20, normed=True, alpha=0.7, label='Duplicate')\n",
"plt.legend()\n",
"plt.title('Label distribution over word_match_share', fontsize=15)\n",
"plt.xlabel('word_match_share', fontsize=15)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "c1cf9ce3-e45d-ad1f-7180-81c822c8675a"
},
"source": [
"Used stops to filter all words, and compute the relateness in question 1 and question 2."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"_cell_guid": "d19059c6-d945-a8d2-454f-1d5153511a53"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>qid1</th>\n <th>qid2</th>\n <th>question1</th>\n <th>question2</th>\n <th>is_duplicate</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>What is the step by step guide to invest in sh...</td>\n <td>What is the step by step guide to invest in sh...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>3</td>\n <td>4</td>\n <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n <td>What would happen if the Indian government sto...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>5</td>\n <td>6</td>\n <td>How can I increase the speed of my internet co...</td>\n <td>How can Internet speed be increased by hacking...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>7</td>\n <td>8</td>\n <td>Why am I mentally very lonely? How can I solve...</td>\n <td>Find the remainder when [math]23^{24}[/math] i...</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4</td>\n <td>9</td>\n <td>10</td>\n <td>Which one dissolve in water quikly sugar, salt...</td>\n <td>Which fish would survive in salt water?</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " id qid1 qid2 question1 \\\n0 0 1 2 What is the step by step guide to invest in sh... \n1 1 3 4 What is the story of Kohinoor (Koh-i-Noor) Dia... \n2 2 5 6 How can I increase the speed of my internet co... \n3 3 7 8 Why am I mentally very lonely? How can I solve... \n4 4 9 10 Which one dissolve in water quikly sugar, salt... \n\n question2 is_duplicate \n0 What is the step by step guide to invest in sh... 0 \n1 What would happen if the Indian government sto... 0 \n2 How can Internet speed be increased by hacking... 0 \n3 Find the remainder when [math]23^{24}[/math] i... 0 \n4 Which fish would survive in salt water? 0 "
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"_cell_guid": "23fe0a4a-60b0-6443-d1ea-2cd5818fa72e"
},
"outputs": [],
"source": [
"from collections import Counter\n",
"\n",
"# If a word appears only once, we ignore it completely (likely a typo)\n",
"# Epsilon defines a smoothing constant, which makes the effect of extremely rare words smaller\n",
"def get_weight(count, eps=10000, min_count=2):\n",
" if count < min_count:\n",
" return 0\n",
" else:\n",
" return 1 / (count + eps)\n",
"\n",
"eps = 5000 \n",
"words = r.split((\" \".join(train_qs)).lower())\n",
"counts = Counter(words)\n",
"weights = {word: get_weight(count) for word, count in counts.items() if word != \"\"}"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ff0a24a3-5086-5356-7cae-847b0772503b"
},
"source": [
"Because we filter the only-once appearance, so we need some specific operation on x==0.\n",
"Also, the re.split() will create a lot null str, I also remove that from weights."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"_cell_guid": "5acb4dcb-b758-2d83-2b81-85868eeb2f65"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Most commonly used words are: \n\n[('the', 2.587951532843693e-06), ('what', 2.9944154152505576e-06), ('is', 3.576717002997289e-06), ('i', 4.318852221833526e-06), ('how', 4.33715291933763e-06), ('a', 4.5256445649271595e-06), ('to', 4.646818555675857e-06), ('in', 4.854910985207086e-06), ('do', 5.853772756541591e-06), ('of', 5.894314933246883e-06)]\n\nLeast commonly used words are: \n\n[('\u30b7', 9.998000399920016e-05), ('\u3057', 9.998000399920016e-05), ('dcx3400', 9.998000399920016e-05), ('(employment', 9.998000399920016e-05), ('1-855-425-3768', 9.998000399920016e-05), ('confederates', 9.998000399920016e-05), ('asahi', 9.998000399920016e-05), ('oitnb', 9.998000399920016e-05), ('samrudi', 9.998000399920016e-05), ('prospering', 9.998000399920016e-05)]\n"
}
],
"source": [
"print(\"Most commonly used words are: \\n\")\n",
"print(sorted(weights.items(),key=lambda x: x[1] if x[1]>0 else 9999)[:10])\n",
"print(\"\\nLeast commonly used words are: \\n\")\n",
"print(sorted(weights.items(),key=lambda x: x[1],reverse = True)[:10])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"_cell_guid": "2c317f05-32a4-1442-5eb9-07c4f65b67b3"
},
"outputs": [],
"source": [
"def tfidf_word_match_share(row):\n",
" q1words = {}\n",
" q2words = {}\n",
" for word in r.split(str(row['question1']).lower()):\n",
" if word not in stops:\n",
" q1words[word] = 1\n",
" for word in r.split(str(row['question2']).lower()):\n",
" if word not in stops:\n",
" q2words[word] = 1\n",
" if len(q1words) == 0 or len(q2words) == 0:\n",
" # The computer-generated chaff includes a few questions that are nothing but stopwords\n",
" return 0\n",
" \n",
" shared_weights = [weights.get(w, 0) for w in q1words.keys() if w in q2words] + [weights.get(w, 0) for w in q2words.keys() if w in q1words]\n",
" total_weights = [weights.get(w, 0) for w in q1words] + [weights.get(w, 0) for w in q2words]\n",
" \n",
" R = np.sum(shared_weights) / np.sum(total_weights)\n",
" return R"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"_cell_guid": "aa3e43e9-4b94-fe44-d060-89b0239a3940"
},
"outputs": [
{
"data": {
"text/plain": "<matplotlib.text.Text at 0x7f75704504e0>"
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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1uzciXgP+RDejZ5IkSZKkrtWySMgDwB7d7J8MTO7DmiRJkgassq5eOuXUvRpd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\ngMxuKqklAAAAAElFTkSuQmCC\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f75500abcc0>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15, 5))\n",
"tfidf_train_word_share = df_train.apply(tfidf_word_match_share,axis = 1, raw = True)\n",
"plt.hist(tfidf_train_word_share[df_train['is_duplicate']==1].fillna(0), bins=20, normed = True, label = 'Duplicate')\n",
"plt.hist(tfidf_train_word_share[df_train['is_duplicate']==0].fillna(0), bins=20, normed = True, label = 'Not Duplicate',alpha = 0.7)\n",
"plt.legend()\n",
"plt.title('Label distribution over tfidf train data')\n",
"plt.xlabel('tfidf train word share')"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"_cell_guid": "f07426a3-3796-a11e-6365-25735c41798b"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Original AUC: 0.749579155461\n TFIDF AUC: 0.738956967713\n"
}
],
"source": [
"from sklearn.metrics import roc_auc_score\n",
"print('Original AUC:', roc_auc_score(df_train['is_duplicate'], train_word_related))\n",
"print(' TFIDF AUC:', roc_auc_score(df_train['is_duplicate'], tfidf_train_word_share.fillna(0)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "17fdf979-7419-80cc-21d2-51af2350d1ee"
},
"source": [
"Rebalancing data"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"_cell_guid": "1cf46e20-9801-cc29-0ca2-6026f39aaff6"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/opt/conda/bin/ipython:17: RuntimeWarning: invalid value encountered in long_scalars\n"
}
],
"source": [
"# First we create our training and testing data\n",
"x_train = pd.DataFrame()\n",
"x_test = pd.DataFrame()\n",
"x_train['word_match'] = train_word_related\n",
"x_train['tfidf_word_match'] = tfidf_train_word_share\n",
"x_test['word_match'] = df_test.apply(word_match, axis=1, raw=True)\n",
"x_test['tfidf_word_match'] = df_test.apply(tfidf_word_match_share, axis=1, raw=True)\n",
"\n",
"y_train = df_train['is_duplicate'].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "9c05da7a-0d85-01dd-2184-e17ae3d2cd47"
},
"outputs": [],
"source": [
"pos_train = x_train[y_train == 1]\n",
"neg_train = x_train[y_train == 0]\n",
"\n",
"# Now we oversample the negative class\n",
"# There is likely a much more elegant way to do this...\n",
"p = 0.165\n",
"scale = ((len(pos_train) / (len(pos_train) + len(neg_train))) / p) - 1\n",
"while scale > 1:\n",
" neg_train = pd.concat([neg_train, neg_train])\n",
" scale -=1\n",
"neg_train = pd.concat([neg_train, neg_train[:int(scale * len(neg_train))]])\n",
"print(len(pos_train) / (len(pos_train) + len(neg_train)))\n",
"\n",
"x_train = pd.concat([pos_train, neg_train])\n",
"y_train = (np.zeros(len(pos_train)) + 1).tolist() + np.zeros(len(neg_train)).tolist()\n",
"del pos_train, neg_train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "90b5c348-71f1-0de7-718e-41987027ea6d"
},
"outputs": [],
"source": [
"from sklearn.cross_validation import train_test_split\n",
"\n",
"x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train, test_size=0.2, random_state=4242)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "83ac7e0b-830d-1335-e8a5-720cd6d4908d"
},
"outputs": [],
"source": [
"x_train"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "bdeb626c-9ef1-bce1-fc60-53d7855c8a36"
},
"outputs": [],
"source": [
"import xgboost as xgb\n",
"\n",
"# Set our parameters for xgboost\n",
"params = {}\n",
"params['objective'] = 'binary:logistic'\n",
"params['eval_metric'] = 'logloss'\n",
"params['eta'] = 0.02\n",
"params['max_depth'] = 4\n",
"\n",
"d_train = xgb.DMatrix(x_train, label=y_train)\n",
"d_valid = xgb.DMatrix(x_valid, label=y_valid)\n",
"\n",
"watchlist = [(d_train, 'train'), (d_valid, 'valid')]\n",
"\n",
"bst = xgb.train(params, d_train, 400, watchlist, early_stopping_rounds=50, verbose_eval=10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "0800558a-6f99-c8c7-aee8-e397f7dc37a0"
},
"outputs": [],
"source": [
"d_test = xgb.DMatrix(x_test)\n",
"p_test = bst.predict(d_test)\n",
"\n",
"sub = pd.DataFrame()\n",
"sub['test_id'] = df_test['test_id']\n",
"sub['is_duplicate'] = p_test"
]
}
],
"metadata": {
"_change_revision": 173,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | 0001/162/1162969.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "57467428-cda7-c05b-83b8-8bb01b2f9402"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "70c742ae-fe84-945f-06e7-26cb2942083a"
},
"outputs": [],
"source": [
"train = pd.read_csv('../input/train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "3b0fe5bb-4e4e-4a7e-979c-2c1496c6b72e"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>MSSubClass</th>\n",
" <th>LotFrontage</th>\n",
" <th>LotArea</th>\n",
" <th>OverallQual</th>\n",
" <th>OverallCond</th>\n",
" <th>YearBuilt</th>\n",
" <th>YearRemodAdd</th>\n",
" <th>MasVnrArea</th>\n",
" <th>BsmtFinSF1</th>\n",
" <th>...</th>\n",
" <th>WoodDeckSF</th>\n",
" <th>OpenPorchSF</th>\n",
" <th>EnclosedPorch</th>\n",
" <th>3SsnPorch</th>\n",
" <th>ScreenPorch</th>\n",
" <th>PoolArea</th>\n",
" <th>MiscVal</th>\n",
" <th>MoSold</th>\n",
" <th>YrSold</th>\n",
" <th>SalePrice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1452.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>...</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>730.500000</td>\n",
" <td>56.897260</td>\n",
" <td>70.049958</td>\n",
" <td>10516.828082</td>\n",
" <td>6.099315</td>\n",
" <td>5.575342</td>\n",
" <td>1971.267808</td>\n",
" <td>1984.865753</td>\n",
" <td>103.685262</td>\n",
" <td>443.639726</td>\n",
" <td>...</td>\n",
" <td>94.244521</td>\n",
" <td>46.660274</td>\n",
" <td>21.954110</td>\n",
" <td>3.409589</td>\n",
" <td>15.060959</td>\n",
" <td>2.758904</td>\n",
" <td>43.489041</td>\n",
" <td>6.321918</td>\n",
" <td>2007.815753</td>\n",
" <td>180921.195890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>421.610009</td>\n",
" <td>42.300571</td>\n",
" <td>24.284752</td>\n",
" <td>9981.264932</td>\n",
" <td>1.382997</td>\n",
" <td>1.112799</td>\n",
" <td>30.202904</td>\n",
" <td>20.645407</td>\n",
" <td>181.066207</td>\n",
" <td>456.098091</td>\n",
" <td>...</td>\n",
" <td>125.338794</td>\n",
" <td>66.256028</td>\n",
" <td>61.119149</td>\n",
" <td>29.317331</td>\n",
" <td>55.757415</td>\n",
" <td>40.177307</td>\n",
" <td>496.123024</td>\n",
" <td>2.703626</td>\n",
" <td>1.328095</td>\n",
" <td>79442.502883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>20.000000</td>\n",
" <td>21.000000</td>\n",
" <td>1300.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1872.000000</td>\n",
" <td>1950.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2006.000000</td>\n",
" <td>34900.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>365.750000</td>\n",
" <td>20.000000</td>\n",
" <td>59.000000</td>\n",
" <td>7553.500000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1954.000000</td>\n",
" <td>1967.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>2007.000000</td>\n",
" <td>129975.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>730.500000</td>\n",
" <td>50.000000</td>\n",
" <td>69.000000</td>\n",
" <td>9478.500000</td>\n",
" <td>6.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1973.000000</td>\n",
" <td>1994.000000</td>\n",
" <td>0.000000</td>\n",
" <td>383.500000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>25.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2008.000000</td>\n",
" <td>163000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1095.250000</td>\n",
" <td>70.000000</td>\n",
" <td>80.000000</td>\n",
" <td>11601.500000</td>\n",
" <td>7.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2000.000000</td>\n",
" <td>2004.000000</td>\n",
" <td>166.000000</td>\n",
" <td>712.250000</td>\n",
" <td>...</td>\n",
" <td>168.000000</td>\n",
" <td>68.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" <td>2009.000000</td>\n",
" <td>214000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1460.000000</td>\n",
" <td>190.000000</td>\n",
" <td>313.000000</td>\n",
" <td>215245.000000</td>\n",
" <td>10.000000</td>\n",
" <td>9.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>1600.000000</td>\n",
" <td>5644.000000</td>\n",
" <td>...</td>\n",
" <td>857.000000</td>\n",
" <td>547.000000</td>\n",
" <td>552.000000</td>\n",
" <td>508.000000</td>\n",
" <td>480.000000</td>\n",
" <td>738.000000</td>\n",
" <td>15500.000000</td>\n",
" <td>12.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>755000.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Id MSSubClass LotFrontage LotArea OverallQual \\\n",
"count 1460.000000 1460.000000 1201.000000 1460.000000 1460.000000 \n",
"mean 730.500000 56.897260 70.049958 10516.828082 6.099315 \n",
"std 421.610009 42.300571 24.284752 9981.264932 1.382997 \n",
"min 1.000000 20.000000 21.000000 1300.000000 1.000000 \n",
"25% 365.750000 20.000000 59.000000 7553.500000 5.000000 \n",
"50% 730.500000 50.000000 69.000000 9478.500000 6.000000 \n",
"75% 1095.250000 70.000000 80.000000 11601.500000 7.000000 \n",
"max 1460.000000 190.000000 313.000000 215245.000000 10.000000 \n",
"\n",
" OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 \\\n",
"count 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 \n",
"mean 5.575342 1971.267808 1984.865753 103.685262 443.639726 \n",
"std 1.112799 30.202904 20.645407 181.066207 456.098091 \n",
"min 1.000000 1872.000000 1950.000000 0.000000 0.000000 \n",
"25% 5.000000 1954.000000 1967.000000 0.000000 0.000000 \n",
"50% 5.000000 1973.000000 1994.000000 0.000000 383.500000 \n",
"75% 6.000000 2000.000000 2004.000000 166.000000 712.250000 \n",
"max 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 \n",
"\n",
" ... WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n",
"count ... 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean ... 94.244521 46.660274 21.954110 3.409589 \n",
"std ... 125.338794 66.256028 61.119149 29.317331 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 0.000000 0.000000 0.000000 0.000000 \n",
"50% ... 0.000000 25.000000 0.000000 0.000000 \n",
"75% ... 168.000000 68.000000 0.000000 0.000000 \n",
"max ... 857.000000 547.000000 552.000000 508.000000 \n",
"\n",
" ScreenPorch PoolArea MiscVal MoSold YrSold \\\n",
"count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean 15.060959 2.758904 43.489041 6.321918 2007.815753 \n",
"std 55.757415 40.177307 496.123024 2.703626 1.328095 \n",
"min 0.000000 0.000000 0.000000 1.000000 2006.000000 \n",
"25% 0.000000 0.000000 0.000000 5.000000 2007.000000 \n",
"50% 0.000000 0.000000 0.000000 6.000000 2008.000000 \n",
"75% 0.000000 0.000000 0.000000 8.000000 2009.000000 \n",
"max 480.000000 738.000000 15500.000000 12.000000 2010.000000 \n",
"\n",
" SalePrice \n",
"count 1460.000000 \n",
"mean 180921.195890 \n",
"std 79442.502883 \n",
"min 34900.000000 \n",
"25% 129975.000000 \n",
"50% 163000.000000 \n",
"75% 214000.000000 \n",
"max 755000.000000 \n",
"\n",
"[8 rows x 38 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "8d47ac97-c47a-4676-e648-29303db95407"
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 1, 2, ..., 1457, 1458, 1459])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.isnull(train).any(1).nonzero()[0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "8c249f5f-c2e6-5fdd-5377-3303d9f3b8c8"
},
"outputs": [],
"source": [
"missing = train.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "189938d6-6fb1-9d97-2732-9c2457eaf8fc"
},
"outputs": [
{
"data": {
"text/plain": [
"(81,)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing.shape"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "1525a151-0eab-d468-8b9b-13995eef8b2c"
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing[0]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "3b8c4d62-dc1e-0460-f2f9-81f4b2c455b2"
},
"outputs": [],
"source": [
"miss_fields = missing.where(missing>0).dropna()\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "9300203e-ceaa-fd2d-0172-c73ff26275ce"
},
"outputs": [
{
"data": {
"text/plain": [
"LotFrontage 259.0\n",
"Alley 1369.0\n",
"MasVnrType 8.0\n",
"MasVnrArea 8.0\n",
"BsmtQual 37.0\n",
"BsmtCond 37.0\n",
"BsmtExposure 38.0\n",
"BsmtFinType1 37.0\n",
"BsmtFinType2 38.0\n",
"Electrical 1.0\n",
"FireplaceQu 690.0\n",
"GarageType 81.0\n",
"GarageYrBlt 81.0\n",
"GarageFinish 81.0\n",
"GarageQual 81.0\n",
"GarageCond 81.0\n",
"PoolQC 1453.0\n",
"Fence 1179.0\n",
"MiscFeature 1406.0\n",
"dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"miss_fields"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "bb06df28-d8aa-4237-664c-49354c108cec"
},
"outputs": [],
"source": [
"from sklearn import model_selection, preprocessing\n",
"lbl = preprocessing.LabelEncoder()\n",
"lbl.fit(list(train['PoolQC'].values)) \n",
"train['PoolQC'] = lbl.transform(list(train['PoolQC'].values))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "9a0d83f8-450b-4c53-993c-1ef87175b121"
},
"outputs": [
{
"data": {
"text/plain": [
"array([3, 0, 1, 2])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['PoolQC'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "9b7db345-5463-0bee-b592-0d80b2adaa0c"
},
"outputs": [
{
"data": {
"text/plain": [
"0 4\n",
"1 4\n",
"2 4\n",
"3 4\n",
"4 4\n",
"5 2\n",
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"7 4\n",
"8 4\n",
"9 4\n",
"10 4\n",
"11 4\n",
"12 4\n",
"13 4\n",
"14 1\n",
"15 0\n",
"16 4\n",
"17 4\n",
"18 4\n",
"19 2\n",
"20 4\n",
"21 0\n",
"22 4\n",
"23 4\n",
"24 2\n",
"25 4\n",
"26 4\n",
"27 4\n",
"28 4\n",
"29 4\n",
" ..\n",
"1430 4\n",
"1431 4\n",
"1432 4\n",
"1433 4\n",
"1434 4\n",
"1435 0\n",
"1436 1\n",
"1437 4\n",
"1438 2\n",
"1439 4\n",
"1440 4\n",
"1441 4\n",
"1442 4\n",
"1443 4\n",
"1444 4\n",
"1445 4\n",
"1446 4\n",
"1447 4\n",
"1448 1\n",
"1449 4\n",
"1450 4\n",
"1451 4\n",
"1452 4\n",
"1453 4\n",
"1454 4\n",
"1455 4\n",
"1456 2\n",
"1457 0\n",
"1458 4\n",
"1459 4\n",
"Name: Fence, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lbl = preprocessing.LabelEncoder()\n",
"lbl.fit(list(train['Fence'].values)) \n",
"train['Fence'] = lbl.transform(list(train['Fence'].values))\n",
"train['Fence']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "ed76391c-a5a8-a0e5-26f9-8a1c75ca9f12"
},
"outputs": [
{
"data": {
"text/plain": [
"array([4, 2, 1, 0, 3])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train['Fence'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "141d8cce-faf2-d458-61a1-9579e348d0c4"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 278,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162984.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "50cfce73-0cd8-092a-fe57-9c3ac000e875"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"# visualization\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"\n",
"import xgboost as xgb\n",
"from sklearn import model_selection, preprocessing\n",
"\n",
"\n",
"pd.set_option('display.max_columns', 500)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "912d98a7-bb10-cab8-0fa8-d38f4b341d7c"
},
"outputs": [],
"source": [
"train_df = pd.read_csv('../input/train.csv',parse_dates=['timestamp'])\n",
"result_df = pd.read_csv('../input/test.csv',parse_dates=['timestamp'])\n",
"train_df.shape\n",
"combine=[train_df,result_df]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c9be57ef-bcb3-c3ff-338f-d6f433c645cc"
},
"outputs": [],
"source": [
"for c in train_df.columns:\n",
" if train_df[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(train_df[c].values)) \n",
" train_df[c] = lbl.transform(list(train_df[c].values))\n",
" #x_train.drop(c,axis=1,inplace=True)\n",
" \n",
"for c in result_df.columns:\n",
" if result_df[c].dtype == 'object':\n",
" lbl = preprocessing.LabelEncoder()\n",
" lbl.fit(list(result_df[c].values)) \n",
" result_df[c] = lbl.transform(list(result_df[c].values))\n",
" #x_test.drop(c,axis=1,inplace=True) \n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "e4063db2-adcf-e873-9247-f752f53c5254"
},
"outputs": [
{
"data": {
"text/plain": [
"(30471, 289)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_train = train_df.drop(['price_doc','id','timestamp'],axis=1)\n",
"Y_train = train_df['price_doc'].values.reshape(-1,1)\n",
"X_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "8b7b7718-d741-ab52-06bc-f83f946bcfbe"
},
"outputs": [
{
"data": {
"text/plain": [
"(7662, 289)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_result = result_df.drop(['id','timestamp'],axis=1)\n",
"id_test = result_df['id']\n",
"X_result.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "46e17dae-f980-cc0f-f8ac-1012251e4d21"
},
"outputs": [],
"source": [
"dtrain = xgb.DMatrix(X_train, Y_train)\n",
"dresult=xgb.DMatrix(X_result)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "59b9c470-d047-916c-7af0-608ea01d4c58"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\ttrain-rmse:8.2095e+06\n",
"[25]\ttrain-rmse:3.49658e+06\n",
"[50]\ttrain-rmse:2.56442e+06\n",
"[75]\ttrain-rmse:2.35659e+06\n",
"[100]\ttrain-rmse:2.26672e+06\n",
"[125]\ttrain-rmse:2.20367e+06\n",
"[150]\ttrain-rmse:2.15031e+06\n",
"[175]\ttrain-rmse:2.11448e+06\n",
"[200]\ttrain-rmse:2.08329e+06\n",
"[225]\ttrain-rmse:2.05057e+06\n",
"[250]\ttrain-rmse:2.02361e+06\n",
"[275]\ttrain-rmse:2.00002e+06\n",
"[300]\ttrain-rmse:1.97267e+06\n",
"[325]\ttrain-rmse:1.95074e+06\n",
"[350]\ttrain-rmse:1.92801e+06\n",
"[375]\ttrain-rmse:1.9092e+06\n",
"[399]\ttrain-rmse:1.89102e+06\n"
]
}
],
"source": [
"xgb_params = {\n",
" 'eta': 0.05,\n",
" 'max_depth': 5,\n",
" 'subsample': 0.7,\n",
" 'colsample_bytree': 0.7,\n",
" 'objective': 'reg:linear',\n",
" 'eval_metric': 'rmse',\n",
" 'silent': 1\n",
"}\n",
"# Uncomment to tune XGB `num_boost_rounds`\n",
"#model = xgb.cv(xgb_params, dtrain, num_boost_round=200,\n",
" #early_stopping_rounds=30, verbose_eval=10)\n",
"model = xgb.train(xgb_params, dtrain, num_boost_round=400,verbose_eval=25, evals=[(dtrain,'train')])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "e5520862-a82a-495c-9f44-cb5db3d4f5d1"
},
"outputs": [],
"source": [
"y_pred=model.predict(dresult)\n",
"output=pd.DataFrame(data={'price_doc':y_pred},index=id_test)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "5beb30f0-e48f-6415-44e7-6766b0c343c5"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>price_doc</th>\n",
" </tr>\n",
" <tr>\n",
" <th>id</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>30474</th>\n",
" <td>5464391.5</td>\n",
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" <tr>\n",
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" <tr>\n",
" <th>30476</th>\n",
" <td>5374278.5</td>\n",
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" <tr>\n",
" <th>30477</th>\n",
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" <tr>\n",
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"</table>\n",
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],
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" price_doc\n",
"id \n",
"30474 5464391.5\n",
"30475 8517044.0\n",
"30476 5374278.5\n",
"30477 5931926.0\n",
"30478 5151043.5"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"output.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "c7369d2d-f24c-ae51-7261-9bcd9ce88501"
},
"outputs": [],
"source": [
"output.to_csv('output.csv',header=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "b749a59f-6954-cc76-fb16-76e3392bb216"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 78,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/162/1162988.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4ce5338a-86f9-2f6b-4025-3cacc7b06cf9"
},
"source": [
"In this notebook, we do a brief exploration of the data and try to discern which factors matter the most in determining why our personnel leave. The notebook will primarily be divided into two sections -- data analysis and machine learning. "
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "abe6bb6a-a0cd-b345-15f1-e83156e1b242"
},
"source": [
"# Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "387ee789-b190-32a4-b24d-f21ad7094ef9"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "67309f99-937d-91e5-26dd-8f7c97e2efcc"
},
"source": [
"First, let's read in and get an overview of the data we'll be working with."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "44488b81-87fe-58ed-fc2f-b920fd880df3"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>sales</th>\n",
" <th>salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.38</td>\n",
" <td>0.53</td>\n",
" <td>2</td>\n",
" <td>157</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.80</td>\n",
" <td>0.86</td>\n",
" <td>5</td>\n",
" <td>262</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.11</td>\n",
" <td>0.88</td>\n",
" <td>7</td>\n",
" <td>272</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.72</td>\n",
" <td>0.87</td>\n",
" <td>5</td>\n",
" <td>223</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.37</td>\n",
" <td>0.52</td>\n",
" <td>2</td>\n",
" <td>159</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project average_montly_hours \\\n",
"0 0.38 0.53 2 157 \n",
"1 0.80 0.86 5 262 \n",
"2 0.11 0.88 7 272 \n",
"3 0.72 0.87 5 223 \n",
"4 0.37 0.52 2 159 \n",
"\n",
" time_spend_company Work_accident left promotion_last_5years sales \\\n",
"0 3 0 1 0 sales \n",
"1 6 0 1 0 sales \n",
"2 4 0 1 0 sales \n",
"3 5 0 1 0 sales \n",
"4 3 0 1 0 sales \n",
"\n",
" salary \n",
"0 low \n",
"1 medium \n",
"2 medium \n",
"3 low \n",
"4 low "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data = pd.read_csv('../input/HR_comma_sep.csv')\n",
"hr_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "13edaa13-41ea-e3db-d576-21d7b7629dcc"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.612834</td>\n",
" <td>0.716102</td>\n",
" <td>3.803054</td>\n",
" <td>201.050337</td>\n",
" <td>3.498233</td>\n",
" <td>0.144610</td>\n",
" <td>0.238083</td>\n",
" <td>0.021268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.248631</td>\n",
" <td>0.171169</td>\n",
" <td>1.232592</td>\n",
" <td>49.943099</td>\n",
" <td>1.460136</td>\n",
" <td>0.351719</td>\n",
" <td>0.425924</td>\n",
" <td>0.144281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.090000</td>\n",
" <td>0.360000</td>\n",
" <td>2.000000</td>\n",
" <td>96.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.440000</td>\n",
" <td>0.560000</td>\n",
" <td>3.000000</td>\n",
" <td>156.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.640000</td>\n",
" <td>0.720000</td>\n",
" <td>4.000000</td>\n",
" <td>200.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.820000</td>\n",
" <td>0.870000</td>\n",
" <td>5.000000</td>\n",
" <td>245.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>7.000000</td>\n",
" <td>310.000000</td>\n",
" <td>10.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project \\\n",
"count 14999.000000 14999.000000 14999.000000 \n",
"mean 0.612834 0.716102 3.803054 \n",
"std 0.248631 0.171169 1.232592 \n",
"min 0.090000 0.360000 2.000000 \n",
"25% 0.440000 0.560000 3.000000 \n",
"50% 0.640000 0.720000 4.000000 \n",
"75% 0.820000 0.870000 5.000000 \n",
"max 1.000000 1.000000 7.000000 \n",
"\n",
" average_montly_hours time_spend_company Work_accident left \\\n",
"count 14999.000000 14999.000000 14999.000000 14999.000000 \n",
"mean 201.050337 3.498233 0.144610 0.238083 \n",
"std 49.943099 1.460136 0.351719 0.425924 \n",
"min 96.000000 2.000000 0.000000 0.000000 \n",
"25% 156.000000 3.000000 0.000000 0.000000 \n",
"50% 200.000000 3.000000 0.000000 0.000000 \n",
"75% 245.000000 4.000000 0.000000 0.000000 \n",
"max 310.000000 10.000000 1.000000 1.000000 \n",
"\n",
" promotion_last_5years \n",
"count 14999.000000 \n",
"mean 0.021268 \n",
"std 0.144281 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "868ef524-9bde-9db1-1c0f-114a88025e68"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 14999 entries, 0 to 14998\n",
"Data columns (total 10 columns):\n",
"satisfaction_level 14999 non-null float64\n",
"last_evaluation 14999 non-null float64\n",
"number_project 14999 non-null int64\n",
"average_montly_hours 14999 non-null int64\n",
"time_spend_company 14999 non-null int64\n",
"Work_accident 14999 non-null int64\n",
"left 14999 non-null int64\n",
"promotion_last_5years 14999 non-null int64\n",
"sales 14999 non-null object\n",
"salary 14999 non-null object\n",
"dtypes: float64(2), int64(6), object(2)\n",
"memory usage: 1.1+ MB\n"
]
}
],
"source": [
"hr_data.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1e79a209-460a-25a3-7595-c5783c9feb3a"
},
"source": [
"Conveniently, there is no missing data. Given that the \"sales' and \"salary\" columns are non-numeric, we can check the number of unique levels and dummy code the variables."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "a8718b8b-20aa-1f75-af3a-37cfb178a458"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Departments: sales, accounting, hr, technical, support, management, IT, product_mng, marketing, RandD\n",
"Salary levels: low, medium, high\n"
]
}
],
"source": [
"print('Departments: ', ', '.join(hr_data['sales'].unique()))\n",
"print('Salary levels: ', ', '.join(hr_data['salary'].unique()))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "2f1575c8-87e1-6dbd-06eb-4dc2cf0985a8"
},
"outputs": [],
"source": [
"dummy_depts = pd.get_dummies(hr_data['sales'], drop_first = True)\n",
"dummy_salaries = pd.get_dummies(hr_data['salary'], drop_first = True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "4b163373-3852-a916-be79-9e5c2c3ba931"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>RandD</th>\n",
" <th>accounting</th>\n",
" <th>hr</th>\n",
" <th>management</th>\n",
" <th>marketing</th>\n",
" <th>product_mng</th>\n",
" <th>sales</th>\n",
" <th>support</th>\n",
" <th>technical</th>\n",
" <th>low</th>\n",
" <th>medium</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>262</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.11</td>\n",
" <td>0.88</td>\n",
" <td>7</td>\n",
" <td>272</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.72</td>\n",
" <td>0.87</td>\n",
" <td>5</td>\n",
" <td>223</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.37</td>\n",
" <td>0.52</td>\n",
" <td>2</td>\n",
" <td>159</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project average_montly_hours \\\n",
"0 0.38 0.53 2 157 \n",
"1 0.80 0.86 5 262 \n",
"2 0.11 0.88 7 272 \n",
"3 0.72 0.87 5 223 \n",
"4 0.37 0.52 2 159 \n",
"\n",
" time_spend_company Work_accident left promotion_last_5years RandD \\\n",
"0 3 0 1 0 0 \n",
"1 6 0 1 0 0 \n",
"2 4 0 1 0 0 \n",
"3 5 0 1 0 0 \n",
"4 3 0 1 0 0 \n",
"\n",
" accounting hr management marketing product_mng sales support \\\n",
"0 0 0 0 0 0 1 0 \n",
"1 0 0 0 0 0 1 0 \n",
"2 0 0 0 0 0 1 0 \n",
"3 0 0 0 0 0 1 0 \n",
"4 0 0 0 0 0 1 0 \n",
"\n",
" technical low medium \n",
"0 0 1 0 \n",
"1 0 0 1 \n",
"2 0 0 1 \n",
"3 0 1 0 \n",
"4 0 1 0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data_new = hr_data.drop(['sales','salary'], axis = 1)\n",
"hr_data_new = hr_data_new.join(dummy_depts)\n",
"hr_data_new = hr_data_new.join(dummy_salaries)\n",
"hr_data_new.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "84f5247f-fa53-51b5-5b5d-58e65169d75f"
},
"source": [
"Observe that \"IT\" and \"high\" are the baseline levels for the assigned department and salary level, respectively. Also note that we saved the data with dummified variables as another dataframe in case we need to access the string values, such as for a cross-tabulation table."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e5fb9a81-b4b9-b9e7-a0f6-0b02539d5970"
},
"source": [
"## Exploring the Data\n",
"\n",
"Now that we have data in an analysis-friendly form, we can do some basic visualizations to see "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "4db9f644-8481-fa86-278f-3b2a2bfd403a"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 164,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163071.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4ce5338a-86f9-2f6b-4025-3cacc7b06cf9"
},
"source": [
"In this notebook, we do a brief exploration of the data and try to discern which factors matter the most in determining why our personnel leave. The notebook will primarily be divided into two sections -- data analysis and machine learning. "
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "abe6bb6a-a0cd-b345-15f1-e83156e1b242"
},
"source": [
"# Data Analysis"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "387ee789-b190-32a4-b24d-f21ad7094ef9"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "67309f99-937d-91e5-26dd-8f7c97e2efcc"
},
"source": [
"First, let's read in and get an overview of the data we'll be working with."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "44488b81-87fe-58ed-fc2f-b920fd880df3"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>sales</th>\n",
" <th>salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.38</td>\n",
" <td>0.53</td>\n",
" <td>2</td>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.80</td>\n",
" <td>0.86</td>\n",
" <td>5</td>\n",
" <td>262</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.11</td>\n",
" <td>0.88</td>\n",
" <td>7</td>\n",
" <td>272</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>medium</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.72</td>\n",
" <td>0.87</td>\n",
" <td>5</td>\n",
" <td>223</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.37</td>\n",
" <td>0.52</td>\n",
" <td>2</td>\n",
" <td>159</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>sales</td>\n",
" <td>low</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project average_montly_hours \\\n",
"0 0.38 0.53 2 157 \n",
"1 0.80 0.86 5 262 \n",
"2 0.11 0.88 7 272 \n",
"3 0.72 0.87 5 223 \n",
"4 0.37 0.52 2 159 \n",
"\n",
" time_spend_company Work_accident left promotion_last_5years sales \\\n",
"0 3 0 1 0 sales \n",
"1 6 0 1 0 sales \n",
"2 4 0 1 0 sales \n",
"3 5 0 1 0 sales \n",
"4 3 0 1 0 sales \n",
"\n",
" salary \n",
"0 low \n",
"1 medium \n",
"2 medium \n",
"3 low \n",
"4 low "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data = pd.read_csv('../input/HR_comma_sep.csv')\n",
"hr_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "13edaa13-41ea-e3db-d576-21d7b7629dcc"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" <td>14999.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.612834</td>\n",
" <td>0.716102</td>\n",
" <td>3.803054</td>\n",
" <td>201.050337</td>\n",
" <td>3.498233</td>\n",
" <td>0.144610</td>\n",
" <td>0.238083</td>\n",
" <td>0.021268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.248631</td>\n",
" <td>0.171169</td>\n",
" <td>1.232592</td>\n",
" <td>49.943099</td>\n",
" <td>1.460136</td>\n",
" <td>0.351719</td>\n",
" <td>0.425924</td>\n",
" <td>0.144281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.090000</td>\n",
" <td>0.360000</td>\n",
" <td>2.000000</td>\n",
" <td>96.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.440000</td>\n",
" <td>0.560000</td>\n",
" <td>3.000000</td>\n",
" <td>156.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.640000</td>\n",
" <td>0.720000</td>\n",
" <td>4.000000</td>\n",
" <td>200.000000</td>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>0.820000</td>\n",
" <td>0.870000</td>\n",
" <td>5.000000</td>\n",
" <td>245.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>7.000000</td>\n",
" <td>310.000000</td>\n",
" <td>10.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project \\\n",
"count 14999.000000 14999.000000 14999.000000 \n",
"mean 0.612834 0.716102 3.803054 \n",
"std 0.248631 0.171169 1.232592 \n",
"min 0.090000 0.360000 2.000000 \n",
"25% 0.440000 0.560000 3.000000 \n",
"50% 0.640000 0.720000 4.000000 \n",
"75% 0.820000 0.870000 5.000000 \n",
"max 1.000000 1.000000 7.000000 \n",
"\n",
" average_montly_hours time_spend_company Work_accident left \\\n",
"count 14999.000000 14999.000000 14999.000000 14999.000000 \n",
"mean 201.050337 3.498233 0.144610 0.238083 \n",
"std 49.943099 1.460136 0.351719 0.425924 \n",
"min 96.000000 2.000000 0.000000 0.000000 \n",
"25% 156.000000 3.000000 0.000000 0.000000 \n",
"50% 200.000000 3.000000 0.000000 0.000000 \n",
"75% 245.000000 4.000000 0.000000 0.000000 \n",
"max 310.000000 10.000000 1.000000 1.000000 \n",
"\n",
" promotion_last_5years \n",
"count 14999.000000 \n",
"mean 0.021268 \n",
"std 0.144281 \n",
"min 0.000000 \n",
"25% 0.000000 \n",
"50% 0.000000 \n",
"75% 0.000000 \n",
"max 1.000000 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "868ef524-9bde-9db1-1c0f-114a88025e68"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 14999 entries, 0 to 14998\n",
"Data columns (total 10 columns):\n",
"satisfaction_level 14999 non-null float64\n",
"last_evaluation 14999 non-null float64\n",
"number_project 14999 non-null int64\n",
"average_montly_hours 14999 non-null int64\n",
"time_spend_company 14999 non-null int64\n",
"Work_accident 14999 non-null int64\n",
"left 14999 non-null int64\n",
"promotion_last_5years 14999 non-null int64\n",
"sales 14999 non-null object\n",
"salary 14999 non-null object\n",
"dtypes: float64(2), int64(6), object(2)\n",
"memory usage: 1.1+ MB\n"
]
}
],
"source": [
"hr_data.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1e79a209-460a-25a3-7595-c5783c9feb3a"
},
"source": [
"Conveniently, there is no missing data. Given that the \"sales' and \"salary\" columns are non-numeric, we can check the number of unique levels and dummy code the variables."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "a8718b8b-20aa-1f75-af3a-37cfb178a458"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Departments: sales, accounting, hr, technical, support, management, IT, product_mng, marketing, RandD\n",
"Salary levels: low, medium, high\n"
]
}
],
"source": [
"print('Departments: ', ', '.join(hr_data['sales'].unique()))\n",
"print('Salary levels: ', ', '.join(hr_data['salary'].unique()))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "2f1575c8-87e1-6dbd-06eb-4dc2cf0985a8"
},
"outputs": [],
"source": [
"dummy_depts = pd.get_dummies(hr_data['sales'], drop_first = True)\n",
"dummy_salaries = pd.get_dummies(hr_data['salary'], drop_first = True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "4b163373-3852-a916-be79-9e5c2c3ba931"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>satisfaction_level</th>\n",
" <th>last_evaluation</th>\n",
" <th>number_project</th>\n",
" <th>average_montly_hours</th>\n",
" <th>time_spend_company</th>\n",
" <th>Work_accident</th>\n",
" <th>left</th>\n",
" <th>promotion_last_5years</th>\n",
" <th>RandD</th>\n",
" <th>accounting</th>\n",
" <th>hr</th>\n",
" <th>management</th>\n",
" <th>marketing</th>\n",
" <th>product_mng</th>\n",
" <th>sales</th>\n",
" <th>support</th>\n",
" <th>technical</th>\n",
" <th>low</th>\n",
" <th>medium</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.38</td>\n",
" <td>0.53</td>\n",
" <td>2</td>\n",
" <td>157</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.80</td>\n",
" <td>0.86</td>\n",
" <td>5</td>\n",
" <td>262</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.11</td>\n",
" <td>0.88</td>\n",
" <td>7</td>\n",
" <td>272</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.72</td>\n",
" <td>0.87</td>\n",
" <td>5</td>\n",
" <td>223</td>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.37</td>\n",
" <td>0.52</td>\n",
" <td>2</td>\n",
" <td>159</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" satisfaction_level last_evaluation number_project average_montly_hours \\\n",
"0 0.38 0.53 2 157 \n",
"1 0.80 0.86 5 262 \n",
"2 0.11 0.88 7 272 \n",
"3 0.72 0.87 5 223 \n",
"4 0.37 0.52 2 159 \n",
"\n",
" time_spend_company Work_accident left promotion_last_5years RandD \\\n",
"0 3 0 1 0 0 \n",
"1 6 0 1 0 0 \n",
"2 4 0 1 0 0 \n",
"3 5 0 1 0 0 \n",
"4 3 0 1 0 0 \n",
"\n",
" accounting hr management marketing product_mng sales support \\\n",
"0 0 0 0 0 0 1 0 \n",
"1 0 0 0 0 0 1 0 \n",
"2 0 0 0 0 0 1 0 \n",
"3 0 0 0 0 0 1 0 \n",
"4 0 0 0 0 0 1 0 \n",
"\n",
" technical low medium \n",
"0 0 1 0 \n",
"1 0 0 1 \n",
"2 0 0 1 \n",
"3 0 1 0 \n",
"4 0 1 0 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data_new = hr_data.drop(['sales','salary'], axis = 1)\n",
"hr_data_new = hr_data_new.join(dummy_depts)\n",
"hr_data_new = hr_data_new.join(dummy_salaries)\n",
"hr_data_new.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "84f5247f-fa53-51b5-5b5d-58e65169d75f"
},
"source": [
"Observe that \"IT\" and \"high\" are the baseline levels for the assigned department and salary level, respectively. Also note that we saved the data with dummified variables as another dataframe in case we need to access the string values, such as for a cross-tabulation table."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e5fb9a81-b4b9-b9e7-a0f6-0b02539d5970"
},
"source": [
"## Exploring the Data\n",
"\n",
"Now that we have data in an analysis-friendly form, we can do some basic visualizations to spot any relationships in the data."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "4db9f644-8481-fa86-278f-3b2a2bfd403a"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f1a3d0f1f28>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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KoJJfIvu38CA6vSfQ5up6WKKiDAAp5ddAL8AE7BRCNCtFW5nAj1LKnpZPayll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\nqBiS0UskEolExfD/pF1lXFLGWTUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f19ff739b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Correlation matrix\n",
"sns.heatmap(hr_data.corr(), annot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "503f6ac1-7553-71a8-442a-e8fa3f9fafee"
},
"source": [
"The matrix above shows that, generally speaking, the data is not correlated. This is good because it means we likely won't have issues with multicollinearity later.\n",
"\n",
"It is notable, though perhaps unsurprising, that our employees' satisfaction level is the variable that is most highly correlated with them leaving."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "d0100ccd-948b-7cb6-6314-32b64cba89dd"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['satisfaction_level', 'last_evaluation', 'number_project',\n",
" 'average_montly_hours', 'time_spend_company', 'Work_accident', 'left',\n",
" 'promotion_last_5years', 'RandD', 'accounting', 'hr', 'management',\n",
" 'marketing', 'product_mng', 'sales', 'support', 'technical', 'low',\n",
" 'medium'],\n",
" dtype='object')"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hr_data_new.columns"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "16c182c4-4e2b-8bdc-8c03-2865a2dad24b"
},
"source": [
"Let's first check if there are any particular departments that our people tend to be leaving from."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "aea2ebe7-53de-932e-ec35-7fc4c22fe45a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>left</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Department</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>IT</th>\n",
" <td>954</td>\n",
" <td>273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RandD</th>\n",
" <td>666</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>accounting</th>\n",
" <td>563</td>\n",
" <td>204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hr</th>\n",
" <td>524</td>\n",
" <td>215</td>\n",
" </tr>\n",
" <tr>\n",
" <th>management</th>\n",
" <td>539</td>\n",
" <td>91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>marketing</th>\n",
" <td>655</td>\n",
" <td>203</td>\n",
" </tr>\n",
" <tr>\n",
" <th>product_mng</th>\n",
" <td>704</td>\n",
" <td>198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sales</th>\n",
" <td>3126</td>\n",
" <td>1014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>support</th>\n",
" <td>1674</td>\n",
" <td>555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>technical</th>\n",
" <td>2023</td>\n",
" <td>697</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"left 0 1\n",
"Department \n",
"IT 954 273\n",
"RandD 666 121\n",
"accounting 563 204\n",
"hr 524 215\n",
"management 539 91\n",
"marketing 655 203\n",
"product_mng 704 198\n",
"sales 3126 1014\n",
"support 1674 555\n",
"technical 2023 697"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dept_table = pd.crosstab(hr_data['sales'], hr_data['left'])\n",
"dept_table.index.names = ['Department']\n",
"dept_table"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a275b42c-d681-b7b8-c6bc-6be0ba0984f0"
},
"source": [
"We can check the above in terms of percentages to more easily see if there are particular departments that tend to have a higher proportion of people leaving."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "1b9cbff2-72b8-ca62-1803-472b2b220886"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>left</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Department</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>IT</th>\n",
" <td>77.750611</td>\n",
" <td>22.249389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RandD</th>\n",
" <td>84.625159</td>\n",
" <td>15.374841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>accounting</th>\n",
" <td>73.402868</td>\n",
" <td>26.597132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>hr</th>\n",
" <td>70.906631</td>\n",
" <td>29.093369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>management</th>\n",
" <td>85.555556</td>\n",
" <td>14.444444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>marketing</th>\n",
" <td>76.340326</td>\n",
" <td>23.659674</td>\n",
" </tr>\n",
" <tr>\n",
" <th>product_mng</th>\n",
" <td>78.048780</td>\n",
" <td>21.951220</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sales</th>\n",
" <td>75.507246</td>\n",
" <td>24.492754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>support</th>\n",
" <td>75.100942</td>\n",
" <td>24.899058</td>\n",
" </tr>\n",
" <tr>\n",
" <th>technical</th>\n",
" <td>74.375000</td>\n",
" <td>25.625000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"left 0 1\n",
"Department \n",
"IT 77.750611 22.249389\n",
"RandD 84.625159 15.374841\n",
"accounting 73.402868 26.597132\n",
"hr 70.906631 29.093369\n",
"management 85.555556 14.444444\n",
"marketing 76.340326 23.659674\n",
"product_mng 78.048780 21.951220\n",
"sales 75.507246 24.492754\n",
"support 75.100942 24.899058\n",
"technical 74.375000 25.625000"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dept_table_percentages = dept_table.apply(lambda row: (row/row.sum())*100, axis = 1)\n",
"dept_table_percentages"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ce9bccee-8e80-8136-85bd-898ff76d9126"
},
"source": [
"R&D and management tend to have lower rates of leaving, and HR and accounting tend to have higher rates of leaving. The other departments are fairly similar, all between around 22 to 25 percent. We can also visualize the above data with a countplot."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "da700fcb-c8b7-d9bf-84d8-bc87713a5161"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f19ff668b00>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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PcEOedlYuMzOzLlKzJqaIWAmslFRe3D8ilue/5wNbAsOAprJp1imPiGZJLZL6RsSKWsV8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\n5ARhZmaFnCDMzKyQE4SZmRX6P0nUUFsP8RAwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f19ff6fac88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='sales', hue='left', data=hr_data)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "22b6169d-f901-c811-86b0-3aa2f28cb178"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f19f5d20da0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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lIpJRQ7laq7d9FQATJkyqarjWCVvEnl6cAtBhZh8HnjKzq4Gnga2rSiQikmFpvToszkng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\ngIhIRqkAiIhk1P8HQT0OBbYqB+YAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f19f5c7af28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x='sales', y='satisfaction_level', data=hr_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6b12e7ea-2905-92d5-2a2d-9f2d4be54b6f"
},
"source": [
"While there doesn't appear to be too much of a difference in the satisfaction, we notice that both HR and accounting, the departments that have the highest rates of leaving, have slightly lower median satisfaction levels than the rest of the departments."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "5eef6474-f00b-89a8-da18-368a8feec5ec"
},
"source": [
"Salary is likely to have a high impact on leaving. In fact, it is highly likely that both R&D and management, the two departments with the lowers leaving rates, have high salaries. Let's first check the relationship between leaving and salary."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "c922cfc4-245b-86ab-7d1b-1e809a0ee502"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f19f5a5aac8>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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NdAELMvPqiBgNLAT2BTYBMzPzsSbrlSS9prEeRETsBnwVuL1H84XAlZk5FXgUmFWvdz5w\nJDANOCsiJgAnAi9k5mHAXOCSpmqVJG2tyVNMG4APAE/1aJsGbB6hupkqFA4BlmfmmsxcD9wNHAoc\nAXyvXndJ3SZJGiKNBURmvlJ/4Pe0W2ZuqJefBfYGJgOreqyzVXtmdgHdETGmqXolSVtq5WWuHYPU\n/qrx48fS2TlqxyvSoJo0aY9Wl6Cd4PF7zUj9XQx1QLwYEbvWPYspVKefnqLqLWw2BbinR/uKesC6\nIzM3bmvnq1eva6Zq7ZBVq9a2ugTtBI/fa4b776KvABzqy1yXANPr5enAbcC9wEERMS4idqcaa1gK\n/BA4vl73OOCOIa5Vkka0xnoQEXEg8GVgP+DliPgwMANYGBGnAk8A12TmyxExB1gMdAMXZOaaiLge\nOCoillENeJ/SVK2SpK01FhCZeT/VVUu9HVVYdxGwqFfbJmBmI8VJkrbLb1JLkooMCElSkQEhSSoy\nICRJRQaEJKnIgJAkFRkQkqQiA0KSVGRASJKKDAhJUpEBIUkqMiAkSUUGhCSpyICQJBUZEJKkIgNC\nklRkQEiSigwISVKRASFJKjIgJElFBoQkqciAkCQVGRCSpCIDQpJUZEBIkooMCElSkQEhSSoyICRJ\nRQaEJKnIgJAkFRkQkqSizlYXIEnt7uxbzmt1CQMy79iLBmU/bR0QEXEp8EdAN3BmZi5vcUmSNGK0\n7SmmiDgc+L3MfBfwMeArLS5JkkaUtg0I4AjgBoDM/BdgfETs2dqSJGnk6Oju7m51DUURsQD4fmbe\nWD9eCnwsM3/W2sokaWRo5x5Ebx2tLkCSRpJ2DoingMk9Hv8u8HSLapGkEaedA+KHwIcBIuIPgacy\nc21rS5KkkaNtxyAAIuKLwHuALuBTmbmixSVJ0ojR1gEhSWqddj7FJElqIQNCklRkQLSZiDglIua3\nug4Nnog4ICLurJdvbHE56iUipkXEol5tl0XEm7exzeMRsXvz1bVWW8/FJA03mfmhVteg7cvM2a2u\noR0YEG0qIs4E/lv98Abg74GvZub7I+LdwK3ABKpe4IOZeUBrKh3+IuIU4HBgIvB24FzgI8DbgBnA\nfwZOpLra7obM/HJEvBH4LrABWNFjX89l5sS6R3FGZj4UEWfU+74TOBN4BfhDYC7wx8AfAGdn5g2N\nv9mRa/eI+CbwDqrj9j7gDOCF+vFG4C5gamZOq7c5IyI+QPU5esxwvAzfU0zt6c3AKcDU+ucEqhlt\n3xgRHcChwANUH1bvBH7SmjJHlN8DPghcAnwW+NN6+Ryq7+scRnVJ9vSIeBPwF8C36w+TpwbwOu8E\nPgqcBnwRmFkvnzIYb0J9ehvwCeBdwJ/3aD8L+E5mHg78h17bPJSZ7wGeoJo7btgxINrTHwD3ZOYr\nmfkKcDfVXzY/Bf4jcDDwN1T/mA+l+stTzbovM7upvs3/T5m5CXgG+E9U4XFH/bMHsB/VB86P623v\nHMDrrMjMDfXr/CwzX6pfZ69BeA/q2z9m5rrMfJEtp/XZn+r/P4Cbem2zrP7vrximx8dTTO2pmy3/\nkY6hOn1xJ9X9McZSfRh9Cdgd+PQQ1zcSvdLH8gSqnsKpPVeOiL+iOmZQ/kOs5xeQRvfjdZyLrFmv\n9NHewWvHsfeXxob98TEg2tMDwLsiYvPxOQS4GPgt4Erg4cx8LiImAbtn5i9bVKfgfuC9ETEWWA9c\nBswBkmps4n7gvYXtfgPsDTxE1Qt8aEiq1UD9guo43ge8v8W1DDlPMbWnx4EFwI+ApcBVmflEZibV\nqYt/qNdbDTzakgq12b9RhcJdwD3AysxcD1wOzIqIxcD4wnYLgCsj4vsMbIxCQ+ty4NSIWELVS9jU\n4nqGlFNtSFIfIuLtwLjMvDsiPgK8NzM/0eq6hoqnmCSpb2uBr0VEN9VYxMwW1zOk7EFIkoocg5Ak\nFRkQkqQiA0KSVGRASA2JiIUR8T9aXYe0owwISVKRl7lKAxARvwt8i+pLU7sCXwN+BvxvqplbxwKn\nZ+Y/9truQl6b0O1J4KOZ+XJE/Aa4GhgFHAicm5l31tv8gGoG31ubfl9SiT0IaWBOAB6pZ2k9nCoQ\nJgKfzMz3UX3z9pyeG9RTpqyjmir6UGAccEz99O7ArZn5F1Rhc0q9zQQggNsafj9SnwwIaWB+ABwZ\nEQuB46g+1FcC8yPiLqp5mCb23KCekXcTsDQifkQ1pffmdTp4bbbQ7wDvq+9U9qfAtzKzC6lFDAhp\nADLzEar5sL4JHEk1w+51wBfrewOc23ubiDgUmAUcXd9XYGmvVTbW+/534P9ShcOHga838y6k/jEg\npAGIiBOBgzJzCXA68CZgCvBwRIwCjmfrG8u8AXg8M1+KiH2ppmzvvc5mC+r9dmTmvzbxHqT+cqoN\naQAi4p3A31INSHdQnRbai+qWo08A86h6FPOpTiUtA74NLKa6n8DDwHLgfKoeSAKj69NQm1/jJ8Bf\nZ+a3h+ZdSWUGhNRGImI/qvuNvyMzX25xORrhPMUktYmIOAe4Efi44aB2YA9CklRkD0KSVGRASJKK\nDAhJUpEBIUkqMiAkSUX/H8nHvclbkaFdAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f19f5caac18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.countplot(x='salary', hue='left', data=hr_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7041b29e-3462-ef60-1eef-a49d81848db3"
},
"source": [
"Confirming our hypothesis, those with low salaries tend to have the highest number of people that leave. Eyeballing the plot shows us that around 40% of those with low salaries leave and 25% of those with median salaries leave. It looks like only 10% of those with high salaries leave."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e39c63c4-a878-5275-50cb-7e92252a1a69"
},
"source": [
"Let's also check the spread of satisfaction level between the different salary ranges."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "2154a824-e508-0d5c-9fae-c1c42077a5c2"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f19f5cd0dd8>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ALouIg4HVmbkBIDO/SDHBPBFxIMUdxpO++UuS2q/KncB/CVwLLKa4aPtD4D9NtVFmrgBW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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f19f5a749b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.boxplot(x='salary', y='satisfaction_level', data=hr_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6b5cf004-54b9-1e52-29fe-1facdc953b1b"
},
"source": [
"Again, in line with some of our prior observations, low salary has the lowest median satisfaction and the highest spread."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "71a11274-d9c3-b3ab-8cc2-4ce27ed4dcd5"
},
"source": [
"Something that may impact employee perception in the company is the number of projects they are assigned."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "c48be365-a050-aa7b-91aa-c82c07d00787"
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f19f59acf60>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WsmrxyW2xOlHSgvylrnsuGGLNWnHrS6eKNauBs5ZlH87g6QNjtB8X+Yq2LYS4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\nS9rGGJNGLGkbY0wa+f/TnZOTv7MnpQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f19f59693c8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.factorplot(x='number_project', y='last_evaluation', hue='sales', data=hr_data)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "719221e7-ba42-cb89-d49e-5856f8a75b4b"
},
"source": [
"It is very clear that evaluation scores are affected by the number of projects assigned to the employee. What's more, we again notice a peculiar trend in accounting -- they have a lower last_evaluation score than the other departments at 7 projects."
]
}
],
"metadata": {
"_change_revision": 310,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163111.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d4589c40-d404-7a9c-b966-147e2d5b1a02"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "db7a71e2-7ab2-853d-9701-7be932d52661"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# File sizes\n",
"movie_metadata.csv 1.49MB\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import os\n",
"import gc\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"pal = sns.color_palette()\n",
"\n",
"print('# File sizes')\n",
"for f in os.listdir('../input'):\n",
" if 'zip' not in f:\n",
" print(f.ljust(30) + str(round(os.path.getsize('../input/' + f) / 1000000, 2)) + 'MB')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3a6bc07c-f0ca-a9f1-61a9-f7b45b11c33a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>color</th>\n",
" <th>director_name</th>\n",
" <th>num_critic_for_reviews</th>\n",
" <th>duration</th>\n",
" <th>director_facebook_likes</th>\n",
" <th>actor_3_facebook_likes</th>\n",
" <th>actor_2_name</th>\n",
" <th>actor_1_facebook_likes</th>\n",
" <th>gross</th>\n",
" <th>genres</th>\n",
" <th>...</th>\n",
" <th>num_user_for_reviews</th>\n",
" <th>language</th>\n",
" <th>country</th>\n",
" <th>content_rating</th>\n",
" <th>budget</th>\n",
" <th>title_year</th>\n",
" <th>actor_2_facebook_likes</th>\n",
" <th>imdb_score</th>\n",
" <th>aspect_ratio</th>\n",
" <th>movie_facebook_likes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Color</td>\n",
" <td>James Cameron</td>\n",
" <td>723.0</td>\n",
" <td>178.0</td>\n",
" <td>0.0</td>\n",
" <td>855.0</td>\n",
" <td>Joel David Moore</td>\n",
" <td>1000.0</td>\n",
" <td>760505847.0</td>\n",
" <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
" <td>...</td>\n",
" <td>3054.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>237000000.0</td>\n",
" <td>2009.0</td>\n",
" <td>936.0</td>\n",
" <td>7.9</td>\n",
" <td>1.78</td>\n",
" <td>33000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Color</td>\n",
" <td>Gore Verbinski</td>\n",
" <td>302.0</td>\n",
" <td>169.0</td>\n",
" <td>563.0</td>\n",
" <td>1000.0</td>\n",
" <td>Orlando Bloom</td>\n",
" <td>40000.0</td>\n",
" <td>309404152.0</td>\n",
" <td>Action|Adventure|Fantasy</td>\n",
" <td>...</td>\n",
" <td>1238.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>300000000.0</td>\n",
" <td>2007.0</td>\n",
" <td>5000.0</td>\n",
" <td>7.1</td>\n",
" <td>2.35</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Color</td>\n",
" <td>Sam Mendes</td>\n",
" <td>602.0</td>\n",
" <td>148.0</td>\n",
" <td>0.0</td>\n",
" <td>161.0</td>\n",
" <td>Rory Kinnear</td>\n",
" <td>11000.0</td>\n",
" <td>200074175.0</td>\n",
" <td>Action|Adventure|Thriller</td>\n",
" <td>...</td>\n",
" <td>994.0</td>\n",
" <td>English</td>\n",
" <td>UK</td>\n",
" <td>PG-13</td>\n",
" <td>245000000.0</td>\n",
" <td>2015.0</td>\n",
" <td>393.0</td>\n",
" <td>6.8</td>\n",
" <td>2.35</td>\n",
" <td>85000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Color</td>\n",
" <td>Christopher Nolan</td>\n",
" <td>813.0</td>\n",
" <td>164.0</td>\n",
" <td>22000.0</td>\n",
" <td>23000.0</td>\n",
" <td>Christian Bale</td>\n",
" <td>27000.0</td>\n",
" <td>448130642.0</td>\n",
" <td>Action|Thriller</td>\n",
" <td>...</td>\n",
" <td>2701.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>250000000.0</td>\n",
" <td>2012.0</td>\n",
" <td>23000.0</td>\n",
" <td>8.5</td>\n",
" <td>2.35</td>\n",
" <td>164000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>Doug Walker</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>131.0</td>\n",
" <td>NaN</td>\n",
" <td>Rob Walker</td>\n",
" <td>131.0</td>\n",
" <td>NaN</td>\n",
" <td>Documentary</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" <td>7.1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" color director_name num_critic_for_reviews duration \\\n",
"0 Color James Cameron 723.0 178.0 \n",
"1 Color Gore Verbinski 302.0 169.0 \n",
"2 Color Sam Mendes 602.0 148.0 \n",
"3 Color Christopher Nolan 813.0 164.0 \n",
"4 NaN Doug Walker NaN NaN \n",
"\n",
" director_facebook_likes actor_3_facebook_likes actor_2_name \\\n",
"0 0.0 855.0 Joel David Moore \n",
"1 563.0 1000.0 Orlando Bloom \n",
"2 0.0 161.0 Rory Kinnear \n",
"3 22000.0 23000.0 Christian Bale \n",
"4 131.0 NaN Rob Walker \n",
"\n",
" actor_1_facebook_likes gross genres \\\n",
"0 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi \n",
"1 40000.0 309404152.0 Action|Adventure|Fantasy \n",
"2 11000.0 200074175.0 Action|Adventure|Thriller \n",
"3 27000.0 448130642.0 Action|Thriller \n",
"4 131.0 NaN Documentary \n",
"\n",
" ... num_user_for_reviews language country content_rating \\\n",
"0 ... 3054.0 English USA PG-13 \n",
"1 ... 1238.0 English USA PG-13 \n",
"2 ... 994.0 English UK PG-13 \n",
"3 ... 2701.0 English USA PG-13 \n",
"4 ... NaN NaN NaN NaN \n",
"\n",
" budget title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n",
"0 237000000.0 2009.0 936.0 7.9 1.78 \n",
"1 300000000.0 2007.0 5000.0 7.1 2.35 \n",
"2 245000000.0 2015.0 393.0 6.8 2.35 \n",
"3 250000000.0 2012.0 23000.0 8.5 2.35 \n",
"4 NaN NaN 12.0 7.1 NaN \n",
"\n",
" movie_facebook_likes \n",
"0 33000 \n",
"1 0 \n",
"2 85000 \n",
"3 164000 \n",
"4 0 \n",
"\n",
"[5 rows x 28 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/movie_metadata.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "66f9f7de-4f86-7c41-3c5e-52264ce93095"
},
"outputs": [
{
"ename": "TypeError",
"evalue": "select() missing 1 required positional argument: 'crit'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-905c96d588e9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: select() missing 1 required positional argument: 'crit'"
]
}
],
"source": [
"df.select()"
]
}
],
"metadata": {
"_change_revision": 20,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163135.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d4589c40-d404-7a9c-b966-147e2d5b1a02"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "db7a71e2-7ab2-853d-9701-7be932d52661"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# File sizes\n",
"movie_metadata.csv 1.49MB\n"
]
}
],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"import os\n",
"import gc\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"pal = sns.color_palette()\n",
"\n",
"print('# File sizes')\n",
"for f in os.listdir('../input'):\n",
" if 'zip' not in f:\n",
" print(f.ljust(30) + str(round(os.path.getsize('../input/' + f) / 1000000, 2)) + 'MB')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3a6bc07c-f0ca-a9f1-61a9-f7b45b11c33a"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>color</th>\n",
" <th>director_name</th>\n",
" <th>num_critic_for_reviews</th>\n",
" <th>duration</th>\n",
" <th>director_facebook_likes</th>\n",
" <th>actor_3_facebook_likes</th>\n",
" <th>actor_2_name</th>\n",
" <th>actor_1_facebook_likes</th>\n",
" <th>gross</th>\n",
" <th>genres</th>\n",
" <th>...</th>\n",
" <th>num_user_for_reviews</th>\n",
" <th>language</th>\n",
" <th>country</th>\n",
" <th>content_rating</th>\n",
" <th>budget</th>\n",
" <th>title_year</th>\n",
" <th>actor_2_facebook_likes</th>\n",
" <th>imdb_score</th>\n",
" <th>aspect_ratio</th>\n",
" <th>movie_facebook_likes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Color</td>\n",
" <td>James Cameron</td>\n",
" <td>723.0</td>\n",
" <td>178.0</td>\n",
" <td>0.0</td>\n",
" <td>855.0</td>\n",
" <td>Joel David Moore</td>\n",
" <td>1000.0</td>\n",
" <td>760505847.0</td>\n",
" <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
" <td>...</td>\n",
" <td>3054.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>237000000.0</td>\n",
" <td>2009.0</td>\n",
" <td>936.0</td>\n",
" <td>7.9</td>\n",
" <td>1.78</td>\n",
" <td>33000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Color</td>\n",
" <td>Gore Verbinski</td>\n",
" <td>302.0</td>\n",
" <td>169.0</td>\n",
" <td>563.0</td>\n",
" <td>1000.0</td>\n",
" <td>Orlando Bloom</td>\n",
" <td>40000.0</td>\n",
" <td>309404152.0</td>\n",
" <td>Action|Adventure|Fantasy</td>\n",
" <td>...</td>\n",
" <td>1238.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>300000000.0</td>\n",
" <td>2007.0</td>\n",
" <td>5000.0</td>\n",
" <td>7.1</td>\n",
" <td>2.35</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Color</td>\n",
" <td>Sam Mendes</td>\n",
" <td>602.0</td>\n",
" <td>148.0</td>\n",
" <td>0.0</td>\n",
" <td>161.0</td>\n",
" <td>Rory Kinnear</td>\n",
" <td>11000.0</td>\n",
" <td>200074175.0</td>\n",
" <td>Action|Adventure|Thriller</td>\n",
" <td>...</td>\n",
" <td>994.0</td>\n",
" <td>English</td>\n",
" <td>UK</td>\n",
" <td>PG-13</td>\n",
" <td>245000000.0</td>\n",
" <td>2015.0</td>\n",
" <td>393.0</td>\n",
" <td>6.8</td>\n",
" <td>2.35</td>\n",
" <td>85000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Color</td>\n",
" <td>Christopher Nolan</td>\n",
" <td>813.0</td>\n",
" <td>164.0</td>\n",
" <td>22000.0</td>\n",
" <td>23000.0</td>\n",
" <td>Christian Bale</td>\n",
" <td>27000.0</td>\n",
" <td>448130642.0</td>\n",
" <td>Action|Thriller</td>\n",
" <td>...</td>\n",
" <td>2701.0</td>\n",
" <td>English</td>\n",
" <td>USA</td>\n",
" <td>PG-13</td>\n",
" <td>250000000.0</td>\n",
" <td>2012.0</td>\n",
" <td>23000.0</td>\n",
" <td>8.5</td>\n",
" <td>2.35</td>\n",
" <td>164000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>Doug Walker</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>131.0</td>\n",
" <td>NaN</td>\n",
" <td>Rob Walker</td>\n",
" <td>131.0</td>\n",
" <td>NaN</td>\n",
" <td>Documentary</td>\n",
" <td>...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>12.0</td>\n",
" <td>7.1</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" color director_name num_critic_for_reviews duration \\\n",
"0 Color James Cameron 723.0 178.0 \n",
"1 Color Gore Verbinski 302.0 169.0 \n",
"2 Color Sam Mendes 602.0 148.0 \n",
"3 Color Christopher Nolan 813.0 164.0 \n",
"4 NaN Doug Walker NaN NaN \n",
"\n",
" director_facebook_likes actor_3_facebook_likes actor_2_name \\\n",
"0 0.0 855.0 Joel David Moore \n",
"1 563.0 1000.0 Orlando Bloom \n",
"2 0.0 161.0 Rory Kinnear \n",
"3 22000.0 23000.0 Christian Bale \n",
"4 131.0 NaN Rob Walker \n",
"\n",
" actor_1_facebook_likes gross genres \\\n",
"0 1000.0 760505847.0 Action|Adventure|Fantasy|Sci-Fi \n",
"1 40000.0 309404152.0 Action|Adventure|Fantasy \n",
"2 11000.0 200074175.0 Action|Adventure|Thriller \n",
"3 27000.0 448130642.0 Action|Thriller \n",
"4 131.0 NaN Documentary \n",
"\n",
" ... num_user_for_reviews language country content_rating \\\n",
"0 ... 3054.0 English USA PG-13 \n",
"1 ... 1238.0 English USA PG-13 \n",
"2 ... 994.0 English UK PG-13 \n",
"3 ... 2701.0 English USA PG-13 \n",
"4 ... NaN NaN NaN NaN \n",
"\n",
" budget title_year actor_2_facebook_likes imdb_score aspect_ratio \\\n",
"0 237000000.0 2009.0 936.0 7.9 1.78 \n",
"1 300000000.0 2007.0 5000.0 7.1 2.35 \n",
"2 245000000.0 2015.0 393.0 6.8 2.35 \n",
"3 250000000.0 2012.0 23000.0 8.5 2.35 \n",
"4 NaN NaN 12.0 7.1 NaN \n",
"\n",
" movie_facebook_likes \n",
"0 33000 \n",
"1 0 \n",
"2 85000 \n",
"3 164000 \n",
"4 0 \n",
"\n",
"[5 rows x 28 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('../input/movie_metadata.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "66f9f7de-4f86-7c41-3c5e-52264ce93095"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>director_name</th>\n",
" <th>gross</th>\n",
" <th>movie_title</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>James Cameron</td>\n",
" <td>760505847.0</td>\n",
" <td>Avatar</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Gore Verbinski</td>\n",
" <td>309404152.0</td>\n",
" <td>Pirates of the Caribbean: At World's End</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Sam Mendes</td>\n",
" <td>200074175.0</td>\n",
" <td>Spectre</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Christopher Nolan</td>\n",
" <td>448130642.0</td>\n",
" <td>The Dark Knight Rises</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Doug Walker</td>\n",
" <td>NaN</td>\n",
" <td>Star Wars: Episode VII - The Force Awakens ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Andrew Stanton</td>\n",
" <td>73058679.0</td>\n",
" <td>John Carter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Sam Raimi</td>\n",
" <td>336530303.0</td>\n",
" <td>Spider-Man 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Nathan Greno</td>\n",
" <td>200807262.0</td>\n",
" <td>Tangled</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Joss Whedon</td>\n",
" <td>458991599.0</td>\n",
" <td>Avengers: Age of Ultron</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>David Yates</td>\n",
" <td>301956980.0</td>\n",
" <td>Harry Potter and the Half-Blood Prince</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Zack Snyder</td>\n",
" <td>330249062.0</td>\n",
" <td>Batman v Superman: Dawn of Justice</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Bryan Singer</td>\n",
" <td>200069408.0</td>\n",
" <td>Superman Returns</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Marc Forster</td>\n",
" <td>168368427.0</td>\n",
" <td>Quantum of Solace</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Gore Verbinski</td>\n",
" <td>423032628.0</td>\n",
" <td>Pirates of the Caribbean: Dead Man's Chest</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Gore Verbinski</td>\n",
" <td>89289910.0</td>\n",
" <td>The Lone Ranger</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Zack Snyder</td>\n",
" <td>291021565.0</td>\n",
" <td>Man of Steel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Andrew Adamson</td>\n",
" <td>141614023.0</td>\n",
" <td>The Chronicles of Narnia: Prince Caspian</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Joss Whedon</td>\n",
" <td>623279547.0</td>\n",
" <td>The Avengers</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Rob Marshall</td>\n",
" <td>241063875.0</td>\n",
" <td>Pirates of the Caribbean: On Stranger Tides</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Barry Sonnenfeld</td>\n",
" <td>179020854.0</td>\n",
" <td>Men in Black 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Peter Jackson</td>\n",
" <td>255108370.0</td>\n",
" <td>The Hobbit: The Battle of the Five Armies</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Marc Webb</td>\n",
" <td>262030663.0</td>\n",
" <td>The Amazing Spider-Man</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Ridley Scott</td>\n",
" <td>105219735.0</td>\n",
" <td>Robin Hood</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Peter Jackson</td>\n",
" <td>258355354.0</td>\n",
" <td>The Hobbit: The Desolation of Smaug</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Chris Weitz</td>\n",
" <td>70083519.0</td>\n",
" <td>The Golden Compass</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Peter Jackson</td>\n",
" <td>218051260.0</td>\n",
" <td>King Kong</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>James Cameron</td>\n",
" <td>658672302.0</td>\n",
" <td>Titanic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Anthony Russo</td>\n",
" <td>407197282.0</td>\n",
" <td>Captain America: Civil War</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Peter Berg</td>\n",
" <td>65173160.0</td>\n",
" <td>Battleship</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Colin Trevorrow</td>\n",
" <td>652177271.0</td>\n",
" <td>Jurassic World</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5013</th>\n",
" <td>Eric Eason</td>\n",
" <td>NaN</td>\n",
" <td>Manito</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5014</th>\n",
" <td>Uwe Boll</td>\n",
" <td>NaN</td>\n",
" <td>Rampage</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5015</th>\n",
" <td>Richard Linklater</td>\n",
" <td>1227508.0</td>\n",
" <td>Slacker</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5016</th>\n",
" <td>Joseph Mazzella</td>\n",
" <td>NaN</td>\n",
" <td>Dutch Kills</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5017</th>\n",
" <td>Travis Legge</td>\n",
" <td>NaN</td>\n",
" <td>Dry Spell</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5018</th>\n",
" <td>Alex Kendrick</td>\n",
" <td>NaN</td>\n",
" <td>Flywheel</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5019</th>\n",
" <td>Marcus Nispel</td>\n",
" <td>NaN</td>\n",
" <td>Exeter</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5020</th>\n",
" <td>Brandon Landers</td>\n",
" <td>NaN</td>\n",
" <td>The Ridges</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5021</th>\n",
" <td>Jay Duplass</td>\n",
" <td>192467.0</td>\n",
" <td>The Puffy Chair</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5022</th>\n",
" <td>Jim Chuchu</td>\n",
" <td>NaN</td>\n",
" <td>Stories of Our Lives</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5023</th>\n",
" <td>Daryl Wein</td>\n",
" <td>76382.0</td>\n",
" <td>Breaking Upwards</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5024</th>\n",
" <td>Jason Trost</td>\n",
" <td>NaN</td>\n",
" <td>All Superheroes Must Die</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5025</th>\n",
" <td>John Waters</td>\n",
" <td>180483.0</td>\n",
" <td>Pink Flamingos</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5026</th>\n",
" <td>Olivier Assayas</td>\n",
" <td>136007.0</td>\n",
" <td>Clean</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5027</th>\n",
" <td>Jafar Panahi</td>\n",
" <td>673780.0</td>\n",
" <td>The Circle</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5028</th>\n",
" <td>Ivan Kavanagh</td>\n",
" <td>NaN</td>\n",
" <td>Tin Can Man</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5029</th>\n",
" <td>Kiyoshi Kurosawa</td>\n",
" <td>94596.0</td>\n",
" <td>The Cure</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5030</th>\n",
" <td>Tadeo Garcia</td>\n",
" <td>NaN</td>\n",
" <td>On the Downlow</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5031</th>\n",
" <td>Thomas L. Phillips</td>\n",
" <td>NaN</td>\n",
" <td>Sanctuary; Quite a Conundrum</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5032</th>\n",
" <td>Ash Baron-Cohen</td>\n",
" <td>NaN</td>\n",
" <td>Bang</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5033</th>\n",
" <td>Shane Carruth</td>\n",
" <td>424760.0</td>\n",
" <td>Primer</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5034</th>\n",
" <td>Neill Dela Llana</td>\n",
" <td>70071.0</td>\n",
" <td>Cavite</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5035</th>\n",
" <td>Robert Rodriguez</td>\n",
" <td>2040920.0</td>\n",
" <td>El Mariachi</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5036</th>\n",
" <td>Anthony Vallone</td>\n",
" <td>NaN</td>\n",
" <td>The Mongol King</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5037</th>\n",
" <td>Edward Burns</td>\n",
" <td>4584.0</td>\n",
" <td>Newlyweds</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5038</th>\n",
" <td>Scott Smith</td>\n",
" <td>NaN</td>\n",
" <td>Signed Sealed Delivered</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5039</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>The Following</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5040</th>\n",
" <td>Benjamin Roberds</td>\n",
" <td>NaN</td>\n",
" <td>A Plague So Pleasant</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5041</th>\n",
" <td>Daniel Hsia</td>\n",
" <td>10443.0</td>\n",
" <td>Shanghai Calling</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5042</th>\n",
" <td>Jon Gunn</td>\n",
" <td>85222.0</td>\n",
" <td>My Date with Drew</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5043 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" director_name gross \\\n",
"0 James Cameron 760505847.0 \n",
"1 Gore Verbinski 309404152.0 \n",
"2 Sam Mendes 200074175.0 \n",
"3 Christopher Nolan 448130642.0 \n",
"4 Doug Walker NaN \n",
"5 Andrew Stanton 73058679.0 \n",
"6 Sam Raimi 336530303.0 \n",
"7 Nathan Greno 200807262.0 \n",
"8 Joss Whedon 458991599.0 \n",
"9 David Yates 301956980.0 \n",
"10 Zack Snyder 330249062.0 \n",
"11 Bryan Singer 200069408.0 \n",
"12 Marc Forster 168368427.0 \n",
"13 Gore Verbinski 423032628.0 \n",
"14 Gore Verbinski 89289910.0 \n",
"15 Zack Snyder 291021565.0 \n",
"16 Andrew Adamson 141614023.0 \n",
"17 Joss Whedon 623279547.0 \n",
"18 Rob Marshall 241063875.0 \n",
"19 Barry Sonnenfeld 179020854.0 \n",
"20 Peter Jackson 255108370.0 \n",
"21 Marc Webb 262030663.0 \n",
"22 Ridley Scott 105219735.0 \n",
"23 Peter Jackson 258355354.0 \n",
"24 Chris Weitz 70083519.0 \n",
"25 Peter Jackson 218051260.0 \n",
"26 James Cameron 658672302.0 \n",
"27 Anthony Russo 407197282.0 \n",
"28 Peter Berg 65173160.0 \n",
"29 Colin Trevorrow 652177271.0 \n",
"... ... ... \n",
"5013 Eric Eason NaN \n",
"5014 Uwe Boll NaN \n",
"5015 Richard Linklater 1227508.0 \n",
"5016 Joseph Mazzella NaN \n",
"5017 Travis Legge NaN \n",
"5018 Alex Kendrick NaN \n",
"5019 Marcus Nispel NaN \n",
"5020 Brandon Landers NaN \n",
"5021 Jay Duplass 192467.0 \n",
"5022 Jim Chuchu NaN \n",
"5023 Daryl Wein 76382.0 \n",
"5024 Jason Trost NaN \n",
"5025 John Waters 180483.0 \n",
"5026 Olivier Assayas 136007.0 \n",
"5027 Jafar Panahi 673780.0 \n",
"5028 Ivan Kavanagh NaN \n",
"5029 Kiyoshi Kurosawa 94596.0 \n",
"5030 Tadeo Garcia NaN \n",
"5031 Thomas L. Phillips NaN \n",
"5032 Ash Baron-Cohen NaN \n",
"5033 Shane Carruth 424760.0 \n",
"5034 Neill Dela Llana 70071.0 \n",
"5035 Robert Rodriguez 2040920.0 \n",
"5036 Anthony Vallone NaN \n",
"5037 Edward Burns 4584.0 \n",
"5038 Scott Smith NaN \n",
"5039 NaN NaN \n",
"5040 Benjamin Roberds NaN \n",
"5041 Daniel Hsia 10443.0 \n",
"5042 Jon Gunn 85222.0 \n",
"\n",
" movie_title \n",
"0 Avatar \n",
"1 Pirates of the Caribbean: At World's End \n",
"2 Spectre \n",
"3 The Dark Knight Rises \n",
"4 Star Wars: Episode VII - The Force Awakens ... \n",
"5 John Carter \n",
"6 Spider-Man 3 \n",
"7 Tangled \n",
"8 Avengers: Age of Ultron \n",
"9 Harry Potter and the Half-Blood Prince \n",
"10 Batman v Superman: Dawn of Justice \n",
"11 Superman Returns \n",
"12 Quantum of Solace \n",
"13 Pirates of the Caribbean: Dead Man's Chest \n",
"14 The Lone Ranger \n",
"15 Man of Steel \n",
"16 The Chronicles of Narnia: Prince Caspian \n",
"17 The Avengers \n",
"18 Pirates of the Caribbean: On Stranger Tides \n",
"19 Men in Black 3 \n",
"20 The Hobbit: The Battle of the Five Armies \n",
"21 The Amazing Spider-Man \n",
"22 Robin Hood \n",
"23 The Hobbit: The Desolation of Smaug \n",
"24 The Golden Compass \n",
"25 King Kong \n",
"26 Titanic \n",
"27 Captain America: Civil War \n",
"28 Battleship \n",
"29 Jurassic World \n",
"... ... \n",
"5013 Manito \n",
"5014 Rampage \n",
"5015 Slacker \n",
"5016 Dutch Kills \n",
"5017 Dry Spell \n",
"5018 Flywheel \n",
"5019 Exeter \n",
"5020 The Ridges \n",
"5021 The Puffy Chair \n",
"5022 Stories of Our Lives \n",
"5023 Breaking Upwards \n",
"5024 All Superheroes Must Die \n",
"5025 Pink Flamingos \n",
"5026 Clean \n",
"5027 The Circle \n",
"5028 Tin Can Man \n",
"5029 The Cure \n",
"5030 On the Downlow \n",
"5031 Sanctuary; Quite a Conundrum \n",
"5032 Bang \n",
"5033 Primer \n",
"5034 Cavite \n",
"5035 El Mariachi \n",
"5036 The Mongol King \n",
"5037 Newlyweds \n",
"5038 Signed Sealed Delivered \n",
"5039 The Following \n",
"5040 A Plague So Pleasant \n",
"5041 Shanghai Calling \n",
"5042 My Date with Drew \n",
"\n",
"[5043 rows x 3 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# checking the director/movie with the biggest grossing\n",
"dfD=df.loc[:,['director_name','gross','movie_title']]\n",
"dfD"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "c851b52c-23da-85fb-1734-7afca576d820"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>director_name</th>\n",
" <th>gross</th>\n",
" <th>movie_title</th>\n",
" <th>grossMil</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>James Cameron</td>\n",
" <td>760505847.0</td>\n",
" <td>Avatar</td>\n",
" <td>760.505847</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>James Cameron</td>\n",
" <td>658672302.0</td>\n",
" <td>Titanic</td>\n",
" <td>658.672302</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Colin Trevorrow</td>\n",
" <td>652177271.0</td>\n",
" <td>Jurassic World</td>\n",
" <td>652.177271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>794</th>\n",
" <td>Joss Whedon</td>\n",
" <td>623279547.0</td>\n",
" <td>The Avengers</td>\n",
" <td>623.279547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Joss Whedon</td>\n",
" <td>623279547.0</td>\n",
" <td>The Avengers</td>\n",
" <td>623.279547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>Christopher Nolan</td>\n",
" <td>533316061.0</td>\n",
" <td>The Dark Knight</td>\n",
" <td>533.316061</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>George Lucas</td>\n",
" <td>474544677.0</td>\n",
" <td>Star Wars: Episode I - The Phantom Menace</td>\n",
" <td>474.544677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3024</th>\n",
" <td>George Lucas</td>\n",
" <td>460935665.0</td>\n",
" <td>Star Wars: Episode IV - A New Hope</td>\n",
" <td>460.935665</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Joss Whedon</td>\n",
" <td>458991599.0</td>\n",
" <td>Avengers: Age of Ultron</td>\n",
" <td>458.991599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Christopher Nolan</td>\n",
" <td>448130642.0</td>\n",
" <td>The Dark Knight Rises</td>\n",
" <td>448.130642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>582</th>\n",
" <td>Andrew Adamson</td>\n",
" <td>436471036.0</td>\n",
" <td>Shrek 2</td>\n",
" <td>436.471036</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3080</th>\n",
" <td>Steven Spielberg</td>\n",
" <td>434949459.0</td>\n",
" <td>E.T. the Extra-Terrestrial</td>\n",
" <td>434.949459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>Francis Lawrence</td>\n",
" <td>424645577.0</td>\n",
" <td>The Hunger Games: Catching Fire</td>\n",
" <td>424.645577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Gore Verbinski</td>\n",
" <td>423032628.0</td>\n",
" <td>Pirates of the Caribbean: Dead Man's Chest</td>\n",
" <td>423.032628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>509</th>\n",
" <td>Roger Allers</td>\n",
" <td>422783777.0</td>\n",
" <td>The Lion King</td>\n",
" <td>422.783777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Lee Unkrich</td>\n",
" <td>414984497.0</td>\n",
" <td>Toy Story 3</td>\n",
" <td>414.984497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Shane Black</td>\n",
" <td>408992272.0</td>\n",
" <td>Iron Man 3</td>\n",
" <td>408.992272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>439</th>\n",
" <td>Gary Ross</td>\n",
" <td>407999255.0</td>\n",
" <td>The Hunger Games</td>\n",
" <td>407.999255</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Anthony Russo</td>\n",
" <td>407197282.0</td>\n",
" <td>Captain America: Civil War</td>\n",
" <td>407.197282</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td>Sam Raimi</td>\n",
" <td>403706375.0</td>\n",
" <td>Spider-Man</td>\n",
" <td>403.706375</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Michael Bay</td>\n",
" <td>402076689.0</td>\n",
" <td>Transformers: Revenge of the Fallen</td>\n",
" <td>402.076689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td>Chris Buck</td>\n",
" <td>400736600.0</td>\n",
" <td>Frozen</td>\n",
" <td>400.736600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>338</th>\n",
" <td>Andrew Stanton</td>\n",
" <td>380838870.0</td>\n",
" <td>Finding Nemo</td>\n",
" <td>380.838870</td>\n",
" </tr>\n",
" <tr>\n",
" <th>236</th>\n",
" <td>George Lucas</td>\n",
" <td>380262555.0</td>\n",
" <td>Star Wars: Episode III - Revenge of the Sith</td>\n",
" <td>380.262555</td>\n",
" </tr>\n",
" <tr>\n",
" <th>339</th>\n",
" <td>Peter Jackson</td>\n",
" <td>377019252.0</td>\n",
" <td>The Lord of the Rings: The Return of the King</td>\n",
" <td>377.019252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Sam Raimi</td>\n",
" <td>373377893.0</td>\n",
" <td>Spider-Man 2</td>\n",
" <td>373.377893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>521</th>\n",
" <td>Pierre Coffin</td>\n",
" <td>368049635.0</td>\n",
" <td>Despicable Me 2</td>\n",
" <td>368.049635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>812</th>\n",
" <td>Tim Miller</td>\n",
" <td>363024263.0</td>\n",
" <td>Deadpool</td>\n",
" <td>363.024263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1805</th>\n",
" <td>Jon Favreau</td>\n",
" <td>362645141.0</td>\n",
" <td>The Jungle Book</td>\n",
" <td>362.645141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>Jon Favreau</td>\n",
" <td>362645141.0</td>\n",
" <td>The Jungle Book</td>\n",
" <td>362.645141</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2794</th>\n",
" <td>Martin Scorsese</td>\n",
" <td>0.0</td>\n",
" <td>New York, New York</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1802</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>Limitless</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3755</th>\n",
" <td>Frank Borzage</td>\n",
" <td>0.0</td>\n",
" <td>A Farewell to Arms</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4335</th>\n",
" <td>Harald Reinl</td>\n",
" <td>0.0</td>\n",
" <td>The Torture Chamber of Dr. Sadism</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4336</th>\n",
" <td>James Cox</td>\n",
" <td>0.0</td>\n",
" <td>Straight A's</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4337</th>\n",
" <td>Richard Lester</td>\n",
" <td>0.0</td>\n",
" <td>A Funny Thing Happened on the Way to the Forum</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4338</th>\n",
" <td>Michael Moore</td>\n",
" <td>0.0</td>\n",
" <td>Slacker Uprising</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4339</th>\n",
" <td>Tanner Beard</td>\n",
" <td>0.0</td>\n",
" <td>The Legend of Hell's Gate: An American Conspir...</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4340</th>\n",
" <td>Scott Dow</td>\n",
" <td>0.0</td>\n",
" <td>The Walking Deceased</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4341</th>\n",
" <td>Derick Martini</td>\n",
" <td>0.0</td>\n",
" <td>The Curse of Downers Grove</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4342</th>\n",
" <td>Jerry Dugan</td>\n",
" <td>0.0</td>\n",
" <td>Shark Lake</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1842</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>It's Always Sunny in Philadelphia</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2765</th>\n",
" <td>John Blanchard</td>\n",
" <td>0.0</td>\n",
" <td>Towering Inferno</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4345</th>\n",
" <td>William Kaufman</td>\n",
" <td>0.0</td>\n",
" <td>The Marine 4: Moving Target</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2767</th>\n",
" <td>Vidhu Vinod Chopra</td>\n",
" <td>0.0</td>\n",
" <td>Broken Horses</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4348</th>\n",
" <td>Jehane Noujaim</td>\n",
" <td>0.0</td>\n",
" <td>The Square</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4362</th>\n",
" <td>J. Lee Thompson</td>\n",
" <td>0.0</td>\n",
" <td>Battle for the Planet of the Apes</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1831</th>\n",
" <td>Kenneth Branagh</td>\n",
" <td>0.0</td>\n",
" <td>The Magic Flute</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2772</th>\n",
" <td>Ted Kotcheff</td>\n",
" <td>0.0</td>\n",
" <td>First Blood</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2775</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>Ghost Hunters</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3752</th>\n",
" <td>King Vidor</td>\n",
" <td>0.0</td>\n",
" <td>Solomon and Sheba</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4354</th>\n",
" <td>John Laing</td>\n",
" <td>0.0</td>\n",
" <td>Abandoned</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3751</th>\n",
" <td>Don Coscarelli</td>\n",
" <td>0.0</td>\n",
" <td>The Beastmaster</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1819</th>\n",
" <td>Walter Murch</td>\n",
" <td>0.0</td>\n",
" <td>Return to Oz</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2786</th>\n",
" <td>John Boorman</td>\n",
" <td>0.0</td>\n",
" <td>Exorcist II: The Heretic</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4358</th>\n",
" <td>Elia Kazan</td>\n",
" <td>0.0</td>\n",
" <td>A Streetcar Named Desire</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4359</th>\n",
" <td>Stanley Kubrick</td>\n",
" <td>0.0</td>\n",
" <td>Dr. Strangelove or: How I Learned to Stop Worr...</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1818</th>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>The Honeymooners</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1815</th>\n",
" <td>Wolfgang Petersen</td>\n",
" <td>0.0</td>\n",
" <td>The NeverEnding Story</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3354</th>\n",
" <td>Serdar Akar</td>\n",
" <td>0.0</td>\n",
" <td>Valley of the Wolves: Iraq</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5043 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" director_name gross \\\n",
"0 James Cameron 760505847.0 \n",
"26 James Cameron 658672302.0 \n",
"29 Colin Trevorrow 652177271.0 \n",
"794 Joss Whedon 623279547.0 \n",
"17 Joss Whedon 623279547.0 \n",
"66 Christopher Nolan 533316061.0 \n",
"240 George Lucas 474544677.0 \n",
"3024 George Lucas 460935665.0 \n",
"8 Joss Whedon 458991599.0 \n",
"3 Christopher Nolan 448130642.0 \n",
"582 Andrew Adamson 436471036.0 \n",
"3080 Steven Spielberg 434949459.0 \n",
"186 Francis Lawrence 424645577.0 \n",
"13 Gore Verbinski 423032628.0 \n",
"509 Roger Allers 422783777.0 \n",
"43 Lee Unkrich 414984497.0 \n",
"32 Shane Black 408992272.0 \n",
"439 Gary Ross 407999255.0 \n",
"27 Anthony Russo 407197282.0 \n",
"161 Sam Raimi 403706375.0 \n",
"36 Michael Bay 402076689.0 \n",
"125 Chris Buck 400736600.0 \n",
"338 Andrew Stanton 380838870.0 \n",
"236 George Lucas 380262555.0 \n",
"339 Peter Jackson 377019252.0 \n",
"31 Sam Raimi 373377893.0 \n",
"521 Pierre Coffin 368049635.0 \n",
"812 Tim Miller 363024263.0 \n",
"1805 Jon Favreau 362645141.0 \n",
"79 Jon Favreau 362645141.0 \n",
"... ... ... \n",
"2794 Martin Scorsese 0.0 \n",
"1802 NaN 0.0 \n",
"3755 Frank Borzage 0.0 \n",
"4335 Harald Reinl 0.0 \n",
"4336 James Cox 0.0 \n",
"4337 Richard Lester 0.0 \n",
"4338 Michael Moore 0.0 \n",
"4339 Tanner Beard 0.0 \n",
"4340 Scott Dow 0.0 \n",
"4341 Derick Martini 0.0 \n",
"4342 Jerry Dugan 0.0 \n",
"1842 NaN 0.0 \n",
"2765 John Blanchard 0.0 \n",
"4345 William Kaufman 0.0 \n",
"2767 Vidhu Vinod Chopra 0.0 \n",
"4348 Jehane Noujaim 0.0 \n",
"4362 J. Lee Thompson 0.0 \n",
"1831 Kenneth Branagh 0.0 \n",
"2772 Ted Kotcheff 0.0 \n",
"2775 NaN 0.0 \n",
"3752 King Vidor 0.0 \n",
"4354 John Laing 0.0 \n",
"3751 Don Coscarelli 0.0 \n",
"1819 Walter Murch 0.0 \n",
"2786 John Boorman 0.0 \n",
"4358 Elia Kazan 0.0 \n",
"4359 Stanley Kubrick 0.0 \n",
"1818 NaN 0.0 \n",
"1815 Wolfgang Petersen 0.0 \n",
"3354 Serdar Akar 0.0 \n",
"\n",
" movie_title grossMil \n",
"0 Avatar 760.505847 \n",
"26 Titanic 658.672302 \n",
"29 Jurassic World 652.177271 \n",
"794 The Avengers 623.279547 \n",
"17 The Avengers 623.279547 \n",
"66 The Dark Knight 533.316061 \n",
"240 Star Wars: Episode I - The Phantom Menace 474.544677 \n",
"3024 Star Wars: Episode IV - A New Hope 460.935665 \n",
"8 Avengers: Age of Ultron 458.991599 \n",
"3 The Dark Knight Rises 448.130642 \n",
"582 Shrek 2 436.471036 \n",
"3080 E.T. the Extra-Terrestrial 434.949459 \n",
"186 The Hunger Games: Catching Fire 424.645577 \n",
"13 Pirates of the Caribbean: Dead Man's Chest 423.032628 \n",
"509 The Lion King 422.783777 \n",
"43 Toy Story 3 414.984497 \n",
"32 Iron Man 3 408.992272 \n",
"439 The Hunger Games 407.999255 \n",
"27 Captain America: Civil War 407.197282 \n",
"161 Spider-Man 403.706375 \n",
"36 Transformers: Revenge of the Fallen 402.076689 \n",
"125 Frozen 400.736600 \n",
"338 Finding Nemo 380.838870 \n",
"236 Star Wars: Episode III - Revenge of the Sith 380.262555 \n",
"339 The Lord of the Rings: The Return of the King 377.019252 \n",
"31 Spider-Man 2 373.377893 \n",
"521 Despicable Me 2 368.049635 \n",
"812 Deadpool 363.024263 \n",
"1805 The Jungle Book 362.645141 \n",
"79 The Jungle Book 362.645141 \n",
"... ... ... \n",
"2794 New York, New York 0.000000 \n",
"1802 Limitless 0.000000 \n",
"3755 A Farewell to Arms 0.000000 \n",
"4335 The Torture Chamber of Dr. Sadism 0.000000 \n",
"4336 Straight A's 0.000000 \n",
"4337 A Funny Thing Happened on the Way to the Forum 0.000000 \n",
"4338 Slacker Uprising 0.000000 \n",
"4339 The Legend of Hell's Gate: An American Conspir... 0.000000 \n",
"4340 The Walking Deceased 0.000000 \n",
"4341 The Curse of Downers Grove 0.000000 \n",
"4342 Shark Lake 0.000000 \n",
"1842 It's Always Sunny in Philadelphia 0.000000 \n",
"2765 Towering Inferno 0.000000 \n",
"4345 The Marine 4: Moving Target 0.000000 \n",
"2767 Broken Horses 0.000000 \n",
"4348 The Square 0.000000 \n",
"4362 Battle for the Planet of the Apes 0.000000 \n",
"1831 The Magic Flute 0.000000 \n",
"2772 First Blood 0.000000 \n",
"2775 Ghost Hunters 0.000000 \n",
"3752 Solomon and Sheba 0.000000 \n",
"4354 Abandoned 0.000000 \n",
"3751 The Beastmaster 0.000000 \n",
"1819 Return to Oz 0.000000 \n",
"2786 Exorcist II: The Heretic 0.000000 \n",
"4358 A Streetcar Named Desire 0.000000 \n",
"4359 Dr. Strangelove or: How I Learned to Stop Worr... 0.000000 \n",
"1818 The Honeymooners 0.000000 \n",
"1815 The NeverEnding Story 0.000000 \n",
"3354 Valley of the Wolves: Iraq 0.000000 \n",
"\n",
"[5043 rows x 4 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# it's kinda hard to read so we will check only millions, and replace the nan\n",
"dfD['gross']=dfD['gross'].fillna(0)\n",
"dfD['grossMil']=dfD['gross']/1000000\n",
"dfD.sort_values(by=\"grossMil\", ascending=False)"
]
}
],
"metadata": {
"_change_revision": 82,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163180.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "5cd0738f-89de-f11d-1adb-e1ff5a4a27d9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hello\n",
"Ms.\n",
"Me\n",
",\n",
"how\n",
"are\n",
"you\n",
"doing\n",
"today\n",
"?\n",
"Its\n",
"a\n",
"funny\n",
"little\n",
"world\n",
".\n",
"And\n",
"i\n",
"am\n",
"learning\n",
";\n",
"nltk\n"
]
}
],
"source": [
"from nltk import sent_tokenize, word_tokenize\n",
"example_test = \"Hello Ms. Me, how are you doing today? Its a funny little world. And i am learning ; nltk\"\n",
"#print(sent_tokenize(example_test))\n",
"#print(word_tokenize(example_test))\n",
"\n",
"for i in word_tokenize(example_test):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "d40ea282-3704-4426-c83f-f569535931e4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['This', 'example', 'showing', 'stop', 'word', 'filtration', '.']\n"
]
}
],
"source": [
"from nltk.corpus import stopwords #words like a, and, more, so which dont give any meaning to analysis\n",
"from nltk.tokenize import word_tokenize\n",
"\n",
"example_sentence = \"This is an example showing off stop word filtration.\"\n",
"stop_words = set(stopwords.words(\"english\"))#set of predefined english stopwords\n",
"#print(stop_words)\n",
"\n",
"words = word_tokenize(example_sentence)\n",
"filtered_sentence = []\n",
"for w in words:\n",
" if w not in stop_words:\n",
" filtered_sentence.append(w)\n",
"#one line replacement \n",
"#filtered_sentence = [w for w in words if not w in stop_words]\n",
"print(filtered_sentence)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "fd664990-2dfe-0fc8-c0cf-61d731e74d79"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"python\n",
"python\n",
"python\n",
"python\n",
"pythonli\n",
"It\n",
"is\n",
"import\n",
"to\n",
"be\n",
"pythonli\n",
"while\n",
"you\n",
"are\n",
"python\n",
"with\n",
"python\n",
".\n",
"all\n",
"python\n",
"ahv\n",
"python\n",
"poorli\n",
"at\n",
"least\n",
"onc\n",
".\n"
]
}
],
"source": [
"#Stemming - data preprocessing method (finding stem of word 'riding-ride')\n",
"#Porerstemmer\n",
"from nltk.stem import PorterStemmer\n",
"from nltk.tokenize import word_tokenize\n",
"\n",
"ps=PorterStemmer()\n",
"example_words = [\"python\",\"pythoner\", \"pythoned\", \"pythoning\", \"pythonly\"]\n",
"for w in example_words:\n",
" print(ps.stem(w))\n",
" \n",
"sent = \"It is important to be pythonly while you are pythoning with python. All pythoners ahve pythoned poorly at least once.\"\n",
"words = word_tokenize(sent)\n",
"\n",
"for w in words:\n",
" print(ps.stem(w))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "38abc1e4-12ea-5c28-e8d4-5f955bc60bdc"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.04020979020979021 0.07357859531772576 0.03836930455635491 5720 299 230 22\n",
"0.002972027972027972 0.0033444816053511705 0.002951484965873455 5720 299 17 1\n",
"0.0008741258741258741 0.006688963210702341 0.0005534034311012728 5720 299 5 2\n",
"0.0024475524475524478 0.016722408026755852 0.0016602102933038186 5720 299 14 5\n",
"0.0013986013986013986 0.0033444816053511705 0.0012912746725696365 5720 299 8 1\n",
"0.0034965034965034965 0.0033444816053511705 0.003504888396974728 5720 299 20 1\n",
"0.017132867132867134 0.04013377926421405 0.01586423169156982 5720 299 98 12\n",
"0.005244755244755245 0.006688963210702341 0.005165098690278546 5720 299 30 2\n",
"0.005944055944055944 0.026755852842809364 0.004796163069544364 5720 299 34 8\n",
"0.04493006993006993 0.043478260869565216 0.04501014572957019 5720 299 257 13\n",
"0.00017482517482517483 0.0033444816053511705 0.0 5720 299 1 1\n",
"0.016258741258741258 0.0802675585284281 0.012728278915329275 5720 299 93 24\n",
"0.001048951048951049 0.0033444816053511705 0.0009223390518354548 5720 299 6 1\n",
"0.029545454545454545 0.006688963210702341 0.030806124331304186 5720 299 169 2\n",
"0.0015734265734265735 0.0033444816053511705 0.0014757424829367274 5720 299 9 1\n",
"0.0038461538461538464 0.006688963210702341 0.003689356207341819 5720 299 22 2\n",
"0.003146853146853147 0.006688963210702341 0.002951484965873455 5720 299 18 2\n",
"0.0022727272727272726 0.0033444816053511705 0.002213613724405091 5720 299 13 1\n",
"0.01381118881118881 0.020066889632107024 0.013466150156797639 5720 299 79 6\n",
"0.006118881118881119 0.0033444816053511705 0.006271905552481092 5720 299 35 1\n",
"0.0026223776223776225 0.010033444816053512 0.002213613724405091 5720 299 15 3\n",
"0.009965034965034964 0.006688963210702341 0.010145729570190002 5720 299 57 2\n",
"0.0012237762237762239 0.016722408026755852 0.00036893562073418186 5720 299 7 5\n",
"0.0015734265734265735 0.006688963210702341 0.0012912746725696365 5720 299 9 2\n",
"0.00017482517482517483 0.0033444816053511705 0.0 5720 299 1 1\n",
"0.0012237762237762239 0.006688963210702341 0.0009223390518354548 5720 299 7 2\n",
"0.00034965034965034965 0.006688963210702341 0.0 5720 299 2 2\n",
"0.0005244755244755245 0.006688963210702341 0.00018446781036709093 5720 299 3 2\n",
"0.004370629370629371 0.0033444816053511705 0.004427227448810182 5720 299 25 1\n",
"0.003146853146853147 0.013377926421404682 0.002582549345139273 5720 299 18 4\n",
"0.0006993006993006993 0.0033444816053511705 0.0005534034311012728 5720 299 4 1\n",
"0.0013986013986013986 0.0033444816053511705 0.0012912746725696365 5720 299 8 1\n",
"0.0022727272727272726 0.0033444816053511705 0.002213613724405091 5720 299 13 1\n",
"0.0013986013986013986 0.0033444816053511705 0.0012912746725696365 5720 299 8 1\n",
"0.0024475524475524478 0.0033444816053511705 0.002398081534772182 5720 299 14 1\n",
"0.0024475524475524478 0.006688963210702341 0.002213613724405091 5720 299 14 2\n",
"0.0006993006993006993 0.0033444816053511705 0.0005534034311012728 5720 299 4 1\n",
"0.00017482517482517483 0.0033444816053511705 0.0 5720 299 1 1\n",
"0.0008741258741258741 0.006688963210702341 0.0005534034311012728 5720 299 5 2\n",
"0.00017482517482517483 0.0033444816053511705 0.0 5720 299 1 1\n",
"0.013111888111888112 0.010033444816053512 0.013281682346430549 5720 299 75 3\n",
"0.003146853146853147 0.0033444816053511705 0.003135952776240546 5720 299 18 1\n",
"0.00017482517482517483 0.0033444816053511705 0.0 5720 299 1 1\n",
"0.001048951048951049 0.013377926421404682 0.00036893562073418186 5720 299 6 4\n",
"0.00017482517482517483 0.0033444816053511705 0.0 5720 299 1 1\n",
"0.00017482517482517483 0.0033444816053511705 0.0 5720 299 1 1\n",
"0.004370629370629371 0.0033444816053511705 0.004427227448810182 5720 299 25 1\n",
"0.015559440559440559 0.0033444816053511705 0.016233167312304002 5720 299 89 1\n",
"0.0008741258741258741 0.006688963210702341 0.0005534034311012728 5720 299 5 2\n",
"0.0005244755244755245 0.0033444816053511705 0.00036893562073418186 5720 299 3 1\n",
"0.00034965034965034965 0.0033444816053511705 0.00018446781036709093 5720 299 2 1\n",
"0.0019230769230769232 0.0033444816053511705 0.0018446781036709095 5720 299 11 1\n",
"0.00034965034965034965 0.0033444816053511705 0.00018446781036709093 5720 299 2 1\n",
"0.0015734265734265735 0.0033444816053511705 0.0014757424829367274 5720 299 9 1\n",
"0.00034965034965034965 0.0033444816053511705 0.00018446781036709093 5720 299 2 1\n",
"0.001048951048951049 0.0033444816053511705 0.0009223390518354548 5720 299 6 1\n"
]
}
],
"source": [
"#Partofspeech\n",
"import nltk\n",
"from nltk.corpus import state_union #presidential address text\n",
"from nltk.tokenize import PunktSentenceTokenizer #trainable ml\n",
"\n",
"train_text = state_union.raw(\"2005-GWBush.txt\")\n",
"sample_text = state_union.raw(\"2006-GWBush.txt\")\n",
"\n",
"custom_sent_tokenizer = PunktSentenceTokenizer(train_text)\n",
"\n",
"tokenized = custom_sent_tokenizer.tokenize(sample_text)\n",
"\n",
"def process_content():\n",
" try:\n",
" for i in tokenized:\n",
" words = nltk.word_tokenize(i)\n",
" tagged= nltk.pos_tag(words)\n",
" \n",
" print(tagged)\n",
" except Exception as e:\n",
" print(str(e))\n",
" \n",
" \n",
" "
]
}
],
"metadata": {
"_change_revision": 15,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163258.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "2480b61d-3b74-80ff-a1ad-858bd14a2958"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "aisles.csv\ndepartments.csv\norder_products__prior.csv\norder_products__train.csv\norders.csv\nproducts.csv\nsample_submission.csv\n\n"
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "e3959be5-327a-d40a-98c2-e02df98e7b8e"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"---------------------------------------------------------------------------",
"NameError Traceback (most recent call last)",
"<ipython-input-1-f4856856aad8> in <module>()\n 1 # Loading the data sets\n----> 2 aisles = pd.read_csv('../input/aisles.csv')\n 3 departments = pd.read_csv('../input/departments.csv')\n 4 order_products_prior = pd.read_csv('../input/order_products__prior.csv')\n 5 order_products_train = pd.read_csv('../input/order_products__train.csv')\n",
"NameError: name 'pd' is not defined"
]
}
],
"source": [
"# Loading the data sets\n",
"aisles = pd.read_csv('../input/aisles.csv')\n",
"departments = pd.read_csv('../input/departments.csv')\n",
"order_products_prior = pd.read_csv('../input/order_products__prior.csv')\n",
"order_products_train = pd.read_csv('../input/order_products__train.csv')\n",
"orders = pd.read_csv('../input/orders.csv')\n",
"products = pd.read_csv('../input/products.csv')\n",
"sample_submission = pd.read_csv('../input/sample_submission.csv')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "ca0bf207-4c88-3feb-d689-308af9e513d7"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>order_id</th>\n <th>products</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>17</td>\n <td>39276</td>\n </tr>\n <tr>\n <th>1</th>\n <td>34</td>\n <td>39276</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " order_id products\n0 17 39276\n1 34 39276"
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sample_submission.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "dce08e34-4858-963e-f291-e5ed26c9d892"
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "Missing parentheses in call to 'print' (<ipython-input-4-50bcf71e3c7a>, line 1)",
"output_type": "error",
"traceback": [
" File \"<ipython-input-4-50bcf71e3c7a>\", line 1\n print 'aisles, shape = ', aisles.shape\n ^\nSyntaxError: Missing parentheses in call to 'print'\n"
]
},
{
"ename": "SyntaxError",
"evalue": "Missing parentheses in call to 'print' (<ipython-input-5-839135e57ff6>, line 1)",
"output_type": "error",
"traceback": [
" File \"<ipython-input-5-839135e57ff6>\", line 1\n print 'aisles, shape = ', aisles.shape\n ^\nSyntaxError: Missing parentheses in call to 'print'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": "aisles, shape = (134, 2)\ndepartments, shape = (21, 2)\norder_procducts_prior, shape = (32434489, 4)\n"
},
{
"ename": "NameError",
"evalue": "name 'order_procducts_train' is not defined",
"output_type": "error",
"traceback": [
"---------------------------------------------------------------------------",
"NameError Traceback (most recent call last)",
"<ipython-input-6-a67b09fec273> in <module>()\n 2 print ('departments, shape = ', departments.shape)\n 3 print ('order_procducts_prior, shape = ', order_products_prior.shape)\n----> 4 print ('order_procducts_train, shape = ', order_procducts_train.shape)\n 5 print ('orders, shape = ', orders.shape)\n 6 print ('products, shape = ', products.shape)\n",
"NameError: name 'order_procducts_train' is not defined"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": "aisles, shape = (134, 2)\ndepartments, shape = (21, 2)\norder_procducts_prior, shape = (32434489, 4)\norder_products_train, shape = (1384617, 4)\norders, shape = (3421083, 7)\nproducts, shape = (49688, 4)\n"
},
{
"ename": "AttributeError",
"evalue": "'list' object has no attribute 'shape'",
"output_type": "error",
"traceback": [
"---------------------------------------------------------------------------",
"AttributeError Traceback (most recent call last)",
"<ipython-input-8-bd30f6cb0275> in <module>()\n 9 '''\n 10 for s in sets:\n---> 11 print (str(s), sets.shape, '\\n')\n",
"AttributeError: 'list' object has no attribute 'shape'"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": " aisle_id aisle\n0 1 prepared soups salads\n1 2 specialty cheeses\n2 3 energy granola bars\n3 4 instant foods\n4 5 marinades meat preparation\n5 6 other\n6 7 packaged meat\n7 8 bakery desserts\n8 9 pasta sauce\n9 10 kitchen supplies\n10 11 cold flu allergy\n11 12 fresh pasta\n12 13 prepared meals\n13 14 tofu meat alternatives\n14 15 packaged seafood\n15 16 fresh herbs\n16 17 baking ingredients\n17 18 bulk dried fruits vegetables\n18 19 oils vinegars\n19 20 oral hygiene\n20 21 packaged cheese\n21 22 hair care\n22 23 popcorn jerky\n23 24 fresh fruits\n24 25 soap\n25 26 coffee\n26 27 beers coolers\n27 28 red wines\n28 29 honeys syrups nectars\n29 30 latino foods\n.. ... ...\n104 105 doughs gelatins bake mixes\n105 106 hot dogs bacon sausage\n106 107 chips pretzels\n107 108 other creams cheeses\n108 109 skin care\n109 110 pickled goods olives\n110 111 plates bowls cups flatware\n111 112 bread\n112 113 frozen juice\n113 114 cleaning products\n114 115 water seltzer sparkling water\n115 116 frozen produce\n116 117 nuts seeds dried fruit\n117 118 first aid\n118 119 frozen dessert\n119 120 yogurt\n120 121 cereal\n121 122 meat counter\n122 123 packaged vegetables fruits\n123 124 spirits\n124 125 trail mix snack mix\n125 126 feminine care\n126 127 body lotions soap\n127 128 tortillas flat bread\n128 129 frozen appetizers sides\n129 130 hot cereal pancake mixes\n130 131 dry pasta\n131 132 beauty\n132 133 muscles joints pain relief\n133 134 specialty wines champagnes\n\n[134 rows x 2 columns] (134, 2) \n\n department_id department\n0 1 frozen\n1 2 other\n2 3 bakery\n3 4 produce\n4 5 alcohol\n5 6 international\n6 7 beverages\n7 8 pets\n8 9 dry goods pasta\n9 10 bulk\n10 11 personal care\n11 12 meat seafood\n12 13 pantry\n13 14 breakfast\n14 15 canned goods\n15 16 dairy eggs\n16 17 household\n17 18 babies\n18 19 snacks\n19 20 deli\n20 21 missing (21, 2) \n\n order_id product_id add_to_cart_order reordered\n0 2 33120 1 1\n1 2 28985 2 1\n2 2 9327 3 0\n3 2 45918 4 1\n4 2 30035 5 0\n5 2 17794 6 1\n6 2 40141 7 1\n7 2 1819 8 1\n8 2 43668 9 0\n9 3 33754 1 1\n10 3 24838 2 1\n11 3 17704 3 1\n12 3 21903 4 1\n13 3 17668 5 1\n14 3 46667 6 1\n15 3 17461 7 1\n16 3 32665 8 1\n17 4 46842 1 0\n18 4 26434 2 1\n19 4 39758 3 1\n20 4 27761 4 1\n21 4 10054 5 1\n22 4 21351 6 1\n23 4 22598 7 1\n24 4 34862 8 1\n25 4 40285 9 1\n26 4 17616 10 1\n27 4 25146 11 1\n28 4 32645 12 1\n29 4 41276 13 1\n... ... ... ... ...\n32434459 3421080 41950 4 0\n32434460 3421080 31717 5 0\n32434461 3421080 12935 6 1\n32434462 3421080 25122 7 0\n32434463 3421080 10667 8 0\n32434464 3421080 38061 9 0\n32434465 3421081 38185 1 0\n32434466 3421081 12218 2 0\n32434467 3421081 32299 3 0\n32434468 3421081 3060 4 0\n32434469 3421081 20539 5 0\n32434470 3421081 35221 6 0\n32434471 3421081 12861 7 0\n32434472 3421082 17279 1 1\n32434473 3421082 12738 2 1\n32434474 3421082 16797 3 0\n32434475 3421082 43352 4 1\n32434476 3421082 32700 5 1\n32434477 3421082 12023 6 0\n32434478 3421082 47941 7 0\n32434479 3421083 7854 1 0\n32434480 3421083 45309 2 0\n32434481 3421083 21162 3 0\n32434482 3421083 18176 4 1\n32434483 3421083 35211 5 0\n32434484 3421083 39678 6 1\n32434485 3421083 11352 7 0\n32434486 3421083 4600 8 0\n32434487 3421083 24852 9 1\n32434488 3421083 5020 10 1\n\n[32434489 rows x 4 columns] (32434489, 4) \n\n order_id product_id add_to_cart_order reordered\n0 1 49302 1 1\n1 1 11109 2 1\n2 1 10246 3 0\n3 1 49683 4 0\n4 1 43633 5 1\n5 1 13176 6 0\n6 1 47209 7 0\n7 1 22035 8 1\n8 36 39612 1 0\n9 36 19660 2 1\n10 36 49235 3 0\n11 36 43086 4 1\n12 36 46620 5 1\n13 36 34497 6 1\n14 36 48679 7 1\n15 36 46979 8 1\n16 38 11913 1 0\n17 38 18159 2 0\n18 38 4461 3 0\n19 38 21616 4 1\n20 38 23622 5 0\n21 38 32433 6 0\n22 38 28842 7 0\n23 38 42625 8 0\n24 38 39693 9 0\n25 96 20574 1 1\n26 96 30391 2 0\n27 96 40706 3 1\n28 96 25610 4 0\n29 96 27966 5 1\n... ... ... ... ...\n1384587 3421026 32237 3 0\n1384588 3421026 10 4 0\n1384589 3421026 4493 5 0\n1384590 3421026 7781 6 0\n1384591 3421049 40800 1 0\n1384592 3421049 17706 2 0\n1384593 3421049 33424 3 1\n1384594 3421049 17299 4 0\n1384595 3421049 26800 5 0\n1384596 3421049 34243 6 0\n1384597 3421056 5750 1 1\n1384598 3421056 9340 2 1\n1384599 3421056 21709 3 1\n1384600 3421056 16475 4 0\n1384601 3421056 12432 5 0\n1384602 3421058 15629 1 1\n1384603 3421058 4347 2 1\n1384604 3421058 34466 3 1\n1384605 3421058 6244 4 1\n1384606 3421058 6858 5 1\n1384607 3421058 30316 6 1\n1384608 3421058 35578 7 0\n1384609 3421058 32650 8 1\n1384610 3421063 49235 1 1\n1384611 3421063 13565 2 1\n1384612 3421063 14233 3 1\n1384613 3421063 35548 4 1\n1384614 3421070 35951 1 1\n1384615 3421070 16953 2 1\n1384616 3421070 4724 3 1\n\n[1384617 rows x 4 columns] (1384617, 4) \n\n order_id user_id eval_set order_number order_dow \\\n0 2539329 1 prior 1 2 \n1 2398795 1 prior 2 3 \n2 473747 1 prior 3 3 \n3 2254736 1 prior 4 4 \n4 431534 1 prior 5 4 \n5 3367565 1 prior 6 2 \n6 550135 1 prior 7 1 \n7 3108588 1 prior 8 1 \n8 2295261 1 prior 9 1 \n9 2550362 1 prior 10 4 \n10 1187899 1 train 11 4 \n11 2168274 2 prior 1 2 \n12 1501582 2 prior 2 5 \n13 1901567 2 prior 3 1 \n14 738281 2 prior 4 2 \n15 1673511 2 prior 5 3 \n16 1199898 2 prior 6 2 \n17 3194192 2 prior 7 2 \n18 788338 2 prior 8 1 \n19 1718559 2 prior 9 2 \n20 1447487 2 prior 10 1 \n21 1402090 2 prior 11 1 \n22 3186735 2 prior 12 1 \n23 3268552 2 prior 13 4 \n24 839880 2 prior 14 3 \n25 1492625 2 train 15 1 \n26 1374495 3 prior 1 1 \n27 444309 3 prior 2 3 \n28 3002854 3 prior 3 3 \n29 2037211 3 prior 4 2 \n... ... ... ... ... ... \n3421053 2789700 206208 prior 35 3 \n3421054 844592 206208 prior 36 6 \n3421055 1541132 206208 prior 37 2 \n3421056 2808240 206208 prior 38 0 \n3421057 3027766 206208 prior 39 2 \n3421058 3356245 206208 prior 40 5 \n3421059 442304 206208 prior 41 2 \n3421060 2675140 206208 prior 42 1 \n3421061 167903 206208 prior 43 4 \n3421062 2393201 206208 prior 44 6 \n3421063 3292671 206208 prior 45 2 \n3421064 3059777 206208 prior 46 1 \n3421065 2239861 206208 prior 47 3 \n3421066 1285346 206208 prior 48 1 \n3421067 1882108 206208 prior 49 1 \n3421068 803273 206208 test 50 5 \n3421069 3154581 206209 prior 1 3 \n3421070 1889163 206209 prior 2 3 \n3421071 1542354 206209 prior 3 5 \n3421072 688306 206209 prior 4 1 \n3421073 2307371 206209 prior 5 4 \n3421074 3186442 206209 prior 6 0 \n3421075 550836 206209 prior 7 2 \n3421076 2129269 206209 prior 8 3 \n3421077 2558525 206209 prior 9 4 \n3421078 2266710 206209 prior 10 5 \n3421079 1854736 206209 prior 11 4 \n3421080 626363 206209 prior 12 1 \n3421081 2977660 206209 prior 13 1 \n3421082 272231 206209 train 14 6 \n\n order_hour_of_day days_since_prior_order \n0 8 NaN \n1 7 15.0 \n2 12 21.0 \n3 7 29.0 \n4 15 28.0 \n5 7 19.0 \n6 9 20.0 \n7 14 14.0 \n8 16 0.0 \n9 8 30.0 \n10 8 14.0 \n11 11 NaN \n12 10 10.0 \n13 10 3.0 \n14 10 8.0 \n15 11 8.0 \n16 9 13.0 \n17 12 14.0 \n18 15 27.0 \n19 9 8.0 \n20 11 6.0 \n21 10 30.0 \n22 9 28.0 \n23 11 30.0 \n24 10 13.0 \n25 11 30.0 \n26 14 NaN \n27 19 9.0 \n28 16 21.0 \n29 18 20.0 \n... ... ... \n3421053 22 4.0 \n3421054 15 10.0 \n3421055 12 3.0 \n3421056 15 19.0 \n3421057 14 9.0 \n3421058 9 10.0 \n3421059 14 11.0 \n3421060 19 6.0 \n3421061 14 3.0 \n3421062 16 2.0 \n3421063 11 3.0 \n3421064 10 13.0 \n3421065 4 9.0 \n3421066 11 5.0 \n3421067 22 7.0 \n3421068 11 4.0 \n3421069 11 NaN \n3421070 17 7.0 \n3421071 11 30.0 \n3421072 10 30.0 \n3421073 15 3.0 \n3421074 16 3.0 \n3421075 13 9.0 \n3421076 17 22.0 \n3421077 15 22.0 \n3421078 18 29.0 \n3421079 10 30.0 \n3421080 12 18.0 \n3421081 12 7.0 \n3421082 14 30.0 \n\n[3421083 rows x 7 columns] (3421083, 7) \n\n product_id product_name \\\n0 1 Chocolate Sandwich Cookies \n1 2 All-Seasons Salt \n2 3 Robust Golden Unsweetened Oolong Tea \n3 4 Smart Ones Classic Favorites Mini Rigatoni Wit... \n4 5 Green Chile Anytime Sauce \n5 6 Dry Nose Oil \n6 7 Pure Coconut Water With Orange \n7 8 Cut Russet Potatoes Steam N' Mash \n8 9 Light Strawberry Blueberry Yogurt \n9 10 Sparkling Orange Juice & Prickly Pear Beverage \n10 11 Peach Mango Juice \n11 12 Chocolate Fudge Layer Cake \n12 13 Saline Nasal Mist \n13 14 Fresh Scent Dishwasher Cleaner \n14 15 Overnight Diapers Size 6 \n15 16 Mint Chocolate Flavored Syrup \n16 17 Rendered Duck Fat \n17 18 Pizza for One Suprema Frozen Pizza \n18 19 Gluten Free Quinoa Three Cheese & Mushroom Blend \n19 20 Pomegranate Cranberry & Aloe Vera Enrich Drink \n20 21 Small & Medium Dental Dog Treats \n21 22 Fresh Breath Oral Rinse Mild Mint \n22 23 Organic Turkey Burgers \n23 24 Tri-Vi-Sol\u00ae Vitamins A-C-and D Supplement Drop... \n24 25 Salted Caramel Lean Protein & Fiber Bar \n25 26 Fancy Feast Trout Feast Flaked Wet Cat Food \n26 27 Complete Spring Water Foaming Antibacterial Ha... \n27 28 Wheat Chex Cereal \n28 29 Fresh Cut Golden Sweet No Salt Added Whole Ker... \n29 30 Three Cheese Ziti, Marinara with Meatballs \n... ... ... \n49658 49659 Organic Creamed Coconut \n49659 49660 Professionals Sleek Shampoo \n49660 49661 Porto \n49661 49662 Bacon Cheddar Pretzel Pieces \n49662 49663 Ultra Protein Power Crunch Peanut Butter N' Ho... \n49663 49664 Lemon Cayenne Drinking Vinegar \n49664 49665 Super Dark Coconut Ash & Banana Chocolate Bar \n49665 49666 Ginger Snaps Snacking Cookies \n49666 49667 Enchilada with Spanish Rice & Beans Meal \n49667 49668 Apple Cinnamon Scented Candles \n49668 49669 K Cup Dark Blend \n49669 49670 Beef Summer Sausage \n49670 49671 Milk Chocolate Drops \n49671 49672 Cafe Mocha K-Cup Packs \n49672 49673 Stone Baked Multi Grain Artisan Rolls \n49673 49674 Frozen Greek Yogurt Bars Chocolate Chip \n49674 49675 Cinnamon Dolce Keurig Brewed K Cups \n49675 49676 Ultra Red Energy Drink \n49676 49677 Thick & Chunky Sloppy Joe Sauce \n49677 49678 Large Chicken & Cheese Taquitos \n49678 49679 Famous Chocolate Wafers \n49679 49680 All Natural Creamy Caesar Dressing \n49680 49681 Spaghetti with Meatballs and Sauce Meal \n49681 49682 California Limeade \n49682 49683 Cucumber Kirby \n49683 49684 Vodka, Triple Distilled, Twist of Vanilla \n49684 49685 En Croute Roast Hazelnut Cranberry \n49685 49686 Artisan Baguette \n49686 49687 Smartblend Healthy Metabolism Dry Cat Food \n49687 49688 Fresh Foaming Cleanser \n\n aisle_id department_id \n0 61 19 \n1 104 13 \n2 94 7 \n3 38 1 \n4 5 13 \n5 11 11 \n6 98 7 \n7 116 1 \n8 120 16 \n9 115 7 \n10 31 7 \n11 119 1 \n12 11 11 \n13 74 17 \n14 56 18 \n15 103 19 \n16 35 12 \n17 79 1 \n18 63 9 \n19 98 7 \n20 40 8 \n21 20 11 \n22 49 12 \n23 47 11 \n24 3 19 \n25 41 8 \n26 127 11 \n27 121 14 \n28 81 15 \n29 38 1 \n... ... ... \n49658 17 13 \n49659 22 11 \n49660 134 5 \n49661 107 19 \n49662 57 14 \n49663 100 21 \n49664 45 19 \n49665 61 19 \n49666 38 1 \n49667 101 17 \n49668 100 21 \n49669 106 12 \n49670 45 19 \n49671 26 7 \n49672 129 1 \n49673 37 1 \n49674 26 7 \n49675 64 7 \n49676 59 15 \n49677 129 1 \n49678 61 19 \n49679 89 13 \n49680 38 1 \n49681 98 7 \n49682 83 4 \n49683 124 5 \n49684 42 1 \n49685 112 3 \n49686 41 8 \n49687 73 11 \n\n[49688 rows x 4 columns] (49688, 4) \n\n"
},
{
"ename": "AttributeError",
"evalue": "'DataFrame' object has no attribute 'name'",
"output_type": "error",
"traceback": [
"---------------------------------------------------------------------------",
"AttributeError Traceback (most recent call last)",
"<ipython-input-10-0ed6abfa8ac1> in <module>()\n 9 '''\n 10 for s in sets:\n---> 11 print (s.name, s.shape, '\\n')\n",
"/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py in __getattr__(self, name)\n 2742 if name in self._info_axis:\n 2743 return self[name]\n-> 2744 return object.__getattribute__(self, name)\n 2745 \n 2746 def __setattr__(self, name, value):\n",
"AttributeError: 'DataFrame' object has no attribute 'name'"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": "aisles, shape = (134, 2)\ndepartments, shape = (21, 2)\norder_products_prior, shape = (32434489, 4)\norder_products_train, shape = (1384617, 4)\norders, shape = (3421083, 7)\nproducts, shape = (49688, 4)\n"
},
{
"name": "stdout",
"output_type": "stream",
"text": "aisles (134, 2)\ndepartments (21, 2)\norder_products_prior (32434489, 4)\norder_products_train (1384617, 4)\norders (3421083, 7)\nproducts (49688, 4)\n"
}
],
"source": [
"sets = [('aisles', aisles), ('departments', departments), ('order_products_prior', order_products_prior), \n",
" ('order_products_train', order_products_train), ('orders', orders), ('products', products)]\n",
"# Observing the dimensions of data sets\n",
"for s in sets:\n",
" print (s[0], s[1].shape)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "ab7feae3-9b6c-5af3-d463-d7a466550e3f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " aisle_id aisle\n0 1 prepared soups salads\n1 2 specialty cheeses\n department_id department\n0 1 frozen\n1 2 other\n order_id product_id add_to_cart_order reordered\n0 2 33120 1 1\n1 2 28985 2 1\n order_id product_id add_to_cart_order reordered\n0 1 49302 1 1\n1 1 11109 2 1\n order_id user_id eval_set order_number order_dow order_hour_of_day \\\n0 2539329 1 prior 1 2 8 \n1 2398795 1 prior 2 3 7 \n\n days_since_prior_order \n0 NaN \n1 15.0 \n product_id product_name aisle_id department_id\n0 1 Chocolate Sandwich Cookies 61 19\n1 2 All-Seasons Salt 104 13\n"
},
{
"name": "stdout",
"output_type": "stream",
"text": " aisle_id aisle\n0 1 prepared soups salads\n1 2 specialty cheeses \n\n department_id department\n0 1 frozen\n1 2 other \n\n order_id product_id add_to_cart_order reordered\n0 2 33120 1 1\n1 2 28985 2 1 \n\n order_id product_id add_to_cart_order reordered\n0 1 49302 1 1\n1 1 11109 2 1 \n\n order_id user_id eval_set order_number order_dow order_hour_of_day \\\n0 2539329 1 prior 1 2 8 \n1 2398795 1 prior 2 3 7 \n\n days_since_prior_order \n0 NaN \n1 15.0 \n\n product_id product_name aisle_id department_id\n0 1 Chocolate Sandwich Cookies 61 19\n1 2 All-Seasons Salt 104 13 \n\n"
},
{
"name": "stdout",
"output_type": "stream",
"text": "aisles \n aisle_id aisle\n0 1 prepared soups salads\n1 2 specialty cheeses \n\ndepartments \n department_id department\n0 1 frozen\n1 2 other \n\norder_products_prior \n order_id product_id add_to_cart_order reordered\n0 2 33120 1 1\n1 2 28985 2 1 \n\norder_products_train \n order_id product_id add_to_cart_order reordered\n0 1 49302 1 1\n1 1 11109 2 1 \n\norders \n order_id user_id eval_set order_number order_dow order_hour_of_day \\\n0 2539329 1 prior 1 2 8 \n1 2398795 1 prior 2 3 7 \n\n days_since_prior_order \n0 NaN \n1 15.0 \n\nproducts \n product_id product_name aisle_id department_id\n0 1 Chocolate Sandwich Cookies 61 19\n1 2 All-Seasons Salt 104 13 \n\n"
}
],
"source": [
"# Observing initial entries of all data sets\n",
"for s in sets:\n",
" print (s[0], '\\n', s[1].head(2), '\\n')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "64e1ce1a-de1a-dd11-5371-02ed506acc1f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "aisles aisle_id <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 10\n10 11\n11 12\n12 13\n13 14\n14 15\n15 16\n16 17\n17 18\n18 19\n19 20\n20 21\n21 22\n22 23\n23 24\n24 25\n25 26\n26 27\n27 28\n28 29\n29 30\n ... \n104 105\n105 106\n106 107\n107 108\n108 109\n109 110\n110 111\n111 112\n112 113\n113 114\n114 115\n115 116\n116 117\n117 118\n118 119\n119 120\n120 121\n121 122\n122 123\n123 124\n124 125\n125 126\n126 127\n127 128\n128 129\n129 130\n130 131\n131 132\n132 133\n133 134\nName: aisle_id, dtype: int64>\naisles aisle <bound method IndexOpsMixin.nunique of 0 prepared soups salads\n1 specialty cheeses\n2 energy granola bars\n3 instant foods\n4 marinades meat preparation\n5 other\n6 packaged meat\n7 bakery desserts\n8 pasta sauce\n9 kitchen supplies\n10 cold flu allergy\n11 fresh pasta\n12 prepared meals\n13 tofu meat alternatives\n14 packaged seafood\n15 fresh herbs\n16 baking ingredients\n17 bulk dried fruits vegetables\n18 oils vinegars\n19 oral hygiene\n20 packaged cheese\n21 hair care\n22 popcorn jerky\n23 fresh fruits\n24 soap\n25 coffee\n26 beers coolers\n27 red wines\n28 honeys syrups nectars\n29 latino foods\n ... \n104 doughs gelatins bake mixes\n105 hot dogs bacon sausage\n106 chips pretzels\n107 other creams cheeses\n108 skin care\n109 pickled goods olives\n110 plates bowls cups flatware\n111 bread\n112 frozen juice\n113 cleaning products\n114 water seltzer sparkling water\n115 frozen produce\n116 nuts seeds dried fruit\n117 first aid\n118 frozen dessert\n119 yogurt\n120 cereal\n121 meat counter\n122 packaged vegetables fruits\n123 spirits\n124 trail mix snack mix\n125 feminine care\n126 body lotions soap\n127 tortillas flat bread\n128 frozen appetizers sides\n129 hot cereal pancake mixes\n130 dry pasta\n131 beauty\n132 muscles joints pain relief\n133 specialty wines champagnes\nName: aisle, dtype: object>\ndepartments department_id <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 10\n10 11\n11 12\n12 13\n13 14\n14 15\n15 16\n16 17\n17 18\n18 19\n19 20\n20 21\nName: department_id, dtype: int64>\ndepartments department <bound method IndexOpsMixin.nunique of 0 frozen\n1 other\n2 bakery\n3 produce\n4 alcohol\n5 international\n6 beverages\n7 pets\n8 dry goods pasta\n9 bulk\n10 personal care\n11 meat seafood\n12 pantry\n13 breakfast\n14 canned goods\n15 dairy eggs\n16 household\n17 babies\n18 snacks\n19 deli\n20 missing\nName: department, dtype: object>\norder_products_prior order_id <bound method IndexOpsMixin.nunique of 0 2\n1 2\n2 2\n3 2\n4 2\n5 2\n6 2\n7 2\n8 2\n9 3\n10 3\n11 3\n12 3\n13 3\n14 3\n15 3\n16 3\n17 4\n18 4\n19 4\n20 4\n21 4\n22 4\n23 4\n24 4\n25 4\n26 4\n27 4\n28 4\n29 4\n ... \n32434459 3421080\n32434460 3421080\n32434461 3421080\n32434462 3421080\n32434463 3421080\n32434464 3421080\n32434465 3421081\n32434466 3421081\n32434467 3421081\n32434468 3421081\n32434469 3421081\n32434470 3421081\n32434471 3421081\n32434472 3421082\n32434473 3421082\n32434474 3421082\n32434475 3421082\n32434476 3421082\n32434477 3421082\n32434478 3421082\n32434479 3421083\n32434480 3421083\n32434481 3421083\n32434482 3421083\n32434483 3421083\n32434484 3421083\n32434485 3421083\n32434486 3421083\n32434487 3421083\n32434488 3421083\nName: order_id, dtype: int64>\norder_products_prior product_id <bound method IndexOpsMixin.nunique of 0 33120\n1 28985\n2 9327\n3 45918\n4 30035\n5 17794\n6 40141\n7 1819\n8 43668\n9 33754\n10 24838\n11 17704\n12 21903\n13 17668\n14 46667\n15 17461\n16 32665\n17 46842\n18 26434\n19 39758\n20 27761\n21 10054\n22 21351\n23 22598\n24 34862\n25 40285\n26 17616\n27 25146\n28 32645\n29 41276\n ... \n32434459 41950\n32434460 31717\n32434461 12935\n32434462 25122\n32434463 10667\n32434464 38061\n32434465 38185\n32434466 12218\n32434467 32299\n32434468 3060\n32434469 20539\n32434470 35221\n32434471 12861\n32434472 17279\n32434473 12738\n32434474 16797\n32434475 43352\n32434476 32700\n32434477 12023\n32434478 47941\n32434479 7854\n32434480 45309\n32434481 21162\n32434482 18176\n32434483 35211\n32434484 39678\n32434485 11352\n32434486 4600\n32434487 24852\n32434488 5020\nName: product_id, dtype: int64>\norder_products_prior add_to_cart_order <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 1\n10 2\n11 3\n12 4\n13 5\n14 6\n15 7\n16 8\n17 1\n18 2\n19 3\n20 4\n21 5\n22 6\n23 7\n24 8\n25 9\n26 10\n27 11\n28 12\n29 13\n ..\n32434459 4\n32434460 5\n32434461 6\n32434462 7\n32434463 8\n32434464 9\n32434465 1\n32434466 2\n32434467 3\n32434468 4\n32434469 5\n32434470 6\n32434471 7\n32434472 1\n32434473 2\n32434474 3\n32434475 4\n32434476 5\n32434477 6\n32434478 7\n32434479 1\n32434480 2\n32434481 3\n32434482 4\n32434483 5\n32434484 6\n32434485 7\n32434486 8\n32434487 9\n32434488 10\nName: add_to_cart_order, dtype: int64>\norder_products_prior reordered <bound method IndexOpsMixin.nunique of 0 1\n1 1\n2 0\n3 1\n4 0\n5 1\n6 1\n7 1\n8 0\n9 1\n10 1\n11 1\n12 1\n13 1\n14 1\n15 1\n16 1\n17 0\n18 1\n19 1\n20 1\n21 1\n22 1\n23 1\n24 1\n25 1\n26 1\n27 1\n28 1\n29 1\n ..\n32434459 0\n32434460 0\n32434461 1\n32434462 0\n32434463 0\n32434464 0\n32434465 0\n32434466 0\n32434467 0\n32434468 0\n32434469 0\n32434470 0\n32434471 0\n32434472 1\n32434473 1\n32434474 0\n32434475 1\n32434476 1\n32434477 0\n32434478 0\n32434479 0\n32434480 0\n32434481 0\n32434482 1\n32434483 0\n32434484 1\n32434485 0\n32434486 0\n32434487 1\n32434488 1\nName: reordered, dtype: int64>\norder_products_train order_id <bound method IndexOpsMixin.nunique of 0 1\n1 1\n2 1\n3 1\n4 1\n5 1\n6 1\n7 1\n8 36\n9 36\n10 36\n11 36\n12 36\n13 36\n14 36\n15 36\n16 38\n17 38\n18 38\n19 38\n20 38\n21 38\n22 38\n23 38\n24 38\n25 96\n26 96\n27 96\n28 96\n29 96\n ... \n1384587 3421026\n1384588 3421026\n1384589 3421026\n1384590 3421026\n1384591 3421049\n1384592 3421049\n1384593 3421049\n1384594 3421049\n1384595 3421049\n1384596 3421049\n1384597 3421056\n1384598 3421056\n1384599 3421056\n1384600 3421056\n1384601 3421056\n1384602 3421058\n1384603 3421058\n1384604 3421058\n1384605 3421058\n1384606 3421058\n1384607 3421058\n1384608 3421058\n1384609 3421058\n1384610 3421063\n1384611 3421063\n1384612 3421063\n1384613 3421063\n1384614 3421070\n1384615 3421070\n1384616 3421070\nName: order_id, dtype: int64>\norder_products_train product_id <bound method IndexOpsMixin.nunique of 0 49302\n1 11109\n2 10246\n3 49683\n4 43633\n5 13176\n6 47209\n7 22035\n8 39612\n9 19660\n10 49235\n11 43086\n12 46620\n13 34497\n14 48679\n15 46979\n16 11913\n17 18159\n18 4461\n19 21616\n20 23622\n21 32433\n22 28842\n23 42625\n24 39693\n25 20574\n26 30391\n27 40706\n28 25610\n29 27966\n ... \n1384587 32237\n1384588 10\n1384589 4493\n1384590 7781\n1384591 40800\n1384592 17706\n1384593 33424\n1384594 17299\n1384595 26800\n1384596 34243\n1384597 5750\n1384598 9340\n1384599 21709\n1384600 16475\n1384601 12432\n1384602 15629\n1384603 4347\n1384604 34466\n1384605 6244\n1384606 6858\n1384607 30316\n1384608 35578\n1384609 32650\n1384610 49235\n1384611 13565\n1384612 14233\n1384613 35548\n1384614 35951\n1384615 16953\n1384616 4724\nName: product_id, dtype: int64>\norder_products_train add_to_cart_order <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 1\n9 2\n10 3\n11 4\n12 5\n13 6\n14 7\n15 8\n16 1\n17 2\n18 3\n19 4\n20 5\n21 6\n22 7\n23 8\n24 9\n25 1\n26 2\n27 3\n28 4\n29 5\n ..\n1384587 3\n1384588 4\n1384589 5\n1384590 6\n1384591 1\n1384592 2\n1384593 3\n1384594 4\n1384595 5\n1384596 6\n1384597 1\n1384598 2\n1384599 3\n1384600 4\n1384601 5\n1384602 1\n1384603 2\n1384604 3\n1384605 4\n1384606 5\n1384607 6\n1384608 7\n1384609 8\n1384610 1\n1384611 2\n1384612 3\n1384613 4\n1384614 1\n1384615 2\n1384616 3\nName: add_to_cart_order, dtype: int64>\norder_products_train reordered <bound method IndexOpsMixin.nunique of 0 1\n1 1\n2 0\n3 0\n4 1\n5 0\n6 0\n7 1\n8 0\n9 1\n10 0\n11 1\n12 1\n13 1\n14 1\n15 1\n16 0\n17 0\n18 0\n19 1\n20 0\n21 0\n22 0\n23 0\n24 0\n25 1\n26 0\n27 1\n28 0\n29 1\n ..\n1384587 0\n1384588 0\n1384589 0\n1384590 0\n1384591 0\n1384592 0\n1384593 1\n1384594 0\n1384595 0\n1384596 0\n1384597 1\n1384598 1\n1384599 1\n1384600 0\n1384601 0\n1384602 1\n1384603 1\n1384604 1\n1384605 1\n1384606 1\n1384607 1\n1384608 0\n1384609 1\n1384610 1\n1384611 1\n1384612 1\n1384613 1\n1384614 1\n1384615 1\n1384616 1\nName: reordered, dtype: int64>\norders order_id <bound method IndexOpsMixin.nunique of 0 2539329\n1 2398795\n2 473747\n3 2254736\n4 431534\n5 3367565\n6 550135\n7 3108588\n8 2295261\n9 2550362\n10 1187899\n11 2168274\n12 1501582\n13 1901567\n14 738281\n15 1673511\n16 1199898\n17 3194192\n18 788338\n19 1718559\n20 1447487\n21 1402090\n22 3186735\n23 3268552\n24 839880\n25 1492625\n26 1374495\n27 444309\n28 3002854\n29 2037211\n ... \n3421053 2789700\n3421054 844592\n3421055 1541132\n3421056 2808240\n3421057 3027766\n3421058 3356245\n3421059 442304\n3421060 2675140\n3421061 167903\n3421062 2393201\n3421063 3292671\n3421064 3059777\n3421065 2239861\n3421066 1285346\n3421067 1882108\n3421068 803273\n3421069 3154581\n3421070 1889163\n3421071 1542354\n3421072 688306\n3421073 2307371\n3421074 3186442\n3421075 550836\n3421076 2129269\n3421077 2558525\n3421078 2266710\n3421079 1854736\n3421080 626363\n3421081 2977660\n3421082 272231\nName: order_id, dtype: int64>\norders user_id <bound method IndexOpsMixin.nunique of 0 1\n1 1\n2 1\n3 1\n4 1\n5 1\n6 1\n7 1\n8 1\n9 1\n10 1\n11 2\n12 2\n13 2\n14 2\n15 2\n16 2\n17 2\n18 2\n19 2\n20 2\n21 2\n22 2\n23 2\n24 2\n25 2\n26 3\n27 3\n28 3\n29 3\n ... \n3421053 206208\n3421054 206208\n3421055 206208\n3421056 206208\n3421057 206208\n3421058 206208\n3421059 206208\n3421060 206208\n3421061 206208\n3421062 206208\n3421063 206208\n3421064 206208\n3421065 206208\n3421066 206208\n3421067 206208\n3421068 206208\n3421069 206209\n3421070 206209\n3421071 206209\n3421072 206209\n3421073 206209\n3421074 206209\n3421075 206209\n3421076 206209\n3421077 206209\n3421078 206209\n3421079 206209\n3421080 206209\n3421081 206209\n3421082 206209\nName: user_id, dtype: int64>\norders eval_set <bound method IndexOpsMixin.nunique of 0 prior\n1 prior\n2 prior\n3 prior\n4 prior\n5 prior\n6 prior\n7 prior\n8 prior\n9 prior\n10 train\n11 prior\n12 prior\n13 prior\n14 prior\n15 prior\n16 prior\n17 prior\n18 prior\n19 prior\n20 prior\n21 prior\n22 prior\n23 prior\n24 prior\n25 train\n26 prior\n27 prior\n28 prior\n29 prior\n ... \n3421053 prior\n3421054 prior\n3421055 prior\n3421056 prior\n3421057 prior\n3421058 prior\n3421059 prior\n3421060 prior\n3421061 prior\n3421062 prior\n3421063 prior\n3421064 prior\n3421065 prior\n3421066 prior\n3421067 prior\n3421068 test\n3421069 prior\n3421070 prior\n3421071 prior\n3421072 prior\n3421073 prior\n3421074 prior\n3421075 prior\n3421076 prior\n3421077 prior\n3421078 prior\n3421079 prior\n3421080 prior\n3421081 prior\n3421082 train\nName: eval_set, dtype: object>\norders order_number <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 10\n10 11\n11 1\n12 2\n13 3\n14 4\n15 5\n16 6\n17 7\n18 8\n19 9\n20 10\n21 11\n22 12\n23 13\n24 14\n25 15\n26 1\n27 2\n28 3\n29 4\n ..\n3421053 35\n3421054 36\n3421055 37\n3421056 38\n3421057 39\n3421058 40\n3421059 41\n3421060 42\n3421061 43\n3421062 44\n3421063 45\n3421064 46\n3421065 47\n3421066 48\n3421067 49\n3421068 50\n3421069 1\n3421070 2\n3421071 3\n3421072 4\n3421073 5\n3421074 6\n3421075 7\n3421076 8\n3421077 9\n3421078 10\n3421079 11\n3421080 12\n3421081 13\n3421082 14\nName: order_number, dtype: int64>\norders order_dow <bound method IndexOpsMixin.nunique of 0 2\n1 3\n2 3\n3 4\n4 4\n5 2\n6 1\n7 1\n8 1\n9 4\n10 4\n11 2\n12 5\n13 1\n14 2\n15 3\n16 2\n17 2\n18 1\n19 2\n20 1\n21 1\n22 1\n23 4\n24 3\n25 1\n26 1\n27 3\n28 3\n29 2\n ..\n3421053 3\n3421054 6\n3421055 2\n3421056 0\n3421057 2\n3421058 5\n3421059 2\n3421060 1\n3421061 4\n3421062 6\n3421063 2\n3421064 1\n3421065 3\n3421066 1\n3421067 1\n3421068 5\n3421069 3\n3421070 3\n3421071 5\n3421072 1\n3421073 4\n3421074 0\n3421075 2\n3421076 3\n3421077 4\n3421078 5\n3421079 4\n3421080 1\n3421081 1\n3421082 6\nName: order_dow, dtype: int64>\norders order_hour_of_day <bound method IndexOpsMixin.nunique of 0 8\n1 7\n2 12\n3 7\n4 15\n5 7\n6 9\n7 14\n8 16\n9 8\n10 8\n11 11\n12 10\n13 10\n14 10\n15 11\n16 9\n17 12\n18 15\n19 9\n20 11\n21 10\n22 9\n23 11\n24 10\n25 11\n26 14\n27 19\n28 16\n29 18\n ..\n3421053 22\n3421054 15\n3421055 12\n3421056 15\n3421057 14\n3421058 9\n3421059 14\n3421060 19\n3421061 14\n3421062 16\n3421063 11\n3421064 10\n3421065 4\n3421066 11\n3421067 22\n3421068 11\n3421069 11\n3421070 17\n3421071 11\n3421072 10\n3421073 15\n3421074 16\n3421075 13\n3421076 17\n3421077 15\n3421078 18\n3421079 10\n3421080 12\n3421081 12\n3421082 14\nName: order_hour_of_day, dtype: int64>\norders days_since_prior_order <bound method IndexOpsMixin.nunique of 0 NaN\n1 15.0\n2 21.0\n3 29.0\n4 28.0\n5 19.0\n6 20.0\n7 14.0\n8 0.0\n9 30.0\n10 14.0\n11 NaN\n12 10.0\n13 3.0\n14 8.0\n15 8.0\n16 13.0\n17 14.0\n18 27.0\n19 8.0\n20 6.0\n21 30.0\n22 28.0\n23 30.0\n24 13.0\n25 30.0\n26 NaN\n27 9.0\n28 21.0\n29 20.0\n ... \n3421053 4.0\n3421054 10.0\n3421055 3.0\n3421056 19.0\n3421057 9.0\n3421058 10.0\n3421059 11.0\n3421060 6.0\n3421061 3.0\n3421062 2.0\n3421063 3.0\n3421064 13.0\n3421065 9.0\n3421066 5.0\n3421067 7.0\n3421068 4.0\n3421069 NaN\n3421070 7.0\n3421071 30.0\n3421072 30.0\n3421073 3.0\n3421074 3.0\n3421075 9.0\n3421076 22.0\n3421077 22.0\n3421078 29.0\n3421079 30.0\n3421080 18.0\n3421081 7.0\n3421082 30.0\nName: days_since_prior_order, dtype: float64>\nproducts product_id <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 10\n10 11\n11 12\n12 13\n13 14\n14 15\n15 16\n16 17\n17 18\n18 19\n19 20\n20 21\n21 22\n22 23\n23 24\n24 25\n25 26\n26 27\n27 28\n28 29\n29 30\n ... \n49658 49659\n49659 49660\n49660 49661\n49661 49662\n49662 49663\n49663 49664\n49664 49665\n49665 49666\n49666 49667\n49667 49668\n49668 49669\n49669 49670\n49670 49671\n49671 49672\n49672 49673\n49673 49674\n49674 49675\n49675 49676\n49676 49677\n49677 49678\n49678 49679\n49679 49680\n49680 49681\n49681 49682\n49682 49683\n49683 49684\n49684 49685\n49685 49686\n49686 49687\n49687 49688\nName: product_id, dtype: int64>\nproducts product_name <bound method IndexOpsMixin.nunique of 0 Chocolate Sandwich Cookies\n1 All-Seasons Salt\n2 Robust Golden Unsweetened Oolong Tea\n3 Smart Ones Classic Favorites Mini Rigatoni Wit...\n4 Green Chile Anytime Sauce\n5 Dry Nose Oil\n6 Pure Coconut Water With Orange\n7 Cut Russet Potatoes Steam N' Mash\n8 Light Strawberry Blueberry Yogurt\n9 Sparkling Orange Juice & Prickly Pear Beverage\n10 Peach Mango Juice\n11 Chocolate Fudge Layer Cake\n12 Saline Nasal Mist\n13 Fresh Scent Dishwasher Cleaner\n14 Overnight Diapers Size 6\n15 Mint Chocolate Flavored Syrup\n16 Rendered Duck Fat\n17 Pizza for One Suprema Frozen Pizza\n18 Gluten Free Quinoa Three Cheese & Mushroom Blend\n19 Pomegranate Cranberry & Aloe Vera Enrich Drink\n20 Small & Medium Dental Dog Treats\n21 Fresh Breath Oral Rinse Mild Mint\n22 Organic Turkey Burgers\n23 Tri-Vi-Sol\u00ae Vitamins A-C-and D Supplement Drop...\n24 Salted Caramel Lean Protein & Fiber Bar\n25 Fancy Feast Trout Feast Flaked Wet Cat Food\n26 Complete Spring Water Foaming Antibacterial Ha...\n27 Wheat Chex Cereal\n28 Fresh Cut Golden Sweet No Salt Added Whole Ker...\n29 Three Cheese Ziti, Marinara with Meatballs\n ... \n49658 Organic Creamed Coconut\n49659 Professionals Sleek Shampoo\n49660 Porto\n49661 Bacon Cheddar Pretzel Pieces\n49662 Ultra Protein Power Crunch Peanut Butter N' Ho...\n49663 Lemon Cayenne Drinking Vinegar\n49664 Super Dark Coconut Ash & Banana Chocolate Bar\n49665 Ginger Snaps Snacking Cookies\n49666 Enchilada with Spanish Rice & Beans Meal\n49667 Apple Cinnamon Scented Candles\n49668 K Cup Dark Blend\n49669 Beef Summer Sausage\n49670 Milk Chocolate Drops\n49671 Cafe Mocha K-Cup Packs\n49672 Stone Baked Multi Grain Artisan Rolls\n49673 Frozen Greek Yogurt Bars Chocolate Chip\n49674 Cinnamon Dolce Keurig Brewed K Cups\n49675 Ultra Red Energy Drink\n49676 Thick & Chunky Sloppy Joe Sauce\n49677 Large Chicken & Cheese Taquitos\n49678 Famous Chocolate Wafers\n49679 All Natural Creamy Caesar Dressing\n49680 Spaghetti with Meatballs and Sauce Meal\n49681 California Limeade\n49682 Cucumber Kirby\n49683 Vodka, Triple Distilled, Twist of Vanilla\n49684 En Croute Roast Hazelnut Cranberry\n49685 Artisan Baguette\n49686 Smartblend Healthy Metabolism Dry Cat Food\n49687 Fresh Foaming Cleanser\nName: product_name, dtype: object>\nproducts aisle_id <bound method IndexOpsMixin.nunique of 0 61\n1 104\n2 94\n3 38\n4 5\n5 11\n6 98\n7 116\n8 120\n9 115\n10 31\n11 119\n12 11\n13 74\n14 56\n15 103\n16 35\n17 79\n18 63\n19 98\n20 40\n21 20\n22 49\n23 47\n24 3\n25 41\n26 127\n27 121\n28 81\n29 38\n ... \n49658 17\n49659 22\n49660 134\n49661 107\n49662 57\n49663 100\n49664 45\n49665 61\n49666 38\n49667 101\n49668 100\n49669 106\n49670 45\n49671 26\n49672 129\n49673 37\n49674 26\n49675 64\n49676 59\n49677 129\n49678 61\n49679 89\n49680 38\n49681 98\n49682 83\n49683 124\n49684 42\n49685 112\n49686 41\n49687 73\nName: aisle_id, dtype: int64>\nproducts department_id <bound method IndexOpsMixin.nunique of 0 19\n1 13\n2 7\n3 1\n4 13\n5 11\n6 7\n7 1\n8 16\n9 7\n10 7\n11 1\n12 11\n13 17\n14 18\n15 19\n16 12\n17 1\n18 9\n19 7\n20 8\n21 11\n22 12\n23 11\n24 19\n25 8\n26 11\n27 14\n28 15\n29 1\n ..\n49658 13\n49659 11\n49660 5\n49661 19\n49662 14\n49663 21\n49664 19\n49665 19\n49666 1\n49667 17\n49668 21\n49669 12\n49670 19\n49671 7\n49672 1\n49673 1\n49674 7\n49675 7\n49676 15\n49677 1\n49678 19\n49679 13\n49680 1\n49681 7\n49682 4\n49683 5\n49684 1\n49685 3\n49686 8\n49687 11\nName: department_id, dtype: int64>\n"
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"text": "aisles aisle_id <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 10\n10 11\n11 12\n12 13\n13 14\n14 15\n15 16\n16 17\n17 18\n18 19\n19 20\n20 21\n21 22\n22 23\n23 24\n24 25\n25 26\n26 27\n27 28\n28 29\n29 30\n ... \n104 105\n105 106\n106 107\n107 108\n108 109\n109 110\n110 111\n111 112\n112 113\n113 114\n114 115\n115 116\n116 117\n117 118\n118 119\n119 120\n120 121\n121 122\n122 123\n123 124\n124 125\n125 126\n126 127\n127 128\n128 129\n129 130\n130 131\n131 132\n132 133\n133 134\nName: aisle_id, dtype: int64>\naisles aisle <bound method IndexOpsMixin.nunique of 0 prepared soups salads\n1 specialty cheeses\n2 energy granola bars\n3 instant foods\n4 marinades meat preparation\n5 other\n6 packaged meat\n7 bakery desserts\n8 pasta sauce\n9 kitchen supplies\n10 cold flu allergy\n11 fresh pasta\n12 prepared meals\n13 tofu meat alternatives\n14 packaged seafood\n15 fresh herbs\n16 baking ingredients\n17 bulk dried fruits vegetables\n18 oils vinegars\n19 oral hygiene\n20 packaged cheese\n21 hair care\n22 popcorn jerky\n23 fresh fruits\n24 soap\n25 coffee\n26 beers coolers\n27 red wines\n28 honeys syrups nectars\n29 latino foods\n ... \n104 doughs gelatins bake mixes\n105 hot dogs bacon sausage\n106 chips pretzels\n107 other creams cheeses\n108 skin care\n109 pickled goods olives\n110 plates bowls cups flatware\n111 bread\n112 frozen juice\n113 cleaning products\n114 water seltzer sparkling water\n115 frozen produce\n116 nuts seeds dried fruit\n117 first aid\n118 frozen dessert\n119 yogurt\n120 cereal\n121 meat counter\n122 packaged vegetables fruits\n123 spirits\n124 trail mix snack mix\n125 feminine care\n126 body lotions soap\n127 tortillas flat bread\n128 frozen appetizers sides\n129 hot cereal pancake mixes\n130 dry pasta\n131 beauty\n132 muscles joints pain relief\n133 specialty wines champagnes\nName: aisle, dtype: object>\ndepartments department_id <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 10\n10 11\n11 12\n12 13\n13 14\n14 15\n15 16\n16 17\n17 18\n18 19\n19 20\n20 21\nName: department_id, dtype: int64>\ndepartments department <bound method IndexOpsMixin.nunique of 0 frozen\n1 other\n2 bakery\n3 produce\n4 alcohol\n5 international\n6 beverages\n7 pets\n8 dry goods pasta\n9 bulk\n10 personal care\n11 meat seafood\n12 pantry\n13 breakfast\n14 canned goods\n15 dairy eggs\n16 household\n17 babies\n18 snacks\n19 deli\n20 missing\nName: department, dtype: object>\norder_products_prior order_id <bound method IndexOpsMixin.nunique of 0 2\n1 2\n2 2\n3 2\n4 2\n5 2\n6 2\n7 2\n8 2\n9 3\n10 3\n11 3\n12 3\n13 3\n14 3\n15 3\n16 3\n17 4\n18 4\n19 4\n20 4\n21 4\n22 4\n23 4\n24 4\n25 4\n26 4\n27 4\n28 4\n29 4\n ... \n32434459 3421080\n32434460 3421080\n32434461 3421080\n32434462 3421080\n32434463 3421080\n32434464 3421080\n32434465 3421081\n32434466 3421081\n32434467 3421081\n32434468 3421081\n32434469 3421081\n32434470 3421081\n32434471 3421081\n32434472 3421082\n32434473 3421082\n32434474 3421082\n32434475 3421082\n32434476 3421082\n32434477 3421082\n32434478 3421082\n32434479 3421083\n32434480 3421083\n32434481 3421083\n32434482 3421083\n32434483 3421083\n32434484 3421083\n32434485 3421083\n32434486 3421083\n32434487 3421083\n32434488 3421083\nName: order_id, dtype: int64>\norder_products_prior product_id <bound method IndexOpsMixin.nunique of 0 33120\n1 28985\n2 9327\n3 45918\n4 30035\n5 17794\n6 40141\n7 1819\n8 43668\n9 33754\n10 24838\n11 17704\n12 21903\n13 17668\n14 46667\n15 17461\n16 32665\n17 46842\n18 26434\n19 39758\n20 27761\n21 10054\n22 21351\n23 22598\n24 34862\n25 40285\n26 17616\n27 25146\n28 32645\n29 41276\n ... \n32434459 41950\n32434460 31717\n32434461 12935\n32434462 25122\n32434463 10667\n32434464 38061\n32434465 38185\n32434466 12218\n32434467 32299\n32434468 3060\n32434469 20539\n32434470 35221\n32434471 12861\n32434472 17279\n32434473 12738\n32434474 16797\n32434475 43352\n32434476 32700\n32434477 12023\n32434478 47941\n32434479 7854\n32434480 45309\n32434481 21162\n32434482 18176\n32434483 35211\n32434484 39678\n32434485 11352\n32434486 4600\n32434487 24852\n32434488 5020\nName: product_id, dtype: int64>\norder_products_prior add_to_cart_order <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 1\n10 2\n11 3\n12 4\n13 5\n14 6\n15 7\n16 8\n17 1\n18 2\n19 3\n20 4\n21 5\n22 6\n23 7\n24 8\n25 9\n26 10\n27 11\n28 12\n29 13\n ..\n32434459 4\n32434460 5\n32434461 6\n32434462 7\n32434463 8\n32434464 9\n32434465 1\n32434466 2\n32434467 3\n32434468 4\n32434469 5\n32434470 6\n32434471 7\n32434472 1\n32434473 2\n32434474 3\n32434475 4\n32434476 5\n32434477 6\n32434478 7\n32434479 1\n32434480 2\n32434481 3\n32434482 4\n32434483 5\n32434484 6\n32434485 7\n32434486 8\n32434487 9\n32434488 10\nName: add_to_cart_order, dtype: int64>\norder_products_prior reordered <bound method IndexOpsMixin.nunique of 0 1\n1 1\n2 0\n3 1\n4 0\n5 1\n6 1\n7 1\n8 0\n9 1\n10 1\n11 1\n12 1\n13 1\n14 1\n15 1\n16 1\n17 0\n18 1\n19 1\n20 1\n21 1\n22 1\n23 1\n24 1\n25 1\n26 1\n27 1\n28 1\n29 1\n ..\n32434459 0\n32434460 0\n32434461 1\n32434462 0\n32434463 0\n32434464 0\n32434465 0\n32434466 0\n32434467 0\n32434468 0\n32434469 0\n32434470 0\n32434471 0\n32434472 1\n32434473 1\n32434474 0\n32434475 1\n32434476 1\n32434477 0\n32434478 0\n32434479 0\n32434480 0\n32434481 0\n32434482 1\n32434483 0\n32434484 1\n32434485 0\n32434486 0\n32434487 1\n32434488 1\nName: reordered, dtype: int64>\norder_products_train order_id <bound method IndexOpsMixin.nunique of 0 1\n1 1\n2 1\n3 1\n4 1\n5 1\n6 1\n7 1\n8 36\n9 36\n10 36\n11 36\n12 36\n13 36\n14 36\n15 36\n16 38\n17 38\n18 38\n19 38\n20 38\n21 38\n22 38\n23 38\n24 38\n25 96\n26 96\n27 96\n28 96\n29 96\n ... \n1384587 3421026\n1384588 3421026\n1384589 3421026\n1384590 3421026\n1384591 3421049\n1384592 3421049\n1384593 3421049\n1384594 3421049\n1384595 3421049\n1384596 3421049\n1384597 3421056\n1384598 3421056\n1384599 3421056\n1384600 3421056\n1384601 3421056\n1384602 3421058\n1384603 3421058\n1384604 3421058\n1384605 3421058\n1384606 3421058\n1384607 3421058\n1384608 3421058\n1384609 3421058\n1384610 3421063\n1384611 3421063\n1384612 3421063\n1384613 3421063\n1384614 3421070\n1384615 3421070\n1384616 3421070\nName: order_id, dtype: int64>\norder_products_train product_id <bound method IndexOpsMixin.nunique of 0 49302\n1 11109\n2 10246\n3 49683\n4 43633\n5 13176\n6 47209\n7 22035\n8 39612\n9 19660\n10 49235\n11 43086\n12 46620\n13 34497\n14 48679\n15 46979\n16 11913\n17 18159\n18 4461\n19 21616\n20 23622\n21 32433\n22 28842\n23 42625\n24 39693\n25 20574\n26 30391\n27 40706\n28 25610\n29 27966\n ... \n1384587 32237\n1384588 10\n1384589 4493\n1384590 7781\n1384591 40800\n1384592 17706\n1384593 33424\n1384594 17299\n1384595 26800\n1384596 34243\n1384597 5750\n1384598 9340\n1384599 21709\n1384600 16475\n1384601 12432\n1384602 15629\n1384603 4347\n1384604 34466\n1384605 6244\n1384606 6858\n1384607 30316\n1384608 35578\n1384609 32650\n1384610 49235\n1384611 13565\n1384612 14233\n1384613 35548\n1384614 35951\n1384615 16953\n1384616 4724\nName: product_id, dtype: int64>\norder_products_train add_to_cart_order <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 1\n9 2\n10 3\n11 4\n12 5\n13 6\n14 7\n15 8\n16 1\n17 2\n18 3\n19 4\n20 5\n21 6\n22 7\n23 8\n24 9\n25 1\n26 2\n27 3\n28 4\n29 5\n ..\n1384587 3\n1384588 4\n1384589 5\n1384590 6\n1384591 1\n1384592 2\n1384593 3\n1384594 4\n1384595 5\n1384596 6\n1384597 1\n1384598 2\n1384599 3\n1384600 4\n1384601 5\n1384602 1\n1384603 2\n1384604 3\n1384605 4\n1384606 5\n1384607 6\n1384608 7\n1384609 8\n1384610 1\n1384611 2\n1384612 3\n1384613 4\n1384614 1\n1384615 2\n1384616 3\nName: add_to_cart_order, dtype: int64>\norder_products_train reordered <bound method IndexOpsMixin.nunique of 0 1\n1 1\n2 0\n3 0\n4 1\n5 0\n6 0\n7 1\n8 0\n9 1\n10 0\n11 1\n12 1\n13 1\n14 1\n15 1\n16 0\n17 0\n18 0\n19 1\n20 0\n21 0\n22 0\n23 0\n24 0\n25 1\n26 0\n27 1\n28 0\n29 1\n ..\n1384587 0\n1384588 0\n1384589 0\n1384590 0\n1384591 0\n1384592 0\n1384593 1\n1384594 0\n1384595 0\n1384596 0\n1384597 1\n1384598 1\n1384599 1\n1384600 0\n1384601 0\n1384602 1\n1384603 1\n1384604 1\n1384605 1\n1384606 1\n1384607 1\n1384608 0\n1384609 1\n1384610 1\n1384611 1\n1384612 1\n1384613 1\n1384614 1\n1384615 1\n1384616 1\nName: reordered, dtype: int64>\norders order_id <bound method IndexOpsMixin.nunique of 0 2539329\n1 2398795\n2 473747\n3 2254736\n4 431534\n5 3367565\n6 550135\n7 3108588\n8 2295261\n9 2550362\n10 1187899\n11 2168274\n12 1501582\n13 1901567\n14 738281\n15 1673511\n16 1199898\n17 3194192\n18 788338\n19 1718559\n20 1447487\n21 1402090\n22 3186735\n23 3268552\n24 839880\n25 1492625\n26 1374495\n27 444309\n28 3002854\n29 2037211\n ... \n3421053 2789700\n3421054 844592\n3421055 1541132\n3421056 2808240\n3421057 3027766\n3421058 3356245\n3421059 442304\n3421060 2675140\n3421061 167903\n3421062 2393201\n3421063 3292671\n3421064 3059777\n3421065 2239861\n3421066 1285346\n3421067 1882108\n3421068 803273\n3421069 3154581\n3421070 1889163\n3421071 1542354\n3421072 688306\n3421073 2307371\n3421074 3186442\n3421075 550836\n3421076 2129269\n3421077 2558525\n3421078 2266710\n3421079 1854736\n3421080 626363\n3421081 2977660\n3421082 272231\nName: order_id, dtype: int64>\norders user_id <bound method IndexOpsMixin.nunique of 0 1\n1 1\n2 1\n3 1\n4 1\n5 1\n6 1\n7 1\n8 1\n9 1\n10 1\n11 2\n12 2\n13 2\n14 2\n15 2\n16 2\n17 2\n18 2\n19 2\n20 2\n21 2\n22 2\n23 2\n24 2\n25 2\n26 3\n27 3\n28 3\n29 3\n ... \n3421053 206208\n3421054 206208\n3421055 206208\n3421056 206208\n3421057 206208\n3421058 206208\n3421059 206208\n3421060 206208\n3421061 206208\n3421062 206208\n3421063 206208\n3421064 206208\n3421065 206208\n3421066 206208\n3421067 206208\n3421068 206208\n3421069 206209\n3421070 206209\n3421071 206209\n3421072 206209\n3421073 206209\n3421074 206209\n3421075 206209\n3421076 206209\n3421077 206209\n3421078 206209\n3421079 206209\n3421080 206209\n3421081 206209\n3421082 206209\nName: user_id, dtype: int64>\norders eval_set <bound method IndexOpsMixin.nunique of 0 prior\n1 prior\n2 prior\n3 prior\n4 prior\n5 prior\n6 prior\n7 prior\n8 prior\n9 prior\n10 train\n11 prior\n12 prior\n13 prior\n14 prior\n15 prior\n16 prior\n17 prior\n18 prior\n19 prior\n20 prior\n21 prior\n22 prior\n23 prior\n24 prior\n25 train\n26 prior\n27 prior\n28 prior\n29 prior\n ... \n3421053 prior\n3421054 prior\n3421055 prior\n3421056 prior\n3421057 prior\n3421058 prior\n3421059 prior\n3421060 prior\n3421061 prior\n3421062 prior\n3421063 prior\n3421064 prior\n3421065 prior\n3421066 prior\n3421067 prior\n3421068 test\n3421069 prior\n3421070 prior\n3421071 prior\n3421072 prior\n3421073 prior\n3421074 prior\n3421075 prior\n3421076 prior\n3421077 prior\n3421078 prior\n3421079 prior\n3421080 prior\n3421081 prior\n3421082 train\nName: eval_set, dtype: object>\norders order_number <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 10\n10 11\n11 1\n12 2\n13 3\n14 4\n15 5\n16 6\n17 7\n18 8\n19 9\n20 10\n21 11\n22 12\n23 13\n24 14\n25 15\n26 1\n27 2\n28 3\n29 4\n ..\n3421053 35\n3421054 36\n3421055 37\n3421056 38\n3421057 39\n3421058 40\n3421059 41\n3421060 42\n3421061 43\n3421062 44\n3421063 45\n3421064 46\n3421065 47\n3421066 48\n3421067 49\n3421068 50\n3421069 1\n3421070 2\n3421071 3\n3421072 4\n3421073 5\n3421074 6\n3421075 7\n3421076 8\n3421077 9\n3421078 10\n3421079 11\n3421080 12\n3421081 13\n3421082 14\nName: order_number, dtype: int64>\norders order_dow <bound method IndexOpsMixin.nunique of 0 2\n1 3\n2 3\n3 4\n4 4\n5 2\n6 1\n7 1\n8 1\n9 4\n10 4\n11 2\n12 5\n13 1\n14 2\n15 3\n16 2\n17 2\n18 1\n19 2\n20 1\n21 1\n22 1\n23 4\n24 3\n25 1\n26 1\n27 3\n28 3\n29 2\n ..\n3421053 3\n3421054 6\n3421055 2\n3421056 0\n3421057 2\n3421058 5\n3421059 2\n3421060 1\n3421061 4\n3421062 6\n3421063 2\n3421064 1\n3421065 3\n3421066 1\n3421067 1\n3421068 5\n3421069 3\n3421070 3\n3421071 5\n3421072 1\n3421073 4\n3421074 0\n3421075 2\n3421076 3\n3421077 4\n3421078 5\n3421079 4\n3421080 1\n3421081 1\n3421082 6\nName: order_dow, dtype: int64>\norders order_hour_of_day <bound method IndexOpsMixin.nunique of 0 8\n1 7\n2 12\n3 7\n4 15\n5 7\n6 9\n7 14\n8 16\n9 8\n10 8\n11 11\n12 10\n13 10\n14 10\n15 11\n16 9\n17 12\n18 15\n19 9\n20 11\n21 10\n22 9\n23 11\n24 10\n25 11\n26 14\n27 19\n28 16\n29 18\n ..\n3421053 22\n3421054 15\n3421055 12\n3421056 15\n3421057 14\n3421058 9\n3421059 14\n3421060 19\n3421061 14\n3421062 16\n3421063 11\n3421064 10\n3421065 4\n3421066 11\n3421067 22\n3421068 11\n3421069 11\n3421070 17\n3421071 11\n3421072 10\n3421073 15\n3421074 16\n3421075 13\n3421076 17\n3421077 15\n3421078 18\n3421079 10\n3421080 12\n3421081 12\n3421082 14\nName: order_hour_of_day, dtype: int64>\norders days_since_prior_order <bound method IndexOpsMixin.nunique of 0 NaN\n1 15.0\n2 21.0\n3 29.0\n4 28.0\n5 19.0\n6 20.0\n7 14.0\n8 0.0\n9 30.0\n10 14.0\n11 NaN\n12 10.0\n13 3.0\n14 8.0\n15 8.0\n16 13.0\n17 14.0\n18 27.0\n19 8.0\n20 6.0\n21 30.0\n22 28.0\n23 30.0\n24 13.0\n25 30.0\n26 NaN\n27 9.0\n28 21.0\n29 20.0\n ... \n3421053 4.0\n3421054 10.0\n3421055 3.0\n3421056 19.0\n3421057 9.0\n3421058 10.0\n3421059 11.0\n3421060 6.0\n3421061 3.0\n3421062 2.0\n3421063 3.0\n3421064 13.0\n3421065 9.0\n3421066 5.0\n3421067 7.0\n3421068 4.0\n3421069 NaN\n3421070 7.0\n3421071 30.0\n3421072 30.0\n3421073 3.0\n3421074 3.0\n3421075 9.0\n3421076 22.0\n3421077 22.0\n3421078 29.0\n3421079 30.0\n3421080 18.0\n3421081 7.0\n3421082 30.0\nName: days_since_prior_order, dtype: float64>\nproducts product_id <bound method IndexOpsMixin.nunique of 0 1\n1 2\n2 3\n3 4\n4 5\n5 6\n6 7\n7 8\n8 9\n9 10\n10 11\n11 12\n12 13\n13 14\n14 15\n15 16\n16 17\n17 18\n18 19\n19 20\n20 21\n21 22\n22 23\n23 24\n24 25\n25 26\n26 27\n27 28\n28 29\n29 30\n ... \n49658 49659\n49659 49660\n49660 49661\n49661 49662\n49662 49663\n49663 49664\n49664 49665\n49665 49666\n49666 49667\n49667 49668\n49668 49669\n49669 49670\n49670 49671\n49671 49672\n49672 49673\n49673 49674\n49674 49675\n49675 49676\n49676 49677\n49677 49678\n49678 49679\n49679 49680\n49680 49681\n49681 49682\n49682 49683\n49683 49684\n49684 49685\n49685 49686\n49686 49687\n49687 49688\nName: product_id, dtype: int64>\nproducts product_name <bound method IndexOpsMixin.nunique of 0 Chocolate Sandwich Cookies\n1 All-Seasons Salt\n2 Robust Golden Unsweetened Oolong Tea\n3 Smart Ones Classic Favorites Mini Rigatoni Wit...\n4 Green Chile Anytime Sauce\n5 Dry Nose Oil\n6 Pure Coconut Water With Orange\n7 Cut Russet Potatoes Steam N' Mash\n8 Light Strawberry Blueberry Yogurt\n9 Sparkling Orange Juice & Prickly Pear Beverage\n10 Peach Mango Juice\n11 Chocolate Fudge Layer Cake\n12 Saline Nasal Mist\n13 Fresh Scent Dishwasher Cleaner\n14 Overnight Diapers Size 6\n15 Mint Chocolate Flavored Syrup\n16 Rendered Duck Fat\n17 Pizza for One Suprema Frozen Pizza\n18 Gluten Free Quinoa Three Cheese & Mushroom Blend\n19 Pomegranate Cranberry & Aloe Vera Enrich Drink\n20 Small & Medium Dental Dog Treats\n21 Fresh Breath Oral Rinse Mild Mint\n22 Organic Turkey Burgers\n23 Tri-Vi-Sol\u00ae Vitamins A-C-and D Supplement Drop...\n24 Salted Caramel Lean Protein & Fiber Bar\n25 Fancy Feast Trout Feast Flaked Wet Cat Food\n26 Complete Spring Water Foaming Antibacterial Ha...\n27 Wheat Chex Cereal\n28 Fresh Cut Golden Sweet No Salt Added Whole Ker...\n29 Three Cheese Ziti, Marinara with Meatballs\n ... \n49658 Organic Creamed Coconut\n49659 Professionals Sleek Shampoo\n49660 Porto\n49661 Bacon Cheddar Pretzel Pieces\n49662 Ultra Protein Power Crunch Peanut Butter N' Ho...\n49663 Lemon Cayenne Drinking Vinegar\n49664 Super Dark Coconut Ash & Banana Chocolate Bar\n49665 Ginger Snaps Snacking Cookies\n49666 Enchilada with Spanish Rice & Beans Meal\n49667 Apple Cinnamon Scented Candles\n49668 K Cup Dark Blend\n49669 Beef Summer Sausage\n49670 Milk Chocolate Drops\n49671 Cafe Mocha K-Cup Packs\n49672 Stone Baked Multi Grain Artisan Rolls\n49673 Frozen Greek Yogurt Bars Chocolate Chip\n49674 Cinnamon Dolce Keurig Brewed K Cups\n49675 Ultra Red Energy Drink\n49676 Thick & Chunky Sloppy Joe Sauce\n49677 Large Chicken & Cheese Taquitos\n49678 Famous Chocolate Wafers\n49679 All Natural Creamy Caesar Dressing\n49680 Spaghetti with Meatballs and Sauce Meal\n49681 California Limeade\n49682 Cucumber Kirby\n49683 Vodka, Triple Distilled, Twist of Vanilla\n49684 En Croute Roast Hazelnut Cranberry\n49685 Artisan Baguette\n49686 Smartblend Healthy Metabolism Dry Cat Food\n49687 Fresh Foaming Cleanser\nName: product_name, dtype: object>\nproducts aisle_id <bound method IndexOpsMixin.nunique of 0 61\n1 104\n2 94\n3 38\n4 5\n5 11\n6 98\n7 116\n8 120\n9 115\n10 31\n11 119\n12 11\n13 74\n14 56\n15 103\n16 35\n17 79\n18 63\n19 98\n20 40\n21 20\n22 49\n23 47\n24 3\n25 41\n26 127\n27 121\n28 81\n29 38\n ... \n49658 17\n49659 22\n49660 134\n49661 107\n49662 57\n49663 100\n49664 45\n49665 61\n49666 38\n49667 101\n49668 100\n49669 106\n49670 45\n49671 26\n49672 129\n49673 37\n49674 26\n49675 64\n49676 59\n49677 129\n49678 61\n49679 89\n49680 38\n49681 98\n49682 83\n49683 124\n49684 42\n49685 112\n49686 41\n49687 73\nName: aisle_id, dtype: int64>\nproducts department_id <bound method IndexOpsMixin.nunique of 0 19\n1 13\n2 7\n3 1\n4 13\n5 11\n6 7\n7 1\n8 16\n9 7\n10 7\n11 1\n12 11\n13 17\n14 18\n15 19\n16 12\n17 1\n18 9\n19 7\n20 8\n21 11\n22 12\n23 11\n24 19\n25 8\n26 11\n27 14\n28 15\n29 1\n ..\n49658 13\n49659 11\n49660 5\n49661 19\n49662 14\n49663 21\n49664 19\n49665 19\n49666 1\n49667 17\n49668 21\n49669 12\n49670 19\n49671 7\n49672 1\n49673 1\n49674 7\n49675 7\n49676 15\n49677 1\n49678 19\n49679 13\n49680 1\n49681 7\n49682 4\n49683 5\n49684 1\n49685 3\n49686 8\n49687 11\nName: department_id, dtype: int64>\n"
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"text": "aisles aisle_id 134\naisles aisle 134\ndepartments department_id 21\ndepartments department 21\n"
},
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"name": "stdout",
"output_type": "stream",
"text": "order_products_prior order_id 3214874\n"
},
{
"name": "stdout",
"output_type": "stream",
"text": "order_products_prior product_id 49677\norder_products_prior add_to_cart_order 145\n"
},
{
"name": "stdout",
"output_type": "stream",
"text": "order_products_prior reordered 2\norder_products_train order_id 131209\norder_products_train product_id 39123\norder_products_train add_to_cart_order 80\norder_products_train reordered 2\n"
},
{
"name": "stdout",
"output_type": "stream",
"text": "orders order_id 3421083\norders user_id 206209\n"
},
{
"name": "stdout",
"output_type": "stream",
"text": "orders eval_set 3\norders order_number 100\norders order_dow 7\norders order_hour_of_day 24\norders days_since_prior_order 31\nproducts product_id 49688\nproducts product_name 49688\nproducts aisle_id 134\nproducts department_id 21\n"
}
],
"source": [
"# Number of unique values in each column\n",
"for s in sets:\n",
" for col in list(s[1].columns):\n",
" print (s[0], col, s[1][col].nunique())"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "21824710-06d2-8cb5-7ba8-573f895821e6"
},
"source": [
"# Joining the data frames"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a053ba65-ab3d-67f9-854f-3210e1d25fd1"
},
"source": [
"## Joining products, their aisles and departments"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "07773bbd-c6d7-d884-7184-f3f0074d8d4b"
},
"outputs": [
{
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>product_id</th>\n <th>product_name</th>\n <th>aisle_id</th>\n <th>department_id</th>\n <th>aisle</th>\n <th>department</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>Chocolate Sandwich Cookies</td>\n <td>61</td>\n <td>19</td>\n <td>cookies cakes</td>\n <td>snacks</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>All-Seasons Salt</td>\n <td>104</td>\n <td>13</td>\n <td>spices seasonings</td>\n <td>pantry</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>Robust Golden Unsweetened Oolong Tea</td>\n <td>94</td>\n <td>7</td>\n <td>tea</td>\n <td>beverages</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>Smart Ones Classic Favorites Mini Rigatoni Wit...</td>\n <td>38</td>\n <td>1</td>\n <td>frozen meals</td>\n <td>frozen</td>\n </tr>\n <tr>\n <th>4</th>\n <td>5</td>\n <td>Green Chile Anytime Sauce</td>\n <td>5</td>\n <td>13</td>\n <td>marinades meat preparation</td>\n <td>pantry</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " product_id product_name aisle_id \\\n0 1 Chocolate Sandwich Cookies 61 \n1 2 All-Seasons Salt 104 \n2 3 Robust Golden Unsweetened Oolong Tea 94 \n3 4 Smart Ones Classic Favorites Mini Rigatoni Wit... 38 \n4 5 Green Chile Anytime Sauce 5 \n\n department_id aisle department \n0 19 cookies cakes snacks \n1 13 spices seasonings pantry \n2 7 tea beverages \n3 1 frozen meals frozen \n4 13 marinades meat preparation pantry "
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products = pd.merge(left = products, right = aisles, on = 'aisle_id', how = 'left')\n",
"products = pd.merge(left = products, right = departments, on = 'department_id', how = 'left')\n",
"products.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1334708c-abf7-b25c-a1b4-26338b008b8b"
},
"source": [
"## Joining the above data set with orders"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "cb5b7bd0-242e-ab46-0772-c649c9a15613"
},
"outputs": [],
"source": [
"order_products_prior = pd.merge(left = order_products_prior, right = products, on = 'product_id', how = 'left')\n",
"order_products_train = pd.merge(left = order_products_train, right = products, on = 'product_id', how = 'left')\n",
"orders = pd.merge(left = orders, right = order_products_prior, on = 'order_id', how = 'left')\n",
"orders = pd.merge(left = orders, right = order_products_train, on = 'order_id', how = 'left')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "584e60bb-1617-6dc0-1e34-b041540a670d"
},
"outputs": [],
"source": [
"orders.head(5)"
]
}
],
"metadata": {
"_change_revision": 179,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | 0001/163/1163436.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8ad618db-cd05-e3dd-aa92-823a3927a752"
},
"source": [
"Hi, everyone. I'm Thomas Colthurst, and I work on a random forest implementation called TensorForest. As the name suggests, TensorForest is built on top of TensorFlow, which makes it easy to use all the goodies that TensorFlow provides (feature preprocessing, distributed training, etc.). You can find out more about TensorForest by reading our [NIPS 2017 paper][1].\n",
"\n",
"This is a simple example to show you how to use TensorForest on the Iris classification task. (TensorForest also works for regression problems, but this won't cover that.)\n",
"\n",
"First, let's load the data:\n",
"\n",
" [1]: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxtbHN5c25pcHMyMDE2fGd4OjFlNTRiOWU2OGM2YzA4MjE"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a42958c9-7e07-f0ad-73a0-1c00ee992093"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training = \n",
" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"0 1 5.1 3.5 1.4 0.2 Iris-setosa\n",
"2 3 4.7 3.2 1.3 0.2 Iris-setosa\n",
"4 5 5.0 3.6 1.4 0.2 Iris-setosa\n",
"6 7 4.6 3.4 1.4 0.3 Iris-setosa\n",
"8 9 4.4 2.9 1.4 0.2 Iris-setosa\n",
"Test = \n",
" Id SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm Species\n",
"1 2 4.9 3.0 1.4 0.2 Iris-setosa\n",
"3 4 4.6 3.1 1.5 0.2 Iris-setosa\n",
"5 6 5.4 3.9 1.7 0.4 Iris-setosa\n",
"7 8 5.0 3.4 1.5 0.2 Iris-setosa\n",
"9 10 4.9 3.1 1.5 0.1 Iris-setosa\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import pandas as pd\n",
"import math\n",
"import os\n",
"from glob import glob\n",
"\n",
"tf.logging.set_verbosity(tf.logging.DEBUG)\n",
"\n",
"all_data = pd.read_csv(\"../input/Iris.csv\")\n",
"\n",
"train = all_data[::2]\n",
"test = all_data[1::2]\n",
"\n",
"print(\"Training = \")\n",
"print(train[:5])\n",
"\n",
"print(\"Test = \")\n",
"print(test[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "19d8211b-3e26-eaf9-a816-aa860d201885"
},
"source": [
"I split the data into halves for training and test. Next, let's split the training data into features and labels:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "04b55bda-37f3-7bd3-08e2-16f75cc99aca"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training features =\n",
"[[ 5.0999999 3.5 1.39999998 0.2 ]\n",
" [ 4.69999981 3.20000005 1.29999995 0.2 ]\n",
" [ 5. 3.5999999 1.39999998 0.2 ]\n",
" [ 4.5999999 3.4000001 1.39999998 0.30000001]\n",
" [ 4.4000001 2.9000001 1.39999998 0.2 ]]\n",
"Training labels =\n",
"[ 0. 0. 0. 0. 0.]\n"
]
}
],
"source": [
"x_train = train.drop(['Species', 'Id'], axis=1).astype(np.float32).values\n",
"label_map = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}\n",
"y_train = train['Species'].map(label_map).astype(np.float32).values\n",
"\n",
"print(\"Training features =\")\n",
"print(x_train[:5])\n",
"print(\"Training labels =\")\n",
"print(y_train[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "51d571e6-6feb-4fe6-98e7-e8dd23bd67da"
},
"source": [
"Great, now let's configure the forest. We need to specify how many classes there are, how many features, how many trees we want, and the maximum size of those trees. TensorForest will intelligently set a bunch of other training parameters based on those values. In this example, we choose to override one of those -- we say we want to split any node once it has seen 50 examples."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "e4b8772a-e8f2-490e-1018-ee2510cb13e4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Params =\n",
"{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 4, 'max_fertile_nodes': 500, 'split_after_samples': 50, 'min_split_samples': 5, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'num_classes': 3, 'num_features': 4, 'bagged_num_features': 4, 'bagged_features': None, 'regression': False, 'num_outputs': 1, 'num_output_columns': 4, 'split_initializations_per_input': 1, 'base_random_seed': 0}\n"
]
}
],
"source": [
"params = tf.contrib.tensor_forest.python.tensor_forest.ForestHParams(\n",
" num_classes=3, num_features=4, num_trees=50, max_nodes=1000, split_after_samples=50).fill()\n",
"\n",
"print(\"Params =\")\n",
"print(vars(params))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "41bcdffc-c9e4-2414-c9fa-82bdbd0ef11a"
},
"source": [
"Everything is set up, so it's time to train the forest:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "cb3cc9b7-b008-ed61-914e-1e6e8092b48c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Using default config.\n",
"INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f9851cb2eb8>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_tf_config': gpu_options {\n",
" per_process_gpu_memory_fraction: 1.0\n",
"}\n",
", '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': None}\n",
"WARNING:tensorflow:From <ipython-input-4-3c2584996fd3>:6: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
"Instructions for updating:\n",
"Estimator is decoupled from Scikit Learn interface by moving into\n",
"separate class SKCompat. Arguments x, y and batch_size are only\n",
"available in the SKCompat class, Estimator will only accept input_fn.\n",
"Example conversion:\n",
" est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
"WARNING:tensorflow:From <ipython-input-4-3c2584996fd3>:6: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
"Instructions for updating:\n",
"Estimator is decoupled from Scikit Learn interface by moving into\n",
"separate class SKCompat. Arguments x, y and batch_size are only\n",
"available in the SKCompat class, Estimator will only accept input_fn.\n",
"Example conversion:\n",
" est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
"DEBUG:tensorflow:Setting feature info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None), Dimension(4)]), is_sparse=False).\n",
"DEBUG:tensorflow:Setting labels info to TensorSignature(dtype=tf.float32, shape=TensorShape([Dimension(None)]), is_sparse=False)\n",
"INFO:tensorflow:Constructing forest with params = \n",
"INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 4, 'max_fertile_nodes': 500, 'split_after_samples': 50, 'min_split_samples': 5, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'num_classes': 3, 'num_features': 4, 'bagged_num_features': 4, 'bagged_features': None, 'regression': False, 'num_outputs': 1, 'num_output_columns': 4, 'split_initializations_per_input': 1, 'base_random_seed': 0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:248: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
" equality = a == b\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:training graph for tree: 0\n",
"INFO:tensorflow:training graph for tree: 1\n",
"INFO:tensorflow:training graph for tree: 2\n",
"INFO:tensorflow:training graph for tree: 3\n",
"INFO:tensorflow:training graph for tree: 4\n",
"INFO:tensorflow:training graph for tree: 5\n",
"INFO:tensorflow:training graph for tree: 6\n",
"INFO:tensorflow:training graph for tree: 7\n",
"INFO:tensorflow:training graph for tree: 8\n",
"INFO:tensorflow:training graph for tree: 9\n",
"INFO:tensorflow:training graph for tree: 10\n",
"INFO:tensorflow:training graph for tree: 11\n",
"INFO:tensorflow:training graph for tree: 12\n",
"INFO:tensorflow:training graph for tree: 13\n",
"INFO:tensorflow:training graph for tree: 14\n",
"INFO:tensorflow:training graph for tree: 15\n",
"INFO:tensorflow:training graph for tree: 16\n",
"INFO:tensorflow:training graph for tree: 17\n",
"INFO:tensorflow:training graph for tree: 18\n",
"INFO:tensorflow:training graph for tree: 19\n",
"INFO:tensorflow:training graph for tree: 20\n",
"INFO:tensorflow:training graph for tree: 21\n",
"INFO:tensorflow:training graph for tree: 22\n",
"INFO:tensorflow:training graph for tree: 23\n",
"INFO:tensorflow:training graph for tree: 24\n",
"INFO:tensorflow:training graph for tree: 25\n",
"INFO:tensorflow:training graph for tree: 26\n",
"INFO:tensorflow:training graph for tree: 27\n",
"INFO:tensorflow:training graph for tree: 28\n",
"INFO:tensorflow:training graph for tree: 29\n",
"INFO:tensorflow:training graph for tree: 30\n",
"INFO:tensorflow:training graph for tree: 31\n",
"INFO:tensorflow:training graph for tree: 32\n",
"INFO:tensorflow:training graph for tree: 33\n",
"INFO:tensorflow:training graph for tree: 34\n",
"INFO:tensorflow:training graph for tree: 35\n",
"INFO:tensorflow:training graph for tree: 36\n",
"INFO:tensorflow:training graph for tree: 37\n",
"INFO:tensorflow:training graph for tree: 38\n",
"INFO:tensorflow:training graph for tree: 39\n",
"INFO:tensorflow:training graph for tree: 40\n",
"INFO:tensorflow:training graph for tree: 41\n",
"INFO:tensorflow:training graph for tree: 42\n",
"INFO:tensorflow:training graph for tree: 43\n",
"INFO:tensorflow:training graph for tree: 44\n",
"INFO:tensorflow:training graph for tree: 45\n",
"INFO:tensorflow:training graph for tree: 46\n",
"INFO:tensorflow:training graph for tree: 47\n",
"INFO:tensorflow:training graph for tree: 48\n",
"INFO:tensorflow:training graph for tree: 49\n",
"INFO:tensorflow:Create CheckpointSaverHook.\n",
"INFO:tensorflow:Saving checkpoints for 1 into ./model.ckpt.\n",
"INFO:tensorflow:loss = -0.0, step = 1\n",
"INFO:tensorflow:TensorForestLossHook resetting last_step.\n",
"INFO:tensorflow:global_step/sec: 41.8541\n",
"INFO:tensorflow:loss = -210.12, step = 101 (2.397 sec)\n",
"INFO:tensorflow:global_step/sec: 43.7869\n",
"INFO:tensorflow:loss = -468.8, step = 201 (2.278 sec)\n",
"INFO:tensorflow:global_step/sec: 39.9615\n",
"INFO:tensorflow:loss = -735.44, step = 301 (2.503 sec)\n",
"INFO:tensorflow:global_step/sec: 41.4266\n",
"INFO:tensorflow:loss = -997.04, step = 401 (2.414 sec)\n",
"INFO:tensorflow:global_step/sec: 38.7636\n",
"INFO:tensorflow:loss = -998.0, step = 501 (2.579 sec)\n",
"INFO:tensorflow:TensorForestLossHook requesting stop.\n",
"INFO:tensorflow:Saving checkpoints for 506 into ./model.ckpt.\n",
"INFO:tensorflow:Loss for final step: -998.0.\n"
]
},
{
"data": {
"text/plain": [
"TensorForestEstimator(params=None)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove previous checkpoints so that we can re-run this step if necessary.\n",
"for f in glob(\"./*\"):\n",
" os.remove(f)\n",
"classifier = tf.contrib.tensor_forest.client.random_forest.TensorForestEstimator(\n",
" params, model_dir=\"./\")\n",
"classifier.fit(x=x_train, y=y_train)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "afb9add8-4590-6ea4-3ce1-aed71247c986"
},
"source": [
"While the forest is training, the **negative** of the current average tree size is reported as the loss. (This is sort of a hack to get around the fact that random forest training isn't loss-based in the way that TensorFlow expects.)\n",
"\n",
"Now let's see how well this forest does on the test data:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "5167b64b-676e-b8f0-a46f-5174702a50bb"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_test = \n",
"[[ 4.9000001 3. 1.39999998 0.2 ]\n",
" [ 4.5999999 3.0999999 1.5 0.2 ]\n",
" [ 5.4000001 3.9000001 1.70000005 0.40000001]\n",
" [ 5. 3.4000001 1.5 0.2 ]\n",
" [ 4.9000001 3.0999999 1.5 0.1 ]]\n",
"test labels =\n",
"[ 0. 0. 0. 0. 0.]\n",
"WARNING:tensorflow:From <ipython-input-5-5311ff63ede6>:10: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
"Instructions for updating:\n",
"Estimator is decoupled from Scikit Learn interface by moving into\n",
"separate class SKCompat. Arguments x, y and batch_size are only\n",
"available in the SKCompat class, Estimator will only accept input_fn.\n",
"Example conversion:\n",
" est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
"INFO:tensorflow:Constructing forest with params = \n",
"INFO:tensorflow:{'num_trees': 50, 'max_nodes': 1000, 'bagging_fraction': 1.0, 'feature_bagging_fraction': 1.0, 'num_splits_to_consider': 4, 'max_fertile_nodes': 500, 'split_after_samples': 50, 'min_split_samples': 5, 'valid_leaf_threshold': 1, 'dominate_method': 'bootstrap', 'dominate_fraction': 0.99, 'num_classes': 3, 'num_features': 4, 'bagged_num_features': 4, 'bagged_features': None, 'regression': False, 'num_outputs': 1, 'num_output_columns': 4, 'split_initializations_per_input': 1, 'base_random_seed': 0}\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py:248: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
" equality = a == b\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from ./model.ckpt-506\n",
"[{'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}, {'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}, {'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}, {'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}, {'classes': array([ 1., 0., 0.], dtype=float32), 'probabilities': 0}]\n"
]
}
],
"source": [
"x_test = test.drop(['Species', 'Id'], axis=1).astype(np.float32).values\n",
"y_test = test['Species'].map(label_map).astype(np.float32).values\n",
"\n",
"print(\"x_test = \")\n",
"print(x_test[:5])\n",
"\n",
"print(\"test labels =\")\n",
"print(y_test[:5])\n",
"\n",
"y_out = list(classifier.predict(x=x_test))\n",
"\n",
"print(y_out[:5])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "5ac1b3e8-69e8-aa60-ade0-6f0f913a658e"
},
"source": [
"The output comes both as soft (probabilities) and hard (single class) predictions, so there are a bunch of ways you can slice and dice them. Here are a few:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "4223af0e-8652-025c-cf57-a197233458f0"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Soft predictions:\n",
"[array([ 1., 0., 0.], dtype=float32), array([ 1., 0., 0.], dtype=float32), array([ 1., 0., 0.], dtype=float32), array([ 1., 0., 0.], dtype=float32), array([ 1., 0., 0.], dtype=float32)]\n",
"Hard predictions:\n",
"[0, 0, 0, 0, 0]\n",
"Accuracy = 0.96\n"
]
}
],
"source": [
"n = len(y_test)\n",
"out_soft = list(y['classes'] for y in y_out)\n",
"out_hard = list(y['probabilities'] for y in y_out)\n",
"\n",
"print(\"Soft predictions:\")\n",
"print(out_soft[:5])\n",
"print(\"Hard predictions:\")\n",
"print(out_hard[:5])\n",
"\n",
"soft_zipped = zip(y_test, out_soft)\n",
"hard_zipped = list(zip(y_test, out_hard))\n",
"\n",
"num_correct = sum(1 for p in hard_zipped if p[0] == p[1])\n",
"print(\"Accuracy = %s\" % (num_correct / n))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "3fa914c5-b107-0073-85a9-791bd61619d4"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Probs of real label:\n",
"[1.0, 1.0, 1.0, 1.0, 1.0]\n",
"Average log loss = -0.10168163037957292\n"
]
}
],
"source": [
"test_ps = list(p[1][int(p[0])] for p in soft_zipped)\n",
"print(\"Probs of real label:\")\n",
"print(test_ps[:5])\n",
"total_log_loss = sum(math.log(p) for p in test_ps)\n",
"print(\"Average log loss = %s\" % (total_log_loss / n))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "4ce24ee0-4461-9fef-2c6a-e9aac8f040ca"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Confusion matrix:\n",
"{(0.0, 0): 25, (1.0, 1): 25, (2.0, 1): 3, (2.0, 2): 22}\n"
]
}
],
"source": [
"confusion = {x: hard_zipped.count(x) for x in set(hard_zipped)}\n",
"print (\"Confusion matrix:\")\n",
"print (confusion)"
]
}
],
"metadata": {
"_change_revision": 1,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163742.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "a09b70bc-05e2-b7aa-770f-9a495a02d6b3"
},
"outputs": [],
"source": [
"import nltk"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "6389bb66-4f2a-3de4-405a-28a5d11824aa"
},
"outputs": [],
"source": [
"from nltk.tokenize import word_tokenize, sent_tokenize\n",
"from nltk.corpus import stopwords"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "5345ab1e-a945-603e-0a68-1505c5d585b5"
},
"outputs": [],
"source": [
"ex=\"Hello there, how you doing? Im awesome\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "b26242dd-38bb-6f00-32e4-62c55bf16322"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Hello there, how you doing?', 'Im awesome']\n"
]
}
],
"source": [
"a=(sent_tokenize(ex))\n",
"stop=set(stopwords.words(\"english\"))\n",
"b=[i for i in a if not i in stop]\n",
"print(b)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "f308201b-bc2d-0874-69a0-b1e68e1dd378"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['Hello', 'there', ',', 'how', 'you', 'doing', '?', 'Im', 'awesome']\n"
]
}
],
"source": [
"print(word_tokenize(ex))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "646353ef-43d5-5c84-3df4-69b0f2592331"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 2,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163762.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "3944a488-e562-538f-3fc8-05472eb5e714"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "2b62968c-608b-bb21-d44f-78db2ec12ae4"
},
"outputs": [
{
"data": {
"text/plain": [
"count 1460.000000\n",
"mean 180921.195890\n",
"std 79442.502883\n",
"min 34900.000000\n",
"25% 129975.000000\n",
"50% 163000.000000\n",
"75% 214000.000000\n",
"max 755000.000000\n",
"Name: SalePrice, dtype: float64"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"train = pd.read_csv('../input/train.csv')\n",
"test = pd.read_csv('../input/test.csv')\n",
"\n",
"train.head()\n",
"train.SalePrice.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "77a9bcc6-0856-657b-add6-b4182b9e8320"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skew is: 1.88287575977\n"
]
},
{
"data": {
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a8/Pz2rx5s7Zs2aKZmRkFAgFNTk6qqqpK3d3dKisrkyQNDw8rHA7L4XCos7NTTU1Nxg8E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\nmzZt0rZt24o+l8t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"text/plain": [
"<matplotlib.figure.Figure at 0x7fb9280ee128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.style.use(style='ggplot')\n",
"plt.rcParams['figure.figsize'] = (10, 6)\n",
"print (\"Skew is:\", train.SalePrice.skew())\n",
"plt.hist(train.SalePrice, color='blue')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "8792bf70-6add-573b-cb8b-5d589f038029"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Skew is: 0.121335062205\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7fb928150e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"target = np.log(train.SalePrice)\n",
"print (\"Skew is:\", target.skew())\n",
"plt.hist(target, color='blue')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "c74651bc-2408-37e2-a14c-73b63968872b"
},
"outputs": [
{
"data": {
"image/png": 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QFKBsnHZv9fa+JXJJ58RuSSmkG2YPH8RIIviWfX4EqyZr7zOG2macXVTfarBk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\nIYQQQggL8X+KOcsPnV5z8QAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb9145d0710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(x=train['GrLivArea'], y=target)\n",
"plt.ylabel('Sale Price')\n",
"plt.xlabel('Above grade (ground) living area square feet')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "f87728bf-30be-fe64-f96a-26a9ea1a4cd8"
},
"outputs": [],
"source": [
"data = train.select_dtypes(include=[np.number]).interpolate().dropna() \n",
"y = np.log(train.SalePrice)\n",
"X = data.drop(['SalePrice', 'Id'], axis=1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "88f21901-19cd-812a-23c8-af2d214f4e66"
},
"source": [
"Définition du test et train set"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "d12fcbd5-d265-5528-ee15-af0d925b8f38"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=.33)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "b2d32e58-1518-1054-c6e6-06977e320508"
},
"source": [
"modèle"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "daa9233c-ab0d-7db2-33b5-4009153fd5ed"
},
"outputs": [],
"source": [
"from sklearn import linear_model\n",
"lr = linear_model.LinearRegression()\n",
"lr_model = lr.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "0d56521a-55e0-a4f0-0f57-a120fe8d0591"
},
"outputs": [],
"source": [
"predictions_lm = lr_model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "eeeff045-4b41-982d-73e0-98c94673292a"
},
"outputs": [],
"source": [
"rm = linear_model.Ridge(alpha=0.1)\n",
"rm_model = rm.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "08d20dfd-b4ac-ad27-5cc0-07ae66a9bfc1"
},
"outputs": [],
"source": [
"predictions_rm = rm_model.predict(X_test)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "d80620a6-0d83-5d69-0721-8aad012d9a86"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fb9098127f0>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Kdu2A1NPwfvIbGJilP69EpNk0WsACgQATJkygX79+VFRU8OCDDzJw4EAGDhzI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\nlmA8AWOMMcYYSzCegDHGGGOMJdg/a5eYH80gwRMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb909f38e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"submission = pd.DataFrame()\n",
"submission['Id'] = test.Id\n",
"feats = test.select_dtypes(\n",
" include=[np.number]).drop(['Id'], axis=1).interpolate()\n",
"\n",
"submission.plot()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "818f90cb-a0e9-5dee-81c6-9eeaf1b3b820"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>SalePrice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1461</td>\n",
" <td>119176.319130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1462</td>\n",
" <td>120836.395320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1463</td>\n",
" <td>168225.543024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1464</td>\n",
" <td>195642.589726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1465</td>\n",
" <td>181910.799054</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Id SalePrice\n",
"0 1461 119176.319130\n",
"1 1462 120836.395320\n",
"2 1463 168225.543024\n",
"3 1464 195642.589726\n",
"4 1465 181910.799054"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions = rm_model.predict(feats)\n",
"final_predictions = np.exp(predictions)\n",
"submission['SalePrice'] = final_predictions\n",
"submission.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "01ef80fc-0363-842f-84b8-bab6b5b47fd7"
},
"outputs": [],
"source": [
"submission.to_csv('submission1.csv', index=False)"
]
}
],
"metadata": {
"_change_revision": 79,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163805.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7da4abe3-92df-0613-28af-3d1c60875510"
},
"source": [
"This notebook is the reproduction of [A Very Extensive Sberbank Exploratory Analysis](https://www.kaggle.com/captcalculator/a-very-extensive-sberbank-exploratory-analysis) notebook in python (work in progress)."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4adbd1a5-36f4-5485-035a-a35c443c71b5"
},
"source": [
"This notebook is the reproduction of [A Very Extensive Sberbank Exploratory Analysis](https://www.kaggle.com/captcalculator/a-very-extensive-sberbank-exploratory-analysis) notebook in python. Not all code is implemented yet. Here i copied only titles for more detailed description please see original notebook."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "2ee37461-707a-4225-d8b0-6feda0fe7f53"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"color = sns.color_palette()\n",
"\n",
"%matplotlib inline\n",
"\n",
"pd.options.mode.chained_assignment = None # default='warn'\n",
"pd.set_option('display.max_columns', 500)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "570eceb9-13e7-c896-d8dd-8ceb7abe4a9e"
},
"source": [
"## Training Data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "dcfd9c4f-d434-7792-6279-85842972a5ca"
},
"outputs": [],
"source": [
"train_df = pd.read_csv(\"../input/train.csv\", parse_dates=['timestamp'])\n",
"train_df['price_doc_log'] = np.log1p(train_df['price_doc'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e34fd6a9-277a-847e-6ea6-f146e71638fe"
},
"source": [
"## Missing Data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "90c5dc41-b939-63fc-3ee2-0e59745b89ff"
},
"outputs": [],
"source": [
"train_na = (train_df.isnull().sum() / len(train_df)) * 100\n",
"train_na = train_na.drop(train_na[train_na == 0].index).sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "ae466f09-2bd6-1e9b-6ef3-29038249418b"
},
"outputs": [
{
"data": {
"text/plain": "[<matplotlib.text.Text at 0x7f914ff8f198>,\n <matplotlib.text.Text at 0x7f914ff1b208>]"
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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jYAuzJEmSVGFgliRJkioMzJIkSVKFgVmSJEmqMDBLkiRJFQZmSZIkqcLALEmSJFUYmCVJ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\nYQYAgAXfqU3l+PqjeMkAAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f914ff71668>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(12, 8))\n",
"plt.xticks(rotation='90')\n",
"sns.barplot(x=train_na.index, y=train_na)\n",
"ax.set(title='Percent missing data by feature', ylabel='% missing')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "199c95d5-9b9c-76f0-a82f-b1c7475d481a"
},
"source": [
"## Data Quality Issues"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "3d07162f-eb17-6c65-9a3e-fb6045f74a85"
},
"outputs": [],
"source": [
"# state should be discrete valued between 1 and 4. There is a 33 in it that is cleary a data entry error\n",
"# Lets just replace it with the mode.\n",
"train_df.loc[train_df['state'] == 33, 'state'] = train_df['state'].mode()\n",
"\n",
"# build_year has an erronus value 20052009. Since its unclear which it should be, let's replace with 2007\n",
"train_df.loc[train_df['build_year'] == 20052009, 'build_year'] = 2007"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "377e7a72-d216-9e1a-cb03-b192747dfb7c"
},
"source": [
"## Housing Internal Characteristics"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "212b1565-f914-66cf-172f-b8ed8b5813c0"
},
"outputs": [],
"source": [
"internal_chars = ['full_sq', 'life_sq', 'floor', 'max_floor', 'build_year', 'num_room', 'kitch_sq', 'state', 'price_doc']\n",
"corrmat = train_df[internal_chars].corr()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "c8d155df-f1a4-d957-8ace-f6458bb88f8b"
},
"outputs": [
{
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7f914ff50a58>"
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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A78YCpdRcpdTPSqlIpdTbecuaKKV2KaV2KqW+Vkp5KaXaKaW+yVvW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\nWqJlPxY+T4yln0PW/WhvX497O3XA1taWpk0a4+jgQMLVRI4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gZaAT8BaQDXQGZgODgPuA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WAAAg\nAElEQVTQREEOvYWWbBoJISHl98dskk1IgbBhJnBez7NPMjuzu59stnznnDNnEhMp71meYWFjqODr\ny8Zf1rNsyULKlClLl7/9jUefeIpLl5IZFzaC2Bg7qampPP9ST0LbdXBJzoitW5g/ZxZu7m60CW1H\n9569rsg5cuhgI2d5T0aMMnKeO3uWEUMHkXb5MvUbNODdQUPJyMhg4tjRHD18CI8yZXh30BBq1Kzl\nkpzOdkZsZUn4bNzc3GgR2pbnerxyxTYb1q1h0uiRTJ+/lFp16gKwe0cEi8Nn4ebmzu3Va/DmoGG4\nubm+R237tq3Gc+rmRuu27eiez/9+5LAhJCUmUr58eYZ/MIaUlBTC3h+Svc3pyEh6v9aPoOBghg0a\nQK3adQCoXacub777nkvzjl/5X/YcPoHNZmPgMw/SpHa17HXb/jrM9E9X4+Zmo2blYEb2eIRLqZcZ\nvOBTEpKSSU1Lp89DXWh7R32XZsqVb8Zc9uz9C2w2Br7xb+5omHPq4M0RO5k+fzHubm60b9OS3t2f\n4+LFZAaNGk/ChURSL1/m3z2eo22rFmzfvYfp8xbj4eFB+fLlGDv0PXwr+JRM5inT2fPnXuM5fbs/\nTRo1zMm8LYIZc+bh5uZG+7Zt6P1yDyJ27OTtQcOoU9t4v9SrU5vB775VItmuRZXG9enz9QLWTl3E\n+tkfmZpl3OyF7NmnjOe07yvc0aBe9rqU1FRGTJ7N4WMnWTVvCgDJl1IYMm4a9tg4UlIv0/uFJ+nU\npoVZ8W9Zlh2zICKPFbH+XyJStqRzzJw8gbBxE5m1YAkRWzdz7MiRXOs/+89K7m7ajFkLFtOhUxc+\nXraUjIwMpk8az/ipM5kxbyGbf93A+XPn2PTrBqRhQ6aHL2T46HHMmTbFZTmnT57AqPGTmLtwKdu2\nbuHokcO51q/6eCX3NGvO3IVL6Ni5C8s/WgrArOlTeOrZ51nw4XLc3Nw5e/YMv/6ynqTERMIXf8ig\nYcOZPX2qy3I6mzN1IsPGTGDqvMXs3LaF40dzP7d7du0gYvMmatepl+v6aeNHM2z0BKbNW8zFi0ls\n37KpRPJNmzyBD8ZPZM7CJURs2czRPP/7Tz9eyT1NmzFnwWI6du7Cio+WEhwSwszwBcwMX8DUWXOp\nWKkSbTt0BODups2y17m6UIjYf4QT56JZMawPYS89ytgV3+ZaP3LpV0zu+wzLhvYmKTmFjX8c5KuN\nO6lZKZjFA19hymvPMG7Fdy7NlCvfrt85fiqSFfNmEDbwLcZNm51r/djps5k66n2WzZ3Gpm07OHz0\nOF/98BM1q9/OkpmTmDpqGOOmzwVgwsxwwga9zZKZk7i7SSM+/fq/JZN55y5OnDzFisXzCRs6iLGT\ncr8Pxk2extTxo1m2MJzNW7Zx+MhRAJo3vZsl4bNYEj7LEoVCWc/yPDlzJPvX/mZ2FCJ2/8mJU6dZ\nOXsiYe/2Y+zM+bnWTwpfQoO6tXNdt37TNhpLXT6cPpYpwwcwYc6iGxm5WDIzXHexCksWCyJSE3i6\niM3eAkq0WDgdeQqfCr6EVKyU3bKwc/u2XNvsjNhGu06dAQht34Ed27YSHxeHt48Pfv7+uLm50bRF\nS3ZEbKVLt/t4+vnuAJw/d47gkBCX5Iw8ZeSsWMnI2Sa0LTsicufcEbGVDo6cbTt0YPu2rWRkZLBn\n1y7aOb7M3n5vEJUqVebUyRM0bNwYgKrVbufsmTOkp6e7JGuWM5Gn8KlQIfu5bdGmLbvyPLd16zfg\n7SHD8ShTJtf1s5csJzikIgC+fv4kxMe7NBsY//sKFXypmPW/b9sun+d0W/ZzGtq+A9sjtuZa/8N3\n39Kxcxc8PT1dni+vrfsO06VpIwBqVwkhISmZxORL2es/GfEalQJ8AQio4EV84kX8vT2JS7wIQMLF\nZPx9vEou345ddGkfCkCdmjVIuJBIYlISACcjz+Dr40PliiHGXnqblmzZsQs/X1/i4xOMfAmJ+PlW\nAMDf15e4rOsvJOLnV6FkMkdsp0vH9gDUrlWThAsXSEzMyhyJb4UKVKpYMbtlYUvE9hLJcb3SUlKZ\n9UB34k+fNzsKW3b+Tpd2rQGoU+N2x+vgYvb6/j2fp2v71rlu8/cu7Xn5aWPf8UxUNBWDA29c4GLK\nzMx02cUqLNENISLVgeVAOkamNKCJiLwPLAaWOTYtA7wIhAKtgR9E5F7gFeAZIAP4Sik12RW5Yux2\n/Pz9s5f9AgI4fepkgdv4+Qdgt0fj5+/PxYtJnDpxgkpVKrNrx3bubto8+zav9exO1PnzjJ08zRUx\niXE8Zhb/gAAiT53KtY3dKae/fwD26CjiYmMp7+XJzKmTUPv3c9fd99C77+vUrlOXVR+v4P+efpbI\nkyc5HXmK+Lg4AgJd9yaNibHj6+f03PoHcCYyd2ZPr/y/vLy8vI2/KTqKndu20L1XH5flymK32/Fz\nyufvH0Bk5Mkrt8n1nEbnWv/dN18yZcac7OVjR48w8O3+JCQk0KNnL1q0yv2heD2i4xNpVLNq9nJA\nBS+i4xPxLl8OIPtnVFwCm/48SN9Hu+Hn7cnXG3fywIBJJCQlM/vNF12W54p89lgaSU4Xh7+fL9H2\nWLy9vIiOicHfzy8nu78fJyNP8+zjD/P196v5+5MvknDhAnMmjAJgwOu96dH3HSr4eFPBx5v+r75c\nQpljaNSgQU4uPz+i7Xa8vb2w2/Nm9udkZCT169bh8NFj9Ht7APEJF+jdswehrVqWSL6rlZGeToaL\ni/3iio6JpXH9OtnL/n6+RMfE4u1lFNRenp7EJVzI97bP9h3A2aho5owZdkOyarlZpWXhcWCNUqoz\n8AbwE/CLUioMqAyEOdYtBv6tlFoGnAX+DlR13L4d0AF4zFF8uFxRVV7WepvNxqD3wxg/agRDB7xN\n5SpVc9129sKljJ40ldEjhpZI5Xi1OTMzM4k+f54nnnqGWfMWckApNm38lTZt29GwcRP69nqZVf9Z\nQY1atUq+wr3G+4+NieH9AW/S952BVPD1K/oG1+lqn9Msf+75neo1auHlbRQ21W6vTo+evRg7aSpD\nho9k3KgwLl++XIJ5r7zOnpBI32nLGPrCQ/h5e/Ltpl1UCvTj+wnvsOi9noxZ/u2VNyqxfAU/n1nr\nvv3pZypXDOGHTz5k0fSJjJ46C4CxU2czbcxwvvt4Cffc2YT/fPnNjclc2DpH5uq3306fnj2YMWk8\no4cPYfiocSX6fy7truVzZcWsCcwaPZSBY6ZYao87PxkZmS67WIUlWhaA1cCXIuIHfAZsAbJ2xc8C\nM0RkJOAP7Mhz25ZAPeB/jmUfoCZworhhvv78U9b9vBo/P39i7Dl7i9FR5wkMDs61bWBwMDF2O97e\nPsb6IGP93U2bMXP+YgDmz55JpcpVUH/twz8ggJCKlahXX0hPSycuNhb/gIBi5fzys1WsXbMaP//c\nOaOiogjKkzMoKJiY6JycQcHB+Pr5UalyZapWux2A5i1bcvTIYULbtadXn9eyb/t/D/+z2Bnz+vaL\nT/ll7Rp8/fyItduzr4+Oisp+7oqSlJTIkLf70ePV12jeqo1LcmX58rOC//dBefIF5fnfO6/ftPFX\nmrfM2aMMDgnh3m73AUbXTmBgIFHnz1OlalVcIcTPh+j4nD2y83EJBPvmDPpLTL5En8lLef2xvxHa\nxBgDsvvgcdo6fpfqlYmKSyA9IwP3EhgsGhwUSLQ9Jns5KtpOcJDxmgoJCsQek7PufJSdkKBAdv2x\nl7atjI+BBvXqEBVtJz09nQOHj9L0ziYAhLZoyner17k8r5EriGin1+j5qGiCgwIdf09+64KoGBLM\n/d26AnB7tWoEBQZw7nwU1apWKZGMpU1IUADRMXHZy1H2GIID/Qu5BexVhwjw96VySDAN69YmLT2D\nmLh4Av1LfiehuG7GQyct0bKglPoTuAv4FRgLOLcMhAE/KaU6ACPzuXkq8F+lVCfH5Q6l1IbryfPQ\nY08wfe4CRo6dwMWkJM6cPk1aWhqbN/5KizxfTi1atWb92p8B2PC/dbRsY/TLDujfl9iYGJKTk9m0\ncQPNWrZkz+6dfLLC6FGJsdtJTk7G16/4L/hHHv8/Zs1byKhxE0lKzMm56dcNV+Rs2boN69auAWD9\nurW0atMWDw8PqlStxskTxwFQf+2jeo0aHDygGBM2AoAtm36jfoMGLjva4J+PPsGk2fMZNnoCFy8m\ncfbMadLT0tj62680a3l1zfLzZ0zl0SefpUXrUJdkcvbI408wM3wBH4ybQJLT/35TAf/7//1s/O/X\nr1tHqzY5efb/tY+69XKa3Vf/+D0fLzdGodujo4mJsbtszApAaJN6rNm+F4B9xyIJ8auAV/nbstdP\n+vh7nr+vLe3uzMlUPSSQP44YXSuno2PxvK1siRQKAKEtm7F6/a9GPnWQ4KBAvBxjOapWrkRi0kUi\nz5wlLS2dXzZtIbRFc6pXrcKeffuNfGfP4Vm+PO7u7gQG+nP4qPGa/fOvA9S43TUF1xWZW7dkzTpj\nH2TffkVIcBBejq6xqlUqk5SUROTpM6SlpfHLxt8IbdWS7378iaXLVwIQHW3HHhNDxZCrK4JvBaHN\n72H1BmOg5b4DhwkODMh+HRRk+569LF31FWB0Y1xMTsbft2TGqbhKZkamyy5WYbNCc46IPAUcUUpt\nE5F2GAVCglLqYRH5FpgB/Ax8BLgrpZ4RkSNAU6CCY93dQDIwDRiolEou7DHPxCVd1R/++64dzJs1\nA4AOne/lqedewG6PZun8cN4eNJSLFy8yevhQEuKNQY1DRo7C29uHDf9by4eLFmCz2Xjy2efpdv8D\npFy6xITRYZw/d5aUlBS69+xFaPuOBT52ZT/jgykq4WKB22TZvXMHc2dNB6Bj56488/wL2KOjWTQ/\nnAGDjZwfvD+E+Ph4vL19eP8DI+epkycYPXI4mRkZ1K5bj3cGDgZgbNgIjh09QtnbyvJ+2BgqVqpU\n4GMHVzDe7MftiVfzlGbbs2sni+YYz227zl144pkXiLFH89HCefR/bwg/fPsVa3/8nsMHFVWrVad6\nzVq8PmAQj93XmYZN7sy+n87d7ucfDz96VY9ZI9Cb8/FJV7Xt7p07CHf87zt2uZennzOe08ULwnl3\nUNZzmvO/HxZmPKcALz79f0ydNTd7nMfFpCRGDhtM4oULXE5Lo0fPXrRp267Axw7x9SJ18+dXlTPL\n1FU/suPAMdxsNoY8/y/+On4aH89yhDapR9vXPuCuOjk1+ANt7uIfre9i2KLPsSckkp6eQd9Hu9Gq\nUZ1CHuFKZdsYA88uRxXdkDd17kK2//6Hke+tfuw/eAhvLy+6dmzH9t17mDp3IQBdO7anxzNPcPFi\nMsPGTsIeG0taejr9enanVbN72PXHXibPmY+Huwe+FXz4YJAxfqEoZYKrkxofXeR2uTLPmsuOXbtx\nc3NjyLtv8deBA/h4eXNv545s37mbqbOMMSndunSi+3PPkJSUxHvDRnLB8X/u3bMHHdpeW1Fb1jeI\n3raa13SbwlRv2oTHJw8lsGY10i9fJi7yHOGPvsrF2OsbGByeeQyAtNPqmm43Zf6H7NhjHI469I3e\n/HXoCD5ennRt34Y3R4zj7PloDh07SeP6dXj8wfvo2r41wybM5GxUNCkpqfR58Sk6h179OBCPKgJg\nu6aQ16lV2BqXfbFufb/bDc1eEKsUC02BcCARY5BjGLAC+BxYC0wCjgEzgflAD+ApjC6ITsD/AS85\nbvuVUmpsUY95tcWCma6lWDBTcYsFM1xLsWCm4hQLZriWYsFsxSkWzODqYqGkFLdYuNHMKBZajFzt\nsu+XiOF/s0SxYIkxC0qpnRhf/M6cuyKcDwDPanNc7XTdHMdF0zRN00xlpe4DV7HEmAVN0zRN06zL\nEi0LmqZpmnazuBlbFnSxoGmapmkuZKX5EVxFd0NomqZpmlYo3bKgaZqmaS5khaMMXU0XC5qmaZrm\nQlY6W6Sr6G4ITdM0TdMKpVsWNE3TNM2FbsYBjrpY0DRN0zQXuhkPndTdEJqmaZqmFUq3LGiapmma\nC92MLQu6WNA0TdM0F8q4CQ+d1N0QmqZpmqYVSrcsaJqmaZoL6W4ITdM0TdMKdTMWC7abcVrKq3TL\n/uGapmm3GNuNfDDp95XLvl/UzIdvaPaC6JYFTdM0TXMhPSnTTeTS93PNjlCkcg/0ASD92G6TkxTO\nvebdAKSdOWhykqJ5VK7H5fPHzI5RpDIhNUmNPWt2jCKV9a8EwPlJb5icpGgh70wnJTHe7BhFus3b\nl7TTyuwYRfKoIgD0ttU0NUdRwjOP3fDHNLvFXkTKAEuBGkA60EMpdSTPNqOBThgHOnyplJpQ2H3q\noyE0TdM07ebyDBCnlGoHjAbGOq8UkSZAZ6VUW6At0ENEKhV2h7dsy4KmaZqmlQQLDHC8F/jI8fvP\nwOI86+OBciJyG+AOZAAXC7tD3bKgaZqmaS6UkZHpsksxVQKiAJRSGUCmiJTNWqmUOgl8Chx3XMKV\nUgmF3aFuWdA0TdO0UkpEegI981zdKs9yriMqRKQ28AhQGygDbBKRT5RS5wt6HF0saJqmaZoLZWak\n37DHUkotBBY6XyciSzFaF353DHa0KaVSnTZpAWxVSl10bL8HaAKsK+hxdLGgaZqmaS50I4uFAqwG\nngB+Av4J/C/P+kNAfxFxwxizcAdwhELoYkHTNE3Tbi6fAN1EZCOQAnQHEJGBwC9Kqc0ishrY6Nh+\noVLqWGF3qIsFTdM0TXMhs1sWlFLpQI98rh/n9PtwYPjV3qcuFjRN0zTNhTLTTe+GcDl96KSmaZqm\naYXSLQuapmma5kJmd0OUBF0saJqmaZoL3YzFgu6G0DRN0zStULplQdM0TdNc6GZsWdDFgqZpmqa5\nkC4WbmETv/yFPcfPYLPZGPBIR5pUzzmb5+eb/+DLrXtxt9moXzWYwY91xmYzpuK+lJrGYxOW0etv\nLXmoZeMbknVc+If8vv8gNmwM6vMid0jd7HUpqamMmL6AQ8dP8eks46yln/+4jm/W/pq9zZ8HDrPj\n64+uuF+X55y1gD379mOz2RjYrxd3NKifkzMllRFTZnH46AlWzZ+Wff2k8MXs3LOXtPQMXnn2Cbp1\nCC2RbONnhLNn336wwcDX+3BHQ8let3n7TqbPX4K7mzvtW7egd/dnycjIIGzSDA4ePUYZjzK8/04/\nateonn2b37Zu59V3hvDnrz+5Puu0Wez5c6/xPL7ZjyaNGuZk3badGeELcHNzo31oa3q/9CIA3/24\nhiXLP8bd3Z2+vV6iQ9s27P7jT6bMDMfDw52yZcsyZvgQAvz9XJ4XwLvTI5SpUgMy4cL/viDt7Ikr\ntvFq/yBlqtQk7pNZ2MqUxefvz+FWzhObuwdJm38k9dj+Esk2YfIU9vzxJzabjffeeZsmjRtlr9uy\ndRszZs8xns+2bXn1lZcBmDJ9Bjt37SY9PZ2Xe3Sna5fOHD16jJGjx2Cz2ahRvTpDB72Hh0fJfOSO\nm72QPfuU8Rro+wp3NKiXvS4lNZURk2dz+NhJVs2bAkDypRSGjJuGPTaOlNTL9H7hSTq1aVEi2a5F\nlcb16fP1AtZOXcT62SX/GaRdPZePWRCRgSLSpgTud7uI1HT1/V6N7YdOcTw6jmX9n2LEU90Y/8X6\n7HXJqZf5cdcBlvR7gg/feJKj52L4/diZ7PUL1mzF17PcDcsasWcfxyPP8vG0UXzw1quMmbs01/qJ\nC5bToE7NXNc9dn8XPpw4nA8nDqfv80/wcLeOJZ9z9x+cOHWalXMmEzbgDcbOmJdr/aTwxTSoWzvX\ndVt37eHQ0eOsnDOZ+RNGMm7WgpLJtmsPx09FsiJ8GmHvvcW46XNzrR87bS5TPxjGsjlT2BSxg8NH\nj7Nu42YuJCWxYu40wga+yaTZOdlSUlJZsPwTggMDXJ91525OnDzFioVzCRs8gLFTZuRaP27KDKaO\n/YBl82ezeWsEh48eIy4+nvBFS/lo3ixmTx7Hug3GJG4ffbyK0cMHs3jOdO5q0pjPv/7W5XkBylSr\ng7t/MLErp5Hw08f4dHn0im3cAytSplqd7OVyTVqRHnueuFWziP9mMd6dr7yNK2zfsZMTJ06yfOli\nRr4/lHETJ+VaP27iZKZMGM9HixeyacsWDh85wraI7Rw6fITlSxczd+Z0JkwyvpCnzpzFyz26s2TB\nPCpXqsRPa34ukcwRu/803kuzJxL2bj/Gzpyfa/2k8CVXvJfWb9pGY6nLh9PHMmX4ACbMWVQi2a5F\nWc/yPDlzJPvX/mZ2lOuWmZHusotVuLxYUEqNU0ptdvX9mmnrwZN0ucP44KpdMYCE5BQSL6UAUL5s\nGRb8+zHKuLuTnHqZxEupBPl4AXD0XAyHz8XQvlHNG5Z1y64/uTe0OQB1qlcj4UISiUk5pyl/s8fT\ndA0teA9izorP6f3MYyWfc+fvdGnX2shZ4/YrcvZ/5QW6tstdcza/szFTRgwCwMfbi+RLl0gvgclP\ntu7YRZf2RotFnZrVSbhwgcSkJABOnj6DbwUfKlcMMfYuW7dky47dnDgZmd36UL1qFc6cO5+dbcGy\nj3n60X9SpkwZ12fdvoMuHdoBULtWTRIuJOZkjTyNb4UKVMrKGtqaLRE72LJtB61bNMPLy5PgoEBG\nDHoXgCljwri9ahUyMzM5FxVFxZBgl+cFKFujPimH9gCQHnMO222e2Mrelmsb704Pk7Txv9nLGRcT\ncStnvK9s5TzJSE4qkWxbt0XQuZNRLNeuVYuEhAskJiYCcOpUpPF8VqqY3bKwdVsEzZrew6TxRiud\nj48PyZeSSU9P58SJk9zhaJUIbdOazVu2lkjmK99LibnfSz2fp2v71rlu8/cu7Xn5aeN9fiYqmorB\ngSWS7VqkpaQy64HuxJ8u8MSHpUZGRrrLLlZxzW1iItIduB+oAFQDpgKDge+B80A94DOME1h8CNQA\nLgEvAGeB+eScFvN9pVSBZ7kSkRlAG0ABZR3XVQMWO5YzgJeVUkdFZADwuOO6QUqpvCfOKDZ7QhKN\nqoVkL/t7lSc64SLe5XI+4Bb9HMHKDbt4tuM9VAvyBWDy1xsY+Fhnvo3Y56ooRYqOjaNRvVo5WX0r\nEB0bh7eXJwBenuWJS7iQ723/UIeoHBxIcEDJND3nyhkTS+P6Od0j/n4ViI6JdcrpSVx87pzu7u54\nlncH4PPv19ChVXPc3d1LJFsjyWnG9ffzJdoei7eXF9H2GPz9fLPXBfj7cTLyNM3uuoOPVn3B8088\nwonI05w6fYbY+AQSE5NQh4/Qt+eLTJ6zML+Hu76s9hgaNcjpIgnw8yXaHoO3lxd2ewz+/s5Z/TkZ\nGcmlSykkX0qh3zuDSLhwgT49e9C6RTMANm7eyrgpM6hVswYP3v83l+cFcPOswOWzJ7OXM5ITcfOq\nQHpqFADlGrfk8snDpMfHZG+TonZRrkkrAl4eils5T+K+mHfF/bpCtN1Oo4YNspf9/f2Ittvx9vYm\n2m7H36lbJiDAn5OnIh2vy/IAfPn1N7Rv2xZ3d3fq1a3Dho2/8a8H/8GmzVuwx8Rc8XguyRwTS+P6\nOa0w/n6+V76XCnjPP9t3AGejopkzZliJZLsWGenpZNyEMx/eLIrbstAY+BfQBRgF3Ab8oJQa7bTN\ni8BZpVRbYIFj+2eAM0qpzsDDwDQKICKNgFCM83IPArI+EcOARUqpTsAcYISI1MMoFFoDzwHPFvPv\nuiqZ+Vz3ctcW/HfoS/y2/zi7jpzm24h93FmzMtUCffPZ+kbKL23+Pvtx3Q3pgshP5tXHZN3GLXzx\n/WqGvNG75AI5KSxbpmNl+9YtuKOh8GLfd1i26ktq1agOmZmMnzmPd/u+ekNyGnkKW2eszCST+Ph4\npo77gFHDBjFs1Ljsde3atOLbVcupVaM6iz5acSMiA7ac38p5Uq5JKy5uz70PcVvD5mQkxBKzaBRx\nq2bhc+/jNybaVTyfWf63/he++OobBg0wWmre7v8Gq9f8zMuv9iEjM+OK7UvKtTzOilkTmDV6KAPH\nTLlh+W4FN2M3RHFH2/yilEoDokUkFqOlYFuebZoCawGUUv8BEJG5QHsRaefYpryIlM1znu0sjTDO\nt50BnBSRrNNnNscoHsA47eb7wD1O2x4Cehbz78pXsK8X0Rdymj2j4hMJrmA0icYnXeLQ2Wia1alG\nubIetGtQk91HT7Pv1Dki7Qls2HeUc3GJlPVwp6KvD62lekEP45qsgf5Ex8ZlL5+3xxIc4H9Vt43Y\ns48h/36ppKLlEhIYQHRMbPZylN1OcGDROTdu28G85Z8wb0IYPt5eJZItOCgwd7ZoO8FBxniDkKBA\n7E7rzkfbCQkymnBff6V79vX3P9md9IwMjp44ycCw8cb92GPo3vcdls7K3Q9+PUKCgoi25+yxno+O\nJjgwMOfvcF4XFU1wUBDly5fj7jub4OHhwe3VquLl6UlMbBy79/zBvZ06YLPZ6Na5I3MWLnFZTmcZ\nSfG4eVXIXnb3rkBGYgIAZavXw83TG/+n3gB3D9z9gvDu9Ah4eGQPaEyLOo2bty/YbNdWZV6F4OAg\nou327OXz0VEEBwXlvy4qipBgY91vmzazYPES5s6cjo+PNwCVKlVk1vSp2eujo6NdmjVLSFAA0TE5\n7/koe0yR76W96hAB/r5UDgmmYd3apKVnEBMXT2AJDWi91VjpS95Vituy4Hw7G0b9nfcLPz2f+08F\nRiulOjku9QooFLLuNyOfx8wkZ1ckqysiv8dymTZSg59/PwTAXyfPE+zrjVe5sgCkZaQzbOVqLqYY\nf8afJ85SM8SfiS/+g5VvPc3y/k/xaOvG9PpbyxIvFADaNr2T1b8afaP7Dh4hJNAfL8/yRd7uvD0G\nz3LlKFvmxhwgE9qiKat/MQYy7TtwiODAQLw8PQu9zYXEJCaHL2HO2OH4VfAp2WzrjaND9qmDBAfl\nZKtauRKJSUlEnjlLWlo6v2zaSmiLZuw/dJihYycDsHFrBI3q16VicBA/frKUlXE19fgAABY9SURB\nVPOms3LedIIDA1xaKACEtmrBmnXrjaz7DxASFISXo/m5apXKJCVdJPL0GdLS0vjlt02EtmpBaMsW\nbN2+k4yMDOLi47mYnIy/ny9zFi5l/4GDAOzZu4+aNUrm9ZpybD/l6t8NgEdINdITE8i8bIwBSjnw\nOzFLxhK7cirxXy8i7fxJEtd/SXpcNB6VawDgVsGfzNQUlxcKAKGtW7NmrdGqse+v/YQEBePlZRSl\nVatUISkpicjTp0lLS2PDrxtp07oVFy4kMmX6TGZOm4Kvb05L4uzw+Wz41Rg8+vW339GxfXuX5wUI\nbX4PqzdkvZcOExwYUOR7afuevSxd9RVgdGNcTE7G37dCobfRbm3F/WZoIyLugD/gA9jz2SYCo5vi\nUxF5ELgT2Ao8BHwsIiFAf6XU4AIeQwFviogNqA5kdcRHAJ2Bj4GOwHZgBzBMRDyAQCBcKfVIMf+2\nK9xdqwoNq4XwwvRPsNlsDH6sM19v24t3udu49866vHpfK3rO/hx3Nxv1qwTTqUntou+0hNzTWGhc\nrzbP9B+Gm5uNoa+9xJer1+Pj5UnXti3pP2oKZ6PsHD11mhffHckTf7+XB7u0IyomjgC/G9dlck+T\nhjSqX5dnX3sHm82Nof178+UPP+Pj7UnX9qG8OXwsZ89Hc/RkJN3fGMjj/7yfi8mXiI1P4O0R2WdZ\nZczgt6hSMaSQRypGtjsa01jq8Wyf/rjZ3Bjy1mt89f1qvL296NqhLcPefp0BI40M93fpQM3q1cjI\nyCAzI5OnevXjtrJlGTfsPZdmKsjddzahUQPhuVf+bWR9tz9fffcDPt5e3NupA0MHvMWA98OMrF27\nULP67QB069KJZ3v2AWDQW2/g5uZG2JABjJo4FXd3d8rddhtjhg8pkcxpp49x+dxJ/J/uT2ZmJolr\nP6Vc45ZkpFwi1THwMa9Lv/+Gz/3P4PdkP3Bz48KaVSWS7e677qRRgwY83+Nl3GxuDB74Ll9/8x3e\n3l7c26UzQwa9x3uDhwJwX7du1KxRg8+++JK4uDjeHZjzUTZ65AgeuP8+hrw/nLnzF9D0nrvp0L5d\nQQ97XbLfS30HYLPZGPpGb778ca3xnm/fhjdHjMt5L/UfzOMP3seT/7qfYRNm8vzrA0lJSWXoG71x\nczN3Qt/qTZvw+OShBNasRvrlyzR9/AHCH32Vi7HxpuYqjpvxrJO2a+2ncgxwfAhjD78uMBH4AGii\nlEoUkaUYAxxXAwsxBjhexhjDcA4Ix+hicAdGKKV+KOSx5gF3AQeABsD/YbROLMIYJ5GKMcAxUkTe\nBh7DaHUYXNQAx0vfz7V8B125B4wP8/Rju01OUjj3msZeYtqZgyYnKZpH5XpcPn/M7BhFKhNSk9TY\ns2bHKFJZf2O+kfOT3jA5SdFC3plOSqL1v3hu8/Yl7bQyO0aRPKoYw8h622qamqMo4ZnHwHlgzA0Q\ncH+Yy75fYn58/4ZmL0hxWxYOK6XecVpelvWLUqq70/Uv5HPbqx5PoJQqaGTY3/PZdjIw+WrvW9M0\nTdO0q2P6DI4i0gvjKIm8Bt1s8zVomqZpN7+bcYDjNRcLSqmlrgyglJqPMfeCpmmappV6N2OxoE9R\nrWmapmlaoUzvhtA0TdO0m0lmRkbRG5UyuljQNE3TNBfS3RCapmmapt1ydMuCpmmaprnQzdiyoIsF\nTdM0TXMhK51a2lV0saBpmqZpLnQzTvesxyxomqZpmlYo3bKgaZqmaS6kxyxomqZpmlaom7FY0N0Q\nmqZpmqYVSrcsaJqmaZoL3YwtC7pY0DRN0zQXuhmLBVtmZqbZGTRN0zRNszA9ZkHTNE3TtELpYkHT\nNE3TtELpYkHTNE3TtELpYkHTNE3TtELpYkHTNE3TtELpYkHTNE3TtELpYkHTNE3TtELpYkHTNE3T\ntELpYkGzDBG5x+wMV0tEBpmdQdM07UbR0z1fBxF5obD1SqmPblSWoojI+4WtV0qF3agshZgsIn9T\nSqWZHeQqhIhINyACSM26Uil10bxIVxKRT5VST5id42qIyL+AHkAFwJZ1vVKqi2mh8iEiHQpbr5Ta\ncKOyFEVEqgE1lVIbReQ2pVSK2ZnyIyKdgGeUUr0cy18A05VSv5gaTMumi4Xr8w+gIfAbkAZ0BBRw\nDLDaPNo1gCrAeoys9wLngO0mZsorCTgoIr+T+wv4/8yLVKB/AA/nuS4TqG1ClsLEiMgYYBu5n9Pv\nzYtUoIlAH4zXpZUNBppivHfSgVbAbiAe4zVgiWJBRN4EHge8gbuA8SJyRik13txk+RoDPO+03Af4\nAmhrThwtL10sXB8v4B6lVDqAiHgAXyul3jU3Vr6qKaXuc1qeLCI/KaVmm5boSpPMDnC1lFL1AUTE\nH8hQSsWbHKkgZYHKwENO12UCViwWdgOblFKXzA5ShCSgtlIqEUBEfIAPLdiC87BSqq2I/M+x/Caw\nCbBiseCulDrstBxlWhItX7pYuD7VMZpMYx3L3sDt5sUpVBURaaiU+gtARBpgtDRYye9Af+BuIANj\nz22GqYkKICJdgdnAJaCsiGQAvZRSv5mbLDelVA/nZREpA8wxKU5RfgSOicgBjNYvwHrdEEAtwLk5\n/xJGy53VuDt+ZrVylsO6n/mfi8gWYCtG7lBgmbmRNGdWfeGUFhOB3SKStVfpC4wwL06h3gQ+FJGs\nYuYUYLUWkA8xmnDDMPaIOwJLAKvtsYGRsZNS6gyA43ldCbQ3NVUeIvIS8AEQhPEF5w58Z2qogg0G\nngPOmB2kCKuA/SKy17HcGON1ajUrRWQdUE9E5gKdgWkmZ8qXUmqCY5zC3RhdOxOVUsdNjqU50cXC\ndVBKLQOWiUggxoCsDKVUjMmx8qWU+llENiqlLolIAFBDKbXL7Fx5+CilJjstbxGRn01LU7jUrEIB\nQCl1UkQumxmoAL2BOsAPSqnOjkGEtUzOVJBdwHqrD3BVSo0TkXCM59UGHFJKxZkcKz/zMbqbWmKM\nVxmD0YViOSJSAxgK3INRLGwXkeHO7zHNXLpYuA4iMhCjC2IF8AtgF5EtSqlCjzwwg4jMxHgDfg+s\nAzaLSKZS6lWTozlzF5HmSqntACLSCuse3ntERGZjDBi1AV2Aw4XewhyXHAViWRFxU0p94+jDnm52\nsHx4AMoxwNW5G8JSA1wdR0GVwWgm/xYIEJFFSqlwc5MZHGOnbsMoFO4npyXJA9gI3GlStMIsAuYC\nb2G0KnZyXPeAiZk0J7pYuD7/dAwgegX4Sin1gYX3hO9SSvUTkTeAxUqpqSKyxuxQebwGTBeRho7l\nPx3XWVEv4GmM0dpZI+A/MTVR/iJEpC+wGlgnIicBT5MzFcSKBUx++mB0Nz0J/K6UGiAiawFLFAvA\n3zG+dFsCe8k5DDUDo7i1Inel1OdOy/9xfK5qFqGLhevjLiJuwDNA1h66j4l5CnObiFTF6BN+xLH3\n4WdyplyUUn+KyENAPYwPtgNKqWSTYxXEhtH/byNnAJnVDpdFKfV21vH1jhaFIMCqBW1pGeCarpRK\nE5EnyBmjVM7EPLkopb4FvhWR55RSy53XOQbmWlGq4/lcT05LnSXnhLhV6WLh+nwJnAU+VUodEJFh\nGKN5rWg2RrPkSqXUKREZBXxmcqZcROQ5YDiwD6MZtbaIvKeU+tLcZPlajNEFtZ6cwZidAUvtDYlI\nBaCviIQopfqLSGes27VTWga47hSRQ4BSSu0WkX7ACbND5eM3EZkIBDqWs55TKx6x9RLG/30oRqEY\nAbxsaiItF10sXAfH5CbOxyxPV0olAIjIq0qpeeYku5JjNknnGSWHKaUyARwDiUaakyyX1zC6Sy4C\niIg38BNGUWY11ZRSzpPI/Mcx8txqlgJrMCaRAgjBOGrDin3BpWKAq1Lqdcd7JuuQ6W9wdEGIyENK\nqa/NS5fLhxjFVn+ML+KHMLrPLENEqjstjiSnyyQTY1yIZhFW3cMolbIKBYcnTQtyFbIKBYeOpgXJ\nLd15umTHpDdWHRlfVkSy56lwTKtrxQ83H6XUXByzNyqlPgHKmxupQO4i0jxrwcoDXJ0KBZRSx5VS\nWUfCvGFSpPxcVkotAeKUUp8rpV4A+pkdKo/PMVo4v8GY/fZ7jB2EAxiHqGoWoVsWSo6t6E0swypZ\nfxOR7zCOLLFhjIj+1dREBRsCrHVMxuSG0XRqqb02BzcRqYNjPIWI3E/OZD1WU5oGuBbEKu8lAJuI\ndMQ4SqsXxtE6ljpsVinVAkBElgEPKqVOOZZrYLQ0aBahi4WSY7nBboWwRFal1Hsi0h5ojvHlO9pq\nMyJmUUqtBxo6pnvOtOhx9gB9gXlAcxE5gzGI0IpFTWkb4FoQS7yXHJ7HmOr7dYxuiAeBt01NVLD6\nWYUCGK01IlLfzEBabrpY0EwnIv/Oc1XWKOi7ROQupZRlpicWkQjy+UIQEQCUUi1vdKYi3As8rZSy\n/Fz7pWyAa2nQQyk1yvH7SwAiMhlrnhdkq4hswxggngE0wyhsNYvQxULJsVJzZFHMztoSOIIxv77V\np3h9GriM8WVWGg7tqgB8LSJxwMfAF0opS87iR+ka4FoQs99LiMijGK/TDiLiPAGTB8bZMi3XuuAY\nNNoQaITxHC5USv0BxtgVpZRVjzK7ZehioRhEpFFh65VS+4ABNyjOVRORNhjTPP9HRCo7TaX6gpm5\ngAYYp9CtjTGwyVkmRhOqVXyBMVfFAqA7FvhyKIxSagwwRkQqA/8EfhCRSCBcKfWLuemucMUAVxGx\n5ABXxyGpvjj9/5VSJ4AppoXKyfGFiOzEOLx3O8beenWM1+uzJkYrlOMkd3/ls2osxrwLmol0sVA8\nhZ3WORPoopSKuFFhrobjeOvqQF3gP8CrIhKglHpdKXXS3HS0wzgD5hQsuNeTx3JgKlAf43XgXCxk\nYsEPNcdRG08CDwN2jOl/e4jIo0opK43e35TPANcNpibKh4jMwzj09Ay5D/Vr6ZgQyXRKqWOOiddW\nY0wY1QMYBrwP3FfYbS3I0gX5rUIXC8WglOpsdoZiaO44kdD/AJRSI0TEEkcaOE4cdAJ43OwsRVFK\nTQAm5Dc7XhYrHWsvIhswJuNZDjymlIp2rFohIpvNS5av9zAKx+YYX75WHeDaDKie5/BjK7rsmDRq\nIjBNKfWbo4Aobaz+PN8SSuMLx3QiEkX+L2Abxsj4kBsc6WqUEZEy5BxCF4SFpqgtbQoqFBzeACxR\nLAC9lFL7C1hntW6I9Uqpjlj3cNksWzGmzbb6oFEPERkC/AsYJiItAG+TM2mllC4WikEpFWx2hmKY\nDGwBqovID0BDjJndNNezTLNpIYUCGANLreSYiKwEtuGYRArAKkfDOB0J445x1tGDGJOGZe0kWO35\nfA6jte5Rx5lHa2Ocsry0scz76Vami4Xr4GjSv6KFQSllmX5rEWnraMqNBjoAjTE+iFUpPIa9tCgt\nzaZW+xA+4vjpa2qKglm+m8yZYyzSVKdlK54VNRcR8XB0SzpbaUoYLRddLFyfvk6/l8Hob7XaB91C\nEXkP+AAY5HR9FRFBKWXFY661G8NSRU1h5ycRkS+VUo/cyDx5KaWOO7I0Ap5USg13LM/EOqenLpUc\nJzibhnFIcgMRGQ1sUEr9pJRaYG46DXSxcF2UUnvzXLVbRH4CRpuRpwAfYJxAJoQrz96XiTUnaCnt\nrLbHfjOw0unUw4HBTsuLgTlY5xwrpdFIjCOJss6EOx1j3M9PpiXSctHFwnXIZ+bBKo6LZSilVgIr\nRaSrUspyZ/ArrUSktVJqS57rHlNKfY4FjrW/SqWpqLFSK0gZpdTGrAWl1C4RKU3PpRVdVkrZRSQT\nQCl13nHeFc0idLFQDCKyRCnVA+MQr2OOqzMxRkdb6tS/IjJXKdUHGCsiY/KszlRKtTIj101gpIgc\nxTjczx+YhTEu5HMrHGsvIoVOtOU4ZbnZk3GVVttE5DPgN4yTiHXGGJSpFd9REQkDgkQka06QfSZn\n0pzoYqF4GjpmSKvDlTMOPou1Rpl7iMgEcooaZ1baWytVlFL3iciDGDPkpWDMw2+libjucPysjTER\nV9YXW1vgD+AjC0zGVSoppd4QkXsxpk5OB8Yrpax+uKfV9QKeATYCbTC6ID41NZGWiy4Wiqc0zTiY\n1Vyad3yFdh1EpBnGfAofA5WAt0VkgGPKX9Mppd4FEJH/As2yRpg75tpYZWa2Yoo1O0AWEflMKfU4\nsNbpui1KqdYmxirtKgJeSql/A4jIQIxxVmcKvZV2w+hioRhK2YyDH5qd4SY1FnhNKXUAQERCgU8w\n9oqs5HaMI3TsjuXyQC3z4hTMMWnQ01x5zoWXlFKPmRbMQUQeAwZinA31vNMqd2CXOaluGh9hnG8l\nyx/Ah8DfzImj5aWLBU0rBqVU3g+xbViz9WYCsFNEEjC6nSpgjDy3ohXAOOCc2UHy4xi8+rmIvKOU\nmmR2nptMeaVUdouXUuq/IvKumYG03HSxoGnFICIvYRyWGoQxyZUbxgmaLMUxLfVyEQnE2Fu3W/ic\nBn8BS6yaT0ReVUrNAyo6xgE5y1RKvWdGrpvEcRGZRM7Ymi5Y/3T1txRdLGha8fTGGOD6g+MEXf/C\nQs37TlMT57cOC05NDMb4j10isgdjGmXA6IYwL1Iuxxw/44CT5HSVVMQ4Jb0uForvRcelK8ag0c0Y\n3XqaRehiQdOK55Jjvv2yIuKmlPrGMf33dLODOVh+PE0+RmF0Q1hyUJtSKmuCIC+ML7WeGBOdvQS8\nZlau0kxEWimltmKMTTgD/NdpdTf0pHGWoYsFTSueCBHpC6wG1onISYzBg1Zxv1JqnuP0xPm1MAy4\n0YGuwj6l1EKzQxRFKTVYRB7HmAdgL9BWKWUv4mZa/jphnMUz7+yyoGeYtRRdLGha8XwC9MCYyz4D\nY0rtNaYmyu2Y4+ef+ayz5JgAIFpENmDMXeHcDWGJwiafwusAUA94z9G1Y4mcpYlSarzj14NKqbyT\nxmkWoosFTSue5Vh75L7znPpWLQ7y+sVxsaq8hZcVj34prYJFpBsQQe7Tk180L5LmTBcLmlY8lh65\n76SJ0+9lgNYYX3ofmROnSJZ9PvWcJSXqH8AjGEcXZWLMC5KBMQOpZgG6WNC04rH6yH0gZybHLCLi\nTs6Z/aymtBU2muuMwRjgehTjKBMfYJipibRcdLGgacVj6ZH7WUTEM89VVYAGZmQpSikrbDTX6g/c\nlTVQVESCgJ8xJurSLEAXC5pWPKVi5D45/eqBGGdFTQAsOftgaSpsNJeLBGKclu3AYZOyaPnQxYKm\nFY+lR+47CXNcss4w6Q8kmxenUHvJGbOQiYULG83lEoDdIvILxgyObYBjWTNlWvB9dcvRxYKmFY/V\nR+5nyWrejQEQkWCMQzxXmpoqfx8A/TBOJOUG+AFDgEVmhtJuiB8dlyxWOt27hi4WNK1YStHI+FMY\n0xNnica6zbvvAA9jNElrt5BS9H66ZeliQdNuQk4TCCVjHLWx0bHcBthvZrZCHMw65bemadaiiwVN\nuzllTSCUd+IgKzfvnheRzRgnEbLyOBBNu+XoYkHTbkKltFl3o+OiaZrF2DIzLTthmqZpmqZpFuBm\ndgBN0zRN06xNFwuapmmaphVKFwuapmmaphVKFwuapmmaphVKFwuapmmaphXq/wHWCVlY9O2sfAAA\nAABJRU5ErkJggg==\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f91502d5c18>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(10, 7))\n",
"plt.xticks(rotation='90')\n",
"sns.heatmap(corrmat, square=True, linewidths=.5, annot=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f9369355-52ef-6194-39f7-9f01b153217c"
},
"source": [
"## Area of Home and Number of Rooms"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "29f327cc-fa0d-49a8-34bd-1484ed080203"
},
"outputs": [
{
"data": {
"text/plain": "<matplotlib.collections.PathCollection at 0x7f915e5e3a58>"
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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U8sTjtI2OLLqeMnD6ni/+5EBjj4GRJCn3Wnw6ry6bG0yfuzd1oDHA6K7ddalb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\nA0OUJElSBoYoSZKkDP5/S57IW41peMgAAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f915ae003c8>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(10, 7))\n",
"plt.scatter(x=train_df['full_sq'], y=train_df['price_doc'], c='r')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "f6ac8c69-bc9d-cf60-6649-c66018b006b5"
},
"outputs": [
{
"data": {
"text/plain": "[<matplotlib.text.Text at 0x7f915e5b03c8>,\n <matplotlib.text.Text at 0x7f915e5ff080>,\n <matplotlib.text.Text at 0x7f915e549908>]"
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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DKfgSqSqVMD3noFRa6JGIiMgyllnoAYgsuDQlt3cP8cEDmMFBfEsLyfU7KN6xGyL9fiIi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\n4ksQBEEQBGEZEfElCIIgCIKwjPz/eEoewcdB32cAAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7f915e5f3080>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(10, 7))\n",
"ind = train_df[train_df['full_sq'] > 2000].index\n",
"plt.scatter(x=train_df.drop(ind)['full_sq'], y=train_df.drop(ind)['price_doc'], c='r', alpha=0.5)\n",
"ax.set(title='Price by area in sq meters', xlabel='Area', ylabel='Price')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "419bc94e-6e7f-bb79-51aa-916af0927665"
},
"outputs": [
{
"data": {
"text/plain": "37"
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(train_df['life_sq'] > train_df['full_sq']).sum()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"_cell_guid": "ab02189c-3d5c-5fc3-27d1-ae731e558115"
},
"outputs": [
{
"data": {
"text/plain": "[<matplotlib.text.Text at 0x7f915e5a1fd0>,\n <matplotlib.text.Text at 0x7f915e4dfef0>]"
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": "<matplotlib.figure.Figure at 0x7f914ff71470>"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(10, 7))\n",
"sns.countplot(x=train_df['num_room'])\n",
"ax.set(title='Distribution of room count', xlabel='num_room')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "b253640d-6ee4-e011-a0c2-e2a05aa7ce2d"
},
"source": [
"## Sale Type"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "a6fbac09-d1ba-175f-ee3b-f121ed6abb69"
},
"outputs": [],
"source": ""
}
],
"metadata": {
"_change_revision": 199,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | 0001/163/1163810.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "38541fd0-854b-b5ae-dd1d-d5a181160c1c"
},
"source": [
"# Import Basic Libraries:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f5fb6410-9e53-24bf-8f8e-0fd19e110c62"
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e628c172-6edc-c818-1532-e787d782d612"
},
"source": [
"# 1. Data Input, Preparation, & Exploration"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "478defd6-4f2f-31bf-8001-ad4e8fd11798"
},
"source": [
"## 1.1 Read in Transaction Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "256f3663-58ec-121d-a1ae-f345bbfb9c6d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No. of Rows: \t\t 284807\n",
"No. of Columns: \t 31\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>V1</th>\n",
" <th>V2</th>\n",
" <th>V3</th>\n",
" <th>V4</th>\n",
" <th>V5</th>\n",
" <th>V6</th>\n",
" <th>V7</th>\n",
" <th>V8</th>\n",
" <th>V9</th>\n",
" <th>...</th>\n",
" <th>V21</th>\n",
" <th>V22</th>\n",
" <th>V23</th>\n",
" <th>V24</th>\n",
" <th>V25</th>\n",
" <th>V26</th>\n",
" <th>V27</th>\n",
" <th>V28</th>\n",
" <th>Amount</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>-1.359807</td>\n",
" <td>-0.072781</td>\n",
" <td>2.536347</td>\n",
" <td>1.378155</td>\n",
" <td>-0.338321</td>\n",
" <td>0.462388</td>\n",
" <td>0.239599</td>\n",
" <td>0.098698</td>\n",
" <td>0.363787</td>\n",
" <td>...</td>\n",
" <td>-0.018307</td>\n",
" <td>0.277838</td>\n",
" <td>-0.110474</td>\n",
" <td>0.066928</td>\n",
" <td>0.128539</td>\n",
" <td>-0.189115</td>\n",
" <td>0.133558</td>\n",
" <td>-0.021053</td>\n",
" <td>149.62</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>1.191857</td>\n",
" <td>0.266151</td>\n",
" <td>0.166480</td>\n",
" <td>0.448154</td>\n",
" <td>0.060018</td>\n",
" <td>-0.082361</td>\n",
" <td>-0.078803</td>\n",
" <td>0.085102</td>\n",
" <td>-0.255425</td>\n",
" <td>...</td>\n",
" <td>-0.225775</td>\n",
" <td>-0.638672</td>\n",
" <td>0.101288</td>\n",
" <td>-0.339846</td>\n",
" <td>0.167170</td>\n",
" <td>0.125895</td>\n",
" <td>-0.008983</td>\n",
" <td>0.014724</td>\n",
" <td>2.69</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>-1.358354</td>\n",
" <td>-1.340163</td>\n",
" <td>1.773209</td>\n",
" <td>0.379780</td>\n",
" <td>-0.503198</td>\n",
" <td>1.800499</td>\n",
" <td>0.791461</td>\n",
" <td>0.247676</td>\n",
" <td>-1.514654</td>\n",
" <td>...</td>\n",
" <td>0.247998</td>\n",
" <td>0.771679</td>\n",
" <td>0.909412</td>\n",
" <td>-0.689281</td>\n",
" <td>-0.327642</td>\n",
" <td>-0.139097</td>\n",
" <td>-0.055353</td>\n",
" <td>-0.059752</td>\n",
" <td>378.66</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>-0.966272</td>\n",
" <td>-0.185226</td>\n",
" <td>1.792993</td>\n",
" <td>-0.863291</td>\n",
" <td>-0.010309</td>\n",
" <td>1.247203</td>\n",
" <td>0.237609</td>\n",
" <td>0.377436</td>\n",
" <td>-1.387024</td>\n",
" <td>...</td>\n",
" <td>-0.108300</td>\n",
" <td>0.005274</td>\n",
" <td>-0.190321</td>\n",
" <td>-1.175575</td>\n",
" <td>0.647376</td>\n",
" <td>-0.221929</td>\n",
" <td>0.062723</td>\n",
" <td>0.061458</td>\n",
" <td>123.50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>-1.158233</td>\n",
" <td>0.877737</td>\n",
" <td>1.548718</td>\n",
" <td>0.403034</td>\n",
" <td>-0.407193</td>\n",
" <td>0.095921</td>\n",
" <td>0.592941</td>\n",
" <td>-0.270533</td>\n",
" <td>0.817739</td>\n",
" <td>...</td>\n",
" <td>-0.009431</td>\n",
" <td>0.798278</td>\n",
" <td>-0.137458</td>\n",
" <td>0.141267</td>\n",
" <td>-0.206010</td>\n",
" <td>0.502292</td>\n",
" <td>0.219422</td>\n",
" <td>0.215153</td>\n",
" <td>69.99</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 31 columns</p>\n",
"</div>"
],
"text/plain": [
" Time V1 V2 V3 V4 V5 V6 V7 \\\n",
"0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n",
"1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n",
"2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n",
"3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n",
"4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n",
"\n",
" V8 V9 ... V21 V22 V23 V24 \\\n",
"0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 \n",
"1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 \n",
"2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 \n",
"3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 \n",
"4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 \n",
"\n",
" V25 V26 V27 V28 Amount Class \n",
"0 0.128539 -0.189115 0.133558 -0.021053 149.62 0 \n",
"1 0.167170 0.125895 -0.008983 0.014724 2.69 0 \n",
"2 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0 \n",
"3 0.647376 -0.221929 0.062723 0.061458 123.50 0 \n",
"4 -0.206010 0.502292 0.219422 0.215153 69.99 0 \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"../input/creditcard.csv\")\n",
"print(\"No. of Rows: \\t\\t\", data.shape[0])\n",
"print(\"No. of Columns: \\t\", data.shape[1])\n",
"data.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "302cfc01-d0c3-ddf7-a897-73d3cca6dc73"
},
"source": [
"## 1.2 Class Distribution"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "d7d5ce17-5636-39fd-e257-787010f848f1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No. of normal transactions: \t\t 284315\n",
"No. of fraudulent transactions: \t 492\n",
"% normal transactions: \t\t 99.82725143693798\n",
"% fraudulent transcations: \t 0.1727485630620034\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7fe7f74a99e8>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7f74bf550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"norm_count = data[data['Class'] == 0].shape[0] # Normal transactions\n",
"fraud_count = data[data['Class'] == 1].shape[0] # Fraudulent transcations\n",
"total_count = data.shape[0]\n",
"print(\"No. of normal transactions: \\t\\t\", norm_count)\n",
"print(\"No. of fraudulent transactions: \\t\", fraud_count)\n",
"print(\"% normal transactions: \\t\\t\", norm_count/total_count * 100)\n",
"print(\"% fraudulent transcations: \\t\", fraud_count/total_count * 100)\n",
"pd.value_counts(data['Class'], sort = True).sort_index().plot(kind='bar')\n",
"plt.title(\"Class Histogram\")\n",
"plt.xlabel(\"Class\")\n",
"plt.ylabel(\"Frequency\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "842e8fc6-56e2-1457-4edc-ab92e4973f5f"
},
"source": [
"## 1.3 Standardize Input Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "f6f35ec1-b928-ac06-da74-4227c584a6f8"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>V1</th>\n",
" <th>V2</th>\n",
" <th>V3</th>\n",
" <th>V4</th>\n",
" <th>V5</th>\n",
" <th>V6</th>\n",
" <th>V7</th>\n",
" <th>V8</th>\n",
" <th>V9</th>\n",
" <th>V10</th>\n",
" <th>...</th>\n",
" <th>V21</th>\n",
" <th>V22</th>\n",
" <th>V23</th>\n",
" <th>V24</th>\n",
" <th>V25</th>\n",
" <th>V26</th>\n",
" <th>V27</th>\n",
" <th>V28</th>\n",
" <th>Class</th>\n",
" <th>Amount_scl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1.359807</td>\n",
" <td>-0.072781</td>\n",
" <td>2.536347</td>\n",
" <td>1.378155</td>\n",
" <td>-0.338321</td>\n",
" <td>0.462388</td>\n",
" <td>0.239599</td>\n",
" <td>0.098698</td>\n",
" <td>0.363787</td>\n",
" <td>0.090794</td>\n",
" <td>...</td>\n",
" <td>-0.018307</td>\n",
" <td>0.277838</td>\n",
" <td>-0.110474</td>\n",
" <td>0.066928</td>\n",
" <td>0.128539</td>\n",
" <td>-0.189115</td>\n",
" <td>0.133558</td>\n",
" <td>-0.021053</td>\n",
" <td>0</td>\n",
" <td>0.244964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.191857</td>\n",
" <td>0.266151</td>\n",
" <td>0.166480</td>\n",
" <td>0.448154</td>\n",
" <td>0.060018</td>\n",
" <td>-0.082361</td>\n",
" <td>-0.078803</td>\n",
" <td>0.085102</td>\n",
" <td>-0.255425</td>\n",
" <td>-0.166974</td>\n",
" <td>...</td>\n",
" <td>-0.225775</td>\n",
" <td>-0.638672</td>\n",
" <td>0.101288</td>\n",
" <td>-0.339846</td>\n",
" <td>0.167170</td>\n",
" <td>0.125895</td>\n",
" <td>-0.008983</td>\n",
" <td>0.014724</td>\n",
" <td>0</td>\n",
" <td>-0.342475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.358354</td>\n",
" <td>-1.340163</td>\n",
" <td>1.773209</td>\n",
" <td>0.379780</td>\n",
" <td>-0.503198</td>\n",
" <td>1.800499</td>\n",
" <td>0.791461</td>\n",
" <td>0.247676</td>\n",
" <td>-1.514654</td>\n",
" <td>0.207643</td>\n",
" <td>...</td>\n",
" <td>0.247998</td>\n",
" <td>0.771679</td>\n",
" <td>0.909412</td>\n",
" <td>-0.689281</td>\n",
" <td>-0.327642</td>\n",
" <td>-0.139097</td>\n",
" <td>-0.055353</td>\n",
" <td>-0.059752</td>\n",
" <td>0</td>\n",
" <td>1.160686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.966272</td>\n",
" <td>-0.185226</td>\n",
" <td>1.792993</td>\n",
" <td>-0.863291</td>\n",
" <td>-0.010309</td>\n",
" <td>1.247203</td>\n",
" <td>0.237609</td>\n",
" <td>0.377436</td>\n",
" <td>-1.387024</td>\n",
" <td>-0.054952</td>\n",
" <td>...</td>\n",
" <td>-0.108300</td>\n",
" <td>0.005274</td>\n",
" <td>-0.190321</td>\n",
" <td>-1.175575</td>\n",
" <td>0.647376</td>\n",
" <td>-0.221929</td>\n",
" <td>0.062723</td>\n",
" <td>0.061458</td>\n",
" <td>0</td>\n",
" <td>0.140534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.158233</td>\n",
" <td>0.877737</td>\n",
" <td>1.548718</td>\n",
" <td>0.403034</td>\n",
" <td>-0.407193</td>\n",
" <td>0.095921</td>\n",
" <td>0.592941</td>\n",
" <td>-0.270533</td>\n",
" <td>0.817739</td>\n",
" <td>0.753074</td>\n",
" <td>...</td>\n",
" <td>-0.009431</td>\n",
" <td>0.798278</td>\n",
" <td>-0.137458</td>\n",
" <td>0.141267</td>\n",
" <td>-0.206010</td>\n",
" <td>0.502292</td>\n",
" <td>0.219422</td>\n",
" <td>0.215153</td>\n",
" <td>0</td>\n",
" <td>-0.073403</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" V1 V2 V3 V4 V5 V6 V7 \\\n",
"0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n",
"1 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n",
"2 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n",
"3 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n",
"4 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n",
"\n",
" V8 V9 V10 ... V21 V22 V23 \\\n",
"0 0.098698 0.363787 0.090794 ... -0.018307 0.277838 -0.110474 \n",
"1 0.085102 -0.255425 -0.166974 ... -0.225775 -0.638672 0.101288 \n",
"2 0.247676 -1.514654 0.207643 ... 0.247998 0.771679 0.909412 \n",
"3 0.377436 -1.387024 -0.054952 ... -0.108300 0.005274 -0.190321 \n",
"4 -0.270533 0.817739 0.753074 ... -0.009431 0.798278 -0.137458 \n",
"\n",
" V24 V25 V26 V27 V28 Class Amount_scl \n",
"0 0.066928 0.128539 -0.189115 0.133558 -0.021053 0 0.244964 \n",
"1 -0.339846 0.167170 0.125895 -0.008983 0.014724 0 -0.342475 \n",
"2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 0 1.160686 \n",
"3 -1.175575 0.647376 -0.221929 0.062723 0.061458 0 0.140534 \n",
"4 0.141267 -0.206010 0.502292 0.219422 0.215153 0 -0.073403 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"data['Amount_scl'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))\n",
"data = data.drop(['Time','Amount'],axis=1)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "dd464857-7a67-7b12-e682-12911cfe145e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X.shape: (284807, 29)\n",
"y.shape: (284807,)\n"
]
}
],
"source": [
"X = data.ix[:, data.columns != 'Class'] # features\n",
"y = data['Class'] # labels\n",
"print(\"X.shape: \", X.shape)\n",
"print(\"y.shape: \", y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "35336b32-eea8-330d-04e4-fa356b077d24"
},
"source": [
"## 1.4 Handle Class Imbalance"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "2779886c-d420-e1fa-be0b-bed2aa09a2d0"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "22fea321-d877-f4e1-8497-aa8447ac0d43"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'y_train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-d7925dbcdbb5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Get the indices of the fraudulent and normal classes:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mfraud_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_train\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mnum_fraud\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfraud_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mnormal_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0my_train\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'y_train' is not defined"
]
}
],
"source": [
"# Get the indices of the fraudulent and normal classes:\n",
"fraud_idx = np.array(y_train[y_train == 1].index)\n",
"num_fraud = len(fraud_idx)\n",
"normal_idx = y_train[y_train == 0].index\n",
"\n",
"# From the normal indices, sample a random subset (subset size = # of frauds):\n",
"normal_idx_sample = np.random.choice(normal_idx, num_fraud, replace=False)\n",
"normal_idx_sample = np.array(normal_idx_sample)\n",
"\n",
"# Group together our normal and fraud indices:\n",
"# (we'll have a balanced class distribution, 50% normal, 50% fraud)\n",
"undersample_idx = np.concatenate([fraud_idx,normal_idx_sample])\n",
"\n",
"# Grab the records at the indices:\n",
"undersample_data = data.iloc[undersample_idx,:]\n",
"\n",
"# Split into features and labels:\n",
"X_undersample = undersample_data.ix[:, undersample_data.columns != 'Class']\n",
"y_undersample = undersample_data['Class']\n",
"\n",
"norm_count = undersample_data[undersample_data['Class'] == 0].shape[0]\n",
"fraud_count = undersample_data[undersample_data['Class'] == 1].shape[0]\n",
"\n",
"print(\"---Undersampled Data Set---\")\n",
"print(\"No. of normal transactions: \\t\", norm_count)\n",
"print(\"No. of fraudulent transactions: \\t\\t\", fraud_count)\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "54570223-ec61-0bbc-22f7-41ae876cbc22"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'y_train' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-184ceb084de2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'y_train' is not defined"
]
}
],
"source": [
"y_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "d0fe1f55-5163-5229-eafd-d96215f417df"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "78ffa6bc-ab94-7c24-9d76-bd0e03d8bfc6"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"# 70% training data, 30% testing data\n",
"X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "b75eb1fd-6c0b-cffa-5be6-0f21c76b3e5d"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>V1</th>\n",
" <th>V2</th>\n",
" <th>V3</th>\n",
" <th>V4</th>\n",
" <th>V5</th>\n",
" <th>V6</th>\n",
" <th>V7</th>\n",
" <th>V8</th>\n",
" <th>V9</th>\n",
" <th>V10</th>\n",
" <th>...</th>\n",
" <th>V20</th>\n",
" <th>V21</th>\n",
" <th>V22</th>\n",
" <th>V23</th>\n",
" <th>V24</th>\n",
" <th>V25</th>\n",
" <th>V26</th>\n",
" <th>V27</th>\n",
" <th>V28</th>\n",
" <th>Amount_scl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>95707</th>\n",
" <td>1.167692</td>\n",
" <td>0.225779</td>\n",
" <td>0.239349</td>\n",
" <td>1.087745</td>\n",
" <td>-0.146246</td>\n",
" <td>-0.503627</td>\n",
" <td>0.128902</td>\n",
" <td>-0.070329</td>\n",
" <td>0.053785</td>\n",
" <td>-0.022168</td>\n",
" <td>...</td>\n",
" <td>-0.173409</td>\n",
" <td>0.059678</td>\n",
" <td>0.190525</td>\n",
" <td>-0.078399</td>\n",
" <td>0.073477</td>\n",
" <td>0.591273</td>\n",
" <td>-0.298849</td>\n",
" <td>0.023668</td>\n",
" <td>0.018848</td>\n",
" <td>-0.267311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134264</th>\n",
" <td>0.174399</td>\n",
" <td>-2.141734</td>\n",
" <td>-0.049317</td>\n",
" <td>0.299497</td>\n",
" <td>-0.987938</td>\n",
" <td>0.588867</td>\n",
" <td>0.170460</td>\n",
" <td>-0.110208</td>\n",
" <td>0.879512</td>\n",
" <td>-0.708416</td>\n",
" <td>...</td>\n",
" <td>1.286884</td>\n",
" <td>0.166210</td>\n",
" <td>-0.587754</td>\n",
" <td>-0.652408</td>\n",
" <td>-0.854682</td>\n",
" <td>0.135290</td>\n",
" <td>1.008324</td>\n",
" <td>-0.137383</td>\n",
" <td>0.108613</td>\n",
" <td>2.045622</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160199</th>\n",
" <td>2.024826</td>\n",
" <td>-0.176528</td>\n",
" <td>-1.434116</td>\n",
" <td>0.085700</td>\n",
" <td>0.148483</td>\n",
" <td>-0.575064</td>\n",
" <td>-0.036593</td>\n",
" <td>-0.053517</td>\n",
" <td>0.677803</td>\n",
" <td>0.098181</td>\n",
" <td>...</td>\n",
" <td>-0.304399</td>\n",
" <td>0.311083</td>\n",
" <td>0.952930</td>\n",
" <td>0.029675</td>\n",
" <td>0.693949</td>\n",
" <td>0.257401</td>\n",
" <td>-0.465650</td>\n",
" <td>-0.005209</td>\n",
" <td>-0.059796</td>\n",
" <td>-0.349231</td>\n",
" </tr>\n",
" <tr>\n",
" <th>202778</th>\n",
" <td>2.063972</td>\n",
" <td>-0.917513</td>\n",
" <td>-0.791070</td>\n",
" <td>-0.050962</td>\n",
" <td>-0.833235</td>\n",
" <td>-0.528734</td>\n",
" <td>-0.449784</td>\n",
" <td>-0.223829</td>\n",
" <td>0.129043</td>\n",
" <td>0.603585</td>\n",
" <td>...</td>\n",
" <td>-0.527694</td>\n",
" <td>-0.727778</td>\n",
" <td>-1.277798</td>\n",
" <td>0.313903</td>\n",
" <td>-0.095561</td>\n",
" <td>-0.281492</td>\n",
" <td>0.396796</td>\n",
" <td>-0.029100</td>\n",
" <td>-0.051121</td>\n",
" <td>-0.183511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>221937</th>\n",
" <td>2.095684</td>\n",
" <td>-0.760005</td>\n",
" <td>-1.177668</td>\n",
" <td>-0.710860</td>\n",
" <td>-0.729446</td>\n",
" <td>-0.619706</td>\n",
" <td>-0.984984</td>\n",
" <td>-0.001752</td>\n",
" <td>-0.147767</td>\n",
" <td>0.252731</td>\n",
" <td>...</td>\n",
" <td>0.062998</td>\n",
" <td>0.274847</td>\n",
" <td>0.819967</td>\n",
" <td>0.044441</td>\n",
" <td>-0.438308</td>\n",
" <td>-0.147740</td>\n",
" <td>-0.088197</td>\n",
" <td>0.021530</td>\n",
" <td>-0.023101</td>\n",
" <td>-0.233487</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" V1 V2 V3 V4 V5 V6 V7 \\\n",
"95707 1.167692 0.225779 0.239349 1.087745 -0.146246 -0.503627 0.128902 \n",
"134264 0.174399 -2.141734 -0.049317 0.299497 -0.987938 0.588867 0.170460 \n",
"160199 2.024826 -0.176528 -1.434116 0.085700 0.148483 -0.575064 -0.036593 \n",
"202778 2.063972 -0.917513 -0.791070 -0.050962 -0.833235 -0.528734 -0.449784 \n",
"221937 2.095684 -0.760005 -1.177668 -0.710860 -0.729446 -0.619706 -0.984984 \n",
"\n",
" V8 V9 V10 ... V20 V21 \\\n",
"95707 -0.070329 0.053785 -0.022168 ... -0.173409 0.059678 \n",
"134264 -0.110208 0.879512 -0.708416 ... 1.286884 0.166210 \n",
"160199 -0.053517 0.677803 0.098181 ... -0.304399 0.311083 \n",
"202778 -0.223829 0.129043 0.603585 ... -0.527694 -0.727778 \n",
"221937 -0.001752 -0.147767 0.252731 ... 0.062998 0.274847 \n",
"\n",
" V22 V23 V24 V25 V26 V27 V28 \\\n",
"95707 0.190525 -0.078399 0.073477 0.591273 -0.298849 0.023668 0.018848 \n",
"134264 -0.587754 -0.652408 -0.854682 0.135290 1.008324 -0.137383 0.108613 \n",
"160199 0.952930 0.029675 0.693949 0.257401 -0.465650 -0.005209 -0.059796 \n",
"202778 -1.277798 0.313903 -0.095561 -0.281492 0.396796 -0.029100 -0.051121 \n",
"221937 0.819967 0.044441 -0.438308 -0.147740 -0.088197 0.021530 -0.023101 \n",
"\n",
" Amount_scl \n",
"95707 -0.267311 \n",
"134264 2.045622 \n",
"160199 -0.349231 \n",
"202778 -0.183511 \n",
"221937 -0.233487 \n",
"\n",
"[5 rows x 29 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_test.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "912bd6a7-b37c-b22a-723a-02ce696b9876"
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.metrics import auc,roc_auc_score,roc_curve,recall_score "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "bb202318-c46c-d889-4693-196003360b78"
},
"outputs": [],
"source": [
"def get_best_hypers_lr(X, y):\n",
" \"\"\" Search parameter space for the optimal values.\n",
" \n",
" -Perform Logistic Regression using a range of C parameter values and two different\n",
" penalty terms (L1 & L2)\n",
" -Compute mean recall scores using kfold cross validation for each run\n",
" -Output the C parameter and penalty term with the best recall score\n",
" \"\"\"\n",
" c_range = [0.01, 0.1, 1.0, 10.0, 100.0]\n",
" recall_max = 0\n",
" best_c = 0\n",
" penalty = ''\n",
" \n",
" for c_param in c_range:\n",
" print('='*25)\n",
" print('C parameter: ', c_param)\n",
" print('='*25)\n",
" print('')\n",
" \n",
" print('-'*25)\n",
" print('L1-penalty')\n",
" print('-'*25)\n",
" print('')\n",
" \n",
" lr_l1 = LogisticRegression(C=c_param, penalty='l1')\n",
" acc_score = cross_val_score(lr_l1, X, y, cv=5)\n",
" recall_score = cross_val_score(lr_l1, X, y, cv=5, scoring='recall')\n",
" l1_recall = np.mean(recall_score)\n",
" \n",
" print(\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
" print(\"Mean Recall: %0.3f (+/- %0.3f)\" % (l1_recall, np.std(recall_score)) )\n",
" print('')\n",
" \n",
" print('-'*25)\n",
" print('L2-penalty')\n",
" print('-'*25)\n",
" print('')\n",
" \n",
" lr_l2 = LogisticRegression(C=c_param, penalty='l2')\n",
" score = cross_val_score(lr_l2, X, y, cv=5)\n",
" recall_score = cross_val_score(lr_l2, X, y, cv=5, scoring='recall')\n",
" l2_recall = np.mean(recall_score)\n",
" \n",
" print(\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
" print(\"Mean Recall: %0.3f (+/- %0.3f)\" % (l2_recall, np.std(recall_score)) )\n",
" print('')\n",
" \n",
" # compare l1_recall & l2_recall:\n",
" if l2_recall > l1_recall:\n",
" # compare to max:\n",
" if l2_recall > recall_max:\n",
" recall_max = l2_recall\n",
" best_c = c_param\n",
" penalty='l2'\n",
" else:\n",
" # compare to max:\n",
" if l1_recall > recall_max:\n",
" recall_max = l1_recall\n",
" best_c = c_param\n",
" penalty='l1'\n",
" \n",
"\n",
" print('*'*25)\n",
" print('Optimal C parameter = ', best_c)\n",
" print('Optimal penalty = ', penalty)\n",
" print('Optimal recall = ', recall_max)\n",
" print('*'*25)\n",
" \n",
" return best_c, penalty"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "6e6ca420-fa59-8a81-3919-10fbb984ec2f"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X_undersample' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-1b1933e0d2b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbest_c_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpenalty_lr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_best_hypers_lr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_undersample\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_undersample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'X_undersample' is not defined"
]
}
],
"source": [
"best_c_lr, penalty_lr = get_best_hypers_lr(X_undersample,y_undersample)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "5a482730-429a-4cfc-fe2f-2787cf9b26a8"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'best_c_lr' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-13-d40f345d98f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Use best hyperparameters:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mlr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbest_c_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpenalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpenalty_lr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;31m# Train on full undersample data set:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_undersample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Test on unseen test data set:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'best_c_lr' is not defined"
]
}
],
"source": [
"# Use best hyperparameters:\n",
"lr = LogisticRegression(C=best_c_lr, penalty=penalty_lr)\n",
"# Train on full undersample data set:\n",
"lr.fit(X_undersample, y_undersample)\n",
"# Test on unseen test data set:\n",
"y_pred_score = lr.decision_function(X_test.values)\n",
"# Compute ROC metrics:\n",
"fpr, tpr, thresholds = roc_curve(y_test.values, y_pred_score)\n",
"# Get AUC:\n",
"roc_auc = auc(fpr,tpr)\n",
"\n",
"# Plot ROC:\n",
"plt.title('ROC Curve - Linear Regression')\n",
"plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)\n",
"plt.legend(loc='lower right')\n",
"plt.plot([0,1],[0,1],'r--')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "ac1cbe69-9309-54ab-9f50-40c175d4e4f6"
},
"outputs": [],
"source": [
"from sklearn.svm import LinearSVC"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "41d4f774-cd1c-dd2a-7478-5c8d86599de2"
},
"outputs": [],
"source": [
"def get_best_hypers_svc(X, y):\n",
" \"\"\" Search parameter space for the optimal values.\n",
" \n",
" -Perform Support Vector Classifier using a range of C parameter values and two different\n",
" penalty terms (L1 & L2)\n",
" -Compute mean recall scores using kfold cross validation for each run\n",
" -Output the C parameter and penalty term with the best recall score\n",
" \"\"\"\n",
" c_range = [0.01, 0.1, 1.0, 10.0, 100.0]\n",
" recall_max = 0\n",
" best_c = 0\n",
" penalty = ''\n",
" \n",
" for c_param in c_range:\n",
" print('='*25)\n",
" print('C parameter: ', c_param)\n",
" print('='*25)\n",
" print('')\n",
" \n",
" print('-'*25)\n",
" print('L1-penalty')\n",
" print('-'*25)\n",
" print('')\n",
" \n",
" svc_l1 = LinearSVC(C=c_param, penalty='l1', dual=False)\n",
" acc_score = cross_val_score(svc_l1, X, y, cv=5)\n",
" recall_score = cross_val_score(svc_l1, X, y, cv=5, scoring='recall')\n",
" l1_recall = np.mean(recall_score)\n",
" \n",
" print(\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
" print(\"Mean Recall: %0.3f (+/- %0.3f)\" % (l1_recall, np.std(recall_score)) )\n",
" print('')\n",
" \n",
" print('-'*25)\n",
" print('L2-penalty')\n",
" print('-'*25)\n",
" print('')\n",
" \n",
" svc_l2 = LinearSVC(C=c_param, penalty='l2')\n",
" score = cross_val_score(svc_l2, X, y, cv=5)\n",
" recall_score = cross_val_score(svc_l2, X, y, cv=5, scoring='recall')\n",
" l2_recall = np.mean(recall_score)\n",
" \n",
" print(\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
" print(\"Mean Recall: %0.3f (+/- %0.3f)\" % (l2_recall, np.std(recall_score)) )\n",
" print('')\n",
" \n",
" # compare l1_recall & l2_recall:\n",
" if l2_recall > l1_recall:\n",
" # compare to max:\n",
" if l2_recall > recall_max:\n",
" recall_max = l2_recall\n",
" best_c = c_param\n",
" penalty='l2'\n",
" else:\n",
" # compare to max:\n",
" if l1_recall > recall_max:\n",
" recall_max = l1_recall\n",
" best_c = c_param\n",
" penalty='l1'\n",
" \n",
"\n",
" print('*'*25)\n",
" print('Optimal C parameter = ', best_c)\n",
" print('Optimal penalty = ', penalty)\n",
" print('Optimal recall = ', recall_max)\n",
" print('*'*25)\n",
" \n",
" return best_c, penalty"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "9e29cbd1-f516-6059-e75c-04a863b372f3"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X_undersample' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-16-fd9e3a17d4e4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbest_c_svc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpenalty_svc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_best_hypers_svc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_undersample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'X_undersample' is not defined"
]
}
],
"source": [
"best_c_svc, penalty_svc = get_best_hypers_svc(X_undersample, y_undersample)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "f98f24bc-f70c-14fc-d13d-b8a8facf5e10"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'best_c_svc' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-6671baf8b2aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Use best hyperparameters:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msvc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearSVC\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbest_c_svc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpenalty\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpenalty_svc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;31m# Train on full undersample data set:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msvc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_undersample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# Test on unseen test data set:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'best_c_svc' is not defined"
]
}
],
"source": [
"# Use best hyperparameters:\n",
"svc = LinearSVC(C=best_c_svc, penalty=penalty_svc)\n",
"# Train on full undersample data set:\n",
"svc.fit(X_undersample, y_undersample)\n",
"# Test on unseen test data set:\n",
"y_pred_score = svc.decision_function(X_test.values)\n",
"# Compute ROC metrics:\n",
"fpr, tpr, thresholds = roc_curve(y_test.values, y_pred_score)\n",
"# Get AUC:\n",
"roc_auc = auc(fpr,tpr)\n",
"\n",
"# Plot ROC:\n",
"plt.title('ROC Curve - SVC')\n",
"plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)\n",
"plt.legend(loc='lower right')\n",
"plt.plot([0,1],[0,1],'r--')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "bef3d4f4-4138-b410-e41e-1babe77eaf12"
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "8342274d-68ea-5479-a2fa-4b4bca3c08ee"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X_undersample' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-8ac805b22eb0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0macc_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mrecall_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'recall'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy Score: %0.3f (+/- %0.3f)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Recall Score: %0.3f (+/- %0.3f)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecall_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecall_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'X_undersample' is not defined"
]
}
],
"source": [
"dt = DecisionTreeClassifier(random_state=0)\n",
"acc_score = cross_val_score(dt, X_undersample, y_undersample, cv=5)\n",
"recall_score = cross_val_score(dt, X_undersample, y_undersample, cv=5, scoring='recall')\n",
"print(\"Accuracy Score: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
"print(\"Recall Score: %0.3f (+/- %0.3f)\" % (np.mean(recall_score), np.std(recall_score)) )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "cf67a52d-685c-c331-3048-4cb8e280a851"
},
"outputs": [
{
"data": {
"image/png": 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vWP6x2iNTpj//hPPPd91NwU1RMXo01KsXbFzGREg4y3HOB4YCs0TkAGC5qv7L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\njCliScEYY0wRSwrGGGOKWFIwxhhT5P8BeON0V8ZeSDYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7d4502eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Train on full undersample data set:\n",
"dt.fit(X_train, y_train)\n",
"# Test on unseen test data set:\n",
"y_pred_score = dt.predict_proba(X_test.values)[:,1]\n",
"# Compute ROC metrics:\n",
"fpr, tpr, thresholds = roc_curve(y_test.values,y_pred_score)\n",
"# Get AUC:\n",
"roc_auc = auc(fpr, tpr)\n",
" \n",
" \n",
"# Plot ROC:\n",
"plt.title('ROC Curve - DecisionTree')\n",
"plt.plot(fpr, tpr, label = 'AUC = %0.2f' % roc_auc)\n",
"plt.plot([0,1],[0,1],'r--')\n",
"plt.legend(loc='lower right')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "1bf91508-e637-7923-aa9c-6290aaac1e09"
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "b6f3348d-b58f-8f95-67be-6529176db193"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'X_undersample' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-22-170257a7f42f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mrfc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0macc_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrfc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mrecall_score\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrfc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_undersample\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'recall'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy Score: %0.3f (+/- %0.3f)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macc_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Recall Score: %0.3f (+/- %0.3f)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecall_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecall_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'X_undersample' is not defined"
]
}
],
"source": [
"rfc = RandomForestClassifier(random_state=0)\n",
"acc_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5)\n",
"recall_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5, scoring='recall')\n",
"print(\"Accuracy Score: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
"print(\"Recall Score: %0.3f (+/- %0.3f)\" % (np.mean(recall_score), np.std(recall_score)) )"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "3523c4b3-4554-3568-fd0f-558cf5ed4e3b"
},
"outputs": [
{
"data": {
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Du0q4sNSZAaKaJQVTpXZmZO2ftWp7obtpS6mXU7OG0LS+66gtrl5O3ll+/Xirl2OCpQrP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\nyff/2wzjUtB7qLAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7cdabd828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Train on full undersample data set:\n",
"rfc.fit(X_train, y_train)\n",
"# Test on unseen test data set:\n",
"y_pred_score = rfc.predict_proba(X_test.values)[:,1]\n",
"# Compute ROC metrics:\n",
"fpr, tpr, thresholds = roc_curve(y_test.values,y_pred_score)\n",
"# Get AUC:\n",
"roc_auc = auc(fpr, tpr)\n",
" \n",
" \n",
"# Plot ROC:\n",
"plt.title('ROC Curve - RandomForest')\n",
"plt.plot(fpr, tpr, label = 'AUC = %0.2f' % roc_auc)\n",
"plt.plot([0,1],[0,1],'r--')\n",
"plt.legend(loc='lower right')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.show()"
]
}
],
"metadata": {
"_change_revision": 232,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163931.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f06aaa16-1098-180b-86e8-b677eee19c7e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python: 3.6.0 |Anaconda custom (64-bit)| (default, Dec 23 2016, 12:22:00) \n",
"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
"scipy: 0.19.0\n",
"numpy: 1.12.1\n",
"matplotlib: 2.0.0\n",
"pandas: 0.19.2\n",
"sklearn: 0.19.dev0\n"
]
}
],
"source": [
"# Check the versions of libraries\n",
"\n",
"# Python version\n",
"import sys\n",
"print('Python: {}'.format(sys.version))\n",
"# scipy\n",
"import scipy\n",
"print('scipy: {}'.format(scipy.__version__))\n",
"# numpy\n",
"import numpy\n",
"print('numpy: {}'.format(numpy.__version__))\n",
"# matplotlib\n",
"import matplotlib\n",
"print('matplotlib: {}'.format(matplotlib.__version__))\n",
"# pandas\n",
"import pandas\n",
"print('pandas: {}'.format(pandas.__version__))\n",
"# scikit-learn\n",
"import sklearn\n",
"print('sklearn: {}'.format(sklearn.__version__))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "eceb8730-f611-6120-fb55-6e1fff05646f"
},
"source": [
"**Load Libraries**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "138e11e6-3d15-418b-45f5-60f12821ed8e"
},
"outputs": [],
"source": [
"# Load libraries\n",
"from pandas.tools.plotting import scatter_matrix\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import model_selection\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.svm import SVC"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c358cbfd-9cac-8a84-95f8-01dc485a00d1"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-121622a80dc8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mnames\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'sepal-length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'sepal-width'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'petal-length'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'petal-width'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'class'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../input/Iris.csv\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"# Load dataset\n",
"names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\n",
"dataset = pd.read_csv(\"../input/Iris.csv\",names=names)\n",
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "e7cfbcb8-56cf-614a-3bbb-ecc073a243d0"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataset' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-d7b3bddcb985>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
]
}
],
"source": [
"# shape\n",
"print(dataset.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "ecba21bc-6392-ecf4-9951-5f0a8eaec61d"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 10,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163932.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "9b53570f-6f00-f0cd-eaca-d7dbb7e1a78c"
},
"source": [
"16-May- 2017\n",
"To get the insights in House Prices Data by Exploratory data analysis and to predict house pricing using various machine learning algorithms."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "d9f57425-11af-ea3e-42f2-ae50df03c49e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample_submission.csv\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "3c1265b6-3c01-4f8f-1e84-1aa528dee508"
},
"outputs": [
{
"data": {
"text/plain": [
"(1460, 81)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Read the Data\n",
"df_train = pd.read_csv('../input/train.csv')\n",
"df_test = pd.read_csv('../input/test.csv')\n",
"df_train.shape\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "e33fe08e-9077-724c-f05a-14dfad6109be"
},
"outputs": [
{
"data": {
"text/plain": [
"(1459, 80)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_test.shape"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "1f2c2e50-42f6-6072-9c11-36c6dd4f5d79"
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
" 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
" 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
" 'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
" 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
" 'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
" 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
" 'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
" 'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
" 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
" 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
" 'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
" 'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
" 'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
" 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
" 'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
" 'SaleCondition', 'SalePrice'],\n",
" dtype='object')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.columns"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "b1f6b09f-b592-7ff1-bf3b-61c3545653a6"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Id</th>\n",
" <th>MSSubClass</th>\n",
" <th>LotFrontage</th>\n",
" <th>LotArea</th>\n",
" <th>OverallQual</th>\n",
" <th>OverallCond</th>\n",
" <th>YearBuilt</th>\n",
" <th>YearRemodAdd</th>\n",
" <th>MasVnrArea</th>\n",
" <th>BsmtFinSF1</th>\n",
" <th>...</th>\n",
" <th>WoodDeckSF</th>\n",
" <th>OpenPorchSF</th>\n",
" <th>EnclosedPorch</th>\n",
" <th>3SsnPorch</th>\n",
" <th>ScreenPorch</th>\n",
" <th>PoolArea</th>\n",
" <th>MiscVal</th>\n",
" <th>MoSold</th>\n",
" <th>YrSold</th>\n",
" <th>SalePrice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1201.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1452.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>...</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" <td>1460.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>730.500000</td>\n",
" <td>56.897260</td>\n",
" <td>70.049958</td>\n",
" <td>10516.828082</td>\n",
" <td>6.099315</td>\n",
" <td>5.575342</td>\n",
" <td>1971.267808</td>\n",
" <td>1984.865753</td>\n",
" <td>103.685262</td>\n",
" <td>443.639726</td>\n",
" <td>...</td>\n",
" <td>94.244521</td>\n",
" <td>46.660274</td>\n",
" <td>21.954110</td>\n",
" <td>3.409589</td>\n",
" <td>15.060959</td>\n",
" <td>2.758904</td>\n",
" <td>43.489041</td>\n",
" <td>6.321918</td>\n",
" <td>2007.815753</td>\n",
" <td>180921.195890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>421.610009</td>\n",
" <td>42.300571</td>\n",
" <td>24.284752</td>\n",
" <td>9981.264932</td>\n",
" <td>1.382997</td>\n",
" <td>1.112799</td>\n",
" <td>30.202904</td>\n",
" <td>20.645407</td>\n",
" <td>181.066207</td>\n",
" <td>456.098091</td>\n",
" <td>...</td>\n",
" <td>125.338794</td>\n",
" <td>66.256028</td>\n",
" <td>61.119149</td>\n",
" <td>29.317331</td>\n",
" <td>55.757415</td>\n",
" <td>40.177307</td>\n",
" <td>496.123024</td>\n",
" <td>2.703626</td>\n",
" <td>1.328095</td>\n",
" <td>79442.502883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>20.000000</td>\n",
" <td>21.000000</td>\n",
" <td>1300.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1872.000000</td>\n",
" <td>1950.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2006.000000</td>\n",
" <td>34900.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>365.750000</td>\n",
" <td>20.000000</td>\n",
" <td>59.000000</td>\n",
" <td>7553.500000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1954.000000</td>\n",
" <td>1967.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" <td>2007.000000</td>\n",
" <td>129975.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>730.500000</td>\n",
" <td>50.000000</td>\n",
" <td>69.000000</td>\n",
" <td>9478.500000</td>\n",
" <td>6.000000</td>\n",
" <td>5.000000</td>\n",
" <td>1973.000000</td>\n",
" <td>1994.000000</td>\n",
" <td>0.000000</td>\n",
" <td>383.500000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>25.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2008.000000</td>\n",
" <td>163000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1095.250000</td>\n",
" <td>70.000000</td>\n",
" <td>80.000000</td>\n",
" <td>11601.500000</td>\n",
" <td>7.000000</td>\n",
" <td>6.000000</td>\n",
" <td>2000.000000</td>\n",
" <td>2004.000000</td>\n",
" <td>166.000000</td>\n",
" <td>712.250000</td>\n",
" <td>...</td>\n",
" <td>168.000000</td>\n",
" <td>68.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" <td>2009.000000</td>\n",
" <td>214000.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1460.000000</td>\n",
" <td>190.000000</td>\n",
" <td>313.000000</td>\n",
" <td>215245.000000</td>\n",
" <td>10.000000</td>\n",
" <td>9.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>1600.000000</td>\n",
" <td>5644.000000</td>\n",
" <td>...</td>\n",
" <td>857.000000</td>\n",
" <td>547.000000</td>\n",
" <td>552.000000</td>\n",
" <td>508.000000</td>\n",
" <td>480.000000</td>\n",
" <td>738.000000</td>\n",
" <td>15500.000000</td>\n",
" <td>12.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>755000.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" Id MSSubClass LotFrontage LotArea OverallQual \\\n",
"count 1460.000000 1460.000000 1201.000000 1460.000000 1460.000000 \n",
"mean 730.500000 56.897260 70.049958 10516.828082 6.099315 \n",
"std 421.610009 42.300571 24.284752 9981.264932 1.382997 \n",
"min 1.000000 20.000000 21.000000 1300.000000 1.000000 \n",
"25% 365.750000 20.000000 59.000000 7553.500000 5.000000 \n",
"50% 730.500000 50.000000 69.000000 9478.500000 6.000000 \n",
"75% 1095.250000 70.000000 80.000000 11601.500000 7.000000 \n",
"max 1460.000000 190.000000 313.000000 215245.000000 10.000000 \n",
"\n",
" OverallCond YearBuilt YearRemodAdd MasVnrArea BsmtFinSF1 \\\n",
"count 1460.000000 1460.000000 1460.000000 1452.000000 1460.000000 \n",
"mean 5.575342 1971.267808 1984.865753 103.685262 443.639726 \n",
"std 1.112799 30.202904 20.645407 181.066207 456.098091 \n",
"min 1.000000 1872.000000 1950.000000 0.000000 0.000000 \n",
"25% 5.000000 1954.000000 1967.000000 0.000000 0.000000 \n",
"50% 5.000000 1973.000000 1994.000000 0.000000 383.500000 \n",
"75% 6.000000 2000.000000 2004.000000 166.000000 712.250000 \n",
"max 9.000000 2010.000000 2010.000000 1600.000000 5644.000000 \n",
"\n",
" ... WoodDeckSF OpenPorchSF EnclosedPorch 3SsnPorch \\\n",
"count ... 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean ... 94.244521 46.660274 21.954110 3.409589 \n",
"std ... 125.338794 66.256028 61.119149 29.317331 \n",
"min ... 0.000000 0.000000 0.000000 0.000000 \n",
"25% ... 0.000000 0.000000 0.000000 0.000000 \n",
"50% ... 0.000000 25.000000 0.000000 0.000000 \n",
"75% ... 168.000000 68.000000 0.000000 0.000000 \n",
"max ... 857.000000 547.000000 552.000000 508.000000 \n",
"\n",
" ScreenPorch PoolArea MiscVal MoSold YrSold \\\n",
"count 1460.000000 1460.000000 1460.000000 1460.000000 1460.000000 \n",
"mean 15.060959 2.758904 43.489041 6.321918 2007.815753 \n",
"std 55.757415 40.177307 496.123024 2.703626 1.328095 \n",
"min 0.000000 0.000000 0.000000 1.000000 2006.000000 \n",
"25% 0.000000 0.000000 0.000000 5.000000 2007.000000 \n",
"50% 0.000000 0.000000 0.000000 6.000000 2008.000000 \n",
"75% 0.000000 0.000000 0.000000 8.000000 2009.000000 \n",
"max 480.000000 738.000000 15500.000000 12.000000 2010.000000 \n",
"\n",
" SalePrice \n",
"count 1460.000000 \n",
"mean 180921.195890 \n",
"std 79442.502883 \n",
"min 34900.000000 \n",
"25% 129975.000000 \n",
"50% 163000.000000 \n",
"75% 214000.000000 \n",
"max 755000.000000 \n",
"\n",
"[8 rows x 38 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "c87e2161-7f05-3a42-6880-e1bfd0ee98b9"
},
"outputs": [
{
"data": {
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" <th>Id</th>\n",
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" <th>MSZoning</th>\n",
" <th>LotFrontage</th>\n",
" <th>LotArea</th>\n",
" <th>Street</th>\n",
" <th>Alley</th>\n",
" <th>LotShape</th>\n",
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" <th>PoolQC</th>\n",
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" <td>RL</td>\n",
" <td>65.0</td>\n",
" <td>8450</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>Reg</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2008</td>\n",
" <td>WD</td>\n",
" <td>Normal</td>\n",
" <td>208500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>20</td>\n",
" <td>RL</td>\n",
" <td>80.0</td>\n",
" <td>9600</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>Reg</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>2007</td>\n",
" <td>WD</td>\n",
" <td>Normal</td>\n",
" <td>181500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>60</td>\n",
" <td>RL</td>\n",
" <td>68.0</td>\n",
" <td>11250</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>IR1</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>9</td>\n",
" <td>2008</td>\n",
" <td>WD</td>\n",
" <td>Normal</td>\n",
" <td>223500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>70</td>\n",
" <td>RL</td>\n",
" <td>60.0</td>\n",
" <td>9550</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>IR1</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>2006</td>\n",
" <td>WD</td>\n",
" <td>Abnorml</td>\n",
" <td>140000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>60</td>\n",
" <td>RL</td>\n",
" <td>84.0</td>\n",
" <td>14260</td>\n",
" <td>Pave</td>\n",
" <td>NaN</td>\n",
" <td>IR1</td>\n",
" <td>Lvl</td>\n",
" <td>AllPub</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" <td>2008</td>\n",
" <td>WD</td>\n",
" <td>Normal</td>\n",
" <td>250000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 81 columns</p>\n",
"</div>"
],
"text/plain": [
" Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
"0 1 60 RL 65.0 8450 Pave NaN Reg \n",
"1 2 20 RL 80.0 9600 Pave NaN Reg \n",
"2 3 60 RL 68.0 11250 Pave NaN IR1 \n",
"3 4 70 RL 60.0 9550 Pave NaN IR1 \n",
"4 5 60 RL 84.0 14260 Pave NaN IR1 \n",
"\n",
" LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal \\\n",
"0 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"1 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"2 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"3 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"4 Lvl AllPub ... 0 NaN NaN NaN 0 \n",
"\n",
" MoSold YrSold SaleType SaleCondition SalePrice \n",
"0 2 2008 WD Normal 208500 \n",
"1 5 2007 WD Normal 181500 \n",
"2 9 2008 WD Normal 223500 \n",
"3 2 2006 WD Abnorml 140000 \n",
"4 12 2008 WD Normal 250000 \n",
"\n",
"[5 rows x 81 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "b7f92418-a4c8-e7f2-bb57-35cf180bc4b7"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Id</th>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSSubClass</th>\n",
" <td>60</td>\n",
" <td>20</td>\n",
" <td>60</td>\n",
" <td>70</td>\n",
" <td>60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSZoning</th>\n",
" <td>RL</td>\n",
" <td>RL</td>\n",
" <td>RL</td>\n",
" <td>RL</td>\n",
" <td>RL</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotFrontage</th>\n",
" <td>65</td>\n",
" <td>80</td>\n",
" <td>68</td>\n",
" <td>60</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotArea</th>\n",
" <td>8450</td>\n",
" <td>9600</td>\n",
" <td>11250</td>\n",
" <td>9550</td>\n",
" <td>14260</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Street</th>\n",
" <td>Pave</td>\n",
" <td>Pave</td>\n",
" <td>Pave</td>\n",
" <td>Pave</td>\n",
" <td>Pave</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alley</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotShape</th>\n",
" <td>Reg</td>\n",
" <td>Reg</td>\n",
" <td>IR1</td>\n",
" <td>IR1</td>\n",
" <td>IR1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LandContour</th>\n",
" <td>Lvl</td>\n",
" <td>Lvl</td>\n",
" <td>Lvl</td>\n",
" <td>Lvl</td>\n",
" <td>Lvl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Utilities</th>\n",
" <td>AllPub</td>\n",
" <td>AllPub</td>\n",
" <td>AllPub</td>\n",
" <td>AllPub</td>\n",
" <td>AllPub</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LotConfig</th>\n",
" <td>Inside</td>\n",
" <td>FR2</td>\n",
" <td>Inside</td>\n",
" <td>Corner</td>\n",
" <td>FR2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LandSlope</th>\n",
" <td>Gtl</td>\n",
" <td>Gtl</td>\n",
" <td>Gtl</td>\n",
" <td>Gtl</td>\n",
" <td>Gtl</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Neighborhood</th>\n",
" <td>CollgCr</td>\n",
" <td>Veenker</td>\n",
" <td>CollgCr</td>\n",
" <td>Crawfor</td>\n",
" <td>NoRidge</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Condition1</th>\n",
" <td>Norm</td>\n",
" <td>Feedr</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Condition2</th>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BldgType</th>\n",
" <td>1Fam</td>\n",
" <td>1Fam</td>\n",
" <td>1Fam</td>\n",
" <td>1Fam</td>\n",
" <td>1Fam</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HouseStyle</th>\n",
" <td>2Story</td>\n",
" <td>1Story</td>\n",
" <td>2Story</td>\n",
" <td>2Story</td>\n",
" <td>2Story</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OverallQual</th>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>7</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OverallCond</th>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YearBuilt</th>\n",
" <td>2003</td>\n",
" <td>1976</td>\n",
" <td>2001</td>\n",
" <td>1915</td>\n",
" <td>2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YearRemodAdd</th>\n",
" <td>2003</td>\n",
" <td>1976</td>\n",
" <td>2002</td>\n",
" <td>1970</td>\n",
" <td>2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RoofStyle</th>\n",
" <td>Gable</td>\n",
" <td>Gable</td>\n",
" <td>Gable</td>\n",
" <td>Gable</td>\n",
" <td>Gable</td>\n",
" </tr>\n",
" <tr>\n",
" <th>RoofMatl</th>\n",
" <td>CompShg</td>\n",
" <td>CompShg</td>\n",
" <td>CompShg</td>\n",
" <td>CompShg</td>\n",
" <td>CompShg</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exterior1st</th>\n",
" <td>VinylSd</td>\n",
" <td>MetalSd</td>\n",
" <td>VinylSd</td>\n",
" <td>Wd Sdng</td>\n",
" <td>VinylSd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Exterior2nd</th>\n",
" <td>VinylSd</td>\n",
" <td>MetalSd</td>\n",
" <td>VinylSd</td>\n",
" <td>Wd Shng</td>\n",
" <td>VinylSd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MasVnrType</th>\n",
" <td>BrkFace</td>\n",
" <td>None</td>\n",
" <td>BrkFace</td>\n",
" <td>None</td>\n",
" <td>BrkFace</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MasVnrArea</th>\n",
" <td>196</td>\n",
" <td>0</td>\n",
" <td>162</td>\n",
" <td>0</td>\n",
" <td>350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExterQual</th>\n",
" <td>Gd</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ExterCond</th>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Foundation</th>\n",
" <td>PConc</td>\n",
" <td>CBlock</td>\n",
" <td>PConc</td>\n",
" <td>BrkTil</td>\n",
" <td>PConc</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BedroomAbvGr</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KitchenAbvGr</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>KitchenQual</th>\n",
" <td>Gd</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>Gd</td>\n",
" <td>Gd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>TotRmsAbvGrd</th>\n",
" <td>8</td>\n",
" <td>6</td>\n",
" <td>6</td>\n",
" <td>7</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Functional</th>\n",
" <td>Typ</td>\n",
" <td>Typ</td>\n",
" <td>Typ</td>\n",
" <td>Typ</td>\n",
" <td>Typ</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fireplaces</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FireplaceQu</th>\n",
" <td>NaN</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>Gd</td>\n",
" <td>TA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageType</th>\n",
" <td>Attchd</td>\n",
" <td>Attchd</td>\n",
" <td>Attchd</td>\n",
" <td>Detchd</td>\n",
" <td>Attchd</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageYrBlt</th>\n",
" <td>2003</td>\n",
" <td>1976</td>\n",
" <td>2001</td>\n",
" <td>1998</td>\n",
" <td>2000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageFinish</th>\n",
" <td>RFn</td>\n",
" <td>RFn</td>\n",
" <td>RFn</td>\n",
" <td>Unf</td>\n",
" <td>RFn</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageCars</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageArea</th>\n",
" <td>548</td>\n",
" <td>460</td>\n",
" <td>608</td>\n",
" <td>642</td>\n",
" <td>836</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageQual</th>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GarageCond</th>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" <td>TA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PavedDrive</th>\n",
" <td>Y</td>\n",
" <td>Y</td>\n",
" <td>Y</td>\n",
" <td>Y</td>\n",
" <td>Y</td>\n",
" </tr>\n",
" <tr>\n",
" <th>WoodDeckSF</th>\n",
" <td>0</td>\n",
" <td>298</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>192</td>\n",
" </tr>\n",
" <tr>\n",
" <th>OpenPorchSF</th>\n",
" <td>61</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>35</td>\n",
" <td>84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EnclosedPorch</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>272</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3SsnPorch</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ScreenPorch</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PoolArea</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>PoolQC</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fence</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MiscFeature</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MiscVal</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MoSold</th>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>YrSold</th>\n",
" <td>2008</td>\n",
" <td>2007</td>\n",
" <td>2008</td>\n",
" <td>2006</td>\n",
" <td>2008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SaleType</th>\n",
" <td>WD</td>\n",
" <td>WD</td>\n",
" <td>WD</td>\n",
" <td>WD</td>\n",
" <td>WD</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SaleCondition</th>\n",
" <td>Normal</td>\n",
" <td>Normal</td>\n",
" <td>Normal</td>\n",
" <td>Abnorml</td>\n",
" <td>Normal</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SalePrice</th>\n",
" <td>208500</td>\n",
" <td>181500</td>\n",
" <td>223500</td>\n",
" <td>140000</td>\n",
" <td>250000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>81 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4\n",
"Id 1 2 3 4 5\n",
"MSSubClass 60 20 60 70 60\n",
"MSZoning RL RL RL RL RL\n",
"LotFrontage 65 80 68 60 84\n",
"LotArea 8450 9600 11250 9550 14260\n",
"Street Pave Pave Pave Pave Pave\n",
"Alley NaN NaN NaN NaN NaN\n",
"LotShape Reg Reg IR1 IR1 IR1\n",
"LandContour Lvl Lvl Lvl Lvl Lvl\n",
"Utilities AllPub AllPub AllPub AllPub AllPub\n",
"LotConfig Inside FR2 Inside Corner FR2\n",
"LandSlope Gtl Gtl Gtl Gtl Gtl\n",
"Neighborhood CollgCr Veenker CollgCr Crawfor NoRidge\n",
"Condition1 Norm Feedr Norm Norm Norm\n",
"Condition2 Norm Norm Norm Norm Norm\n",
"BldgType 1Fam 1Fam 1Fam 1Fam 1Fam\n",
"HouseStyle 2Story 1Story 2Story 2Story 2Story\n",
"OverallQual 7 6 7 7 8\n",
"OverallCond 5 8 5 5 5\n",
"YearBuilt 2003 1976 2001 1915 2000\n",
"YearRemodAdd 2003 1976 2002 1970 2000\n",
"RoofStyle Gable Gable Gable Gable Gable\n",
"RoofMatl CompShg CompShg CompShg CompShg CompShg\n",
"Exterior1st VinylSd MetalSd VinylSd Wd Sdng VinylSd\n",
"Exterior2nd VinylSd MetalSd VinylSd Wd Shng VinylSd\n",
"MasVnrType BrkFace None BrkFace None BrkFace\n",
"MasVnrArea 196 0 162 0 350\n",
"ExterQual Gd TA Gd TA Gd\n",
"ExterCond TA TA TA TA TA\n",
"Foundation PConc CBlock PConc BrkTil PConc\n",
"... ... ... ... ... ...\n",
"BedroomAbvGr 3 3 3 3 4\n",
"KitchenAbvGr 1 1 1 1 1\n",
"KitchenQual Gd TA Gd Gd Gd\n",
"TotRmsAbvGrd 8 6 6 7 9\n",
"Functional Typ Typ Typ Typ Typ\n",
"Fireplaces 0 1 1 1 1\n",
"FireplaceQu NaN TA TA Gd TA\n",
"GarageType Attchd Attchd Attchd Detchd Attchd\n",
"GarageYrBlt 2003 1976 2001 1998 2000\n",
"GarageFinish RFn RFn RFn Unf RFn\n",
"GarageCars 2 2 2 3 3\n",
"GarageArea 548 460 608 642 836\n",
"GarageQual TA TA TA TA TA\n",
"GarageCond TA TA TA TA TA\n",
"PavedDrive Y Y Y Y Y\n",
"WoodDeckSF 0 298 0 0 192\n",
"OpenPorchSF 61 0 42 35 84\n",
"EnclosedPorch 0 0 0 272 0\n",
"3SsnPorch 0 0 0 0 0\n",
"ScreenPorch 0 0 0 0 0\n",
"PoolArea 0 0 0 0 0\n",
"PoolQC NaN NaN NaN NaN NaN\n",
"Fence NaN NaN NaN NaN NaN\n",
"MiscFeature NaN NaN NaN NaN NaN\n",
"MiscVal 0 0 0 0 0\n",
"MoSold 2 5 9 2 12\n",
"YrSold 2008 2007 2008 2006 2008\n",
"SaleType WD WD WD WD WD\n",
"SaleCondition Normal Normal Normal Abnorml Normal\n",
"SalePrice 208500 181500 223500 140000 250000\n",
"\n",
"[81 rows x 5 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.head().T"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "add0fbe5-9d14-cb1a-027d-a96f164e7c7d"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1460 entries, 0 to 1459\n",
"Data columns (total 81 columns):\n",
"Id 1460 non-null int64\n",
"MSSubClass 1460 non-null int64\n",
"MSZoning 1460 non-null object\n",
"LotFrontage 1201 non-null float64\n",
"LotArea 1460 non-null int64\n",
"Street 1460 non-null object\n",
"Alley 91 non-null object\n",
"LotShape 1460 non-null object\n",
"LandContour 1460 non-null object\n",
"Utilities 1460 non-null object\n",
"LotConfig 1460 non-null object\n",
"LandSlope 1460 non-null object\n",
"Neighborhood 1460 non-null object\n",
"Condition1 1460 non-null object\n",
"Condition2 1460 non-null object\n",
"BldgType 1460 non-null object\n",
"HouseStyle 1460 non-null object\n",
"OverallQual 1460 non-null int64\n",
"OverallCond 1460 non-null int64\n",
"YearBuilt 1460 non-null int64\n",
"YearRemodAdd 1460 non-null int64\n",
"RoofStyle 1460 non-null object\n",
"RoofMatl 1460 non-null object\n",
"Exterior1st 1460 non-null object\n",
"Exterior2nd 1460 non-null object\n",
"MasVnrType 1452 non-null object\n",
"MasVnrArea 1452 non-null float64\n",
"ExterQual 1460 non-null object\n",
"ExterCond 1460 non-null object\n",
"Foundation 1460 non-null object\n",
"BsmtQual 1423 non-null object\n",
"BsmtCond 1423 non-null object\n",
"BsmtExposure 1422 non-null object\n",
"BsmtFinType1 1423 non-null object\n",
"BsmtFinSF1 1460 non-null int64\n",
"BsmtFinType2 1422 non-null object\n",
"BsmtFinSF2 1460 non-null int64\n",
"BsmtUnfSF 1460 non-null int64\n",
"TotalBsmtSF 1460 non-null int64\n",
"Heating 1460 non-null object\n",
"HeatingQC 1460 non-null object\n",
"CentralAir 1460 non-null object\n",
"Electrical 1459 non-null object\n",
"1stFlrSF 1460 non-null int64\n",
"2ndFlrSF 1460 non-null int64\n",
"LowQualFinSF 1460 non-null int64\n",
"GrLivArea 1460 non-null int64\n",
"BsmtFullBath 1460 non-null int64\n",
"BsmtHalfBath 1460 non-null int64\n",
"FullBath 1460 non-null int64\n",
"HalfBath 1460 non-null int64\n",
"BedroomAbvGr 1460 non-null int64\n",
"KitchenAbvGr 1460 non-null int64\n",
"KitchenQual 1460 non-null object\n",
"TotRmsAbvGrd 1460 non-null int64\n",
"Functional 1460 non-null object\n",
"Fireplaces 1460 non-null int64\n",
"FireplaceQu 770 non-null object\n",
"GarageType 1379 non-null object\n",
"GarageYrBlt 1379 non-null float64\n",
"GarageFinish 1379 non-null object\n",
"GarageCars 1460 non-null int64\n",
"GarageArea 1460 non-null int64\n",
"GarageQual 1379 non-null object\n",
"GarageCond 1379 non-null object\n",
"PavedDrive 1460 non-null object\n",
"WoodDeckSF 1460 non-null int64\n",
"OpenPorchSF 1460 non-null int64\n",
"EnclosedPorch 1460 non-null int64\n",
"3SsnPorch 1460 non-null int64\n",
"ScreenPorch 1460 non-null int64\n",
"PoolArea 1460 non-null int64\n",
"PoolQC 7 non-null object\n",
"Fence 281 non-null object\n",
"MiscFeature 54 non-null object\n",
"MiscVal 1460 non-null int64\n",
"MoSold 1460 non-null int64\n",
"YrSold 1460 non-null int64\n",
"SaleType 1460 non-null object\n",
"SaleCondition 1460 non-null object\n",
"SalePrice 1460 non-null int64\n",
"dtypes: float64(3), int64(35), object(43)\n",
"memory usage: 924.0+ KB\n"
]
}
],
"source": [
"df_train.info()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "6b0614e2-9683-eb0a-b64d-e6895b9e0ad9"
},
"outputs": [
{
"data": {
"text/plain": [
"Id 0\n",
"MSSubClass 0\n",
"MSZoning 0\n",
"LotFrontage 259\n",
"LotArea 0\n",
"Street 0\n",
"Alley 1369\n",
"LotShape 0\n",
"LandContour 0\n",
"Utilities 0\n",
"LotConfig 0\n",
"LandSlope 0\n",
"Neighborhood 0\n",
"Condition1 0\n",
"Condition2 0\n",
"BldgType 0\n",
"HouseStyle 0\n",
"OverallQual 0\n",
"OverallCond 0\n",
"YearBuilt 0\n",
"YearRemodAdd 0\n",
"RoofStyle 0\n",
"RoofMatl 0\n",
"Exterior1st 0\n",
"Exterior2nd 0\n",
"MasVnrType 8\n",
"MasVnrArea 8\n",
"ExterQual 0\n",
"ExterCond 0\n",
"Foundation 0\n",
" ... \n",
"BedroomAbvGr 0\n",
"KitchenAbvGr 0\n",
"KitchenQual 0\n",
"TotRmsAbvGrd 0\n",
"Functional 0\n",
"Fireplaces 0\n",
"FireplaceQu 690\n",
"GarageType 81\n",
"GarageYrBlt 81\n",
"GarageFinish 81\n",
"GarageCars 0\n",
"GarageArea 0\n",
"GarageQual 81\n",
"GarageCond 81\n",
"PavedDrive 0\n",
"WoodDeckSF 0\n",
"OpenPorchSF 0\n",
"EnclosedPorch 0\n",
"3SsnPorch 0\n",
"ScreenPorch 0\n",
"PoolArea 0\n",
"PoolQC 1453\n",
"Fence 1179\n",
"MiscFeature 1406\n",
"MiscVal 0\n",
"MoSold 0\n",
"YrSold 0\n",
"SaleType 0\n",
"SaleCondition 0\n",
"SalePrice 0\n",
"dtype: int64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.isnull().sum()"
]
}
],
"metadata": {
"_change_revision": 48,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163937.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "29287bb0-e866-8783-add3-36ff9aca79f9"
},
"source": [
"some first tests"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f758a3dd-5d46-bf61-fe95-920c0b148b3a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"genderclassmodel.csv\n",
"gendermodel.csv\n",
"gendermodel.py\n",
"myfirstforest.py\n",
"test.csv\n",
"train.csv\n",
"\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load in \n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the \"../input/\" directory.\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
"\n",
"from subprocess import check_output\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output.\n",
"\n",
"# Visualisation\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.pylab as pylab\n",
"import seaborn as sns\n",
"\n",
"# Configure visualisations\n",
"%matplotlib inline\n",
"mpl.style.use( 'ggplot' )\n",
"sns.set_style( 'white' )\n",
"pylab.rcParams[ 'figure.figsize' ] = 8 , 6"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "f0ab400f-e7c6-3772-dc5e-3395184d632c"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train: (891, 12)\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# get titanic & test csv files as a DataFrame\n",
"train = pd.read_csv(\"../input/train.csv\")\n",
"test = pd.read_csv(\"../input/test.csv\")\n",
"\n",
"print ('train:' , train.shape)\n",
"train.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "886341a4-f985-d423-493f-10bac89b1b36"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Fare</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>714.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" <td>891.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>446.000000</td>\n",
" <td>0.383838</td>\n",
" <td>2.308642</td>\n",
" <td>29.699118</td>\n",
" <td>0.523008</td>\n",
" <td>0.381594</td>\n",
" <td>32.204208</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>257.353842</td>\n",
" <td>0.486592</td>\n",
" <td>0.836071</td>\n",
" <td>14.526497</td>\n",
" <td>1.102743</td>\n",
" <td>0.806057</td>\n",
" <td>49.693429</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>223.500000</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>20.125000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.910400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>446.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" <td>28.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>14.454200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>668.500000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>891.000000</td>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" <td>8.000000</td>\n",
" <td>6.000000</td>\n",
" <td>512.329200</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "52e65464-ef27-88d4-4533-7d4808d7011d"
},
"outputs": [],
"source": [
"def plot_correlation_map( df ):\n",
" corr = df.corr()\n",
" _ , ax = plt.subplots( figsize =( 12 , 10 ) )\n",
" cmap = sns.diverging_palette( 220 , 10 , as_cmap = True )\n",
" _ = sns.heatmap(\n",
" corr, \n",
" cmap = cmap,\n",
" square=True, \n",
" cbar_kws={ 'shrink' : .9 }, \n",
" ax=ax, \n",
" annot = True, \n",
" annot_kws = { 'fontsize' : 12 }\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "252f6aa9-1a29-4b19-d270-04c2d88cd481"
},
"outputs": [
{
"data": {
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Z0rwuLS0Nk8mEt7d3tsd4eXmRdtu3n9vjvLy8SE9PN69LTU3F69bJugMHDjTf\n36JFC6ZPn86+ffto0qSJVcYmjmfzimX8snI5AK6uRfAp7mdel5Gejslkwj2XPyZPvfQKbV7owsZl\nS5j06SDejhzKkjkzee2DCJvkbkuLvvuWH259Ll1z+Vx63eFzmZ6e83N5e9zaVSt5qn2HHNu7eOEC\nJ/84QcMH/nO3h2ITd3s/BuDq6sqSJUu4cuUKffr0MU/YK2z7MS9PT9Iz0rPdl5Kaivdtkyzyivnf\nlCkEB5XmyxGfkZmZyQdDPmXmggV0uzWx8eSZ0/T473957fkXaPPoo9YfkA15uXuQnpGR7b7U9HS8\nb/sS8v827N7FyPlzGdf7PfMh6aJe3tQOrUwJHx8AOjR7hK9XLKPnMzk/o4WdwUlmQ+c5ykWLFrFw\n4UKqVq3K6tWrWb58OStXrmTVqlVUutWOtpeOHTuaT9Tu0KEDsbGx5nVnzpzB39+fYsWyz7qsUKFC\nrnHly5fnzJkz2dZVqlSJ5OTkbIej4eY39yJFCs1EcrHAQ63bMHDcZAaOm0zjx1oTH3fevO7S+XP4\n+JXA+57ss1Zjjh3l1NEjwM0/4I1btibm2FHO/HGCa5cvM27QAD58rQuLZ05lz7YtTBk22KZjsob2\nz3UyTzp5un0HYm/7TMWeOU3JO3wuy5WvkGdc8o0bHPxtPw3ub8Rfbd/6C+EN78f11iHDguZu78eW\nL19u7hb6+fnRsmVLtm/fXij3YxXLlc12yDkxKYnrSUmUCw62KGbbrp20atYMtyJF8PL0pNkD/yF6\n/81D8xcuXeLNAQN4r3t3OjzxhO0GZSMVSpfmzMUL5uXE5GSuJ9+g3G2n18DNjuKob+Yx6f0PqF6h\novn+0iVLknRbx9vVxQUXg3MUTc7Kov/dU6dOmU+mBggODs6x47Gnpk2bsmPHDnNO8+bN47HHHvtH\ncS1atGDBggUYjUbi4+NZvXo1LVq04MKFC3Tr1s28c46KiuLq1avUrFnTJmMTx1Mr/H6OHtjHhbM3\n3xMblv1I/cYP5oi7cC6Wb6dMIuXGDQAO7IzGz78UofdWZ8SsbxgybTZDps3mmVe6U+8/TXgj4iOb\njsPamjRtxu7oaE7f+rx9N28uzVu2+sdxp06dxNfPD+87XJroxNFjlK9YMcf9BdHd2I8tXbqU+fPn\nA5CZmUlUVBRVqlQplPuxhnXrce7CBXb/9hsAsxctpGmjRtk6i3nFVChblk1RUcDNwnlrdDRVbhVE\nn44by4vtdt0RAAAgAElEQVTPtOexps1sOygbCQ+7l/OXL7Pn2FEA5q1ZxYO16+J1W2cxJS2Nj2dM\nY3TPd6h023mfAC0aNGR19K9cSEjAmJXFkl82cX/1GjYdg8NwknMWLfpaWadOHTp06ECdOnUwGAwc\nPHiQqneYkWgvAQEBDBgwgL59+2I0GgkLC+ODDz4AYMOGDWzevJnIyMg8455//nlOnTpF+/btcXV1\n5bXXXjOP8f333+e9994jKysLHx8fxowZc8drnxVWrn7FCZk42rwcPGEUGI3E9u6PMf5yHo8snIqX\nLMmzr/Vg+qhhGI1GylYM5fFurwOw79ftHNy1gxfe6k2Dhx7m0vlz/C+iL5jA6557ePn9fnbO3nZK\nBQTwXv8BRHzwPkajkarVwuj9QX8ANm9Yz7ZfNjPgo4/zjAO4dOFCtsOzt7t48QKhVQvudRVvdzf2\nY5GRkQwfPpz27dtjNBqpU6cOXbt2xcvLq9Dtxzw9PBj14SCGjB9HSmoq5YKDGdqvP78dOcyEmTP5\n6rORucYA9H+rJ5+OHcvjXW6e71orLIzXO3fmYnw8G7dv5+TpM3y39Cfz9vq8/gbN/lMwT3f4K093\nd4a/0YMRc2eTkp5G2YBAPun2Ggf+OMEXSxbzxfsfsGnvbq4kJvLh1CnZHju133+pHVqZN9o+TbcR\nQyni6kq9KlV55fE2dhqNnbk4RjFnbQaTyWSyJPDEiRMcP34ck8lExYoVqVatmkUbKMgnUNtDXGvn\nO+fj3zrxxXh7p1Cg1KtQeGZ12oq3IcveKRQonteu/32QZJN+6szfB0k29zTJeWqKrcUN/szq2wj6\nqP/fB1mZRYehk5KSWLNmDTt37qRVq1ZcuXIlx+UcRERERJyKkxyGtqhYHDBgAD4+Pvx267yPhIQE\n+vTpY9XERERERMT+LCoWb9y4wQsvvIDbresyPf744+ZrEIqIiIg4I4OLi9VvjsCiLLKysjh9+rT5\np7Q2b95MVpbO4REREREp7CyaDf3RRx/x0UcfceDAAZo0aUK1atUYPLjgXxNOREREJN+c5PqSFhWL\n27dvZ+TIkQQEBPx9sIiIiIgUGhYVi1evXuXNN9/E09OTli1b0qpVq2wX6RYRERFxOk5ynUWL+qdv\nv/02ixcvZsyYMbi5ufHRRx/x/PPPWzs3EREREbEzi38YNCkpiT179rBnzx4uXbpEvXr1rJmXiIiI\niEMzOMh1EK3NomKxa9euXLp0iWbNmvHiiy9St25da+clIiIiIg7AomIxIiLC4p/3ExEREXEKmg0N\nPXv2ZNKkSXTt2jVbq9VkMmEwGNi+fbvVExQRERER+8mzWJw0aRIAs2bNUmdRRERE5HZOMhvaosPQ\nQ4cOJSEhgebNm9OqVSvuvfdea+clIiIiIg7AomJx9uzZXLt2jY0bNzJ58mTOnDlDkyZN6NOnj7Xz\nExEREXFMTjIb2uIzM319fWncuDEPPvggwcHB/PLLL9bMS0REREQcgEWdxUmTJrFx40ZcXFxo3rw5\nffr0oWLFitbOTURERMRhGXTOYnYTJkzQT/yJiIiIOBmLDkP/+uuv+Pv7WzsXERERkYLD4GL9mwOw\nqLPo7e1Ny5YtCQsLw83NzXz/uHHjrJaYiIiIiNifRcVit27drJ2HiIiISMHiJLOhLSoWd+zYccf7\nGzZseFeTERERESkwNMHlT35+fuZ/Z2RksHv3bgIDA62WlIiIiIg4BouKxc6dO2dbfvnll3nzzTet\nkpCIiIhIQWBwcYwJKNZmUbF4/PjxbMsXL17k5MmTVklIRERERByHRcXiJ598Yv63i4sLbm5uRERE\nWC0pEREREYfnIJe2sbY8i8Xt27fzxRdfMGfOHIxGI6+88gpxcXFkZWXZKj8RERERsaM8i8XPP/+c\n0aNHA7B69WqSk5NZuXIl165d4+2336Zp06Y2SVJERETE4TjJbOg8+6ceHh6UK1cOgM2bN9O2bVsM\nBgPFixfH1dXVJgmKiIiIiP3kWSymp6eTlZVFSkoKmzZtokmTJuZ1ycnJVk9ORERExFEZDAar3xxB\nnoeh27ZtyzPPPEN6ejoPPvgglSpVIj09nUGDBhEeHm6rHEVERETETvIsFjt37kyzZs1ITEwkLCwM\nAHd3d8LDw2nfvr1NEhQRERFxSA7S+bO2v710TnBwcI77nn32WaskIyIiIiKOxaLrLIqIiIjIXzjJ\nL7g4xyhFREREJF/UWXQwJ74Yb+8UCpzQt3rZO4UC5ddxn9s7hQKndaCvvVMoUFJ9feydQoEzIzHD\n3ikUOL3tnQA4zTmL6iyKiIiISK7UWRQRERHJB0e5DqK1qbMoIiIiIrlSZ1FEREQkPzQbWkRERESc\nnTqLIiIiIvnhJOcsqlgUERERyQ8dhhYRERERZ6fOooiIiEg+GFyc4zC0OosiIiIikit1FkVERETy\nw0kmuKizKCIiIlJADRs2jI4dO9KpUyf2799/x5gxY8bw0ksv5Xsb6iyKiIiI5IfBvj23HTt2EBMT\nw4IFCzhx4gQREREsWLAgW8zx48eJjo7Gzc0t39tRZ1FERESkANq+fTuPPvooAKGhoVy7do2kpKRs\nMSNGjOC99977V9tRsSgiIiKSDwYXg9VveYmPj8fPz8+8XKJECS5dumReXrx4MQ0bNiQ4OPhfjVPF\nooiIiEghYDKZzP++evUqixcv5pVXXvnXz6tzFkVERETyw86zoQMCAoiPjzcvX7x4kVKlSgEQFRVF\nQkICnTt3Jj09ndOnTzNs2DAiIiL+8XbUWRQREREpgBo3bsyqVasAOHjwIAEBARQtWhSAVq1a8fPP\nP/Pdd98xceJEatSoka9CEdRZFBEREckfO8+Grl+/PjVq1KBTp04YDAYiIyNZvHgxxYoVo0WLFndt\nOyoWRURERAqovn37ZlsOCwvLERMSEsKcOXPyvQ0ViyIiIiL5od+GFhERERFnp86iiIiISD4Y9NvQ\nIiIiIuLs1FkUERERyQ+dsygiIiIizk6dRREREZH8cHGOnpuKRREREZH8sPNFuW3FOUYpIiIiIvmi\nzqKIiIhIPujSOSIiIiLi9ApNZ3HVqlVMnz6dzMxMQkNDiYyMpGjRohbHJSUlMXToUI4ePYrJZKJF\nixb06NEDgPDwcMqXL29+joCAACZPnmyzsdnC7q2bWbXoO7IyjQSVK8cLPXrhdc89OeK2rPqZX1Yu\nJ8uYRYmAADq9+TZ+/qXM67Oyshg7sB+BwSF0fvtdWw7Bsbi64t/jVfw6tedku85kXoq3d0Z2t3fb\nL6xd8j1ZRiOBIeV47o238fLO+R7btmYl21b/TFaWEb9SgTzb/S2Kl/Rn8qcfknj1qjnuRuJ1wh96\nmCdffMWWw7CaX/fsZvSXX5KckkrpwECG9OtHUKlSFsUkp6QwZNw49h8+hIuLKw82bMj7r7+Oq6sr\nR//4g2ETxnP56lVcXVzo2fVlWjz0kJ1GeXdFR0czduxYUlJSCAoKIjIyksDAQItjYmNj6d+/P76+\nvnzxxRfmxxw4cIBRo0aRlJSEl5cXb775Jk2aNLHp2Gzl2O4d7Fq9jCyjkRKlg3n4+Zfx8PLOFnP2\n2BGWfzWeon4lzPdVrFWPB55sz7p5Mzjz+0HcPb3M65p37kZg+Uo2G4NdOcmlcwpFsRgXF8eoUaOY\nO3cuQUFBfP7550yaNIn+/ftbHDdu3Dj8/f0ZPnw4iYmJvPjii9SqVcu8g1i0aJE9hmYTCZcusXD6\nV/T97HNKlCrFD7Oms/ybOXR47c1scSd/P8z6pUvoO+J/eBctyuKvp7Fk9gxeef/P13nr6hUkXrtK\nYHCIrYfhUMqM+JjUw0ftnYbDuBJ/iSWzptF76Gj8/EuxdO5MVi6YR7tXXs8Wd+roETYtX0LvIaPx\nLlqUn+bMYOncmbzU+wN6DBpijsvKMjJu4Afc92AzG4/EOpJTUvhgyBC+HD6C6lWrMnfxYgZ//jlf\nDBtmUczU+fPJyMzgp5lfk5mZyev9+/HDypV0eOIJ3v/kY97r/jrNmzTh8LFjdHm3Nw3r1sXXx8eO\nI/73UlJSiIiIYMKECYSFhfHtt98yfPhwxo4da1HMqVOn6Nu3L/Xq1ePs2bPmx5hMJvr160dERARN\nmjTh+PHjvPbaayxbtuyODYiCLPHKZX5ZNJ9n+w6imF9Jti5ZwK/Lf+ChDp1zxAaUq8DT7/S74/M0\neuIZwu5vbO10xY4KxWHojRs30qBBA4KCggB46qmnWLdu3T+Ka968OV27dgWgWLFiVKtWjZiYGBuN\nwL4O7PyVqrXqUOJWF+OBR1qwJ2prjriiPr689M57eN/aYVatVYeL5/7cyV67ksDmFcto+kRb2yTu\nwBK+nk/CjDn2TsNhHNy1g8o1apu70A2bPcr+X7fliCvq48vzb71rfo9VrlGLS+fP5YiLWreG4IqV\nKFO+onUTt5Ede/YQUro01atWBeCZ1q3ZtmsnN5KTLYo5dvIPGtSpi4uLC+7u7tStUZPjp06SkZnJ\nW11f5pHGN/+Q31ulCh7u7py7cMH2g7zLoqOjCQ4OJiwsDIC2bdsSFRXFjRs3LIrx8PDgyy+/pHbt\n2tme9/r161y8eJGGDRsCULlyZTw9PbMVlIXFyd/2ElL1Xor5lQTg3kYPcmLvTjtnVcAYDNa/OYB/\nXCxmZWVx/fp1a+SSb6dPnyYk5M9OVkhICAkJCTnyzCuuUaNG+Pv7AxATE8OhQ4do1KiROXbQoEE8\n++yzdO/enX379ll5RLZ18dxZ/AODzMv+QaVJunaN5KSkbHGlSpehYrV7AUhPS2PXLxupFX6/ef0P\nM6fR6tlOdzy06GxSDx62dwoOJf78OUredniwZGAQSddzvsf8g0pToerNP+wZ6Wns2bqZGvc1yBaT\nmZnBhqWLaf5UB+snbiOnYmMpW6aMednby4viPj6cvq1AySvm/nr1Wbd1C6lpaSQmJbF99y4euC8c\ntyJFePyRR8wn4a/bsgWfYsUIve20moLqr/tzb29vfH19OXPmjEUxpUuXNu/zb+fr60u1atVYuXIl\nAHv37sXV1ZWKFQvHF5PbXbt0AZ/bTiPy9S9FSlIiqck3csQmXU1g6eTPmT90ICtnTibp6hXzuqO7\nf+X7MUP4Zvggdq1Zjslkskn+YjsWHYb+6quv8PHxoU2bNnTp0oXixYtTp04devfube38LJKamkqJ\nEn+eS+Hu7o7BYCAlJQWf2w61/F2c0Wikffv2xMfH06tXL0JDQwFo164dzz33HFWqVGHNmjW8//77\nLFmyhGLFitlukFaUnpZGMV9f83IRNzcMBgPpaanmDs/tfpwzk61rVlIprDrNn3oGgMN7dpF8I4n7\nmjTl1w05u7ri3NLT07jHx/L32LL5s4hat4qKVe+l2ZPtsq3bs2Uz5SpVpuRtX3AKutS0NNzd3LPd\n5+nhQUpqqkUxzz/9NBu3b+PBZ9qRaTTyaJMmPHT/n1/k9h48SJ9PB2PKymLUh4Nwd8/+PAVRampq\njnF4enqSevtrZkHMnXz44Yf07NmTsWPHkpqayrBhwwrFa/ZXGenpeBX98++YaxE3MBjITE+D2770\ne/sUp2Lt+tRv3hp3Ly+2/fg96+ZN56mefSlTuSomk4mwho25ce0qSyf/j3t8/Qhr+B97DMn2dJ3F\nP61fv55OnTrx888/07x5c2bMmMGePXusnVueFixYQPv27Wnfvj0HDx4kLS3NvC4tLQ2TyYS3d/aT\ndL28vPKMc3V1ZcmSJSxdupSVK1eycOFCAAYOHEiVKlUAaNGiBaVKlSrw3cXNK5YxtHcPhvbuwenj\nx8hIzzCvy0hPx2Qy4e7pecfHPvXSKwyfOZ8qNWoy6dNBpKelsWTOTJ79yzmO4ty2rvqZkX3eZmSf\ntzlz4hiZGenmdf//HvO47aT427V5oSuffDWHStVr8tWwj7Ot27PtF+r+50Frpm5zXp6epN/2+gCk\npKbi7eVlUcz/pkwhOKg02378iW1LfiQlNZWZCxaY4+rWqMG6bxfwxfARfDDkU46cOGHdAdmAp6cn\n6enZX4/U1FS8bnvNLIn5q9TUVPr27cuIESNYv349c+fOZdiwYZw/f/7uDsBOfvtlPfOHfcj8YR9y\n8fRJMjP/3PdnZmSAyYSbe/Z9v19gEI2feg6vosVwdS1Cg8ee5Ozx38lIS+Pe+5tQvdGDuLi4UMyv\nBNUfeIiYQ/ttPSyxMouKxaysLLKysli6dCmPP/44QLbzQuyhY8eOLFq0iEWLFtGhQwdiY2PN686c\nOYO/v3+Ozl+FChVyjVu+fDmJiYkA+Pn50bJlS7Zv305ycjKnTp3K9jxGo5EiRQr23KCHWrdh4LjJ\nDBw3mcaPtSY+7s8d4aXz5/DxK4H3Pdk7PjHHjnLq6BHgZmHduGVrYo4d5cwfJ7h2+TLjBg3gw9e6\nsHjmVPZs28KUYYNtOiZxLI0fe5x+YybSb8xEHni0FZcvxJnXxcedx6e4X44Z96ePHyXm2O/AzffY\nA4+24vTxo6Tc2t+kpqQQc+x3qtSqa7uB2EDFcmWzHXJOTErielIS5YKDLYrZtmsnrZo1w61IEbw8\nPWn2wH+I3r+Pa9evs2ztWvNjwkJDqV29OtF77ftl/26oUKFCtkPOSUlJXL9+nXLlyv2jmL/6448/\nyMrKMp+zWKlSJcqWLcvBgwetMArbq/XgI7wQMYQXIoZQs3Ezrl26aF537dIFvH188fhLoyU58Vq2\nw85ZWVkYABdXFy6fP4vxtoLTlGXExdXV6uNwFAYXg9VvjsCiYvHRRx+lcePGVK5cmYoVKzJp0iTq\n1Klj7dws1rRpU3bs2GEu6ubNm8djjz32j+KWLl3K/PnzAcjMzCQqKooqVapw4cIFunXrZi4yo6Ki\nuHr1KjVr1rT+wGykVvj9HD2wjwtnb45xw7Ifqd84Z+fmwrlYvp0yyfyH+8DOaPz8SxF6b3VGzPqG\nIdNmM2TabJ55pTv1/tOENyI+suk4xHHVuK8hxw7sN0+I2vzzT3fsDl48d5aF0yaTcuucqcO7oynu\nX8pcVF48e4Z7fHzwzKMzVBA1rFuPcxcusPu33wCYvWghTRs1ytZZzCumQtmybIqKAm5+md0aHU2V\nChUpUqQIwyaM59c9uwG4fOUKvx0+TNVKBf+yJuHh4cTFxbF3717g5v68SZMm2bqGlsT8VenSpUlM\nTDQXh3Fxcfzxxx+F8pzFCjXrcvbYEa7c+iK3b+NqqtRvmCPu5G97WTVzMhm3jszt37yW4Kr34lrE\njY0LZrN/83oAUpNv8Hv0dspXr53jOaRgM5j+4ZmoWVlZXLhwgdKlS1sU///dOmtbs2YNU6ZMwWg0\nEhYWxqBBg/D29mbDhg1s3ryZyMjIPOPOnz/P8OHDOXv2LEajkTp16jBgwAC8vLxYtmwZs2bNIisr\nCx8fH957770cM+julq0nc878tIU927aw4rv5GI1GylYM5fke7+Dh5cW+X7dzcNcOXnirNyaTiZ8X\nzGPv9q1gAq977uGZbt2pUKVatuf6dcM6jh/8zWbXWQx9q5dNtmMpV7/ihEwcDYB7+bKkx54Do5HY\n3v0xxl+2c3ZweNzndtnuvqitrF74DVnGLIIrVuLZ13vi4enFb9FRHN4dzXNvvIPJZGLV99/cmilt\nwtP7Hp7u+hrlKt+cAfzbju1sXLaEdwZ/ZtPcWwf6/n3Qv7Rj715GTJpISmoq5YKDGdqvP+cvXmDC\nzJl89dnIXGP8S5Tg/MWLfDp2LKdib3bRaoWFMaj3uxS95x527NnDmKlfkZycTFaWiWdat+bV55+3\n6lhSfW1zWZ6dO3cyZswYUlJSKFu2LJGRkcTFxfHll18yceLEXGP8/f1ZuHAh33zzDUlJSdy4cYPA\nwEBq1KjB4MGDWb9+PV999RXp6em4uLjQuXNn2rVr9zfZ/Dsztuy16vPn5vieaHas+BFTVhb+IeV4\n5PmXcfPw5I/9uzl1YB+PvPAKpqwsti1dyMnf9uBicMEvqAwPtn+BosX9uHbpAhu/m0PS1SsYDAaq\nNXiA+o8+bpNfNund2v6noySu2WD1bRRr8bDVt/F3LCoW/3+Cy5NPPslLL71E8eLFqVu3Lr16/f0f\naVsVi4WFvYrFgszRikVHZ69isSCzRbFYmNiqWCxM7FUsFmQqFm3nH01wWb58uXmCy+7du62dm4iI\niIjjcnGx/s0BFNgJLiIiIiJifYVigouIiIiIrRkMBqvfHIFF1395/fXXef31P3/DtWvXrqxZs8Zq\nSYmIiIiIY7CoWPztt9+YOnUqV69eBSAjI4P4+Hirzw4TERERcVgOck6htVk0yiFDhvDCCy+QnJxM\nv379aNiwIREREdbOTURERETszKLOoqenJ40aNcLd3Z2aNWtSs2ZNXn31VR5+2P7TuUVERETswkHO\nKbQ2i4pFLy8v1q1bR0hICP/73/8oW7ZsofmdTBEREZF8cZCf47M2iw5Djx49mtDQUD766CPc3d35\n/fff+ewz2/6CgoiIiIjYXp6dxU2bNmVbjomJoVatWphMJhISEqyamIiIiIgjMxicY4JLnsXiypUr\n83xw06ZN72oyIiIiIuJY8iwWhw8fDtz8BZcDBw5Qu3ZtALZv306jRo2sn52IiIiIo3KSCS4W9U8H\nDBjA6tWrzcvR0dEMGDDAakmJiIiIiGOwqFg8d+4cffv2NS/36tWLc+fOWS0pEREREYfnYrD+zQFY\nVCwaDAY2bNjAtWvXuHLlCitWrKBIEYuuuiMiIiIiBdjfVnzp6en06tWL77//ntGjR+Pq6kqtWrXM\n5zOKiIiIOCXNhoa1a9cybNgwSpUqxdWrVxk5ciR16tSxVW4iIiIiYmd5FovTpk3jhx9+wNfXl9jY\nWD7++GOmTZtmq9xEREREHJbBQc4ptLY8+6dubm74+voCEBISQlpamk2SEhERERHHkGdn0fCX6wf9\ndVlERETEaTlJXZRnsXjgwAE6dOgAgMlk4uTJk3To0AGTyYTBYGDhwoU2SVJERERE7CPPYnHp0qW2\nykNERESkYFFnEYKDg22Vh4iIiIg4IF1ZW0RERCQfDC7OcZ1F5xiliIiIiOSLOosiIiIi+aHOooiI\niIg4O3UWRURERPLDSWZDq7MoIiIiIrlSZ1FEREQkP5zkt6FVLIqIiIjkg8HgHAdonWOUIiIiIpIv\n6iyKiIiI5IcmuIiIiIiIszOYTCaTNTeQmJhozacvdJJNqt//qV+Pxdg7hQLl3t7v2TuFAufMlEn2\nTqFACfAtZu8UCpyqxnR7p1DguJcva+8USD38u9W34XlvNatv4++oMhERERGRXOmcRREREZH80DmL\nIiIiIuLs1FkUERERyQddZ1FEREREnJ46iyIiIiL54SQ/96fOooiIiIjkSp1FERERkfxwcY6em3OM\nUkRERETyRZ1FERERkXww6DqLIiIiIuLs1FkUERERyQ+dsygiIiIizk6dRREREZH80DmLIiIiIuLs\n1FkUERERyQ8n6SyqWBQRERHJB4N+7k9EREREnJ06iyIiIiL5YXCOnptzjFJERERE8kWdRREREZH8\ncJIJLuosioiIiEiu1FkUERERyQ/NhhYRERERZ6fOooiIiEg+GDQbWkREREScnTqLIiIiIvmhcxZF\nRERExNmpsygiIiKSDymeHlbfRjGrb+HvqbMoIiIiIrlSsSgiIiIiuSoUh6Gjo6MZO3YsKSkpBAUF\nERkZSWBgoEUxmZmZjB49mujoaEwmE+Hh4fTr14/4+Hh69uyZ7Tni4uIYPnw4Dz30kC2HZzXrVq9i\n9vRpZGZmUjE0lAEfRVK0aM6Gd25xvd7oTsLly+a4a1ev8tgTbWjWvDkjBn+S7TnOxsYybe48QitX\nsfq4bGHvtl9Yu+R7soxGAkPK8dwbb+PlfU+OuG1rVrJt9c9kZRnxKxXIs93fonhJfyZ/+iGJV6+a\n424kXif8oYd58sVXbDkMx+Lqin+PV/Hr1J6T7TqTeSne3hnZXfSWTaz4fgFGYyZlypWnS8938bon\n5/ts08rlbFqxDKPRiH9AIJ3f6kUJ/1IAxJw4xrTRI6haszYv9ext6yHY1Jb161g4bzbGzEzKVqxI\nz74DuKdo0RxxKSnJTPl8NFs3bOD7NRvM919NSGDK2DGcOXUSg8HAa++8S53wBrYcglX9umcPY6ZO\nITklhdIBgXza9wOCSpWyKCY5JYVhkyay79BBMjIz6dnlZZ589FEAMo1Ghk+awKaoX3F3d6PLM+3p\n1PYpewxRrKTAdxZTUlKIiIhg0KBBLF68mIceeojhw4dbHDNnzhwSEhL47rvv+Oabbzh27Bg//PAD\nQUFBLFq0yHybOHEigYGBNGzY0B7DvOsuxJ1n7KjPGDluPPMW/UBQ6TJM/WLSP4obP2UqcxcuZu7C\nxcxa8D0BgYG0eqINNWvXMd8/d+FiIj7+hCrVqlEptLKth2kVV+IvsWTWNF7tN4h+YyZRolQAKxfM\nyxF36ugRNi1fwluRw+g3ZhKBwSEsnTsTgB6DhtBvzET6jZlI31HjKF7Sn/sebGbjkTiWMiM+Jis5\nxd5pOIyESxf5btqXvP3hx3wy8StKlgrkx/mzc8SdOHKItT8ups/QkXwy8SuCQsqy6OtpABw9+Btz\nJo6jQpWqtk7f5i5duMD0iWMZOGwkE2bNIyAwiPkzpt4xNuKdtygVEJTj/umTxhFUpgwTZ8+nb+Rg\nxg0fQkpysrVTt4nklBT6DRvKx+/1YdnMWTRr9ACfjh9rccyX8+aSkprCj9Nm8PWYz/l82lRiz58H\nYMaCb7l85Sqr5sxlzufjWLFxA9euX7f5GMV6CnyxGB0dTXBwMGFhYQC0bduWqKgobty4YVFM/fr1\neeedd3B1dcXDw4M6deoQExOTYzvjx4/n1VdfxdPT0zYDs7ItmzZxX4OGBAaVBqDNU0+zcd3afMct\n/WExVcLCqFw15x+l8WNG0fPd9zAUkh9cP7hrB5Vr1MbvVuemYbNH2f/rthxxRX18ef6td/G+1dmo\nXHcCkvEAACAASURBVKMWl86fyxEXtW4NwRUrUaZ8Resm7uASvp5Pwow59k7DYezbEUW1WnUpUSoA\ngMaPtmT3ti054or5Fufl3n2459ZRgbDadblw9uzNdT6+9Bk6ksAyIbZL3E6it22hVr37KHXrqFLz\n1m3YvmnjHWPffK8vLdo8meP+/bt28kirJwAoXymUSlWrsn/3LqvlbEs79u4lpHQQ1avcPLrTrlUr\ntu3axY3biuG8YqJ27+Kplo/h4uJCUKlSPPKf/7Bh+8393g+rVtL9+edxdXWlpJ8fs/43Fl8fH9sP\n0kkNGzaMjh070qlTJ/bv359t3bZt2+jQoQMdO3Zk0qScDSFLFfhi8fTp04SE/Lkj9Pb2xtfXlzNn\nzlgUU6dOHcqWLQtAfHw827Zto0mTJtm2cfz4cY4cOULr1q2tPBrbOXM6huDbXpMyISFcSUgg8S/f\nBi2Jy8jIYN6smXR55dUc29m+5Rc8PDypU6++FUZhH/Hnz1HyttMcSgYGkXT9GslJSdni/INKU6Hq\nzS8oGelp7Nm6mRr3ZT+klZmZwYali2n+1P+xd9/hUVTv38ffKaQREhICIVWKQEQEVIogCBYs2ECw\nl6+iICiK0kURRLogIEi1UAREigVQiggiPfQmIDUEkhAIkLYl2d3nj/BbCAkh5GE37fO6rr10Zu6d\nOWfZnb1zzzmz7R3f8CLOuO/fwm5CkXLm9CkqVr5c/QqqHELKxQukpaZki6sUEkr1qNoAmE0mtqxd\nTb1GjQEIiYjE28fHeY0uRKdjTxIcGmZfrhwaysUL50lNSckRW+v2OtfYiwtWq8W+5OXtQ/zp2Jvd\n1EJx4lQs4SGh9mUfb2/K+/kRc/pUPmNcsFqs2bbFnD5NusFAbFwcew4coH3nt2nXuRNL/1rllD4J\nbNmyhRMnTjBv3jyGDBnCkCFDsm0fPHgw48ePZ+7cuaxfv57Dhw8X6Dj5ShbNZjOxsUXzA2M0GvHw\n8Mi2zsvLC6PReEMxHTt25Omnn6Zly5Y0btw4W+ysWbN48cUXcXUt9rm1XdZrcnnKv4eHBy4uLhgM\nhhuOW/nH79x2ex1Cw3NWL+bMnMELr7zqgB4UHrPZhHuZy+8n9zJlcHFxwWwy5hq/ZM4MPuvyBsb0\ndFo+2Tbbth3r1hJZ7VYqBOe8JCalm9lsoswV77My//c+M5pyjV808zv6dHgZQ3o6rdqUvj8+TFed\n58tcOleZjPkf2lDv7gYsWTgfi8XC8SNH2LtjOxlmsyOa63QGownPq74HPT08MFzxPZhXTJO77+LH\nxb9iMpuJO5PAqvXrMZvNJF/6Izku8Qw/TZzEkF69+fyrcRyNiXF8p4SNGzfy0KWxo9WrV+fixYuk\nXvo3OXnyJP7+/oSEhODq6kqLFi3YuHFjgY5z3exn6dKlPPPMM3Tu3BnIylJ/+eWXAh3MEby8vDBf\n9WE2Go14e3vfUMy0adNYvnw5x44dY/z48fb1ZrOZNWvW0KpVKwf1wHkW/vQjr7R/hlfaP8O/+/Zh\nNl/+0jGZTNhsthxVCG9v7+vG/bl8GQ8+/EiO451JSODY0SM0atLUAb1xrvXLf2dkj66M7NGVk0f+\nIzPj8vspw2zGZrPh6eWd63OfeOl/fDZ1FtVq12Hq0IHZtu3Y8A/1mzZ3ZNOlGFnz+2IGvvc2A997\nm+P/HSIjt/eZd+5DYZ55rQOjZs6j5u13MG7gx85qcqH6/ZeFvPf6K7z3+iscPvhvtvO82Zx1rvLy\nzv1zmZs3u3YjLS2Vbm+8ysI5M7mzYSP7MJLiztvLC9PV34MmEz5XvD55xbz90itUqhBEu7c7Mmjc\nOJo1bEg5X1/KXZpw1f6xx3F1dSWq+q00rFuPLTt3OL5TwtmzZwkICLAvBwYGkpiYCEBiYiKBgYG5\nbrtR100WZ8+ezaJFi+yN6dWrF3PmzCnQwRyhSpUq2S45p6amkpycTGRkZL5i1qxZQ3x8PAC+vr48\n+eSTbNq0yR67bds2qlatmu0fo7hq99wL9kknbdq1J/aK1yT2ZAwVgoIoVy77bOjIW6rkGZeelsa+\nPbtp2PieHMfbuP4fGjRqjJubm4N65Dz3PtLaPiGlyUOPci4h3r7tbHwcfuUDcsxSjTl8iBP/HQTA\nzc2NJg89SszhQxgujac1Ggyc+O8gNe6o77yOSJHWsvWTDBw/hYHjp3DfI61JvDSBAOBM3Gn8AwLx\nKZs9eTn+30GOHjwAZL3P7nu0Ncf/O0h6WvZhESVR6zbtGD/9B8ZP/4FHnmyT7ZJxXGwsARUq2Mdy\n5od/QAC9Bw5mwsw59Oj/GUnnznJL1eqOaLrTVY2M4OTpy2OmU9JSSU5NJfKKS/d5xfh4ezOoR0+W\nfD+DSUOGkm40UKNKVcr6+OBXrhwpV8wTcHV1xdW1+J/3iyObzeaQ/V43WXRzc7NfegRyXM4tbA0a\nNCA+Pp6dO3cCWclts2bNslUN84r5+++/mTJlClarFZvNxrp167j11suzdg8dOkTVqiVv4kGzFi3Z\nHh1NzPHjAPw0+wcefPjRG447fvwY/gEB+ORyO48jh/7jlhL42t1+dyP+27ubM5fG+qz9/bdcq4Nn\nTp9iwTeTMKRnnUT/3R5N+aCK9qTyzKmTlPXzu6HKh5Qe9Rrdw4E9u4g/lZUArfrtZxo0a5EjLj42\nljmTx9v/CNkdvYXAoIo5ksqSrmHTZuzZvp1TJ7Mufy5e8BPN7n/whvYx7asxLF7wEwB7d+4g6exZ\nourccdPbWhga1avP6TMJbN+7B4BZCxfSonHjbJXFvGK+nfcjX0yZDMCREyfYtH079zfNumr0aIsW\nzFgwH5vNRmxcHNG7d9GwXj0n97B0qlSpEmfPXr7N2JkzZ6h46XZIV29LSEigUqVKBTrOde+zeNdd\nd9GrVy8SEhKYOnUqf/31F02aNCnQwRzBy8uLIUOGMGLECAwGAxEREQwYMIC9e/cyefJkJkyYcM0Y\ngA8++IARI0bQvn17bDYb1apVo1+/fvb9nzlzhgoVKhRW9xymYqVKfNinL/16dcdisVCzVhTdevUB\nYO3qv9jwz1r6fjowzzjIul3FtV6fM2cSqF6zZNxX8Ur+gRV4psPbzPhyGFaLlbCq1Xi4/VsA7Ine\nxL/bo3nu7fe4u3lLzsbHMb5/H8CGl09ZXn2/p30/F5POUc6/fCH1omhxCyhP+IRR9uWw8V+AxUJs\ntz5Yzp7L45klV/kKQbzY6R2mDP8ci9VKZNXqPPdW1nCgnZs2sHvrFl7r+gGNWz7AmbjTjOjbHZvN\nhk/ZsrzVsy8Av82ZxfaN60hNTsZqsXDkwH7qN25Cm1deL8SeOUaFihXp2O1DRnzaD4vFQrUaNXnz\nvaz7Sm5et5atGzfwbq++HD10kDFDP8eSmYnVauG9118BYPz0H3iszTN8NWwwf/yyCN9y5eg5YFCJ\nuDIC4OXpyRf9PmbIhPEYjEYiQ0MZ3LM3ew4cYMKM75kybMQ1YwCefvgReg8dzKOvvYKXpydDe/fB\n79Il+u5vdaL/qC94+JWX8PH25qN3ulL10sRRcax7772X8ePH88ILL7Bv3z4qVaqE76V/l/DwcFJT\nU4mNjaVy5cqsXr2aUaNGXWePuXOx5aNmuXXrVnbs2IGHhwd169blzjvvzPcBUnKZiSbXlm4rOZNo\nnGXzfzlvdSTXdlu3Dwu7CcXOySkFv+VEaVTJvyj8mm3xUtNSMibSOJPHLYWfkDojx7l6eNjVRo0a\nxdatW3FxcWHAgAHs37+fcuXK0apVK6Kjo+0J4sMPP8ybb+a8a0l+XDdZnDBhQo51bm5uREZG8sgj\nj+DunndxUsnijVGyeOOULN4YJYs3TsnijVGyeOOULN44JYvOc93MJCkpiXXr1uHm5oa7uzubN28m\nISGBzZs307Nnz+s9XURERESKseuOWTx+/Dhz5861T3Dp2LEj7777LpMnT+aVV15xeANFREREpPBc\nt7KYmJjIwYMH7csxMTHExsZy+vTpbD+pJyIiIiIlz3Urix999BH9+vUj7tL9vgwGA126dOHYsWP0\n6NHD4Q0UERERkcJz3WSxadOmTJo0iT/++IOlS5dy8eJFrFYr9957rzPaJyIiIiKF6JrJ4oULF1i+\nfDlLlizhxIkTPPzww6SkpLBixQpntk9ERESkSMpwK1PYTXCKayaLzZo1IzIykj59+tC8eXNcXV1p\n06aNM9smIiIiIoXsmsni8OHDWbJkCR9//DH3338/rVu3dma7RERERIo0B/0Uc5FzzdnQTzzxBJMn\nT2bp0qXUqVOHiRMncvToUUaMGMHhw4ed2UYRERERKSTXvXWOv78/zz//PLNmzWLlypUEBQXRu3dv\nZ7RNREREpMiy2mwOfxQFN/TbcsHBwbz55pssWrTIUe0RERERkSLkurfOEREREZGcbEWk8udoN1RZ\nFBEREZHSRZVFERERkQJQZVFERERESj1VFkVEREQKoKjMVnY0JYsiIiIiBVBKckVdhhYRERGRa1Nl\nUURERKQANMFFREREREo9VRZFRERECsCKKosiIiIiUsqpsigiIiJSABqzKCIiIiKlniqLIiIiIgVQ\nWm7KrcqiiIiIiFyTKosiIiIiBWC1qrIoIiIiIqWcKosiIiIiBVBKhiyqsigiIiIi16bKooiIiEgB\n6D6LIiIiIlLqqbIoIiIiUgD6bWgRERERKfUcXlk0uqh4eSN8yCzsJhQ7jwX7F3YTipV/pnxd2E0o\ndiLefrewm1CsBL3zVmE3odix1rujsJsgBaAxiyIiIiJS6qnsJyIiIlIAqiyKiIiISKmnyqKIiIhI\nAZSSn4ZWsigiIiJSELoMLSIiIiKlniqLIiIiIgWgyqKIiIiIlHqqLIqIiIgUgFWVRREREREp7VRZ\nFBERESkAVRZFREREpNRTZVFERESkADQbWkRERERKPVUWRURERApAYxZFREREpNRTZVFERESkAEpJ\nYVGVRRERERG5NlUWRURERApAs6FFREREpNRTZVFERESkADQbWkRERERKPVUWRURERApAYxZFRERE\npNRTZVFERESkAEpJYVHJooiIiEhBaIKLiIiIiJR6qiyKiIiIFIAmuIiIiIhIqafKooiIiEgBaMyi\niIiIiJR6Jaay+OfyZcz4dhqZmZlUq34rH306EN9y5fIdN2RAf7Zs3EhZX1977CeDPqd2nTvY8M9a\npk6cgNlkxr+8P+9170ntOnc4s3sOsXz5cr799lsyMzOpXr06AwYMwPeK/l8vLjU1lSFDhnDo0CFs\nNhutWrWiS5cuADRo0IBbbrnFvo9KlSoxadIkp/XtZtu8YzujJk8m3WAkJDiYwb17U7lixXzFpBsM\nDB43jt3/7sfV1Y3mjRrRvVMn3NzcOHT0KEPHf8W5Cxdwc3Xl3f+9Tqv77iukXjpW9Lq/+WP+PCyW\nTEIjb+G1dz/Au2zZHHF/L1vK338swWKxEFQpmJffeZ/AoKzX+sSR//hm1HBq1qnLq+92c3YXihY3\nN4K6vEnAC+041vZlMhPPFnaLCtXWY0f4asUyDGYzlcuXp//Tz1DJzz/X2PWHDtJj7iwWdetBaPkA\nAGKTztFv/o/4eXsz4bUOzmx6odi8ZzdfTv+edKOR0IoVGdT1fYKDgrLFZGRmMm7WTGYt/pUVU7/N\ntn3Vpo2MnTUDi9VKVNVqDOr6Pr4+Ps7uRqFTZbEYiY+LY+zIEXwxbgJzF/1K5ZBQpk6ccMNxb3d9\njzmLfrE/ate5g5SUZD77uB+fDBrMnEW/8L+3OvFJ757O7J5DxMfH88UXX/DVV1+xaNEiQkND+frr\nr28obty4cQQFBbFw4UJmzJjBsmXLWLdunf25CxcutD+Kc6KYbjDQa/BgPuvRk6UzZ9KySRMGjRmT\n75hpc+aQkZnBb99PZ8GUKew7dJCfly0DoPtnA3m1XXsWfz+dYX0/ot+I4VxMTnZ6Hx0tKfEMP30z\nma6fDOSzCVOpUDGYX+fMzBF35MB+/vx1ET2GjOSzCVOpHB7BwunfAHBo3x5mTRhHlRo1nd38Iil0\n+ECs6YbCbkaRYDCb6b/gJ/o91Zb5731Is5pRjFjyW66xxgwzE1etwM/b277uxNlEesz9gdqhYc5q\ncqFKNxrpM3oUA9/pyuKvJ3Ffw4Z8PiXnOfqD4UPx8fbKsT42IYGhU6fw9ScDWDpxCpUrBLF2a7Qz\nmi6FpEQki+v+XsPdjRpROSQEgCfatGH1nysLHHel07Gn8PLy4tZLX1B3N2zEmYQEUlKK9xf6mjVr\naNiwIZUrVwbg6aefZtWqVTcU9+CDD/K///0PgHLlylGrVi1OnDjhpB44z5YdOwgPCaF2zaz3wDOP\nPcaGbVtJS0/PV8x/x47SsF59XF1d8fDwoP7tdTh8/BgZmZm887/XeeDeewG4rUYNPD08OJ2Q4PxO\nOtiuLZuodUd9AitWAuDehx5m+4Z1OeLK+Zfn9W49KOubdVUgqm59Ek6dytrm50+PISMJDg13XsOL\nsKTpc0j6blZhN6NI2HrsKKEBAUSFhALw5J13sfnIYdJMphyx09b8xaN16+Pj4Wlf5+HuztevdaBO\nRKTT2lyYtuzZTXhwMLdVrw5A2wceYuOunaQZ0rPFdXr2Od554aUcz1/69xoebNKEyJAQXFxc6P3m\nW7S+r4VT2l7U2Gw2hz+KghtKFpOSkjh//ryj2lJgJ2NOEBp++QskLDyC80lJJF9Voble3Mplf/DW\nqy/xSvtnmPndN9hsNqpUrYqrmyvbtmwBYPWfK4mqXZty5fyc0DPHiYmJIfyK1yI8PJykXF6zvOLu\nuecegi5dljhx4gT79+/nnnvuscf279+fZ599lo4dO7Jr1y4H98hxjsfGEhEaal/28famvJ8fMZeS\nmOvFNL7zLlatX4fRZCIlNZWN27fR5O4GlHF3p/UDD+Di4gLAqnXr8CtXjupXXL4vKc6cPkXFS39w\nAARVDiHl4gXSUlOyxVUKCaV6VG0AzCYTW9aupl6jxgCERETiXQovc12Lcd+/hd2EIiPm3FnCAgPt\nyz4envj7eBObdC5b3OGEeKKPHuHFe5pmWx9SPoCgXIYtlVQnTp8m4orPo4+3N+V9yxETF58trl6t\nqFyff+j4Mcq4u/P2wE958t0ufD55IoZcEnMpOfI1ZnHRokWMHTsWf39/bDYb6enpfPjhhzz55JOO\nbl++GI1GAgIunyg8PDxwcXHBaDDg5+eXr7j6d9+NzWrjsSef4mxiIh++05mKlYJ57Ikn6f1xf3p9\n8B6enp7YrFZGTZjo1P45gtFoJDAw52thyOU1yyvOYrHQrl07zp49y/vvv0/1//tLtW1bnnvuOWrU\nqMHKlSvp3r07v/zyC+WK4QnZaDLhUcYj2zovT08MRmO+Yl5s04Y1GzfQ/Jm2ZFosPNSsGfc1bmyP\n27lvHz0+H4TNauWLT/rj4ZF9PyWB2WyinH95+3KZMmVwcXHBbDTZq4hXWjTzO/5Z/jvVb7udVm3a\nO7OpUgyZMjLwdMv+debpXgZDhtm+bLPZGLH0N7o/9jjubm7ObmKRYjSZcpxnPD09MJiM13hGdinp\naRzfdZqpAwfh7eXFh8OH8u3C+XR96RVHNLdIKyqVP0fLV7I4Y8YMfv31VwICsgYCJyUl8cYbbxRq\nsrhw3o8snPcjAO7u7gRWqGDfZjKZsNlsePt4Z3uOt5c3JrMp17jHn2pjXx9cuTJPPdOODf+spWHj\nxgwf9BnTZvxA9Ro12L41mo97dGfuL7/hU8yqHPPmzeOnn34Csl6zCrm8Zlf3ydvbG5Mp52v2f3Fu\nbm788ssvnD9/nh49euDq6kr79u35+OOP7c9p1aoV3377Lbt27aJZs2aO7KJDeHt5Yb7iSwfAYDTi\nc8WYp7xivpwyhbDKIUwePoLMzEx6Df6c7+fNo8MLLwBQ//bbWfXjPA4cOcI7H/Vl4rDhRF1Kuouz\nNb8vZs0fS4Cs94nfpYkEABlmMzabDc9cxkMBPPNaB55++X+s+u1nxg38mD4jvnRKm6V48vLwwGTJ\nzLbOmJGR7VLzL9uiqRpUkfqRVZzcuqLH28sLszn7+cpoMuHjlfvn8Wq+PmWpWzOKCuWz/gB89tHH\n+G7RwlKZLJYW+boMHRwcTPnyl6sCAQEBREYW7tiOds+/YJ+I0qb9s5w6edK+LTYmhgpBFXNcKo6s\nUuWacUcPH8724bFYMnF3d2fPrl2EhodRvUYNAO5q0BBXN1dOHDvq4B7efM8//7x9wkn79u2JjY21\nbzt58iRBQUE5Kn9VqlS5ZtzSpUtJScm6jBgQEMDDDz/Mxo0bSU9P5/jx49n2Y7FYcHcvnpPvq0ZG\nZLvknJKaSnJqKpFhYfmK2bBtK4+2bEkZd3e8vbxo2aQp0bt3cTE5mSV//ml/TlT16tStXZvonTuc\n0zEHa9n6SQaOn8LA8VO475HWJMbF2bediTuNf0AgPmWzz74//t9Bjh48AGQlmPc92prj/x0kPS3V\nqW2X4qVKUBCxSUn25VSjkRSjgYjAy38Qrz14gH8OHqD1qOG0HjWcM8kX6TBtEtuK4bn8/1fVsDBi\n4i9/HlPS0rLOVyGheTzrspCKFUlNT7Mvu7m64uZaIqZA3DCrzfGPoiBf/7q+vr48/fTTDB48mEGD\nBtGuXTsARo4cyciRIx3awPxo3rIl27ZsIeZSgjJv9iweeuTRG4obOWQQC36cC0BycjLLli6hSbPm\nRNxyC8eOHCHudFYicPDff0lNTSUsPMLxHXOgFi1asGXLFntSN3v2bB555JEbilu8eDFz5swBIDMz\nk02bNlGjRg0SEhLo0KGDPcnctGkTFy5coE6dOo7vmAM0qn8npxMS2L5nDwAzFy6gxT33ZKss5hVT\nJSKCvzdtArKS5vXR0dSoUhV3d3eGjv+KzTu2A3Du/Hn2/PsvNatVc3IPHa9eo3s4sGcX8aey3hOr\nfvuZBs1yDoiPj41lzuTxGNKyvoh2R28hMKhijqRS5Ep3ValG/IUL7Iw5DsDcTeu5t2YtvK+41Drm\n5df4o9dH/N6zL7/37EslP3++69iFu6uWvM/b9TSscwdxiYls/3c/AD8s/o37GjTMd2XxkabNWL5+\nHQlnz2KxWPj5z5U0rlvPkU2WQuZiy8cF959//jnP7W3btr3mtsRU59zaYdWK5Xw3ZTIWSyY1o26j\n76cD8fHx4e+//mL9P3/Tb8BnecbFnozhiyGDSUiIx83VjUcef5xX33gTFxcXflkwn/lz52C1WfEo\n48FbXd6hecv7HdIPL1vm9YNukpUrVzJlyhQsFgtRUVH0798fHx8fVq9ezdq1axkwYECecXFxcQwb\nNoxTp05hsVioV68effv2xdvbmyVLljBjxgysVit+fn58+OGH1K1b1yH98Lro+JnpW3buZPjXEzAY\njUSGhTGkdx/iziQw/vvvmTpi5DVjggIDiTtzhs/HjuV4bFZV+46oKPp3+wDfsmXZsmMHo6dNJT09\nHavVxjOPPcabL77o0L78c7Fwbreybf0/LPnxByxWK5FVq/PKu93w8vZm56YN7N66hde6foDNZmPx\n3B/YvnFd1nCHsmV57s23qVozit/mzGL7xnWkJidjtVjwCwigfuMmtHnldYe3PeLtdx1+jBvhFlCe\n8AmjAPC4JQJz7GmwWIjt1gfL2XPXebbjBb3zltOPue34UcYs+x2j2Ux4YAX6t3mG+IsXmbr6T8bl\n8h5pM3YUE19/k9DyASzauoV5mzaQajKSZjIR7OdP7bBwBrR13nhZ73rOvXdv9N49jPz2GwwmIxGV\nQ/j8vW7EnU3k67mzmfzpZ5y7cIEO/fsBcPzUKSIqV8bNzY2pAz8nuEIFflr2B9//vAh3dzfuvK02\nfd/qlO9k82bxuj33CTjOtHLPfw4/Rqs7ajj8GNdz3WRx//791K6dNTvx0KFDrFy5koiICJ566ql8\nHcBZyWJJ4cxksaRwRrJYkhRWslicFbVksagrjGSxuHN2slgSKFl0njwvQ48aNcp+A+bExEReffVV\nbDYb0dHRjBgxwikNFBERESmKSst9FvOccbBx40YWLlwIZI1Pa9GiBV27dgXg5ZdfdnzrRERERKRQ\n5ZksXnkblfXr19O+/eXxG26l/D5VIiIiUrpZKRqVP0fL8zK0q6sr+/btY+PGjezZs4fmzZsDWZek\nr75Hk4iIiIiUPHlWFj/++GMGDx5Mamoqw4YNw9fXF5PJxPPPP8/AgQOd1EQRERGRoqeojCl0tDyT\nxZo1azJz5sxs6zw9Pfntt9/w9dV9z0RERKT0Kio3zXa0fP2kxrp16/jyyy9JSEjAxcWF0NBQevTo\nQeMrft9WREREREqefCWLI0aM4Msvv6TGpZ+8O3DgAL169WLx4sUObZyIiIhIUWUtJaXFfP3cX6VK\nleyJIkBUVBTh4eEOa5SIiIiIFA15VhZnz54NQMWKFenUqRONGjXCxcWFbdu2ERQU5JQGioiIiBRF\nmuACnD9/HoDw8HDCw8MxGo0A9p//ExEREZGSLc9ksW3btoSFhXH48GFntUdERESkWFBlEZg5cyYf\nffQRn332GS4uLthsNuLi4qhQoQKenp45bqsjIiIiIiVLnsliy5YtefXVV5k1axYWi4U33ngDNzc3\nkpKS+OSTT5zVRhEREZEipyj+3F9GRgZ9+/bl9OnTuLm5MWzYMCIiInKN7d69Ox4eHgwfPjzPfeY5\nG3rMmDEMGTIEgBUrVpCens6yZcuYP38+06ZNK2A3RERERMQRlixZgp+fH3PnzqVz586MHj0617j1\n69cTExOTr33mmSx6enoSGRkJwNq1a3nqqadwcXGhfPnyuLm53WDzRUREREoOm83m8MeN2rhxI61a\ntQKgadOmbN++PUeM2Wxm0qRJdOnSJV/7zDNZNJvNWK1WDAYDf//9N82aNbNvS09Pv5G2i4iIiIiD\nnT17lsDAQABcXV1xcXHBbDZni5kyZQovvvhivn+6Oc8xi0899RTPPPMMZrOZ5s2bU61aNcxmlNBP\nqAAAIABJREFUM/3796dBgwYF7IaIiIhI8VfYk6Hnz5/P/Pnzs63btWtXtuWrq5PHjx9n7969vPfe\ne2zevDlfx8kzWXz55Zdp2bIlKSkpREVFAeDh4UGDBg1o165dvg4gIiIiIjffs88+y7PPPpttXd++\nfUlMTCQqKoqMjAxsNhseHh727WvWrOH06dM899xzpKamkpSUxLRp0+jYseM1j3Pd34YOCwvLtXEi\nIiIipZm1sEuLubj33ntZtmwZzZs3Z/Xq1TRu3Djb9tdff53XX38dgM2bN/Pzzz/nmShCPn8bWkRE\nRESKvtatW2O1WnnxxReZPXs2PXr0AGDq1Kns2LGjQPu8bmVRRERERHIqir/g8n/3Vrxap06dcqxr\n3LhxjspjblRZFBEREZFrUmVRREREpACKYmXREVRZFBEREZFrUmVRREREpACK4mxoR1CyKCIiIlIA\npSVZ1GVoEREREbkmVRZFRERECkATXERERESk1FNlUURERKQArKWjsKjKooiIiIhcmyqLIiIiIgWg\nMYsiIiIiUuo5vLLoZct09CFKFK+LyYXdhGLH6O9X2E0oVirpgsINC3rnrcJuQrFyduI3hd2EYids\nzLDCboIUgCqLIiIiIlLqqcQgIiIiUgD6BRcRERERKfVUWRQREREpgFJSWFRlUURERESuTZVFERER\nkQLQbGgRERERKfVUWRQREREpAM2GFhEREZFST5VFERERkQLQmEURERERKfVUWRQREREpAI1ZFBER\nEZFST5VFERERkQIoLZVFJYsiIiIiBaAJLiIiIiJS6qmyKCIiIlIApaSwqMqiiIiIiFybKosiIiIi\nBVBaJriosigiIiIi16TKooiIiEgBaDa0iIiIiJR6qiyKiIiIFIAqiyIiIiJS6qmyKCIiIlIAmg0t\nIiIiIqWeKosiIiIiBVA66oqqLIqIiIhIHlRZFBERESkAjVkUERERkVJPlUURERGRAigt91ks1sni\n8uXL+fbbb8nMzKR69eoMGDAAX1/ffMdlZGQwfPhwduzYgaurK+3bt+eFF14Ast4As2bN4uuvv2bK\nlCnUr18fgHHjxrF27Vr7vo1GIwEBAfzwww/O6fRNsnnHdkZNnky6wUhIcDCDe/emcsWK+YpJNxgY\nPG4cu//dj6urG80bNaJ7p064ublx5MQJPh83lnPnz+Pu5sY7//sfrZrfV0i9vHmio6MZO3YsBoOB\nypUrM2DAAIKDg/MdExsbS58+ffD392fixIn25+zdu5cvvviC1NRUvL296dy5M82aNXNq35xl3V+r\nWDB7JpbMTCKqVuXdnn0pm8vn1WBIZ8qYUaxfvZr5K1fb119ISmLK2NGcPH4MFxcX3nrvA+o1aOjM\nLjjN1mNH+GrFMgxmM5XLl6f/089Qyc8/19j1hw7SY+4sFnXrQWj5AABik87Rb/6P+Hl7M+G1Ds5s\netHm5kZQlzcJeKEdx9q+TGbi2cJuUaHZsm8vY+bMJt1kJCQoiM86dia4QoVsMWu2bWXSwgVkZGbg\n71uOj994k1sjIsi0WBj1w0w2792L1WalYe3b6fu/N3B3cyuk3oijFdvL0PHx8XzxxRd89dVXLFq0\niNDQUL7++usbips9ezbJycksWLCA6dOnM3fuXPbv3w/AsGHDiImJITAwMNv+unXrxsKFC+2P5s2b\n88QTTzi+wzdRusFAr8GD+axHT5bOnEnLJk0YNGZMvmOmzZlDRmYGv30/nQVTprDv0EF+XrYMgB6D\nPuPphx9m8ffTGdHvY/oNH05KaqrT+3gzGQwG+vXrR//+/Vm0aBH33Xcfw4YNy3fM8ePH+eCDD6hd\nu3a259hsNnr37k3Hjh1ZuHAhAwcO5JNPPiG1mL9euUlMSODbCWP5eOhIxs+YTaXgysz5blqusf3e\ne4eKlSrnWP/t1+OoHBrKhJlz6DlgEOOGDcaQnu7opjudwWym/4Kf6PdUW+a/9yHNakYxYslvucYa\nM8xMXLUCP29v+7oTZxPpMfcHaoeGOavJxUbo8IFY0w2F3YxCZzAa6fv1eD59qxO/jhrDfXfezZDv\nv80WcyYpiU+nTGbou11ZNHI0jzVpyuDvvgFgzrI/OBEXx0/DRrBg+BcciY3lt7VrCqEnhc9qtTn8\nURQU22RxzZo1NGzYkMqVs75Unn76aVatWnVDcX/++Sdt27bF1dUVX19fHnjgAf78808AnnjiCT75\n5BPc3a9dfD18+DDbt2+nffv2N7t7DrVlxw7CQ0KoXbMmAM889hgbtm0l7Yov3rxi/jt2lIb16uPq\n6oqHhwf1b6/D4ePHsFgsvP3KqzzZ6mEAalarRpkyZTgVH+/8Tt5E0dHRhIWFERUVBcBTTz3Fpk2b\nSEtLy1eMp6cnkydPpm7dutn2m5yczJkzZ2jUqBEAt956K15eXpw6dcpJPXOe6A3ruOPOu6l4qdL6\n4GNPsPHvNbnGdv6wJ62eeDLH+t3btvLAo48DcEu16lSrWZPd27c5rM2FZeuxo4QGBBAVEgrAk3fe\nxeYjh0kzmXLETlvzF4/WrY+Ph6d9nYe7O1+/1oE6EZFOa3NxkTR9DknfzSrsZhS6Lfv3EV6xErdV\nrQpAmxYt2bhnN2mGy4m0u5sbw97tSvWwcADq16rFkVOxANwVFUXv1/5HGXd3yri7c3u16hyJjXV+\nR8Rpim2yGBMTQ3h4uH05PDycpKQkkpOT8x2X27bjx48D5Phiz820adN47bXX8kwoi6LjsbFEhIba\nl328vSnv50fMFUlKXjGN77yLVevXYTSZSElNZeP2bTS5uwFubm48dv/99ksRu//9F4BbrniNi6Or\n3yc+Pj74+/tz8uTJfMWEhIQQFBSUY7/+/v7UqlWLZZeqsjt37sTNzY2ql07gJcnp2JMEX1Hpqhwa\nysUL50lNSckRW+v2OtfYiwtWq8W+5OXtQ/zpkvcFFXPuLGFXXNHw8fDE38eb2KRz2eIOJ8QTffQI\nL97TNNv6kPIBBJUr55S2FjfGff8WdhOKhBPxcYRfMYzGx8uL8r7lOJlw+Q/7QH9/7q1X3768ftcu\n7qh+KwB1qt9K1Uuf50yLhc1791Dn0rbSxmazOfxRFOQry4mPj2fFihWkpKRka3jXrl0d1rDrMRqN\n2S4Re3h44OLigsFgwM/PL19xRqMRDw8P+zZPT0+MRmO+jn/y5En27t3LkCFDbkJvnMtoMuFRxiPb\nOi9PTwxX9D2vmBfbtGHNxg00f6YtmRYLDzVrxn2NG2eLjTtzht5DBtOv63t4e3k5rjNOcPX7BMDL\nyyvbeyU/Mbn55JNPePfddxk7dixGo5GhQ4fm2E9JYDIa8b80ng6gzKXPoclowDefiU29uxuwZOF8\nOnfvxcnjx9m7YztVqlVzVJMLjSkjA0+37KdmT/cyGDLM9mWbzcaIpb/R/bHHNU5MbpjRZMajTJls\n6zw9PDDkUr0G2Lx3L7OX/c6Ufp9kW2+z2Rg2/TsqBQby8D1NHNbeoqy03DonX8lily5daN68eY4B\n/c42b948fvrpJwDc3d2pcMVgXJPJhM1mw8fHJ9tzvL29MV3xAbgyztvbG7P58gnYaDTifcXYn7ys\nWLGCli1bFruqIoC3lxfmK754IGsMi88Vfc8r5sspUwirHMLk4SPIzMyk1+DP+X7ePDpcmhx07GQM\nXT76iLdefIknHnrI8R1yMC8vr2zvE8j5XslPzNWMRiM9e/Zk+PDhNGrUiKNHj9K5c2dq1apFSEjI\nze1EIfj9l4X88cvPALi7u1E+8PLn1WzO+hx65fPzBvBm125MGTeabm+8StUaNbizYSN8cpkgU9x5\neXhgsmRmW2fMyMh2qfmXbdFUDapI/cgqTm6dlATenp6YMzKyrTOaTfjk8of96q3RjJg5nXE9etkv\nSUNWRXHgtCmcT05m9AfdcXMtthcqJR/ylen4+/vTvXt3R7flup5//nmef/55AObPn8/27dvt206e\nPElQUBDlrqpSVKlS5Zpxt9xyCydPniQyMtK+rVo+KxXr1q2jY8eO/79dKhRVIyNYtubyLNOU1FSS\nU1OJDAvLV8yGbVvp3eUd+3iVlk2asmr9Ojq88AIJiYl07tuX7p068UiLls7slsNUqVKFlStX2pdT\nU1NJTk62v2/yG3O1o0ePYrVa7WMWq1WrRkREBPv27SsRyWLrNu1o3aYdAMt+/Zl9u3fat8XFxhJQ\noQJlffN/udQ/IIDeAwfblwf06MYtVavfvAYXEVWCgvhz3x77cqrRSIrRQMQVyfbagwc4cPoUrUcN\nB+BCehodpk1iSPsXuLtqyau2ys1VJTSUFZs32pdT0tNJTksjMjj7xLJNe/cwctZMJvbpR7Ww7BOm\nPv9mGiazmbHde1KmGBZNbpbSUVe8zpjFw4cPc/jwYe666y5mz57NgQMH7OsOHz7srDbmqkWLFmzZ\nssU+xnD27Nk88sgjNxTXqlUr5s2bh8Vi4ezZs6xYsYJWrVrl6/j//fdfsR1b1qj+nZxOSGD7nqwv\npJkLF9DinnuyVRbziqkSEcHfmzYBYLFYWB8dTY0qWa/F5+PG8soz7UpMogjQoEED4uPj2bkzK9mZ\nPXs2zZo1y1Y1zE/M1UJCQkhJSWHfvn1A1nCPo0ePFtv3VV4aNm3Gnu3bOXUyBoDFC36i2f0P3tA+\npn01hsULsq4s7N25g6SzZ4mqc8dNb2thu6tKNeIvXGBnzHEA5m5az701a+F9xfCEMS+/xh+9PuL3\nnn35vWdfKvn5813HLkoUJV8a1r6duLNn2XHwAACz//id5nfelW3IkMFkYuDUyYz+4MMcieKq6C0c\nPR3L0He6lupEsTRxseUxevLVV1+99hNdXJg5c+Z1D5CSywD2m2XlypVMmTIFi8VCVFQU/fv3x8fH\nh9WrV7N27VoGDBiQZ1xmZibDhg1j27ZtuLm58dJLL9GuXVYl5LnnnsNisRAbG0vFihXx9PTks88+\no06dOly8eJEHH3yQDRs23PTxZV4Xk68fdBNs2bmT4V9PwGA0EhkWxpDefYg7k8D4779n6oiR14wJ\nCgwk7swZPh87luOxWRM87oiKon+3D0g3GHjg+ee4JSwcV1cX+7F6dHqblk2b5tqOm8Ho73f9oP9P\nW7duZfTo0RgMBiIiIhgwYADx8fFMnjyZCRMmXDMmKCiIBQsWMHfuXFJTU0lLSyM4OJjbb7+dQYMG\n8ddffzF16lTMZjOurq68/PLLtG3b1qF9OXGxcG43s37NX8yb8R0Wi4VqNWryTs8+eHv7sHndWrZu\n3MC7vfpy9NBBxgz9HEtmJglxpwm9NKN3/PQfiI05wVfDBpOakoJvuXK826svt1RzTmUx7Ip7qzrD\ntuNHGbPsd4xmM+GBFejf5hniL15k6uo/GffK6zni24wdxcTX3yS0fACLtm5h3qYNpJqMpJlMBPv5\nUzssnAFtnXfXhrMTv3HasfLLLaA84RNGAeBxSwTm2NNgsRDbrQ+Ws+eu82zHCxsz7PpBN9HW/fsZ\n+cMMjEYTEcHBfPZ2F+LPnWXigvlM7PMRf2xYz8BpUwgNyn7/3W8+6U//yZM4eOI4fmUvDwOpV6MG\nAzt1dmoffBre5dTj5WbQwhUOP8an7R52+DGuJ89k8UomkwlPz6wxMykpKTku916LI5PFkshZyWJJ\n4oxksSQprGSxOHN2sljcFcVksahzdrJYEihZdJ58jUidOXMm3bp1sy/36tUrX1VFERERkZLKarM5\n/FEU5CtZ/P3337P9RNmkSZP4/fffHdYoERERESka8jUyNTMzk+TkZMqXLw9AYmKiQxslIiIiUtQV\nlZtmO1q+ksXu3bvz/PPP4+npidVqxWq12iePiIiIiEjJla9kMSMjg+XLl5OUlISrq6u9wigiIiJS\nWhWVMYWOlq8xiz/88APJyckEBgYqURQREREpRfJVWUxNTaVFixZERkZSpkwZbDYbLi4uLFiwwNHt\nExERESmSSklhMX/J4qhRo3KsS01NvemNEREREZGiJV/JYrly5Vi8eDHnz58HssYw/vLLL/z9998O\nbZyIiIhIUVVaZkPna8xit27dOHfuHIsXL8bHx4edO3fSv39/R7dNRERERApZvpJFq9XK+++/T6VK\nlejQoQPTpk1j0aJFjm6biIiISJGlX3C5QkZGBgcOHMDLy4v169cTHx9PTEyMo9smIiIiIoXsumMW\nzWYzn376KefPn6dnz54MGTKECxcu8NprrzmjfSIiIiJFUlGp/Dlansnin3/+ydChQ6lYsSIXLlxg\n5MiRzJw501ltExEREZFClmey+M033/Dzzz/j7+9PbGwsAwcO5JtvvnFW20RERESKrNIyGzrPZLFM\nmTL4+/sDEB4ejslkckqjRERERIq60pIs5jnBxcXFJc9lERERESnZ8qws7t27l/bt2wNZ2fOxY8do\n3769fu5PRERESj1r6Sgs5p0sLl682FntEBEREZEiKM9kMSwszFntEBERESlWNGZRREREREq9696U\nW0RERERyUmVRREREREo9VRZFRERECqC0/NyfKosiIiIick2qLIqIiIgUgMYsioiIiEipp8qiiIiI\nSAGUll9wUWVRRERERK5JlUURERGRArDarIXdBKdQZVFERERErkmVRREREZECKCWToVVZFBEREZFr\nU2VRREREpAB0n0URERERKfVUWSxizMdPFnYTip3vUjIKuwnFSpfaVQq7CcWOtd4dhd2EYiVszLDC\nbkKxc+rDjwq7CcVOjXXLC7sJRfK3oTMyMujbty+nT5/Gzc2NYcOGERERkS1mzJgxbN68GZvNxkMP\nPUTHjh3z3KcqiyIiIiIFYLPZHP64UUuWLMHPz4+5c+fSuXNnRo8enW37oUOH2Lx5Mz/++CNz585l\n0aJFJCYm5rlPJYsiIiIiJcTGjRtp1aoVAE2bNmX79u3ZtpcrVw6TyYTZbMZkMuHq6oq3t3ee+9Rl\naBEREZECKIoTXM6ePUtgYCAArq6uuLi4YDab8fDwACAkJIRHH32U+++/H4vFwrvvvouvr2+e+1Sy\nKCIiIlIMzZ8/n/nz52dbt2vXrmzLVye0J0+eZOXKlfz5559kZmbywgsv0Lp1aypUqHDN4yhZFBER\nESkAayEXFp999lmeffbZbOv69u1LYmIiUVFRZGRkYLPZ7FVFgD179lCvXj37pedatWpx6NAhmjRp\ncs3jaMyiiIiISAlx7733smzZMgBWr15N48aNs22PjIxk7969WK1WMjIyOHToUI7Z0ldTZVFERESk\nAIrimMXWrVuzYcMGXnzxRTw8PBg+fDgAU6dOpWHDhtx5553ce++9vPTSSwC0b9+e8PDwPPepZFFE\nRESkhPi/eyterVOnTvb/f//993n//ffzvU8liyIiIiIFYKXoVRYdQWMWRUREROSaVFkUERERKYCi\nOGbREVRZFBEREZFrUmVRREREpACshX2jRSdRZVFERERErkmVRREREZEC0JhFERERESn1VFkUERER\nKYBSMmRRlUURERERuTZVFkVEREQKQGMWRURERKTUU2VRREREpABs+m1oERERESntVFkUERERKQBr\nKRmzqGRRREREpAA0wUVERERESj1VFkVEREQKQDflFhEREZFST5VFERERkQLQmEURERERKfVUWRQR\nEREpAFUWRURERKTUKxGVxejoaMaOHYvBYKBy5coMGDCA4ODgfMfExsbSp08f/P39mThxov05hw8f\nZuTIkSQlJeHq6srbb7/Ngw8+6NS+OcOWf/cz9qcfSTcZCakQxMA33iI4MDBbzN87tzPpl58xZ2ZQ\nvqwv/V59nVvDwwH4a/tWxs3/CavVSq3IWxjY4S18vb0LoytO89/2LWxbsQSrxUJgSBj3v/g6nt4+\n2WJO/XeApVO/wjfg8mtZ9Y47afJkO1bN/o6TB/fh4XX5dXrw5Q4E31LNaX1wtM07djB62hTSDQZC\nKgXzec9eVK5YMV8x6QYDQ7+ewK79+8jIzOTd117nyYceAiDTYmHY1+P5e9NmPDzK8Noz7XjhqacL\no4sOs3nPbr6c/j3pRiOhFSsyqOv7BAcFZYvJyMxk3KyZzFr8Kyumfptt+6pNGxk7awYWq5WoqtUY\n1PV9fH18rj5MibJl317GzJmddR4LCuKzjp0JrlAhW8yabVuZtHABGZkZ+PuW4+M33uTWiAgyLRZG\n/TCTzXv3YrVZaVj7dvr+7w3c3dwKqTdFhJsbQV3eJOCFdhxr+zKZiWcLu0VFTmm5KXexrywaDAb6\n9etH//79WbRoEffddx/Dhg3Ld8zx48f54IMPqF27do599+7dm5deeokFCxYwaNAgBg4cyMWLF53S\nL2cxmEx8NGUi/V/vwC9DR3JfvfoMmTU9W8yZ80l8+u00hnbqzKLBw3m0cROGzPoegFOJiQz/YSbj\nP+jBb8O/IDgwkH927SyEnjhPyvlz/LNwDo+/3Y2XPh5CucAKbF76c66xlSKr8FK/wfZHkyfb2bfd\n8/gz2baVpEQx3WCg99AhDPywB0u+n0HLe5rw+Vdj8x0zefYPGIwGfv3mO6aPHsOYb6YRGxcHwHfz\nfuTc+Qssn/UDs8aM4481q7mYnOz0PjpKutFIn9GjGPhOVxZ/PYn7Gjbk8ymTcsR9MHwoPt5eOdbH\nJiQwdOoUvv5kAEsnTqFyhSDWbo12RtMLjcFopO/X4/n0rU78OmoM9915N0O+/zZbzJmkJD6dMpmh\n73Zl0cjRPNakKYO/+waAOcv+4ERcHD8NG8GC4V9wJDaW39auKYSeFC2hwwdiTTcUdjOkCCj2yWJ0\ndDRhYWFERUUB8NRTT7Fp0ybS0tLyFePp6cnkyZOpW7dutv1mZmby9ttv06JFCwCioqLw8PAg7tIX\nVkmx5d/9hFWsxG23VAHg6Wb3sWnfXtIMl08Q7m7uDO3UhWqhYQDUr1GDI6dOA/D7pg08cHcDIoOD\ncXFxodeLL/PYPU2c3g9nOrZnJ+E1b6NcQFbV4rZ7mnNk59ZCblXRsmXnTsJDKlO7Rg0A2j76KBu2\nbSMtPT1fMZu2b+Pphx/B1dWVyhUr8kDTpqzeuAGAn5cvo+OLL+Lm5kaFgABmfDkWfz8/53fSQbbs\n2U14cDC3Va8OQNsHHmLjrp2kGdKzxXV69jneeeGlHM9f+vcaHmzShMiQEFxcXOj95lu0vq+FU9pe\nWLbs30d4xUrcVrUqAG1atGTjnt1XncfcGPZuV6qHZV0RqV+rFkdOxQJwV1QUvV/7H2Xc3Snj7s7t\n1apzJDbW+R0pYpKmzyHpu1mF3YwizWqzOfxRFBT7y9AxMTGEX7ocCuDj44O/vz8nT560J4f5ibma\nu7s7jzzyiH15zZo1+Pn5Ua1ayan+AMQkxBNRsZJ92cfLi/K+vpw8c4aoW24BINDPj3vvuJxMb9iz\nhzqXXodDJ2OoHFiBLqNHEnfuLA2jatP9+Rfx9vR0bkec6GJiAn5Bly+n+gdVxJCagjE9DS+fstli\nUy8ksXjSGFKSzhIYGk6zti/gWz4AgEPbN7Nn3WoyzSZqNriHux5qjYuLi1P74ignTsUSHhJqX/bx\n9qa8nx8xp09x2601rhsDLlgt1mzbYk6fJt1gIDYujj0HDjDgyy+xYaPDc8/z+AMlZ3jIidOniahc\n2b7s4+1Ned9yxMTFc9sV5596tXI/dx06fozKFSvy9sBPOZ2YSKM77qDnG2+W6M/kifg4wq8YepR1\nHivHyYR4oqpkJZCB/v7cW6++PWb9rl3cUf1WAOpc+i9kDXPYvHcPHUrY0IaCMO77t7CbIEVEviqL\nhw4dokOHDjz//PMATJ8+nX379jm0YfllNBrx8PDIts7Lywuj0XhDMdeye/duHn/8cUaMGMGnn36a\nYz/FndFsxqNMmWzrPMt4YDCbco3fvH8fs1cup+elikZKejqb9+9jSMfOzB3wObGJZ/hu6WKHt7sw\nZZjNuLtffs3c3MuAiwuZV71mPn7lqVr3Lh569S2e7/sZZf3Ls2p21qWx0FtrcuudDWn3YT+e6Pwh\nB6M3cjB6o1P74UgGownPqz4rnh4eGK74zOUV0+Tuu/hx8a+YzGbiziSwav16zGYzyampAMQlnuGn\niZMY0qs3n381jqMxMY7vlJMYTaYc5xlPTw8MpuufrwBS0tPYtGsXQz/ozrzRY4iNj+fbhfMd0dQi\nw2jK5Tzm4YHBdI3z2N69zF72Oz1eeTXbepvNxrDp31EpMJCHS/gVErk5bDabwx9FQb6Sxc8//5yP\nP/7YfgJr1qwZgwcPdmjD8svLywuz2ZxtndFoxPuKCRb5ibmWunXrsnTpUsaNG0e/fv04dOjQzWl4\nEeHt4Yk5IyPbOqPZjE8uVYjV27cx8LtvGNftQ/slaV9vH1reeReBfn54e3rSvuUDbNq/1yltd6Y9\n//zFnKGfMGfoJ5yJOUZm5uXXLDMjA2w2ynhkHz8WEFyZe59+Dm/fcri5udPwkSc5dfggGSYTtzVu\nRu17muPq6kq5gEBqN7mPE/t3O7tbDuPt5YXp6s+cyYTPFZ+5vGLefukVKlUIot3bHRk0bhzNGjak\nnK8v5cpmVW7bP/Y4rq6uRFW/lYZ167Fl5w7Hd8pJvHM7X5lM+HjlHJ+YG1+fstzfqDEVypfHx8uL\nZx99jA07S/Y4Ym/P3M5jub9mq7dGM2DqJMb16GW/JA1ZFcX+UyYRf+4coz/ojptrsR+lJXLT5OvT\n4O7uTvVL42cAbr31VlyLyAepSpUqnDx50r6cmppKcnIykZGRNxRztYsXL/LHH3/Yl2vWrEmdOnXY\nurVkjU2rEhLCyTMJ9uWU9HSS09OIDK6cLW7z/n18MXc2X3fvRe1Ll3UAQipUIPWKcUFurq64uhSN\n98bNdEfzB+wTUerc25KLiWfs2y4mJuDj54/nVbNN01MuknrhvH3ZarXiAri6uXIu7hSWKxJOm9WC\nawmaeVk1MoKTp0/bl1PSUklOTSXy0h8Z14vx8fZmUI+eLPl+BpOGDCXdaKBGlaqU9fES/lsWAAAU\nu0lEQVTBr1w5Uq4Yk+zq6oqrawl67cLCiIm/PDY6JS0t63W54pJ9XkIqViQ1/fLr4+bqWuITnyqh\noZxMiLcvp6Snk5yW8zy2ae8eRs6aycQ+/bi9WvVs2z7/Zhoms5mx3XviVcKuIInj2GyOfxQF+TqD\nlCtXjgULFmAwGNi1axejRo2iwlW3JCgsDRo0ID4+np2X/nKePXs2zZo1y1Y1zE/M1dzd3Rk5ciTR\n0VmzCJOSkti3bx81Lg3GLykaRN1G3Llz7Pgvq2I6e+Vymtetn218k8FkYuB33zDq3feoFpr9C6tV\nw0asiN5MQlISFquVX/75m8a1b3dqH5ytSp36nPrvAOcvfTntWrOCGnc1yhF3bM9Oln8/iYxLl8J2\nr/2TsJq34eZehjXzZrJ77V8AGNPTOBi9kVtq182xj+KqUb36nD6TwPa9ewCYtXAhLRo3zlZZzCvm\n23k/8sWUyQAcOXGCTdu3c3/TpgA82qIFMxbMx2azERsXR/TuXTSsV8/JPXSchnXuIC4xke3/7gfg\nh8W/cV+DhvmuLD7StBnL168j4exZLBYLP/+5ksZ1S87rk5uGtW8n7uxZdhw8AMDsP36n+Z134X3F\na2YwmRg4dTKjP/iQamFh2Z6/KnoLR0/HMvSdrpRxL/ZD+UVuOhdbPi6Ip6WlMWPGDHbs2EGZMmWo\nV68er7zyCmXLlr3eU0lJSbkpDc3L1q1bGT16NAaDgYiICAYMGEB8fDyTJ09mwoQJ14wJCgpiwYIF\nzJ07l9TUVNLS0ggODub2229n0KBBbN26la+++oq0tDRsNhtPPfUUr7/+ukP74rrL+WNBtx74ly/m\nzsZgNhFRKZjPOrxF/LlzTPxlERO792LZ5o0M/O5bQq+6z9u03h9Rwd+f+atXMf2P33F3c+POGjXp\n8/KrTh1M/01KxvWDbrLDO6LZ8sev2KxWgsIjeeDF1ynj6cXR3ds5vncXD7z0BjarlQ2LF3Bszw5c\nXVwJqBxK83Yv4Vs+gIuJCaz5aRapF87j4uJCrYZNnDbBpUvtKg4/BkD0rp0MnzQRg9FIZGjo/2vv\n3qOiKvc+gH9nBkbAQkBbIJfykuarhVGpXYyiBZqEFYnxqgOpnVITrDdFUqCjRipmF5H0aJBLRV0a\n4g3T0pWSRHYzUzllRqiIcR8U5DbM/N4/Os6RdCOazAz4/azlWsMze2Z+e7v3PN955tl7kDhjJv4o\nLUXK6lVYsSBJcZlubm4o1+sxc34izpaUwKFTJ8RFRWPQf05OuFBbi4TF7+Do8V/g5OiIf/zvWPM1\nGNuKqebC1Re6gb47dhSL0lJR11APH4/ueCv6VfxRXoYPN6zDv96ci4qqKkxMmA0AOFlUBB8PD2g0\nGqyc8xbcu3bFpt27sGpLJuzsNPD7n/544x8vtzps3gim2tqrL3SDff/vf2NR+mrU1zfAx90dcydN\nQXFFOZZlfIJlsbOwK/crzPloBTy7Nb/WZ2p8AhL+tRzHT52Ec+dbzO0D+/TBnJcnW6z+ov+bZbHX\nag2Nqwu8UxYDALR3+KDxzFnAaMSZV2NhLK+wcnV/6pPzmbVLwJPzV7T5a+yePanNX+NqWhUWExMT\nER8ff10vYImw2JFYIyy2d9YIi+2ZpcJiR2LpsNjeWSMstne2FhbbA4ZFy2nVeLuIYOPGjfD19YX9\nJWec3XnnnS08ioiIiKjjspWzldtaq8Lir7/+il9//RVZWVnmNpVKhTVr1rRZYURERERkfa0Ki2vX\nXn4F9w8//PCGF0NERETUXtjKL6y0tVaFxezsbCxZssT8u8gGgwEeHh6YOnVqmxZHREREZKtulq+h\nW3XpnKVLl2LJkiXw8PBARkYGpk6disjIyLaujYiIiIisrFVh0dHRET4+PjCZTHB1dUV4eDg2b97c\n1rURERER2ayb5aLcrfoa2t3dHVu3bkX//v0xY8YMeHt7o6LCNq6zRERERERtp8WRxQULFgAAkpKS\n4O/vD1dXVwwdOhRdunTB8uXLLVIgERERkS0yibT5P1vQ4sjizz//DADQaDRwc3PDt99+i6ioKIsU\nRkRERETW12JY/OtZPjfLWT9EREREV3Oz5KIWv4b+6+/UWuJ3a4mIiIjIdrQ4snjs2DGEhYUB+DM9\nFxQUICwsDCIClUqFjIwMixRJREREZGv2z7k5pua1GBZ37NhhqTqIiIiIyAa1GBa9vLwsVQcRERER\n2aBWXZSbiIiIiG5ODItEREREpIhhkYiIiIgUMSwSERERkSKGRSIiIiJSxLBIRERERIoYFomIiIhI\nEcMiERERESliWCQiIiIiRQyLRERERKSIYZGIiIiIFDEsEhEREZEihkUiIiIiUsSwSERERESKGBaJ\niIiISBHDIhEREREpYlgkIiIiIkUMi0RERESkiGGRiIiIiBQxLBIRERGRIpWIiLWLICIiIiLbxJFF\nIiIiIlLEsEhEREREihgWiYiIiEgRwyIRERERKWJYJCIiIiJFDItEREREpMjO2gVc6syZMxg5ciTu\nvvtuiAgaGxvx0ksvISgoyNqlXVVmZiZOnDiB2NjYZu1JSUno06cPnnvuOStVBqxbtw7btm2DVqtF\nfX09Xn/9dTz88MPX9VxTpkzB8uXLr7uW5557DsnJyfD29r7u57CGa9k333jjDQwfPhwBAQFWqNT2\nZWVlITY2FgcOHICbm5u1y7E5Vzpe9+3bh8jISGzduhWurq7Q6XTNHnP8+HG8/fbbMJlMqK2txUMP\nPYQZM2ZApVJZaS0s50b3GxEREUhISEDfvn1vcKW259Jtd1G/fv0QFxdnxarIFtlUWASAnj17Yu3a\ntQCAqqoqhIaG4tFHH4WDg4OVK2ufzpw5g02bNiEjIwP29vY4efIk4uPjrzss/p2g2N5x37wxsrKy\n4OPjg88++wxjxoyxdjk2Rel4TU9Pb/FxiYmJiImJga+vL0wmE6ZOnYq8vLxmIaAj47F5/S7ddkRK\nbC4sXsrFxQW33XYbTp48iblz58LOzg5qtRpLlixB586dERMTg7KyMjQ2NiI6OhoPPfTQZW3+/v5Y\nt24dduzYAbVajcDAQEycOBFLly7F+fPnUVBQgMLCQsyePRuPPfYYVq5ciZ07d8LHxwdNTU2YMGEC\nBgwYgNmzZ+PcuXMwGo2Ij49Hv379MGzYMPj7+6Nr165wd3c3171t2zakpqbC3d0dDg4O6NOnj9W2\nYU1NDRoaGmAwGGBvb48ePXogPT292afn9PR06PV6DB48GB9//DFqa2sxZMgQAEBUVBSAPz9tx8XF\n4YUXXsDq1asxf/58rFmzBgCQkpICZ2dnPPzww5g3bx5UKhU6d+6MhQsXwtnZGYmJifjxxx/Rs2dP\nGAwGq22LG+nivnnkyBEsXboURqMRnp6eSEpKMi9TU1OD6dOno7a2FvX19UhISICvry9WrlyJPXv2\nQK1WIyAgAJMnT75iW0dUVVWFI0eOYP78+UhNTcWYMWOQm5uL+fPno1u3bujZsyfc3NwQHR2N999/\nH99//z2MRiN0Oh1CQkKsXX6bu9rxCgBHjx7FxIkTUVpaipkzZ8Lf3x/V1dWoqakBAKjVavOHuszM\nTBw4cAA1NTUoLi7G+PHjMWrUKKutnyW01G/U1NQgJiYGTk5O0Ol00Gq1eO+996DRaBAcHIzx48cD\nAHbt2oW3334bVVVVWL58OTw9Pa27UhbU1NSE2NhYlJSUoLa2FtHR0QgICEBERIS5L3v99dev2CdS\nByY2pLCwUEJDQ5v9HRQUJDk5OZKXlyciIh988IGsWbNGjh07JpGRkSIicu7cOdm+ffsV206fPi06\nnU5MJpOYTCYJDw+XoqIiSU5OlujoaBERyc7OlilTpoherxd/f3+pq6uTsrIy8fPzk4MHD0pKSops\n2rRJREROnDgh48ePFxGRgIAAyc7OFhGRzZs3y8KFC8VkMsljjz0m5eXl0tjYKCEhIbJ582bLbEAF\nMTEx8uCDD0psbKzs3LlTDAaD6HQ6OX78uIiIrF27VpKTk+XgwYPy+OOPS0NDg5w9e1ZGjRolIiJ6\nvV5GjBghIiKDBw8WEZHhw4fLuXPnREQkNDRUiouLJTIyUgoKCkREJD09XZYtWyYnTpyQ0NBQMRqN\ncvbsWRkwYIAUFhZaeAv8fUr75vTp02Xv3r0iIpKUlCSHDx+W2NhY+eKLL+T333+XPXv2iIhIbm6u\nREVFiYjIkCFDxGAwiMlkknXr1im2dUQbNmyQWbNmSVNTkzzyyCNSXFwsoaGhkpeXJ01NTRIeHi7J\nycny3XffyfTp00VEpKGhQYKDg6Wurs7K1VtGS8drcnKyvPjiiyIicvz4cfM+uWfPHnnggQdkwoQJ\nkpqaKiUlJSLy5/tSSEiIGAwGqaiokKFDh4rRaLTaurWFa+k3CgsLZeDAgVJZWSkmk0mCgoKkoqJC\nmpqa5OWXX5a6ujrR6XSydu1aERFZvHixrFq1yhqrZRF/3XYiIuXl5ZKZmSkiIqdPnzbfr9PpZP36\n9SIiin0idVw2N7JYUFCAiIgIiAg6deqEpKQkODo6YvHixaivr0dpaSlGjhyJXr164cKFC4iJiUFQ\nUBCeeuopNDQ0XNa2e/dunDp1CpGRkQCACxcuoKioCABw3333AQA8PDxQXV2N06dPo2/fvnBwcICD\ngwN8fX0BAD/++CMqKyuxfft2AEBdXZ253ovLXKTX69G5c2d07dq12WtY06JFi5Cfn48DBw4gNTUV\nGzZsgCj8yuNdd90FrVaL7t27Q6VSobS0FLm5uQgMDGy2XEBAAA4cOAA/Pz9otVq4u7vjyJEj5tGP\nxsZG3HPPPfjtt98wcOBAqNVqdO/eHT4+Pm2+vm3lSvtmXFyceX7PzJkzAQAbNmwAAHTr1g3Lli1D\nWloaGhsb4eTkBAAYPnw4JkyYgJCQEDz99NOKbR1RVlYWXnnlFWg0Gjz55JP49NNPUVRUhP79+wMA\n/P39YTQacejQIfz000+IiIgAAJhMJpSVlbXr/ae1rna8Dh48GADQt29f/PHHHwCAwMBADB48GDk5\nOdi3bx9WrFhhHvkfNGgQ7Ozs4Obmhi5dukCv15vfnzqK1vYbAODj4wNXV1dUVFSgU6dO5nmzK1as\nMD/f/fffDwBwd3dHVVWV5VfIgi5uu4uGDBmCyspKbNy4EWq1utn6t6ZPpI7J5sLileZPRERE4KWX\nXoK/vz/S0tJQW1sLR0dHbNq0CYcOHcKWLVuwb98+LFiw4LK2J554Ao8//jjmzZvX7DkPHjwIO7vm\nqy8iUKv/e4L4xcnh9vb2SEhIgJ+f32X12tvbX9Z26XMohTJLkf9M+O7duzd69+6NiIgIjBgxotnX\n5k1NTebbWq3WfDswMBD79+9HTk4OJk2a1Ox5hw0bZv76evjw4QAAR0dHrFmzptmk+l27djXbHiaT\n6Yavo6Vcad/UaDSK/8erV6+Gu7s73nnnHRw9ehSLFi0CAMydOxf5+fnYtWsXIiIi8Mknn1yx7a/7\nZ3tXXFyMn376CQsXLoRKpUJ9fT1uvfXWZstc3He0Wi3CwsIu2+86OqXj9dJj9NLj6+Lt+vp6ODs7\nIzg4GMHBwUhJScHevXvh6enZ7JgTkQ550ktr+w3gv+/ZarVa8f1Io9GYb1v7Pbyt/XXbbdmyBQUF\nBVi/fj2qqqoQFhZmvu/itmupT6SOqV1cOqeqqgq33347GhsbkZ2dDYPBgLy8POzYsQMPPPAA5syZ\ng/z8/Cu2DRgwAN988w3q6uogIkhMTER9ff0VX8fLywsnTpyAwWBAZWUljh07BgAYOHAg9u7dCwD4\n7bffsGrVKsVaXVxcUF1djfPnz8NgMODQoUM3foNcg4yMDCQkJJjf8Kqrq2EymaDValFWVgYAijUG\nBQUhOzsbp06dwoABA5rdd++99yI/Px/79+83h8V+/frhyy+/BADs3LkTX3/9NXr27Im8vDyICIqK\nisyjuh3F3XffjYMHDwIAlixZgtzcXPN9er0et99+OwBg7969MBgMqK6uRkpKCnr37o2oqCh06dIF\nJSUll7VdnH/WkWRlZWHcuHHYvn07tm3bht27d+PcuXOoq6tDfn4+jEYjvvrqKwB/jmDs27cPJpMJ\nDQ0NeOutt6xcvWUoHa+XjgT+8MMPAIBffvkFnp6eqKmpwYgRI1BaWmpepri42HzFgcOHD8NoNKKy\nshIXLlyAi4uLBdfIeq7Ub1zK1dUVRqMRJSUlEBFMmjQJ58+ft1K1tkOv18Pb2xtqtRp79uxBY2Pj\nZctcS59IHUO7GLrQ6XSYOnUqfHx8EBERgXnz5mHo0KHYvn07Nm7cCI1GgxdffBHe3t547733mrV5\nenoiMjIS48aNg0ajQWBgoOIZct26dUNISAhGjx6N3r17w9fXFxqNBjqdDrNmzcLYsWNhMplavKyA\nWq1GVFQUdDodvLy8rHpyC/DnpWp+//13jB49Gk5OTmhqakJ8fDwAYN68ebjjjjvMgeavevXqhcLC\nQgwdOvSy+1QqFfz8/PDzzz+bJ3/HxcUhISEBH330ETp16oR3330XLi4u6Nu3L8LDw9GjR48ONwl6\n2rRpmDVrFtavX4/u3bsjKirK/NXMM888g9jYWOzevRvjxo1DVlYWPv/8c+j1eoSFhcHJyQl+fn7w\n8vK6rK0jdug7d+5sdgKQSqXCs88+C7VajejoaHh7e6NXr15Qq9W47777MGTIEISHh0NEMHbsWCtW\nbjlKx2taWpp5ma5du2Ly5Mk4c+YM4uLicMstt2DOnDmYNm0a7O3t0dTUBF9fXzz99NPYunUrvLy8\n8Oqrr+LUqVN47bXXmo30d2RX6jeCg4ObLfPPf/4T06ZNAwCMGDECzs7O1ijVpgwbNgxTpkzB4cOH\nMWrUKHh4eCAlJaXZMtfSJ1LHoJKOPsZ+jTIzMxESEgI7OzuMHDkSaWlp8PDwsHZZRB1WTk4OevTo\nAW9vb7z55psYNGiQeX4Z/T1K138lIroW7WJk0ZLKy8vx/PPPQ6vVYuTIkQyKRG1MRBAVFWU+Mezi\ntAYiIrINHFkkIiIiIkU3x+QVIiIiIrouDItEREREpIhhkYiIiIgUMSwSERERkSKGRSIiIiJSxLBI\nRERERIr+HxC0hwHMuvBwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f45a08e3f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_correlation_map( train )"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "040b5b57-74d6-262f-df86-ee86b01c1906"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 135,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163939.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f06aaa16-1098-180b-86e8-b677eee19c7e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python: 3.6.0 |Anaconda custom (64-bit)| (default, Dec 23 2016, 12:22:00) \n",
"[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
"scipy: 0.19.0\n"
]
},
{
"ename": "NameError",
"evalue": "name 'numpy' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-b059179d354e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# numpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'numpy: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0;31m# matplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'numpy' is not defined"
]
}
],
"source": [
"# Check the versions of libraries\n",
"\n",
"# Python version\n",
"import sys\n",
"print('Python: {}'.format(sys.version))\n",
"# scipy\n",
"import scipy\n",
"print('scipy: {}'.format(scipy.__version__))\n",
"# numpy\n",
"import numpy as np\n",
"print('numpy: {}'.format(numpy.__version__))\n",
"# matplotlib\n",
"import matplotlib\n",
"print('matplotlib: {}'.format(matplotlib.__version__))\n",
"# pandas\n",
"import pandas as pd\n",
"print('pandas: {}'.format(pandas.__version__))\n",
"# scikit-learn\n",
"import sklearn\n",
"print('sklearn: {}'.format(sklearn.__version__))"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "eceb8730-f611-6120-fb55-6e1fff05646f"
},
"source": [
"**Load Libraries**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "138e11e6-3d15-418b-45f5-60f12821ed8e"
},
"outputs": [],
"source": [
"# Load libraries\n",
"from pandas.tools.plotting import scatter_matrix\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import model_selection\n",
"from sklearn.metrics import classification_report\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.svm import SVC"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "c358cbfd-9cac-8a84-95f8-01dc485a00d1"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-9066045c1cef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Load dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"../input/Iris.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mdel\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"# Load dataset\n",
"dataset = pd.read_csv(\"../input/Iris.csv\")\n",
"del dataset['Id']\n",
"dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "e7cfbcb8-56cf-614a-3bbb-ecc073a243d0"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataset' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-4-d7b3bddcb985>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# shape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
]
}
],
"source": [
"# shape\n",
"print(dataset.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "ecba21bc-6392-ecf4-9951-5f0a8eaec61d"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataset' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-6fac0e50cb91>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# descriptions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
]
}
],
"source": [
"# descriptions\n",
"print(dataset.describe())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "cd430156-7db6-7cc1-159b-f81e79244dc4"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataset' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-1559c3049b36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# class distribution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Species'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
]
}
],
"source": [
"# class distribution\n",
"print(dataset.groupby('Species').size())"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ed5d8b56-9e78-8c12-ba95-fc00922c1f6e"
},
"source": [
"- Data Visualization\n",
"- Univariate plots to better understand each attribute\n",
"- Multivariate plots to better understand the relationships between attributes"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "308d9e14-d399-f2a2-09ec-eca01e630f3f"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataset' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-e50b6fd5f68c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# box and whisker plots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'box'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
]
}
],
"source": [
"# box and whisker plots\n",
"dataset.plot(kind='box')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "4029fb49-cd30-bdf7-f6ea-7d5e4194748c"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'dataset' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-5be73a3b3e36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# histograms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;31m# scatter plot matrix\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mscatter_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'dataset' is not defined"
]
}
],
"source": [
"# histograms\n",
"dataset.hist()\n",
"# scatter plot matrix\n",
"scatter_matrix(dataset)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "7d776d7b-3ec2-8c06-87f1-f3b3676a7f59"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 204,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/163/1163955.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "53e65f34-8897-d0b3-a11f-9c71be4dab0a"
},
"source": [
"This is a python jupyter notebook for Digit Recognition Competition in Kaggle were the objective will not be only to develop a model, will also be analyze the features and compare a variety of models that can help to solve this challenge.\n",
"\n",
"This notebook will be divided on the next sections:\n",
"\n",
" 1. Intro \n",
" 2. Data cleaning and Transformation\n",
" 3. Data Exploration \n",
" 4. Data Modeling with\n",
" 4.1 Dimensionality Reduction\n",
" 4.1.1 PCA\n",
" \n",
" 4.2 Classification\n",
" 4.2.1 Decision Tree\n",
" 4.2.2 Logistic Regression\n",
" 4.2.3 SVM\n",
" 4.2.4 Random Forest \n",
" 4.2.5 Classifier Perceptron\n",
" 5. Comparation\n",
" 6. Conclusion\n",
"\n",
"let's take on count that the predictive models might need different preprocessing steps, so I will use different dataframes if needed"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "429053cf-aff1-7435-ff9a-c37ff654af5e"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"import matplotlib.gridspec as gridspec #to manipulate subplots grids\n",
"%matplotlib inline\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn import preprocessing, manifold, svm, tree, linear_model, metrics, ensemble\n",
"from sklearn.decomposition import PCA"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a9f74047-e9b9-d4e0-3db3-22cafce970e1"
},
"source": [
"### Intro"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "630cf2d4-6ee0-2e38-545c-d8893a6515ff"
},
"source": [
"Data load and first look on the Dataset"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "11cef696-a426-4f51-7a28-39a3f2d44ed6"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 42000 entries, 0 to 41999\n",
"Columns: 785 entries, label to pixel783\n",
"dtypes: int64(785)\n",
"memory usage: 251.5 MB\n"
]
}
],
"source": [
"df_digits = pd.read_csv('../input/train.csv')\n",
"df_digits.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "5ddca021-3c9e-e1b7-e499-2f0b5c8e00f2"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>label</th>\n",
" <th>pixel0</th>\n",
" <th>pixel1</th>\n",
" <th>pixel2</th>\n",
" <th>pixel3</th>\n",
" <th>pixel4</th>\n",
" <th>pixel5</th>\n",
" <th>pixel6</th>\n",
" <th>pixel7</th>\n",
" <th>pixel8</th>\n",
" <th>...</th>\n",
" <th>pixel774</th>\n",
" <th>pixel775</th>\n",
" <th>pixel776</th>\n",
" <th>pixel777</th>\n",
" <th>pixel778</th>\n",
" <th>pixel779</th>\n",
" <th>pixel780</th>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
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"</div>"
],
"text/plain": [
" label pixel0 pixel1 pixel2 pixel3 pixel4 pixel5 pixel6 pixel7 \\\n",
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"2 1 0 0 0 0 0 0 0 0 \n",
"3 4 0 0 0 0 0 0 0 0 \n",
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"\n",
" pixel8 ... pixel774 pixel775 pixel776 pixel777 pixel778 \\\n",
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"2 0 ... 0 0 0 0 0 \n",
"3 0 ... 0 0 0 0 0 \n",
"4 0 ... 0 0 0 0 0 \n",
"\n",
" pixel779 pixel780 pixel781 pixel782 pixel783 \n",
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"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
"[5 rows x 785 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_digits.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a4061a05-15d7-173b-62e5-b003be10191a"
},
"source": [
"So, we have 42000 rows with 785 features, the first feature is the digit that the image represent and the others are pixel value of the image. Let's see how are the original images."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "c0b367f7-df9b-e21b-cb4d-5580305b4457"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f2f5b09dcc0>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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1Mtx93jjg90Od7j5J0vWSFmebt23JK/ts7XSudpmky1UZw7FH0tIym8mGlV8n6Vfu3te/\nVuayG6CvUpZbGeHfJ2lcv+c/zqa1BXffl/3ulfSsKrsp7WT/iRGSs9+9Jffzf9x9v7t/7+7HJa1U\nicsuG1Z+naTV7v5MNrn0ZTdQX2UttzLC/4ak8WZ2mZkNlzRX0voS+vgBMxuRHYiRmY2QNE3tN/T4\nekkLsscLJD1XYi8naZdh26sNK6+Sl13bDXfv7i3/kXSDKkf8/0fSP5fRQ5W+Lpf0bvbzQdm9SXpC\nlc3A71Q5NvL3ks6XtFXSx5JektTRRr09pspQ7u+pErSxJfXWqcom/XuS3sl+bih72SX6KmW58fVe\nICgO+AFBEX4gKMIPBEX4gaAIPxAU4QeCIvxAUP8LY5bcMlzFj/sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f5b0c5470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"img = df_digits.iloc[1][1:].as_matrix()\n",
"img = img.reshape((28,28))\n",
"plt.imshow(img,cmap='gray')\n",
"plt.title(df_digits.iloc[1,0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0c313200-3960-38dc-3655-1929d4f41a3c"
},
"source": [
"So now let's plot only 9 images in a matrix"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "80daee29-53fc-7d6b-11f1-1a08f197e57c"
},
"outputs": [
{
"data": {
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GWjDK0EVEEqIoM/QDDjgAgI8+Su1O1aBBAyC/DL1JkyYAXHbZZYAy9Cj59nzttdcA2GWX\nXfI+54033ggE/0duu+22vM8p4brqqquAVGYOcMcdd5RdL/G22247AA488EAAxo0bB8DOO+8MwIIF\neS+ijpQydBGRhCjKDN3PWFi3bh2QuhoOcOqpp+Z9bn81ff/99wfgpZdeyvuckpuSkhIgnMy8skGD\nBgFBpn7PPfcA8NNPP4X+WpKdXr16AXDuuecCMHv2bADOOeecnz32iy++AGD58uVA8M36qKOOAoJv\nYjWVMnQRkYQoygzdGz9+PBBk1XXr1gXyG0v3tthCvyurW/PmzQF46qmnNvk4/83sP//5D0DZXGUI\n5qpvueWWGZ/729/+FoA777wTgJdffhmAuXPnVjVsqSI/i8mPnfvP7xlnnAEE7Vxe3759AfBlgf11\nFj+b6ZZbbgHC6QOioF5HRCQhijpD/9///gfAiSeeCMBWW20FwNKlS3M+148//gjAd999F1J0kqvz\nzjsPgJ122inj/V9++SUAp512GpB5JlK3bt2AYBbLDjvssMnXfOKJJwAYOnQoEMx3lsLzY+S+vf31\njDfffHOzz12xYkWF2/4cfhbMp59+GlaY1UoZuohIQhR1hv7222+Hdq5ly5YByasNURP4FYJ//vOf\nN/m4jz/+GNj0GoHnnnsOCGY7XHzxxQC0atUq4+P9mLpff+DH1BcuXJjx8ZK/+vXrA3DCCSdUOH7t\ntdcC2c04aty4MRBcM0kKZegiIglR1Bm6H/cuhMMPPxyAqVOnFuw1JMWPpe64444Z7/czFoYNG5b1\nOe+44w4AJkxI7dny2GOPAbDXXntlfLzP1J9//nkgGJNdv3591q8p2fGrP/17fPfddwM1d9w7TMrQ\nRUQSoqgzdH+luxCr/HwVNz/zQgrn+uuvBzJXTQSYPn06QFnlvVz4lYV+NeLmMvV27doByd7ZPmp+\n/rnn1wDk8jn2de49Pzvt+++/zy+4iClDFxFJiKLO0N944w0gmJFw9dVXAzBw4EAg80qzzfFZ4JAh\nQwBo1KgRACtXrswvWKkyXx87Hz5T79mzJwDvvPMOANtss03Gx7dp0wYIau9LeHr06FHh9uOPP57z\nOfw3Kc/vZPTVV19VPbAYKOoO3fNFuXyR+3/9618AfPjhhzmfy3/w/SKlzp07AzB58uS845ToLV68\nGIAffvhhk4/zi9Uuv/zygsdULHxph9/85jdAsDDQLxjLhR8S8z+zWYxUE2jIRUQkIZShA1OmTAHg\nm2++AeDf//43AAcffHDO5/JDLmvWrAkpOokjP4yjDLz6+Yvf77//PgCrV6/O+rl+UVKzZs0qnGvR\nokVhhhgZZegiIgmhDD2DfApsffvttwC8++67QFB0/9VXXwWUuSdFw4YNN3m/34BcwuNLGvstBn0h\nrVz4a1tbb711heOffPJJntHFgzJ0EZGEUIZejp/+1LFjRwBq1069PeWXb/usYNdddwWCWSyHHnoo\nEBSK8vd7vsiTL+IkNZMvAOa3otuYRx55pDrCKSr+c5jP5hN//OMfAWjatGmFc/nZaTWdMnQRkYRQ\nhl7O6NGjATjllFOAIJv24+KHHHII++yzDxBsd+XLpfqlxF9//TUQLEC58MILgWCrK6l+vg18obSq\njJf6LcsqfxOrzGfuKsoVPv+Z82PouejSpQsAt99+e4XjvkxyUhaAKUMXEUkIZejlzJ49G4B58+YB\ncPrpp1e4f9KkSZx//vkAzJgxo8LPypYvXw4E2aEUzqxZswDYbbfdMt7vl3mfddZZAGVtuCmtW7cG\n4OyzzwagX79+QDD2WtnIkSMBGD58OLDxQmESHj+n3M9+yVQOe8899wSComp+dtK0adOAYFPopNhs\nhm5mrcxsqpl9YGbvm9k56eNNzGyymX2U/vnLwocrIiIbk02Gvh443zn3tpk1Amaa2WSgPzDFOTfM\nzIYAQ4CLChdq4fn55+3bt8/7XH5LOim8Aw88EIAXXngBgN133z3j43y23bVrVyDYxKK8/v37A0FW\nX3m+cmV+y8FLLrkEgA0bNuQSuuTAr+b0hbT2228/ALp37w4Em5F4TZs2LZuV5DNzvx7kpJNOAqpW\nBybONpuhO+cWO+feTv99JTAHaAn0AEalHzYK6FmoIEVEZPNyGkM3s7bAHsCbQHPn3OL0XV8CzTfy\nnAHAAAjGJaXmi1O7+llIQ4cOBeDRRx/N+LhatWoBsMsuuwBw2223Vfk1fWbus/0lS5ZU+VxxEqd2\nrcyXs37ggQeAIEP3tZf8/d26dQNSm0j7ax4+u/ePTcqslsqynuViZg2BR4HBzrkV5e9zqStAGa8C\nOedGOOdKnXOlviCO1Hxq12RSu9ZsWWXoZlaHVGc+1jk3Pn34KzNr4ZxbbGYtgGSkKCHxG1r4GRh+\nHrMUjl/p27dvXwDGjBkT2rl9bXz/LWD8+NTHoJAbjUtmTz/9NACrVq0Cgs9Wpi0G/TUNX1NpY9/e\nkiKbWS4GjATmOOduKnfXBKBf+u/9gCfCD09ERLKVTYa+D9AXmG1ms9LH/gYMAx42s5OBBUDvwoRY\nM/nxPD/bpVOnTkAwT1nC5+d++zFWn8kNHjwYCLYu82PomfjVwp999hkQVE0cN24coBWgceDbxs9E\n+t3vfgcEu0R16NABSNVnuemmVA7q550n3WY7dOfcNGBjW5h3CTccERGpKq0ULRBfd8Lvg+gzPCk8\nn6n71bp+VyHtLpQsfkNn//PFF1+MMJp4UC0XEZGEUIZeIL7O8sbqi4iIhE0ZuohIQqhDFxFJCHXo\nIiIJoQ5dRCQh1KGLiCSEVefOKma2lNSq0kIoAeJahDxusbVxzoVWeUntGiuhta3aNVayatdq7dAL\nycxmOOdKo44jkzjHFndxfu/iHFvcxfm9i3Nsm6MhFxGRhFCHLiKSEEnq0EdEHcAmxDm2uIvzexfn\n2OIuzu9dnGPbpMSMoYuIFLskZegiIkVNHbqISEKoQxcRSQh16CIiCaEOXUQkIdShi4gkhDp0EZGE\nUIeeBTNbVenPT2Z2S9RxSf7M7H4z+9LMVpjZPDM7JeqYJBxm9qKZ/VDuczs36pgKTR16FpxzDf0f\nYFvge2BcxGFJOIYBv3bONQb+DFxtZh0jjknCM7Dc53fHqIMpNHXouTsSWAK8EnUgkj/n3HvOuTX+\nZvrPDhGGJFJl6tBz1w8Y7VQzITHM7HYzWwN8CCwGJkUckoTnWjNbZmavmtkBUQdTaKrlkgMzawN8\nAvzGOfe/qOOR8JhZLeD3wAHAP51z66KNSPJlZnsDHwBrgWOBW4HdnXMfRxpYASlDz01fYJo68+Rx\nzv3knJsG/Ao4I+p4JH/OuTedcyudcz8650YBrwJ/ijquQlKHnpsTgVFRByEFVRuNoSeVAyzqIApJ\nHXqWzOz/gJZodktimNk2ZnasmTU0s1pm1h3oA0yJOjbJj5ltbWbdzayemdU2s+OBPwDPRB1bIdWO\nOoAapB8w3jm3MupAJDSO1PDKHaSSmwXAYOfchEijkjDUAa4G2gM/kbrg3dM5Ny/SqApMF0VFRBJC\nQy4iIgmhDl1EJCHy6tDN7GAzm2tm881sSFhBiYhI7qo8hp5eiDEPOAj4HJgO9HHOfRBeeCIikq18\nZrl0AuY75z4BMLOHgB6kVmZlVFJS4tq2bZvHS2Y2c+bM0M8ZFx07hl8naubMmcucc83COp/aNXeF\naFcIt23VrrmLul3z6dBbAgvL3f4c2Lvyg8xsADAAoHXr1syYMSOPl8zMLLlrBQr0fi0I4Rxq1zwU\n4v2C/NtW7ZqfqNu14BdFnXMjnHOlzrnSZs1CSwolYmrXZFK71mz5dOiLgFblbv8qfUxERCKQT4c+\nHWhnZtubWV1S1cy0wk5EJCJVHkN3zq03s4HAs0At4B7n3PuhRSYiIjnJq5aLc24S2gxARCQWtFJU\nRCQh1KGLiCSEOnQRkYRQPXQRSaT27dszaNAgALbccksAmjdvDsChhx5a4bHTp08HYPz48QA8/fTT\nALz77rvVEmtYlKGLiCSEMnRJPL/i0Wdr++67LwAHHHBA2WPWr18PwMSJEwH48MMPAZg7d26Fcz3+\n+OMArFq1qsLzJHqNGjUC4JprrgHgxBNPpGHDhhUe48sOVC5KWFpaWuHn5ZdfDsC4cakdJ/v371+Y\noEOmDF1EJCGKMkPv1asXAN27dwfgscceA2DZsmUVHvfZZ58B0LRpUwAaNGiw0XP+4Q9/AKBnz54A\nzJkzBwiyBX8uKZztttsOgMMOOwyAo446CoCuXbtWeNzatWsB+PTTT8uO1apVC4AePXpU+FnZPffc\nA8CsWbMAGD16NAC33noroIw9Cm3atAHgpZdeAqBVq6AiyaRJqWUy69atAzaeoVe2xx57AHDMMccA\n8N133wFwwQUXAMH/obhRhi4ikhBFmaG3b98egFNPPRWAU045Bfj5b++FC1PVgUtKSgCoX79+2X2V\nH1v5tn8Nn6FL4fnx7912263C8SeffBKAadOmATBhQqrkUPnx8c6dOwPw4osvAnD22WcD8NZbb1U4\n1957pypE9+nTB4CbbroJCGZPXHzxxSH8SyQbfubKAw88AKTK/ULwGXzooYfo27cvABs2bMjp3H7s\n/bjjjgPgiCOOAFJ9AChDFxGRAivKDH2LLVK/x8444wwAXn75ZSAYB8+FnzFxwgknVDg+duxYQGPn\n1emGG24Agm9UPmOfP3/+Zp+79dZbA8G3tfvvvz/j4/zYuW/f9957DwjmNfvZEX7MVgrn5ptvBoJv\nV55vm8GDB+ecmXt+FtOIESMq/Iw7ZegiIglRlBm6n4ly1113AcGcY/8zF37GjB+3++CD1JaqGjuv\nfj4zq4pnnnlmk/fvueeeQDB27q+/NG7cGIAuXboAysyr05FHHgkE16/uu+8+AM4991wgmJlSTJSh\ni4gkRFFm6J6fiVIVfk66v7Lus4Rhw4YBP5/TLjWDnzlx3nnnAXDyyScD8Otf/xqA1atXA/DOO+8A\ncPjhhwPFmQ1G5ZBDDgFgq622AoJvx5vKzP01ktq1a1d4ztdff13YYKuZMnQRkYQoqgzdZ+T+px9D\nz+dcO+64IxBUafOrTiU69erVA4Lsuk6dOhkft3jxYgBatGhRtrrQZ9z+m9ezzz4LwOmnnw4Es1z0\nDaz6+W9PfiaRX93rVc7MW7RoUTaTzf/0q75//PFHIJi9EvcVoNlShi4ikhBFlaF7YWRXY8aMAYKx\n8+eeew6ANWvW5H1uyc9BBx0EBOPg22+//Waf41cFX3vttQBMnToV+Hm1RYmOr6bYqVOnCsefeuop\nIFhDcNFFFwGpKpv+OZXVrVsXgIEDBwJBnzB06NCQo65eRdWh+2mJe+21V97n8kMtmyvyI9XPL/Wf\nMmUKANtss80mH3/SSSdx9NFHA3D88ccD8PrrrxcwQqkKP6TiSzj4RX2+GJsfLiv/mfQbV8yePbvC\nufyUR39h1Q+p+SGYr776Kvx/QDXQkIuISEIUVYbu5TPk4ssD+KEWz5cPkPjww1/ly+Rmcvnll3P1\n1VcDwcUzv9DIZ+q+jKoWDkXHv/eXXHIJEHwD8xe9V65cCQQLzIYNG7bR0hs+u/fTGVu0aAHADjvs\nAChDFxGRiBVlhp4PP13Rj9P56YpVKRsg4fDlcv2FzeXLl+d8Dj9dzRd88tMVJ0+eDMAbb7wBQO/e\nvQH4+OOP84hY8uHH0HfaaScgmL74/fffA9kVxPOf38oLjBYtWhRusNVMGbqISEIoQ8/RfvvtBwRj\n6H7TYKl+fvaKz6L9ps9VydAr89+4/OwXvwjNT2f029rNmzcv79eSqsmmLHJl/ht2y5YtKxyfOXMm\nAAsWLMg/sAgpQxcRSQhl6DmqPIbuN4OW6venP/0JCOad+9LFYfJj534DCz+2fvvttwPB3Gc/fivx\nNmrUKCDYYs5LSskOZegiIgmx2QzdzFoBo4HmgANGOOduNrMmwH+AtsCnQG/n3DeFCzV6HTt2LNvo\noPI8dIlOdZSu9TMnrrjiCiC1ATHAPvvsA8Dzzz9f8Bik6nxpXb9K3H/DHjlyJAD33ntvNIGFLJsM\nfT1wvnOuA9AZOMvMOgBDgCnOuXbAlPRtERGJyGYzdOfcYmBx+u8rzWwO0BLoARyQftgo4EXgooJE\nGSOq3RKF9VC3AAAFJklEQVQfvvztmWeeCQR1OQqZsftZTX4WjK8Jogw9nvzK7htvvBEIvln7VaV+\nhXBSVgDnNIZuZm2BPYA3gebpzh7gS1JDMpmeM8DMZpjZjKVLl+YRqsSJ2jWZ1K41W9azXMysIfAo\nMNg5t6L8GLJzzplZxtTVOTcCGAFQWlpa49Nb/+8u9jH0OLTrK6+8AlC2OUX37t0BeOSRRwDYsGFD\n6K/pV5T6Wh+dO3cO/TWiFId2zVf9+vWBVGlcv3GF/2btM/ELL7wQyG5VaU2SVYZuZnVIdeZjnXPj\n04e/MrMW6ftbAEsKE6KIiGQjm1kuBowE5jjnbip31wSgHzAs/fOJgkQYM/43vR9DVQ2X6Phqij7b\nGj16NBDU+LjmmmuAYLuxMPiMz9ePueqqq0I7t2Rn7733BmC77bYDgjnkAwYMAGDQoEEAdOjQ4WfP\nvemmVBd25513FjzOKGQz5LIP0BeYbWaz0sf+Rqojf9jMTgYWAL0LE6KIiGQjm1ku04CNDRh3CTec\neDv11FPLxs4vvfRSQFvOxUHl7QD9rjM9e/YEYMiQ1IxaP+a+atWqrM/ts7zKmw3fcMMNQHIzvTjb\ndtttgeAbmV+lW1JSAlScifbRRx8BwXzz66+/vtrijIJWioqIJIRqueSgV69eZb/9k1L7IUl8xvbu\nu+8CMHjwYCAYN/Xz1P1uROPGjQNSGV7r1q2BYOVnt27dgKAqn6/s58dnhw8fXsB/iWyKr4i4xRap\nfLRp06YV7vft/9hjj5Vl5jW9znm2lKGLiCSEMvQsNGvWDEjV3y7E3GYJ16xZqWv3/fv3B6BBgwZA\nMBvG7yfpK++tWbOGNm3aAME4+4MPPgjAq6++CgQ11/08dImOb1/frhJQhi4ikhDK0LPgx803bNhQ\nkJrbUlirV68GgkqJIkmlDF1EJCGUoWdh2bJlQLC7uIhIHClDFxFJCKvO+t5mtpRUmYBCKAGWFejc\n+YpbbG2cc83COpnaNVZCa1u1a6xk1a7V2qEXkpnNcM6VRh1HJnGOLe7i/N7FOba4i/N7F+fYNkdD\nLiIiCaEOXUQkIZLUoY+IOoBNiHNscRfn9y7OscVdnN+7OMe2SYkZQxcRKXZJytBFRIqaOnQRkYSo\n8R26mR1sZnPNbL6ZDYk4llZmNtXMPjCz983snPTxJmY22cw+Sv/8ZZRx1hRxaVu1a7ji0q7pWBLV\ntjV6DN3MagHzgIOAz4HpQB/nXCQVtMysBdDCOfe2mTUCZgI9gf7AcufcsPR/4F865y6KIsaaIk5t\nq3YNT5zaNR1Potq2pmfonYD5zrlPnHNrgYeAHlEF45xb7Jx7O/33lcAcoGU6plHph40i9R9GNi02\nbat2DVVs2hWS17Y1vUNvCSwsd/vz9LHImVlbYA/gTaC5c25x+q4vgeYRhVWTxLJt1a55i2W7QjLa\ntqZ36LFkZg2BR4HBzrkV5e9zqTGumjvOVcTUrsmVlLat6R36IqBVudu/Sh+LjJnVIfUfY6xzbnz6\n8FfpsTo/ZrckqvhqkFi1rdo1NLFqV0hW29b0Dn060M7MtjezusCxwISogjEzA0YCc5xzN5W7awLQ\nL/33fsAT1R1bDRSbtlW7hio27QrJa9saPcsFwMz+BPwbqAXc45z7R4Sx7Au8AswG/G7SfyM1Jvcw\n0JpUOdLezrnlkQRZg8SlbdWu4YpLu6ZjSVTb1vgOXUREUmr6kIuIiKSpQxcRSQh16CIiCaEOXUQk\nIdShi4gkhDp0EZGEUIcuIpIQ/w8aBbIB0ujkFgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f5b008358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axarr = plt.subplots(nrows = 3, ncols=3, sharey = True, sharex= True)\n",
"i = 0\n",
"for ir in range(3):\n",
" for ic in range(3):\n",
" img = df_digits.iloc[i][1:].as_matrix()\n",
" img = img.reshape((28,28))\n",
" axarr[ir,ic].imshow(img,cmap = 'gray')\n",
" axarr[ir,ic].set_title(df_digits.iloc[i,0])\n",
" i+=1\n",
"f.subplots_adjust(hspace = 0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "6138ecef-a475-2b6a-102f-d9af67d96878"
},
"source": [
"We can see digits are centered, so we are guessing that the more dense pixel features will b the ones for the center of the picture and the less dense will be the ones that corresponds to the borders"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "c60e1034-63da-eb44-a9f1-4ac17363f603"
},
"source": [
"### Data Cleaning and transformation"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "62b62bd3-b678-cb8d-baf8-30df700edf92"
},
"source": [
"There is no need to transform the existent data due:\n",
" \n",
" 1.- All the features are numeric, we don't have to parse features\n",
" \n",
" 2.- We are guessing there are no missing data: the data are images converted to array\n",
" \n",
" 3.- All the features have the same range of values, we can standarize but i will not do it yet."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "934e0ec9-6a5d-0e49-b502-c4504ce0d321"
},
"source": [
"### The exploration"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "401baf60-1e89-dbc7-fcf4-316e3b1aa1f3",
"collapsed": true
},
"outputs": [],
"source": [
"df_features = df_digits.iloc[:,1:]\n",
"digits_labels = df_digits['label']"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e335ec60-2ccc-c155-564d-88c6bc259cb0"
},
"source": [
"First, I want to know the distribution between the labels"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "0f024013-3307-a8a2-c095-f50056ec2e17"
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 4132., 4684., 4177., 4351., 4072., 3795., 4137., 4401.,\n",
" 4063., 4188.]),\n",
" array([ 0. , 0.9, 1.8, 2.7, 3.6, 4.5, 5.4, 6.3, 7.2, 8.1, 9. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f5afb7208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(digits_labels)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "7b167c41-c02d-7f66-9f7e-acc15399d88a"
},
"source": [
"Well, is a mostly equal distribution between the labels, so there shouldn't be a bias towards a certain digit"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "da0514f9-9178-d201-4bf9-ee2075d5995f"
},
"source": [
"Now, I want to know how many pixels are not used in general"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "2437cb33-7fc9-1753-91e2-ba1c615aaf37"
},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 81., 11., 9., 3., 10., 9., 7., 8., 8., 638.]),\n",
" array([ 0. , 25.5, 51. , 76.5, 102. , 127.5, 153. , 178.5,\n",
" 204. , 229.5, 255. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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L9LuQFd1vVV2pqq914x8CLzL/ifkmr+89+l3Iz7XflRz0d/s1C/f6i12pCvhikme7TxMD\nTFbVlW58FZgcT2kjsVBvLV/vdyd5rnu0c/tRRjP9JtkEPAY8wwNwfe/oF+6D67uSg/5B8ZaqehR4\nJ7AvyVt7d9b8vwObfOtUy731+DDzjx8fBa4AfzvecoYryWuATwHvraof9O5r8frepd/74vqu5KDv\n+2sWWlBVl7vtdeAzzP/z7lqSdQDd9vr4Khy6hXpr8npX1bWqulVVPwH+gf/75/uK7zfJK5kPvY9X\n1ae76Wav7936vV+u70oO+uZ/zUKSVyd57e0x8A7gBeb73NMt2wMcG0+FI7FQb8eB3UnWJNkMbAFO\njaG+obodep0/YP76wgrvN0mAjwIvVtUHe3Y1eX0X6ve+ub7j/mn1Mn/S/S7mf7r9LeB9465nBP39\nGvM/mf8GcOZ2j8AvAyeB88AXgbXjrnWJ/X2C+X/O/g/zzyifuldvwPu6a30OeOe46x9Sv/8MPA88\nx/z//Ota6Bd4C/OPZZ4DTnd/3tXq9b1Hv/fF9fWTsZLUuJX86EaSNACDXpIaZ9BLUuMMeklqnEEv\nSY0z6CWpcQa9JDXOoJekxv0vJxzOQTD8jKwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f5b050cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(df_features.max())"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "55ee450e-e090-7ce5-6493-79b5821383cb"
},
"source": [
"We can see that are 81 are pixel not used in all the images (the first and second arrays are the qty and bin boundary values), but the great majority is used and have values greater than 229. This is not helpful."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "07e94c84-8c48-6b35-b7b1-7d9ac687685c",
"collapsed": true
},
"source": [
"Next, I want to check the average pixels usage for each digit in an image"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "956661fc-1519-67f3-2a9a-ab4fdf8d9bfb"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.6/site-packages/ipykernel/__main__.py:7: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n"
]
},
{
"data": {
"image/png": 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8Hn3shuE2bEzChrUxStxECXBaFTyiNw0RK24jUj6dS16EZBSSErjeQCecsCcu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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f5ab1bc50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, axarr = plt.subplots(2,5, figsize = (9,9))\n",
"gs1 = gridspec.GridSpec(2,5)\n",
"i=0\n",
"for ir in range(2):\n",
" for ic in range(5):\n",
" temp = df_features[digits_labels==i] \n",
" axarr[ir,ic].imshow(temp.mean().reshape(28,28))\n",
" i+=1\n",
"f.subplots_adjust(hspace = -0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "b23e845f-172c-4a30-3d2c-5fc78d542515"
},
"source": [
"it seems that 0,2,3,5,6,8,7 and 9 have very common pixel arrange, but for 1 and 4 there is not so defined."
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "ea087731-6ef8-15f8-461a-17db3e380c47"
},
"source": [
"Remember, we have 784 features representing pixel values, but not every pixel is used nor representative of a digit. So lets use dimensionality reduction to choose the most digit's representative features. "
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "a32ff163-2de7-3ed8-9c70-8b1a6fc35aa4"
},
"source": [
"But how many features do we need to have? We can try several options, so one of them is to count the average digit's pixels which have more than a certain value, like 200."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "5a615a4b-5e5d-47c0-f8fc-de0615c6a078"
},
"outputs": [
{
"data": {
"text/plain": [
"139.82614285714286"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"count, bins= np.histogram(df_features.mean(),bins = 14)\n",
"max(bins)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "afc7f824-73ad-fdf9-b003-b41f665613e4"
},
"outputs": [
{
"data": {
"text/plain": [
"[(413, 9.9875816326530611),\n",
" (49, 19.975163265306122),\n",
" (35, 29.962744897959183),\n",
" (27, 39.950326530612244),\n",
" (25, 49.937908163265305),\n",
" (22, 59.925489795918367),\n",
" (20, 69.913071428571428),\n",
" (31, 79.900653061224489),\n",
" (28, 89.88823469387755),\n",
" (38, 99.875816326530611),\n",
" (30, 109.86339795918367),\n",
" (29, 119.85097959183673),\n",
" (25, 129.83856122448981),\n",
" (12, 139.82614285714286)]"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[x for x in zip(count, bins[1:])]"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "f2bfdfec-b179-b45b-952f-39d9aefad1f9"
},
"source": [
"take in count that te bins array is bigger than the count array by 1, so I take from the second smallest bin boundary"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "15bbc621-cbaa-c8be-a444-882a3dea0b6c"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f2f59893080>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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J78vKPyFpg6SV2e303D4XSVoraY2kU6sVW8V8roWZGVDdK+XtBD4YEXdLmgTcJWl5tu5z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\nynKyMDOzspwszMysLCcLMzMr6/8D62F3SqEA0FMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f5aa9f940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[1:]-5, count, 'ro-')\n",
"plt.title('Mean Pixel\\'s Value Count')\n",
"plt.xlabel('Mean Pixel Value')\n",
"plt.ylabel('Count')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "941cc935-518b-a745-c346-fa80eb5adbca"
},
"outputs": [
{
"data": {
"text/plain": [
"37"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(count[-2:])"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0c721dee-b2c9-cd65-6725-65ccd25248c2"
},
"source": [
"So, from the 784 features using 14 bins, at least 400 features have values near to 0. Only 37 features have values greather than 119, so there is an option for the dimensional reduction. so lets use PCA for Exploratory Analysis using 35 features (for sake of the square like plots)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "e1c8c37a-d0dd-5cf8-ab5e-8493efab3e08",
"collapsed": true
},
"outputs": [],
"source": [
"n_components = 35"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "dcb37986-2a82-c908-0c4d-98eaba1a21bc"
},
"source": [
"### Training - test Split"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "bcf7ac30-56c9-d342-75fc-4624d186af33"
},
"outputs": [],
"source": [
"sc = preprocessing.StandardScaler()\n",
"df_features_std = sc.fit_transform(df_features)\n",
"x_train, x_test, y_train, y_test = train_test_split(df_features_std, digits_labels, test_size = 0.25)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "090b5dfd-21fc-80db-bb47-90cc5fc8e57f"
},
"source": [
"### Modeling: Dimensionality Reduction"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "8c0bd270-13dd-c4d5-bab6-dacef0e6feae"
},
"source": [
"#### Modeling PCA"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "8318b98b-a7c9-f5b9-2c47-48cdf28f78b7"
},
"outputs": [],
"source": [
"pca_ = PCA(n_components=n_components)\n",
"pca_.fit(df_features, digits_labels)\n",
"eigen_val = pca_.components_.reshape(n_components, 28,28)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "31d3b4c9-fefe-9cbc-5ba7-5f19780a244c"
},
"outputs": [
{
"data": {
"image/png": 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ay51NlEy0uSJ2Tj+VYdKMlGyH18c1yaUcPVuY6BAtB/b1Comb+2z7jwAABa/t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\ncDgcGcIfpg6Hw+FwZIj/DyGMjv55UpGuAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f59592278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_row = 5\n",
"n_col = 7\n",
"fig = plt.figure(figsize=(8,9))\n",
"for i in range(n_row * n_col):\n",
" offset =0\n",
" plt.subplot(n_row, n_col, i + 1)\n",
" plt.imshow(eigen_val[i].reshape(28,28), cmap='jet')\n",
" title_text = 'Eigenvalue ' + str(i + 1)\n",
" plt.title(title_text, size=6.5)\n",
" plt.xticks(())\n",
" plt.yticks(())\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "dacb565b-5a9b-8ae3-f8f8-5c28a051525e"
},
"source": [
"we can see that the greater is the dimension the greater is the complexity.\n",
"I Will like to use 7, 21, 35 and 42 features to check their performance on PCA."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "0fdc1910-9797-186e-7a2e-532fb86f6ae1",
"collapsed": true
},
"outputs": [],
"source": [
"n_components_ = 7"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "86d7f37c-65eb-a7f8-73e4-a05a13092616",
"collapsed": true
},
"outputs": [],
"source": [
"from random import randint\n",
"colors = []\n",
"for i in range(10):\n",
" colors.append('%06X' % randint(0, 0xFFFFFF))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "ec1b22e1-d1eb-1dac-1c6e-bef816e140f3"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f06295ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pca_ = PCA(n_components=n_components_)\n",
"pca_features = pca_.fit_transform(x_train)\n",
"fig = plt.figure(figsize= (12,12))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "ce2d42de-d66a-b8a6-4ad5-d5c3196a8250"
},
"outputs": [
{
"data": {
"image/png": 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KRXFOfh1nc23NjFKRu5eugtederU1viXXyvA+bG3+w6S/b/TNi9FyjWw2bUXH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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f062a1f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"for i in range(10):\n",
" to_plot = pca_features[:2000,:2][y_train[:2000].values==i]\n",
" plt.scatter(to_plot[:,0],to_plot[:,1], marker='.', cmap=colors[i])\n",
" plt.grid(b='on')"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "bea04376-e4be-aea0-72eb-deb86287a1b8"
},
"source": [
"we can see some clusters, but they aren't defined, so let's see how it work with the classifiers"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"_cell_guid": "b4376991-5031-cf05-c55c-01c1a0e2732a"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ready to learn\n"
]
}
],
"source": [
"clfs = [tree.DecisionTreeClassifier(), linear_model.LogisticRegression(),\n",
" svm.SVC(C=1000.0), ensemble.RandomForestClassifier(),\n",
" linear_model.Perceptron(n_iter=40, eta0=0.1, random_state = 0)]\n",
"n_components_ = [7,21,35,37]\n",
"clf_accu = np.zeros((len(clfs), len(n_components_)))\n",
"print('ready to learn')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"_cell_guid": "c3120a00-2b8e-edde-3757-b082dd201104"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----- # of components: 7 -----\n",
"----- # of components: 21 -----\n",
"----- # of components: 35 -----\n",
"----- # of components: 37 -----\n"
]
}
],
"source": [
"for i,n_component_ in enumerate(n_components_):\n",
" print('----- # of components: %d -----' % (n_component_))\n",
" pca_ = PCA(n_components=n_component_)\n",
" pca_features = pca_.fit_transform(x_train)\n",
" pca_test = pca_.transform(x_test)\n",
" for j,clf_ in enumerate(clfs): \n",
" clf_.fit(pca_features,y_train)\n",
" y_pred = clf_.predict(pca_test)\n",
" #print('Accuracy with %d components: %0.3f' % (n_components_,metrics.accuracy_score(y_test,y_pred)))\n",
" clf_accu[j,i] = metrics.accuracy_score(y_test,y_pred)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"_cell_guid": "e40d2b68-48ca-a1c6-da68-cc20c64ed8d2"
},
"outputs": [
{
"data": {
"image/png": 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tgw/4a983aGA+0OIlGYkZYYG5OLN4wJzM/QPz5KTJxHlGp2a4p6dlkPAcHqR7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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2f594f6748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"colors = ['ro-','yo-','go-','bo-','ko-']\n",
"ax = fig.add_subplot(111)\n",
"legends = []\n",
"for i in range(len(clfs)):\n",
" ax.plot(n_components_,clf_accu[i], colors[i])\n",
" legends.append(str(type(clfs[i])).strip('>').split('.')[-1])\n",
"ax.set_title('Classifiers Performance over # of components')\n",
"ax.set_xlabel('Number of PCA components')\n",
"ax.set_ylabel('Accuracy')\n",
"ax.legend(legends)\n",
"plt.show()"
]
},
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| 0001/164/1164098.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4f6efbe0-1afa-f1bb-0490-461ad661d162"
},
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "99bd3245-4345-09ab-06c4-db4dc5e08dde"
},
"outputs": [],
"source": [
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"from sklearn import linear_model\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.utils import shuffle\n",
"from sklearn.model_selection import GridSearchCV\n",
"import seaborn as sns\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn import neural_network\n",
"from sklearn import preprocessing\n",
"from sklearn import svm\n",
"from sklearn import metrics\n",
"from sklearn import tree\n",
"\n",
"\n",
"\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"# list all the input file"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "a553b76a-8c00-801f-b3b8-272de5e2cad7"
},
"outputs": [],
"source": [
"# Load the training data\n",
"\n",
"data_dir = '../input/'\n",
"Realty = pd.read_csv(data_dir + 'train.csv')\n",
"Realty['timestamp'] = pd.to_datetime(Realty['timestamp'])\n",
"Realty.set_index('id', inplace = True)\n",
"Macro = pd.read_csv(data_dir + 'macro.csv')\n",
"Macro['timestamp'] = pd.to_datetime(Macro['timestamp'])\n",
"Macro.set_index('timestamp', inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "187d051b-af36-1c9f-b3c9-3e26c2aff9a6"
},
"outputs": [],
"source": [
"# correlate relaty price with macro economics\n",
"RealtyMacro = pd.concat([Realty.reset_index(), Macro.loc[Realty['timestamp']].reset_index().drop('timestamp', axis = 1)], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "4a6958f0-0259-dc7d-d697-e6406f9bcf2c"
},
"outputs": [
{
"data": {
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oKGDChIksX/4+bW1tnWokP/nkExYteoVp07ZN26w70LKnh+YIFCnIZ1LZGIU6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\n+P9zbXfc7TvDiwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34e8e1da0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"FontSize = 20\n",
"FigSize = (20, 10)\n",
"fig, RealtyScatter= plt.subplots(figsize=(10, 10))\n",
"\n",
"RealtyScatter.scatter(Realty['full_sq'], Realty['price_doc'])\n",
" \n",
" \n",
"RealtyScatter.legend(fontsize = FontSize)\n",
"RealtyScatter.set_xlabel('Apartment area', color = 'k', fontsize = FontSize)\n",
"RealtyScatter.set_ylabel('Price', color = 'k', fontsize = FontSize)\n",
"RealtyScatter.set_title('House Price vs. Apartment Area Scatter Plot', fontsize = FontSize)\n",
"\n",
"RealtyScatter.spines['bottom'].set_color('k')\n",
"RealtyScatter.spines['left'].set_color('k')\n",
"\n",
"RealtyScatter.tick_params('x', colors = 'k', labelsize = FontSize)\n",
"RealtyScatter.tick_params('y', colors = 'k', labelsize = FontSize)\n",
"\n",
"# RealtyScatter.set_xlim([0, 1000])\n",
"# RealtyScatter.set_ylim([-1000, 1000])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "e3482450-80ca-7472-7b6f-137686780545"
},
"outputs": [
{
"data": {
"image/png": 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v0/vvAiAWq9pkXyyW2siybt16AKqqNn38yeW7utb3Pa6BWiyLZXjUIuJmTZ/M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\ndQgDO0mSpA5hYCdJktQhDOwkSZI6xP8FQfEX5cpe46QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34e8e1588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig2, RealtyScatter2= plt.subplots(figsize=(10, 10))\n",
"\n",
"RealtyScatter2.scatter(Realty['life_sq'], Realty['price_doc'])\n",
" \n",
" \n",
"RealtyScatter2.legend(fontsize = FontSize)\n",
"RealtyScatter2.set_xlabel('Living room area', color = 'k', fontsize = FontSize)\n",
"RealtyScatter2.set_ylabel('Price', color = 'k', fontsize = FontSize)\n",
"RealtyScatter2.set_title('House Price vs. Leaving Room Area Scatter Plot', fontsize = FontSize)\n",
"\n",
"RealtyScatter2.spines['bottom'].set_color('k')\n",
"RealtyScatter2.spines['left'].set_color('k')\n",
"\n",
"RealtyScatter2.tick_params('x', colors = 'k', labelsize = FontSize)\n",
"RealtyScatter2.tick_params('y', colors = 'k', labelsize = FontSize)\n",
"\n",
"# RealtyScatter2.set_xlim([0, 200])\n",
"# RealtyScatter.set_ylim([-1000, 1000])\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "b2305247-de52-4cb6-0256-7d9dd381c476"
},
"outputs": [
{
"data": {
"image/png": 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CJUmSGpBJoCRJUgMyCZQkSWpAJoGSJEkNyCRQkiSpAZkESpIkNSCTQEmSpAZk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\nRESkACkJFBERESlA/x99w6/nZ6OfOwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34cf6a780>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig2, RealtyScatter3= plt.subplots(figsize=(10, 10))\n",
"\n",
"RealtyScatter3.scatter(Realty['floor'], Realty['max_floor'])\n",
"RealtyScatter3.legend(fontsize = FontSize)\n",
"RealtyScatter3.set_xlabel('Floorr #', color = 'k', fontsize = FontSize)\n",
"RealtyScatter3.set_ylabel('Building floor #', color = 'k', fontsize = FontSize)\n",
"RealtyScatter3.set_title('Floor # vs. Building Floor #', fontsize = FontSize)\n",
"\n",
"RealtyScatter3.spines['bottom'].set_color('k')\n",
"RealtyScatter3.spines['left'].set_color('k')\n",
"\n",
"RealtyScatter3.tick_params('x', colors = 'k', labelsize = FontSize)\n",
"RealtyScatter3.tick_params('y', colors = 'k', labelsize = FontSize)\n",
"\n",
"# RealtyScatter2.set_xlim([0, 200])\n",
"# RealtyScatter.set_ylim([-1000, 1000]) "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "9f4881ee-bb6b-ad32-2bd3-d0b8c32d4e62"
},
"outputs": [
{
"data": {
"image/png": 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FcX/g0rIsVwGUZfm/ZVnuXJblQ4CnAL8sy/JV3YbftITkiojouQWtJElqyaLobppBWZan\nAWcURXEaVQ+blxdFcUBRFE/pd/hN25BsAXw7Iq4BrupckZn36XdQkiRpbtq6l01Zlm+YtuisNWxz\nEbBHL8dpmpAcXU+SJGkYzU+j1tY0Skgy83MAEbEdsBVwWWZe0mZgkiRpDhb40PFNG7UWEXEWcAFV\nf+OLI+JnEbF9q9FJkqRm2mnUOm+aRvJR4IvA0sy8HbAx8E3giLYCkyRJzUVEV9OwaNqG5A6ZecjU\nTGauAt4ZEee0E5YkSZqTcWhDAiyKiA0z87qpBRGxYUsxSZKkuRqi0o5uNE1IvgYsj4gjqcax35Lq\nrn8ntBWYJEmagyFqD9KNpgnJm6mGit2XagjZy4DPAh9vKS5JkjQH83Qvm9Y07fZ7M/DhiPgIsBlw\nZWZmq5FJkqTmFniVTdNuv9tExNeBG4C/ADdExHERsXmr0UmSpLEwl26/lwD3Be4APAC4BvhIS3FJ\nkqS5WLSou2lING1DUmRm5410roiIFwO/bSEmSZI0R8M0pkg35tLtd526LcmUxW0EJEmSujBEpR3d\naJqQLAe+FhFHcGu335fVyyVJ0qCNSQnJa4B3Ah/j1m6/JwBvbSkuSZI0F2PS7fc64FX1JEmShkyM\n8sBoEXH4bDvIzIP6F44kSerKiFfZTMxLFJIkqTcjXmVzVmZ+KCJem5kfmJeIJEnS3I1ylQ3wuoj4\nOfCyiPgecJv0KzN/2UpkkiSpsVG/l81JwA+pEpEz1rA+cTwSSZIGb4G3IZmxfCczD8zMdYDfZOai\nNUwmI5IkDYOI7qYh0bTb77/Mtk1EnJOZO/cekiRJmqsYk5FamxieNEuSpHGzwBOSyMz+7Gj2EpL+\nHEiSpIVj3n6s//2ss7v6nt3gvvcaigKFfpaQzGrVqlXzebi+m5iohmUZhfMYhXOA0Xgvzt9tr0GH\n0ZMdT10GjMZ7AaNxHqu+t7BvMzax5+7AaLwX82qB97JZ2OU7kiRpJMxrCYkkSWrHSN/LZo4WdlmR\nJEkL2RB14e1Go3QqIjZfy/KdOmaP6UtEkiRp7hZFd9OQaFq+c3ZEPLFzQUS8Bvjx1HxmvqufgUmS\npDkYh4HRgGcBH4+IpwAfBg4HJoEHtxWYJElqbqG3IWkUfWaeAtwH2Aj4JfCTzHxkZp7fZnCSJKmh\nBV5l06iEJCKWAu8F7gX8B/DKiLgKODQzJ1uMT5IkNbHAR2ptGv25VFU098vMDwAPBHYHft5WYJIk\nqbmI6GoaFk3bkByQmSdPzWTmZcDeEfHadsKSJElzMg4lJFPJSERsHBGb1tN2wMtajU6SJDUzDr1s\nIuJxwOeBTaetOrXvEUmSpLkbouSiG03Ld94HvBnYCbiQqnHrx4FXtRSXJEmag1gUXU3DomkbknUz\n85MAEXFTZp5btx85GXh4a9FJkqRmxmEcEuDmiNi2fnxLRGyWmTcAW7UUlyRJmotxaEMCHAH8PiI2\nAk4CvhsRFwLXthaZJElqboiqX7rRtJfNJ6jGILmRamC0r1K1JXlGi7FJkqSGIhZ1NQ2Lpr1sngm8\nq+7quwiYSsNeCSxuKTZJkjQmmlbZHAq8Ffg1cEt74UiSpK4s8CqbpgnJlZl5ZKuRSJKkrv19/fW6\net7ELOuLovgg8BAggYPLsjy9Y92jgXdTFVZ8uyzLd3YVBM172ZwQES+OiA27PZAkSVpYiqLYHdix\nLMtdgRcAh0/b5HDgacDDgMcWRbFzt8dqmpC8mKra5pqIuKVz6vbAkiRp6O1J1ZGFsizPBTYpimIp\nQFEUdwVWlmX5p7IsJ4Fv19t3pWmVzQRwIvAlYGW3B5MkSQvK1sAZHfMr6mXX1v+v6Fh3BbBDtwdq\nmpBsSFUk89SOZUFVn2QvG0mSxsNMLWd7alXbNCE5mCoT+jYOhiZJ0ri4lOr7f8o2wGVrWbdtvawr\nTROSD1JlPv/RsSw6JkmSNHpOAt4OfLIoivsDl5ZluQqgLMuLiqJYWhTFXYBLgL2BZ3d7oKaNWvcB\nzgauA66vpxuB33d7YEmSNNzKsjwNOKMoitOoetS8vCiKA4qieEq9yUuBY4EfAceVZfm7bo/VtITk\nLcC3gFOATwEvoer+89ZuDyxJkoZfWZZvmLborI51PwR27cdxmiYkG2fmGwEi4vrMPDEifgJ8GXhs\nPwKRJEnjq2lCcktEbJiZ1wFExO0y8+qIuFOLsUmSpIZuWrxk0CH0pGlC8iXg/Ii4M3Aa1cit57YX\nliRJmovMQUfQm0aNWjPz7cCBmXkj8BrgL8Cdgee1GJskSWpoMrOraVg0LSEhM79TP1ycmQe0E44k\nSepGDlFy0Y1GJSQRsVlEfC4i/g6cUy97T0Tcu9XoJElSI5nZ1TQsmo5D8klgNbAL8Nd62U+47V3/\nJEmS5qxplc0DMnN7gIiYBMjMr0fEe1qLTJIkNTZM7UG60TQhuTEi1svM1VMLImIJzUtYJElSixZ4\nPtI4oTgR+FZE/CuwfkQ8BvhfqjHuJUnSgI1LG5I3AOcCn6a609/HqBq3Th9OVpIkDcAk2dU0LJpW\n2TwCOCgzX9FmMJIkqTvDVNrRjaYJyWcAIuILwOcy85z2QpIkSXO10Bu1Nq2yuROwP7AU+EFEnBER\nB0fEFu2FJkmSmpqczK6mYdF06PjMzB9k5kuBOwD/BRwMXBIRX4mIh7UZpCRJmllmd9OwmFO33Yi4\nP/Bh4Ciq6p4PAN8APh0Rr+57dJIkqZGF3sumURuSiDgIOBAogK8Czwa+m/WZRMTXgF8Ch7UUpyRJ\nmsEw9ZjpRtNGrc8CPgF8KTOvmb4yM6+KiE/2NTJJktTYMJV2dKNRQpKZuzbYbH/g3b2FI0mSurHQ\nE5Lo1wlExLmZudMMmyzsV0qSpLmL+TrQOZeu6Op7dudttpi3GGfStMqmCRMOSZIGZKGXkPQzIZnV\nn1/9pvk8XN9te1hVI/X3X/1mwJH0ZoN/uTerVq0adBg9mZiYABiJ8xiFcwA4f7e9BhxJb3Y8dRkw\nGtfUTZf8edBh9GTJHbcF4Kojjx5wJL3Z7IXPndfjmZBIkqSBG5eRWiVJklrTzxKSoWgUI0nSOFro\nJST9TEgO6eO+JEnSHIx0G5KI+A2z9J7JzPvU/x/Vv7AkSdJcjHoJyQfq/wvgCcBxwApga2Dfel6S\nJA3YAs9HZk5IMvNzABGxHHhE57DxEfER4JvAf7caoSRJmtVIV9l0uNP0e9hk5tURsW0LMUmSpDka\n9SqbKRdGxDHAMcBKYCNgP+CPbQUmSZKaW+glJE3HIXkOVePWTwM/BI4GNqiXS5KkAcvsbhoWTe/2\neykmH5IkDa2RrrKJiMNn20FmHtS/cCRJUjcWepXNbCUkE/MShSRJ6slIl5Bk5oHzFYgkSereSCck\nEfGmzHz3TFU3VtlIkjR4o15ls2H9v1U3kiQNsZFOSDLzTfX/Vt1IkjTEJhd2PtKs229EfGZt6zLz\n+f0LR5IkdWOhl5A0HRjtb9OmdYHHA1e1FJckSZqDzOxqGhZNB0Z7xfRlEXEX4D19jkeSJI2hpvey\nuY3MvCgi7tvPYCRJUncmGZ7Sjm40bUPyVGAzYAuqap5FwN2x940kSUNhmKpfutG0hOSzwFL4p/Rr\nEljd94gkSdKcjUUvG+B64B1AAWwKXAl8F/i/luKSJElzMLnAM5KmCUkALwdOBM4DNgE+ADwcOLid\n0CRJUlPzWWVTFMUS4CjgzsAtwIFlWV6wlm2PBVaXZXnATPts2u03gB9RJSQnAccBLwP+X8PnS5Kk\nFs1zt9/9gKvLstwNeBdr6XVbFMVjgB2a7LBpCcli4DnAM6gyoSm3a/h8SZLUonnuZbMncHT9+LvA\nbQZQLYpiPeDNwH8DT51thzOWkETEphGxKbBefeAHAncDHgR8AnjRHIKXJEktmecSkq2BFQBlWU4C\nWRTFutO2eSPwceDaJjucrYTkSqqeNYuA59bTVPRRP17rsPKSJGl+tNWEpCiKFwIvnLb4wdPmY9pz\ndgR2KcvybUVR7NHkOLMlJNvX/z8XeASwDFjVZMeSJGn+TLaUkZRleSRwZOeyoiiOoiolOatu4Bpl\nWd7YsckTgO2Kovgp1bAhWxRF8fqyLN+3tuPMdrffiwEiYuoGenebvgnwydlPR5IktWmeB0Y7iapd\n6TLgicApnSvLsvwQ8CGAuoTkgJmSEWh+L5vtZ99KkiQNyjwnJMcBjymK4lSqQVIPACiK4g3A8rIs\nfzLXHTa+l01EPIUqC9oKuAz4v8w8ca4HlCRJ/ddWlc2alGV5C3DgGpa/dw3LfgD8YLZ9NhqHJCLe\nRFX0sgJYDvwV+FREHNTk+ZIkqV2TmV1Nw6JpCcl+wAMy88qpBRFxKHAycHgbgUmSpObG5eZ6izuT\nEYDM/EtENB3pVZIktWiB38qm8dDxV0bEszsXRMR+wFX9D0mSJI2bpiUkrwK+WlfTXAlsCdwA7NNW\nYJIkqbmxqLLJzF9ExN2oRmbbmqqXzc8z84Y2g5MkSc2MRUJS2xnYnVu7/a4Ezm4jKEmSNDfD1GOm\nG027/b6Aqg/xPanGq78vcFpE7NteaJIkqanM7qZh0bSE5GDgXzLzgqkFEVFQjdT25TYCkyRJzY1L\nlc06nckIQGaWEbGkhZgkSdIcjUWVDXB9RDy8c0FEPAy4vv8hSZKkucrMrqZh0bSE5E3AtyPiHKrh\n47cECuCpbQUmSZKaW+glJE27/Z4UETsCe3Frt99lmXl5m8FJkqRmxiIhiYjPZ+ZzgM+1HI8kSerC\nMFW/dCOanEBEnAy8JDP/0MOxFvYrJUnS3MV8Hegj3zm1q+/ZVzxut3mLcSZN25CsAH5RtyH5p/vX\nZOaT+h6VJEmak7GosgHKeurJqlWret3FQE1MTACjcR6jcA7gezEMRum9ADh/t70GHElvdjx1GTdf\nvmLQYfRkna22AEbnmpovC73Kpmmj1rfPtk1EHJWZB/QckSRJmrOxSEgaelAf9yVJkuZgXKpsJEnS\nEFvY6UjzkVolSZJaYwmJJEkjwCobSZI0cDZqlSRJAzc5aUIy5fw+7kuSJM3BWJSQRMTtgKcB2wGL\nO9dl5jvq//fpe3SSJKmRcWlD8nVge6rRWm/pWL6wz16SpBGx0L+QmyYkdwd2yMyb2gxGkiR1Zyyq\nbIBzgPUAExJJkobQuFTZvB74UUR8D7i2c8VUGxJJkjQ441JC8lFgKbAztiGRJGnojEsJyV2A7TPz\n5hZjkSRJXVrg+UjjhOR0YCPgqhZjkSRJXRqXKpsLgTMiYjlwTeeKzDyo71FJkqQ5GZcqm02BU+rH\nEy3FIkmSujQWCUlmHth2IJIkqXtjUWUTEZ9Z27rMfH7/wpEkSd1Y6AnJoobb/W3atC7weGzkKkmS\n+qBpG5KXs+YxR14TEa+emsnMxWvYRpIktWxyYReQNE5IXgE8FjgSuALYEngxsAvwhHZCkyRJTS30\nKpumCckrgTcCS4BtgcVU97d5dGae0VJskiSpoXFJSLYH3t8xfwvwR6aNSSJJkgZjLLr9Al8FdgR+\nCVwPbAA8CLikpbgkSdIcjEsJyQ3ATlRJySKqEpK/AStaikuSJM3BuDRqfRSwTWZeCRARGwCTmbm6\ntcgkSVJjkzk56BB60jQhuZaqDcmBEbE38GUgIuJZmfnV1qKTJEmNzGeNTVEUS4CjgDtT1ZocWJbl\nBdO2eRewB1XNylfKsnzfTPtsOjDaesBkROxAlZi8karU5J1ziF+SJLUkM7uaurQfcHVZlrsB7wLe\n07myKIp7AY8sy/JhwMOAA4ui2HqmHTYtIbkLcCBwAFUScxgQ9SRJkgZsnnvZ7AkcXT/+LjD9FjPX\nAOsXRbEe1VAhk1SdYtaqaQnJBcDOVFnQt4G7UjVwvbjh8yVJUovmuYRka+qOLWVZTgJZFMW6UyvL\nsvwTcDxVnnAx8ImyLK+daYdNS0g+B/yMKst5VGZeHBHHAifO+RQkSVLftdXttyiKFwIvnLb4wdPm\n/6nGpCiKuwJPoSrAWAKcVhTFcWVZXrG24zRKSDLzHRFxPLAyMy+vFx8FnPKPSCJ2ycxfNNmfJEnq\nr7a6/ZZleSTVrWP+oSiKo6hKSc6qG7hGWZY3dmzyQOBnZVleX2//a+BewPfXdpymJSRk5rnT5pdN\n2+RoqmqCACDIAAAY6klEQVQdSZI0z+Z5YLSTgGcAy4An0lFAUfs98MqiKBZR1a7cm6r5x1o1Tkga\nsIGrJEkDMsm8JiTHAY8piuJUYDVVpxeKongDsLwsy58URXEScGq9/ZFlWV400w77mZAs8DHiJEla\nuOazhKQsy1uoet9OX/7ejsdvBd7adJ9Ne9lIkiS1pp8lJJIkaUAmF/jNbExIJEkaAeNyt98mbNQq\nSdKALPACkr4mJAf1cV+SJGkOFnoJSaNGrRHxlIi4MCJuiohbOqepbTLz5PbClCRJM8ku/w2LpiUk\nH6W6s+8ZVLcZliRJQ2Seb67Xd00Tkqsz8+OtRiJJkro2FlU2wIkR8S+tRiJJkro2md1Nw2LGEpKI\n+AbVCKyLgFMi4jfA1Z3bZOaT2gtPkiQ1sdBLSGarsum8e+/pvR5sYmKi110MhVE4j1E4BxiN8xiF\nc4DROY8dT51+39CFZ52tthh0CH0xKtfUfBnphCQz3z71OCJun5l/qx+vC6ybmdfN5WCXXj2nzYfO\nNhtvCMAVq64fcCS92XLidvxx5TWDDqMn2226EQCXX/u3AUfSm62W3p5V31s+6DB6MrHn7gDcdMmf\nBxxJb5bccVsAbr58xYAj6c06W23B+bvtNegwejKVFK7+w4UDjqQ36+2w/bweb6E3am3a7XdP4M8R\nsWG9aJt6/jGtRSZJkhqbzOxqGhZNe9m8D3jiVIlIZl4UEY8G/ge4X1vBSZKkZka6yqbDhpn5o84F\nmXl6R4mJJEkaoAWejzTu9nv19OqZiHgGcG3/Q5IkSXM1LlU2BwFfr4eKvxrYHLgJ2LutwCRJUnNj\nUWWTmT+LiO2ARwCbAZcDPwY2aDE2SZLU0DCVdnSjUUISEWdm5v2Ak6ctv4iqx40kSVLXZhup9VnA\nfsAOEfH1aas3pnkbFEmS1KJRr7JZBtwMPITqTr+dbgQObiMoSZI0Nws8H5l1pNaVwPERcUVmLuzh\nJCVJGmEj3YYkIt6Ume8Gnh4RT1vTNpl5UCuRSZKkxk5568tj0DH0YrYqm6mBz+4G/GUN67fubziS\nJGkczVZl86b64Q7ASzPzoql1EfEfwJPbC02SJI2LpgOjvR34fkTsDVwBHAPcAXhYW4FJkqTx0XRg\ntC9ExF+AbwNLgK8A+2Tm6jaDkyRJ42HGcUQiYtOpCTgTeD4wCXwBuH29XJIkqSezlZBcCUzvRxTA\nqfX/CSxuIS5JkjRGZktItp+XKCRJ0librZfNxfMViCRJGl/ei0aSJA2cCYkkSRq4xglJRNw1It4Q\nEYfU8/dsLyxJkjROGiUkEbE/8Cvg/sAz68Wvjoi3tRSXJEkaI01LSP4T2CUz9wWur5cdDOzbSlSS\nJGmsNE1IFmXm7+rHCZCZ17UTkiRJGjdNE5KrI2KvzgUR8TBgVf9DkiRJ46bpzfVeD3wrIs4B7hQR\nPwbugXf7lSRJfdD05nrLI+IewBOATYBLgO9k5pVtBidJksZDo4QkIr6ZmXsDn5y2/NzM3KmVyCRJ\n0tiYMSGJiMcD/wrsGhGHT1u9CXCHtgKTJEnjY7YSkhK4J9UdfSemrbseeFYbQUmSpPEy2831LgA+\nEBGXZuYXp6+PiN1bi0ySJI2Npo1av1gPFb8jt3YV3hA4FNiipdgkSdKYaNqo9XXAe4DLgc2Bq4El\nwMfaC02SJI2LpgOjvQS4V2ZuC1yQmVsBbwXOay0ySZI0NpomJDdk5lTyEQCZ+RHgVa1EJUmSxkrT\nhOS6iHhGRARwbUTcOyKWYPsRSZLUB00TktcBH6bq/vsZ4GfAH4EzWopLkiSNkaa9bH4IbFPPfiIi\nzgOWAj9pKzBJkjQ+mvayOTMz7zc1n5k/qJdfyq2JiiRJUldmGzr+WcB+wA4R8fVpqzemeZWPJEnS\nWs1WQrIMuBl4CPAL6h42tdXAwS3FJUmSxshsQ8evBI6PiO2AzTLzTRHxUOBYIIFT5yFGSZI04ppW\nuTwdOK1+fBhwFPBSqqHjJUmSetKoUSuwNDO/GRFbAfcGds/M1RHx/hZjkyRJY6JpCcmSiFgE7A0s\nr5ORANZrLzRJkjQumpaQnAycC2wN7Fsv+yAOjCZJkvogMnP2jarSkb2AyzPzl/WyA4GvZObVDY81\n+4EkSRotMfsmgoYJSZ+YkEiSxo0JSUNNq2z64tpvLpvPw/Xd0r33AuCGs88dcCS9Wf9eO7H6DxcO\nOoyerLfD9gDcePGfBhxJb9a9851YtWrVoMPoycTEBABXHXn0gCPpzWYvfC7ASLwfo/L5Pn+3vQYc\nSW92PHVhf+fNN0dalSRJA2dCIkmSBs6ERJIkDZwJiSRJGjgTEkmSNHAmJJIkaeBMSCRJ0sCZkEiS\npIEzIZEkSQNnQiJJkgbOhESSJA2cCYkkSRo4ExJJkjRwJiSSJGngTEgkSdLAmZBIkqSBMyGRJEkD\nZ0IiSZIGzoREkiQNnAmJJEkauK4SkoiIfgciSZLGV6OEJCLuFxG/j4jb1YvuGhEXRsT9WoxNkiSN\niaYlJEcAb8/M6+v5C4DXAx9rJSpJkjRWmiYkm2bm56dmsnI8sGk7YUmSpHHSNCG5MSJ26lwQEQ8G\nJvsfkiRJGjfrNNzuTcBPI+K3wNXAlsA9gH3aCkySJI2PRglJZn4rInYGHg9sDlwOfCszr2gzOEmS\nNB7m0u13ErgRuAW4qZ1wJEnSOGra7Xdv4PfAy4BHAgcD50fEo1qMTZIkjYmmbUjeDuyZmT+dWhAR\njwA+ADyojcAkSdL4aFpls0FnMgKQmT8ENux/SJIkadw0TUhujogdOxdExN2wLYkkSeqDplU27wN+\nGRHfBlZQdft9HPBvbQUmSZLGR6MSksw8BngEcA6wGDgb2C0zv9RibJIkaUw0LSEhM88EzmwxFkmS\nNKZmTEgiYhWQM22TmUv7GpEkSRo7s5WQ7F3//1Cq8UeOompDcgfgOcC3WotMkiSNjRkTksxcDhAR\nhwAPz8x/9KqJiOOAU4EPtRqhJEkaeU27/W5DNWR8p5uBrfobjiRJGkdNG7X+AvheXSqyEtgI2Bcb\nuUqSpD5ompAcALyOqt3IZsBfgeXAIe2EJUmSxkmjhCQzrwX+q57WKCKOyswD+hSXJEkaI03bkDTh\nTfYkSVJX+pmQSJIkdcWERJIkDZwJiSRJGjgTEkmSNHCROeOtaprvKOKczNx5hk36cyBJkhaOGHQA\nC0Xju/02cP4s631TJEnSGjUuIYmIPYBnAhOZ+eyIeCRwauf9bSRJkrrRqA1JRLwGOAa4Fti1XvxE\n4IMtxSVJksZIoxKSiPg98LDMvDwizs3MnSJiCfCbzLxH61FKkqSR1rSXzS2ZeXnngrqqxoaqkiSp\nZ00Tkj9HxL/VjxMgIp4J/KWVqCRJ0lhpWmVzH2AZsBhYClwJ3AQ8MTPPbjVCSZI08hqVkGTmr4G7\nAs8BDqDqbVP0KxmJiHdExF8i4tAZttkjIq6b/niYRMRdIiIj4s4RcV5EbFwvn/X8FoLO84iIbw46\nnkGIiM9GxGUR8eqIOCoijpjj86eukc3n+Lz3RMSb68drPW5EHBAR/kjo0drep2nvw2YRsV+Dfb1t\noX1emp5bve0OEfGvbce0huP+e0R8fB6PN5TfO6Ok0TgkEbEu8DrgXZl5S0RsDfxnRLw7M1f3IY5n\nA6/NzGP6sK9h8LdpjX1H5fyeDbwWuNugAxmg5wK7Z+apEXHUfB00M984X8fS2k17Hx4F7Ad8cUDh\ntGku5/ZU4M7At1uNaJrMnNOPAQ2/pm1IPg48lFsTmOuB+wIf6zWAiPg2sB1wSER8uPOXRETsEhE9\nNZyNiLdExO8i4pyIOCsiHlcvXxoR/xsRf4qIX9TZdr8a6f7j19Uazm9RRPxXRJQRcWFEfC8idhji\nc5k69j/OA9ikY/mSiHhvXSJ0bkR8PyJ26lj/mjre8yLipxHx0Hr5HhHxh4g4rF63uGEcd4mIyYh4\ndkScGRErIuKFEfGqiPh1XXqxX73tSyLit/VrfXZEPL5e/tiIuLSjBOuREXF5RGw5y7HPovrMHBMR\nr5+2biIi/qc+1nkR8bWI2Hb6awR8t37KKyPiVxGxMiL+HBFfjogbIuLpHfs8YirpWVupSERsW7/m\nF0XEj4Ci4ev404h4Zcf8ojqOx0XEBhHxofpauzgiToiILTq2/WB9fV8WEddFxJURcXTcWqrwufp1\n2G4N53BYRHwlIi6JiB9HxH0jYll97X4vIjZsEPvbIuLYiPhuRHyhXvbYiDijfu3PjogXdmy/S0Sc\nWsd0TkT8Z0REve6iiHhFRPyoPo9PRcSewAn10/+73u7ZEXF+RHynPqc9qP4u7lFfF0TEgyPi5/V2\nZ0bEo/857Dgsqs/IXyLiaQ3fp1Y/8xGxOCI+Wsd8bkT8LCJeu4Zze0R9budE9Xfr9VOvC/Bm4NlR\n/Y0gInavt/1d/Zo/Zw7xTF1DL6zP96qI+GRErFO/V/9Vx/CM6Ch5qs/jsIi4oD7uZyJivXrdfSLi\nlHr5BfX5NYllo/ra/1NE/JzqO7Bz/f4R8Zv6mjszIp7Yse4JUf09KiPiJxFx/6avwVjLzFkn4Dxg\n0bRli4Hzmjy/wf4vAp4OvA34ZsfyXaoQE2AP4Lrpj2fZ785UY6csrecfCHyufvwO4FRg3Xr6+tSx\nejiPu1A1+t2l/n/zzvOrH78GOAfYtJ5/C/DDYTuXpu8TcDBwJrBRPf8G4NdUI/PuA1wCbFOveyZw\nOXC7+j28AXhel6/xG+r5FwF/A17WMX8hsBOwGtiuXv4S4MqO/XwM+ASwHlACT254/AR2qR8fBRxR\nP/4gcGK9v6D6g/6N6a9RR/xX1tu9DJgE3t15ndTPOwI4ag3H6nz8eapfsQFsXL/2Zzc4j5cAZ3XM\n7wFcSvW5/gjw/fp9WgQcCRxdb/d4YEV9Du8B3gucRfUj5f/Vy4+Z4Rz+CGxJdZ1eUD93o475Wa8H\nquvvGuDu9fwdgOuAPer5HYBVVJ/DDYA/A8+t121Rx/C0jmv6m1Q/tnas4/9Mx/uUwLPq7e467bV/\nG7d+DtYHrgAeV88/gurzOtER7wPqdS8DLhqGzzzwr8DvgCX1/JPq97Tz3AK4DHhmR1y3APdew/V4\nR6rP47/W8/eo34u7zfHzfUR93E3q9+s59XvwLWDxGl7/VwM/rF+LdYCT6vW3o+p88dKOa+XPwKMa\nxPJOYDmwhOpzfTK3fgfdj+qau0c9vyvVZ2DberoWuFe97rlU1/aiJq/BOE9NS0iWcNuh35fUb/Yw\nW0l1cb4oIu6Smadn5vPqdY8Gjs3MGzPzRqo/uvPhGcDHMnNlPf9BYLeI2GaW5w3juQA8BTgyM6+p\n5z8C3IuqCPcpdVyXAmTml+ptHlT/vx5wXJfHPb7+/9dU1+ExHfPbZea5VEnSH+vl3wc2i4hN6/nX\nURVLHwv8JDO/2mUcU55C9Ud5dVZ/hT4CPD4i1uG2rxHAplSv0fXA36m+KLuxJ1UCkJl5Nc1fzy8B\nd4+IXer5ZwJfzMxbqK7RwzLz+sycpLpG942IRZl5IrBb/ZzPUL2u21N9aUxdwz+f4bjLM/OK+jot\nge9l5jUd89s1jP+czPxd/Xgv4NzM/AFAZv6BKjncG3gw1Zft0fW6FfW5P7FjX1/JzJsz83yqJLnz\nWrixPv8nZuYFM8TzcGAyM79TH+eHwB0zc1W9/jeZeUb9+AzgTg3OcT4+85dTfUk/LyK2zsyvZ+Yb\nOjeor+c7A1+u58+pn7fjGva3N9V78e162/OA7wD7zjGuj9fX9F+pEsY96uVfr6/R6Z5Gdf3emJk3\nU/0YehdVYrhOZn68jucyqgT+mQ1i2BM4LjNvyqppwlEd6/YBTqzPj8z8CXA28BiqJO+cvLWN5THA\nferPkmbQNCH5GvC9iPi3iNg3Il4O/IghrzvNzL9QfensCpxVF/U9qV69GXBVx+ZXzFNYmwBvrIv5\nzgNOp/q1vPVMTxrSc4Eq7hVTM5n5N6qSia2mr6utrNdB1dbmhi6PO/Xlfkt93Gs75hfVxbXvjrrK\nhuoLCuprvo7zU1TJwoe7jKHT9HO9iqq0YbM1rINbXyOoflFuSnc2o3pNp0w/zhrVyctXgefXSdPT\ngM/VqzcBjui4Rk+g+jW4WURsQvXrHKrX9Eiq1/RmqvOF6hfx2nQmZbdQ/ZLsnG9Udcc/X+9ren2v\n4tZr8Mq1rGsS05J6+qdxmNZgc+DqzgUd1yTT1t1Cg7+98/GZr5Okfane/wvqqpaHrWHT5wM/q6s9\nzqNKoNd0DpsA95i6duptH0JHNW9Dne/Zyo7nX7WGbWHa65+Zf89qrKxNgIlp8TyDqrfobGb6bM10\nzU2PZTIzbQzbQNOb670WeCVV0dPmVB/OzwMf7XM80/8gzfUivo3M/Cnw9PqP7vOB46JqOb+Sqoh7\nSre/UOfqEuDLmfnJuT5xCM8FqqLcf7S9iIgJquLry9awLqiun8tongx3641Uv3AemZlXRMTOwG87\nYrkD1TX9fuDDEbFHj79g/ulcqd6Dm6n+sE5fB7e+RjtRXfNXsubrf7Z7Rf2VqspjyoyJ7TSfBb5A\nVQx+SWb+pl5+CVUV2LLpT4iqPcgd6tmHUJV2fXnaZnM9h16t6fXdgqq07DJgi4iI+pf+1LrLGu77\nBqofX5+far+xFpcDm3ceJyK2p6oG69p8fObrUq8TI2IDqs/NsVSlX8A/7mP2QWDXzPxVvWxtCdAl\nVKVBu65lfVNT3zNw28RgTS6n4zWIiI2oSk4vAa7K7kYUn+mzdRlw72nbT11X60yLZRFVdd+Faynd\nUa1pt9/JzDwsM3fLzHtk5u6ZeXgLL+6fgJ0iYv2oGjke0MvOomqg96WIWLcuxjuV6pwnqeob960b\nS61HVac+H44HXhgRS+sYHxhVI8AZ74Y8pOcC1a/nF9SJCFRf8r+gei9PAJ4VEVO/Rven+gN/+jzE\ntRHVH4ArImJ94OX18qlGk0dSVav8B1Wd9at7PN4JwMuiasAaVO1GvlZ/Rqa/RlD9Af0T1R/NCeB7\n9fz9oWrcR5VQzWY5VW8IomqkO5ei8e9SvR9HAEd3LD8e+PeoetcREU+KiEPqdRsBv68fLwVeQFVv\n3+k+czyHXi0Dioh4eH3cAngcVcnuz6lKo55dr9uaqk3ICWve1W1MUrU32obqWul0I7Bx/X7/mCrx\n2rc+zgOpEqL1uj2p+fjMR8Tzo25sn5l/B366hnPbiKrU6Nz6Oa+ges837Nh26sfjd6hKSHart90w\nqgam07+8Z3NA/fyNgSdQfT5m8n/AAVE1yF5MdT2/jOr9vykinlXvb0lUjbKbfrY6X+Pndqz7KvC4\niLh7vd9HUDUoP4kqwd8pIqaSsqfV8VtlM4sZS0gi4qOZ+fKI+AZrGSY+M5+0puVdOp6qYdzvqeqk\nP079x7ZLpwBPBs6NiNVUv1j3y8y/139gj6JqALmCqvppPvrSf4oq0/5ZVK3ir6NqoDlbC/lhPBeo\n3qM7AD+v/3hdSNX4LYFvRMTdgO/XfyRWAPvUMbcd1xHAl6O6D9PlVAnCfYAfRMRH65g/kJkZES+q\n4z8xM3+79l3O6K3AodzaoPcsqj+I0PEaceuX94eBX1K1mVhF9Qf1cuDTEbFP/fwvUP06nMnrqX69\nX0zVWO/LNExKMnMyIj5P1Z6ms/r1HVS9qX5dX6NXAAfV697bse1ngX8H7sk/dwXfOyIeO4dz6Elm\n/iUingp8MCJuT5UYvCgzp3qI7AN8KCL+k+pL4QOZOZdxQW6kanNwGtUX9vn18u9QXVdTJV1PAI6M\niHdTfa6fkZnX9nCtz8dn/itUbXDOj4gbqNo0PYfqmpw6t/sCPwF+HxFXUpWWfAw4tL7uvgocGxFl\nZhYR8WTgsKkfXVTX5Fw/V3+KiKnPx/H1Pt4zw/YfpWpM+juqNlmnAe/OzNVR9X45PCLeRvXZ/C5V\ncjebQ6iqMS+k+gx8gapUkMw8K6rRy/+3TtyvA55aV7NNXXOfrf/uraRqRO2tVmYx40itEfHyzPxo\nRLx1bdtk5ttbiWyeRdW47/TMbP2bsm2jdC5qV0S8AHhSZu4z6FjUvVH5zNelahcCW2Tm9LY/GnEz\nlpBk5lQbkbMz8//mIR5J8yQiNqMqYXnRoGORpKaNWt9OVaQ8VOpi4cNn2OS5mTlTF8ShMUrnslBF\nNST4/mtb32XDuIGIiAO5bZuHTp8HXgx8pO6iOjQi4uvA3dey+ueZ+dy1rFtQ/MwP1rhcZwtJ05vr\nvZmqP//XmdbtKjObNg6TJElao6YJyYX1w86Ng2rMnLu2EZgkSRofTROSxVRd5fag6t61kqql8vGO\nPidJknrVdHCqI6huMrWa6j4sNwHvY+b6T0mSpEaalpCsAHash5ueWrYx8LvMnPEOqZIkSbNpWkLy\n585kBP5xL4w/9T8kSZI0bpqWkLyAarS+j1H1stmcamjfP9ExwmPeegdbSZKkxpomJJ0NV5Oqh830\n+czMpnfqlCRJ+oemA6Nt32oUkiRprDUqIZEkSWpT00atkiRJrTEhkSRJA2dCIkmSBs6ERJIkDZwJ\niSRJGrj/D30VNENrWyPlAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34ce8d5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f, ax = plt.subplots(figsize=(10,10))\n",
"\n",
"# Generate a custom diverging colormap\n",
"cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
"\n",
"corr = Realty.loc[:,['full_sq', 'life_sq', 'floor', 'max_floor', 'build_year', 'num_room', \n",
" 'kitch_sq', 'state', 'price_doc']].corr()\n",
"# Draw the heatmap with the mask and correct aspect ratio\n",
"sns.heatmap(corr, cmap=cmap,\n",
" square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax)\n",
"\n",
"ax.tick_params('x', colors='k', labelsize = 13)\n",
"ax.tick_params('y', colors='k', labelsize = 13)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "a89fcb82-3a4e-b491-3c5e-2e541a2bda59"
},
"outputs": [
{
"data": {
"image/png": 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G3FFVuwL/H/C2JEub9QdWVQc4Bjj+PgdKNgdOBz5VVTsB+wCvTrL/mgqoqguAQ4Fbq2pJ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\nkkVakiRJkiRJhWSRliRJkiRJUiH/HyygJHa3LILsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34ce04a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f1, ax1 = plt.subplots(figsize=(10,10))\n",
"\n",
"# Generate a custom diverging colormap\n",
"cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
"\n",
"corr1 = Realty.loc[:,['area_m', 'raion_popul', 'full_all', 'male_f', 'female_f',\n",
" 'young_all', 'young_female', 'work_all', 'work_male', 'work_female', 'price_doc']].corr()\n",
"# Draw the heatmap with the mask and correct aspect ratio\n",
"sns.heatmap(corr1, cmap=cmap,\n",
" square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax1)\n",
"\n",
"ax1.tick_params('x', colors='k', labelsize = 13)\n",
"ax1.tick_params('y', colors='k', labelsize = 13)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "5502f1c8-4a64-7c73-fd58-7af403134273"
},
"outputs": [
{
"data": {
"image/png": 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lfRanMgplbwLPdsFQzMzM7N3qJSf1jfAdAetuvwTGSNopfT4QuJ18alC97gL2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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fa34cde7e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"f2, ax2 = plt.subplots(figsize=(10,10))\n",
"\n",
"# Generate a custom diverging colormap\n",
"cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
"\n",
"corr2 = Realty.loc[:,['children_preschool', 'preschool_quota', 'preschool_education_centers_raion',\n",
" 'children_school', 'school_quota', 'school_education_centers_raion', \n",
" 'school_education_centers_top_20_raion', 'university_top_20_raion',\n",
" 'additional_education_raion','price_doc']].corr()\n",
"# Draw the heatmap with the mask and correct aspect ratio\n",
"sns.heatmap(corr2, cmap=cmap,\n",
" square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax2)\n",
"ax2.tick_params('both', colors='k',labelsize = 13)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "6a9919e1-c9af-3883-eb0d-c682655f0cde"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "09581265-4f60-0891-6d0e-827480f5f4e9"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "261b5e76-61fe-6b01-4ae6-903aa46e22c3"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "97a205c7-39eb-b001-b6f0-666de459167e"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "c03b5d7f-e1b7-14f4-5937-6d9e45efd73c"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "1d2f50a8-81f9-9ec4-37a0-76fb0cd5fcaf"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "9d7f21f1-0b09-f618-d090-d80a4652154b"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "966c7b6d-6efb-9c19-4ab7-d7ab2fa350c1"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "743b22d8-941a-2d53-80df-228d7baaa6d2"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "544a34eb-0b17-58e4-8038-d95297f45b4f"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "c19e10d2-8f39-6e68-be17-4d7119356f77"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "e9eb02db-d871-1536-ae96-d1960780aaa4"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "f8551be8-5449-a19c-84d4-00146bf1f7b6"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "80d73eb4-46fc-a43b-80b9-5d9e546bbfc7"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "fd4b5455-eba7-8f65-dd0d-fc764dd6af0e"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "2895d9fa-309c-8045-0a92-edb7f9499953"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "c77def24-2277-8045-e31d-2b330f8e5aeb"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "eb34526f-8b1d-c080-d6af-fd54f9946bc3"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
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| 0001/164/1164140.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "38541fd0-854b-b5ae-dd1d-d5a181160c1c"
},
"source": [
"# Import Basic Libraries:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "f5fb6410-9e53-24bf-8f8e-0fd19e110c62"
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"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e628c172-6edc-c818-1532-e787d782d612"
},
"source": [
"# 1. Data Input, Preparation, & Exploration"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "478defd6-4f2f-31bf-8001-ad4e8fd11798"
},
"source": [
"## 1.1 Read in Transaction Data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "256f3663-58ec-121d-a1ae-f345bbfb9c6d"
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No. of Rows: \t\t 284807\n",
"No. of Columns: \t 31\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Time</th>\n",
" <th>V1</th>\n",
" <th>V2</th>\n",
" <th>V3</th>\n",
" <th>V4</th>\n",
" <th>V5</th>\n",
" <th>V6</th>\n",
" <th>V7</th>\n",
" <th>V8</th>\n",
" <th>V9</th>\n",
" <th>...</th>\n",
" <th>V21</th>\n",
" <th>V22</th>\n",
" <th>V23</th>\n",
" <th>V24</th>\n",
" <th>V25</th>\n",
" <th>V26</th>\n",
" <th>V27</th>\n",
" <th>V28</th>\n",
" <th>Amount</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.0</td>\n",
" <td>-1.359807</td>\n",
" <td>-0.072781</td>\n",
" <td>2.536347</td>\n",
" <td>1.378155</td>\n",
" <td>-0.338321</td>\n",
" <td>0.462388</td>\n",
" <td>0.239599</td>\n",
" <td>0.098698</td>\n",
" <td>0.363787</td>\n",
" <td>...</td>\n",
" <td>-0.018307</td>\n",
" <td>0.277838</td>\n",
" <td>-0.110474</td>\n",
" <td>0.066928</td>\n",
" <td>0.128539</td>\n",
" <td>-0.189115</td>\n",
" <td>0.133558</td>\n",
" <td>-0.021053</td>\n",
" <td>149.62</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.0</td>\n",
" <td>1.191857</td>\n",
" <td>0.266151</td>\n",
" <td>0.166480</td>\n",
" <td>0.448154</td>\n",
" <td>0.060018</td>\n",
" <td>-0.082361</td>\n",
" <td>-0.078803</td>\n",
" <td>0.085102</td>\n",
" <td>-0.255425</td>\n",
" <td>...</td>\n",
" <td>-0.225775</td>\n",
" <td>-0.638672</td>\n",
" <td>0.101288</td>\n",
" <td>-0.339846</td>\n",
" <td>0.167170</td>\n",
" <td>0.125895</td>\n",
" <td>-0.008983</td>\n",
" <td>0.014724</td>\n",
" <td>2.69</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.0</td>\n",
" <td>-1.358354</td>\n",
" <td>-1.340163</td>\n",
" <td>1.773209</td>\n",
" <td>0.379780</td>\n",
" <td>-0.503198</td>\n",
" <td>1.800499</td>\n",
" <td>0.791461</td>\n",
" <td>0.247676</td>\n",
" <td>-1.514654</td>\n",
" <td>...</td>\n",
" <td>0.247998</td>\n",
" <td>0.771679</td>\n",
" <td>0.909412</td>\n",
" <td>-0.689281</td>\n",
" <td>-0.327642</td>\n",
" <td>-0.139097</td>\n",
" <td>-0.055353</td>\n",
" <td>-0.059752</td>\n",
" <td>378.66</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.0</td>\n",
" <td>-0.966272</td>\n",
" <td>-0.185226</td>\n",
" <td>1.792993</td>\n",
" <td>-0.863291</td>\n",
" <td>-0.010309</td>\n",
" <td>1.247203</td>\n",
" <td>0.237609</td>\n",
" <td>0.377436</td>\n",
" <td>-1.387024</td>\n",
" <td>...</td>\n",
" <td>-0.108300</td>\n",
" <td>0.005274</td>\n",
" <td>-0.190321</td>\n",
" <td>-1.175575</td>\n",
" <td>0.647376</td>\n",
" <td>-0.221929</td>\n",
" <td>0.062723</td>\n",
" <td>0.061458</td>\n",
" <td>123.50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2.0</td>\n",
" <td>-1.158233</td>\n",
" <td>0.877737</td>\n",
" <td>1.548718</td>\n",
" <td>0.403034</td>\n",
" <td>-0.407193</td>\n",
" <td>0.095921</td>\n",
" <td>0.592941</td>\n",
" <td>-0.270533</td>\n",
" <td>0.817739</td>\n",
" <td>...</td>\n",
" <td>-0.009431</td>\n",
" <td>0.798278</td>\n",
" <td>-0.137458</td>\n",
" <td>0.141267</td>\n",
" <td>-0.206010</td>\n",
" <td>0.502292</td>\n",
" <td>0.219422</td>\n",
" <td>0.215153</td>\n",
" <td>69.99</td>\n",
" <td>0</td>\n",
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" Time V1 V2 V3 V4 V5 V6 V7 \\\n",
"0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n",
"1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n",
"2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n",
"3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n",
"4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n",
"\n",
" V8 V9 ... V21 V22 V23 V24 \\\n",
"0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 \n",
"1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 \n",
"2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 \n",
"3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 \n",
"4 -0.270533 0.817739 ... -0.009431 0.798278 -0.137458 0.141267 \n",
"\n",
" V25 V26 V27 V28 Amount Class \n",
"0 0.128539 -0.189115 0.133558 -0.021053 149.62 0 \n",
"1 0.167170 0.125895 -0.008983 0.014724 2.69 0 \n",
"2 -0.327642 -0.139097 -0.055353 -0.059752 378.66 0 \n",
"3 0.647376 -0.221929 0.062723 0.061458 123.50 0 \n",
"4 -0.206010 0.502292 0.219422 0.215153 69.99 0 \n",
"\n",
"[5 rows x 31 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pd.read_csv(\"../input/creditcard.csv\")\n",
"print(\"No. of Rows: \\t\\t\", data.shape[0])\n",
"print(\"No. of Columns: \\t\", data.shape[1])\n",
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "302cfc01-d0c3-ddf7-a897-73d3cca6dc73"
},
"source": [
"## 1.2 Class Distribution\n",
"\n",
"As described in the Credit Card Fraud summary, this is a highly unbalanced dataset. Check out the histogram and class counts below (Class 0 for normal, Class 1 for fraudulent)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "d7d5ce17-5636-39fd-e257-787010f848f1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"No. of normal transactions: \t\t 284315\n",
"No. of fraudulent transactions: \t 492\n",
"% normal transactions: \t\t 99.82725143693798\n",
"% fraudulent transcations: \t 0.1727485630620034\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f0915361ac8>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f09153b24a8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"norm_count = data[data['Class'] == 0].shape[0] # Normal transactions\n",
"fraud_count = data[data['Class'] == 1].shape[0] # Fraudulent transcations\n",
"total_count = data.shape[0]\n",
"print(\"No. of normal transactions: \\t\\t\", norm_count)\n",
"print(\"No. of fraudulent transactions: \\t\", fraud_count)\n",
"print(\"% normal transactions: \\t\\t\", norm_count/total_count * 100)\n",
"print(\"% fraudulent transcations: \\t\", fraud_count/total_count * 100)\n",
"pd.value_counts(data['Class'], sort = True).sort_index().plot(kind='bar')\n",
"plt.title(\"Class Histogram\")\n",
"plt.xlabel(\"Class\")\n",
"plt.ylabel(\"Frequency\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "842e8fc6-56e2-1457-4edc-ab92e4973f5f"
},
"source": [
"## 1.3 Standardize Input Data\n",
"\n",
"Before we tackle the class imbalance, let's bring the transaction dollar amount onto a standard scale (i.e. mean = 0, std = 1.0). This is a common preprocessing step. The other feature variables have been outputted from Principal Component Analysis so we can leave them alone. Additionally, we'll drop the unscaled dollar amount column and the time column. A time series analysis would be interesting, but we don't have information for which account is making a particular transaction so the time column won't be very helpful."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "f6f35ec1-b928-ac06-da74-4227c584a6f8"
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>V1</th>\n",
" <th>V2</th>\n",
" <th>V3</th>\n",
" <th>V4</th>\n",
" <th>V5</th>\n",
" <th>V6</th>\n",
" <th>V7</th>\n",
" <th>V8</th>\n",
" <th>V9</th>\n",
" <th>V10</th>\n",
" <th>...</th>\n",
" <th>V21</th>\n",
" <th>V22</th>\n",
" <th>V23</th>\n",
" <th>V24</th>\n",
" <th>V25</th>\n",
" <th>V26</th>\n",
" <th>V27</th>\n",
" <th>V28</th>\n",
" <th>Class</th>\n",
" <th>Amount_scl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-1.359807</td>\n",
" <td>-0.072781</td>\n",
" <td>2.536347</td>\n",
" <td>1.378155</td>\n",
" <td>-0.338321</td>\n",
" <td>0.462388</td>\n",
" <td>0.239599</td>\n",
" <td>0.098698</td>\n",
" <td>0.363787</td>\n",
" <td>0.090794</td>\n",
" <td>...</td>\n",
" <td>-0.018307</td>\n",
" <td>0.277838</td>\n",
" <td>-0.110474</td>\n",
" <td>0.066928</td>\n",
" <td>0.128539</td>\n",
" <td>-0.189115</td>\n",
" <td>0.133558</td>\n",
" <td>-0.021053</td>\n",
" <td>0</td>\n",
" <td>0.244964</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.191857</td>\n",
" <td>0.266151</td>\n",
" <td>0.166480</td>\n",
" <td>0.448154</td>\n",
" <td>0.060018</td>\n",
" <td>-0.082361</td>\n",
" <td>-0.078803</td>\n",
" <td>0.085102</td>\n",
" <td>-0.255425</td>\n",
" <td>-0.166974</td>\n",
" <td>...</td>\n",
" <td>-0.225775</td>\n",
" <td>-0.638672</td>\n",
" <td>0.101288</td>\n",
" <td>-0.339846</td>\n",
" <td>0.167170</td>\n",
" <td>0.125895</td>\n",
" <td>-0.008983</td>\n",
" <td>0.014724</td>\n",
" <td>0</td>\n",
" <td>-0.342475</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-1.358354</td>\n",
" <td>-1.340163</td>\n",
" <td>1.773209</td>\n",
" <td>0.379780</td>\n",
" <td>-0.503198</td>\n",
" <td>1.800499</td>\n",
" <td>0.791461</td>\n",
" <td>0.247676</td>\n",
" <td>-1.514654</td>\n",
" <td>0.207643</td>\n",
" <td>...</td>\n",
" <td>0.247998</td>\n",
" <td>0.771679</td>\n",
" <td>0.909412</td>\n",
" <td>-0.689281</td>\n",
" <td>-0.327642</td>\n",
" <td>-0.139097</td>\n",
" <td>-0.055353</td>\n",
" <td>-0.059752</td>\n",
" <td>0</td>\n",
" <td>1.160686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.966272</td>\n",
" <td>-0.185226</td>\n",
" <td>1.792993</td>\n",
" <td>-0.863291</td>\n",
" <td>-0.010309</td>\n",
" <td>1.247203</td>\n",
" <td>0.237609</td>\n",
" <td>0.377436</td>\n",
" <td>-1.387024</td>\n",
" <td>-0.054952</td>\n",
" <td>...</td>\n",
" <td>-0.108300</td>\n",
" <td>0.005274</td>\n",
" <td>-0.190321</td>\n",
" <td>-1.175575</td>\n",
" <td>0.647376</td>\n",
" <td>-0.221929</td>\n",
" <td>0.062723</td>\n",
" <td>0.061458</td>\n",
" <td>0</td>\n",
" <td>0.140534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>-1.158233</td>\n",
" <td>0.877737</td>\n",
" <td>1.548718</td>\n",
" <td>0.403034</td>\n",
" <td>-0.407193</td>\n",
" <td>0.095921</td>\n",
" <td>0.592941</td>\n",
" <td>-0.270533</td>\n",
" <td>0.817739</td>\n",
" <td>0.753074</td>\n",
" <td>...</td>\n",
" <td>-0.009431</td>\n",
" <td>0.798278</td>\n",
" <td>-0.137458</td>\n",
" <td>0.141267</td>\n",
" <td>-0.206010</td>\n",
" <td>0.502292</td>\n",
" <td>0.219422</td>\n",
" <td>0.215153</td>\n",
" <td>0</td>\n",
" <td>-0.073403</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" V1 V2 V3 V4 V5 V6 V7 \\\n",
"0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n",
"1 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n",
"2 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n",
"3 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n",
"4 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n",
"\n",
" V8 V9 V10 ... V21 V22 V23 \\\n",
"0 0.098698 0.363787 0.090794 ... -0.018307 0.277838 -0.110474 \n",
"1 0.085102 -0.255425 -0.166974 ... -0.225775 -0.638672 0.101288 \n",
"2 0.247676 -1.514654 0.207643 ... 0.247998 0.771679 0.909412 \n",
"3 0.377436 -1.387024 -0.054952 ... -0.108300 0.005274 -0.190321 \n",
"4 -0.270533 0.817739 0.753074 ... -0.009431 0.798278 -0.137458 \n",
"\n",
" V24 V25 V26 V27 V28 Class Amount_scl \n",
"0 0.066928 0.128539 -0.189115 0.133558 -0.021053 0 0.244964 \n",
"1 -0.339846 0.167170 0.125895 -0.008983 0.014724 0 -0.342475 \n",
"2 -0.689281 -0.327642 -0.139097 -0.055353 -0.059752 0 1.160686 \n",
"3 -1.175575 0.647376 -0.221929 0.062723 0.061458 0 0.140534 \n",
"4 0.141267 -0.206010 0.502292 0.219422 0.215153 0 -0.073403 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"data['Amount_scl'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))\n",
"data = data.drop(['Time','Amount'],axis=1)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "dd464857-7a67-7b12-e682-12911cfe145e"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X.shape: (284807, 29)\n",
"y.shape: (284807,)\n"
]
}
],
"source": [
"X = data.ix[:, data.columns != 'Class'] # features\n",
"y = data['Class'] # labels\n",
"print(\"X.shape: \", X.shape)\n",
"print(\"y.shape: \", y.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2779886c-d420-e1fa-be0b-bed2aa09a2d0"
},
"source": [
"## 1.4 Split Entire Dataset into Train-Test Sets\n",
"\n",
"Next, we will break up the data into training and test sets. The test set will be untouched until the final evaluation of each model so we have an unbiased estimate of model performance."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "78ffa6bc-ab94-7c24-9d76-bd0e03d8bfc6"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"# 70% training data, 30% testing data\n",
"X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "fb839282-f238-85e1-d33d-25f4aa9569f1"
},
"source": [
"## 1.5 Undersample Training Data to Create Balanced Class Distributions\n",
"\n",
"Now we will handle the skewed class distribution using a technique known as undersampling:\n",
"\n",
" 1. Get the number of fraudulent transactions and the indices of the corresponding rows in our training data\n",
" 2. Get the indices of normal transactions in our training data\n",
" 3. Take a random sample of normal transactions with a sample size equal to the number of fraudulent transactions\n",
" 4. Combine the fraudulent transactions and the random sample of normal transactions to get balanced training data set (50% fraud, 50% normal)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "22fea321-d877-f4e1-8497-aa8447ac0d43"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---Undersampled Data Set---\n",
"No. of normal transactions: \t 336\n",
"No. of fraudulent transactions: \t\t 336\n"
]
}
],
"source": [
"# Get the indices of the fraudulent and normal classes:\n",
"fraud_idx = np.array(y_train[y_train == 1].index)\n",
"num_fraud = len(fraud_idx)\n",
"normal_idx = y_train[y_train == 0].index\n",
"\n",
"# From the normal indices, sample a random subset (subset size = # of frauds):\n",
"normal_idx_sample = np.random.choice(normal_idx, num_fraud, replace=False)\n",
"normal_idx_sample = np.array(normal_idx_sample)\n",
"\n",
"# Group together our normal and fraud indices:\n",
"# (we'll have a balanced class distribution, 50% normal, 50% fraud)\n",
"undersample_idx = np.concatenate([fraud_idx,normal_idx_sample])\n",
"\n",
"# Grab the records at the indices:\n",
"undersample_data = data.iloc[undersample_idx,:]\n",
"\n",
"# Split into features and labels:\n",
"X_undersample = undersample_data.ix[:, undersample_data.columns != 'Class']\n",
"y_undersample = undersample_data['Class']\n",
"\n",
"norm_count = undersample_data[undersample_data['Class'] == 0].shape[0]\n",
"fraud_count = undersample_data[undersample_data['Class'] == 1].shape[0]\n",
"\n",
"print(\"---Undersampled Data Set---\")\n",
"print(\"No. of normal transactions: \\t\", norm_count)\n",
"print(\"No. of fraudulent transactions: \\t\\t\", fraud_count)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d0fe1f55-5163-5229-eafd-d96215f417df"
},
"source": [
"# 2. Logistic Regression Classifier"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "912bd6a7-b37c-b22a-723a-02ce696b9876"
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.metrics import auc,roc_auc_score,roc_curve,recall_score,f1_score"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "4d42b86c-ced4-6952-002b-56c041e9c5fc"
},
"source": [
"## 2.1 Tune Hyperparameters with Cross Validation\n",
"\n",
"Here we'll search the hyperparameter space and use 5-fold cross validation on the undersampled training data to output accuracy, recall, and F1 metrics. The goal is to find the best combination of hyperparameters that gives the highest F1-score. The cross validation step here further subdivides the training data into two smaller components: (1) training subset and (2) validation subset. The training subset is used to train the model on with a pair of hyperparameters, and the validation subset is used to score the trained model. 5-fold CV means we repeat this process of splitting up the data 5 different times to get a more accurate score. \n",
"\n",
"Once we identify the pair of hyperparameters with the highest score, we will use them to train the model on the full training data (i.e. training subset + validation subset). Keep in mind, we still haven't touched the test dataset from Section 1.4. We will save that for final model evaluation to get an unbiased result."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "bb202318-c46c-d889-4693-196003360b78"
},
"outputs": [],
"source": [
"def get_best_hypers_lr(X, y):\n",
" \"\"\" Search parameter space for the optimal values.\n",
" \n",
" -Perform Logistic Regression using a range of C parameter values and two different\n",
" penalty terms (L1 & L2)\n",
" -Compute mean recall,accuracy and f1-scores using kfold cross validation \n",
" for each run\n",
" -Output the C parameter and penalty term with the best f1 score\n",
" \"\"\"\n",
" c_range = [0.01, 0.1, 1.0, 10.0, 100.0]\n",
" f1_max = 0\n",
" best_c = 0\n",
" penalty = ''\n",
" \n",
" for c_param in c_range:\n",
" print('='*25)\n",
" print('C parameter: ', c_param)\n",
" print('='*25)\n",
" print('')\n",
" \n",
" print('-'*25)\n",
" print('L1-penalty')\n",
" print('-'*25)\n",
" print('')\n",
" \n",
" lr_l1 = LogisticRegression(C=c_param, penalty='l1')\n",
" acc_score = cross_val_score(lr_l1, X, y, cv=5)\n",
" recall_score = cross_val_score(lr_l1, X, y, cv=5, scoring='recall')\n",
" f1_score = cross_val_score(lr_l1, X,y,cv=5, scoring='f1')\n",
" l1_f1=np.mean(f1_score)\n",
" \n",
" print(\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
" print(\"Mean Recall: %0.3f (+/- %0.3f)\" % (np.mean(recall_score), np.std(recall_score)) )\n",
" print(\"Mean F1: %0.3f (+/- %0.3f)\" % (np.mean(f1_score), np.std(f1_score)) )\n",
" print('')\n",
" \n",
" print('-'*25)\n",
" print('L2-penalty')\n",
" print('-'*25)\n",
" print('')\n",
" \n",
" lr_l2 = LogisticRegression(C=c_param, penalty='l2')\n",
" score = cross_val_score(lr_l2, X, y, cv=5)\n",
" recall_score = cross_val_score(lr_l2, X, y, cv=5, scoring='recall')\n",
" f1_score = cross_val_score(lr_l2, X, y, cv=5, scoring='f1')\n",
" l2_f1 = np.mean(f1_score)\n",
" \n",
" print(\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
" print(\"Mean Recall: %0.3f (+/- %0.3f)\" % (np.mean(recall_score), np.std(recall_score)) )\n",
" print(\"Mean F1: %0.3f (+/- %0.3f)\" % (np.mean(f1_score), np.std(f1_score)) )\n",
" print('')\n",
" \n",
" # compare l1_f1 & l2_f1:\n",
" if l2_f1 > l1_f1:\n",
" # compare to max:\n",
" if l2_f1 > f1_max:\n",
" f1_max = l2_f1\n",
" best_c = c_param\n",
" penalty='l2'\n",
" else:\n",
" # compare to max:\n",
" if l1_f1 > f1_max:\n",
" f1_max = l1_f1\n",
" best_c = c_param\n",
" penalty='l1'\n",
" \n",
"\n",
" print('*'*25)\n",
" print('Optimal C parameter = ', best_c)\n",
" print('Optimal penalty = ', penalty)\n",
" print('Optimal F1 = ', f1_max)\n",
" print('*'*25)\n",
" \n",
" return best_c, penalty"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "6e6ca420-fa59-8a81-3919-10fbb984ec2f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=========================\n",
"C parameter: 0.01\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.784 (+/- 0.038)\n",
"Mean Recall: 0.958 (+/- 0.011)\n",
"Mean F1: 0.817 (+/- 0.024)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.784 (+/- 0.038)\n",
"Mean Recall: 0.946 (+/- 0.012)\n",
"Mean F1: 0.952 (+/- 0.012)\n",
"\n",
"=========================\n",
"C parameter: 0.1\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.943 (+/- 0.015)\n",
"Mean Recall: 0.911 (+/- 0.025)\n",
"Mean F1: 0.941 (+/- 0.016)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.943 (+/- 0.015)\n",
"Mean Recall: 0.929 (+/- 0.020)\n",
"Mean F1: 0.951 (+/- 0.013)\n",
"\n",
"=========================\n",
"C parameter: 1.0\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.952 (+/- 0.011)\n",
"Mean Recall: 0.926 (+/- 0.013)\n",
"Mean F1: 0.951 (+/- 0.011)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.952 (+/- 0.011)\n",
"Mean Recall: 0.926 (+/- 0.013)\n",
"Mean F1: 0.950 (+/- 0.013)\n",
"\n",
"=========================\n",
"C parameter: 10.0\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.945 (+/- 0.022)\n",
"Mean Recall: 0.923 (+/- 0.019)\n",
"Mean F1: 0.944 (+/- 0.022)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.945 (+/- 0.022)\n",
"Mean Recall: 0.923 (+/- 0.019)\n",
"Mean F1: 0.944 (+/- 0.022)\n",
"\n",
"=========================\n",
"C parameter: 100.0\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.948 (+/- 0.020)\n",
"Mean Recall: 0.929 (+/- 0.019)\n",
"Mean F1: 0.947 (+/- 0.020)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.948 (+/- 0.020)\n",
"Mean Recall: 0.929 (+/- 0.019)\n",
"Mean F1: 0.947 (+/- 0.020)\n",
"\n",
"*************************\n",
"Optimal C parameter = 0.01\n",
"Optimal penalty = l2\n",
"Optimal F1 = 0.952230431393\n",
"*************************\n"
]
}
],
"source": [
"best_c_lr, penalty_lr = get_best_hypers_lr(X_undersample,y_undersample)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "aab23f26-2d3b-8ffb-038e-fee04ccdc38e"
},
"source": [
"## 2.2 Train Model On Undersampled Training Data & Evaluate on Full Test Data\n",
"\n",
"With our optimal hyperparameters found, let's set them on a fresh Linear Regression model and train it on the \"full\" training data (this is still our 50/50 balanced training set).\n",
"\n",
"The final step is to use the unseen test data from Section 1.4 to evaluate the model performance. It's important to remember that the test set has a similar class imbalance as the original transaction data we started with in Section 1.2. For evaluation, we're using Receiver Operation Characteristic (ROC) curves and Area Under the Curve (AUC) to strike a balance between model sensitivity and specificity."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "5a482730-429a-4cfc-fe2f-2787cf9b26a8"
},
"outputs": [
{
"data": {
"image/png": 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sgThLCqpKj+E/Ziy/ctUpnFa/eoARGWMiaudO+Oc/3bMHM2ZAu3ZuEDuTIa6SwvOf/wpA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\n2SwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f09153da940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Use best hyperparameters:\n",
"lr = LogisticRegression(C=best_c_lr, penalty=penalty_lr)\n",
"# Train on full undersample data set:\n",
"lr.fit(X_undersample, y_undersample)\n",
"# Test on unseen test data set:\n",
"y_pred_score = lr.decision_function(X_test.values)\n",
"# Compute ROC metrics:\n",
"fpr, tpr, thresholds = roc_curve(y_test.values, y_pred_score)\n",
"# Get AUC:\n",
"roc_auc = auc(fpr,tpr)\n",
"\n",
"# Plot ROC:\n",
"plt.title('ROC Curve - Linear Regression')\n",
"plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)\n",
"plt.plot([0,1],[0,1],'r--')\n",
"plt.legend(loc='lower right')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "39f3ba8c-df8f-4f26-0d11-54abdb5c060d"
},
"source": [
"# 3. Support Vector Machine Classifier"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "ac1cbe69-9309-54ab-9f50-40c175d4e4f6"
},
"outputs": [],
"source": [
"from sklearn.svm import LinearSVC"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "9f1ab31d-3884-05ea-a62c-134b668cb185"
},
"source": [
"## 3.1 Tune Hyperparameters with Cross Validation"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "41d4f774-cd1c-dd2a-7478-5c8d86599de2"
},
"outputs": [],
"source": [
"def get_best_hypers_svc(X, y):\n",
" \"\"\" Search parameter space for the optimal values.\n",
" \n",
" -Perform Support Vector Classifier using a range of C parameter values and two different\n",
" penalty terms (L1 & L2)\n",
" -Compute mean recall, accuracy, and f1-scores using kfold cross validation\n",
" for each run\n",
" -Output the C parameter and penalty term with the best f1-score\n",
" \"\"\"\n",
" c_range = [0.01, 0.1, 1.0, 10.0, 100.0]\n",
" f1_max = 0\n",
" best_c = 0\n",
" penalty = ''\n",
" \n",
" for c_param in c_range:\n",
" print('='*25)\n",
" print('C parameter: ', c_param)\n",
" print('='*25)\n",
" print('')\n",
" \n",
" print('-'*25)\n",
" print('L1-penalty')\n",
" print('-'*25)\n",
" print('')\n",
" \n",
" svc_l1 = LinearSVC(C=c_param, penalty='l1', dual=False)\n",
" acc_score = cross_val_score(svc_l1, X, y, cv=5)\n",
" recall_score = cross_val_score(svc_l1, X, y, cv=5, scoring='recall')\n",
" f1_score = cross_val_score(svc_l1, X, y, cv=5, scoring='f1')\n",
" l1_f1 = np.mean(f1_score)\n",
" \n",
" print(\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
" print(\"Mean Recall: %0.3f (+/- %0.3f)\" % (np.mean(recall_score), np.std(recall_score)) )\n",
" print(\"Mean F1: %0.3f (+/- %0.3f)\" % (np.mean(f1_score), np.std(f1_score)) )\n",
" print('')\n",
" \n",
" print('-'*25)\n",
" print('L2-penalty')\n",
" print('-'*25)\n",
" print('')\n",
" \n",
" svc_l2 = LinearSVC(C=c_param, penalty='l2')\n",
" score = cross_val_score(svc_l2, X, y, cv=5)\n",
" recall_score = cross_val_score(svc_l2, X, y, cv=5, scoring='recall')\n",
" f1_score = cross_val_score(svc_l2, X, y, cv=5, scoring='f1')\n",
" l2_f1 = np.mean(f1_score)\n",
" \n",
" print(\"Mean Accuracy: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
" print(\"Mean Recall: %0.3f (+/- %0.3f)\" % (np.mean(recall_score), np.std(recall_score)) )\n",
" print(\"Mean F1: %0.3f (+/- %0.3f)\" % (np.mean(f1_score), np.std(f1_score)) )\n",
" print('')\n",
" \n",
" # compare l1_recall & l2_recall:\n",
" if l2_f1 > l1_f1:\n",
" # compare to max:\n",
" if l2_f1 > f1_max:\n",
" f1_max = l2_f1\n",
" best_c = c_param\n",
" penalty='l2'\n",
" else:\n",
" # compare to max:\n",
" if l1_f1 > f1_max:\n",
" f1_max = l1_f1\n",
" best_c = c_param\n",
" penalty='l1'\n",
" \n",
"\n",
" print('*'*25)\n",
" print('Optimal C parameter = ', best_c)\n",
" print('Optimal penalty = ', penalty)\n",
" print('Optimal F1 = ', f1_max)\n",
" print('*'*25)\n",
" \n",
" return best_c, penalty"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"_cell_guid": "9e29cbd1-f516-6059-e75c-04a863b372f3"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"=========================\n",
"C parameter: 0.01\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.943 (+/- 0.015)\n",
"Mean Recall: 0.902 (+/- 0.029)\n",
"Mean F1: 0.941 (+/- 0.016)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.943 (+/- 0.015)\n",
"Mean Recall: 0.926 (+/- 0.016)\n",
"Mean F1: 0.950 (+/- 0.012)\n",
"\n",
"=========================\n",
"C parameter: 0.1\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.948 (+/- 0.012)\n",
"Mean Recall: 0.917 (+/- 0.020)\n",
"Mean F1: 0.946 (+/- 0.013)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.948 (+/- 0.012)\n",
"Mean Recall: 0.923 (+/- 0.014)\n",
"Mean F1: 0.948 (+/- 0.014)\n",
"\n",
"=========================\n",
"C parameter: 1.0\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.946 (+/- 0.015)\n",
"Mean Recall: 0.917 (+/- 0.015)\n",
"Mean F1: 0.943 (+/- 0.016)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.946 (+/- 0.015)\n",
"Mean Recall: 0.917 (+/- 0.015)\n",
"Mean F1: 0.943 (+/- 0.016)\n",
"\n",
"=========================\n",
"C parameter: 10.0\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.945 (+/- 0.020)\n",
"Mean Recall: 0.920 (+/- 0.020)\n",
"Mean F1: 0.944 (+/- 0.020)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.945 (+/- 0.020)\n",
"Mean Recall: 0.920 (+/- 0.015)\n",
"Mean F1: 0.945 (+/- 0.019)\n",
"\n",
"=========================\n",
"C parameter: 100.0\n",
"=========================\n",
"\n",
"-------------------------\n",
"L1-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.946 (+/- 0.018)\n",
"Mean Recall: 0.920 (+/- 0.020)\n",
"Mean F1: 0.945 (+/- 0.019)\n",
"\n",
"-------------------------\n",
"L2-penalty\n",
"-------------------------\n",
"\n",
"Mean Accuracy: 0.946 (+/- 0.018)\n",
"Mean Recall: 0.926 (+/- 0.009)\n",
"Mean F1: 0.942 (+/- 0.014)\n",
"\n",
"*************************\n",
"Optimal C parameter = 0.01\n",
"Optimal penalty = l2\n",
"Optimal F1 = 0.949671472332\n",
"*************************\n"
]
}
],
"source": [
"best_c_svc, penalty_svc = get_best_hypers_svc(X_undersample, y_undersample)"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "87823185-ea69-23aa-1f1d-46dd166f9d46"
},
"source": [
"## 3.2 Train Model on Undersampled Training Data & Evaluate on Full Test Data"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"_cell_guid": "f98f24bc-f70c-14fc-d13d-b8a8facf5e10"
},
"outputs": [
{
"data": {
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JFYM+ZWNMif3k6pXRsyc88ohbDMcSgi/8/ITcAjQL2W7q7Qs1BHhDnXXABqCdH8Goup/N\n6yVunXRjYs6ePW6d5LZtXc2iChXgr3+FI2xs0C9+JoV5QGsRaeENHg/EdRWF2gScASAiDYG2wHo/\ngsnxskKFCtZ9ZExMmD4dOnSAiRNdYqhdO+iIEoJvYwqqmiUiw4EPgIrARFVdISLXevePA+4HJonI\nMkCA21R1hx/xZGa7pJB+MNuPpzfGlJeMDHfR2auvQufObvGb1NSgo0oYvlZJVdUZwIx8+8aF/L4V\n+L2fMeRXJcnGFIyJapUrQ2Ym3H8/3Habm2pqIiZhPiEV11KokmR1j4yJOps3u/WR169300tffx3u\nvNMSQgASJyl4A802omBMFMnJgWefdWMH770HS5e6/TZ1PDAJkxRy2d+aMVFi7Vo47TS4/no31XT5\ncvjDH4KOKuElzMprGnQAxpjf+sc/XO2if//brYxm39iiQsK0FNTrPxLrQDImOEuWuKJ1AI89BitX\nuplGlhCiRsIkhTz2t2dM5B08CHfd5aaW/t//uX316kGjRsHGZQ6TON1H1n9kTDDmzIFrrnEthKuu\ngn/+M+iITBESrqVgDQVjIuj11+HkkyE93c0uevFF10IwUSvxkoL1XRrjv/373c/f/x5uv93NLOrb\nN9iYTFjCSgoikiwirfwOxk/WfWRMBOzeDX/8I/To4cYRjjgCHngAatYMOjITpmKTgoicAywDPvK2\njxORN/0OrLzlXtFs7QRjfPLGG+4itJdegvPPDzoaU0rhtBTuwy2OswdAVRcDMdtqsN4jY8rZ7t2u\nRMVFF8FRR8G8ee4ahMqVg47MlEI4SSFTVffk2xdznTHWfWSMT6pUgW+/dYlg7lw4/vigIzJlEE5S\nWCUilwAVvLURHge+9jmucpebE6ylYEw5+P57N800PR2qVoUFC9yAshWwi3nhJIXhQDcgB3gDOAjc\n6GdQfrIrmo0pg5wcePpp6NjRrXewYIHbXylhLnmKe+EkhT6qepuqHu/dRgH9/A6svKn1HxlTNqtX\nQ+/e8Je/QK9ebp3k3r2DjsqUs3CSwp0F7LujvAPxm3UfGVMGqq67aOVKmDTJXYh29NFBR2V8UGib\nT0T6AH2BJiISel36EbiuJGNMvFu0yH34163rqpkecYSbYWTiVlEthR+B5UAGsCLk9iEx2X0UdATG\nxJCMDDdwfMIJMHq029emjSWEBFBoS0FVFwGLRORlVc2IYEw+8S5es/4jY4o2a5brKlq7FoYMgXvv\nDToiE0HhjCk0EZEpIrJURNbm3nyPrJzZcpzGhGHsWDd4fOgQfPghTJwIdeoEHZWJoHCSwiTg37jP\n037A/4BXfYzJV9ZQMKYAmZnu5+9/Dzfd5FZEO+usYGMygQgnKVRT1Q8AVPU7Vb2TWBxTCDoAY6LR\nrl1uKcyBA91269ZuvYMaNYKNywQmnKRwUEQqAN+JyLUich4QcyUPf+0+sqaCMai6tQ7at4dXXnGF\n7LKzg47KRIFwLkO8GagOjAAeAGoBf/QzKD9Z95FJeNu3w3XXwZtvQrdubuygS5egozJRotikoKrf\neL/uAwYBiEgTP4Pyg1oHkjFOZibMng0PPww332wlKsxvFNl9JCIniMgfRKS+t91RRF4CvinqcdHI\nZh+ZhLZhA9xxh/uP0LSp2771VksI5jCFJgUReRB4GbgCeF9E7gU+BZYAbSISnQ+s+8gklOxs+Ne/\n4Nhj4amnYM0at79q1WDjMlGrqK8J/YEuqnpAROoCm4FOqro+MqGVL7ui2SSclSth6FCYMwf69YPn\nnoNmzYKOykS5opJChqoeAFDVXSKyNlYTAoSOKVhTwSSAzEzo0wcOHID//hcuv9yaySYsRSWFliLy\nhve7AC1CtlHVC4t7chHpC/wLqAhMUNUxBRxzGvAEkATsUNVTww+/5Oz/hYlry5a56aVJSTB5sqtX\ndOSRQUdlYkhRSeGifNtPl+SJRaQiMBY4C0gD5onIW6q6MuSY2sAzQF9V3SQivv31WveRiWsHDrga\nRY89Bk88AcOHuzUPjCmhogrifVzG5+4OrMvtchKRKbhxipUhx1wOvKGqm7zX/LGMr1ksayiYuPP5\n5/CnP7l1kocOhSuvDDoiE8PCuaK5tJrgBqdzpXn7QrUB6ojIZyKyQESuKuiJRGSYiMwXkfk//fRT\nmYKyKqkmrtx7L5x2mptlNHMmPP881K4ddFQmhvmZFMJRCbf+8zlAH+AuETlsuquqjlfVVFVNbdCg\nQaleyLqPTFzJ/YM+8UR3AdrSpXDGGcHGZOJC2FeuiEhlVT1YgufeAoTOf2vq7QuVBuxU1XQgXUS+\nALoA5V6aO3f2kbUTTEzbscNVMT3mGLf4Tb9+7mZMOSm2pSAi3UVkGfCtt91FRJ4K47nnAa1FpIWI\nJAMDgbfyHTMd6CUilUSkGnAisKpEZ1BC1ntkYpIqvPqqm1n06quQnBx0RCZOhdNSeBI4F5gGoKpL\nROT04h6kqlkiMhz4ADcldaKqrhCRa737x6nqKhF5H1iKW/d5gqouL+W5FBOPH89qTARs3eoK2L31\nFqSmwscfQ6dOQUdl4lQ4SaGCqn6fb4A2rBq7qjoDmJFv37h8248Aj4TzfGWRd+matRRMrFm/3g0i\nP/oo3Hij1Ssyvgrnr2uziHQH1Lv24C/40OfvN9XcMQXLCiYGrF8PH30Ef/6zu95g0yaoVy/oqEwC\nCGf20XXASCAF2A708PbFlAOHXONm/8GsgCMxpgjZ2fD4466A3ahRsHOn228JwURIOC2FLFUd6Hsk\nPkuu5PJf7WpJAUdiTCGWL4drroG5c+Hcc+HZZy0ZmIgLJynME5E1wKu4q4/3+RyTMYlnxw53zUG1\naq5m0aWX2gCYCUSx3Ueqegzwd9xFZstEZJqIxFzLwSYfmai0YYP7Wb8+vPgirFoFAwdaQjCBCeuK\nZlWdraojgK7AXtziOzHJBppNVPjlF7jlFmjVyg0oAwwY4JKDMQEqtvtIRGrgCtkNBNrjLjg7yee4\njIlfn33mCtd9952bXdS9e9ARGZMnnDGF5cDbwMOq+qXP8RgT3266yS2Pecwx8OmnrpidMVEknKTQ\nUlVzfI/EZ3ZFs4kKKSmu22j0aDeobEyUKTQpiMhjqvpXYKqIHPaRGs7Ka9HIxu9MRP30k7sK+YIL\n4OKLYeTIoCMypkhFtRRe9X6WaMU1YwyuaTp5MowYAXv3uummxsSAolZem+v92l5Vf5MYvEJ3ZV2Z\nLaLU+o9MpKSluQJ277zjksELL0DHjkFHZUxYwpmS+scC9l1T3oFEivUeGd+99x588okrV/HVV5YQ\nTEwpakzhUtw01BYi8kbIXTWBPX4HZkxMWbcO1q6Fs892pSr69oVmzYp/nDFRpqgxhbnATtyKaWND\n9u8DFvkZlDExIysLnngC7roLGjaEb7+FpCRLCCZmFTWmsAHYAMyMXDj+sREFU+6WLnWtgvnzoX9/\neOYZlxCMiWFFdR99rqqnishufvuZKoCqal3fo/ODDSqY8rBiBXTrBnXquOUxL77Y5jubuFBU91Hu\nkptWjMWYXDt2uPpEHTrAI4/AlVdavSITVwqdfRRyFXMzoKKqZgM9gT8D1SMQW7myGammTNLT3YVn\nLVq4QWURV7LCEoKJM+FMSZ2GW4rzGODfQGvgFV+j8pFVSTUl9vHH0KmTm2I6aBAceWTQERnjm3CS\nQo6qZgIXAk+p6s1AE3/DMiYKZGe7aqZnngmVKsHnn7vB5COOCDoyY3wTTlLIEpGLgUHAO94+m2Jh\n4l/FipCTA7fdBkuWQO/eQUdkjO/CvaL5dFzp7PUi0gKY7G9Y5U9tUqoJx/btcPnlbropuBIVY8ZA\n1arBxmVMhISzHOdyYAQwX0TaAZtV9QHfI/OJzRo0BVKF//7XzSqaOhUWeddn2h+MSTDFJgUROQVY\nB7wATATWisjJfgdmTMRs2gTnnOMGkdu2hcWL4eqrg47KmECEs8jO48DZqroSQETaA/8BUv0MrNxZ\n75EpzBNPwBdfwJNPwvXXu7EEYxJUOEkhOTchAKjqKhFJ9jEmX1lngAFgzRr45Rc4/ni3Ctpf/uKu\nQTAmwYUz0LxQRMaJSC/v9ixWEM/EqqwsN3DcpQsMH+721axpCcEYTzgthWtxA83/521/CTzlW0TG\n+GXxYlfAbuFCuPBCeNoWFTQmvyKTgoh0Ao4B3lTVhyMTkj9sSCHBzZwJ/fpBvXrw+utw0UVBR2RM\nVCq0+0hE/oYrcXEF8JGIFLQCW8wRm2KYWNLT3c9eveCWW2DlSksIxhShqDGFK4DOqnoxcAJwXUmf\nXET6isgaEVknIqOKOO4EEckSkQElfQ1jCrR/P4wY4WoW7dsHVarAgw9C3dis+G5MpBSVFA6qajqA\nqv5UzLGHEZGKuBXb+gEdgMtEpEMhxz0EfFiS5y8pq5KaQD78EI491o0ZnHOOXYBmTAkUNabQMmRt\nZgGOCV2rWVUvLOa5uwPrVHU9gIhMAfoDK/Md9xdgKq414jv7fIhj6eluRtGkSe4itC++cN1Gxpiw\nFZUU8ne8lnSqRhNgc8h2GnBi6AEi0gS4AFdbqdCkICLDgGEAKSkpJQzDJIzKlWH1arj9drj7btdl\nZIwpkaLWaP44Aq//BHCbquYUNQCsquOB8QCpqanWEWR+9cMPcOed8NBDbmbRl1+6MtfGmFIp0ThB\nCW3BrdqWq6m3L1QqMEVENgIDgGdE5A9+BGNVUuOMqusm6tDBFbL7+mu33xKCMWXiZ1KYB7QWkRZe\nWYyBwFuhB6hqC1VtrqrNgdeB61V1mo8xWZmLeLBxI/TtC0OGQMeObq2Dc84JOipj4kLYX6tEpLKq\nHgz3eFXNEpHhwAdARWCiqq4QkWu9+8eVOFpjwNUpmj0bxo6Fa6+FCn5+tzEmsRSbFESkO65sdi0g\nRUS6AENV9S/FPVZVZwAz8u0rMBmo6uBwAjYJavVqtwxm48bw1FNuGtnRRwcdlTFxJ5yvWE8C5wI7\nAVR1CW62UEyx6xRiVGYm/OMfroDdbbe5fc2bW0IwxifhdB9VUNXv880OyvYpHt/ZdQoxZOFCV8Bu\n8WIYMAAefTToiIyJe+G0FDZ7XUgqIhVF5CZgrc9xmUT38svQvbubcjp1Krz2GjRsGHRUxsS9cJLC\ndcBIIAXYDvSgFHWQgma9RzEiM9P9PO00GDbMFbC7sLiL540x5aXY7iNV/RE3nTROWP9RVNq3D0aN\ncgPKM2dCkybwzDNBR2VMwgln9tHzFPBFW1WH+RKRSTzvvQd//jOkpcGNN7rWQnLMrvhqTEwLZ6B5\nZsjvVXC1ijYXcqwx4du92yWB//wH2reHr76Cnj2DjsqYhBZO99Grodsi8h9glm8R+URtTmr0ycmB\nTz6Bu+6CO+5wBe2MMYEqTaGYFkDMTgOxKakB27YNnngCHnjAFbBbuxaqVQs6KmOMp9jZRyKyW0R2\nebc9wEfA7f6HZuKKKkyc6ArYPfmkuwYBLCEYE2WKbCmIu2KtC79WN83RGO2Hicmg48WGDW566cyZ\n0Ls3PP88tGkTdFTGmAIUmRRUVUVkhqoeG6mA/Ga9RxGWkwPnngubN8Ozz7rkYAXsjIla4YwpLBaR\n41V1ke/RmPixerWrUVSlCvz739CoETRrVuzDjDHBKvQrm4jkJozjgXkiskZEForIIhFZGJnwTMw5\ndAjuv98VsHv4Ybeve3dLCMbEiKJaCnOBrsD5EYrFXzao4L/5810Bu6VLYeBAuC7mqqEYk/CKSgoC\noKrfRSiWiChqLWhTBk8+CTffDEcdBdOnw/nx8V3CmERTVFJoICIjC7tTVf/pQzwm1qi6iz9OOMG1\nEh5+GGrXDjoqY0wpFZUUKgI1iJMJO2r9R+Vr71636E1Skmsl9OxpJSqMiQNFJYVtqnpfxCKJkLjI\ncEF79123NvLWra7LKLe1YIyJeUVNGLf/5ea3duyAK6901x3UqgWzZ7vV0CwhGBM3ikoKZ0QsChMb\ntm6FN9+Ee+5xZSpOPDHoiIwx5azQ7iNV3RXJQPwWm8U5osCWLW45zBEjoHNn2LTJFbIzxsSlhKs3\nYD0dYVJ1NYo6dHArom3a5PZbQjAmriVcUjBh+O47OOMMV6eoWzdYtgxSUoKOyhgTAaVZTyEmWfdR\nmH75BXrGyRHPAAASlUlEQVT0cOUqxo+HoUOteWVMAkmYpJBLbFJVwTZuhKOPdusbTJjgWghNmwYd\nlTEmwqz7KNEdOgSjR7v1DV5/3e3r398SgjEJKuFaCibE3LmuNMXy5XD55XD66UFHZIwJWMK0FGxI\nIZ/Ro11Zit274e234eWXoX79oKMyxgQsYZJCLhsz9TRtCn/6E6xY4a5QNsYYfE4KItLXW5xnnYiM\nKuD+K0RkqYgsE5HZItLFz3gS2s8/uymm48e77WuugXHjXLkKY4zx+DamICIVgbHAWUAabvW2t1R1\nZchhG4BTVXW3iPQDxgO+1E7QRJ6T+vbbroDdDz/YALIxpkh+thS6A+tUdb2qHgKmAP1DD1DV2aq6\n29v8GrBPrPL0449w2WVuwZt69eCbb+Duu4OOyhgTxfxMCk2AzSHbad6+wlwDvFfQHSIyTETmi8j8\nn376qRxDjHNffunqFt13n1sqMzU16IiMMVEuKqakisjpuKTQq6D7VXU8rmuJ1NTUBO4HCsPmzS4B\nXHABXHghfPutuyjNGGPC4GdLYQvQLGS7qbfvN0SkMzAB6K+qO/0KJu4zSU4OPPccdOzoZhWlp7up\nVpYQjDEl4GdSmAe0FpEWIpIMDATeCj1ARFKAN4BBqrrWx1hCXjMSrxJh334Lv/udG0zu3t1dlFa9\netBRGWNikG/dR6qaJSLDgQ9w6z1PVNUVInKtd/844G6gHvCMuE/rLFW1ju+S2LwZunSB5GR44QUY\nMiROM58xJhJ8HVNQ1RnAjHz7xoX8PhQY6mcMv75WJF4lgnbscFcgN2sGjzzixhAaNw46KmNMjEu8\nK5pjvUrqwYNuWmlKCixa5PbdcIMlBGNMuYiK2UcmTHPmuCuRV62Cq66yhW+MMeUu4VoKMUkVRo6E\nk0+G/fthxgx48UVbGtMYU+4SKCnE8KCCiEsM11/vCtj16xd0RMaYOJVw3UcxMzFnzx645RYYPBh6\n9YJ//jOGgjfGxKoEainEkGnToEMHmDQJFi50+ywhGGMiIGGSQkxMSd2+HS65xE0vPfJIV8BuxIig\nozLGJJCESQq5ovoL9/PPw/Tp8MADMG8edOsWdETGmASTcGMKUWfTJti6FXr0gFtvhYsvhrZtg47K\nGJOgEq6lEDVycmDsWFfA7o9/dNuVK1tCMMYEKmGSQlQNKaxZA6eeCsOHQ8+e7rqDCgnzT2GMiWIJ\n130UeJmLefPglFOgWjU3u+iqq6J8oMMYk0js62mkpKe7n127uquTV66Eq6+2hGCMiSoJkxT2/JIJ\nQGZ2TmRfOCMD7rgD2rRxlU0rVoR//AOOOiqycRhjTBgSpvuoVtUkAJIqRjAPzp7tCtitXu1aBRUr\nRu61jTGmFBKmpZArIr01hw65i8569YJffoH333fjB3XqRODFjTGm9BIuKUREUpJrHdxwAyxfDn36\nBB2RMcaExZJCedm1y62RnJbmmiMzZsBTT0HNmkFHZowxYbOkUB6mTnUF7CZMgM8/d/sqJcxwjTEm\njlhSKItt2+Cii2DAALcc5vz5cMUVQUdljDGlZkmhLEaNgnffhTFjYO5cOO64oCMyxpgysT6Oktq4\n0dXhbtHCJYO//c3qFRlTiMzMTNLS0sjIyAg6lIRRpUoVmjZtSlJSUqkeb0khXLkF7G6/3dUtevdd\naNTI3YwxBUpLS6NmzZo0b94csav3faeq7Ny5k7S0NFq0aFGq57Duo3CsXg29e7trD045BZ55JuiI\njIkJGRkZ1KtXzxJChIgI9erVK1PLzFoKxXn3XTeYXL06vPQSXHml1SsypgQsIURWWd9vaykUJtPV\nSqJnT1fJdOVKGDTIEoIxJq5ZUsjvwAE3q+jkkyErC+rWhfHjoWHDoCMzxpTStGnTEBFWr16dt++z\nzz7j3HPP/c1xgwcP5vXXXwfcIPmoUaNo3bo1Xbt2pWfPnrz33ntljuXBBx+kVatWtG3blg8++KDA\nY5YsWULPnj3p1KkT5513Hnv37s2L6eqrr6ZTp060b9+eBx98sMzx5GdJIdSXX7pppQ89BJ07w8GD\nQUdkjCkHkydPplevXkyePDnsx9x1111s27aN5cuXs3DhQqZNm8a+ffvKFMfKlSuZMmUKK1as4P33\n3+f6668nOzv7sOOGDh3KmDFjWLZsGRdccAGPPPIIAK+99hoHDx5k2bJlLFiwgOeee46NGzeWKab8\nbEwBYP9+uO02N4DcogXMnAlnnBF0VMbEldFvr2Dl1r3l+pwdGh/BPed1LPKY/fv3M2vWLD799FPO\nO+88Ro8eXezz/vLLLzz//PNs2LCBypUrA9CwYUMuueSSMsU7ffp0Bg4cSOXKlWnRogWtWrVi7ty5\n9OzZ8zfHrV27lt69ewNw1lln0adPH+6//35EhPT0dLKysjhw4ADJyckcccQRZYopP2sp5Hr/fbjp\nJli2zBKCMXFk+vTp9O3blzZt2lCvXj0WLFhQ7GPWrVtHSkpKWB+4N998M8cdd9xhtzFjxhx27JYt\nW2jWrFnedtOmTdmyZcthx3Xs2JHp06cDrnWwefNmAAYMGED16tVp1KgRKSkp3HLLLdStW7fYGEsi\ncVsKO3fCww/D6NFQo4ZLBtWqBR2VMXGruG/0fpk8eTI33ngjAAMHDmTy5Ml069at0Fk6JZ298/jj\nj5c5xvwmTpzIiBEjuP/++zn//PNJTk4GYO7cuVSsWJGtW7eye/duTjnlFM4880xatmxZbq/ta1IQ\nkb7Av4CKwARVHZPvfvHuPxv4BRisqgv9jAlVeO01GD7cVTY980w46yxLCMbEoV27dvHJJ5+wbNky\nRITs7GxEhEceeYR69eqxe/fuw46vX78+rVq1YtOmTezdu7fY1sLNN9/Mp59+etj+gQMHMmrUqN/s\na9KkSd63fnAX9zVp0uSwx7Zr144PP/wQcF1J7777LgCvvPIKffv2JSkpiSOPPJKTTz6Z+fPnl2tS\nQFV9ueESwXdASyAZWAJ0yHfM2cB7gAA9gG+Ke95u3bppabyzZKuecP2Luq/fuaqg2q2b6uLFpXou\nY0x4Vq5cGejrP/fcczps2LDf7Ovdu7d+/vnnmpGRoc2bN8+LcePGjZqSkqJ79uxRVdVbb71VBw8e\nrAcPHlRV1R9//FH/97//lSme5cuXa+fOnTUjI0PXr1+vLVq00KysrMOO2759u6qqZmdn66BBg/SF\nF15QVdUxY8bo4MGDVVV1//792r59e12yZMlhjy/ofQfmaxif3X6OKXQH1qnqelU9BEwB+uc7pj/w\nkhfz10BtEfGlbkSnJrV4Z9ZTVP90pus2+vpr6NLFj5cyxkSJyZMnc8EFF/xm30UXXcTkyZOpXLky\n//3vfxkyZAjHHXccAwYMYMKECdSqVQuAv//97zRo0IAOHTpw7LHHcu6555Z5ULdjx45ccskldOjQ\ngb59+zJ27Fgqesv0Dh06lPnz5+fF3aZNG9q1a0fjxo0ZMmQIADfccAP79++nY8eOnHDCCQwZMoTO\nnTuXKab8xCWQ8iciA4C+qjrU2x4EnKiqw0OOeQcYo6qzvO2PgdtUdX6+5xoGDANISUnp9v3335cu\nqCVLoGpVaNOmdI83xpTIqlW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"text/plain": [
"<matplotlib.figure.Figure at 0x7f08fb008be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Use best hyperparameters:\n",
"dual_svc = (penalty_svc == 'l2') # 'dual' option must be set to false if penalty is 'l1'\n",
"svc = LinearSVC(C=best_c_svc, penalty=penalty_svc, dual=dual_svc)\n",
"# Train on full undersample data set:\n",
"svc.fit(X_undersample, y_undersample)\n",
"# Test on unseen test data set:\n",
"y_pred_score = svc.decision_function(X_test.values)\n",
"# Compute ROC metrics:\n",
"fpr, tpr, thresholds = roc_curve(y_test.values, y_pred_score)\n",
"# Get AUC:\n",
"roc_auc = auc(fpr,tpr)\n",
"\n",
"# Plot ROC:\n",
"plt.title('ROC Curve - SVC')\n",
"plt.plot(fpr, tpr, label='AUC = %0.2f' % roc_auc)\n",
"plt.plot([0,1],[0,1],'r--')\n",
"plt.legend(loc='lower right')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "e269cd92-dc59-a611-6ef5-0277884a4e7f"
},
"source": [
"# 4. Decision Tree Classifier"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"_cell_guid": "bef3d4f4-4138-b410-e41e-1babe77eaf12"
},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "2a8e9ff8-7a68-ded6-3178-baba32a98ca7"
},
"source": [
"## 4.1 Omit Hyperparameter Tuning, Use Cross Validation to Get Mean F1-Score"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"_cell_guid": "8342274d-68ea-5479-a2fa-4b4bca3c08ee"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy Score: 0.894 (+/- 0.024)\n",
"Recall Score: 0.914 (+/- 0.017)\n",
"Mean F1: 0.897 (+/- 0.021)\n"
]
}
],
"source": [
"dt = DecisionTreeClassifier(random_state=0)\n",
"acc_score = cross_val_score(dt, X_undersample, y_undersample, cv=5)\n",
"recall_score = cross_val_score(dt, X_undersample, y_undersample, cv=5, scoring='recall')\n",
"f1_score = cross_val_score(dt, X_undersample, y_undersample, cv=5, scoring='f1')\n",
"print(\"Accuracy Score: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
"print(\"Recall Score: %0.3f (+/- %0.3f)\" % (np.mean(recall_score), np.std(recall_score)) )\n",
"print(\"Mean F1: %0.3f (+/- %0.3f)\" % (np.mean(f1_score), np.std(f1_score)) )\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "1cb4dc32-5f6f-686e-b3de-bbc9d02b3f60"
},
"source": [
"## 4.2 Train Model on Undersampled Data & Evaluate on Full Test Data"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"_cell_guid": "cf67a52d-685c-c331-3048-4cb8e280a851"
},
"outputs": [
{
"data": {
"image/png": 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cjK/JWOz/rSmPTZvc/ESdOsGDD8LFF9t8RSauFFl9FHYXcyugqqrmAr2AvwB1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\nAHJ6OkyZAn37Ris0YxLCqHf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"text/plain": [
"<matplotlib.figure.Figure at 0x7f08e9808cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Train on full undersample data set:\n",
"dt.fit(X_train, y_train)\n",
"# Test on unseen test data set:\n",
"y_pred_score = dt.predict_proba(X_test.values)[:,1]\n",
"# Compute ROC metrics:\n",
"fpr, tpr, thresholds = roc_curve(y_test.values,y_pred_score)\n",
"# Get AUC:\n",
"roc_auc = auc(fpr, tpr)\n",
" \n",
" \n",
"# Plot ROC:\n",
"plt.title('ROC Curve - DecisionTree')\n",
"plt.plot(fpr, tpr, label = 'AUC = %0.2f' % roc_auc)\n",
"plt.plot([0,1],[0,1],'r--')\n",
"plt.legend(loc='lower right')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "0ad95cd4-8c1e-2f06-2fba-149f4be541bd"
},
"source": [
"# 5. Random Forest Classifier"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"_cell_guid": "1bf91508-e637-7923-aa9c-6290aaac1e09"
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "d871ac79-4584-90b3-6ebe-b7e40a7bf760"
},
"source": [
"## 5.1 Omit Hyperparameter Tuning, Use Cross Validation to Get Mean F1-Score"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"_cell_guid": "b6f3348d-b58f-8f95-67be-6529176db193"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy Score: 0.940 (+/- 0.021)\n",
"Recall Score: 0.911 (+/- 0.027)\n",
"Mean F1: 0.939 (+/- 0.021)\n"
]
}
],
"source": [
"rfc = RandomForestClassifier(random_state=0)\n",
"acc_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5)\n",
"recall_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5, scoring='recall')\n",
"f1_score = cross_val_score(rfc, X_undersample, y_undersample, cv=5, scoring='f1')\n",
"print(\"Accuracy Score: %0.3f (+/- %0.3f)\" % (np.mean(acc_score), np.std(acc_score)) )\n",
"print(\"Recall Score: %0.3f (+/- %0.3f)\" % (np.mean(recall_score), np.std(recall_score)) )\n",
"print(\"Mean F1: %0.3f (+/- %0.3f)\" % (np.mean(f1_score), np.std(f1_score)) )"
]
},
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "fdc4efd8-40b2-1415-e240-09ddc56c2818"
},
"source": [
"## 5.2 Train Model on Undersampled Data & Evaluate on Full Test Data"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"_cell_guid": "3523c4b3-4554-3568-fd0f-558cf5ed4e3b"
},
"outputs": [
{
"data": {
"image/png": 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OBh5V1RuBFH/Dqn7F6nQYU57cXFfN9OSToV49+PJL15nctGnQkRnjm3CSQo6I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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f08e8c4c908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Train on full undersample data set:\n",
"rfc.fit(X_train, y_train)\n",
"# Test on unseen test data set:\n",
"y_pred_score = rfc.predict_proba(X_test.values)[:,1]\n",
"# Compute ROC metrics:\n",
"fpr, tpr, thresholds = roc_curve(y_test.values,y_pred_score)\n",
"# Get AUC:\n",
"roc_auc = auc(fpr, tpr)\n",
" \n",
" \n",
"# Plot ROC:\n",
"plt.title('ROC Curve - RandomForest')\n",
"plt.plot(fpr, tpr, label = 'AUC = %0.2f' % roc_auc)\n",
"plt.plot([0,1],[0,1],'r--')\n",
"plt.legend(loc='lower right')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.show()"
]
}
],
"metadata": {
"_change_revision": 6,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/164/1164152.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "eb4de64d-63cc-b64a-e9c0-5a7830f6202a"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"train = pd.read_csv('../input/train.csv')\n",
"test = pd.read_csv('../input/test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "b47c08d7-20fe-0c24-fda9-fc6bc8182fdf"
},
"outputs": [],
"source": [
"million1 = np.where (train.price_doc==1e6, 1, 0)\n",
"million2 = np.where (train.price_doc==2e6, 1, 0)\n",
"million3 = np.where (train.price_doc==3e6, 1, 0)\n",
"basedate = pd.to_datetime(\"2010-08-19\").toordinal()\n",
"time = pd.to_datetime(train.timestamp).apply(lambda x: x.toordinal()) - basedate\n",
"times = pd.DataFrame({\"time\":time })\n",
"testtime = pd.to_datetime(test.timestamp).apply(lambda x: x.toordinal()) - basedate\n",
"testtimes = pd.DataFrame({\"time\":testtime })"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "19374122-01be-39af-0b59-567e4178abca"
},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"logit = LogisticRegression(max_iter=1000)\n",
"\n",
"logit.fit(times, million1)\n",
"p1train = logit.predict_proba(times)\n",
"p1test = logit.predict_proba(testtimes)\n",
"\n",
"logit.fit(times, million2)\n",
"p2train = logit.predict_proba(times)\n",
"p2test = logit.predict_proba(testtimes)\n",
"\n",
"logit.fit(times, million3)\n",
"p3train = logit.predict_proba(times)\n",
"p3test = logit.predict_proba(testtimes)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "993d6bfe-9ca2-8754-de09-60e21cb504b9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0246604369554\n",
"0.0131234830445\n",
"0.0252902034333\n",
"0.0202878764523\n",
"0.0110977245936\n",
"0.012630247156\n"
]
}
],
"source": [
"print(p1train[:,1].mean())\n",
"print(p1test[:,1].mean())\n",
"\n",
"print(p2train[:,1].mean())\n",
"print(p2test[:,1].mean())\n",
"\n",
"print(p3train[:,1].mean())\n",
"print(p3test[:,1].mean())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "568e9f1b-d83e-3474-6cc2-dcebfc4be418"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 550,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/164/1164155.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"_cell_guid": "b934473c-1fe1-7a54-3128-535411714f31"
},
"source": [
"This notebook explores the .tif files based on the notebook: https://www.kaggle.com/fppkaggle/making-tifs-look-normal-using-spectral-fork/notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"_cell_guid": "71f263cf-564c-1e80-a7e9-320625329853"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"sample_submission_v2.csv\n",
"test-jpg-v2\n",
"test-tif-v2\n",
"train-jpg\n",
"train-tif-v2\n",
"train_v2.csv\n",
"\n"
]
}
],
"source": [
"import numpy as np \n",
"import pandas as pd\n",
"from spectral import *\n",
"from skimage import io\n",
"import os\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from subprocess import check_output\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"%matplotlib inline\n",
"print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
"\n",
"# Any results you write to the current directory are saved as output."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"_cell_guid": "6b10a2fd-a24f-c368-20ac-3c4deae50131"
},
"outputs": [],
"source": [
"basepath = '../input/train-tif-v2'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"_cell_guid": "605b2132-8cd7-728b-1cf1-b25431613208"
},
"outputs": [],
"source": [
"tif_image = io.imread(os.path.join(basepath, 'train_213.tif'))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"_cell_guid": "6370014d-661c-1331-544f-dc9669a9ba53"
},
"outputs": [],
"source": [
"def scale(img):\n",
" rescaleIMG = np.reshape(img, (-1, 1))\n",
" scaler = MinMaxScaler(feature_range=(0, 255))\n",
" rescaleIMG = scaler.fit_transform(rescaleIMG) # .astype(np.float32)\n",
" img_scaled = (np.reshape(rescaleIMG, img.shape))\n",
" return img_scaled\n",
"\n",
"def ndwi(image):\n",
" return (image[:, :, 2] - image[:, :, 0]) / (image[:, :, 2] + image[:, :, 0])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"_cell_guid": "b1b9104d-826a-8997-5d39-57e200013255"
},
"outputs": [],
"source": [
"def calculate_ndvi(image):\n",
" \"\"\"\n",
" Calculate normalized difference vegetation index as follows: (NIR-R)/(NIR+R)\n",
" Input image is in the format (NIR, R, G)\n",
" \"\"\"\n",
" return (image[:, :, 0] - image[:, :, 1]) / (image[:, :, 0] + image[:, :, 1])\n",
"\n",
"def calculate_nwvi(image):\n",
" \"\"\"\n",
" Calculate normalized difference water index as follows: (G-NIR)/(G+NIR)\n",
" Input image is in the format (NIR, R, G)\n",
" \"\"\"\n",
" return (image[:, :, 2] - image[:, :, 0])/(image[:, :, 2] + image[:, :, 0])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"_cell_guid": "7cefa15d-75b6-31f4-9634-f56b72a6f907"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-0.64620708 -0.61404804 -0.64696179 ..., -0.08567782 -0.09723438\n",
" -0.12993439]\n",
" [-0.59258377 -0.60934832 -0.64268934 ..., -0.08312351 -0.08922028\n",
" -0.10483405]\n",
" [-0.55659178 -0.60568732 -0.63023793 ..., -0.09236684 -0.09652823\n",
" -0.09617846]\n",
" ..., \n",
" [-0.40093921 -0.43867698 -0.44076132 ..., -0.36296032 -0.39482716\n",
" -0.41896656]\n",
" [-0.39008329 -0.42150767 -0.43093556 ..., -0.37194927 -0.37042462\n",
" -0.38110279]\n",
" [-0.41886856 -0.41449818 -0.42542998 ..., -0.38399311 -0.35288765\n",
" -0.35929043]]\n"
]
}
],
"source": [
"img2 = scale(get_rgb(tif_image, [2, 1, 0])) # RGB\n",
"img3 = scale(get_rgb(tif_image, [3, 2, 1])) # RG NIR\n",
"img4 = scale(get_rgb(tif_image, [3])) # \n",
"img5 = ndvi(tif_image, 2, 3) \n",
"img6 = ndwi(get_rgb(tif_image, [3, 2, 1]))\n",
"print(img6)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"_cell_guid": "f75fd72b-b52a-f59a-781b-8cf6a11dde31"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fac1044f518>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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P0sr0CgRq/bkTCYb94I5y83kwGIUPDCeehWHIl+Bwez8whAjl0iwLUYDDV4vHo4RyFpSH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kKQrZ4n+LjcFVzeFdq//fSM0LIAaEZTF5/pYUxLLxl87H18hwaIS6It43EfDN+FAQS13EkhYTzj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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fac104cd9b0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img6*255)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"_cell_guid": "84fe4fe9-a14e-3139-2c6e-32394166e328"
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'water' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-22468024cd01>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwater\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mNameError\u001b[0m: name 'water' is not defined"
]
}
],
"source": [
"plt.imshow(water)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"_cell_guid": "e0945e00-8fa1-4a2c-65f4-1134b7b03a24"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fac05182860>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JNoZPUFJcOMUR7SzFSMkmfpAMcehDGFEptmNlI6vwdjbqE1fAZ7LBBfeODOZ76DDG+aQc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LXzxEZHZkSlt/WFWy2h7zv4dHRGJ4L54JYzLpMlKx+3hZdcae09JunTSG6IbMzu0bcgEtASEbI4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\nDAyFQmGFMjAUCoUVysBQKBRW+C+S5OLjGVu2XAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fac104cda20>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img2/255.)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"_cell_guid": "8efd5e81-2487-c3c3-3a88-c90e4c285ce4"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fac050a0710>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7PHLLBobPWIm/RhVGFdm2YHVlTnzgbrnLGz4PVww6taDgGbSIsTZ64dodWYrh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PwHwN8p///3n3z8v1ZK/afI8PEnwP/5Z30zBTQq0SnNPmd0zuxSy4QRyArSPgxZcUqWbepxKrBU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\nrU+tu8Bwt+7W3frU+ldyp8073VAfLQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fac051b8978>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img5)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"_cell_guid": "c35ce98b-1747-bd73-8c2d-f209359b551a"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fac0502d3c8>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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AwMCAzMxMtm7diq2tLXV1dURGRmJiYkJ0dDTDhw9n7NixPHr0iLi4ODIzM3n58iWTJ09G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\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cOXPw9/dn79693L59m8jISExMTBQE/ebNm5W0pqNHj+Lh4UF9fb0ShDNv3jxyc3Pp0aMH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\nxGDGjJnbMCcGM2bM3IY5MZgxY+Y2/h8RkMhaONM0QQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fac050bf4e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img4)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"_cell_guid": "8c3c0986-9e90-7d67-a049-13c74108ada8"
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7fac04fb2cf8>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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NgyfywHEte/ePpX7/Gt7/XYi2exQGXdtSrjSVUVdWoKJoR8VrT+JyVfjLLYyp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w2+wXNwXxWNmyc6kL9yISpKh4F6BpillWFqpoP1CrxorN2Ge5xzmFJ1ws+u3Sg4GwzVJcDG6mCs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18MkxA7ygCzL/5FuGI1Zil4oWPnZhT4XcSTNin4JkvDKeMISmoHYCORhFMhElhl4AaxZDOM\nnrkCe8RssL/IAiPD8zD16RmsO78Q3o2lWPU7DZ0R5zBafTw+StsiOewFukYEoV7pCDYvLsEPsRSk\nWgdBO3I6bJRdMUlXD6Mm3EXArEh0xaug/qYJtg+yRl+gNLK6VRAuRGSsWY/VP/fg2M0iLGgcB/NV\nq9Aa+wLhJmfhde0m3q9pgHNILkSerMbFLF10D07AimmNWCpyFIkxq5EWsxk7orvw5M80xPeUwnnw\nHlSrjoCt6l4IzQ3AiDxVKHtuxqR2Pdgod2Kd5zA0LxOG1nUn3N+mg0a3i3AR14dWYRJ8OzyxO04C\niUJAc4csHuVlQeyrMybMzsTBXF3YzdqDz1faoXFgM/SaNuFniRomDZ+Hbb/NUNGeiRzPEMQLb0R6\n5n5o1Abh+Qpj5ERE4eL8FEwuEkdlZh0GF9Si0ecHykyTkRRSjd67EigX0sWfvXFYpHQBRtKNyFm8\nA9q94Sg9lQvnHaI4626A3ANhqBaNwrLh6Rh/NAJThdQx9/cFXFROgkPQUYSEGcD4Uz8sh2qhens0\npozyBiqyEW1tDa292rhkcBaBK4ohM9gQfYJ5GN0XDsubevhSDjza0onaugwcNPOGmIUvskeko1ch\nCImfvNG6gLjhGAWvjYsxRdcFDzM70PzcEo4Ft5HrGY0tV6djsP00WCg4wV48GKOTN+MK5mNivBVy\n7dLxZv0GFCmvwfhxw+F7VQWddTow8TbBmwvm2LSmATNEW7CtWx8/k9SRbOaGbEUg5rw0Qq7cgqxW\nBnacSID2Hz/c7JXAlDxpDGgdwWpdKziu9MHj9XH4Njcd2lIjccFpKk6e24ITZ4RwvyUZUze7o2Rq\nCUY/E8YLD3W87pCA/ZssGIp0wbflNrJHp6Ltxmm83XIGfa/GYWGdMOKXbMde747/LMUAaQ2KZyrT\nybuW+yZncbdWKC9ND+F06yiOlchmtfpnhixL4WT1Kzw9J4aRJ08zZ/lbiilVsMrLnR0LhelRrckN\nXic4Z/QpbqgXppiRPJWl5Hil2JUzL6RwhYYoDYrDOSa3iJ1Nu/lxoyjXBOpz96toBh3bx6y2KHrI\n+nD1UD8uatlHkbYabhIvYPr9SrYF67Dz8jiWVXpRzc2dP5w2U7DRlzJn7PirWoQv/DWZ4rOP29/M\n5sG396nn5UfPkYMYsecvPX5O5UyDSrZPC+H9gnQ2V9fw36TXHHGwhfL3Pdk+dB9t7kjx3bH59Bdf\nRcuRtlwb1cSIvyM4KeUPOxakU1pvKNtljKgR1sTdKl4UL+nk97GTeWSaERWX3OLJbFAsPpomCqt5\nWSGEBbFttDvrya0v/vH1aVA235q+93RoFPWQryYcpu2FqTw1r5U3L36khEYePxY95sevJhxuJc6n\nxz4y4sV7ah42otzrFbw88Ra/S4rTpCONwX1pPNh5gctqbjDr9nsm2Z9gpfZ1nlgbw9wgBx5dtpwd\nT4wZr3+b36eNo/PmKlYp7uaSPkfqXBlP+fYPfJ3qydJZzZQ4vpN/vDJ4ef0ltng60EquhdfFUhlV\n+pKie6Zw28BKprmN4eMAC6oZV7L9eD4PXTjLKwat3P7gLNvlLlD6LMglBbwY1c8aoVjK37jNlqwH\nFBmlxId/b7HxZxBDrDy5fXckx5i3coLSGO6XF2ND3BjuaH5Iz1BFSrQ40FVzOZ/tr6BBjxGLvVq5\n3fgDX3Wf5a6213zTdJOiEe/pp1PB+tz3XPOllb9MNnCsxHm2mIiyPVuEYvLPqRyrycphGjRO0aDi\nrGQGzx7J5P05NMh6SUU7MZpbFLDxyy6WfFjPweefU1V3Nj/f/02Zg440fKXAZs9JrDk9mGMs5aib\n38P5EYN5I6WRRmYPGTFqEkOUNlLlawRXLgJ3XfWj/PLj/PhoAieMu8nBFu/54ekfijwN4M/vojTR\nDaH2JjE+vKjJ0RNmMuWVEu0Guf6fn5X43x0i0q2wrRuHA0kZ8BAwx8edUSjOccMWvxBY57WhxOw4\nEtVL8Hb2eVgla0BgbCZ8a8fBhB0QUIlHvkMi6ppqcLUpDp8L1kJ4RSdkNVUhwW4ovm2Ct6IenpQn\nItttNlxuBeNdlSCeO6Yjzfs1FDujYHv2B/xzx2FlsQR+B7kj1mIGTuWZY75PLVrntuPGL0E4pI7H\n/btP4er/HZ+WhcNuZRBu/BgHZ+da/F2YjR/rMsC9UYg8UIvfvua4+3wUrKz84fgkAY/sQqCQfBS7\nSv8hNPUJpHR9cG2NC7KuD8EpdSnk9ZXis2w+1j5+gaOJX5A4MxTfnLJxWbgM5x2DoRL6CR/DZmBG\n5AEc03dH9th1GOS0GSN+FOB2XwbOT/eBxYsiWMnvwyPrfvzaYIMlg29jq6AydkgEIfmkHZzwCDH/\ngrHngBrUR8TDO3ocvhgWo73cGuO1MhAdU4TFdRex6okqRIXy8fl3AcqWFkHNuwsXU29DfUEchIo0\nofGqHYdSMyA7IgqywrJIMtHFkI9bEDsyAqe74mE1Igx//0jhVfQARh8djvK136E65juurytF83N7\nZGcY4bDKTWy5b4HVc4RxonknRBYch+RV4mq0EIwNZLHc3hYW9oAmw1DsqYRCwRpIvUuD790olOsD\nSW+tcFVCAyFCNRhsIIGUlYkYfFoaW1LdMVGtHfu2BKE1KwoHlKXQOTgBtZk12FbQCRm3Ggwt3oN3\nkIOr4kV0H7DAEnd/ZE2OwHm5OmR03IJ1ZzBSlVVw+sc43BRfBSktKQhtS4dHbRWyK2VQnRsEUbt4\nKKX342lIB9xMBSC6IAgW7kUodxfAJu1PWPsoHXv2A0l+R6F/PhJ2c8+BfRvh9e8JHtoNYJunMpaE\nd2GbaTGiKhZiUIIniiIkcdxkNbQWemPUdEmcmyWI5xrT8fphNDxi6pElcB0v+q1wWSIKCWdHQFFG\nF9r7q5DXuhGBCroY7VMCrxVx8EgF6qMzsGuWICYrL8KdLB8YGcshLdcWNw3+G6D8v60WSGLKlCnc\nNlOCAYukme98mRaWJewo6Obl5tHsFzvAvV80GXTyLjuXj+f6pz28JD2Tv32EmTA5jOPDzCkfIMeG\n86U0ibZj08A9Cs1/Q/czfdx5spn3v6ZyYasHHX0dWHHEl89kLjDRTokilQGc5aTBa4576REvzzCN\n4wyv7+HRqT+54tZdrlBQoV/VOHZ98ORWcyc2yk/jJVkhjqpNZGmXEL3Uw6kmcIkHl7zltHsHqe/Q\nx60Sfbz8cRKrfJfxodohjji4iwXaDnyj+pzXDiswMeAOFUTLeXbVcQba+bHXaz0f2Q9QZtp3zl1h\nQ4faHVTt9eKcZFtO1FjCeTppVG6eTJve3RSfnMkzJ7Oo8X0tb5cr8c0xLd4TteCrzeksNfFleXgt\n/cx8OQfplBh/i03vP9K8qJKrKvspM96Gb+51EAlKzP5+iNlzlbjoyVn2rvHk3ocS1Js7j68qg2j+\n14K9c+cwL/gPjR6854Oarzw5ejL/1kzin4GvrO/W49Vt7Rz0dg+XmefTPzGPvcd+U/GzMXsfG9Go\n+SUj/ryjWvlQ/lAU46GgV+xY30YtK2OumDaHzuMSKeEmSUfxCgorneKRty8YuieVq0JruCJxFg+U\nfOPLoTuptngH92k5cpi2MztLOzmyMpG3h4dzx1s55qCHHd2irEt7R58H3yhbpUuH2mLqnrjJ3cdL\n6H5bj+V1/Qx3OkwzuTEMEWtlyrWVnHfZmDJRc+l1tpXvMoNYXKhEa7zg19RD1Eg9RIGdZyl3XZSR\nBb/ZbTiHpb/fM+rxKW7ae4qj/vTQ7Nsx9ig85/6AAAb8HMWDRRN463ozr32axT17P7NIaSLdhXLp\nGfqAsxdsZaZaH+cWXOemr8eoMOILT4x4R1/P91RSukpfHT/er1zLk29bWDpJiUfO9TJd4S79n2dz\n63ZJHuj+Rzf7TZxx+w73nNHlY8U49szq4Cstbxa1b+Hr4F4KJBvQJ/Uzh17woIbxXSpf28ZC0XKO\nfFfGNr9xjJV/xQ9Cr9jhDU6KcmPT7Mz/PEu0tKgqjWIEuDUqm6If1nCksTwTbTR59lIqnSbk0s6w\nifudDTnzqh9LzuSwMFmEujeWsOmMOj8dTaCK6F6+Tn3KQbOj+XbDJdr4a1Jf+ho/DPlI48EvabFr\nJhtF7Xm/+QwzpnxlUXAzd4mOYI2TOq9etePi5VspKJpCh34bDosRYq6bPI26xjPGr4iHTfJJ2dWM\nGtJB73Qzyjepc8HTN4z0H89K7SbGXr7G7zqp/NHhwNwZQ1ltXc3o0incuVuU6xvOct3UHtqecOHi\nLm8q2nmz/eNe7ltaxpcLvFi+LoQu136ysLWVJTlVjOq4ztHnh1N5+3juevybQyq06W3wh8cLL3K9\nzAHKZHzh+EU7ubwgj5dfV7LfP42vM9K5Lr6eGj5C3CWuz8k7VTmnTIKB2cuZp6ZF1W1jKRWXQKsA\n8teaYHo4W7Bv7AeO3rqRSfJOjPN6QCOrI1y0Zx6znSYxJ/Atld5Mof2EQFpePs2q5zvZXfSXP6bc\nZOTuAbY9fs22v9rUk3/N1uff+PLBVw7sC6Lqt78UdVPgwr4zDPe4SqcxfZxULsb+AAv2/93NhfcO\n8aDTKc65IkOF36Novi6AW09XcvxPRy4978xXH6MZK3mZzm5+lLcQovxyQ64Ks+Olym38uXkHD8W8\nZLKuEvOinvDXVz82C3XzZO8+vhpzj5OPtdJlyWumb1fmkzYn9riv5owT31hvn0YFqcN0cdZm+Pp+\nqi804sOJ4zhTbRWvlDtydd1H3hFP5fYRu5iv9ZtLDZaz1/0gX2ZNpqRmAH8+XsJ1n+9zfGkj3eaJ\ncsHabbygd5dy/wJ5uX8qNSas5ssCMcbqDqH1qweMxxS27LxJt9xJPHF6PRd8XUdHpwBKakhwot9s\n3mzq46ItuXz97znDmyaxvsqRmep/KHDTgz71P+iwMp1Lmnv4wMmXP52fMkWsjEkxIaxf6cSTWMDr\nPg3seruTt2WFGdB9h4ZjT7JuyAJaRqrScHInM893MlDUmkoYy7id/yiU1EbrWYVcOXcem04r/+c1\nH6WEZPn8rTtcjiyAx/1gnH9Sj5dSC+F9haiXfQSTHaZIGGSGx/kqkBOKw1kfH5gcXQ3PwYdQNKYD\ni21s4HriEybZh8PPohRSltLIOSKFkD9HsdAkCMsntcKgswOCA5lwSy+E3io9fF/0BDImBTj/IRPr\np8Zj+IgnGHzOCvllPtD7tA/q9p6QejYLt/Z6IcfUBb+PbIFD22bUTBPGZ5U1WHI+AhmGZqjomoYU\nnUSUSgni1BVlbAuQRYFWO0bEOaEgeg88f8Vig5UW1GevRlq9JjKdurGvKQDa+vtQ1eKDw4vj4Nj6\nDy1SPZifMg56MeNwbKgQps28CpGqOqjcaMCb0hboNQQh4ksJ3CQHYWxMJ8RL3TFTvQurpaxRNVID\nsWHpMBEowoewDHjsNsUE080IfnISpf1hiB01ApGJ4hh+Rw8WNx9DrbsIU2y9cbGhClcQArHLvahW\ns0RNehGUPphh54oilA9+jQzlCHw8U4OtPgYwlkqG9R1x3PzhjU7HC8j2L4a5ohBe2JmCP42hFXgU\n2mbf4WIWjD0HAmEkp4qsZT+wXyQOXrOj4PRBHR1bpCEt1A6V1bMxLfcR7iYUYNS+Qyi4K43a6YBi\nVxTu27hCLVwVEx5KIP3IKihmWiL1tyWCj15CQr8QLLsSYTv3I0aKPUdhWShwYjYeSzaiRCMczVfm\nY8aJUpi804eseBTOWxZi7AgXTBePx6/LdvjZ0oGP4trQvbYBv5d6QSLLFchRxxlLX6wKicJN3Vrk\nH7yJxJFJWFlkhdEJY7C/MBP1f6KwYdRIVBcPwDxNAIUXvfDihBVEJAThfxM4J1sHywnR+Gx+G56t\nD4HcOLwt70ZS7ycMaRsO6fvzEGCojMkHQ1FWtwAeaQk4OskXv1cWonm2Lqbs3IQZtgbQ2rMdz3ru\nYtimOAwU7cezilFo/fcPHsGxWN5ZguxgAxzjKXi1ReHNsZfI2maBebOMkF5VhZDgKbARXYOfsq6Y\npPMaZbkJKGy/iG8TXeDwuR/fuxOg0Kf6n3W7UmySCp0z30FpyBEs/6MHq7guvB65EG/HmWB2YQi+\nf6rEXL+nuJdWC1Mbady7mQ6xztfQ2nYOewvCYf+qAPefzcCN1YPwdVsCMnaV4GFVAcoXN2Dh4BpM\nvLIJWHQBusNmYMSQKLQdLsCoUCl8sfqLH3PW4NlX4ojBKnzwP4bkHV0YkhuMi6OUsdBTA2FjE+Gw\nIhz35szAbI8yrPu2FXNr2/H2yGq8XDwDx+arY/SdCLznfPy8Qqx6tBCyr3Swb7saFCqnYmLrBlwf\nnobOogv4YDUK4oY5uPBGCP/OXIWP03ckTU/D+fpaJLSnotR7GOqt+pEW44flNRNgX34RoXqJSDY9\nhx8fgOVendhbHQ7NwGpcubYMS0K0IaM+DTE31mKR5WakH7oI9xIvzBvkAJVnz3DS6xtKj6hg271L\n8Bz+CkP0/RCy/jXMj2ZCQQnoHWeDL+UXIZC7CNlxrVgSHgWH7kwsV1iNA0d18e7rBnwykYCzZAA8\numfi+5RZEFPvws2G+XC26UKfbyYk5x7G5LJCePhl49ezLgRej4fxUnF0jjPFx7UC+BoUDv8rNQir\nE0BTzBpUWkeifN5cbNWzQJDzI0zZLIb1nQXI1szHt0MDyBlyGTo5WbBrq8GZuY449XQEBuvdQtTU\nDggMnYGK8jsQLquC+ApTuLnsQ+78FIgn2sFMyxUzn+rDKU0Dr6ProXXOBXV3I3FFph8pfZeRfLQL\nm+4WochSG++K2rDBUwK5EztQNlkDZ00votqoCO1FQRB/ogwlpONjzlMs3t6GyZ0FmBX8A26F+5Cd\n7oQ29SIcc9TD7J8RiLHRh6aBM24qumC7EGB0MAOemo/gZb8KSrEh+DVPEzfSSpDVYYwHWab43KSH\ng+OL8HxkMcZaz4fVQgH8WhSM8mHvoSiliXESn+FwLBCJY2UxZastdo28ApnBnbB8O4BZX8fgw049\nxKyYhZkjIhB0bO8Sk4gAACAASURBVBPmiP/DujHiGOi4gLHmSmiWj8Yn62I0HCtFict5pBgNh+kq\nU/Q+/ot/53xwZ83m/2Vi+H+i+Sje1YihY/oR+lcLz3pUELE2GSK9avDKLMBCN2uYZc+D7XRtjBFc\niMEikTh2IB6BmbtxbmgnHs8IgLaVLer3m+KomC02nc6HkUQ29qe5Qa88EamrTJFv2Y5FX/Ohe2Ms\nVkAF0vZFWJXqjvNfzuD39X5IF93D2S/ZeD9vD6o99LG/WRD3FTyh8u4vomtGoOVdD4Z6yULI4wnG\nlmsjxLYGl+ZMR01oJoxXRuGttwVUa4ow7YU3Llcpo3lSDc5E2+D5UhXUvRKA8r2FsOl3heQ0Tdxr\n6MLP4B5M7hpA4PTXuBF8A7ojjbG/dQVSvoYgMfQSHFKPQl1TDRrrOvFIzAoOtqNweWA8pEbuhd03\nQcwdU4LgxB6k7Y+Ax7If2FY8H9md7VC8GopVJsDoKz1oFImG4kAgRJOk4C7iiSPWysjZ5YyefElk\n29Qj6bgk8i7KwfvjPsRqmWGtwhqEVJhgmMRChBkKIC9aF2FYiGlTXBE2ezPO6vrhzZES/Apsg4KE\nDOzM5ZC9YA+MhsTj41IfRMWEI1xfBjUVVlgS2w8ZuGLN9DXYFxSFu5OAobO7oH9EFysD3YBd36Bx\nrxsOvmU4r70Tsw57Y9D2XSi4GgmXM8NxOS0Oikfd0FqgACH1eLx2WgOLyVJYm9j1P6g5q6gulLDd\n//6g6EZFysQiLRSkjI1KK7bSttIGJmknISaiSJgYpLoNOmyUEmxaFJsSRVFhzs13ce7O2ldnf1ez\n1qw1a65+z3remed9eX6kGmnf3/SuqMTplB5bKjeyJVGdVMUW7n2wxf2qDKPnKxCSXsD0ohr+nu1N\nzEs1FivEEXiuiMJTD1n71IDf+8aQXlTF0HYHvBbp83SdAqlf3fh0MJ5FKo/pcjaSpZF7Me6txKJI\nWLRyH83r6nHK1qRCMwHtdAk+2oasWgFrnhZyU2o3R67F89Z9EOs01RDLspiTuIUOSze2m+lT4mXO\n9x0WlHqFsEDVlvVHfAi4lo2Xpi+T1X/SEVtNVHs/BgwOZpGhFvV39RnU5QQuPQRbR8tjGT4ExVk5\ntO7twuy9Jmz55seGzz2w7eNDP5lORM5QRapzAb2ME/jikM22Malcr5NiQngSnc61k7vM6l8x+Z+I\nREt6KlCh50rSrFAML0jhrJfAiRNqWCkY8Om7Ofcve/PA3YhKjWDkluZxd2Qa6TE1nNYJwf6PGoXP\ncjhoPoXr5S6EWblS1maKt3wjhj5yrNyqy+Y9X9hfos6om5WcK2qiwUYRJfU9hFlZMs3InzafjYRv\naqRwWQ5VqS30jy7mQ89gbvtJ8XHOZZwqD2DS1oiWpi+D2qoYbmTJYPcEfr7cy6PvHQx6qY5Xzz14\ntQ0hOD8d3dvSpNXqYzijnk1FCqyvaOLFCg/uT8wi6qMha8e7E3ekmsU/PLE60MH0jOtsT6nkV0xf\neof1ZodtCWdK7KnX02BfgAWXbt8gV6OMDuNApnSx5bq3L6tinAiUzOeIRxaxZoI1LfrImpWzfacd\n2y/LM/rqTbyU2ll115wpl9sJcHuE42wfviiVs6rqBPWhOYzbFEHw7BMsCJVC470DsV0KmObuSkOW\nPEeGXGCuqgWxM4q5UNVM+MB4csv1mKuVw9YL6sg/kKPELQ/b9dFEbd9P8Dh3FBPzeSjfhMtpQ+q3\nJZLX5MbPNgmhH5spjVFl9YkKtqvtx+tIMinangzcFcBax1ym317BEsUcorudwWNuLe2hm6g8vpJ/\nRpVwP02XY9b1aBgq0L5NlSGycfx8VEZn08ts6ypNeHIt+zXMWKzRiE7vep6kpOI3MwHJS3fMrzWw\nIaWBNfYWdB9fwqXWeDaZxiGzrRg9WQlbSopZNsKN/V+qSLdRYDbptI92Z4iTFT3lgokYHkCbihrb\nV0by5FcNF2tXMP5DMBoLLcibV0/W6IfIvLag3/ga+seOZYBhFBMagzi2T56ndxs4/GA39W2/qexS\nT/XSIBpkqigZnUHpfDeitbNI2eTGvOvBfIouwunqY8I8peg2yJ7KrobYtQWRaqZK6Yx0rod9ZenC\n8Uz825vTZz5zsmUoVTNec9dXjd5m7XTP8EOifYLMcxk8+GzH2QGmVBtX8VetL8e7WTLDdhOOkSf/\nFZP/Cccg/bkvo8MSsOoD+687MGBGJJ9bPCnvoUHxLU2+aebyOyOXCcGnSW0wxF7PhaKdagy45kZ2\ndgNOndtZ87yK3acDuOWUSOye4/wcdJtgc4Hx2o2s9PVh6rvj/LVPCbcV/rS21vNEs4OLozeSvr8H\nl0xPMKc1mL7FFizI96FI2oB3fW+xorGBJIVNpOr4ETzGm7eHHNhgmEqqTAE6jWPQkVnGhHOC+C5f\neTWjmVXtgvgYPexXHWR+zHzqJg3DUN6cM0GZXBuYxT61rzSvsaL88VXarq4gakscMrdP06WuL1dP\nn2LUi1m88ismOswTp4y9TAqeT61MIPumGVK+dBiGsm5sandEVz6E83kLKAjpzWilbHaWFnJysAJN\nQdlMC5HDNb0a281FWP3S4baqOxGuz1Eaupq+Olfod/oPtbZFqCZbcdu4kuhsZXY88+bRAiv8Oto5\nuEaZsnlxLN9+jIKBkTTGJ3FebirPVCq5bhvPm63KXNeeitMDcwJny2FzTsKTGfJ8nh1PfrgaOUfc\naesSxZF8T16MDOLGm1Dkh1owrnwMY+s9kR1pygLzi1jXe9NgV4vr7jpsqnqglXCb8ZO30m9ADO8v\nh9D6LB/vI4q8VjIkbUYc2xYvZ93gYPLHWyI76gRui5K5ouXIDtdOyMfk0uRnRMcMJZbLquPzfSpV\nDpaUHlcnXVWNqlK4vWwsNpbdiVh0jWz9UMrz9ZmbYcj1wodc32uIwWlXHKKOc/X9YwbpRKEyuYNf\nr0IY+rgWZaRRnJDB/T37eN2qStm2YhaF/uHmimqGysdz8ZovC/qHoHzMk6cFFeQu1ad9iB4GeyBM\n059hSYVcGOnG+zmFpJVaYmeVxcuwfcxMj8DcawzXxitwqPgc26NyWX30Ov80RrFsvCKlmv68WFdE\n4e/vGEs0UNlexip5Cf3Do0h4aUL3DWHo1V4iM/Uk6tMSyW+yZ7F9FTfkXYiV1WLOeiP2tGaz/Us2\nv6U7/hWT/wnH8NegTtz47ItqnS9lOzJIedmE8zE4t9gNtQwr+ueMY1a/x9ScmkuncabYbbhIaJ0d\nTcP0eGoxifoDczje04wdu10xIYjaN2+xVajGbH4wYUtT0XaUsCommsUe6oxbkU6DgwKfJtshdz2a\n5H4tdLdWoDlJwrDEKiyuPUJLtYyDKvE4quVwvu4EM1rkiTlUzdhCXZIDqtDUAd/ERmxk41mzOo0L\nst6cXRzBxBZFjqVHUnWyP+bvN9LtbhTJzyXMXmTBAQtpos5EM1SziuKr/tQFSzGvUwcyNmX82NpE\nQFAg2cPGcP2jHWOcGoiLd2Das0w2T9/CGrkarO9WoLLVlL+s7RjXN5P6P+2UO77C1saWk8cauLD6\nIldeeuOVVciA0ATcBgUytE8Kiqe6URKnwOMWA7xz5jDJSpGEVCMelOhhWOePlkkwVb6q6Epf4v2+\nPIKlj/NlqwtlXs3c+uTIIvsIwpzaqbuyl1tXDHhrDM4yDUQGmhPVEk30s2xkH5cxWs0NthdjZKsA\niwponVzI/E4GuPTIZ/IbCV7LDYn5UEb0lXZMutWg8sANH/ck5pqHYvC8N88SSvl+uQvvpEr4vaQF\nr3QJrvKB7DeYTVh6HolDj/NnqYSIRw3kTOvMtS6ZzLoQROzoAr5vksf6jisbXVswkC3kywx9vPBF\nqs6b94fcSMuW4umoV7yftY8Ws+080NpMjlw03+VU+V6cSNlLVQxTgjjbLQpdSzO6vZNGSruZGbVR\npD0zYOegCkrWS/H7VjDPhu+j5wEdXD6msWCFGbKDgD1uTDYvIOqiJdlLE5gyw4C2XJDWqGJQdSO3\nFlkSNh/+Vs7kZFUBAxsNyDespmpOFCNHBeJf5oJXaE/6tuqRWNaDPurHUP2URlJ6PN/6jcdJ7zHz\nN6iS3L8S41Zb3HpcQr09mo23HNDP1ydjSRKTSsYQLVNJRie4XpJOkPwutn3fyeU/FQyvkGPmn8H/\nisn/hGOof1mMps0VJrfXsPmuIV2PFGDw+ATBXfy48iOSPePiSAtYyI/aT7ht1CWh/yhM/ccyNvUw\nD3xGs2qbIxP0DVmc58nqpmrWpyxAq1QBFRlb2tPe4jjoPkPPdMPy0jEeWQ0k7pk6Za+ysKtw58cX\nTyZqFLIgXPBaT4FvQ3QoLlJHvcdFMg/aYfdLgTM7CjHXiEB3cgjORXEcDwrgs99V3rTHoBVjheMG\nV86WT2JwZh7JRjpMtM7G4Y4J85PG0DDpHluMVnLWugb97KHEfeyN5p6+HPoaT9laU8bLKDLCvoR1\nh45hGreYXctWkqcyFO8e36k/70/sckvu768g+PF+xr77isrDeRiXaXEqJ4HeqkOYe8yGr+s0eL7W\nmwmKybQNNWfyZTcSNd05F7qaXeo+fHt3DPUmR9bJZTIvtYa1SzVY4jKeqtmVfNDLYH9TJc1XfjNI\nIxWXF/rMlstmkl4k+8qC0ArSoEISgt8zQ96ZdZD8IRxvTWV2vM/l4cEm1hbGMf6qMnV+Wdgdi+OC\nrAW2WyV0tXHHaFoZLiNrUMyz5dEDRVqXvWHqZCmUok8Q5CdFTps3R24K9keMRXvWNoapu2LctZjD\n8mUwcRJrnEs5HlGM43MHPp+2IG7zNyImh3DAuZjAU8XM9LdDdkgSlyzaGelXwetcI9xCJcRaJbJ2\nfjsvLuoTVyBNyJtb7GvIpu9CBbT76FPXRZ/pwxr5MFGTphuNHE0txKm/Lik5j/DYNoVRiwyxK9An\nstUO0WaD9c0EWocGMffBFw74/mH7VVdmHS5HZfwXfrhEUnUtmJpnhexut2DiwAj6Py3jl1IfHu/v\nzYXGRKZp+JAvN5WRD+B7bCLX0q14EuTK2VP5uLvYsvdMM0YBFuQOfEHx+PksDbfBrNSAl9uCyZcM\nY0HNAeziGunj9QV9mVSq4tW4XCXHsnQjjEZFU37lKOmba7h2ypycpHgcZRt4YeZOgn8tOT6L2ffX\nKJZ9j/hXTP4nHENvRRVODrXj6w5Fvr3JJrGPH+VWVYQNuURzSRCWyzahMl6P3/XnOdI7BQffHrw7\nOo/ce1t5r7WFEd+08E51YqxsFcMsyzi+MY22WX/Ii3bjtZ4BEbmCSYvN+bQxkyM9bFFu12BHsSHO\nnZToe6WY/rXVKOars8P/MRd+NHN0phLTLOURM+ZxYHQ86/Qa6bt3Kq3uGiyQzeJzsQGqZ9s5uCmB\nVfK2jPQuxLybDNPfaLBSI5/JUjb0tEnnTvQehrVrctK4iUy9IM4FNbOiRYOkj6H435lA4Owseja+\n4lJVOedvnEJ5SDCnNwaTfCiK51mVJBmeILDdAfXz3+lvE8yDwkc80+zgoWo43uUR+KSocXhrEb6v\nEjD1SMR1vAej7iXiskzwtsWCMMtKCvWa2DBzE82NVVxe6gub9NHRlub2XTdcjSvJ/aEIR8zYbh2P\nybLX4BKMb2A5O0Za8u7nJrRmJmI6ooId1rY8TU8nbJ0vPwfms8ZJnXE2CnSs9GaNbRNrZTPQrg5i\ni0MiPoeCSZY/zvn2ZCoipdBa4cPR+WYofU9h3mgbShUyqd8pT0yXKJq8A8gZ6Me4Y440v2pEZnQB\nnhfcmBphw4xXx3COucyFwzO4e+8MypqvkVPsRc8BJWR/l6PfmXsUbH9Oid1y4m9VsfqZKgOUPbAJ\nXMXlvkaUpwcyNT2eqWmvmGTvjdM6Pw4lnKRYkkxn20S69o/GxlIdSRd3Ur/EU3ZaiQeKyshrx7Ny\nYT4yWQlMDc9nakk8X46dQNEQFI0LmfxBigrbXBZ2moLusGCm/wih0SgBk5cGjHYJwU0lmKMKRsSo\nwo63uoxZ4sqSR3u56w3N8W7cud3M+An2DCoSfNMJ5nNOG1UlZbQ8lOPKER/cfk9l15pNaBU30nT2\nOM33dzHDOIRTRxK5nRuI6xxd3l70oLhElczN1fTzHEvCiWTWV0ghu1OJ3Nh92DdNZW/SJjoqI1iR\nmET16X/3+PifcAwfv7Xw2tuPkm9ZlCZdRuPoQzRLFdHyqsS4xZ2C4dlY/LFFrvYMRvkNPJ5ZzaOV\nJ3g+uZaj8fko/SXHj+lmmKhFsUG2mMEH1Vk92YDnUXO48XQl2apDKLvbQUDLam54CfakatIwzgGb\nTUUccxasTLFDwXkjfhvk+DXVnIp1U5hSNpcg40r0reKIDq9h4sxGCuq92dcjlRD7XF4/82FPeTp2\nSZn81LNA/WA+dTKRXOubiKyeEis0DVgUeol9t2oIClKm86RA3g/qTtdzfgxYtIipfX+hP6GKY+cl\nHA0QeJtU4Xq7E4WvOiPbdolQ843s98unuGMc5nOP0vDxKWebw9DocpkHPsdwHmPJxzArVGzU2Isf\n2+1t0FF3Qf1DObE27lhXmRO214/td+34p8genXc5aE2sJHD2PMIuW6HlOBbruVFUzLPEdmskO+4a\noqJiz/kdkZw4kk2dVyWLnmahY1vFwzPHmZbQTNW+SmxkIwkrtmNNx0sGaDgTG2xOWJInbl0U6NtY\nzeSXHgy1VWLs669EHQsmqioBlfmNjC6KY9GBdIa8tcdQ04rYtSdYoyxPqu9uXGrjaKCKTnoXuXS5\ngs7TdLnyuifa/e3IJYyjkz1Y2WczP9uKOfl0PS2n1uKsNIifL7IJ7WRIxNRmytW/s3mYH2nHXvMu\nSANf1UcYtWZRVTsE2Up5Jq+4TPTWXoi19cS/mMTz1RexWj6H7R1RmCVtostPRXxjslhSrYX2OV8O\nz5bi3YLLxA35g4KsB5MOzMXTywAfFQmzWqYwRceSessOJHoK2IyTR2q0Pk/8LJg7yJy1HgVMfVhN\nSkwkQ5OK8RyYjNTQJ7zsa0VerwB0jYfQ1vs4v7vZ0vd3HLMHT8RN5hJt1WbMqNJgQM8VOCdlcGWt\nPG+qj+MxtwoL1XQe17lwz9kWU2U3ppiroRCeyUj/RbR7WvOiix9PFdSZY6aKlUMDloFf+WeEIelz\npKip/8O52EJk/g2U/79Tj0IIZPtLRFLD38LZbqSYaeIjqp1GC3u3AaJnyC7hUtxNtOZ1Fap5+8Tu\nhZ+FyQttkfNnt/Cy7xCb7ItF5z2dxJxrRSL83lDRKU9TqMyMEQdik0W0iqaw8x0kRnxMECnP9wv7\nVilR8AURtshWvG5pFp7zq0W79xvxsPMhsTB2oQivGC+SdO+ICTvHil/1X4VKvqGo/1Esbi/zFCO+\nKIqjVWriwOvjwrlqiPglv1fkj44UH39bi+tWMWJkRq2Y4u0kPH61iNn6q0Xwt+FC7kmdUK5fJYbX\nbxaFBUvE+MXbhd36bFHarVnc6j5JlBgWiIN2yeLItx3iZM/F4pSvrZBoZwsT1yei8dEo8XbkLrG2\n/IOoObJB7B4xXdiZ9RejVNaJoin5wn9LpdCRPSKs1nUTz0w2ioejPUTzEjkhXZkmrrZZif36Q8S9\nO59Fe9olMdIxXKz4Z6/Yd9JFkHZJrJ6UJrpuSBc+pyrFo4FuoueKfFF6f7C42a+7aJ1gIlyGbxSG\nlxvE5P7HxS2NvcJdf5I42b5amL2ZJWwyzomF728IpYT7ok3/oNDqPFtsLZso/v4QLjK+lonF0upi\n+rgCcWddksiwcRfuWSfEmFtVYuipNJE/PVVMm3pTbG64II5NnyvujjEVr+ofiMPXtwurmhTxcqGu\nUHxzU0ze3ya6t34VKW/fim0KHSL+cav4WRsupNMGiT2zR4uS5nui+eZEYa6jKWRerRRJrbuE+4nl\nQvuMq4hJvy+yh7uK75ZNIiXWW2Q9NxOPQgeIlwnLROf1OuJYoIIIrdgs3BM7xMWynaKzqa5YnnlJ\nOE0yFa+XHBc7K9XEB48eYlrkBbE6bJfYvvKiuHvvt/j0aY6Ysfmo8JkwUGwcXCZmbk8XgwaniN5v\nzgvFvHUiPMNE6O3aLbZseym8858J7ymjhWr8R7HX6pXo/+uLODlluTjtVin6bP4kLjXPFT3qf4nf\nM7uJTN++Yo6ejFh3o58I77ZPxCleFX+fnSTaFhiI+j2WIqp9jHBVFiLsgZ9wy5gi1IoqRcucS+LC\n0nbRWpcvblZIxKqxvcUXiUR06xsjFJIChMwzI/G3oYK43K4ppkv1Ede1v4mUlTFC859//vdFogf1\n7ituGbSJ0asnikP7lgnjhJkic5escJr1QWyX/CW0n88Sfu2lostiRVH5eKCQnqQjzszpEPeKNonr\nOx2FRqiG2PtRQ/j+FSc2azeJhMgWsXrNAXGhwlJ8PeQv7Oq/Co3TyaJT2m7x5MQYUfAqVJwlUiTq\n1gmbeR9Ez2gtYTB4ulgYv0AEyr4Wfx41iA2KmqL3LE8xz+eJMO5TLJ5GXRIWT9PFpGx1ofZVXrQb\n+ogtSepi5Dp50bHSU3Rec1p4BZ8Wg1cOE/273xXdP90RHzppiiGp04Tzh1digOd58exIunggdVJ8\nerVUVI8bI/ZEFgkZqzyx2l9b9K6SE8oe0iK1n5foabhddOxrEcJppbiRsV9cGl0kJF2viNYNV0Xt\nlcUi8+4H0XDgHxFv1iZum38S3nr+wnaUt/CdflG0vSgSGc+HiNcj2sXKxAfi09RX4vRzHbE2+IMw\n0/8tTGeZCYW6eNHlc5D461GEOKvkIlLGDBJKA/YIF915Ys6bcGE04rioFLvEyQ1zxZL1L8SEmpFC\nymS7ENkfxOSj7WKuRYr4GbZYDPh5SXwe8VCsWb1BNP6dIpSLVEVbWoOYrj1FKGuuELfMqkRTW5bY\n99lc6FwbI3Jvpog+cmtE75yJYrDPevHS5L7YePWF2KB+VOwanCJqmn4Js06zxfosaaFkdUPc0msW\ndZJcEfF2kjj3dKNwsbshnszaIib8s0Vc3B4jipSVRdnN/kJ53D3xd+NNsWDxKPHqaLu49zZDGOzV\nFQGH9oojFQNEVkSC0N8dL8bcVRQ2ZebikW6KcOs+UPzj1kOMdPwstBefFzc+DBE1swuFavdysWaC\nnPhcfEH8SXIWo1L/iKceeeLwmY1i5q8yUal/QZzU/y2WDBsgjD9piwXD7ov3g/+IPxPzxByFUaKu\nV6w4eqZN9O82WkSl2Yod/xiIq38KRZymlBh8/rO4OuG6yLCSER3r3oqyJdlC59dxURm4Rcx7VSNG\nzOojLF7/Ej3vRgrpX1nCdomOWJZvJ6zW7BXr2tVFZ+V0YftLTaiZJIqNRsEiSDNEhKnFiCITJXHT\nSFkM1s4Vnu0B4ld/BdH1zRbhsrWr0O22TXy38xILsw7/75v5qPSthYv2PdFc6M0ddyV6f4ziZ3Yw\n6jI2SBbpo3NVk2cqieRPKuadYi0VY08xZlotO1V8KdDR50qbPq0rXPl+IwvXSR3kOQ3h85hBvAqK\nI2GbB/dq5ek8uYDHizJZ4thOeL4NB+cWIf+wli76MYyId0BmRhHbNP2R8vLn6PpTnHR35On4bwQZ\nBzJyZiajfmWT+ugSydfMkSRbEbyziFN7Jei6JjGlJI6lja6ciaun2zA91KINOLhRjejMCGL2KrOl\nahgZ8v+w4sAwYmNzmHV6LKl1Zayeq8DyTfM5kFPDsCPxWAYpgKY8EUVyPHKSZ2/xCxbNU6HQbT5B\nhx7zpLWA/DQF4m8VYV5rxpH1hnRct2Dy8N3URyuwcEwWJvHqqP+yxUga5qv0YaJVNn119jFW+ys7\nI2uROgZJQ0xxVtAjubSRp8uy+GmsQYq1BQ1B3kTPOYHNeg0Uukzlelgi+3USuWQVQu/JGkTqJNP1\nli++w4I4dz0HI2dbXL4aUN4/kflJARh1nMRl3knKzoUwKvMUjcd7E71Gi32uTpi2WJH87iuLlkvz\n7i8fTAN0KO3vgrWfFf0Ngyjr1EytchKdto/Ff9oBupmPpe8EGVSOt7DWIYQBJebYdwnhunQzbnUn\nqHhzAd0UN9yKowkvVSd3ZwTWp3oyd6s9MdGCi+5WlI735MQgD7qcK6RrujsVhtHEaJVQ8cCDXMNO\n1LvtZpThQ6IGBbFmRyDNQeXMWCpPyLgCthU1opbfTmf5E9xRteSKYSZ/em3ibUsIY3dOoVuwIXWF\n8kwvucTPGeVMu5tBvnUhAVnq3FG148q5AKTmKDFwTgVrXGsI89nE6l2X6VhmRcccBc73sKFbhhua\nO8PZnZZBrz16NE+V54+KA8b3h7LZZRx6hRMo0X5C0rex3A/txDUrZb59LeCLZjX9g7/Ss66QwzYV\nPF3RRNtZOJmSyesgRRyuJ/FOfROTW5XwrWvgR/JB6j9q/Ssm/xPC8LRtKClLG/kREsGyGXpYmNkh\nCVMg9kYtsZs7kTNwEGFdLRl4yIWC+VbMaoT7WTVgo8G7s1a8v7gPF4/5FJWUsNtjJWbzXTlbYcjd\nC1NIHf6RGZ9SycoahH/9FMRiA+QeFGObLUP6ZXeu93OmMgUMLszjlb4bLw+H8jxsL8s69mI1dxLd\n1wxhkYIhwycG4bi3ijizSNRWruAS/pR+ieQdhpQ6aLL5+xBkrnVg0X8MZoePMVjahkQdM1pwJbGu\nAKsWD96kWjG7Szr3dj6iRfkG86T+sORUHEHje6OS+YyUd8Opss/k41d1FvjVkLb9IKEHtLE/oEK8\n3TpcFE4jtf4Od0auhKH5LDi8iyk/B9NpcQKzXNR4dL6abn9fplDSQkd8FUMb9QiY7YFufjo3zwzj\n1WIFPt8LZsTcZCw3JnD2VBOOUuYsO5PB94zHLLQo5dFEIx5es4ft6+iyKInZHa95uXQhVspnEWui\n6arZyLZ3auSFuvNdtQB1lwwe2ptjrRaMi3Izf9VvwS1nHN1kQxgXYUevhDxOHlHDdv0bLuo9ZudP\nD3olOaG7HApG7QAAIABJREFUsZi3K4uwWupGv/5+dPUdTKROE72LGgmYdplrS19hUzGGbdcmojw8\ngmkKn8k9OoncDdrs2W2OidplZnxs5ud3O96U5mL5o41nF8dxwSqETtcN+OhThFKSN4dy/Lkq1c4n\nwwBO+Aq2tUaiKbMAlVJ55r8Mob9xEX8tPsHoPs6YNUzim2EHh3Qy6axvx8XFRmy9Is8mzzykB5rh\nY+NK6pF08r8P4qhhPfbBFlRf7sP6PpVsavPlhYI+I4p9Kfp5EUlXHR42/yFpcjGru//N9mJ9xvVY\nzYXPOfResonIUHU2h6uiO0KT5zWnudqrlfzv/RjprYyKfRxLpwSwQ+81Ubl2lBg18zDJkdzMYt78\n9uRjD2v0PDNY5FJMR3s2dpoNTPJP58pdsNxVzScrB8ZPl8fI9RUlq/y55fIB6V2R/4rJ/4QwdB7x\nneMrgjlQb8nRjbpohlrgtdEdG2NVgtelcEOvkXVjzHGy96RuQS9k28eyQ0UB1dNVOOkVMtXMAO3o\nSuqr3Rhl3MBSL00+9cjn6/wh3N4K2n5u9BkH/tN6Ean7hmO/7FhhaMvIlS50/2DDs7Z8XlhlMfhv\nmLFhEouP2HLSu4KC8hCuPEjnokIEOisMWHuriSenC/kifwn5c/kcjQsk7qka47r0pGuxHxfygmkb\nsZF3By/Rz8oerf2OPHavp8BGnznvPJhgac/9teu41buMvUk29HgEh0sNCTtmSGcZe+6luFFtlojf\naUOqggzY+LAPO7afxlCYcV2umISBV5k8V4pJfVcw1XIe5+/Gc/5pBdrjFDBc6EM362RSD51j3Yeu\nKORd5Uz2IWYpH+OuczyHX0aQlWiH6vdUVq22Q1gWMeWRPB1NzWhHnObt/BPEDmsgdasFA79+om7U\nHm59tMF06xcab1VzcbUBGg4GWF/z5nteJWU2Fhx2kaA4P4MQL0X8vyTjtymInzXuDLrgxq+tuhw5\nqMBfm7OJ3WaJ19J0XGd6wFwHwrwEst/smdejmcNq/mwjiwvtGry7J8XlqUFMOx2E7dvLLL6zgD6/\nZSjSusUKFTdmHw/CzmIfW7Q1aBt4G4d5fTENyeXgqB+0/B5KwU4Hnta1c7n0K+PyqwlWUKL1bWfm\nTDNi2YBsDIyHMHu5Ln3D9zJKtZC6DCuO/KkiVz8AF9UEvmzLokpFjb7Gapw57ckyZUM6uWRy1Psx\nizcW4eYrh06bKtNKpbjVqsDgr7DW7BipQZfQVY3gZNsn/K+GsS97CU66s3GWOk/e4K4YRv7N/NRp\n6M1czgG5Uxgs2o/itUw2Sw1i3ZjzmHzxQcm6gWc/ivhc7Y9x2ApMXzbgaf+IpT0GEv7hI1t7P2Tl\n6Sp0fRpZNWYuf+1+yYNfu5HtUoXfUjXSWoq5UtTIT41E1hYo0c89EZWZOcy2KkJn9CYubFT7V0z+\nJ5qoZGQGieVfH1K21YzvKa4sftWJifu1OGe4lzWmu5Be1YsH6nLsma1P97xMdisrYflhL2p37Qje\nZMmWanirrI/EO4FeH0Zj3NWaiOGfeF2Rxt/5Dhz9EsrXmxm8mJlJiVE1+13c6OgRwGbcMR0Pmwc2\nsLSrJxnX/XFW7EJP50gyDxnw/a46wxe5YR1fxfk+J1g2WY9HT914P6WJzapVzNy8AulHVRRvkkfD\n6SED2jx4U2zAnqdxbDeZwkyHIkr35/LPozDiz4yneJIW0+drYXpmK9Fzd3NyWDVbDDfTKtwwHhRM\nXbfL7DrohhYWLCmuQXvKAdK9mrj41oiYO/1RLldjZtFyzG6q4Z2jg8nzcTSUG2Ar9ZZrui3IHfnB\n5m/ryVy+htWzIwlTyGH8nKVc7LmQubsrWdklCadHKVgNHsZfD3UY+T6K1Q5VvE9X48lUXbp7V7PJ\nz5+53Ufx8lA2n2rPE5jvSOkNBypH6TM6ZjgLnEaj6aHAUZuvGI31xtsmAevibJS6VVFX0YLv4Qn4\nWzdQr6HHwTejObrJgIqCKN50jkFUFbJMbhUun3R42yKhxLsGbZkCJg+2pJtqAQvlnPgicSRoaCrO\n41RZkBLMscJH2AT84k3NDxx/3kSqZAx977sRWHeXtV4nORtrxYN7QxnRrsy2RBUGh6sT8MSTyuyL\nxK4wxVprKgvf2KJ4tRnf3HK+P1PifP8x7HpWzrEvLlRucUFX7QRSXYpIqTNnkWoWoyq+MuyjJ2Oc\nhpChasG97CAKoo4z9J4/t/UD0O2jxyClBOztLdAOTsd9XAdD5SRcHjKIpo4ByNcOp9ZqORfGh6I8\nbB6jC6cTs8qbLSvm4//Tl9ndS2lLnYHfuiyct9Zx38mZhYsV6fwyhtPBl/lzchj1Px+xcsI2moa2\n8/rhApLWrKIyuDuh8uU8clnEo8BY7BbsxEbmJ3MeZDFlwTjcxqnhfqSA1ruKXP80lMOBu3HPS2Dl\n90NIW7uiP1L6f1cTlZJ8F+afdiMo0Yz2rkFsG6hMQlQyKZ4nGB9bRd34O/RSeojV0yau3Q3C7nQU\nM3vAwJe2rIqwZat2Mw+NHRjvmMvBGCuG5ppgLZ2OQUUQkksX6Og3lonTtJj8IJCkJ5oYrynDe/96\n7q/I4o+CIqEmBiwJX8et4Q3U7pYjTNedg/e9sRrjyfAfisgPM2PvMCX2p8ZwSZhSoxpM1/P5/Oro\nyQXfIiysInhoIk/q0ePkWNuTmytP/9RQDIfV8NC8GqPYHlRcvs6BJ2+RuvKGUc5PUBkmQbExi0jV\nE4wdFErdM0cuxxaiE2PASPNqbqqs4MgzNXRNHKlyrsNVdELqmRyhM5s4HBHPkgOJxH9PYHVHEcHd\nEnG+P47SgdOQlLUxLmgzDUNmILyu0GX5TBb2fsGWgUZMDJ7Mlq1n6D3lK0rXCgiZIXgYZYn78jyi\nL/pR2acaO+M4BnoZwr1mnO4EcX3EN9ysPSExn/7tFrg0pRNtoEveAT/KvjQSpFfAXwUVaDXEc1ml\nM0qGtTx1ycZk/ET+SEro6lHJtnxFIve4EzS/gq1XGrnZtYZtb3pifc+A2JsWnKo7gY6iNMMcL5Ll\nPRkdZUHskQjqjatQ9vVh1FYXFFZ1o9XwFLs/rMPq+D6kY7YwdHMTw8YtYNrxN2x21eT9NTkiyqRJ\ni8+ictElfnxLRte5nEWfqpgsOwaVKynI7zGkbE8jCq1uPJTqQOeVA0+s7Lmwy5GUdTmEL48jP9mD\nZQfTub/UldczvjCzpoOTw6UY6+qBbS97Pu6v55eRAsHXi9ipu4ez2//C91YRVfdNuL7zPqrT5xJz\neBdr3qtwq90ZRqzGVD2Q61P/5mm/GzStKuO89BVKPu3G9K01qg9P0afKm7tFynwyKeJPowElLWkM\nKncgc9o3vvrf4U+QBSpvzFhWU4TtHD/mfUpHcbItU6KzGbxemr/1Spj5KBJdl0zytgxkyOA3nF07\nEKXOTVgEn8DtZtu/YvI/UUrINAsW2T8k/ZU8h8oV0PjRjKZRE4+WBfPujzrha9v5/NkVq3KBU2gN\n5nM7sTUsi4V6BUh/0WX+wHrSqw1oOTqaO0vkcG8H0/Yi+hRJuK8TwHDH+TgquLH+V2faJjylUT0U\nrS+w/WUGfnvUCFctpMmvmt0ZTUzftZmkc0bQxZz2aWMwWFvAY2M/nv09hH0P0vmcdILYYZlMW/YY\n2ZmP0VwqMNtQxraRfsxfNQXfcAvWjrfipLsH0Ys9GBsk4V6XSExsowlolENj+H5aAkeTcMef2pdJ\nFHZyIuOID8v3BJMdE0+HijzuW4Np2RzNh8e96LY3Bs/AoVxbVUJ7aSitswuQnxNIeXkFIyoVmWl/\nnHJNZzyG5RMf+oU3U8fQcf0PRreOUDZpDZsG3CDEci5eh+p5n2+B+8Px3LvYSFZsB50dgvgg24z/\nsFyej8liyLh6ZpTboHFXETFnCvsM1RjzqIC2rt0Jc+yOEidJXXYFza4l/DBSwjfektIKRTR1mjn/\nwx9fy0vk7bZB3iGLLZ3n85dbDK3LipnkYIl2bRpzV9sS/U6JCy6dCH/nidmKBq5cyaQyTIJkaBWl\nHxsxueBCVJ4gxSsCL2tforcpE2zymrqGVqbvuot0sDR6h4M4byNh+4poput5UNjuwVL/XN4nj+Un\ncdQN6yDnuRJGKpvIe9WXqIxIxK8ANLI1UP9VSH+peUweEsh2uWJ0Hvjh29cQvxfymGp580QhkpCt\n3uwL6MF0hUBu1iiifsGKKyeqeT8lGtsjYLohCZt9J1hrMBr9KdKsN5TwNHgk2zYP50z5Da5vi6On\nQg4Kre58vSbHOdMeaCeWELFlPMUdm6n5s5fpq1SRFOSyIn4zl4tPs+RVZ7xamxkwNY0Nd3sSoFeM\nUnoNQRpSiHIfvlo0kl89hcYluoQ/b6LC9ziqod5sfpqOUXoSNWaJHLcuYrBsDZeSN7H7gzmfTxRS\nNXEM8rnn+Kf7g38H5f/vr0ohBF1U5cSf5L7CZXqeMJz4UVydNEiclzkuYmvOiTqlPFERsVTY9msT\n4+1dxaUGOVE4TkfkhHcVBxsCxJdDl0WdyxRx98cW8fKrpTD2dRRj92iKycrKwnaMnpAkxonGOGlR\n0uWkCDUaITq96iOMQlVF0oulIvlgquib0iws80JFu3KMSBi7TTww+SbGee0TN44FiGYFAzGvVCJy\n6ptE47QiYbvYTLz5VSC6hO8RD/e4i1mbY0RsXpYwtjIXRm9Wim+DpcWblCxx42OCcCsNEZlHF4jP\nv86L7UYmormtRiweHyxO6iuKKvt14n3ZBHF8SifR2G+YKBgxQ5RG3RCjjywTXUrlxHTtfHH1dqSw\n0q4SCxSjxPdiGaHZNVkUNgSLzzdSROeNt8Xtfp3FtxXyomNwjohJSBWu40JFu6pE9B0cJW5ajRQa\nG7uJni27RIpPtDB4aiFUdJeJIzcvCJ9tGmK7LcI0pUHM+v5F+O36IoabtItZTbKirOalqP5xXTzd\nnimyX50R3w1MxevESiEZYSs+ZwaJex254s+MQLGhsafYHL1XtIyWE7Nel4maD80i7uw+kRHvKa4+\nqxYhufkieQvC9MxhkWz2USRNSBYqtwJFkl6C8I/eJy726CSGarWLgWeOiTWfjolbXcNF8plI4TIz\nXHQU7BXG3SPEupuZYtea7sI46L34NsxL3DefIwp7K4mHvioi0vOHOP/ls5g721Xs/NlZLLq+Tmxc\nZC96fX8qdmV6ibsGh8Qp31ni0ryu4pTcaRFn1ihGtFkI42JH0WOyRGSkVYkVbs1i1CpzMfDeXBER\nVS5GTXUWsgdShN/9TyJ4QIqYuMBZpK48Kp5pvhCvHCaK7LBY4bv+laj2vyH07QaJhffaxZleH8We\n1mXCcuJY0eYRIkYpI2bdFeJ0UISYWrdT9Co5LsTEGIHZNFH0dKxI05UW2UO/i9cy70TJzy3CxatG\n1J2eL1TWnBL7+lSJS6+7iwUb1oquvX1Ej7YiIXmfL9buthX3Ng4RyYd2is6Xl4mya0VivvNucXfQ\nBZH51x7xUrmXmJX1QHwr6CsqzreK/PwAkbKgRLw4qChMikPFypmqwtPKTCTWHfvfNwxWu0GRJ4Yv\nKRgZwLy5Cvwj2849JRMOHjQn6ew+Bsw14q6shLa2NF7XBGB16w9F1/7Q9/MD/oRaEa/ugtbqYuJm\nW6Azp4rUe3Y49c3hV40prX4eXDLJQr3FA1e5XjxrCGfWjEl4bzTHug/4B8WxZq8fd3yUeK67Aftj\nA7n/pxIHmX0oGPsjqiv5qaCK7+p0TFbsZtT1cQTHBlJ+wgHrGYWUvSigoaWYyxtLSFiaznjlRgxx\nZ0Yfe+ZqGVFsrMSek9/YkGtMvsEGBiWX0ffQAAzWfmJNf1eU/YaR3msfv9aCTZgmltvUyHAvYkq/\nQWgN/cLtbH3OKe9ltVMzk9MNsA3YheOSXRTrhTLk0wyW6lmzrugZOudiGFI/mrQtjrg6lqD2qogz\n4Q5Y/erH49NrWZwfh11zCMnT5Ik64kr5Rn2ulOgx0mwIu2wMKN0rQbvRjb7tmXRKHomKJJvgZktu\n7Ffga8RvZqwsRP6zFPO2ZFBR4opNYxA7dsShcrMdzSeulNcpUUI8RhWVjKwvpKuvJ18aNxKp5Yyr\nfU8epn4l+UIByda1mFrOwVV+LmLwYzR2mBG98ATJNkp0zbPCd34w7r6uaJz05aayOb/b5FliBir1\nG1iib0j7mmuor0ynefJCBlwYivDyoONSFS9jx7L0a0++/TzP7LeKhGh6E3n9HjEan1H+2kB6jwRM\nbI0ovVOA+R4bnq1IRNM6jjWeugyxOcZ+aVsuyWZx+IgqjvG+bBrmwbWsDDKyGonQU6fcQMKOrUHc\nzT6G3cdHLNvlyxsVGzxVehJYPYZDq81o3KmAq3sBu+7tY9/nufiZGvNr2C2GeAeimjGASssWvk2e\nx9vfU3BbLc/T0TUc03Gj7+dZ5NgM4U/YXGQvLyHOaylapb058FuXKacKuSgXwYyDafiamnBLK5yJ\n0ZH8abdgnsdjBld0oul+Gudkpajy6URLsxZ5hQXcLKkm/nYA0/YGYO5rw92Chn/F5H+ilHguJyFz\nsSF7O2r47KdPhIkHl4x8CLEN5mm9AmNez6Nl22ZavLIYXOqHaEqidqcVDy+dZMT1GFJvr0A2pgql\n4mpC+sKqVet4qVrO/Kx4fl6MZFacD1uOCLKOHiPedAp9ivyQb4ukTtUSqWjB/h5y6PUfRPOxHqRI\nyTFJOg7H9444peijsSiSaZv0wSaBuou1dG+zZfMtRVpaM1nTV5PgfcfRnBpP5MFqauWiUeumRKxO\nAPVXtdhdr8uNy6YMX+BEzcLxBLzewtqHV/BcVsLgLSkMvdbAdoUqehq4UCJ/nD97XlPQpoHmzlvk\n6ltTpdabv7t4Uq7tzNy/VQmMUEWjxYmYMGtK0vszdZAnK18n02lkA4uUfNB8mMb3A5f4NCCa2g2+\nFBtFMlJlHKsUvzEtrwil5p7scIrDzbiA/C/myLqbsa3ChaQ/ppTnxpF9qJqlkih+PM1n94yHLAkx\nxc4xlm7Ld7Oz1gebwJkoTTlJcrU5T1YH4dsnGOsjCbT6djAgVw4vm0hKw+OokJLnnZYmXlN3c6vs\nAsx2462SISqDxtJVpp4bH4MY1bcEr5V5rLmZzxHtx1ibtRNjUs34Yfk8X23KTJt5OHwZQ59fDbh3\nvUHt62+ExUxkbuIl7liFkt/4mrjiJPxu38K6IoC8bVlkBzagXZRN1OMEGnJ00Dn/mbZRn4j7os4d\n90iGnR3CR5UiXsg08sfqBB7nBBcU3Fjww4hD4QpMVHXngGQfEy9GMsdsHs61BqAbjG27PWuM1Mkf\nvgLljzW0bbVD4pTKtKepXEsbxNJ8C6w2L6DnZFVaTuVj+TiNZTdWceuKP1fifpNT6ILCMz+OfPqF\n2lJd+t0UzJVvYJuqYM7qdo7fzuT9MoFCeE98pp5isbkaW+wNGCdx4mjuG2xmQmu4HoZznSir8eCO\nZy7FRsmk3lKiSVMdnfH5/PkRwJxqJX71HoP942U8va9A6ZECzvY/zJvm+QwwKf5XTP4nhEHprx58\n+iuVzfaPWLbVm1nK6qS1erK21o3BDiEMX2rBt1/yWNTKcPJTLj6PN/H9UA5790hju/AzGuPcULJs\noKFLPGtehuDbawtGalrUbXvM6CIYH9/IrjZVVr4M58O9WXz4Zw/y4+1IjWnAX7YDjx6q2H/+jN+M\nAJJTpIktKGHu+4e8PTCEhZ0s0IpzRSmmiYqtMWSslmK4VwNfNXui/HIDZRvL0VhTQOBOPSYRT9eR\nEgxPO7BqZTEvfYyY889JLGc9wW5WLu8ubEb2iit/XdbhokweZtftyOufzid7C0IannDEVZmlIoc0\nnWAWrZSioDib3wOHY1r+iAEjHlFaqo/Gj2q0JMo8nBJDbuJW5LsupLCPIedH+jAi8hGybWOwefad\n6oHDcSiewYZDl/k8pROaAwUVV0PwnQ86dyupKk8nui2K8itVdCn2JSjSCvUrwaiObqROfgfuL1sI\nPeXD0G/LqbwSyK31DnSrO8a0WdWE2fsxb/cjfmT7EqupgeuZFB4tXUd5dT0aLs08Kchm4NAKvD5U\n8kg6HaUTvqjEmbNqZjCT4wuYfLcJw8lfOT9egqtmFm/PCSqq3ZlmoMvA786k9Snkl3kzs/YvI3Oe\nDtF3Y2lfORR3v6OEFBVwLGsvPXIriJ+TyJDeu0hztsHk73wiG/bg3FxGuUwZmq0JNLYroDwjjd49\nCkhXOs6z1y8plJnD3yr6DLmlwJJsdxYvcmdytDzXe0RyQEef4Ro+bCaD/t41jF2sRX65A7pzGjHf\nr4Xd6otU91zPjOkDmdbtMGdDwshcpMC9LAMMYu25bqbKi6sSmn8UMylagenlHdiGN3DDS4+X663o\niFHHOciD1kUa7NN0RGtbA/JVzkSujmRzVCEzP45H/5cM3cNyeaOvwD9q0VzoE0jkJRMmritgYowb\nBvFF3HlqxZJxzTSohODvFsG26CF0fjCP4stpeGQHY1yUyOJb3gTrXMB+wTA2JZ/k1T/7/xWT/09h\nkEgkAyUSSY5EInkukUieSSSSNf+zryiRSDIkEkn5/6wK/9cZf4lEUiGRSF5JJJIp/687end0YunR\ncex8PI7wgz70NjvBnXcaTC/4xobKrrxZsY72lcpMiPrCkykWjM3LYrTvQbQUGvGx7M7sPt2Ys1Lg\nekuDReMdGFxrzt9D2glwjOHoygBMCEItOpC9ynqkestjHVvEiq9V/JBVpyJfnWRnVeoWWdL66TY5\n0zR45uVAfNwPdq+3QioCFK9I4WYcTWhJI3q1g3nSJ41zqQ58v6dH61Eplm91ZYCDL9F7ijhn7s+c\na3YkDQzk0s4A9CMfU7i8F25qW5hX0pkRfXtj/iSCTzuDCSq1oMO4EDUFSypmW9GvczJfjpdSfHgT\nmwzeIJ12khl+hWwaVE/3Yvv/Q91bRgXVfmu/P8DAAClbkRALBWl9LKRsRVJRHwuwsVAQGwsEW0k7\nUCm7aBspxVZSFJO0A5jnw3nOGHvvcc6793+MPc673/llzXXNNdf96ZrjXvO+r3tREz4B48OzyaqM\nQ63ddhwKLblgWozPFX82mWqiXNWG/ABvXqvG0eLsF9RV59Fl6neW6Tehs1ESriVR7KvIJjtDeGOz\nFeVVFgx/CFLcirv7k7mY5saMjm4U+Glz4+AAzvpYYdzFm8wR7hjO0kP58lWu6P1kpO1p/I7MptP3\nYlZYCN0epLNfXYuhZadxuaJLhlou6RUlTHqcxDG/KjLdbPg+oJ4FpzXwsy9CeWwX4o/85tPMWAYb\nz8Gnvpblz4KQNWaUX/cn47A3609v5vHiWfgUTcMy4QmD1xjRfJQtxRvmsmKhBRMiJqKeMJQdnhX8\nCWrghq4Lyvle9Nl3jZ/VZrimNOLmCytCRnbl2PurHHncBYeYKCJXbkH5rAtOFjasm+qJhnEtlQY1\nrBmozjgff8qXOPDDGYZoRdA19Q8KG9zodtySZ/tt6Wq9nN6hqSwxzcFF4zLHVjxj+eMC/Dz9cKlL\n4rGqPvFp5jQp1GdIoBtvQ73QUbXFYV82PXwcmP9wGKNKUrD1rcd6iQJdO+YSnWDKd99EPBwzSNFS\novPKObj3iKadRw6FmVk88ghA7VJPGg1ryv0EDxym2lDz8xv9Vn3hS2wA1CsQtkmD7Xm5bJvbiu/Z\nXQmarsbfLskU1rRGX9mamFVa1I2c/C8Vhv9cTAHtAdN/fBXgJdAL2Ar4/4P7A8H/+L2AfKApoAsU\nAUr/qzEa920jQ+PDZNXde9JHZbHErdATvfGpUtTXSoYdbysql9vJ3sapYvcoWBysakWtg4FEr18p\nI1a+kOCjk+V39gp5/e6cjKpdI+ezHspFqqTF7Fo5N71UuvgEi3F5nFChIIH1xaL065T83m8jw7Sz\nxSjGV7wfGcnyHXaSWH5KNqlGyX5lc6n4kCB5yxrLM79rMsAnQk5FtpLSD8hXzUz5pVUt0eP9peml\nPLl91V60b8+Sd71DJGXUQ+mX01h+fMyXoKob8vrRWck4uFHedagRd4e+cufaWKk43l4C85tKTvwd\n+dB1sWzZoycxhf3ku04LicgrkizXUBlpcVhiV9yRLPV10vT2ILnmM1XGfrklAx+Ey+2AKJkQUC1l\n1kNl15FB0nTvFenVLFk+1LpIq2dxss1yo9RdXSI3j60Tt24uMuBON+nv/FhOLNklF/delvSPn2R5\n43uy5lsr+fhtvCipXpaLX9dJxy9NZf6wnzJ/W4wY1OlLwaY5suXhIOntnSijB9nKcMf5MuwWotc0\nTizafpSGlu1ktU8XKTuhI2mJzyR/n4k46v+RVyvGS6OJNXLy/krpd/y1JLTZIjOLnGTLLD/5+A3Z\njJY474qTDtcD5U7QeZl/LlPSO46XrFV/RKGvo3SLniG1Rc8kJVRZDLuvEuVOQbK7oI+UK8dLkdtm\nsdlfIPabtaRj52ZieNhV3D4g8y9/lnVT78tqnyfi2vulnM9pkJp7drLFPlWCbexFc5SRTNxwTfSi\nT8snv3gxGKwnASGnZNWnCqnV+imdcj5J+l5faer1SSa+WiZGj2LEO3qcrNm4QRw6PpVYrytyZ34L\n6VF4Qw73GibpKfWi4rFOnPa6y47iV/Jjepx4qUbL67NZskBrnCyvL5DRXu4SsDZNKl/HyaoVUdLG\n0VlMHbMlpq2XqDRHRtSUilflAZncNkfyV6nL+Of2Ylk6WVKuzxavbG8x6R8lc+sR3bZRUlOQJyM7\ndpUe5/Tll0u8VJ6LE/MDC0Qn7ryo/dVebj79Ku+qJsmV8B7i3eKcXIqcLeNPnZBfKpVSNqZaZpvf\nkAmZM0RLw+u/VyshIu9EJO8f/wvwDOgIjAOO/PPYEcDxH38ccEpEfolICVAIWP4vB3nXiGQzK8r6\np/Ex5ALGZ0LYGnWAieWHOSWP2G0dTwe7fJ78KEMvy5eZlwt5oVKDwpupaNR14WKgL1Erp3PZdBGW\nyh1leDB5AAAgAElEQVRx7f0Nk305PP+iw2QPF2xrkrm1Uo0B+bHkhmpwMsGOQZsimXpaULnrQSbX\nmDKrFes1u1BQtoIn7ULYHelHjz0rcP6sRrjrdn70q2b6uybkxsaRuMifWfqe8LmBhGnqfJ+ynKeX\nl/NsrT9GI7KJvl/CszuNMK5/RfbqXZR3XkPLkiVMWbWD9Wd34tc7gfxiU45EV9BWTZXMC7qYHWtA\nO8eKrCtK2E8yYWGUEmf3FXMutCsH3M258dGRgA3H6DvEBpsbjbDukYFTQDWdbFpyPesDQakd8bM8\nRU6L/aiuOETXpK3EvN1EzqgpjG7oydLd9/j2xY70gfEY+oeT1/o2kYX2rNUwRCMwjNDfo/hYmcrW\n0WGg3JSI7uORri9pkhjMdYcSPpclkZE4nvFL4+jtsJK7RRGs9ixi7UhF6DkDpzA9olzv09RQhfs/\nHdFb7sPD1q95UzqRxLaxPLXwpDTxMjczNmIypZiNTzJwOlVP5M8+LDhUx4kgG1zUNtHmsxNtp0Wg\nMCQL8/O+qH32ZGlAEfvKizijrY9mtg4+5l3odNqOAQcPcWijIiNqbvDt0zXGOpuQPXsOXzXfMK3p\nBFbvM2XHqUe8GrSVoL7D6TS4MQ+qMghvuRrDN7Vs/sud+/vVOaI+kfhkA5onBGGcl8LToBLCjurh\n2M+JUINuKEyayejxrhxqG0vZtSjqjr0mpHIArxbuweJ7MXsrTFgcmMbXfV/otcaBpRc/Y5vnhlFB\nEMce78PtazR/vd9C3tsAlP2TuOKow+U1oTTrN56WvqG0SbAis3ki83c2YvzYxqyO9CZIyY9zA80Z\n01+XQbjxqUke/gmzGTSyEe4r0vhkEEyfDff53sqDLXM9yX+zjgGDm6Fj9ZvWm5K5Or0jPbvc44DC\nJLKPh6K/euR/RvV/Z/9Sj0FBQUEHMAHuAW1F5N0/ofdA23/8jsDrf5P25h/s/9NUWlVzV+UaRxdH\nsivejB9DWpOwtyvKpRN4mDmaPQqT+T5pBI/mlTKn9BuHDrTh3GsLprg0wuXSaoy/7iRGIYF+CqtZ\n3WUSLdTn0WTgBJr90qR5j2HkhYbQbXQcQ8a40WxAFTllLrRMyGV0WGvsiicwgG30ywzAYrcbSpqV\n3It5wKmUoyRPOkTfDfv55aTC5rfhFGyyQ2OWP5felPHtRi0VFn7Mb27LIHUNkhbqMyfLhKZDvLlq\nVMgMkyRabltDUuPW/HENJcq+DdM6raVl5Q3s70yncZgml/Tssfo4B09fYXK1H06XlzNqdxAlG9vQ\ndrcZI5eXsizlPrfCVvC5USyNGhXzIK8KtOr5pdWFt3Fe+Fw3w2rgMh6W6HH5WByP0hIpCpvAz7iJ\nNN/tzYlUV7LzgjhYs4pLt/czYZNwqUMle5zaMSpiPsZVlpx0cOD+ifsc72CGYd0nvjReTKebv2lx\nt5pZZRboDkwgKusqI+IzyejhxqPsufQ7Xkv2YTtOhGlQGNEOf5VaOjmdwsihmJ9rLTiR2Y0+1mZM\nyfTn4F1dAi8EUeNpgtUmO6zKIyl4fgUF5wqMFZSYb/2G4KaWTFrvikwUDK9f5dPl3ziU/aG3Wz2P\nygsJ9LAn0dqS8Uk1HImp5y/VIFqnZLF0dzabB1uj+syJIU296HQ4BStHP5xemDP43DC+H45jUdh+\nVA4X0yirgM+pM/mr3Iz5JcMYfPAGr0+1JWHYcCo35XJgmhmH1qYwICOChMER/Ihqwdec3jR+8IzW\nzZpxfO9Y2h+diZ1HGn9ONua5TTDlHnFYz0qkbdp1pkbqs3NBDesOfqLw5DyeSy8uNB3Op5un0Wjd\njznnl2E3x47zHxKwnRRHm9pMNhYlkXI2iKd2J1iYlU2r+OVctTqNtoo6OxzUSI5J4WfXCuobRbFp\nhD/LuwezKUyT6OMT6bEylSW3ZnFgkTbhrkbYGsZQHaiDx/1qcr0z6NvCgWWBtWQ5e2NTU/qvUP2/\nvo8BaAnkAk7/3Nf8h3j1P9e9wOR/gx8AXP5f3ucN5AA5Ki0aSXLZCxm6SUdqDE3k4ykHSV6fIHMu\nK4j/2kx5oO8la+PvilHvDfLza6b0930nrffcFu12g8S14qckZb+VBIs2ohjRTd50iZb9Jb6Su3Kp\nTLOZIrfmb5e6v7Xkkm6KrIp1lwXlIt79PkmVmon8zC6UPL96mZppLmdn+QmPr8rdgGy50Oi6fL50\nW4wGlor3nihp6TRePgamSNTTRFHd2FkirqeKe/YEaVEfJ+nnciRVR0mMFzWWwmXb5NfERzJcbbfc\nOvdSHCe2kE/Gf8Rlm57YOm2UHaeNJLWXi7R5O0/Gxu+R5aGn5fzEtdJr22KxaasqOau7SRf1TrJr\nga0U7p0hU85oy9DB08Rs1CU5WtlHhh/3lYD2ddJwWVWKa55JwTZHGRa+QdpN7CIfRm2UsFGLpX/R\nUlFUjpHt53XEcZKv6LWqEPNJYfLy3WL50OGT2HhvEsVDMyX0kLe42+iLRs9V8vScgZx7tVmyFNpL\nwKAeop7fVf5UzZaHN6NllcVXmRL1Tvp0WSh3glvIC9tfsvn0S1nW4Ypc0oyRqLvXZPeCLBm+arzU\n7cqUg4kvxNtNR5Y/N5WvGxeI38Z5MsYnR7Yp+krp1Q7y5GAfmWJ9WdY7dJDdYZek4OAmmdj3nky2\nnCaL9t+Qp+7d5fBldRl13FkMzivLxCblom5sLpds4+THa3858VFfaszz5UV+uCwri5BXmV/EPeWN\npKy1kFeezyThU6R0um4gOV+sROVGoowzLZE9e5Tkg0W16B3Ql0xjd5nYJUR+zk+Q+sJU2b7HQHJU\nNGTAZzMxDlCVBQv6yNJVY2SwbndRyOgohV+VRMckXJTnWsvhzsdEcndKh1lXRWGblXzqkSpWI+fK\nwUpLabOpVnTLsyVztLOop52WosGLZXarxqJ6fag83ZElGS5z5fTNILHtkCUjMk3ky+E5MlF5kmQX\ne8m5q22lLm6K3AzsLh3blsiotuYy1OygbD+rI8oFfaWobrp82JEnKq8TRanFI/HT3iBfB56Ux2WZ\ncrxpnSi2PSWJq4+KYqujMqbpKdk5Z69YS53Ebw8Ts4He8njrfZmW5PHfL7tWUFBoDCQAJ0Qk8R/4\ng4KCQvt/4u2Bj//g5UDnf5Pe6R/sPxakKBExFxHz3y17srSuL3/c4VjZMpJVB3LIYSB/ehmzv+4P\nPeIbaLlUl5+N4tiqW0q/OQ/Yo74SzfooFFEl59ZpJhsHE3xAj+bzSshfeo6j7R/itEyLM+1as6hC\nC9NvtthqF2MzT4GzPhaMaWpB+O4iTG1rWZ2kTpvvwl+Pi9HZFoDW9RS2HTPF4HA0eoe2sq1dNxZ5\nRNP5iwZBKV94nq7PwBV6tDq/mg7u+YQ0jyetzhyfdSuYOFGJ1sGv0N21jlMn79NyvT6r/urAguAS\nDN2+4aBxnKa70th1P527v1eyNEsYOF+fhz7ubM1UwmpDGsEXkvBynEX7gEI+DVTE0zAPxzR/Ipsu\nw2GmLTN2WOCpVcP+9bPYO28rNjbVaGtHcfbLRIy+mlHwWwGL8EjSEjVY65PHmz3xBN9W4kqHErZ6\nVnPmaA6PtWxpMy+KD6Fq6OQ6kRvows/DCmxcchrvh9+4uf4xfwc95UmXETiUdSU+ypxEKws6Du3K\n/vcFjHALx2eEHsELXNAYkYLCsFS2WgXRwqIS/6pW9N8fR43pKwya+zHsUj8K5TDr7bqTeH8re+4p\noHFAn26mVTyyL8B7ZTiZJeZcKlSi6egQXiwbQWLtPN496sbFaUVc3VXAnwxt/EdH8nOtHlfL1bHe\nqUSFnwsvlVLIVBQySyyp/toV4yER/G2+CZu2OYw6U8nVrmokm8/g9oMTtNrqycXvFnx5b8sGrzpy\nnn2ii8Yl5lQ9Z/RoY+Kn2OFY7kmF5nU80i2ZEazH6ixPenXMYVJbPaKfhzLU4wxf9J05/hksex7k\nhKEl3oYKPA4LIntZKSsso7E/647NtiNsSmlBYGQJo0docuK9DqWhxsRPucajznocnNLA2AXavFlQ\nwcmYLF435BP+0oGGSFtunZvNsreWmCZ2JXzAMnQc9Si9r84AZ31irnfleeNMlsb0xf4DbNm6gYR2\n47CUGir65zN0ugfpX8toveEba4pucaZyP60UDpGck/Ffofq/I+h/NlNQAI4CO/8DHsK/bz5u/cc3\n5N83H4v5T5qP6s0U5E6fo5JgcUlGar+Q5hOni6PSRXHUfi8xx91Ea6ih1M6plXd7nsnjhi3S96Wz\naDVaLIes10mTZWPkZZO2sju4p1zZpSUF07Rk5s8MCf6rRGwv+cnYkvGS9WiBlNkqSfGUOHl6NFam\neZ+Ssvd9ZW30FCmK1xbLmTlSVHxKPAf4yfouX6R8WC/ZvdJK4keGik5Noih1UZKaFT2k56K3cmpM\nsCRvLZMeXg8l+fFoeaZ+R5QPDJYBMQHSZ/RqKdc5INse/pC3dxZKlLW/OFyeKRXD1onlyFOi3vOu\nKD/aLqFDDWVtTZCUjG8kl1zOyXWLmXKwrFp0SzrK+Qcx8vDqEKnudUrWGlXIoM9O8nxLSzG75ChH\nelpLnwGbpfXFaNnT3knOPHWUkVX7pGpLjKTvfSYz/xiJdoS1dN7aRYp6ecn70hi5vdRYNivGyI7B\n9eI+NFPszh+X0qPaMjGmSkwKosVYZ5l82LZeti7/LZ2HbhQvg3VyY1GyPBr0TlKD78qAa0tl4KMN\nEtvMSI5UF8qdTyflQcvjMt/8oyjf3CsNVi9kyC4d+XFlhHxrpSMDM5bJOZPN0rKti7TY8lgMJtyQ\n6wlz5MOV/RLRz05U7l2RFTXNRUFrrSj8WCNnDB7L7h/TxGKQkVwy6ytRx+6INR5y4JOFJNe9lJjo\nc0KliSRMvSy3xiJzltbK6dqDUl7XWV6d3C0l8VvkybvJMnBxe8nv8lweL9onpVtyxVBXS15n5cnk\ndZmybv4xOXVbWY7uvSMJa6okbke2eJ7ZKzsCu4jq9g1Skr9RWvTZIE0vXJUnKkMkw+KqcHiN9LfY\nLV20c6T+YoDEBnnLQgKkqtJKtAIV5USTXXLBuqd0Sv8h7ks85N7tUFH6003Kfots7XpFYpcPlvqP\n66Vw8F4pv1giCz8oyAqnIaKfv09+FL+UqjB3GageIBmfxstRjzjJyB0jK9ftkAtqwbIxz0qUww9L\ni7Q4+RIRKFNO9RZfh2xRPR4qHm/TpHP6GUkeliH6VaPEd/MIaR3lL89XvZfw4pfifi5EBs7WEvOs\nUVJhWycLt66WL979ReXr9//ef1cqKCgMBG4Cj4D/53D6AP7vPkMsoA28AtxEpOqfnJXADKAOWCQi\nV/5XY2i17yblp3YyflYscydcp8XuJPI+qzLJYza7lvTg9dp0Tn4MxSkxA/WV9qjH6LK8pohTA6N4\nNr2WaxlJdG1uT3Tnu4x/4U4nPSumPP3NmSbJOCtmsvXoCX6cr8P1RkcyLCZwYkEmP/y3YJBWz2E3\ne9qVzWODuh6dr2tyqKciE0zTKZ4eQbtFYUTeLWJ4ZT9avOyJ6Wt9tjb+zbCeqxl3X58ee2+wKfY0\na9Tvk+D+go221wk/mE7Ewo/sPz2KiNIGDLyeoVC7mbe7XpDVr5b+xg3sjx3B8cPd2TrqG1pd0vE4\nm4hxr5E4t2nMpd6hvEqezd/NFTHUvMpd9zwm+SZwwlHIwJWsenO6jyxjxp9fWDtl0icnnt5T7tPn\nZAiFwQ5ErDfD8noAP3Z+4Wrne5R3HY+mqj4znd3ps+8lmnugxb5UblXdZPmQaPzKe5B1UZvv7oos\nDdzPiLIcFDJakl90nD4OqTzUMyEmpArN6fGUGzYw8kocVrpxJA4Mpvx0Ed+GBHP9RiOubrLHw9eI\nB8W5FHwJoE9BMkZ1j+kzJY2MXUNZuLKYzoqLGTzzGpfOpuJxdyP5A8JZdySRq+0+s2b0CiYVN7D7\nvjtW27UpHG7Dvu0xRC5Mo6jekGaxJVz62QOfxHx0NQtxDInHMuEzu+134tG3H2f2zGXXsLl03qBF\nluMEum+vxm1nLnXvOpK56APnb57koWoxc253Z1RiCLs+BtM9vDHq/S2wryjl2YmZbNepw/ixNo2T\nQ5l0XpWcxyu507YHYav8yVI9h3tIFNFfrhER449zYgnNvf14vqwNEcXWNB5xjTjb01w6sRG7nADu\nL2yGzRQtzju4ofZzMl3bwo99M7l4OYlOb0tJnd2dtiYNPLgaiO+kBFzvXGSpjwKPzMcT+XoOi0ys\niUwIR6OuHq9Ye96MHIZyWDy9z2jjdHMnijEBPNI2o5f5JhTPqHMurRWzb+gR6taZQ6+usb1nAiF3\n2rG3xJITllv4/NyWkO86/2V15f92nYSI0KdzF4k9YCqakSvEVue2HDA/KH22DZOygq+SdWCxJPke\nE9/Fx2TcjE+SYrZXTiVskJYdZ8jZeUPl0NoIaTs+R4bdVJB1HU/Ko1V1sjHHRloprZdNMb1EJ3+E\nTFKLlil/d5GRy2ZLY0OR4PIkOWqYJIMdvKSn8mv5UrtMWlSLNFcpllEurWX5pR9y5HInqXy+V0yG\nVcrzaD1p675GTo+dLgPWWYjCk1Eyw3KMKPzVTHoqTxWLZr/l3q4fUqFtIlNcP4punor4uqrK0nlF\n0m3eZpmTaSOVv23kXO8ESVEwl3s6PhK2/YoMn+Qukzr0krMpN6W7czNJcIqQqS27SPsbQyS9oU76\nBFtLRekfsdDQliNFx2XjopYycNhi2Tnog5wJT5Jfc2ZKoN07mX/wokx3fSavPB9Lcu1mObTKRpLM\nTMTWq5Ws7GQjpoP3SWfDYNE0UpMrz0zEdneyOL5NkNMvDGTWX51koVVnedCkWGy/FcrawUXSWr2R\ndP5mKu8Gd5Hco9aid+2eVK4vFFHMlMqsT3L5TJ1UxWcLf6bJd8vbEpedKxOLl8mEufWS8r6F3H7v\nJI1XvRS3db5iUvRSSgKWypduv6U+pYW876QitqetZZSaicjLveJ7KUZ8z32SH9PHydCHH0RfpZV8\ndDORba8Npe2UqWJ0vIu0NXkvOQtXyZ6q8zJpa1uZtHyxBMTPkMEOSXJ7/QE5PDxGhm35JDe8G8n3\nIQby/sco6RW4WHaqX5TWW1PFu8Nb6b8tQKrHdZf+QU/k+/ROsj5mqzTP7iN7Tf/IEu9f0rFhgOQ1\nXiwlcQNl+rTVsrjlCrnQfZ2sKCmRhuemEvI9UU606y5d26+VJ/1dpT7bTwYNSJGBZX/kXe0eCV90\nU1IV3KXAv1hOZY+X5r9iZMXHUBmgnC9lg65Ll+ON5bXZRXlRNE3mKd+W+usxIkqBMq/eQNbamMuq\n+sNSprdd7LVLZNeHAzInIU1mOpRK6Y5EqX6fJb4aymJq30J6TnwpbE2UjO6jxTDlnBhkZ0iC5igJ\n9movbnN1ZNzvWaL2PF6OOoSIZ8IEyQ16JXG7Z8uDgtP/52klXtS0wKVbKcEWDhx0LWCAv9BYOZd5\nazQ40zGUxCs1OL+OQFNFk3tPIkjP1sT2hT/VmrnET4ngaM9XqJi6YNfHgdrWWsye04fc2mA6q0Ry\nU6cvvxN0ORjihatlT0wfvuLlAXMuaKfQ9fcETPea0H1+GKOzz7BE1ZvvOVG8GBTJ+Z65fO6Yhtfk\nRzh+vkPXqc8JDhnI5zGnuLbRncdXE2lVfJyzrW35fngYt+7E8vVwS16dvkijsS85ceAzjk0amNJP\nkdxb/ty0nIfpmK3U/HjF6U6++Dy6gfGYg7i8LMN/Qh7Drlpxfqc/jfx16VxmTuGHYqoPmKA9tYQh\nz71Z759Lw/Mk+re3Zf0qD4ry1OkzJJITKlnMnR3CzE+e9OlwmmEPftC1RwgVdnokOLVCq6MfTkqR\nlK3LIL0+iqBjs7mjkMfiidHM6l3D7zht/Iw1GbXGnsuebjxwrMSqsJLhswbzaFAk2W7OjMpR59pv\nc3b1eIH1lUg6Nitk/a1orKdWUdXbij1uw0gvT6JgrT4PBwez+6E73y3dGJJvR0FNMJZvTqA1spKh\nbYswbO5AVXdvmhnp4VcYzQj/VHprziKjUQFXjwt5M7dQmaSG1Q7h5TZTzrbvQ+fpDjis/0Fbn3qe\nv19MjPVklo/djV1dKHNv9iO/mQWxuV4cLbyDdtQoFh5tS/XxndxW3Ul32wE4eLbi82kVKiJq8FAz\n5pJ2Cm3Lz3J75ms6z+jK4tBQ2p+7jU9DFx5+XMLe1i9ZtbSePh3XcqPHQ7Srl7Mq1Y4+A0Np+Nud\ny4on6XIul7AdKZR3O0bu9XAM/oSz8NUVCiI7cergdLolv8FvoDUnCsDwihfnY6oJ+3meWvUSyg28\nObb7DM4Tf5PcwgPTVUvo+n4/59/1JfD5XZR6DGXXlzQOLdLB2XAm58t96ejSGoYXMd/anylrppFu\nZcKo00UseVJK7dqJLN74nXHpn/n9PZ1Uz1l4jbnPCPMrZPqks264NRnrdP8lTv6PKAytGr2kvV4w\nTXvoMGvBcX6cdefaneeEaXjQzcCPZs/TGJSSwWjVVHwcZ9PHzQ/PYym8LvDkQMckds9ezvl8odvJ\nlThX3+fyqb/wUAriuFIkVk1dcP9bgxZz21K6qYJ1dklcV4qio2cRD531Sagv4lHpXbYY59HmQSq9\n2h3g7mszVOzacOFGGg+CktBRHEpd6lfm7A4nPruIVR4XODOsMW0MrIjSO0X3Le/Q91xC+tUgWPyD\nbtrpWNyZikJwA9s7aLMkZjkbOyaTNN2d8E5z0L7TjZKU3vy5+YUHvbVItkvE/qoSmoYRuJ2NJPPy\nct50GMqCgkKumOTQxy2CXU7qjLfUJ8ohgHRFE9qNXo7CwE1snlvJJCtnFpT4MWRVDcrZD+jQczXe\nd/2oVlOjY38Dgm2qUFZayRY1JTLuzEJjUgiGb2vYoa3B3z/mYZ2Zg+NpN9TKWtFhWiGT93jzM/IM\n74aacbPeggPdg1lUV8Aje12smkfxs8SNTukPsHprgLNlBgVd6/llX8WvzmB8WBOrmhT8DgfhFBHF\ngq3paHTS4u3EKgyiopnpbcKJmQpEbw7l7TINvB9qEGVYRO+ku6yO0aPsYmcudajlsqU5mfZXCarT\n4V7Xbux4NYXcJovIHaNIuLEPS55uY9uoMzwbV01akAW/jV+TPusnB9os5ef2XaTHHefv2QHkBIbj\ne3QoPo+rMMy7zwXD+/z1eB+jGrpT6HMDjQEz+ZZ8mfKrBQRYhXLrxjT2uVqj0TQYS7+eNHo8jvKT\nHswz3E5xBwtcN2rTcFSdT5/GQ5kJPy/EkDRZhy83QpjjZMnYThGM2lPIpgZF1pSrEf14ODELrqG8\nz4YuuUfwuriMbS7mKObNpXn5R2J/pPHgz1gOfjLmZIkrdrNN6F++kgn+K3jsmMdzFVt6OK9g7NkJ\ndH+TgmnFSv6MnszUrmqsahfCjDvbWBamyXo9D+x8XnHz0Uo6DrdBsGXG15XoNYtiaYw/q7LDofd/\nnZP/I7QS6u1VcXsbh003f9SfuLDllgkDH9oyOqWBffUu3OgBJzLVyKg7xZ7fp9Gs9MfHQ51Ck5dY\nuRWwzt8W+nnz8LotlZ0c0DjeleM+VVROCsbTJZf0bp9JfB1K0UV3rraxYqv1fmzyc3jvo0kbdQPa\nzJiAyuYaGuW9JLSJLYt8DlKzMJ5K+52k33rD56XbsFkczJtmexg1Vh+fK344Pu9M47zb/L0hCscT\nluzTS+Rcjy+0D07C6UAo1/o24e6wP1z2e88at5uov0ikqflB1pgZ4KjxnV7alvxZ/Yq2q2bTsyYN\npWr4ObIKz452JKq7cK+rLUO2RXN5Uw3D63L4oRpMoZo7QWNC+NajgWUOfnwtXk7kw2C+GVvwyKaY\nH28T6FMCy78G0KfLHPyGJGNUn4z1lFd066pKi7tdyNqfTJ8HRVwycuVrB1s+zThF0BkXXuQJ48dF\nMny6P86aOmgpFvD3qSRqIupZdLYVRkZ+bC1xJWaELj96qVDw0pg+TSLxWZzFXi19ro0OR2GAMT5E\nM8LPlSByCC7Rx+uvoRibNMA3Ezq8X85D/zyc9g3FKlSN6LP+xPQYRsvd9mjMnUXq0PGoTXNFPSOH\n4xWmLEhSI+hDIYe7ZLAh/Qc7nC6ydttF+q47g2qBH7e0glDZdgDtv20Jnn+ZOVfN6Kn8kgHz3mHy\ndCwdJ+wj6F4Eu6bVkKk6h9crS5mlVY/z8DD22RSS3SaMkZPH07H4GQyo5GDjT5QsncGUAx3xdO2E\nVzNXYu1e82jzdtwfb+LI542EG7WiwHgT85pksb95HiGXVfHad5KlG2bwqG0e+WbD2fm1CZ0c0xkY\nrEjWLVNcPII587AYvYp7nB1gTKP3JxigcRTtcU7Eaamx40hTykek0TVTlfS/FYlvHsFz719Y5SnQ\nZUwNzruT0ZzujdbQkcx+E01KW3t0l2hhce8Vlb3TKO2kROHfpazJPk3crgpKrwSTa6iLzmBfUtY7\no2sQxO209H+Jk/8zjnZTayM1Gg0sHrAJg3xnCKykanAwLSwXYHFjDVbX3XkdPwKjC6fwez2bBzez\nWJZvjseqO/RW0aH5hgwWtRyE0ioLkm5V0Lp6E28eb+ZnRz8e7ZlN0iZ99pqksjrADeXWN+muqcOI\nHQrU7i/GMyqfyJ/tmdA8iJxdm9nseoQLZ0bj++cak/LWEjdxLS2eq+Bck4u+nj31TycQ0XCBVOcM\nYvUOM2peA/7Nm7HwchXfFp3lTeFOfn79gu7EECLa7eTkrScMzUuijcV+7pw14VhSa75P7c2zd/1Y\nvCCXUWne/F04h42ZJZhlvUNrhybNT9vBaTWKvnii+eIMZUe9cNKzp/yXG33mzWH87lZsOa5I9a1s\nmqvkMCVLDSdTb3a7hOJrZIuy/TqyiuO4N1EYaVLA/ooG+i/civ77LI7FlPBi+gHSLfxIWDML0w/+\ntMjXIaOrMwW2s9Bf7M2VX6m88ygi7IASJieGYDEG7qmbou2TQ8E+eIk6er012b1dD0XjEoqfB8OD\nuz8AACAASURBVBEdWYQ55hxtq0F7n2ou5QmTgmZjkJzL0J+f8JmXi+foYpq+6IdRDxd23/LDo2Mw\n6xwm0C/SlpQzy4lS28CJMHu8YnLpatyV9KRkHtiG43q6kMr0zSxq2Qrd5624fiiNp/dW0GPYRHw9\nltGzaWumtV3OsLCX9B3gi4NBGZZnbdkX3ojLZ+q5phdCXm8LkppFcKX8Ez9VnFlsUMZwFRM8kpcz\ns9kWzrs+YdMXJeZU3UOzsw2WYbFkxwRRdjeJFEsHNAsWczRVn1bnJhA7JBjtAgUiTa1QHmhM/po4\nPjrUsuGQM5o3Y3jXuCuLP5hxNjWW5o1SmNlWgV7V5lztVobBwGC+FJwm44kLEbq1KAae4VynRhi1\n6IVHq8eoWs1hf4Qd0YGxNGven0D/PL7bmPLFoIaQ1ivw+LSEOwebE9O0GK/W87jXO4BsH1fGrb9D\nsHESUbGgPLoGyzxXNFKHEvM8h6BQNzxXxtPgZvxfbj7+jygMncxM5bTmMEJO92VdOzuMJp7EaNpK\nYk7UMXETfPeBg4qTsXBwoexYMOZbCrk70Yujo25xT90Vr09uFO0fyN4gMwZuX07MF2eaNamg8Ks7\n67NncTXqOs21FOhz6xDrHbYzu9EvEhVmU3xUF2XZwqB7XWj3soJ3S7ai3m4M3+6vxrGyFuvnb3h0\nsD1j5k7BrNMQnrZuoHPNfer9V5Di8Ip6Zx8eq7xgfM1IvH7mMWT+QrSPaBG2egS95xyk86ffvAo7\nyui7jZgW7UGluTeFodlMDexJtEYNlt+yCD5wgMR8C1QGqrPhZDhXm7XCJ7OW6woryXHqQlZzb+qC\nGig8EEDzIHWczhRROTuCpo6z2ZoGWtXxdOviisG3LHy2B9O+WTdmao8gveIB5TXpJDZX5W2bJYw2\nTsapXI2gwa0YVOiAY1MzXu+fxeJkJVzNK9mt5QWmsyiziUChUR4rlDQ4r9IKpZVVNKz1ZtesbHp+\nWU5Xfw0WmOty5OQKOsxdgcltDW4uCkfjcBGpjbrTPmQWho38mPm5AE/fdMr756DpV8C6A3lEq8Ti\nmL2StWNieXPLm6rb+Uw2Lqb8sherhj5nWffl1Eyp4uqerWTsM+fuFTtsD8zgx4VSgi/4cXlXOD5r\nowlIrKWF1VCKRp1lgfJeTp9TZfCkOt4uMOCgrwbGCw+gcekk+ca5XHSxJFW9BgvnFdw/5sci9b7c\neDcImyQHLALNufTDCp0hQ5m64hDFuhrcveyH61ktThQPJbkwh+u5VawcVMr6iYdprgJXqtMwclbk\nV+wBckcYoVISjZlBKYm/FclQf83CrBbkTGzFzpN65LXTY6NmK1peMEF50ByGJjSiVvs02r1smLpz\nFkfHpTBj2Daa2rXh56ONePR7T0K+NeMXxVKXr85Xl7dcdzpD7+JiJobaM9d2JSsH9OHUutUo2Xuj\nPfEr1q8PsmxMJCc392OLTRRL7k3AokcV5ftSSTykzp8v41Cbq0+HcTV4zu7yf9aZj/X5j8h2/kpg\nZTburo50MVXDS3krRs9HsMv0IObFqYy7/Inn47eg+iYC1c/xDGhSwfYJF/h7xBY6HbuA0R8vrvYM\n5IPpabJ+xXHs791YD9Bl/sUobpdqkq/nyNjAb2z8cYRjJQ+ZU76P9haReCvoMzr3BDM7JOF2SZkF\nY/pxKXAJGianSFygRtGrLuh+f4Fb2BxG7f/BoyErmWU0m5jHhewfv4N1PeppcF1CwYkWnOm/g9uu\nrtjVJ3PslDrWGbns7bubt/fdmTqkEX3S5xFV/poziZOxuF+KxWsDmg7TpUWADh2u1JLnPIGnnpFM\nKQBLG2G3oxIHeiSw7kIS24dosFI1DZ/H/hj1r8euySyi+5ZgGbCCo89tUazYSvWXA9hedGZxY0sO\nHPWlrN1YrB6dR6NnJwYqWtK0exZvDCOxMbVlsf5SesZbk/FcCwOFANRagOWSFVx5X09rowr6fqjg\n2lkvrlj4Y5N+kkDvCna+ciZo53RmHh/B4+AAtKblsHPrUPL3RGGlU8kjI3NeFIWzwEGX5HnGlJ1L\nZ1LvKIZbaqE3ToGtf99HMkPQ+h3EiQZFaL+aE3NKWN++DSed8vi924z1nmlUz5pNcNsg1tyKxLGH\nJ9cjDFg/pwcOBmV8znehLMCYc83vsmD8Eh59d2awShlWnY6x7chI8pPVUA+8z9FuESyfNIy5sVrM\ncsrlYIgZY+/WY/WpL2GNgjkbqI9Zx0YcdKsitKMtDd3n4LVdjxmRdix3aMy6aeM5decXh+e9RH1v\nMW/L+5Kj0JWNQarMu2tH+x2pOO/wYuMfe77vO8PUBfZ8sDMgLDuMjNgiso/WcPhhKi86nCS6MVQs\nek5zJ0uGD3xJMyc1BrzSZ2Nvf84GmaFaPQVv25vc87qK4/devLCaR7O8ztx9N5rrhxsx4N5Aln8w\nZOrGKHprp/Aoag/1PS3x2OfE3RwLVsyzxWxhOmMrq/nqHszOFyt5U9QW7Q8xfLYJI7lgC5P2l/xr\npPzfvVQpIqh30ZEPnybIh2l5kvvUUKZsHCetxyXJ6LNp0jtnnfTy7SFZvhVyd+YgUfq4QXqnZYrb\nzx+ibnZZHnpay4nX4bL+Zo48OfBMPjaNEffAe/J0jY98m9hCEs8aioN1Zym1GCXmleZiOzJbojfP\nFfuZn8TV1EbslEvkWXN1cXl9X/Z/fiZba1qJ85EZopv3VbY07yAXLteIWn2NaC9uIn3sbMRp+hJp\nsVpfSpUcZJnnY3m4Qlkyfb/I2BGrZKOZobz+MUi82q+XilFNpf7mL9mZvUasxryTqxueScvVy6RR\n+xcS8WaDfNTJlMb2V6X65gsZMuq3HD90Sr7f3Csv7l2RPMUuYldrIhEpVTJYR0fUltpK1/cmknh5\ni1Qd8hPNB5+kNq6ldG+0QaZsGy/7rU7Jo49XZICvibS/uUEM3z2T2uEtpYVnqbw7ekyK9w2VMJsn\nUnv+oLTcHCI3g/yljdUSKX1mLbVHpoq2WoWYnDSTz5O2SW2anqzquUt2jrotXaPuiJF6MzG6/1Oq\nw3bJwafK0lK5l4y2Giv2vZ7LkZ4/JKxphhy6tlvsRu2RwIPjxWftOPkx6Jn4KPaRqx83iGaEqoxX\nWy6+3jZy+tpHudZjv/zsoS+T73eTn9pjZejOChnl7CsjTs0Ufeux8v1BZ3FaNUTo+1sqXneU5TNa\niN9NVXFT9JUWb0xl1/gsyfq5T+ZGWom7q74obQ2QljajZeXAOBn/1EMmRoyUqonfpPPTN3LuUb4E\n9G4snu7zZLNXD+nRXEk022nKjesF0uTMWDlp9UYeGxwQvww1qUm0kkVnHsvLkCKJM7KQwOAR8vdM\nc/mRaycn/OxFt+yK9E8dJ+2VMmVoraG0mz9QDJodkwmXHCSlz2upnDpcjswPlQE77GTjrThZt6Sx\n3JizQBpwk6NGEfLHJ1bizi6Vg9WbZEqQqWx6oiclYaZyOKFQXi9+JxdGXJE7utPlyfnhstNaSaoO\nxYjJ+VLxcj4h5tN1pW14lbh2thCbsDPyJcxKjGJGiKt2I8k1DpDDh9Vk5KXZ4tltvnjW9ZKK553l\n0rFwcX7aW36sqZWMsfpiGfP6v3eD0/8fptrMUJZXLWXE45ko1jszQkmXxxQTcFSNcyva8ULbHZVW\nZ/E1n8mEzaGcHnGfaZ+ScbtqgpHHAZzqEnBKruFMB2OGts3l+Mp4rBNcmN1kEwHtignano3+tDr0\nYt/RRH8p2xc5MuD0Fp4PMePm9xJm3DalPmU709zXUF81iiZTK/G2vMlTiyu0eXaUMlU9fB5+xfRz\nA/5nwgm8MYc2z/Nw2zgS9079OO2QR+f+tWRaKPCz6RYUHFyZVx7EZm1vRlwaT06RIqPsIzA50p0Q\ntdmodBGcBqrT0cib5+7pDNuohtV4Db65udD7uxnxT2oJ2q1P4bgc3iam4qU6ga2nhnKj0RY6PdSk\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1bTCus6VbyEd2zrlAu+n6pDXzJFB9LTk1hmxMKqRk2EJmFjsyeo4Sfw4qUPB8EnsGrEPj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\nLjHUUUcdv6EuMdRRRx2/4f8A9ybVIwbagjcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fac050570f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img3)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "e604445e-003f-cae9-9ad4-d11d9a3a1f3a"
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"_cell_guid": "c033d0fd-1460-c68d-27f7-a1171aa8b6d3"
},
"outputs": [],
"source": []
}
],
"metadata": {
"_change_revision": 459,
"_is_fork": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| 0001/164/1164317.ipynb | s3://data-agents/kaggle-outputs/sharded/017_00001.jsonl.gz |
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