path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
16150255/cell_16 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
import pandas as pd # For loading and processing the dataset
import torch
import torch.nn.functional as F
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked'], prefix='Embarked')], axis=1)
df_train = df_train.drop('Embarked', axis=1)
X_train = df_train.drop('Survived', axis=1).as_matrix()
y_train = df_train['Survived'].as_matrix()
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2)
X_train, y_train, X_test, y_test = map(torch.tensor, (X_train, y_train, X_test, y_test))
def model(x):
a1 = torch.matmul(x, weights1) + bias1
h1 = a1.sigmoid()
a2 = torch.matmul(h1, weights2) + bias2
h2 = a2.sigmoid()
a3 = torch.matmul(h2, weights3) + bias3
h3 = a3.exp() / a3.exp().sum(-1).unsqueeze(-1)
return h3
def loss_fn(y_hat, y):
return -y_hat[range(y.shape[0]), y].log().mean()
def accuracy(y_hat, y):
pred = torch.argmax(y_hat, dim=1)
return (pred == y).float().mean()
torch.manual_seed(0)
weights1 = torch.randn(9, 128) / math.sqrt(2)
weights1.requires_grad_()
bias1 = torch.zeros(128, requires_grad=True)
weights2 = torch.randn(128, 256) / math.sqrt(2)
weights2.requires_grad_()
bias2 = torch.zeros(256, requires_grad=True)
weights3 = torch.randn(256, 2) / math.sqrt(2)
weights3.requires_grad_()
bias3 = torch.zeros(2, requires_grad=True)
learning_rate = 0.2
epochs = 10000
X_train = X_train.float()
y_train = y_train.long()
loss_arr = []
acc_arr = []
for epoch in range(epochs):
y_hat = model(X_train)
loss = F.cross_entropy(y_hat, y_train)
loss.backward()
loss_arr.append(loss.item())
acc_arr.append(accuracy(y_hat, y_train))
with torch.no_grad():
weights1 -= weights1.grad * learning_rate
bias1 -= bias1.grad * learning_rate
weights2 -= weights2.grad * learning_rate
bias2 -= bias2.grad * learning_rate
weights3 -= weights3.grad * learning_rate
bias3 -= bias3.grad * learning_rate
weights1.grad.zero_()
bias1.grad.zero_()
weights2.grad.zero_()
bias2.grad.zero_()
weights3.grad.zero_()
bias3.grad.zero_()
plt.plot(loss_arr, 'r-')
plt.plot(acc_arr, 'b-')
plt.show()
print('Loss before training', loss_arr[0])
print('Loss after training', loss_arr[-1]) | code |
16150255/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import math
import matplotlib.pyplot as plt
import pandas as pd # For loading and processing the dataset
import torch
import torch.nn.functional as F
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked'], prefix='Embarked')], axis=1)
df_train = df_train.drop('Embarked', axis=1)
X_train = df_train.drop('Survived', axis=1).as_matrix()
y_train = df_train['Survived'].as_matrix()
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2)
X_train, y_train, X_test, y_test = map(torch.tensor, (X_train, y_train, X_test, y_test))
def model(x):
a1 = torch.matmul(x, weights1) + bias1
h1 = a1.sigmoid()
a2 = torch.matmul(h1, weights2) + bias2
h2 = a2.sigmoid()
a3 = torch.matmul(h2, weights3) + bias3
h3 = a3.exp() / a3.exp().sum(-1).unsqueeze(-1)
return h3
def loss_fn(y_hat, y):
return -y_hat[range(y.shape[0]), y].log().mean()
def accuracy(y_hat, y):
pred = torch.argmax(y_hat, dim=1)
return (pred == y).float().mean()
torch.manual_seed(0)
weights1 = torch.randn(9, 128) / math.sqrt(2)
weights1.requires_grad_()
bias1 = torch.zeros(128, requires_grad=True)
weights2 = torch.randn(128, 256) / math.sqrt(2)
weights2.requires_grad_()
bias2 = torch.zeros(256, requires_grad=True)
weights3 = torch.randn(256, 2) / math.sqrt(2)
weights3.requires_grad_()
bias3 = torch.zeros(2, requires_grad=True)
learning_rate = 0.2
epochs = 10000
X_train = X_train.float()
y_train = y_train.long()
loss_arr = []
acc_arr = []
for epoch in range(epochs):
y_hat = model(X_train)
loss = F.cross_entropy(y_hat, y_train)
loss.backward()
loss_arr.append(loss.item())
acc_arr.append(accuracy(y_hat, y_train))
with torch.no_grad():
weights1 -= weights1.grad * learning_rate
bias1 -= bias1.grad * learning_rate
weights2 -= weights2.grad * learning_rate
bias2 -= bias2.grad * learning_rate
weights3 -= weights3.grad * learning_rate
bias3 -= bias3.grad * learning_rate
weights1.grad.zero_()
bias1.grad.zero_()
weights2.grad.zero_()
bias2.grad.zero_()
weights3.grad.zero_()
bias3.grad.zero_()
weights3.grad | code |
16150255/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # For loading and processing the dataset
df_train = pd.read_csv('../input/train.csv')
df_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Cabin'], axis=1)
df_train = pd.concat([df_train, pd.get_dummies(df_train['Embarked'], prefix='Embarked')], axis=1)
df_train = df_train.drop('Embarked', axis=1)
X_train = df_train.drop('Survived', axis=1).as_matrix()
y_train = df_train['Survived'].as_matrix()
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2)
y_train | code |
48165864/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)] | code |
48165864/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.describe(include='all') | code |
48165864/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.info() | code |
48165864/cell_34 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date = pd.to_datetime(df['Date'])
Date
df2 = pd.DataFrame(df)
df2
def longitude(x):
b1 = x.split('(')[1]
b2 = b1.split(')')
for i in b2:
return i.split()[0]
def latitude(x):
b1 = x.split('(')[1]
b2 = b1.split(')')
for i in b2:
return i.split()[1]
df2['latitude'] = df2.Location.apply(latitude)
df2['longitude'] = df2.Location.apply(longitude)
Border_count = df2.Border.value_counts()
Border_count | code |
48165864/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
port = df[['Port Name', 'Port Code']].drop_duplicates()
port[port['Port Name'].duplicated(keep=False)] | code |
48165864/cell_6 | [
"text_html_output_1.png"
] | import os
import seaborn as sns
import warnings
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt
sns.set(palette='Set1') | code |
48165864/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date = pd.to_datetime(df['Date'])
Date
df2 = pd.DataFrame(df)
df2
df2['Year'] = Date.dt.year
df2['Month'] = Date.dt.month
df2 | code |
48165864/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date = pd.to_datetime(df['Date'])
Date | code |
48165864/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df | code |
48165864/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date = pd.to_datetime(df['Date'])
Date
df2 = pd.DataFrame(df)
df2
def longitude(x):
b1 = x.split('(')[1]
b2 = b1.split(')')
for i in b2:
return i.split()[0]
def latitude(x):
b1 = x.split('(')[1]
b2 = b1.split(')')
for i in b2:
return i.split()[1]
df2['latitude'] = df2.Location.apply(latitude)
df2['longitude'] = df2.Location.apply(longitude)
df2 | code |
48165864/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum() | code |
48165864/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
print('Unique values in Port Name: ' + str(df['Port Name'].nunique()))
print('Unique values in Port Code: ' + str(df['Port Code'].nunique())) | code |
48165864/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date = pd.to_datetime(df['Date'])
Date
df2 = pd.DataFrame(df)
df2
def longitude(x):
b1 = x.split('(')[1]
b2 = b1.split(')')
for i in b2:
return i.split()[0]
def latitude(x):
b1 = x.split('(')[1]
b2 = b1.split(')')
for i in b2:
return i.split()[1]
df2['latitude'] = df2.Location.apply(latitude)
df2['longitude'] = df2.Location.apply(longitude)
Border_count = df2.Border.value_counts()
Border_count
plt.figure(figsize=(7, 6))
plt.pie(x=Border_count.values, explode=[0.03, 0.03], labels=Border_count.index, autopct='%0.2f%%')
plt.title('Composition of the Borders')
plt.show() | code |
48165864/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum() | code |
48165864/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/border-crossing-entry-data/Border_Crossing_Entry_Data.csv')
df
df.isnull().sum()
df.duplicated().sum()
df.loc[(df['Port Code'] == 103) | (df['Port Code'] == 3302)]
df.loc[(df['Port Name'] == 'Eastport') & (df['State'] == 'Maine'), 'Port Name'] = 'Eastport Maine'
Date = pd.to_datetime(df['Date'])
Date
df2 = pd.DataFrame(df)
df2 | code |
16124388/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]))
df.columns = ['age', 'income', 'spending']
df.insert(0, 'gender', dataset['Gender_code'])
df.head() | code |
16124388/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
print(dataset.keys())
print(len(dataset))
print(dataset.head()) | code |
16124388/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
dataset.describe().transpose() | code |
16124388/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]))
df.columns = ['age', 'income', 'spending']
df.insert(0, 'gender', dataset['Gender_code'])
plot_gender = sns.distplot(df['gender'], label='gender', color='grey')
plot_age = sns.distplot(df['age'], label='age', color='blue')
plot_income = sns.distplot(df['income'], label='income', color='lightgreen')
plot_spend = sns.distplot(df['spending'], label='spend', color='orange')
plt.xlabel('')
plt.legend()
plt.show() | code |
16124388/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
print(dataset['Gender'].unique())
dataset['Gender_code'] = np.where(dataset['Gender'] == 'Male', 1, 0) | code |
16124388/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]))
df.columns = ['age', 'income', 'spending']
df.insert(0, 'gender', dataset['Gender_code'])
# Histograms
plot_gender = sns.distplot(df["gender"], label="gender",color="grey")
plot_age = sns.distplot(df["age"], label="age",color="blue")
plot_income = sns.distplot(df["income"], label="income",color="lightgreen")
plot_spend = sns.distplot(df["spending"], label="spend",color="orange")
plt.xlabel('')
plt.legend()
plt.show()
# Violin plot
f, axes = plt.subplots(2,2, figsize=(12,6), sharex=True, sharey=True)
v1 = sns.violinplot(data=df, x="gender", color="gray",ax=axes[0,0])
v2 = sns.violinplot(data=df, x="age", color="skyblue",ax=axes[0,1])
v3 = sns.violinplot(data=df, x="income",color="lightgreen", ax=axes[1,0])
v4 = sns.violinplot(data=df, x="spending",color="pink", ax=axes[1,1])
wcss = []
k_s = [i * i for i in range(1, 8)]
print(k_s)
for i in k_s:
km = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
km.fit(df)
wcss.append(km.inertia_)
plt.plot(k_s, wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('wcss')
plt.show() | code |
16124388/cell_16 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]))
df.columns = ['age', 'income', 'spending']
df.insert(0, 'gender', dataset['Gender_code'])
# Histograms
plot_gender = sns.distplot(df["gender"], label="gender",color="grey")
plot_age = sns.distplot(df["age"], label="age",color="blue")
plot_income = sns.distplot(df["income"], label="income",color="lightgreen")
plot_spend = sns.distplot(df["spending"], label="spend",color="orange")
plt.xlabel('')
plt.legend()
plt.show()
# Violin plot
f, axes = plt.subplots(2,2, figsize=(12,6), sharex=True, sharey=True)
v1 = sns.violinplot(data=df, x="gender", color="gray",ax=axes[0,0])
v2 = sns.violinplot(data=df, x="age", color="skyblue",ax=axes[0,1])
v3 = sns.violinplot(data=df, x="income",color="lightgreen", ax=axes[1,0])
v4 = sns.violinplot(data=df, x="spending",color="pink", ax=axes[1,1])
wcss = []
k_s = [i * i for i in range(1, 8)]
for i in k_s:
km = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
km.fit(df)
wcss.append(km.inertia_)
wcss = []
k_s = [4, 7, 9]
print(k_s)
for i in k_s:
km = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
km.fit(df)
wcss.append(km.inertia_)
plt.plot(k_s, wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('wcss')
plt.show() | code |
16124388/cell_17 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]))
df.columns = ['age', 'income', 'spending']
df.insert(0, 'gender', dataset['Gender_code'])
# Histograms
plot_gender = sns.distplot(df["gender"], label="gender",color="grey")
plot_age = sns.distplot(df["age"], label="age",color="blue")
plot_income = sns.distplot(df["income"], label="income",color="lightgreen")
plot_spend = sns.distplot(df["spending"], label="spend",color="orange")
plt.xlabel('')
plt.legend()
plt.show()
# Violin plot
f, axes = plt.subplots(2,2, figsize=(12,6), sharex=True, sharey=True)
v1 = sns.violinplot(data=df, x="gender", color="gray",ax=axes[0,0])
v2 = sns.violinplot(data=df, x="age", color="skyblue",ax=axes[0,1])
v3 = sns.violinplot(data=df, x="income",color="lightgreen", ax=axes[1,0])
v4 = sns.violinplot(data=df, x="spending",color="pink", ax=axes[1,1])
wcss = []
k_s = [i * i for i in range(1, 8)]
for i in k_s:
km = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
km.fit(df)
wcss.append(km.inertia_)
wcss = []
k_s = [4, 7, 9]
for i in k_s:
km = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
km.fit(df)
wcss.append(km.inertia_)
wcss = []
k_s = [4, 6, 7]
print(k_s)
for i in k_s:
km = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
km.fit(df)
wcss.append(km.inertia_)
plt.plot(k_s, wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('wcss')
plt.show() | code |
16124388/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/Mall_Customers.csv')
pd.set_option('display.max_columns', 10)
scaler = StandardScaler()
df = pd.DataFrame(scaler.fit_transform(dataset[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']]))
df.columns = ['age', 'income', 'spending']
df.insert(0, 'gender', dataset['Gender_code'])
# Histograms
plot_gender = sns.distplot(df["gender"], label="gender",color="grey")
plot_age = sns.distplot(df["age"], label="age",color="blue")
plot_income = sns.distplot(df["income"], label="income",color="lightgreen")
plot_spend = sns.distplot(df["spending"], label="spend",color="orange")
plt.xlabel('')
plt.legend()
plt.show()
f, axes = plt.subplots(2, 2, figsize=(12, 6), sharex=True, sharey=True)
v1 = sns.violinplot(data=df, x='gender', color='gray', ax=axes[0, 0])
v2 = sns.violinplot(data=df, x='age', color='skyblue', ax=axes[0, 1])
v3 = sns.violinplot(data=df, x='income', color='lightgreen', ax=axes[1, 0])
v4 = sns.violinplot(data=df, x='spending', color='pink', ax=axes[1, 1]) | code |
130011352/cell_3 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import json
import os
source_list = set()
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
with open(str(os.path.join(dirname, filename)), 'r') as file:
json_file = json.load(file)
for record in json_file:
source_list.add(record)
df_dictionary = {}
for record in source_list:
df_dictionary[record] = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
name = filename.split('.')
city = name[0]
with open(str(os.path.join(dirname, filename)), 'r') as file:
json_file = json.load(file)
for source in json_file:
temp_df = pd.DataFrame(json_file[source])
temp_df['city'] = city
df_dictionary[source].append(temp_df)
for df in df_dictionary:
result = pd.concat(df_dictionary[df])
df_dictionary[df] = result
airbnbHotels = df_dictionary['airbnbHotels']
bookingHotels = df_dictionary['bookingHotels']
hotelsComHotels = df_dictionary['hotelsComHotels']
L = bookingHotels['price'].apply(lambda x: x['value'])
print(np.mean(L)) | code |
130011352/cell_5 | [
"text_plain_output_1.png"
] | import json
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import json
import os
source_list = set()
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
with open(str(os.path.join(dirname, filename)), 'r') as file:
json_file = json.load(file)
for record in json_file:
source_list.add(record)
df_dictionary = {}
for record in source_list:
df_dictionary[record] = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
name = filename.split('.')
city = name[0]
with open(str(os.path.join(dirname, filename)), 'r') as file:
json_file = json.load(file)
for source in json_file:
temp_df = pd.DataFrame(json_file[source])
temp_df['city'] = city
df_dictionary[source].append(temp_df)
for df in df_dictionary:
result = pd.concat(df_dictionary[df])
df_dictionary[df] = result
airbnbHotels = df_dictionary['airbnbHotels']
bookingHotels = df_dictionary['bookingHotels']
hotelsComHotels = df_dictionary['hotelsComHotels']
L = bookingHotels['price'].apply(lambda x: x['value'])
print(pd.value_counts(airbnbHotels['price'].apply(lambda x: x['currency'])))
print(np.mean(airbnbHotels['price'].apply(lambda x: x['value'])))
print(pd.value_counts(bookingHotels['price'].apply(lambda x: x['currency'])))
print(np.mean(bookingHotels['price'].apply(lambda x: x['value'])))
print(pd.value_counts(hotelsComHotels['price'].apply(lambda x: x['currency'])))
print(np.mean(hotelsComHotels['price'].apply(lambda x: x['value']))) | code |
1005853/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbottom='on')
plt.xlim([0, 4])
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.tight_layout()
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
plt.xlim([-1, 2])
ax.set_xticks([0, 1])
ax.set_xticklabels(['Not survived', 'Survived'], rotation='vertical')
plt.tight_layout()
y_surv = [len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
y_not_surv = [len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
(y_surv, y_not_surv)
x = np.array([1, 2, 3])
width=0.3
fig, ax = plt.subplots()
bar1 = ax.bar(x - width, y_surv, width, color='lightblue', label='Survived')
bar2 = ax.bar(x, y_not_surv, width, color='pink', label='Not survived')
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.xlim([0, 4])
plt.ylabel('Count')
plt.grid(True)
plt.legend(loc='upper left')
counts = train_data.groupby(['Age_group', 'Survived']).Age_group.count().unstack()
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
y_surv_2 = [len(train_data[(train_data['Survived'] == 1) & (train_data['Embarked'] == 'S')]['Embarked'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Embarked'] == 'C')]['Embarked'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Embarked'] == 'Q')]['Embarked'].tolist())]
y_not_surv_2 = [len(train_data[(train_data['Survived'] == 0) & (train_data['Embarked'] == 'S')]['Embarked'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Embarked'] == 'C')]['Embarked'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Embarked'] == 'Q')]['Embarked'].tolist())]
(y_surv_2, y_not_surv_2) | code |
1005853/cell_13 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
sum(train_data[train_data['Survived'] == 1]['Age'].isnull()) / len(train_data) | code |
1005853/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data['Pclass'].unique() | code |
1005853/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data.describe() | code |
1005853/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbottom='on')
plt.xlim([0, 4])
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.tight_layout()
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
plt.xlim([-1, 2])
ax.set_xticks([0, 1])
ax.set_xticklabels(['Not survived', 'Survived'], rotation='vertical')
plt.tight_layout()
y_surv = [len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
y_not_surv = [len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
(y_surv, y_not_surv)
x = np.array([1, 2, 3])
width = 0.3
fig, ax = plt.subplots()
bar1 = ax.bar(x - width, y_surv, width, color='lightblue', label='Survived')
bar2 = ax.bar(x, y_not_surv, width, color='pink', label='Not survived')
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.xlim([0, 4])
plt.ylabel('Count')
plt.grid(True)
plt.legend(loc='upper left') | code |
1005853/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbottom='on')
plt.xlim([0, 4])
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.tight_layout()
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
plt.xlim([-1, 2])
ax.set_xticks([0, 1])
ax.set_xticklabels(['Not survived', 'Survived'], rotation='vertical')
plt.tight_layout()
y_surv = [len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
y_not_surv = [len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
(y_surv, y_not_surv)
x = np.array([1, 2, 3])
width=0.3
fig, ax = plt.subplots()
bar1 = ax.bar(x - width, y_surv, width, color='lightblue', label='Survived')
bar2 = ax.bar(x, y_not_surv, width, color='pink', label='Not survived')
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.xlim([0, 4])
plt.ylabel('Count')
plt.grid(True)
plt.legend(loc='upper left')
counts = train_data.groupby(['Age_group', 'Survived']).Age_group.count().unstack()
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
train_data['Embarked'].value_counts() | code |
1005853/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.hist(train_data['Pclass'], color='lightblue')
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbottom='on')
plt.xlim([0, 4])
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.xlabel('Pclass')
plt.ylabel('Count')
plt.grid(True)
plt.tight_layout() | code |
1005853/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbottom='on')
plt.xlim([0, 4])
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.tight_layout()
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
plt.xlim([-1, 2])
ax.set_xticks([0, 1])
ax.set_xticklabels(['Not survived', 'Survived'], rotation='vertical')
plt.tight_layout()
y_surv = [len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
y_not_surv = [len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
(y_surv, y_not_surv)
x = np.array([1, 2, 3])
width=0.3
fig, ax = plt.subplots()
bar1 = ax.bar(x - width, y_surv, width, color='lightblue', label='Survived')
bar2 = ax.bar(x, y_not_surv, width, color='pink', label='Not survived')
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.xlim([0, 4])
plt.ylabel('Count')
plt.grid(True)
plt.legend(loc='upper left')
counts = train_data.groupby(['Age_group', 'Survived']).Age_group.count().unstack()
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
sum(train_data['Embarked'].isnull()) | code |
1005853/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbottom='on')
plt.xlim([0, 4])
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.tight_layout()
plt.hist(train_data['Survived'], color='lightblue')
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
plt.grid(True)
plt.xlim([-1, 2])
ax.set_xticks([0, 1])
ax.set_xticklabels(['Not survived', 'Survived'], rotation='vertical')
plt.ylabel('Count')
plt.tight_layout() | code |
1005853/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
plt.tick_params(top='off', bottom='on', left='off', right='off', labelleft='on', labelbottom='on')
plt.xlim([0, 4])
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.tight_layout()
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
plt.xlim([-1, 2])
ax.set_xticks([0, 1])
ax.set_xticklabels(['Not survived', 'Survived'], rotation='vertical')
plt.tight_layout()
y_surv = [len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
y_not_surv = [len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
(y_surv, y_not_surv)
x = np.array([1, 2, 3])
width=0.3
fig, ax = plt.subplots()
bar1 = ax.bar(x - width, y_surv, width, color='lightblue', label='Survived')
bar2 = ax.bar(x, y_not_surv, width, color='pink', label='Not survived')
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
ax.set_xticks([1, 2, 3])
plt.xlim([0, 4])
plt.ylabel('Count')
plt.grid(True)
plt.legend(loc='upper left')
counts = train_data.groupby(['Age_group', 'Survived']).Age_group.count().unstack()
counts.plot(kind='bar', stacked=True, color=['lightblue', 'pink'])
plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='on', labelbottom='on')
ax = plt.gca()
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(True)
ax.spines['bottom'].set_visible(True)
plt.grid(True) | code |
1005853/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
sum(train_data[train_data['Survived'] == 0]['Age'].isnull()) / len(train_data) | code |
1005853/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
y_surv = [len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 1) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
y_not_surv = [len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 1)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 2)]['Pclass'].tolist()), len(train_data[(train_data['Survived'] == 0) & (train_data['Pclass'] == 3)]['Pclass'].tolist())]
(y_surv, y_not_surv) | code |
1005853/cell_12 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
sum(train_data['Age'].isnull()) / len(train_data) | code |
1005853/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], inplace=True, axis=1)
train_data.head() | code |
2017164/cell_9 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
test.shape
test.dtypes | code |
2017164/cell_25 | [
"text_html_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import accuracy_score
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape
test.shape
train.dtypes
test.dtypes
test.fillna('missing', inplace=True)
target_labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
X = train.comment_text
test_X = test.comment_text
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
vect = TfidfVectorizer(max_features=50000, min_df=2)
X_dtm = vect.fit_transform(X)
test_X_dtm = vect.transform(test_X)
from sklearn.metrics import accuracy_score
from sklearn.ensemble import ExtraTreesClassifier
etc = ExtraTreesClassifier(n_jobs=-1, random_state=3)
for label in target_labels:
print('... Processing {}'.format(label))
y = train[label]
etc.fit(X_dtm, y)
y_pred_X = etc.predict(X_dtm)
print('Training accuracy is {}'.format(accuracy_score(y, y_pred_X)))
test_y_prob = etc.predict_proba(test_X_dtm)[:, 1]
sub[label] = test_y_prob | code |
2017164/cell_4 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
test.shape | code |
2017164/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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape
test.shape
train.dtypes
test.dtypes
test.fillna('missing', inplace=True)
X = train.comment_text
test_X = test.comment_text
print(X.shape, test_X.shape) | code |
2017164/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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
test.shape
test.head() | code |
2017164/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2017164/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
sub.head() | code |
2017164/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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape
train.dtypes | code |
2017164/cell_15 | [
"text_plain_output_1.png"
] | import seaborn as sns
colormap = plt.cm.plasma
plt.figure(figsize=(8, 8))
plt.title('Correlation of features & targets', y=1.05, size=14)
sns.heatmap(data.astype(float).corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True) | code |
2017164/cell_3 | [
"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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape | code |
2017164/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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
test.shape
test.dtypes
test[test['comment_text'].isnull()] | code |
2017164/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/train.csv')
test = pd.read_csv('../input/test.csv')
sub = pd.read_csv('../input/sample_submission.csv')
train.shape
train.head() | code |
105218033/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
num_pipeline = Pipeline([('num_imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
from sklearn.preprocessing import OneHotEncoder
cat_pipeline = Pipeline([('cat_imputer', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder())])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures
num_labels = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
cat_labels = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num_trans', num_pipeline, num_labels), ('cat_trans', cat_pipeline, cat_labels)])
x_train_prepared = full_pipeline.fit_transform(x_train)
def model_fit_and_print_acc(model):
model.fit(x_train_prepared, y_train)
pred = model.predict(x_train_prepared)
cv_scores = cross_val_score(model, x_train_prepared, y_train, cv=3, scoring='accuracy')
from sklearn.ensemble import RandomForestClassifier
forest_clf = RandomForestClassifier()
from sklearn.model_selection import GridSearchCV
param_grid = [{'n_estimators': [150, 200, 250], 'max_features': [5, 10], 'max_depth': [5, 10, 20], 'min_samples_leaf': [5, 10, 20], 'min_samples_split': [5, 10, 20]}]
forest_clf = RandomForestClassifier()
grid_search = GridSearchCV(forest_clf, param_grid, cv=3, scoring='accuracy', return_train_score=True)
grid_search.fit(x_train_prepared, y_train)
best_search_rf = grid_search.best_estimator_
grid_search.best_params_
model_fit_and_print_acc(best_search_rf) | code |
105218033/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
x_train.describe() | code |
105218033/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
num_pipeline = Pipeline([('num_imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
from sklearn.preprocessing import OneHotEncoder
cat_pipeline = Pipeline([('cat_imputer', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder())])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures
num_labels = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
cat_labels = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num_trans', num_pipeline, num_labels), ('cat_trans', cat_pipeline, cat_labels)])
x_train_prepared = full_pipeline.fit_transform(x_train)
def model_fit_and_print_acc(model):
model.fit(x_train_prepared, y_train)
pred = model.predict(x_train_prepared)
cv_scores = cross_val_score(model, x_train_prepared, y_train, cv=3, scoring='accuracy')
poly_log_reg = Pipeline([('poly_features', PolynomialFeatures(degree=3, include_bias=False)), ('log_reg', LogisticRegression(penalty='elasticnet', l1_ratio=0.7, max_iter=100000, solver='saga'))])
from sklearn.ensemble import RandomForestClassifier
forest_clf = RandomForestClassifier()
from sklearn.model_selection import GridSearchCV
param_grid = [{'n_estimators': [150, 200, 250], 'max_features': [5, 10], 'max_depth': [5, 10, 20], 'min_samples_leaf': [5, 10, 20], 'min_samples_split': [5, 10, 20]}]
forest_clf = RandomForestClassifier()
grid_search = GridSearchCV(forest_clf, param_grid, cv=3, scoring='accuracy', return_train_score=True)
grid_search.fit(x_train_prepared, y_train)
best_search_rf = grid_search.best_estimator_
grid_search.best_params_
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(20)
from sklearn.ensemble import VotingClassifier
voting = VotingClassifier(estimators=[('poly_logres', poly_log_reg), ('grid_randomforest', best_search_rf), ('knn', knn)], voting='hard')
voting.fit(x_train_prepared, y_train)
pred = voting.predict(x_train_prepared)
print(accuracy_score(pred, y_train))
cv_scores = cross_val_score(voting, x_train_prepared, y_train, cv=3, scoring='accuracy')
cv_scores.mean() | code |
105218033/cell_20 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
num_pipeline = Pipeline([('num_imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
from sklearn.preprocessing import OneHotEncoder
cat_pipeline = Pipeline([('cat_imputer', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder())])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures
num_labels = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
cat_labels = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num_trans', num_pipeline, num_labels), ('cat_trans', cat_pipeline, cat_labels)])
x_train_prepared = full_pipeline.fit_transform(x_train)
def model_fit_and_print_acc(model):
model.fit(x_train_prepared, y_train)
pred = model.predict(x_train_prepared)
cv_scores = cross_val_score(model, x_train_prepared, y_train, cv=3, scoring='accuracy')
from sklearn.ensemble import RandomForestClassifier
forest_clf = RandomForestClassifier()
from sklearn.model_selection import GridSearchCV
param_grid = [{'n_estimators': [150, 200, 250], 'max_features': [5, 10], 'max_depth': [5, 10, 20], 'min_samples_leaf': [5, 10, 20], 'min_samples_split': [5, 10, 20]}]
forest_clf = RandomForestClassifier()
grid_search = GridSearchCV(forest_clf, param_grid, cv=3, scoring='accuracy', return_train_score=True)
grid_search.fit(x_train_prepared, y_train)
best_search_rf = grid_search.best_estimator_
print(best_search_rf)
grid_search.best_params_ | code |
105218033/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
num_pipeline = Pipeline([('num_imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
from sklearn.preprocessing import OneHotEncoder
cat_pipeline = Pipeline([('cat_imputer', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder())])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures
num_labels = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
cat_labels = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num_trans', num_pipeline, num_labels), ('cat_trans', cat_pipeline, cat_labels)])
x_train_prepared = full_pipeline.fit_transform(x_train)
def model_fit_and_print_acc(model):
model.fit(x_train_prepared, y_train)
pred = model.predict(x_train_prepared)
cv_scores = cross_val_score(model, x_train_prepared, y_train, cv=3, scoring='accuracy')
from sklearn.ensemble import RandomForestClassifier
forest_clf = RandomForestClassifier()
model_fit_and_print_acc(forest_clf) | code |
105218033/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
x_train.info() | code |
105218033/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
num_pipeline = Pipeline([('num_imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
from sklearn.preprocessing import OneHotEncoder
cat_pipeline = Pipeline([('cat_imputer', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder())])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures
num_labels = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
cat_labels = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num_trans', num_pipeline, num_labels), ('cat_trans', cat_pipeline, cat_labels)])
x_train_prepared = full_pipeline.fit_transform(x_train)
def model_fit_and_print_acc(model):
model.fit(x_train_prepared, y_train)
pred = model.predict(x_train_prepared)
cv_scores = cross_val_score(model, x_train_prepared, y_train, cv=3, scoring='accuracy')
poly_log_reg = Pipeline([('poly_features', PolynomialFeatures(degree=3, include_bias=False)), ('log_reg', LogisticRegression(penalty='elasticnet', l1_ratio=0.7, max_iter=100000, solver='saga'))])
model_fit_and_print_acc(poly_log_reg) | code |
105218033/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
x_test.info() | code |
105218033/cell_17 | [
"text_html_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
num_pipeline = Pipeline([('num_imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
from sklearn.preprocessing import OneHotEncoder
cat_pipeline = Pipeline([('cat_imputer', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder())])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures
num_labels = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
cat_labels = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num_trans', num_pipeline, num_labels), ('cat_trans', cat_pipeline, cat_labels)])
x_train_prepared = full_pipeline.fit_transform(x_train)
def model_fit_and_print_acc(model):
model.fit(x_train_prepared, y_train)
pred = model.predict(x_train_prepared)
cv_scores = cross_val_score(model, x_train_prepared, y_train, cv=3, scoring='accuracy')
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
log_reg = LogisticRegression()
model_fit_and_print_acc(log_reg) | code |
105218033/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler
import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
labels = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']
x_train = x_train[labels]
x_test = x_test[labels]
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
num_pipeline = Pipeline([('num_imputer', SimpleImputer(strategy='median')), ('std_scaler', StandardScaler())])
from sklearn.preprocessing import OneHotEncoder
cat_pipeline = Pipeline([('cat_imputer', SimpleImputer(strategy='most_frequent')), ('encode', OneHotEncoder())])
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PolynomialFeatures
num_labels = ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
cat_labels = ['Sex', 'Embarked']
full_pipeline = ColumnTransformer([('num_trans', num_pipeline, num_labels), ('cat_trans', cat_pipeline, cat_labels)])
x_train_prepared = full_pipeline.fit_transform(x_train)
def model_fit_and_print_acc(model):
model.fit(x_train_prepared, y_train)
pred = model.predict(x_train_prepared)
cv_scores = cross_val_score(model, x_train_prepared, y_train, cv=3, scoring='accuracy')
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(20)
model_fit_and_print_acc(knn) | code |
105218033/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
x_train = pd.read_csv('/kaggle/input/titanic/train.csv')
x_test = pd.read_csv('/kaggle/input/titanic/test.csv')
y_train = np.array(x_train['Survived'].copy())
id_test = np.array(x_test['PassengerId'].copy())
x_train.head() | code |
72097528/cell_9 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna().sum().sum() + test.isnull().sum().sum()
from sklearn.model_selection import train_test_split
y = train['target']
X = train.drop(['target'], axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X.columns if X[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid[col]).issubset(set(X_train[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
label_X_train = X_train.drop(bad_label_cols, axis=1)
label_X_valid = X_valid.drop(bad_label_cols, axis=1)
label_X_train[good_label_cols] = enc.fit_transform(X_train[good_label_cols])
label_X_valid[good_label_cols] = enc.transform(X_valid[good_label_cols])
label_X_train.head() | code |
72097528/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna().sum().sum() + test.isnull().sum().sum()
print('Missing values - train data: {}'.format(missing_train))
print('Missing values - test data: {}'.format(missing_test)) | code |
72097528/cell_6 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna().sum().sum() + test.isnull().sum().sum()
from sklearn.model_selection import train_test_split
y = train['target']
X = train.drop(['target'], axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X.columns if X[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid[col]).issubset(set(X_train[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
if good_label_cols == object_cols:
print('All object columns are good!')
else:
print('These are the bad object columns: ', bad_label_cols) | code |
72097528/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 |
72097528/cell_15 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna().sum().sum() + test.isnull().sum().sum()
from sklearn.model_selection import train_test_split
y = train['target']
X = train.drop(['target'], axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X.columns if X[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid[col]).issubset(set(X_train[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
label_X_train = X_train.drop(bad_label_cols, axis=1)
label_X_valid = X_valid.drop(bad_label_cols, axis=1)
label_X_train[good_label_cols] = enc.fit_transform(X_train[good_label_cols])
label_X_valid[good_label_cols] = enc.transform(X_valid[good_label_cols])
low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]
high_cardinality_cols = list(set(object_cols) - set(low_cardinality_cols))
from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder(sparse=False)
OH_X_train = pd.DataFrame(enc.fit_transform(X_train[low_cardinality_cols]))
OH_X_valid = pd.DataFrame(enc.transform(X_valid[low_cardinality_cols]))
OH_X_train.index = X_train.index
OH_X_valid.index = X_valid.index
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)
OH_X_train = pd.concat([num_X_train, OH_X_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_X_valid], axis=1)
OH_X_train.describe() | code |
72097528/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
train.describe() | code |
72097528/cell_17 | [
"text_html_output_1.png"
] | from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna().sum().sum() + test.isnull().sum().sum()
from sklearn.model_selection import train_test_split
y = train['target']
X = train.drop(['target'], axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X.columns if X[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid[col]).issubset(set(X_train[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
label_X_train = X_train.drop(bad_label_cols, axis=1)
label_X_valid = X_valid.drop(bad_label_cols, axis=1)
label_X_train[good_label_cols] = enc.fit_transform(X_train[good_label_cols])
label_X_valid[good_label_cols] = enc.transform(X_valid[good_label_cols])
low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]
high_cardinality_cols = list(set(object_cols) - set(low_cardinality_cols))
from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder(sparse=False)
OH_X_train = pd.DataFrame(enc.fit_transform(X_train[low_cardinality_cols]))
OH_X_valid = pd.DataFrame(enc.transform(X_valid[low_cardinality_cols]))
OH_X_train.index = X_train.index
OH_X_valid.index = X_valid.index
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)
OH_X_train = pd.concat([num_X_train, OH_X_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_X_valid], axis=1)
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(label_X_train)
X_scaled = pd.DataFrame(scaler.transform(label_X_train), columns=label_X_train.columns, index=label_X_train.index)
X_scaled | code |
72097528/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna().sum().sum() + test.isnull().sum().sum()
from sklearn.model_selection import train_test_split
y = train['target']
X = train.drop(['target'], axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X.columns if X[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid[col]).issubset(set(X_train[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
label_X_train = X_train.drop(bad_label_cols, axis=1)
label_X_valid = X_valid.drop(bad_label_cols, axis=1)
label_X_train[good_label_cols] = enc.fit_transform(X_train[good_label_cols])
label_X_valid[good_label_cols] = enc.transform(X_valid[good_label_cols])
low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]
high_cardinality_cols = list(set(object_cols) - set(low_cardinality_cols))
from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder(sparse=False)
OH_X_train = pd.DataFrame(enc.fit_transform(X_train[low_cardinality_cols]))
OH_X_valid = pd.DataFrame(enc.transform(X_valid[low_cardinality_cols]))
OH_X_train.index = X_train.index
OH_X_valid.index = X_valid.index
num_X_train = X_train.drop(object_cols, axis=1)
num_X_valid = X_valid.drop(object_cols, axis=1)
OH_X_train = pd.concat([num_X_train, OH_X_train], axis=1)
OH_X_valid = pd.concat([num_X_valid, OH_X_valid], axis=1)
OH_X_train.head() | code |
72097528/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna().sum().sum() + test.isnull().sum().sum()
from sklearn.model_selection import train_test_split
y = train['target']
X = train.drop(['target'], axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X.columns if X[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid[col]).issubset(set(X_train[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
label_X_train = X_train.drop(bad_label_cols, axis=1)
label_X_valid = X_valid.drop(bad_label_cols, axis=1)
label_X_train[good_label_cols] = enc.fit_transform(X_train[good_label_cols])
label_X_valid[good_label_cols] = enc.transform(X_valid[good_label_cols])
label_X_train.describe() | code |
72097528/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/30-days-of-ml/train.csv', index_col='id')
test = pd.read_csv('/kaggle/input/30-days-of-ml/test.csv', index_col='id')
missing_train = train.isna().sum().sum() + train.isnull().sum().sum()
missing_test = test.isna().sum().sum() + test.isnull().sum().sum()
from sklearn.model_selection import train_test_split
y = train['target']
X = train.drop(['target'], axis=1)
X_train, X_valid, y_train, y_valid = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)
object_cols = [col for col in X.columns if X[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(X_valid[col]).issubset(set(X_train[col]))]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
from sklearn.preprocessing import OrdinalEncoder
enc = OrdinalEncoder()
label_X_train = X_train.drop(bad_label_cols, axis=1)
label_X_valid = X_valid.drop(bad_label_cols, axis=1)
label_X_train[good_label_cols] = enc.fit_transform(X_train[good_label_cols])
label_X_valid[good_label_cols] = enc.transform(X_valid[good_label_cols])
low_cardinality_cols = [col for col in object_cols if X_train[col].nunique() < 10]
high_cardinality_cols = list(set(object_cols) - set(low_cardinality_cols))
print('Categorical columns that will be one-hot encoded:', low_cardinality_cols)
print('\nCategorical columns that will be dropped from the dataset:', high_cardinality_cols) | code |
74060776/cell_9 | [
"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('/kaggle/input/mba-admission/Admission.csv')
ndf = df.copy()
ndf['Decision'] = ndf['Decision'].astype('category')
ndf['Decision'] = ndf['Decision'].cat.codes
sns.heatmap(ndf.corr() ** 2, annot=True, cmap='viridis') | code |
74060776/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
df.describe() | code |
74060776/cell_6 | [
"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('/kaggle/input/mba-admission/Admission.csv')
plt.figure(figsize=(12, 6))
sns.kdeplot(data=df[df['Decision'] == 'admit'], x='GMAT', shade=True, label='Admitted')
sns.kdeplot(data=df[df['Decision'] == 'border'], x='GMAT', color='green', shade=True, label='Border')
sns.kdeplot(data=df[df['Decision'] == 'notadmit'], x='GMAT', color='orange', shade=True, label='Declined')
plt.title('GMAT Distributions')
plt.legend() | code |
74060776/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
df.head() | code |
74060776/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74060776/cell_7 | [
"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('/kaggle/input/mba-admission/Admission.csv')
plt.figure(figsize=(12, 6))
sns.kdeplot(data=df[df['Decision'] == 'admit'], x='GPA', shade=True, label='Admitted')
sns.kdeplot(data=df[df['Decision'] == 'border'], x='GPA', color='green', shade=True, label='Border')
sns.kdeplot(data=df[df['Decision'] == 'notadmit'], x='GPA', color='orange', shade=True, label='Declined')
plt.title('GPA Distributions')
plt.legend() | code |
74060776/cell_8 | [
"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
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
sns.jointplot(data=df, x='GPA', y='GMAT', hue='Decision') | code |
74060776/cell_16 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
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('/kaggle/input/mba-admission/Admission.csv')
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
rfr = RandomForestClassifier()
knn = KNeighborsClassifier()
logmod = LogisticRegression()
rfr.fit(X_train, y_train)
knn.fit(X_train, y_train)
logmod.fit(X_train, y_train)
error = []
for k in range(1, 40):
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
p = knn.predict(X_test)
error.append(np.mean(y_test != p))
plt.plot(range(1, 40), error, ls='--', marker='X', markerfacecolor='red') | code |
74060776/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
df.info() | code |
74060776/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
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('/kaggle/input/mba-admission/Admission.csv')
ndf = df.copy()
ndf['Decision'] = ndf['Decision'].astype('category')
ndf['Decision'] = ndf['Decision'].cat.codes
X = ndf.drop('Decision', axis=1)
y = ndf['Decision']
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X = scaler.fit_transform(X)
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
rfr = RandomForestClassifier()
knn = KNeighborsClassifier()
logmod = LogisticRegression()
rfr.fit(X_train, y_train)
knn.fit(X_train, y_train)
logmod.fit(X_train, y_train)
error = []
for k in range(1, 40):
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
p = knn.predict(X_test)
error.append(np.mean(y_test != p))
score = []
for i in range(1, 50):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
score.append(knn.score(X_test, y_test))
np.array(score).mean() | code |
74060776/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
rfr = RandomForestClassifier()
knn = KNeighborsClassifier()
logmod = LogisticRegression()
rfr.fit(X_train, y_train)
knn.fit(X_train, y_train)
logmod.fit(X_train, y_train)
print('Random Forest Regressor Score: {:.2f}'.format(rfr.score(X_test, y_test)))
print('K Nearest Neighbors Score: {:.2f}'.format(knn.score(X_test, y_test)))
print('Logistic Regression Score: {:.2f}'.format(logmod.score(X_test, y_test))) | code |
74060776/cell_10 | [
"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('/kaggle/input/mba-admission/Admission.csv')
ndf = df.copy()
ndf['Decision'] = ndf['Decision'].astype('category')
ndf['Decision'] = ndf['Decision'].cat.codes
ndf.head() | code |
74060776/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/mba-admission/Admission.csv')
df['Decision'].unique() | code |
73061015/cell_21 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
y_train = kr.utils.to_categorical(y_train, 10)
y_test = kr.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result1 = model.fit(x_train, y_train, batch_size=128, epochs=20)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result2 = model.fit(x_train, y_train, batch_size=128, epochs=20) | code |
73061015/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
plt.figure(figsize=(8, 8))
for i in range(10):
plt.subplot(5, 5, i + 1)
plt.title(i)
plt.imshow(x_train[i].reshape(32, 32, 3)) | code |
73061015/cell_23 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
y_train = kr.utils.to_categorical(y_train, 10)
y_test = kr.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result1 = model.fit(x_train, y_train, batch_size=128, epochs=20)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result2 = model.fit(x_train, y_train, batch_size=128, epochs=20)
metrics = ['loss', 'accuracy']
plt.figure(figsize=(10, 5))
for i in range(len(metrics)):
metric = metrics[i]
plt.subplot(1, 2, i + 1)
plt.title(metric)
plt_result1 = result1.history[metric]
plt_result2 = result2.history[metric]
plt.plot(plt_result1, label='1st model')
plt.plot(plt_result2, label='2nd model')
plt.legend()
plt.show() | code |
73061015/cell_26 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
import matplotlib.pyplot as plt
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
y_train = kr.utils.to_categorical(y_train, 10)
y_test = kr.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result1 = model.fit(x_train, y_train, batch_size=128, epochs=20)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result2 = model.fit(x_train, y_train, batch_size=128, epochs=20)
metrics = ['loss', 'accuracy']
for i in range(len(metrics)):
metric = metrics[i]
plt_result1 = result1.history[metric]
plt_result2 = result2.history[metric]
plt.imshow(x_test[[98]].reshape(32, 32, 3)) | code |
73061015/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
y_train = kr.utils.to_categorical(y_train, 10)
y_test = kr.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result1 = model.fit(x_train, y_train, batch_size=128, epochs=20)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary() | code |
73061015/cell_7 | [
"text_plain_output_1.png"
] | from keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data() | code |
73061015/cell_28 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
import numpy as np
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
y_train = kr.utils.to_categorical(y_train, 10)
y_test = kr.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result1 = model.fit(x_train, y_train, batch_size=128, epochs=20)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result2 = model.fit(x_train, y_train, batch_size=128, epochs=20)
prediction = model.predict(x_test[[98]])
prediction
names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
list1 = []
[list1.append(i) for i in range(26)]
list2 = []
[list2.append(i) for i in names]
dic = dict(zip(list1, list2))
print('The answer is', dic[np.argmax(prediction)], '!') | code |
73061015/cell_15 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary() | code |
73061015/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
y_train = kr.utils.to_categorical(y_train, 10)
y_test = kr.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result1 = model.fit(x_train, y_train, batch_size=128, epochs=20) | code |
73061015/cell_31 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
import numpy as np
import pandas as pd
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
y_train = kr.utils.to_categorical(y_train, 10)
y_test = kr.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result1 = model.fit(x_train, y_train, batch_size=128, epochs=20)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result2 = model.fit(x_train, y_train, batch_size=128, epochs=20)
prediction = model.predict(x_test[[98]])
prediction
names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
list1 = []
[list1.append(i) for i in range(26)]
list2 = []
[list2.append(i) for i in names]
dic = dict(zip(list1, list2))
predictions = model.predict(x_test)
results = np.argmax(predictions, axis=1)
results = pd.Series(results, name='Label')
results.tail() | code |
73061015/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import Dense, Activation, Flatten
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.models import Sequential
x_train = x_train.astype('float32') / 255
y_train = y_train.astype('float32')
x_test = x_test.astype('float32') / 255
y_train = kr.utils.to_categorical(y_train, 10)
y_test = kr.utils.to_categorical(y_test, 10)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result1 = model.fit(x_train, y_train, batch_size=128, epochs=20)
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
result2 = model.fit(x_train, y_train, batch_size=128, epochs=20)
prediction = model.predict(x_test[[98]])
prediction | code |
Subsets and Splits