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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
90108519/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
df.describe() | code |
90108519/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
fig, ax1 = plt.subplots(figsize=(12, 5))
plt.title('The difference between the genders in total and percentage terms', fontweight='bold', fontsize = 12)
ax1.set_xlabel('year', fontsize = 10,fontweight='bold')
ax1.set_ylabel('total',fontweight='bold', fontsize = 10, color = 'green')
plt.plot(df['year'], df['male'] - df['female'], linewidth=3,label= 'total', color = 'green')
ax1.tick_params(axis='y')
ax2 = ax1.twinx()
ax2.set_ylabel('percent', fontweight='bold', fontsize = 10)
plt.plot(df['year'], (df['male'] - df['female'])/df['total']*100, linewidth=3, color = 'black', label= 'percent')
ax2.tick_params(axis='y')
ax2.yaxis.set_major_formatter(mtick.PercentFormatter())
fig.tight_layout()
fig, ax1 = plt.subplots(figsize=(12, 5))
plt.title('Changing of population growth', fontweight='bold', fontsize=12)
ax1.set_xlabel('year', fontsize=10, fontweight='bold')
ax1.set_ylabel('total number', fontweight='bold', fontsize=10, color='green')
plt.plot(df['year'], df['total'], linewidth=3, label='total', color='green')
ax1.tick_params(axis='y')
ax2 = ax1.twinx()
ax2.set_ylabel('total growth', fontweight='bold', fontsize=10)
plt.plot(df['year'], df['total'] - df['total'].shift(), linewidth=3, color='black', label='percent')
ax2.tick_params(axis='y')
plt.axhline(y=0, color='red', linestyle='--')
fig.tight_layout() | code |
90108519/cell_26 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
fig, ax1 = plt.subplots(figsize=(12, 5))
plt.title('The difference between the genders in total and percentage terms', fontweight='bold', fontsize=12)
ax1.set_xlabel('year', fontsize=10, fontweight='bold')
ax1.set_ylabel('total', fontweight='bold', fontsize=10, color='green')
plt.plot(df['year'], df['male'] - df['female'], linewidth=3, label='total', color='green')
ax1.tick_params(axis='y')
ax2 = ax1.twinx()
ax2.set_ylabel('percent', fontweight='bold', fontsize=10)
plt.plot(df['year'], (df['male'] - df['female']) / df['total'] * 100, linewidth=3, color='black', label='percent')
ax2.tick_params(axis='y')
ax2.yaxis.set_major_formatter(mtick.PercentFormatter())
fig.tight_layout() | code |
90108519/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
df.info() | code |
90108519/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.head() | code |
90108519/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
print('Cases of nonconformity by territory: {}'.format(sum(df['total'] - df['urban'] - df['rural']))) | code |
90108519/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask] | code |
90108519/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/chinas-population-by-gender-and-urbanrural/Chinas Population En.csv')
df.columns = ['year', 'total', 'male', 'female', 'urban', 'rural']
df.sort_values(by='year', ignore_index=True, inplace=True)
tmp_mask = df['total'] - df['male'] - df['female'] != 0
df[tmp_mask]
df.loc[tmp_mask, 'total'] = df.loc[tmp_mask, 'male'] + df.loc[tmp_mask, 'female']
print('Cases of nonconformity by gender: {}'.format(sum(df['total'] - df['male'] - df['female']))) | code |
16119977/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.windspeed.plot(kind='box') | code |
16119977/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum() | code |
16119977/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)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.info() | code |
16119977/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum() | code |
16119977/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['temp'].unique() | code |
16119977/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
16119977/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)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape | code |
16119977/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.registered.plot(kind='box') | code |
16119977/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['count'].plot(kind='box') | code |
16119977/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)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.head() | code |
16119977/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf['count'].value_counts() | code |
16119977/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum()
dataf.casual.plot(kind='box') | code |
16119977/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T
dataf.duplicated().sum()
dataf.shape
dataf.drop_duplicates(inplace=True)
dataf.duplicated().sum()
dataf.isna().sum() | code |
16119977/cell_12 | [
"text_html_output_1.png"
] | dataf.season.plot(kind='box') | code |
16119977/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)
dataf = pd.read_csv('../input/bike_share.csv')
dataf.describe().T | code |
2016288/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
img_rows, img_cols = (28, 28)
input_shape = (img_rows, img_cols, 1)
X = fashion_train.drop(['label'], axis=1).values
y = to_categorical(fashion_train['label'].values)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
X_test = fashion_test.drop(['label'], axis=1).values
y_test = to_categorical(fashion_test['label'].values)
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
batch_size = 256
num_classes = 10
epochs = 50
img_rows, img_cols = (28, 28)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal', input_shape=input_shape))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary() | code |
2016288/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
print(fashion_train.isnull().sum().sum())
print(fashion_test.isnull().sum().sum()) | code |
2016288/cell_11 | [
"text_plain_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
img_rows, img_cols = (28, 28)
input_shape = (img_rows, img_cols, 1)
X = fashion_train.drop(['label'], axis=1).values
y = to_categorical(fashion_train['label'].values)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
X_test = fashion_test.drop(['label'], axis=1).values
y_test = to_categorical(fashion_test['label'].values)
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_val = X_val.astype('float32')
X_train /= 255
X_test /= 255
X_val /= 255
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
batch_size = 256
num_classes = 10
epochs = 50
img_rows, img_cols = (28, 28)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal', input_shape=input_shape))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_val, y_val))
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1]) | code |
2016288/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 |
2016288/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
fashion_train.head() | code |
2016288/cell_10 | [
"text_html_output_1.png"
] | from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Dropout, Flatten
from keras.models import Sequential
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
img_rows, img_cols = (28, 28)
input_shape = (img_rows, img_cols, 1)
X = fashion_train.drop(['label'], axis=1).values
y = to_categorical(fashion_train['label'].values)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
X_test = fashion_test.drop(['label'], axis=1).values
y_test = to_categorical(fashion_test['label'].values)
X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
X_val = X_val.reshape(X_val.shape[0], img_rows, img_cols, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_val = X_val.astype('float32')
X_train /= 255
X_test /= 255
X_val /= 255
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
batch_size = 256
num_classes = 10
epochs = 50
img_rows, img_cols = (28, 28)
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', kernel_initializer='he_normal', input_shape=input_shape))
model.add(MaxPooling2D((2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.summary()
history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(X_val, y_val))
score = model.evaluate(X_test, y_test, verbose=0) | code |
2016288/cell_5 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
img_rows, img_cols = (28, 28)
input_shape = (img_rows, img_cols, 1)
X = fashion_train.drop(['label'], axis=1).values
y = to_categorical(fashion_train['label'].values)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
X_test = fashion_test.drop(['label'], axis=1).values
y_test = to_categorical(fashion_test['label'].values) | code |
326868/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
plt.hist(ticket_nums, 50)
plt.xlabel('Ticket number')
plt.ylabel('Count')
plt.show() | code |
326868/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = full[col].apply(encoders[col].transform)
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
train = full[full.PassengerId < 892]
test = full[full.PassengerId >= 892]
rf = RandomForestClassifier(n_estimators=100, oob_score=True)
rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived']) | code |
326868/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
plt.hist(ticket_nums, 50)
plt.xlabel('Ticket number')
plt.ylabel('Count')
plt.show() | code |
326868/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp'] | code |
326868/cell_18 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = full[col].apply(encoders[col].transform)
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
full['Deck'].fillna('N', inplace=True)
encoders['Deck'] = LabelEncoder()
encoders['Deck'].fit(full['Deck'])
full['Deck'] = full['Deck'].apply(encoders['Deck'].transform) | code |
326868/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True) | code |
326868/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = full[col].apply(encoders[col].transform)
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
full_with_deck = full[full['Deck'].notnull()]
full_without_deck = full[~full['Deck'].notnull()]
full_with_deck_means, full_without_deck_means = ([], [])
for col in full_with_deck:
if col not in ['Deck', 'PassengerId']:
sum_means = np.nanmean(full_with_deck[col].values) + np.nanmean(full_without_deck[col].values)
full_with_deck_means.append(np.nanmean(full_with_deck[col].values) / sum_means)
full_without_deck_means.append(np.nanmean(full_without_deck[col].values) / sum_means)
bar_width = 0.35
opacity = 0.4
x_index = np.arange(len(full_with_deck_means))
plt.bar(x_index, full_with_deck_means, bar_width, alpha=opacity, color='b', label='With deck value')
plt.bar(x_index + bar_width, full_without_deck_means, bar_width, alpha=opacity, color='r', label='Missing deck value')
plt.legend()
plt.ylabel('Ratio of means')
plt.xticks(x_index + bar_width, [col for col in full_with_deck if col not in ['PassengerId', 'Deck']])
plt.show() | code |
326868/cell_24 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = full[col].apply(encoders[col].transform)
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
full_with_deck = full[full['Deck'].notnull()]
full_without_deck = full[~full['Deck'].notnull()]
full_with_deck_means, full_without_deck_means = ([], [])
for col in full_with_deck:
if col not in ['Deck', 'PassengerId']:
sum_means = np.nanmean(full_with_deck[col].values) + np.nanmean(full_without_deck[col].values)
full_with_deck_means.append(np.nanmean(full_with_deck[col].values) / sum_means)
full_without_deck_means.append(np.nanmean(full_without_deck[col].values) / sum_means)
bar_width = 0.35
opacity = 0.4
x_index = np.arange(len(full_with_deck_means))
plt.xticks(x_index + bar_width, [col for col in full_with_deck if col not in ['PassengerId', 'Deck']])
train = full[full.PassengerId < 892]
test = full[full.PassengerId >= 892]
rf = RandomForestClassifier(n_estimators=100, oob_score=True)
rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.oob_score_
features = list(zip(train.drop(['Survived', 'PassengerId'], axis=1).columns.values, rf.feature_importances_))
features.sort(key=lambda f: f[1])
names = [f[0] for f in features]
lengths = [f[1] for f in features]
pos = np.arange(len(features)) + 0.5
plt.barh(pos, lengths, align='center', color='r', alpha=opacity)
plt.yticks(pos, names)
plt.xlabel('Gini importance')
plt.show() | code |
326868/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from collections import Counter
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = full[col].apply(encoders[col].transform)
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
Counter(full['Deck'].values) | code |
326868/cell_22 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = full[col].apply(encoders[col].transform)
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
train = full[full.PassengerId < 892]
test = full[full.PassengerId >= 892]
rf = RandomForestClassifier(n_estimators=100, oob_score=True)
rf.fit(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.score(train.drop(['Survived', 'PassengerId'], axis=1), train['Survived'])
rf.oob_score_ | code |
326868/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = full[col].apply(encoders[col].transform)
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId') | code |
326868/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import string
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from collections import Counter
import string
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LinearRegression
pd.options.mode.chained_assignment = None
def get_title(name):
name = name.split(',')[1]
name = name.split('.')[0]
return name.strip()
def get_title_grouped(name):
title = get_title(name)
if title in ['Rev', 'Dr', 'Col', 'Major', 'the Countess', 'Sir', 'Lady', 'Jonkheer', 'Capt', 'Dona', 'Don']:
title = 'Rare'
elif title in ['Ms', 'Mlle']:
title = 'Miss'
elif title == 'Mme':
title = 'Mrs'
return title
def get_deck(cabin):
if isinstance(cabin, str):
if cabin[0] == 'T':
return np.nan
return cabin[0]
return cabin
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
full = pd.concat([train, test])
# feature engineering described in previous notebooks
full['Embarked'].fillna('C', inplace=True)
full['Fare'].fillna(8.05, inplace=True)
full['Title'] = full['Name'].apply(get_title_grouped)
full['Deck'] = full['Cabin'].apply(get_deck)
full['Family size'] = full['Parch'] + full['SibSp']
ticket_nums = [int(n.split()[-1]) for n in full['Ticket'].values if n.split()[-1].isdigit()]
ticket_nums = [num for num in ticket_nums if num < 2000000]
def get_ticket_num(ticket):
ticket_num = ticket.split()
ticket_num = ''.join((char for char in ticket_num[-1].strip() if char not in string.punctuation))
if not ticket_num.isdigit():
return np.nan
return int(ticket_num)
full['Ticket number'] = full['Ticket'].apply(get_ticket_num)
full['Ticket number'].fillna(np.nanmedian(full['Ticket number'].values), inplace=True)
full.drop(['Name', 'Ticket', 'Cabin', 'Parch', 'SibSp'], axis=1, inplace=True)
encoders = {}
to_encode = ['Embarked', 'Sex', 'Title']
for col in to_encode:
encoders[col] = LabelEncoder()
encoders[col].fit(full[col])
full[col] = full[col].apply(encoders[col].transform)
age_train = full[full['Age'].notnull()]
age_predict = full[~full['Age'].notnull()]
lr = LinearRegression()
lr.fit(age_train.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1), age_train['Age'])
predicted_ages = lr.predict(age_predict.drop(['Deck', 'Survived', 'PassengerId', 'Age'], axis=1))
age_predict['Age'] = [max(0.0, age) for age in predicted_ages]
full = pd.concat([age_train, age_predict]).sort_values('PassengerId')
ages = age_train.Age
ages.plot.kde(label='Original')
ages = full.Age
ages.plot.kde(label='With predicted missing values')
plt.xlabel('Age')
plt.legend()
plt.show() | code |
50227272/cell_21 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import pycountry
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
def drop(df):
df = df.drop(df.index[0])
return df
df_2017 = pd.read_csv('../input/kaggle-survey-2017/multipleChoiceResponses.csv', encoding='latin1')
df_2018 = pd.read_csv('../input/kaggle-survey-2018/multipleChoiceResponses.csv', encoding='latin1')
df_2019 = pd.read_csv('../input/kaggle-survey-2019/multiple_choice_responses.csv')
df_2020 = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df_2018 = drop(df_2018)
df_2019 = drop(df_2019)
df_2020 = drop(df_2020)
num_qn = df_2020.columns
stat_2017 = df_2017['GenderSelect'][0:].value_counts()
f_2017 = round(stat_2017['Female'] / np.sum(stat_2017) * 100, 2)
stat_2018 = df_2018['Q1'][0:].value_counts()
f_2018 = round(stat_2018['Female'] / np.sum(stat_2018) * 100, 2)
stat_2019 = df_2019['Q2'][0:].value_counts()
f_2019 = round(stat_2019['Female'] / np.sum(stat_2019) * 100, 2)
stat_2020 = df_2020['Q2'][0:].value_counts()
f_2020 = round(stat_2020['Woman'] / np.sum(stat_2020) * 100, 2)
color = ['rgb(49,130,189)']
color_m = ['rgb(49,130,189)', '#de6560']
mode_size = [12]
line_size = [5]
x_data = np.vstack((np.arange(2017, 2021),) * 1)
y_data = np.array([[f_2017, f_2018, f_2019, f_2020]])
fig = go.Figure()
for i in range(0, 1):
fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines', line=dict(color=color[i], width=line_size[i]), connectgaps=True))
fig.add_trace(go.Scatter(x=[x_data[i][0], x_data[i][-1]], y=[y_data[i][0], y_data[i][-1]], mode='markers', marker=dict(color=color_m[i], size=mode_size[i])))
fig.update_layout(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Relative number of female participants</span>", xaxis=dict(showline=True, showgrid=False, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=15, color='rgb(82, 82, 82)')), yaxis=dict(showgrid=False, zeroline=False, showline=False, showticklabels=False), autosize=False, margin=dict(autoexpand=True, l=200, r=20, t=100), width=600, height=400, showlegend=False, plot_bgcolor='white')
annotations = []
for y_trace, color in zip(y_data, color):
annotations.append(dict(xref='paper', x=0.04, y=y_trace[0], xanchor='left', yanchor='bottom', text='{}%'.format(y_trace[0]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', x=0.9, y=y_trace[3], xanchor='right', yanchor='middle', text='{}%'.format(y_trace[3]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.2, xanchor='center', yanchor='top', text='Source: 2017 - 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.update_layout(annotations=annotations)
age_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q1'].value_counts()
age = []
percent_age = []
for i, j in enumerate(age_2020.index):
age.append(j)
percent_age.append(round(age_2020[i] / np.sum(age_2020) * 100, 2))
color_first = '#de6560'
color_rest = '#98c1d9'
x_data = age
y_data = percent_age
white = "<b style='color: #fff; font-size:15px; font-family:PT Sans'> %{text}% </b>"
black = "<b style='color: #000; font-size:15px; font-family:PT Sans'> %{text}% </b>"
trace = go.Bar(y=x_data, x=y_data, orientation='h', marker=dict(color=[color_first] + [color_rest] * 10), text=y_data, texttemplate=[white] * 6 + [black] * 5, textposition=['inside'] * 6 + ['outside'] * 5)
layout = dict(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Age groups</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female paricipants</span><br>", margin=dict(t=150), legend=dict(orientation='h', yanchor='top', xanchor='center', y=1.06, x=0.5, font=dict(size=16)), xaxis=dict(side='top', showline=True, showgrid=True, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=14, color='rgb(82, 82, 82)')), yaxis=dict(autorange='reversed', tickfont=dict(family='PT sans', size=18), color='rgb(82, 82, 82)'), barmode='group', bargap=0.05, bargroupgap=0.1, width=800, height=600, plot_bgcolor='white')
fig = go.Figure(data=trace, layout=layout)
fig.add_annotation(dict(xref='paper', yref='paper', x=0.5, y=0, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
import pycountry
country_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q3']
country_2020 = country_2020.value_counts()
country = []
percent = []
for i, j in enumerate(country_2020.index):
country.append(j)
percent.append(round(country_2020[i] / np.sum(country_2020) * 100, 3))
country[1] = 'United States'
country[4] = 'United Kingdom'
country[7] = 'Russian Federation'
country[12] = 'Iran, Islamic Republic of'
country[13] = 'Taiwan, Province of China'
country[20] = 'Korea, Republic of'
country[-1] = "Korea, Democratic People's Republic of"
input_countries = country
countries = {}
for cntry in pycountry.countries:
countries[cntry.name] = cntry.alpha_3
codes = [countries.get(cntry, 'Unknown code') for cntry in input_countries]
del codes[2:3]
del percent[2:3]
fig = go.Figure(data=go.Choropleth(locations=codes, z=percent, text=percent, colorscale='Reds', autocolorscale=False, reversescale=False, marker_line_color='darkgray', marker_line_width=0.5, colorbar_title="<span style='color:#000; font-size:16px; font-family:PT Sans'>Percentage</span><br>"))
fig.update_layout(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Location</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female participants</span><br>", margin=dict(t=150), geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular'), width=700, height=600, annotations=[dict(xref='paper', yref='paper', x=0.5, y=0, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False)])
fig.show() | code |
50227272/cell_25 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import pycountry
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
def drop(df):
df = df.drop(df.index[0])
return df
df_2017 = pd.read_csv('../input/kaggle-survey-2017/multipleChoiceResponses.csv', encoding='latin1')
df_2018 = pd.read_csv('../input/kaggle-survey-2018/multipleChoiceResponses.csv', encoding='latin1')
df_2019 = pd.read_csv('../input/kaggle-survey-2019/multiple_choice_responses.csv')
df_2020 = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df_2018 = drop(df_2018)
df_2019 = drop(df_2019)
df_2020 = drop(df_2020)
num_qn = df_2020.columns
stat_2017 = df_2017['GenderSelect'][0:].value_counts()
f_2017 = round(stat_2017['Female'] / np.sum(stat_2017) * 100, 2)
stat_2018 = df_2018['Q1'][0:].value_counts()
f_2018 = round(stat_2018['Female'] / np.sum(stat_2018) * 100, 2)
stat_2019 = df_2019['Q2'][0:].value_counts()
f_2019 = round(stat_2019['Female'] / np.sum(stat_2019) * 100, 2)
stat_2020 = df_2020['Q2'][0:].value_counts()
f_2020 = round(stat_2020['Woman'] / np.sum(stat_2020) * 100, 2)
color = ['rgb(49,130,189)']
color_m = ['rgb(49,130,189)', '#de6560']
mode_size = [12]
line_size = [5]
x_data = np.vstack((np.arange(2017, 2021),) * 1)
y_data = np.array([[f_2017, f_2018, f_2019, f_2020]])
fig = go.Figure()
for i in range(0, 1):
fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines', line=dict(color=color[i], width=line_size[i]), connectgaps=True))
fig.add_trace(go.Scatter(x=[x_data[i][0], x_data[i][-1]], y=[y_data[i][0], y_data[i][-1]], mode='markers', marker=dict(color=color_m[i], size=mode_size[i])))
fig.update_layout(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Relative number of female participants</span>", xaxis=dict(showline=True, showgrid=False, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=15, color='rgb(82, 82, 82)')), yaxis=dict(showgrid=False, zeroline=False, showline=False, showticklabels=False), autosize=False, margin=dict(autoexpand=True, l=200, r=20, t=100), width=600, height=400, showlegend=False, plot_bgcolor='white')
annotations = []
for y_trace, color in zip(y_data, color):
annotations.append(dict(xref='paper', x=0.04, y=y_trace[0], xanchor='left', yanchor='bottom', text='{}%'.format(y_trace[0]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', x=0.9, y=y_trace[3], xanchor='right', yanchor='middle', text='{}%'.format(y_trace[3]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.2, xanchor='center', yanchor='top', text='Source: 2017 - 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.update_layout(annotations=annotations)
age_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q1'].value_counts()
age = []
percent_age = []
for i, j in enumerate(age_2020.index):
age.append(j)
percent_age.append(round(age_2020[i] / np.sum(age_2020) * 100, 2))
color_first = '#de6560'
color_rest = '#98c1d9'
x_data = age
y_data = percent_age
white = "<b style='color: #fff; font-size:15px; font-family:PT Sans'> %{text}% </b>"
black = "<b style='color: #000; font-size:15px; font-family:PT Sans'> %{text}% </b>"
trace = go.Bar(y=x_data, x=y_data, orientation='h', marker=dict(color=[color_first] + [color_rest] * 10), text=y_data, texttemplate=[white] * 6 + [black] * 5, textposition=['inside'] * 6 + ['outside'] * 5)
layout = dict(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Age groups</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female paricipants</span><br>", margin=dict(t=150), legend=dict(orientation='h', yanchor='top', xanchor='center', y=1.06, x=0.5, font=dict(size=16)), xaxis=dict(side='top', showline=True, showgrid=True, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=14, color='rgb(82, 82, 82)')), yaxis=dict(autorange='reversed', tickfont=dict(family='PT sans', size=18), color='rgb(82, 82, 82)'), barmode='group', bargap=0.05, bargroupgap=0.1, width=800, height=600, plot_bgcolor='white')
fig = go.Figure(data=trace, layout=layout)
fig.add_annotation(dict(xref='paper', yref='paper', x=0.5, y=0, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
import pycountry
country_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q3']
country_2020 = country_2020.value_counts()
country = []
percent = []
for i, j in enumerate(country_2020.index):
country.append(j)
percent.append(round(country_2020[i] / np.sum(country_2020) * 100, 3))
country[1] = 'United States'
country[4] = 'United Kingdom'
country[7] = 'Russian Federation'
country[12] = 'Iran, Islamic Republic of'
country[13] = 'Taiwan, Province of China'
country[20] = 'Korea, Republic of'
country[-1] = "Korea, Democratic People's Republic of"
input_countries = country
countries = {}
for cntry in pycountry.countries:
countries[cntry.name] = cntry.alpha_3
codes = [countries.get(cntry, 'Unknown code') for cntry in input_countries]
del codes[2:3]
del percent[2:3]
fig = go.Figure(data=go.Choropleth(locations=codes, z=percent, text=percent, colorscale='Reds', autocolorscale=False, reversescale=False, marker_line_color='darkgray', marker_line_width=0.5, colorbar_title="<span style='color:#000; font-size:16px; font-family:PT Sans'>Percentage</span><br>"))
fig.update_layout(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Location</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female participants</span><br>", margin=dict(t=150), geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular'), width=700, height=600, annotations=[dict(xref='paper', yref='paper', x=0.5, y=0, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False)])
education_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q4'].value_counts()
education = []
percent_edu = []
for i, j in enumerate(education_2020.index):
education.append(j)
percent_edu.append(round(education_2020[i] / np.sum(education_2020) * 100, 2))
color_first = '#de6560'
color_rest = '#98c1d9'
x_data = percent_edu
y_data = education
white = "<b style='color: #fff; font-size:15px; font-family:PT Sans'> %{text}% </b>"
black = "<b style='color: #000; font-size:15px; font-family:PT Sans'> %{text}% </b>"
trace = go.Bar(x=x_data, y=y_data, orientation='h', marker=dict(color=[color_first] + [color_rest] * 6), text=x_data, texttemplate=[white] * 2 + [black] * 5, textposition=['inside'] * 2 + ['outside'] * 5)
layout = dict(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Level of education</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female participants</span><br>", margin=dict(t=150), legend=dict(orientation='h', yanchor='top', xanchor='center', y=1.06, x=0.5, font=dict(size=16)), xaxis=dict(autorange='reversed', side='right', showline=True, showgrid=True, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=14, color='rgb(82, 82, 82)')), yaxis=dict(autorange='reversed', side='right', tickfont=dict(family='PT sans', size=18, color='rgb(82, 82, 82)')), barmode='group', bargap=0.05, bargroupgap=0.1, width=700, height=600, plot_bgcolor='white')
fig = go.Figure(data=trace, layout=layout)
fig.add_annotation(dict(xref='paper', yref='paper', x=1, y=-0.11, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.show() | code |
50227272/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import pycountry
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
def drop(df):
df = df.drop(df.index[0])
return df
df_2017 = pd.read_csv('../input/kaggle-survey-2017/multipleChoiceResponses.csv', encoding='latin1')
df_2018 = pd.read_csv('../input/kaggle-survey-2018/multipleChoiceResponses.csv', encoding='latin1')
df_2019 = pd.read_csv('../input/kaggle-survey-2019/multiple_choice_responses.csv')
df_2020 = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df_2018 = drop(df_2018)
df_2019 = drop(df_2019)
df_2020 = drop(df_2020)
num_qn = df_2020.columns
stat_2017 = df_2017['GenderSelect'][0:].value_counts()
f_2017 = round(stat_2017['Female'] / np.sum(stat_2017) * 100, 2)
stat_2018 = df_2018['Q1'][0:].value_counts()
f_2018 = round(stat_2018['Female'] / np.sum(stat_2018) * 100, 2)
stat_2019 = df_2019['Q2'][0:].value_counts()
f_2019 = round(stat_2019['Female'] / np.sum(stat_2019) * 100, 2)
stat_2020 = df_2020['Q2'][0:].value_counts()
f_2020 = round(stat_2020['Woman'] / np.sum(stat_2020) * 100, 2)
color = ['rgb(49,130,189)']
color_m = ['rgb(49,130,189)', '#de6560']
mode_size = [12]
line_size = [5]
x_data = np.vstack((np.arange(2017, 2021),) * 1)
y_data = np.array([[f_2017, f_2018, f_2019, f_2020]])
fig = go.Figure()
for i in range(0, 1):
fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines', line=dict(color=color[i], width=line_size[i]), connectgaps=True))
fig.add_trace(go.Scatter(x=[x_data[i][0], x_data[i][-1]], y=[y_data[i][0], y_data[i][-1]], mode='markers', marker=dict(color=color_m[i], size=mode_size[i])))
fig.update_layout(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Relative number of female participants</span>", xaxis=dict(showline=True, showgrid=False, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=15, color='rgb(82, 82, 82)')), yaxis=dict(showgrid=False, zeroline=False, showline=False, showticklabels=False), autosize=False, margin=dict(autoexpand=True, l=200, r=20, t=100), width=600, height=400, showlegend=False, plot_bgcolor='white')
annotations = []
for y_trace, color in zip(y_data, color):
annotations.append(dict(xref='paper', x=0.04, y=y_trace[0], xanchor='left', yanchor='bottom', text='{}%'.format(y_trace[0]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', x=0.9, y=y_trace[3], xanchor='right', yanchor='middle', text='{}%'.format(y_trace[3]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.2, xanchor='center', yanchor='top', text='Source: 2017 - 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.update_layout(annotations=annotations)
age_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q1'].value_counts()
age = []
percent_age = []
for i, j in enumerate(age_2020.index):
age.append(j)
percent_age.append(round(age_2020[i] / np.sum(age_2020) * 100, 2))
color_first = '#de6560'
color_rest = '#98c1d9'
x_data = age
y_data = percent_age
white = "<b style='color: #fff; font-size:15px; font-family:PT Sans'> %{text}% </b>"
black = "<b style='color: #000; font-size:15px; font-family:PT Sans'> %{text}% </b>"
trace = go.Bar(y=x_data, x=y_data, orientation='h', marker=dict(color=[color_first] + [color_rest] * 10), text=y_data, texttemplate=[white] * 6 + [black] * 5, textposition=['inside'] * 6 + ['outside'] * 5)
layout = dict(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Age groups</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female paricipants</span><br>", margin=dict(t=150), legend=dict(orientation='h', yanchor='top', xanchor='center', y=1.06, x=0.5, font=dict(size=16)), xaxis=dict(side='top', showline=True, showgrid=True, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=14, color='rgb(82, 82, 82)')), yaxis=dict(autorange='reversed', tickfont=dict(family='PT sans', size=18), color='rgb(82, 82, 82)'), barmode='group', bargap=0.05, bargroupgap=0.1, width=800, height=600, plot_bgcolor='white')
fig = go.Figure(data=trace, layout=layout)
fig.add_annotation(dict(xref='paper', yref='paper', x=0.5, y=0, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
import pycountry
country_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q3']
country_2020 = country_2020.value_counts()
country = []
percent = []
for i, j in enumerate(country_2020.index):
country.append(j)
percent.append(round(country_2020[i] / np.sum(country_2020) * 100, 3))
country[1] = 'United States'
country[4] = 'United Kingdom'
country[7] = 'Russian Federation'
country[12] = 'Iran, Islamic Republic of'
country[13] = 'Taiwan, Province of China'
country[20] = 'Korea, Republic of'
country[-1] = "Korea, Democratic People's Republic of"
input_countries = country
countries = {}
for cntry in pycountry.countries:
countries[cntry.name] = cntry.alpha_3
codes = [countries.get(cntry, 'Unknown code') for cntry in input_countries]
del codes[2:3]
del percent[2:3]
fig = go.Figure(data=go.Choropleth(locations=codes, z=percent, text=percent, colorscale='Reds', autocolorscale=False, reversescale=False, marker_line_color='darkgray', marker_line_width=0.5, colorbar_title="<span style='color:#000; font-size:16px; font-family:PT Sans'>Percentage</span><br>"))
fig.update_layout(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Location</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female participants</span><br>", margin=dict(t=150), geo=dict(showframe=False, showcoastlines=False, projection_type='equirectangular'), width=700, height=600, annotations=[dict(xref='paper', yref='paper', x=0.5, y=0, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False)])
education_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q4'].value_counts()
education = []
percent_edu = []
for i, j in enumerate(education_2020.index):
education.append(j)
percent_edu.append(round(education_2020[i] / np.sum(education_2020) * 100, 2))
color_first = '#de6560'
color_rest = '#98c1d9'
x_data = percent_edu
y_data = education
white = "<b style='color: #fff; font-size:15px; font-family:PT Sans'> %{text}% </b>"
black = "<b style='color: #000; font-size:15px; font-family:PT Sans'> %{text}% </b>"
trace = go.Bar(x=x_data, y=y_data, orientation='h', marker=dict(color=[color_first] + [color_rest] * 6), text=x_data, texttemplate=[white] * 2 + [black] * 5, textposition=['inside'] * 2 + ['outside'] * 5)
layout = dict(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Level of education</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female participants</span><br>", margin=dict(t=150), legend=dict(orientation='h', yanchor='top', xanchor='center', y=1.06, x=0.5, font=dict(size=16)), xaxis=dict(autorange='reversed', side='right', showline=True, showgrid=True, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=14, color='rgb(82, 82, 82)')), yaxis=dict(autorange='reversed', side='right', tickfont=dict(family='PT sans', size=18, color='rgb(82, 82, 82)')), barmode='group', bargap=0.05, bargroupgap=0.1, width=700, height=600, plot_bgcolor='white')
fig = go.Figure(data=trace, layout=layout)
fig.add_annotation(dict(xref='paper', yref='paper', x=1, y=-0.11, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
qns = [x for x in num_qn]
def data(qnn):
qn = [x for x in qns if qnn in x]
name = []
for q in qn:
for x in df_2020[q].unique():
name.append(x)
name = [x for x in name if str(x) != 'nan']
name = [x.strip(' ') for x in name]
name_percent = (df_2020.shape[0] - df_2020[qn].isnull().sum()) / df_2020.shape[0]
name_percent.index = name
name_percent = name_percent.sort_values(ascending=False)
name = []
percent_name = []
for i, j in enumerate(name_percent.index):
name.append(j)
percent_name.append(round(name_percent[i] * 100, 2))
return (name, percent_name)
language, percent_lng = data('Q7')
color_first = '#de6560'
color_rest = '#98c1d9'
x_data = percent_lng
y_data = language
white = "<b style='color: #fff; font-size:15px; font-family:PT Sans'> %{text}% </b>"
black = "<b style='color: #000; font-size:15px; font-family:PT Sans'> %{text}% </b>"
trace = go.Bar(x=x_data, y=y_data, orientation='h', marker=dict(color=[color_first] + [color_rest] * 12), text=x_data, texttemplate=[white] * 7 + [black] * 6, textposition=['inside'] * 7 + ['outside'] * 6)
layout = dict(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Programming language</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>all participants</span><br>", margin=dict(t=150), legend=dict(orientation='h', yanchor='top', xanchor='center', y=1.06, x=0.5, font=dict(size=16)), xaxis=dict(side='top', showline=True, showgrid=True, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=14, color='rgb(82, 82, 82)')), yaxis=dict(autorange='reversed', tickfont=dict(family='PT sans', size=18, color='rgb(82, 82, 82)')), barmode='group', bargap=0.05, bargroupgap=0.1, width=700, height=600, plot_bgcolor='white')
fig = go.Figure(data=trace, layout=layout)
fig.add_annotation(dict(xref='paper', yref='paper', x=0.5, y=0, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.show() | code |
50227272/cell_17 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
def drop(df):
df = df.drop(df.index[0])
return df
df_2017 = pd.read_csv('../input/kaggle-survey-2017/multipleChoiceResponses.csv', encoding='latin1')
df_2018 = pd.read_csv('../input/kaggle-survey-2018/multipleChoiceResponses.csv', encoding='latin1')
df_2019 = pd.read_csv('../input/kaggle-survey-2019/multiple_choice_responses.csv')
df_2020 = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df_2018 = drop(df_2018)
df_2019 = drop(df_2019)
df_2020 = drop(df_2020)
num_qn = df_2020.columns
stat_2017 = df_2017['GenderSelect'][0:].value_counts()
f_2017 = round(stat_2017['Female'] / np.sum(stat_2017) * 100, 2)
stat_2018 = df_2018['Q1'][0:].value_counts()
f_2018 = round(stat_2018['Female'] / np.sum(stat_2018) * 100, 2)
stat_2019 = df_2019['Q2'][0:].value_counts()
f_2019 = round(stat_2019['Female'] / np.sum(stat_2019) * 100, 2)
stat_2020 = df_2020['Q2'][0:].value_counts()
f_2020 = round(stat_2020['Woman'] / np.sum(stat_2020) * 100, 2)
color = ['rgb(49,130,189)']
color_m = ['rgb(49,130,189)', '#de6560']
mode_size = [12]
line_size = [5]
x_data = np.vstack((np.arange(2017, 2021),) * 1)
y_data = np.array([[f_2017, f_2018, f_2019, f_2020]])
fig = go.Figure()
for i in range(0, 1):
fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines', line=dict(color=color[i], width=line_size[i]), connectgaps=True))
fig.add_trace(go.Scatter(x=[x_data[i][0], x_data[i][-1]], y=[y_data[i][0], y_data[i][-1]], mode='markers', marker=dict(color=color_m[i], size=mode_size[i])))
fig.update_layout(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Relative number of female participants</span>", xaxis=dict(showline=True, showgrid=False, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=15, color='rgb(82, 82, 82)')), yaxis=dict(showgrid=False, zeroline=False, showline=False, showticklabels=False), autosize=False, margin=dict(autoexpand=True, l=200, r=20, t=100), width=600, height=400, showlegend=False, plot_bgcolor='white')
annotations = []
for y_trace, color in zip(y_data, color):
annotations.append(dict(xref='paper', x=0.04, y=y_trace[0], xanchor='left', yanchor='bottom', text='{}%'.format(y_trace[0]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', x=0.9, y=y_trace[3], xanchor='right', yanchor='middle', text='{}%'.format(y_trace[3]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.2, xanchor='center', yanchor='top', text='Source: 2017 - 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.update_layout(annotations=annotations)
age_2020 = df_2020[df_2020['Q2'] == 'Woman']['Q1'].value_counts()
age = []
percent_age = []
for i, j in enumerate(age_2020.index):
age.append(j)
percent_age.append(round(age_2020[i] / np.sum(age_2020) * 100, 2))
color_first = '#de6560'
color_rest = '#98c1d9'
x_data = age
y_data = percent_age
white = "<b style='color: #fff; font-size:15px; font-family:PT Sans'> %{text}% </b>"
black = "<b style='color: #000; font-size:15px; font-family:PT Sans'> %{text}% </b>"
trace = go.Bar(y=x_data, x=y_data, orientation='h', marker=dict(color=[color_first] + [color_rest] * 10), text=y_data, texttemplate=[white] * 6 + [black] * 5, textposition=['inside'] * 6 + ['outside'] * 5)
layout = dict(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Age groups</span><br><span style='color:#969696; font-size: 20px; font-family:PT Sans'>female paricipants</span><br>", margin=dict(t=150), legend=dict(orientation='h', yanchor='top', xanchor='center', y=1.06, x=0.5, font=dict(size=16)), xaxis=dict(side='top', showline=True, showgrid=True, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=14, color='rgb(82, 82, 82)')), yaxis=dict(autorange='reversed', tickfont=dict(family='PT sans', size=18), color='rgb(82, 82, 82)'), barmode='group', bargap=0.05, bargroupgap=0.1, width=800, height=600, plot_bgcolor='white')
fig = go.Figure(data=trace, layout=layout)
fig.add_annotation(dict(xref='paper', yref='paper', x=0.5, y=0, xanchor='center', yanchor='top', text='Source: 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.show() | code |
50227272/cell_14 | [
"text_html_output_2.png"
] | import numpy as np
import pandas as pd
import plotly.graph_objects as go
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings(action='ignore')
import plotly as py
import plotly.graph_objects as go
from plotly import tools
def drop(df):
df = df.drop(df.index[0])
return df
df_2017 = pd.read_csv('../input/kaggle-survey-2017/multipleChoiceResponses.csv', encoding='latin1')
df_2018 = pd.read_csv('../input/kaggle-survey-2018/multipleChoiceResponses.csv', encoding='latin1')
df_2019 = pd.read_csv('../input/kaggle-survey-2019/multiple_choice_responses.csv')
df_2020 = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv')
df_2018 = drop(df_2018)
df_2019 = drop(df_2019)
df_2020 = drop(df_2020)
num_qn = df_2020.columns
stat_2017 = df_2017['GenderSelect'][0:].value_counts()
f_2017 = round(stat_2017['Female'] / np.sum(stat_2017) * 100, 2)
stat_2018 = df_2018['Q1'][0:].value_counts()
f_2018 = round(stat_2018['Female'] / np.sum(stat_2018) * 100, 2)
stat_2019 = df_2019['Q2'][0:].value_counts()
f_2019 = round(stat_2019['Female'] / np.sum(stat_2019) * 100, 2)
stat_2020 = df_2020['Q2'][0:].value_counts()
f_2020 = round(stat_2020['Woman'] / np.sum(stat_2020) * 100, 2)
color = ['rgb(49,130,189)']
color_m = ['rgb(49,130,189)', '#de6560']
mode_size = [12]
line_size = [5]
x_data = np.vstack((np.arange(2017, 2021),) * 1)
y_data = np.array([[f_2017, f_2018, f_2019, f_2020]])
fig = go.Figure()
for i in range(0, 1):
fig.add_trace(go.Scatter(x=x_data[i], y=y_data[i], mode='lines', line=dict(color=color[i], width=line_size[i]), connectgaps=True))
fig.add_trace(go.Scatter(x=[x_data[i][0], x_data[i][-1]], y=[y_data[i][0], y_data[i][-1]], mode='markers', marker=dict(color=color_m[i], size=mode_size[i])))
fig.update_layout(title="<span style='color:#000; font-size:25px; font-family:PT Sans'>Relative number of female participants</span>", xaxis=dict(showline=True, showgrid=False, showticklabels=True, linecolor='rgb(204, 204, 204)', linewidth=2, ticks='outside', tickfont=dict(family='PT sans', size=15, color='rgb(82, 82, 82)')), yaxis=dict(showgrid=False, zeroline=False, showline=False, showticklabels=False), autosize=False, margin=dict(autoexpand=True, l=200, r=20, t=100), width=600, height=400, showlegend=False, plot_bgcolor='white')
annotations = []
for y_trace, color in zip(y_data, color):
annotations.append(dict(xref='paper', x=0.04, y=y_trace[0], xanchor='left', yanchor='bottom', text='{}%'.format(y_trace[0]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', x=0.9, y=y_trace[3], xanchor='right', yanchor='middle', text='{}%'.format(y_trace[3]), font=dict(family='PT sans', size=18, color='rgb(82, 82, 82)'), showarrow=False))
annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.2, xanchor='center', yanchor='top', text='Source: 2017 - 2020 Kaggle Machine Learning & ' + 'Data Science Survey', font=dict(family='PT sans', size=12, color='rgb(150,150,150)'), showarrow=False))
fig.update_layout(annotations=annotations)
fig.show() | code |
18135360/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': 'Private'}) | code |
18135360/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult.head() | code |
18135360/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes | code |
18135360/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': 'Private'})
adult.occupation.value_counts()
adult = adult.fillna({'occupation': 'Prof-specialty'})
adult.dropna(axis=0, inplace=True)
adult.isnull().sum()
adult.dtypes
adult = pd.get_dummies(adult)
y = adult['income']
X = adult.drop('income', axis=1)
X | code |
18135360/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape | code |
18135360/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN']) | code |
18135360/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.head() | code |
18135360/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': 'Private'})
adult.occupation.value_counts()
adult = adult.fillna({'occupation': 'Prof-specialty'})
adult.dropna(axis=0, inplace=True)
adult.isnull().sum()
adult.dtypes
adult = pd.get_dummies(adult)
y = adult['income']
X = adult.drop('income', axis=1) | code |
18135360/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns | code |
18135360/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': 'Private'})
adult.occupation.value_counts()
adult = adult.fillna({'occupation': 'Prof-specialty'})
adult.dropna(axis=0, inplace=True)
adult.isnull().sum()
adult.dtypes
adult = pd.get_dummies(adult) | code |
18135360/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1) | code |
18135360/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': 'Private'})
adult.occupation.value_counts()
adult = adult.fillna({'occupation': 'Prof-specialty'})
adult.dropna(axis=0, inplace=True) | code |
18135360/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': 'Private'})
adult.occupation.value_counts()
adult = adult.fillna({'occupation': 'Prof-specialty'})
adult.dropna(axis=0, inplace=True)
adult.isnull().sum() | code |
18135360/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.head() | code |
18135360/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': 'Private'})
adult.occupation.value_counts()
adult = adult.fillna({'occupation': 'Prof-specialty'})
adult.dropna(axis=0, inplace=True)
adult.isnull().sum()
adult.dtypes | code |
18135360/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum()
adult.workclass.value_counts()
adult = adult.fillna({'workclass': 'Private'})
adult.occupation.value_counts()
adult = adult.fillna({'occupation': 'Prof-specialty'}) | code |
18135360/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0) | code |
18135360/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.shape
adult.columns
adult = adult.drop('fnlwgt', axis=1)
adult = adult.replace('>50K', 1)
adult = adult.replace('<=50K', 0)
adult.isnull().sum() | code |
18135360/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
adult = pd.read_csv('adult.csv', na_values=['?', ',', 'NaN'])
adult.dtypes
adult.head() | code |
34123573/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
y_k | code |
34123573/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k2 = list(zip(x_k, y_k))
x_k2 = np.array(x_k2)
x_k2
model = KMeans(n_clusters=5)
model.fit(x_k2)
y_kmeans = model.predict(x_k2)
y_kmeans
label = model.labels_
label | code |
34123573/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import plotly
import plotly.express as px
import cufflinks as cf
import plotly.offline as pyo
from plotly.offline import init_notebook_mode, plot, iplot
pyo.init_notebook_mode(connected=True)
cf.go_offline() | code |
34123573/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
f,axes=plt.subplots(1,3,figsize=(20,20))
sns.distplot(df['Annual Income (k$)'],color='red',label="nannualincome",ax=axes[0])
sns.distplot(df['Age'],color='yellow',label="age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'],color="skyblue", label="Spending Score",ax=axes[2])
f, axes = plt.subplots(1, 3, figsize=(20, 5)) #sharex=True)
sns.distplot(df['Annual Income (k$)'][df['Gender']=="Male"],color="salmon", label="Annual Income (k$)",ax=axes[0])
sns.distplot(df['Annual Income (k$)'][df['Gender']=="Female"],color="skyblue", label="Annual Income (k$)",ax=axes[0])
sns.distplot(df['Age'][df['Gender']=="Male"],color="salmon", label="Age",ax=axes[1])
sns.distplot(df['Age'][df['Gender']=="Female"],color="skyblue", label="Age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'][df['Gender']=="Male"],color="salmon", label="Spending Score",ax=axes[2])
sns.distplot(df['Spending Score (1-100)'][df['Gender']=="Female"],color="skyblue", label="Spending Score",ax=axes[2])
plt.show()
f, axes = plt.subplots(1, 2, figsize=(20, 10)) #sharex=True)
sns.scatterplot(x="Age", y="Spending Score (1-100)",hue="Gender", data=df, ax=axes[0])
sns.scatterplot(x="Age", y="Annual Income (k$)",hue="Gender", data=df, ax=axes[1])
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k2 = list(zip(x_k, y_k))
x_k2 = np.array(x_k2)
x_k2
model = KMeans(n_clusters=5)
model.fit(x_k2)
y_kmeans = model.predict(x_k2)
y_kmeans
label = model.labels_
label
unique_labels = set(model.labels_)
unique_labels
target = pd.DataFrame({'target': model.labels_})
df_new = pd.concat([df, target], axis=1, sort=False)
df_new = df_new.drop(['CustomerID'], axis=1)
df_new | code |
34123573/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k | code |
34123573/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
print(df.head()) | code |
34123573/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
f,axes=plt.subplots(1,3,figsize=(20,20))
sns.distplot(df['Annual Income (k$)'],color='red',label="nannualincome",ax=axes[0])
sns.distplot(df['Age'],color='yellow',label="age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'],color="skyblue", label="Spending Score",ax=axes[2])
f, axes = plt.subplots(1, 3, figsize=(20, 5)) #sharex=True)
sns.distplot(df['Annual Income (k$)'][df['Gender']=="Male"],color="salmon", label="Annual Income (k$)",ax=axes[0])
sns.distplot(df['Annual Income (k$)'][df['Gender']=="Female"],color="skyblue", label="Annual Income (k$)",ax=axes[0])
sns.distplot(df['Age'][df['Gender']=="Male"],color="salmon", label="Age",ax=axes[1])
sns.distplot(df['Age'][df['Gender']=="Female"],color="skyblue", label="Age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'][df['Gender']=="Male"],color="salmon", label="Spending Score",ax=axes[2])
sns.distplot(df['Spending Score (1-100)'][df['Gender']=="Female"],color="skyblue", label="Spending Score",ax=axes[2])
plt.show()
f, axes = plt.subplots(1, 2, figsize=(20, 10)) #sharex=True)
sns.scatterplot(x="Age", y="Spending Score (1-100)",hue="Gender", data=df, ax=axes[0])
sns.scatterplot(x="Age", y="Annual Income (k$)",hue="Gender", data=df, ax=axes[1])
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k2 = list(zip(x_k, y_k))
x_k2 = np.array(x_k2)
x_k2
model = KMeans(n_clusters=5)
model.fit(x_k2)
y_kmeans = model.predict(x_k2)
y_kmeans
label = model.labels_
label
unique_labels = set(model.labels_)
unique_labels
for c in unique_labels:
plt.scatter(x_k2[model.labels_ == c, 0], x_k2[model.labels_ == c, 1], label='cluster{}'.format(c))
plt.scatter(model.cluster_centers_[:, 0], model.cluster_centers_[:, 1], s=300, c='red', label='Centroids')
plt.xlabel('Income')
plt.ylabel('Spending Score')
plt.legend()
plt.show() | code |
34123573/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.describe() | code |
34123573/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
f,axes=plt.subplots(1,3,figsize=(20,20))
sns.distplot(df['Annual Income (k$)'],color='red',label="nannualincome",ax=axes[0])
sns.distplot(df['Age'],color='yellow',label="age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'],color="skyblue", label="Spending Score",ax=axes[2])
f, axes = plt.subplots(1, 3, figsize=(20, 5)) #sharex=True)
sns.distplot(df['Annual Income (k$)'][df['Gender']=="Male"],color="salmon", label="Annual Income (k$)",ax=axes[0])
sns.distplot(df['Annual Income (k$)'][df['Gender']=="Female"],color="skyblue", label="Annual Income (k$)",ax=axes[0])
sns.distplot(df['Age'][df['Gender']=="Male"],color="salmon", label="Age",ax=axes[1])
sns.distplot(df['Age'][df['Gender']=="Female"],color="skyblue", label="Age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'][df['Gender']=="Male"],color="salmon", label="Spending Score",ax=axes[2])
sns.distplot(df['Spending Score (1-100)'][df['Gender']=="Female"],color="skyblue", label="Spending Score",ax=axes[2])
plt.show()
f, axes = plt.subplots(1, 2, figsize=(20, 10))
sns.scatterplot(x='Age', y='Spending Score (1-100)', hue='Gender', data=df, ax=axes[0])
sns.scatterplot(x='Age', y='Annual Income (k$)', hue='Gender', data=df, ax=axes[1]) | code |
34123573/cell_3 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34123573/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
f,axes=plt.subplots(1,3,figsize=(20,20))
sns.distplot(df['Annual Income (k$)'],color='red',label="nannualincome",ax=axes[0])
sns.distplot(df['Age'],color='yellow',label="age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'],color="skyblue", label="Spending Score",ax=axes[2])
f, axes = plt.subplots(1, 3, figsize=(20, 5)) #sharex=True)
sns.distplot(df['Annual Income (k$)'][df['Gender']=="Male"],color="salmon", label="Annual Income (k$)",ax=axes[0])
sns.distplot(df['Annual Income (k$)'][df['Gender']=="Female"],color="skyblue", label="Annual Income (k$)",ax=axes[0])
sns.distplot(df['Age'][df['Gender']=="Male"],color="salmon", label="Age",ax=axes[1])
sns.distplot(df['Age'][df['Gender']=="Female"],color="skyblue", label="Age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'][df['Gender']=="Male"],color="salmon", label="Spending Score",ax=axes[2])
sns.distplot(df['Spending Score (1-100)'][df['Gender']=="Female"],color="skyblue", label="Spending Score",ax=axes[2])
plt.show()
f, axes = plt.subplots(1, 2, figsize=(20, 10)) #sharex=True)
sns.scatterplot(x="Age", y="Spending Score (1-100)",hue="Gender", data=df, ax=axes[0])
sns.scatterplot(x="Age", y="Annual Income (k$)",hue="Gender", data=df, ax=axes[1])
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k2 = list(zip(x_k, y_k))
x_k2 = np.array(x_k2)
x_k2
model = KMeans(n_clusters=5)
model.fit(x_k2)
y_kmeans = model.predict(x_k2)
y_kmeans
label = model.labels_
label
unique_labels = set(model.labels_)
unique_labels
plt.figure(1, figsize=(17, 8))
plt.scatter(x_k2[y_kmeans == 0, 0], x_k2[y_kmeans == 0, 1], s=100, c='red', label='Standard people')
plt.scatter(x_k2[y_kmeans == 1, 0], x_k2[y_kmeans == 1, 1], s=100, c='yellow', label='Tightwad people')
plt.scatter(x_k2[y_kmeans == 2, 0], x_k2[y_kmeans == 2, 1], s=100, c='aqua', label='Normal people')
plt.scatter(x_k2[y_kmeans == 3, 0], x_k2[y_kmeans == 3, 1], s=100, c='violet', label='Careless people(TARGET)')
plt.scatter(x_k2[y_kmeans == 4, 0], x_k2[y_kmeans == 4, 1], s=100, c='lightgreen', label='Rich people(TARGET)')
plt.scatter(model.cluster_centers_[:, 0], model.cluster_centers_[:, 1], s=300, c='black', label='Centroids')
plt.title('Clusters of customers')
plt.xlabel('Annual Income (k$)')
plt.ylabel('Spending Score (1-100)')
plt.legend()
plt.show() | code |
34123573/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k2 = list(zip(x_k, y_k))
x_k2 = np.array(x_k2)
x_k2
model = KMeans(n_clusters=5)
model.fit(x_k2)
y_kmeans = model.predict(x_k2)
y_kmeans | code |
34123573/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
f,axes=plt.subplots(1,3,figsize=(20,20))
sns.distplot(df['Annual Income (k$)'],color='red',label="nannualincome",ax=axes[0])
sns.distplot(df['Age'],color='yellow',label="age",ax=axes[1])
sns.distplot(df['Spending Score (1-100)'],color="skyblue", label="Spending Score",ax=axes[2])
f, axes = plt.subplots(1, 3, figsize=(20, 5))
sns.distplot(df['Annual Income (k$)'][df['Gender'] == 'Male'], color='salmon', label='Annual Income (k$)', ax=axes[0])
sns.distplot(df['Annual Income (k$)'][df['Gender'] == 'Female'], color='skyblue', label='Annual Income (k$)', ax=axes[0])
sns.distplot(df['Age'][df['Gender'] == 'Male'], color='salmon', label='Age', ax=axes[1])
sns.distplot(df['Age'][df['Gender'] == 'Female'], color='skyblue', label='Age', ax=axes[1])
sns.distplot(df['Spending Score (1-100)'][df['Gender'] == 'Male'], color='salmon', label='Spending Score', ax=axes[2])
sns.distplot(df['Spending Score (1-100)'][df['Gender'] == 'Female'], color='skyblue', label='Spending Score', ax=axes[2])
plt.show() | code |
34123573/cell_22 | [
"image_output_1.png"
] | import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k2 = list(zip(x_k, y_k))
x_k2 = np.array(x_k2)
x_k2 | code |
34123573/cell_10 | [
"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 seaborn as sns
df = pd.read_csv('/kaggle/input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
sns.heatmap(df.drop(['CustomerID'], axis=1).corr(), annot=True) | code |
34123573/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
import numpy as np
import numpy as np # linear algebra
import pandas as pd
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/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x_k = df['Annual Income (k$)'].values
y_k = df['Spending Score (1-100)'].values
x_k2 = list(zip(x_k, y_k))
x_k2 = np.array(x_k2)
x_k2
model = KMeans(n_clusters=5)
model.fit(x_k2)
y_kmeans = model.predict(x_k2)
y_kmeans
label = model.labels_
label
unique_labels = set(model.labels_)
unique_labels | code |
88104888/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
data_path = '../input/nuclio10-dsc-1121/sales_train_merged.csv'
df = pd.read_csv(data_path, index_col=0)
df.head() | code |
72115608/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(features[object_cols])
X_test[object_cols] = ordinal_encoder.transform(test[object_cols])
X.head() | code |
72115608/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.head() | code |
72115608/cell_23 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(features[object_cols])
X_test[object_cols] = ordinal_encoder.transform(test[object_cols])
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary()
X_train.shape
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)
history = model.fit(x=X_train, y=y_train, validation_split=0.1, batch_size=128, epochs=150, callbacks=[early_stop])
losses = pd.DataFrame(model.history.history)
result = model.predict(X_test)
result = pd.DataFrame(result, columns=['target'])
result = result[['id', 'target']]
result.head() | code |
72115608/cell_20 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(features[object_cols])
X_test[object_cols] = ordinal_encoder.transform(test[object_cols])
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary()
X_train.shape
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)
history = model.fit(x=X_train, y=y_train, validation_split=0.1, batch_size=128, epochs=150, callbacks=[early_stop])
losses = pd.DataFrame(model.history.history)
result = model.predict(X_test)
result = pd.DataFrame(result, columns=['target'])
result | code |
72115608/cell_29 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(features[object_cols])
X_test[object_cols] = ordinal_encoder.transform(test[object_cols])
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary()
X_train.shape
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)
history = model.fit(x=X_train, y=y_train, validation_split=0.1, batch_size=128, epochs=150, callbacks=[early_stop])
losses = pd.DataFrame(model.history.history)
result = model.predict(X_test)
result = pd.DataFrame(result, columns=['target'])
result['id'] = X_test.index
result = result[['id', 'target']]
result.to_csv('submission.csv', index=False)
output = result[['id', 'target']]
output.to_csv('submission.csv', index=False)
output = pd.DataFrame({'Id': X_test.index, 'target': predictions})
output.to_csv('submission.csv', index=False) | code |
72115608/cell_11 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(features[object_cols])
X_test[object_cols] = ordinal_encoder.transform(test[object_cols])
X_test.head() | code |
72115608/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
features.head() | code |
72115608/cell_18 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(features[object_cols])
X_test[object_cols] = ordinal_encoder.transform(test[object_cols])
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary()
X_train.shape
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)
history = model.fit(x=X_train, y=y_train, validation_split=0.1, batch_size=128, epochs=150, callbacks=[early_stop])
losses = pd.DataFrame(model.history.history)
result = model.predict(X_test)
result | code |
72115608/cell_15 | [
"text_html_output_1.png"
] | from keras.layers import Dense
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary()
X_train.shape | code |
72115608/cell_16 | [
"text_html_output_1.png"
] | from keras.layers import Dense
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary()
X_train.shape
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)
history = model.fit(x=X_train, y=y_train, validation_split=0.1, batch_size=128, epochs=150, callbacks=[early_stop])
losses = pd.DataFrame(model.history.history)
losses.plot() | code |
72115608/cell_14 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary() | code |
72115608/cell_22 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(features[object_cols])
X_test[object_cols] = ordinal_encoder.transform(test[object_cols])
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary()
X_train.shape
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)
history = model.fit(x=X_train, y=y_train, validation_split=0.1, batch_size=128, epochs=150, callbacks=[early_stop])
losses = pd.DataFrame(model.history.history)
result = model.predict(X_test)
result = pd.DataFrame(result, columns=['target'])
result | code |
72115608/cell_27 | [
"text_plain_output_1.png"
] | from keras.layers import Dense
from sklearn.preprocessing import OrdinalEncoder
import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
y = train['target']
features = train.drop(['target'], axis=1)
object_cols = [col for col in features.columns if 'cat' in col]
X = features.copy()
X_test = test.copy()
ordinal_encoder = OrdinalEncoder()
X[object_cols] = ordinal_encoder.fit_transform(features[object_cols])
X_test[object_cols] = ordinal_encoder.transform(test[object_cols])
def create_model():
model = Sequential()
model.add(Dense(320, input_dim=X_train.shape[1], activation='relu'))
model.add(Dense(384, activation='relu'))
model.add(Dense(352, activation='relu'))
model.add(Dense(448, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(160, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
model.compile(optimizer=Adam(learning_rate=0.0001), loss='mse')
return model
model = create_model()
model.summary()
X_train.shape
early_stop = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=10)
history = model.fit(x=X_train, y=y_train, validation_split=0.1, batch_size=128, epochs=150, callbacks=[early_stop])
losses = pd.DataFrame(model.history.history)
result = model.predict(X_test)
result = pd.DataFrame(result, columns=['target'])
result = result[['id', 'target']]
result.to_csv('submission.csv', index=False)
output = result[['id', 'target']]
output | code |
72115608/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv', index_col=0)
test = pd.read_csv('../input/30-days-of-ml/test.csv', index_col=0)
train.info() | code |
105197097/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values | code |
105197097/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.head() | code |
105197097/cell_23 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test_df = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')
submission_df = pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')
train_df.shape
train_df.isna().sum().sum()
train_df.values
train_df.values[0]
train_df.values[:, :1]
train_df.values[:, 1:]
X = train_df.values[:, 1:]
y = train_df.values[:, :1]
X.shape
X_scale = X / 255.0
dim = int(np.sqrt(X_scale.shape[1]))
dim
N = X_scale.shape[0]
N
X_scale = X_scale.reshape((N, dim, dim, 1))
X_scale[0] | code |
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