path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
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|---|---|---|---|
72101116/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import math, random
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import LabelEncoder
pd.set_option('display.max_columns', 100)
from lightgbm import LGBMRegressor
SEED = 47
PATH = '../input/30-days-of-ml/'
df_train = pd.read_csv(PATH + '/train.csv')
df_test = pd.read_csv(PATH + '/test.csv')
df_sub = pd.read_csv(PATH + '/sample_submission.csv')
target = df_train['target']
features = df_train.drop(['id', 'target'], axis=1)
features.head() | code |
72101116/cell_28 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | test_feature_matrix.head() | code |
72101116/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import math, random
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import LabelEncoder
pd.set_option('display.max_columns', 100)
from lightgbm import LGBMRegressor
SEED = 47 | code |
72101116/cell_17 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=5)
print('Number of finished trials:', len(study.trials))
print('Best trial:', study.best_trial.params)
print('Best score:', study.best_trial.value) | code |
72101116/cell_22 | [
"text_html_output_2.png"
] | study.best_params | code |
72101116/cell_27 | [
"text_plain_output_1.png"
] | import featuretools as ft
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import math, random
from sklearn.model_selection import KFold, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import LabelEncoder
pd.set_option('display.max_columns', 100)
from lightgbm import LGBMRegressor
SEED = 47
PATH = '../input/30-days-of-ml/'
df_train = pd.read_csv(PATH + '/train.csv')
df_test = pd.read_csv(PATH + '/test.csv')
df_sub = pd.read_csv(PATH + '/sample_submission.csv')
target = df_train['target']
features = df_train.drop(['id', 'target'], axis=1)
es = ft.EntitySet(id='data')
es.entity_from_dataframe(entity_id='january', dataframe=features, index='id')
feature_matrix, feature_defs = ft.dfs(entityset=es, target_entity='january', trans_primitives=['add_numeric', 'multiply_numeric'], verbose=1)
cat_features = ['cat0', 'cat1', 'cat2', 'cat3', 'cat4', 'cat5', 'cat6', 'cat7', 'cat8', 'cat9']
df_cat = feature_matrix[cat_features]
feature_matrix = feature_matrix.drop(cat_features, axis=1)
features = df_test.drop(cat_features, axis=1)
es = ft.EntitySet(id='data')
es.entity_from_dataframe(entity_id='target', dataframe=features, index='id')
test_feature_matrix, test_feature_defs = ft.dfs(entityset=es, target_entity='target', trans_primitives=['add_numeric', 'multiply_numeric'], verbose=1) | code |
130022661/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.countplot(x='Pclass', data=trainbeforedummies) | code |
130022661/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.kdeplot(data=x_reg_df, x='Age') | code |
130022661/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.countplot(data=trainbeforedummies, x='Pclass', hue='Survived') | code |
130022661/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test = test.drop(['Cabin'], axis=1)
print('Na after fixing cabin and embark')
for column in train.columns:
countna(train, column)
test = test.drop(['PassengerId'], axis=1)
test = pd.get_dummies(test, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
test.Sex = [gender[item] for item in test.Sex]
test = test.drop(['Name', 'Ticket'], axis=1)
test = imputer.transform(test)
test_norm = norm.transform(test)
test_stand = test.copy()
for i in [1, 2, 3, 4]:
test_stand[i] = scale.transform(test_stand[[i]]) | code |
130022661/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.countplot(x='Sex', data=x_reg_df) | code |
130022661/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.countplot(data=trainbeforedummies, x='Parch', hue='Survived') | code |
130022661/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 |
130022661/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.kdeplot(data=trainbeforedummies, x='Fare', hue='Survived', multiple='stack') | code |
130022661/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
import seaborn as sns
sns.countplot(x='Survived', data=trainbeforedummies) | code |
130022661/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.countplot(data=trainbeforedummies, x='Sex', hue='Survived') | code |
130022661/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
print(train.head())
print('na after downloading data')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
print(column, ' has', num, 'na values')
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
print('Na after fixing cabin and embark')
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]]) | code |
130022661/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.kdeplot(data=trainbeforedummies, x='Age', hue='Survived', multiple='stack') | code |
130022661/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.countplot(x='Embarked', data=trainbeforedummies) | code |
130022661/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.kdeplot(data=x_reg_df, x='Fare') | code |
130022661/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train = pd.read_csv('/kaggle/input/titanic/train.csv')
def countna(df, column):
col = pd.Series(list(df.loc[:, column]))
num = col.isna().sum()
for column in train.columns:
countna(train, column)
train = train.drop(['Cabin'], axis=1)
train = train.dropna(subset=['Embarked'])
for column in train.columns:
countna(train, column)
irrelevant_columns = ['PassengerId']
categorical_columns = ['Pclass', 'Embarked']
to_binary_columns = ['Sex']
numerical_columns = ['Age', 'SibSp', 'Parch', 'Fare']
other_columns = ['Name', 'Ticket']
label_columns = ['Survived']
train = train.drop(['PassengerId'], axis=1)
trainbeforedummies = train.copy()
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['0'], 'Did Not Survive')
trainbeforedummies['Survived'] = trainbeforedummies['Survived'].replace(['1'], 'Survived')
train = pd.get_dummies(train, columns=categorical_columns)
gender = {'male': 1, 'female': 0}
train.Sex = [gender[item] for item in train.Sex]
train = train.drop(['Name', 'Ticket'], axis=1)
y = train.pop('Survived')
x = train
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
from sklearn.impute import KNNImputer
imputer = KNNImputer(n_neighbors=2, weights='uniform')
imputer = imputer.fit(x)
x_train = imputer.transform(x_train)
x_test = imputer.transform(x_test)
from sklearn.preprocessing import MinMaxScaler
norm = MinMaxScaler().fit(x_train)
x_train_norm = norm.transform(x_train)
x_test_norm = norm.transform(x_test)
from sklearn.preprocessing import StandardScaler
x_train_stand = x_train.copy()
x_test_stand = x_test.copy()
for i in [1, 2, 3, 4]:
scale = StandardScaler().fit(x_train_stand[[i]])
x_train_stand[i] = scale.transform(x_train_stand[[i]])
x_test_stand[i] = scale.transform(x_test_stand[[i]])
x_train_stand_df = pd.DataFrame(x_train_stand, columns=list(x.columns))
x_train_norm_df = pd.DataFrame(x_train_norm, columns=list(x.columns))
x_train_reg_df = pd.DataFrame(x_train, columns=list(x.columns))
x_test_stand_df = pd.DataFrame(x_test_stand, columns=list(x.columns))
x_test_norm_df = pd.DataFrame(x_test_norm, columns=list(x.columns))
x_test_reg_df = pd.DataFrame(x_test, columns=list(x.columns))
x_stand_df = pd.concat([x_train_stand_df, x_test_stand_df])
x_norm_df = pd.concat([x_train_norm_df, x_test_norm_df])
x_reg_df = pd.concat([x_train_reg_df, x_test_reg_df])
x_stand_df_with_y = x_stand_df.copy()
x_stand_df_with_y['Survived'] = y
x_norm_df_with_y = x_norm_df.copy()
x_norm_df_with_y['Survived'] = y
x_reg_df_with_y = x_norm_df.copy()
x_reg_df_with_y['Survived'] = y
import seaborn as sns
sns.countplot(x='Parch', data=x_reg_df) | code |