path
stringlengths 13
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sequencelengths 1
873
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stringlengths 0
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stringclasses 1
value |
|---|---|---|---|
89139379/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape | code |
89139379/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum() | code |
74042979/cell_21 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df_test.shape
df.shape
df.isna().sum()[df.isna().sum() > 0]
df.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False)
fig, ax = plt.subplots(figsize=(16,8))
ax= sns.heatmap(df.corr(), cmap ='bwr', linewidths=.5)
df.corr()
sns.set(font_scale=1.5)
plt.tight_layout()
model = LinearRegression()
target = df.loc[:, 'SalePrice']
features = df.loc[:, ['OverallQual', 'GrLivArea']]
model.fit(features, target)
cv_results = cross_val_score(estimator=model, X=features, y=target, cv=10, scoring='neg_root_mean_squared_error')
RMSE_train = np.round(cv_results.mean(), 0)
features_test = df_test.loc[:, ['OverallQual', 'GrLivArea']]
fc = model.predict(features_test)
si_train = RMSE_train / df.loc[:, 'SalePrice'].mean()
r2_train = cross_val_score(estimator=model, X=features, y=target, cv=10, scoring='r2').mean()
print('scatter index training', np.round(si_train, 2))
print('r2 cross validation training', np.round(r2_train, 2)) | code |
74042979/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape
df.info() | code |
74042979/cell_23 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df_test.shape
df.shape
df.isna().sum()[df.isna().sum() > 0]
df.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False)
fig, ax = plt.subplots(figsize=(16,8))
ax= sns.heatmap(df.corr(), cmap ='bwr', linewidths=.5)
df.corr()
sns.set(font_scale=1.5)
plt.tight_layout()
model = LinearRegression()
target = df.loc[:, 'SalePrice']
features = df.loc[:, ['OverallQual', 'GrLivArea']]
model.fit(features, target)
cv_results = cross_val_score(estimator=model, X=features, y=target, cv=10, scoring='neg_root_mean_squared_error')
RMSE_train = np.round(cv_results.mean(), 0)
features_test = df_test.loc[:, ['OverallQual', 'GrLivArea']]
fc = model.predict(features_test)
si_train = RMSE_train / df.loc[:, 'SalePrice'].mean()
r2_train = cross_val_score(estimator=model, X=features, y=target, cv=10, scoring='r2').mean()
df_forecast = pd.DataFrame(fc, columns=['SalePrice'])
df_forecast['Id'] = df_test.loc[:, 'Id']
df_forecast.to_csv('fc_linear_1.csv', index=False)
df_forecast
model = LinearRegression()
target = df.loc[:, 'SalePrice']
features = df.loc[:, ['OverallQual', 'GrLivArea', 'YearBuilt', 'YearRemodAdd']]
model.fit(features, target)
cv_results = cross_val_score(estimator=model, X=features, y=target, cv=10, scoring='neg_root_mean_squared_error')
RMSE_train = np.round(cv_results.mean(), 0)
print('mean of the cross validation neg RMSE', RMSE_train)
features_test = df_test.loc[:, ['OverallQual', 'GrLivArea', 'YearBuilt', 'YearRemodAdd']]
fc = model.predict(features_test)
si_train = RMSE_train / df.loc[:, 'SalePrice'].mean()
r2_train = cross_val_score(estimator=model, X=features, y=target, cv=10, scoring='r2').mean()
print('scatter index training', np.round(si_train, 2))
print('r2 cross validation training', np.round(r2_train, 2)) | code |
74042979/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df_test.shape
df.shape
df.isna().sum()[df.isna().sum() > 0]
df.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False)
fig, ax = plt.subplots(figsize=(16,8))
ax= sns.heatmap(df.corr(), cmap ='bwr', linewidths=.5)
df.corr()
sns.set(font_scale=1.5)
plt.tight_layout()
model = LinearRegression()
target = df.loc[:, 'SalePrice']
features = df.loc[:, ['OverallQual', 'GrLivArea']]
model.fit(features, target)
cv_results = cross_val_score(estimator=model, X=features, y=target, cv=10, scoring='neg_root_mean_squared_error')
RMSE_train = np.round(cv_results.mean(), 0)
print('mean of the cross validation neg RMSE', RMSE_train)
features_test = df_test.loc[:, ['OverallQual', 'GrLivArea']]
fc = model.predict(features_test) | code |
74042979/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
example.shape | code |
74042979/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74042979/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape
df.isna().sum()[df.isna().sum() > 0]
df.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False)
fig, ax = plt.subplots(figsize=(16,8))
ax= sns.heatmap(df.corr(), cmap ='bwr', linewidths=.5)
df.corr()
sns.pairplot(df.loc[:, ['OverallQual', 'GrLivArea', 'SalePrice', 'YearBuilt', 'YearRemodAdd']])
df.head()
sns.set(font_scale=1.5)
plt.tight_layout()
plt.show() | code |
74042979/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape | code |
74042979/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape
df.isna().sum()[df.isna().sum() > 0]
df.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False) | code |
74042979/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape
df.isna().sum()[df.isna().sum() > 0]
df.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False)
fig, ax = plt.subplots(figsize=(16, 8))
ax = sns.heatmap(df.corr(), cmap='bwr', linewidths=0.5) | code |
74042979/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.head() | code |
74042979/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape
df.isna().sum()[df.isna().sum() > 0]
df.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False)
fig, ax = plt.subplots(figsize=(16,8))
ax= sns.heatmap(df.corr(), cmap ='bwr', linewidths=.5)
df.corr() | code |
74042979/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape
df.isna().sum()[df.isna().sum() > 0]
sns.histplot(data=df, x='SalePrice') | code |
74042979/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df_test.shape
df.shape
df.isna().sum()[df.isna().sum() > 0]
df.corr()[['SalePrice']].sort_values(by='SalePrice', ascending=False)
fig, ax = plt.subplots(figsize=(16,8))
ax= sns.heatmap(df.corr(), cmap ='bwr', linewidths=.5)
df.corr()
sns.set(font_scale=1.5)
plt.tight_layout()
model = LinearRegression()
target = df.loc[:, 'SalePrice']
features = df.loc[:, ['OverallQual', 'GrLivArea']]
model.fit(features, target)
cv_results = cross_val_score(estimator=model, X=features, y=target, cv=10, scoring='neg_root_mean_squared_error')
RMSE_train = np.round(cv_results.mean(), 0)
features_test = df_test.loc[:, ['OverallQual', 'GrLivArea']]
fc = model.predict(features_test)
df_forecast = pd.DataFrame(fc, columns=['SalePrice'])
df_forecast['Id'] = df_test.loc[:, 'Id']
df_forecast.to_csv('fc_linear_1.csv', index=False)
print(df_forecast.shape)
df_forecast | code |
74042979/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape
df.describe() | code |
74042979/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df.shape
df.isna().sum()[df.isna().sum() > 0] | code |
74042979/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
example = pd.read_csv('../input/house-prices-advanced-regression-techniques/sample_submission.csv')
pd.read_csv
df_test.shape | code |
72065751/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
lin.predict([[40, 35, 2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
lin.predict([[40, 35, 2]]) | code |
72065751/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
lin.predict([[40, 35, 2]]) | code |
72065751/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
plt.scatter(df.bmi, df.charges)
plt.xlabel('BMI')
plt.ylabel('Charges')
lin.fit(df[['bmi']], df.charges)
plt.plot(df.bmi, lin.predict(df[['bmi']]), color='red')
lin.predict([[35]]) | code |
72065751/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
df.describe() | code |
72065751/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
df1 = df[df['smoker'] == 'yes']
df1 | code |
72065751/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df | code |
72065751/cell_8 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
plt.scatter(df.age, df.charges)
plt.xlabel('Age')
plt.ylabel('Charges')
lin.fit(df[['age']], df.charges)
plt.plot(df.age, lin.predict(df[['age']]), color='red')
lin.predict([[40]]) | code |
72065751/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
import seaborn as sns #Visualization
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
sns.scatterplot(x='bmi', y='charges', data=df, palette='viridis', hue='smoker') | code |
72065751/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
import seaborn as sns #Visualization
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
sns.scatterplot(x='age', y='charges', data=df, palette='magma', hue='sex') | code |
72065751/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
import seaborn as sns #Visualization
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
sns.scatterplot(x='bmi', y='charges', data=df, palette='viridis', hue='sex') | code |
72065751/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
import seaborn as sns #Visualization
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
sns.scatterplot(x='age', y='charges', data=df, palette='magma', hue='smoker') | code |
72065751/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges)
lin.predict([[40, 35, 2]])
df1 = df[df['smoker'] == 'yes']
df1
lin.fit(df[['age', 'bmi', 'children']], df.charges)
lin.predict([[40, 35, 2]])
lin.fit(df1[['age', 'bmi', 'children']], df1.charges)
lin.predict([[40, 35, 2]]) | code |
72065751/cell_10 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
plt.scatter(df.children, df.charges)
plt.xlabel('Children')
plt.ylabel('Charges')
lin.fit(df[['children']], df.charges)
plt.plot(df.children, lin.predict(df[['children']]), color='red')
lin.predict([[2]]) | code |
72065751/cell_12 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt # Visualization
import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
from sklearn import linear_model
lin = linear_model.LinearRegression()
lin.fit(df[['age']], df.charges)
lin.predict([[40]])
lin.fit(df[['bmi']], df.charges)
lin.predict([[35]])
lin.fit(df[['children']], df.charges)
lin.predict([[2]])
lin.fit(df[['age', 'bmi', 'children']], df.charges) | code |
72065751/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd #Data manipulation
df = pd.read_csv('../input/insurance/insurance.csv')
df
df.info() | code |
73100919/cell_16 | [
"text_html_output_1.png"
] | from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.preprocessing import LabelEncoder
from tqdm import tqdm
import numpy as np
import pandas as pd
X = pd.read_csv('../input/30-days-of-ml/train.csv', encoding='utf-8', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', encoding='utf-8', index_col=0)
y = X['target']
X = X.drop(['target'], axis=1)
label = LabelEncoder()
categorical_feature = np.where(X.dtypes != 'float64')[0].tolist()
categorical_feature_columns = X.select_dtypes(exclude=['float64']).columns
for column in categorical_feature_columns:
label.fit(X[column])
X[column] = label.transform(X[column])
test[column] = label.transform(test[column])
lgbm_parameters = {'metric': 'rmse', 'n_jobs': -1, 'n_estimators': 50000, 'reg_alpha': 10.924491968127692, 'reg_lambda': 17.396730654687218, 'colsample_bytree': 0.21497646795452627, 'subsample': 0.7582562557431147, 'learning_rate': 0.009985133666265425, 'max_depth': 18, 'num_leaves': 63, 'min_child_samples': 27, 'max_bin': 523, 'cat_l2': 0.025083670064082797}
lgbm_val_pred = np.zeros(len(y))
lgbm_test_pred = np.zeros(len(test))
mse = []
kf = KFold(n_splits=10, shuffle=True)
for trn_idx, val_idx in tqdm(kf.split(X, y)):
x_train_idx = X.iloc[trn_idx]
y_train_idx = y.iloc[trn_idx]
x_valid_idx = X.iloc[val_idx]
y_valid_idx = y.iloc[val_idx]
lgbm_model = LGBMRegressor(**lgbm_parameters)
lgbm_model.fit(x_train_idx, y_train_idx, eval_set=(x_valid_idx, y_valid_idx), verbose=-1, early_stopping_rounds=400, categorical_feature=categorical_feature)
lgbm_test_pred += lgbm_model.predict(test) / 10
mse.append(mean_squared_error(y_valid_idx, lgbm_model.predict(x_valid_idx))) | code |
73100919/cell_3 | [
"text_html_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from lightgbm import LGBMRegressor
from sklearn.model_selection import KFold
from tqdm import tqdm
from sklearn.metrics import mean_squared_error
import warnings
warnings.filterwarnings('ignore') | code |
73100919/cell_5 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_18.png",
"application_vnd.jupyter.stderr_output_19.png",
"application_vnd.jupyter.stderr_output_13.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_16.png",
"application_vnd.jupyter.stderr_output_15.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_output_17.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"text_plain_output_12.png",
"application_vnd.jupyter.stderr_output_21.png"
] | import pandas as pd
X = pd.read_csv('../input/30-days-of-ml/train.csv', encoding='utf-8', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', encoding='utf-8', index_col=0)
y = X['target']
X = X.drop(['target'], axis=1)
X.head() | code |
2014551/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
air = pd.merge(avd_di, asi, on=['air_store_id'])
airdata = air
airdata['year'] = air['visit_date'].dt.year
airdata['month'] = air['visit_date'].dt.month
airdata['day'] = air['visit_date'].dt.day
airdata = airdata.drop(['visit_date'], axis=1)
airdata = airdata[airdata.air_store_id.isin(air_selected_store_ids)]
visitors_data = airdata['visitors']
features_data = airdata.drop('visitors', axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
numerical = ['latitude', 'longitude', 'year', 'month', 'day']
features_minmax_transform = pd.DataFrame(data=features_data)
features_minmax_transform[numerical] = scaler.fit_transform(features_data[numerical])
display(features_minmax_transform.head(n=5))
air_selected_store_ids = features_minmax_transform['air_store_id'].unique()
print(len(air_selected_store_ids)) | code |
2014551/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
display(features_to_train.head())
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
print(len(features_to_train.air_store_id.unique()))
print(len(features_to_train.visit_date.unique()))
display(features_to_train.head())
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
print(len(features_to_train))
display(features_to_train.head())
air_selected_store_ids = features_to_train.air_store_id.unique()
print(len(air_selected_store_ids)) | code |
2014551/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
display(asi.head())
print('Total AIR restaurents: ', len(asi))
print('Total AIR restaurents in ASI', len(asi.air_store_id.value_counts())) | code |
2014551/cell_2 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2014551/cell_11 | [
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
air = pd.merge(avd_di, asi, on=['air_store_id'])
airdata = air
airdata['year'] = air['visit_date'].dt.year
airdata['month'] = air['visit_date'].dt.month
airdata['day'] = air['visit_date'].dt.day
airdata = airdata.drop(['visit_date'], axis=1)
print(len(airdata.air_store_id.unique()))
airdata = airdata[airdata.air_store_id.isin(air_selected_store_ids)]
print(len(airdata.air_store_id.unique()))
visitors_data = airdata['visitors']
features_data = airdata.drop('visitors', axis=1) | code |
2014551/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
air = pd.merge(avd_di, asi, on=['air_store_id'])
display(air.head(2))
print('Total AIR restaurents count: ', len(air.air_store_id.value_counts())) | code |
2014551/cell_15 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
air = pd.merge(avd_di, asi, on=['air_store_id'])
airdata = air
airdata['year'] = air['visit_date'].dt.year
airdata['month'] = air['visit_date'].dt.month
airdata['day'] = air['visit_date'].dt.day
airdata = airdata.drop(['visit_date'], axis=1)
airdata = airdata[airdata.air_store_id.isin(air_selected_store_ids)]
visitors_data = airdata['visitors']
features_data = airdata.drop('visitors', axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
numerical = ['latitude', 'longitude', 'year', 'month', 'day']
features_minmax_transform = pd.DataFrame(data=features_data)
features_minmax_transform[numerical] = scaler.fit_transform(features_data[numerical])
air_selected_store_ids = features_minmax_transform['air_store_id'].unique()
features_final = pd.get_dummies(features_minmax_transform)
encoded = list(features_final.columns)
print(len(encoded), ' total features after one-hot encoding.')
print(encoded) | code |
2014551/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import MinMaxScaler
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
air = pd.merge(avd_di, asi, on=['air_store_id'])
airdata = air
airdata['year'] = air['visit_date'].dt.year
airdata['month'] = air['visit_date'].dt.month
airdata['day'] = air['visit_date'].dt.day
airdata = airdata.drop(['visit_date'], axis=1)
airdata = airdata[airdata.air_store_id.isin(air_selected_store_ids)]
visitors_data = airdata['visitors']
features_data = airdata.drop('visitors', axis=1)
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
numerical = ['latitude', 'longitude', 'year', 'month', 'day']
features_minmax_transform = pd.DataFrame(data=features_data)
features_minmax_transform[numerical] = scaler.fit_transform(features_data[numerical])
air_selected_store_ids = features_minmax_transform['air_store_id'].unique()
features_final = pd.get_dummies(features_minmax_transform)
encoded = list(features_final.columns)
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(features_final, visitors_data, test_size=0.2, random_state=0)
print('Training set has ', X_train.shape[0], ' samples.')
print('Testing set has ', X_test.shape[0], ' samples.')
print(X_train.shape, X_test.shape, len(y_train), len(y_train) / 10, len(y_train) / 100) | code |
2014551/cell_14 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
air = pd.merge(avd_di, asi, on=['air_store_id'])
airdata = air
airdata['year'] = air['visit_date'].dt.year
airdata['month'] = air['visit_date'].dt.month
airdata['day'] = air['visit_date'].dt.day
airdata = airdata.drop(['visit_date'], axis=1)
airdata = airdata[airdata.air_store_id.isin(air_selected_store_ids)]
visitors_data = airdata['visitors']
features_data = airdata.drop('visitors', axis=1)
print(len(features_data.air_store_id.unique()))
print(len(features_data.day_of_week.unique()))
print(len(features_data.air_genre_name.unique()))
print(len(features_data.air_area_name.unique()))
print(6) | code |
2014551/cell_22 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.metrics import make_scorer
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import ShuffleSplit
from sklearn import tree
from sklearn.ensemble import AdaBoostRegressor
from sklearn.linear_model import BayesianRidge, LinearRegression
from time import time | code |
2014551/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
air = pd.merge(avd_di, asi, on=['air_store_id'])
airdata = air
airdata['year'] = air['visit_date'].dt.year
airdata['month'] = air['visit_date'].dt.month
airdata['day'] = air['visit_date'].dt.day
airdata = airdata.drop(['visit_date'], axis=1)
airdata.head() | code |
2014551/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
asi = pd.read_csv('../input/air_store_info.csv')
air = pd.merge(avd_di, asi, on=['air_store_id'])
airdata = air
airdata['year'] = air['visit_date'].dt.year
airdata['month'] = air['visit_date'].dt.month
airdata['day'] = air['visit_date'].dt.day
airdata = airdata.drop(['visit_date'], axis=1)
airdata = airdata[airdata.air_store_id.isin(air_selected_store_ids)]
visitors_data = airdata['visitors']
features_data = airdata.drop('visitors', axis=1)
print('Check the training data: ')
display(visitors_data.head())
display(features_data.head()) | code |
2014551/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from IPython.display import display
import matplotlib.pyplot as plt
from datetime import date
pd.set_option('display.float_format', lambda x: '%.5f' % x)
from subprocess import check_output
sample_result = pd.read_csv('../input/sample_submission.csv')
features_to_train = sample_result.copy(deep=True)
features_to_train['air_store_id'] = features_to_train.id.str.slice(0, 20)
features_to_train['visit_date'] = pd.to_datetime(features_to_train.id.str.slice(21, 31))
features_to_train.drop(['visitors', 'id'], axis=1, inplace=True)
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
features_to_train = pd.merge(features_to_train, di, left_on='visit_date', right_on='calendar_date', how='left')
features_to_train.drop(['calendar_date'], axis=1, inplace=True)
airstore = pd.read_csv('../input/air_store_info.csv')
features_to_train = pd.merge(features_to_train, airstore, on=['air_store_id'])
result_data_stub = features_to_train
features_to_train['year'] = features_to_train['visit_date'].dt.year
features_to_train['month'] = features_to_train['visit_date'].dt.month
features_to_train['day'] = features_to_train['visit_date'].dt.day
features_to_train = features_to_train.drop(['visit_date'], axis=1)
air_selected_store_ids = features_to_train.air_store_id.unique()
avd = pd.read_csv('../input/air_visit_data.csv', parse_dates=['visit_date'])
di = pd.read_csv('../input/date_info.csv', parse_dates=['calendar_date'])
display(avd.head(2))
display(di.head(2))
avd_di = pd.merge(avd, di, left_on='visit_date', right_on='calendar_date', how='left')
avd_di.drop(['calendar_date'], axis=1, inplace=True)
display(avd_di.head(2))
print('The AIR visitor data count', len(avd), '. The AIR visitor data with holiday info : ', len(avd_di), 'records.')
print('Total AIR restaurents in AVD', len(avd.air_store_id.value_counts())) | code |
16148624/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features
corrplot = sns.heatmap(df_train[num_features].corr(), cmap=plt.cm.Reds, annot=True)
g = sns.FacetGrid(df_train, col='Survived')
g.map(sns.distplot, 'Pclass')
sns.boxplot(data=df, x='Fare', y='Pclass', orient='h') | code |
16148624/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
print('{:.2f}% survival rate, {} out of {} not survived'.format(df_train.Survived.sum() / len(df_train) * 100, df_train.Survived.sum(), len(df_train))) | code |
16148624/cell_25 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features
corrplot = sns.heatmap(df_train[num_features].corr(), cmap=plt.cm.Reds, annot=True)
g = sns.FacetGrid(df_train, col='Survived')
g.map(sns.distplot, 'Pclass')
df_train.groupby('Pclass').agg(['mean', 'count'])['Survived']
df_train.Age.isnull().sum() | code |
16148624/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.info() | code |
16148624/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min() | code |
16148624/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features
df_train[num_features].describe() | code |
16148624/cell_19 | [
"image_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
df_train.groupby('Pclass').agg(['mean', 'count'])['Survived'] | code |
16148624/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features
corrplot = sns.heatmap(df_train[num_features].corr(), cmap=plt.cm.Reds, annot=True)
g = sns.FacetGrid(df_train, col='Survived')
g.map(sns.distplot, 'Pclass') | code |
16148624/cell_28 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features
corrplot = sns.heatmap(df_train[num_features].corr(), cmap=plt.cm.Reds, annot=True)
g = sns.FacetGrid(df_train, col='Survived')
g.map(sns.distplot, 'Pclass')
df_train.groupby('Pclass').agg(['mean', 'count'])['Survived']
df_train.Age.isnull().sum()
df_train.Age.isnull().sum() | code |
16148624/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
print('{} numerical features:\n{} \nand {} categorical features:\n{}'.format(len(num_features), num_features, len(cat_features), cat_features)) | code |
16148624/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features
abs(df_train[num_features].corr()['Survived']).sort_values(ascending=False) | code |
16148624/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features
corrplot = sns.heatmap(df_train[num_features].corr(), cmap=plt.cm.Reds, annot=True)
g = sns.FacetGrid(df_train, col='Survived')
g.map(sns.distplot, 'Pclass')
df_train.groupby('Pclass').agg(['mean', 'count'])['Survived']
sns.distplot(df_train[df_train.Age.notnull()].Age) | code |
16148624/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['figure.figsize'] = (16, 10)
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features
corrplot = sns.heatmap(df_train[num_features].corr(), cmap=plt.cm.Reds, annot=True) | code |
16148624/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_train['dataset'] = 'train'
df_test = pd.read_csv('../input/test.csv')
df_test['dataset'] = 'test'
df = pd.concat([df_train, df_test], sort=True, copy=False)
df_train.nunique().min()
num_features = df_train.select_dtypes(['float64', 'int64']).columns.tolist()
cat_features = df_train.select_dtypes(['object']).columns.tolist()
num_features.remove('PassengerId')
num_features = sorted(num_features)
num_features | code |
34130785/cell_13 | [
"text_html_output_1.png"
] | from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df = df.sample(frac=1).reset_index(drop=True)
max_seq_len = 458
MAX_NB_WORDS = round(int(df.doc_length.sum() / max_seq_len))
tokenizer = Tokenizer(num_words=MAX_NB_WORDS, lower=True, char_level=False)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
word_seq_train = tokenizer.texts_to_sequences(X_train)
word_seq_train = sequence.pad_sequences(word_seq_train, maxlen=max_seq_len)
word_seq_train.shape | code |
34130785/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
print(df.groupby(['label'])['label'].count())
df = df.sample(frac=1).reset_index(drop=True) | code |
34130785/cell_20 | [
"text_plain_output_1.png"
] | from gensim.models import Word2Vec
from keras import optimizers
from keras import regularizers
from keras.callbacks import EarlyStopping
from keras.layers import Dense, Activation, Dropout, Flatten,Input
from keras.layers import Embedding, Conv1D, MaxPooling1D, Dense
from keras.models import Sequential
from keras.preprocessing import sequence
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df = df.sample(frac=1).reset_index(drop=True)
max_seq_len = 458
MAX_NB_WORDS = round(int(df.doc_length.sum() / max_seq_len))
tokenizer = Tokenizer(num_words=MAX_NB_WORDS, lower=True, char_level=False)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
word_seq_train = tokenizer.texts_to_sequences(X_train)
word_seq_train = sequence.pad_sequences(word_seq_train, maxlen=max_seq_len)
word_seq_train.shape
gensim_news_desc = []
chunk_data = X_train
for record in range(0, len(chunk_data)):
news_desc_list = []
for tok in chunk_data[record].split():
news_desc_list.append(str(tok))
gensim_news_desc.append(news_desc_list)
vsize = max_seq_len
vmin_count = 4
gensim_model = Word2Vec(gensim_news_desc, min_count=vmin_count, size=vsize, sg=1)
words = list(gensim_model.wv.vocab)
batch_size = 1024
num_epochs = 10
num_filters = 128
embed_dim = max_seq_len
weight_decay = 0.0001
class_weight = {0: 1, 1: 1}
gensim_words_not_found = []
gensim_nb_words = len(gensim_model.wv.vocab)
gensim_embedding_matrix = np.zeros((gensim_nb_words, embed_dim))
for word, i in word_index.items():
if i >= gensim_nb_words:
continue
if word in gensim_model.wv.vocab:
embedding_vector = gensim_model.wv[word]
if embedding_vector is not None and len(embedding_vector) > 0:
gensim_embedding_matrix[i] = embedding_vector
else:
gensim_words_not_found.append(word)
model = Sequential()
model.add(Embedding(gensim_nb_words, embed_dim, weights=[gensim_embedding_matrix], input_length=max_seq_len))
model.add(Conv1D(num_filters, 5, activation='relu', padding='same'))
model.add(MaxPooling1D(2))
model.add(Dense(64, activation='relu', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Dropout(0.5))
model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(weight_decay)))
model.add(Dropout(0.5))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
adam = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
model.compile(loss='binary_crossentropy', optimizer=adam, metrics=['accuracy'])
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0.01, patience=4, verbose=1)
callbacks_list = [early_stopping]
hist = model.fit(word_seq_train, y_train, batch_size=batch_size, epochs=num_epochs, callbacks=callbacks_list, validation_split=0.1, shuffle=True, verbose=2, class_weight=class_weight) | code |
34130785/cell_11 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df = df.sample(frac=1).reset_index(drop=True)
max_seq_len = 458
MAX_NB_WORDS = round(int(df.doc_length.sum() / max_seq_len))
tokenizer = Tokenizer(num_words=MAX_NB_WORDS, lower=True, char_level=False)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
print('dictionary size: ', len(word_index)) | code |
34130785/cell_18 | [
"text_plain_output_1.png"
] | from gensim.models import Word2Vec
from keras.preprocessing.text import Tokenizer
import numpy as np
import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df = df.sample(frac=1).reset_index(drop=True)
max_seq_len = 458
MAX_NB_WORDS = round(int(df.doc_length.sum() / max_seq_len))
tokenizer = Tokenizer(num_words=MAX_NB_WORDS, lower=True, char_level=False)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
gensim_news_desc = []
chunk_data = X_train
for record in range(0, len(chunk_data)):
news_desc_list = []
for tok in chunk_data[record].split():
news_desc_list.append(str(tok))
gensim_news_desc.append(news_desc_list)
vsize = max_seq_len
vmin_count = 4
gensim_model = Word2Vec(gensim_news_desc, min_count=vmin_count, size=vsize, sg=1)
words = list(gensim_model.wv.vocab)
batch_size = 1024
num_epochs = 10
num_filters = 128
embed_dim = max_seq_len
weight_decay = 0.0001
class_weight = {0: 1, 1: 1}
print('preparing embedding matrix...')
gensim_words_not_found = []
gensim_nb_words = len(gensim_model.wv.vocab)
print('gensim_nb_words : ', gensim_nb_words)
gensim_embedding_matrix = np.zeros((gensim_nb_words, embed_dim))
for word, i in word_index.items():
if i >= gensim_nb_words:
continue
if word in gensim_model.wv.vocab:
embedding_vector = gensim_model.wv[word]
if embedding_vector is not None and len(embedding_vector) > 0:
gensim_embedding_matrix[i] = embedding_vector
else:
gensim_words_not_found.append(word)
print(gensim_embedding_matrix.shape) | code |
34130785/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | print(len(X_train))
print(len(X_test))
print(len(y_train))
print(len(y_test)) | code |
34130785/cell_15 | [
"text_plain_output_1.png"
] | from gensim.models import Word2Vec
max_seq_len = 458
gensim_news_desc = []
chunk_data = X_train
for record in range(0, len(chunk_data)):
news_desc_list = []
for tok in chunk_data[record].split():
news_desc_list.append(str(tok))
gensim_news_desc.append(news_desc_list)
vsize = max_seq_len
vmin_count = 4
gensim_model = Word2Vec(gensim_news_desc, min_count=vmin_count, size=vsize, sg=1)
print(gensim_model) | code |
34130785/cell_17 | [
"text_plain_output_1.png"
] | from gensim.models import Word2Vec
max_seq_len = 458
gensim_news_desc = []
chunk_data = X_train
for record in range(0, len(chunk_data)):
news_desc_list = []
for tok in chunk_data[record].split():
news_desc_list.append(str(tok))
gensim_news_desc.append(news_desc_list)
vsize = max_seq_len
vmin_count = 4
gensim_model = Word2Vec(gensim_news_desc, min_count=vmin_count, size=vsize, sg=1)
words = list(gensim_model.wv.vocab)
words[0:3] | code |
34130785/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
datasets_dir = ''
vnrows = None
datasets_dir = '../input/'
df = pd.read_csv(datasets_dir + 'fakenews_preprocessed_35k.csv', nrows=vnrows, encoding='utf-8')
df = df.loc[:, ~df.columns.str.contains('^Unnamed')]
df = df.sample(frac=1).reset_index(drop=True)
df[['label', 'target_text']].head(5) | code |
89135227/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import warnings
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
warnings.simplefilter(action='ignore')
df = pd.read_csv('../input/sepsis-dataset/Dataset.csv')
df.head() | code |
89122519/cell_9 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
def binom(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res /= m
for n in range(1, n - k + 1):
res /= n
return int(res)
def binomf(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res = float(res / m)
for n in range(1, n - k + 1):
res = float(res / n)
return res
def main():
n = int(input())
k = int(input())
main()
def prime(n):
for i in range(2, n):
if n % i == 0:
return False
else:
return True
def main():
continue_or_not = True
while continue_or_not:
n = int(input())
if n <= 0:
continue_or_not = False
main()
def rich(n):
total_divisors = 0
for i in range(1, n):
if n % i == 0:
total_divisors += i
if total_divisors > n:
return True
else:
return False
def main():
k = abs(int(input('Enter a integer: ')))
main()
def f(x):
if x < 2:
return f(17 - abs(x))
if x > 1:
divisor = 0
for i in range(2, x):
if x % i == 0:
divisor = i
if divisor != 0:
d = divisor
return f(d)
else:
return x
def main():
continue_or_not = True
while continue_or_not:
x = int(input('Enter a integer: '))
if x != 0:
print(f(x))
if x == 0:
continue_or_not = False
main() | code |
89122519/cell_11 | [
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
def binom(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res /= m
for n in range(1, n - k + 1):
res /= n
return int(res)
def binomf(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res = float(res / m)
for n in range(1, n - k + 1):
res = float(res / n)
return res
def main():
n = int(input())
k = int(input())
main()
def prime(n):
for i in range(2, n):
if n % i == 0:
return False
else:
return True
def main():
continue_or_not = True
while continue_or_not:
n = int(input())
if n <= 0:
continue_or_not = False
main()
def rich(n):
total_divisors = 0
for i in range(1, n):
if n % i == 0:
total_divisors += i
if total_divisors > n:
return True
else:
return False
def main():
k = abs(int(input('Enter a integer: ')))
main()
def f(x):
if x < 2:
return f(17 - abs(x))
if x > 1:
divisor = 0
for i in range(2, x):
if x % i == 0:
divisor = i
if divisor != 0:
d = divisor
return f(d)
else:
return x
def main():
continue_or_not = True
while continue_or_not:
x = int(input('Enter a integer: '))
if x == 0:
continue_or_not = False
main()
def g(x):
return int(x / 2) + 1
def h(x):
return int(x / 2 + 1)
def f(x):
if x < 9:
return x * x
if x >= 9:
if x % 2 == 0:
return f(g(x))
else:
return f(h(x + 1))
'\nThe above code can also write as below: (another way)\ndef g(x):\n return f(int(x/2)+1)\ndef h(x):\n return f(int(x/2+1))\ndef f(x):\n if x<9:\n return x*x\n if x>=9:\n if (x%2==0):\n return g(x)\n else:\n return h(x+1)\n'
def main():
continue_or_not = True
while continue_or_not:
x = int(input('Enter a integer: '))
if x != 0:
print(f(x))
if x == 0:
continue_or_not = False
main() | code |
89122519/cell_1 | [
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
print(max_digit(n, d, r))
main() | code |
89122519/cell_7 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
def binom(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res /= m
for n in range(1, n - k + 1):
res /= n
return int(res)
def binomf(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res = float(res / m)
for n in range(1, n - k + 1):
res = float(res / n)
return res
def main():
n = int(input())
k = int(input())
main()
def prime(n):
for i in range(2, n):
if n % i == 0:
return False
else:
return True
def main():
continue_or_not = True
while continue_or_not:
n = int(input())
if n <= 0:
continue_or_not = False
main()
def rich(n):
total_divisors = 0
for i in range(1, n):
if n % i == 0:
total_divisors += i
if total_divisors > n:
print(n)
return True
else:
return False
def main():
k = abs(int(input('Enter a integer: ')))
for n in range(1, k):
print(rich(n))
main() | code |
89122519/cell_3 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
def binom(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res /= m
for n in range(1, n - k + 1):
res /= n
return int(res)
def binomf(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res = float(res / m)
for n in range(1, n - k + 1):
res = float(res / n)
return res
def main():
n = int(input())
k = int(input())
print(binom(n, k))
print(binomf(n, k))
main() | code |
89122519/cell_5 | [
"text_plain_output_1.png"
] | def max_digit(n, d, r):
max_number = -1
n = abs(n)
while n > 0:
digit = n % 10
if digit % d == r:
if digit > max_number:
max_number = digit
n //= 10
return max_number
def main():
n = int(input())
d = int(input())
r = int(input())
main()
def binom(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res /= m
for n in range(1, n - k + 1):
res /= n
return int(res)
def binomf(n, k):
res = 1
if n < 0 or k < 0:
return 0
else:
for i in range(1, n + 1):
res *= i
for m in range(1, k + 1):
res = float(res / m)
for n in range(1, n - k + 1):
res = float(res / n)
return res
def main():
n = int(input())
k = int(input())
main()
def prime(n):
for i in range(2, n):
if n % i == 0:
return False
else:
return True
def main():
continue_or_not = True
while continue_or_not:
n = int(input())
if n > 0:
print(prime(n))
if n <= 0:
continue_or_not = False
main() | code |
72092307/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.info
y = train['target']
features = train.drop(['target'], axis=1)
col_data = []
for col in features.columns:
if 'cat' in col:
col_data.append(col)
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[col_data] = ordinal_encoder.fit_transform(features[col_data])
X_test[col_data] = ordinal_encoder.transform(test[col_data])
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
model = XGBRegressor()
model.fit(X_train, y_train)
preds = model.predict(X_valid)
sub = pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv')
model.fit(X, y)
preds = model.predict(X_test)
sub.target = preds
sub.to_csv('submission.csv', index=False)
print('Saved') | code |
72092307/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
sub = pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv')
sub.head() | code |
72092307/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.head() | code |
72092307/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
import numpy as np # linear algebra
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
model = XGBRegressor()
model.fit(X_train, y_train)
preds = model.predict(X_valid)
print('RMAE: ', np.sqrt(mean_squared_error(y_valid, preds))) | code |
72092307/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
72092307/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.describe() | code |
72092307/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.info | code |
72092307/cell_16 | [
"text_html_output_1.png"
] | print(X_train) | code |
72092307/cell_17 | [
"text_plain_output_1.png"
] | print(y_train) | code |
72092307/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.info
y = train['target']
features = train.drop(['target'], axis=1)
col_data = []
for col in features.columns:
if 'cat' in col:
col_data.append(col)
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[col_data] = ordinal_encoder.fit_transform(features[col_data])
X_test[col_data] = ordinal_encoder.transform(test[col_data])
X.head() | code |
72092307/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import OrdinalEncoder
from xgboost import XGBRegressor
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.info
y = train['target']
features = train.drop(['target'], axis=1)
col_data = []
for col in features.columns:
if 'cat' in col:
col_data.append(col)
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[col_data] = ordinal_encoder.fit_transform(features[col_data])
X_test[col_data] = ordinal_encoder.transform(test[col_data])
from sklearn.metrics import mean_squared_error
from xgboost import XGBRegressor
model = XGBRegressor()
model.fit(X_train, y_train)
preds = model.predict(X_valid)
sub = pd.read_csv('/kaggle/input/30-days-of-ml/sample_submission.csv')
model.fit(X, y)
preds = model.predict(X_test)
sub.target = preds
sub.head() | code |
72092307/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape
train.info
y = train['target']
features = train.drop(['target'], axis=1)
print(y) | code |
72092307/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.shape | code |
73069645/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
sns.set(rc={'figure.figsize': (10, 8)})
sns.set_style('white')
top_host_df = pd.DataFrame(top_host)
top_host_df.reset_index(inplace=True)
top_host_df.rename(columns={'index': 'Host_ID', 'host_id': 'P_Count'}, inplace=True)
top_host_df
viz_1 = sns.barplot(x='Host_ID', y='P_Count', data=top_host_df, palette='Blues_d')
viz_1.set_title('Hosts with the most listings in NYC')
viz_1.set_ylabel('Count of listings')
viz_1.set_xlabel('Host IDs')
viz_1.set_xticklabels(viz_1.get_xticklabels(), rotation=45) | code |
73069645/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique() | code |
73069645/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
top_host_check = data.calculated_host_listings_count.max()
top_host_check
sns.set(rc={'figure.figsize': (10, 8)})
sns.set_style('white')
top_host_df = pd.DataFrame(top_host)
top_host_df.reset_index(inplace=True)
top_host_df.rename(columns={'index': 'Host_ID', 'host_id': 'P_Count'}, inplace=True)
top_host_df
viz_1=sns.barplot(x="Host_ID", y="P_Count", data=top_host_df,
palette='Blues_d')
viz_1.set_title('Hosts with the most listings in NYC')
viz_1.set_ylabel('Count of listings')
viz_1.set_xlabel('Host IDs')
viz_1.set_xticklabels(viz_1.get_xticklabels(), rotation=45)
sub_1 = data.loc[data['neighbourhood_group'] == 'Brooklyn']
price_sub1 = sub_1[['price']]
sub_2 = data.loc[data['neighbourhood_group'] == 'Manhattan']
price_sub2 = sub_2[['price']]
sub_3 = data.loc[data['neighbourhood_group'] == 'Queens']
price_sub3 = sub_3[['price']]
sub_4 = data.loc[data['neighbourhood_group'] == 'Staten Island']
price_sub4 = sub_4[['price']]
sub_5 = data.loc[data['neighbourhood_group'] == 'Bronx']
price_sub5 = sub_5[['price']]
price_list_by_n = [price_sub1, price_sub2, price_sub3, price_sub4, price_sub5]
sub_6 = data[data.price < 500]
viz_2 = sns.violinplot(data=sub_6, x='neighbourhood_group', y='price')
viz_2.set_title('Density and distribution of prices for each neighberhood_group') | code |
73069645/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
top_host_check = data.calculated_host_listings_count.max()
top_host_check
sns.set(rc={'figure.figsize': (10, 8)})
sns.set_style('white')
top_host_df = pd.DataFrame(top_host)
top_host_df.reset_index(inplace=True)
top_host_df.rename(columns={'index': 'Host_ID', 'host_id': 'P_Count'}, inplace=True)
top_host_df
viz_1=sns.barplot(x="Host_ID", y="P_Count", data=top_host_df,
palette='Blues_d')
viz_1.set_title('Hosts with the most listings in NYC')
viz_1.set_ylabel('Count of listings')
viz_1.set_xlabel('Host IDs')
viz_1.set_xticklabels(viz_1.get_xticklabels(), rotation=45)
sub_1 = data.loc[data['neighbourhood_group'] == 'Brooklyn']
price_sub1 = sub_1[['price']]
sub_2 = data.loc[data['neighbourhood_group'] == 'Manhattan']
price_sub2 = sub_2[['price']]
sub_3 = data.loc[data['neighbourhood_group'] == 'Queens']
price_sub3 = sub_3[['price']]
sub_4 = data.loc[data['neighbourhood_group'] == 'Staten Island']
price_sub4 = sub_4[['price']]
sub_5 = data.loc[data['neighbourhood_group'] == 'Bronx']
price_sub5 = sub_5[['price']]
price_list_by_n = [price_sub1, price_sub2, price_sub3, price_sub4, price_sub5]
#we can see from our statistical table that we have some extreme values, therefore we need to remove them for the sake of a better visualization
#creating a sub-dataframe with no extreme values / less than 500
sub_6=data[data.price < 500]
#using violinplot to showcase density and distribtuion of prices
viz_2=sns.violinplot(data=sub_6, x='neighbourhood_group', y='price')
viz_2.set_title('Density and distribution of prices for each neighberhood_group')
viz_4 = sub_6.plot(kind='scatter', x='longitude', y='latitude', label='availability_365', c='price', cmap=plt.get_cmap('jet'), colorbar=True, alpha=0.4, figsize=(10, 8))
viz_4.legend() | code |
73069645/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
top_host_df = pd.DataFrame(top_host)
top_host_df.reset_index(inplace=True)
top_host_df.rename(columns={'index': 'Host_ID', 'host_id': 'P_Count'}, inplace=True)
top_host_df | code |
73069645/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape | code |
73069645/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns | code |
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