path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
18139612/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
r... | code |
18139612/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for... | code |
18139612/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.head() | code |
18139612/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
r... | code |
18139612/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max()
returns = pd.DataFrame()
for tick in tickers:
r... | code |
18139612/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
tickers = ['BAC', 'C', 'GS', 'JPM', 'MS', 'WFC']
Banks_Stock = pd.concat([BAC, C, GS, JPM, MS, WFC], axis=1, keys=tickers)
Banks_Stock.columns.names = ['bank ticker', 'stock info']
Banks_Stock.xs(key='Close', axis=1, level='stock info').max() | code |
105190066/cell_25 | [
"text_plain_output_1.png"
] | from scipy import stats
from sklearn.linear_model import LinearRegression
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)
df_train = pd.read_csv('../input/house-prices-advanced-regres... | code |
105190066/cell_23 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
... | code |
105190066/cell_2 | [
"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 |
105190066/cell_7 | [
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test... | code |
105190066/cell_8 | [
"text_plain_output_1.png"
] | from scipy import stats
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)
df_train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('... | code |
105190066/cell_24 | [
"image_output_1.png"
] | from scipy import stats
from sklearn.linear_model import LinearRegression
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)
df_train = pd.read_csv('../input/house-prices-advanced-regres... | code |
105194794/cell_9 | [
"text_plain_output_1.png"
] | liability = 14589
asset = 4000
liability = 4000
asset = 4000
liability = 4000
asset = 4000
if liability >= asset:
print(' asset deficiency')
else:
print('going good') | code |
105194794/cell_11 | [
"text_plain_output_1.png"
] | liability = 14589
asset = 4000
liability = 4000
asset = 4000
liability = 4000
asset = 4000
liability = 14589
asset = 4000
if liability <= asset:
print(' going good')
else:
print('asset deficiency') | code |
105194794/cell_7 | [
"text_plain_output_1.png"
] | liability = 14589
asset = 4000
if liability >= asset:
print(' asset deficiency')
else:
print('going good') | code |
105194794/cell_8 | [
"text_plain_output_1.png"
] | liability = 14589
asset = 4000
liability = 4000
asset = 4000
if liability > asset:
print(' asset deficiency')
else:
print('going good') | code |
105194794/cell_3 | [
"text_plain_output_1.png"
] | a = 49
b = 2
if a % b == 1:
print('odd number')
else:
print('even number') | code |
105194794/cell_5 | [
"text_plain_output_1.png"
] | a = 49
b = 2
a = 49
b = 2
if a % b == 1:
print('even number')
else:
print('old number') | code |
2030468/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/harddrive.csv', usecols=['failure', 'smart_1_normalized'], nrows=100000)
x = df['smart_1_normalized']
y = df['failure']
x = sm.add_constant(x)
model = sm.GLM(y, x, family=sm.families.Binomial()).fit()
model.summary() | code |
2030468/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/harddrive.csv', usecols=['failure', 'smart_1_normalized'], nrows=100000)
sns.regplot(df['smart_1_normalized'], df['failure'], line_kws={'color': 'k', 'lw': 1}) | code |
2030468/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/harddrive.csv', usecols=['failure', 'smart_1_normalized'], nrows=100000)
df.head() | code |
2030468/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm | code |
2030468/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/harddrive.csv', usecols=['failure', 'smart_1_normalized'], nrows=100000)
x = df['smart_1_normalized']
y = df['failure']
x = sm.add_constant(x)
model = sm.GLM(y, x, family=sm.families.Binomial()).fit()
model.summary()
(model.null_deviance, ... | code |
327848/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(gro... | code |
327848/cell_4 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
titanic_df['Survived'].mean() | code |
327848/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
print(class_sex_grouping['Survived']) | code |
327848/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import random
import numpy as np
import pandas as pd
from sklearn import datasets, svm, cross_validation, tree, preprocessing, metrics
import sklearn.ensemble as ske
import tensorflow as tf
from tensorflow.contrib import skflow | code |
327848/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
class_sex_grouping['Survived'].plot.bar() | code |
327848/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(gro... | code |
327848/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
titanic_df.head() | code |
327848/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean()
class_sex_grouping = titanic_df.groupby(['Pclass', 'Sex']).mean()
group_by_age = pd.cut(titanic_df['Age'], np.arange(0, 90, 10))
age_grouping = titanic_df.groupby(gro... | code |
327848/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
titanic_df = pd.read_csv('../input/train.csv', dtype={'Age': np.float64})
titanic_df.groupby('Pclass').mean() | code |
2044953/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('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_y = pd.read_csv('../input/gender_submission.csv')
X_train = train_df[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
sex = pd.get_dummies(X... | code |
2044953/cell_6 | [
"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('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_y = pd.read_csv('../input/gender_submission.csv')
X_train = train_df[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
sex = pd.get_dummies(X... | code |
2044953/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_y = pd.read_csv('../input/gender_submission.csv')
X_train = train_df[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
sex = pd.get_dummies(X... | code |
2044953/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'))
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
from sklearn.linear_model import LogisticRegression
from sklearn.svm imp... | code |
2044953/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import Imputer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_y = pd.read_csv('../input/gender_submission.csv')
X_train =... | code |
2044953/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_y = pd.read_csv('../input/gender_submission.csv')
X_train = train_df[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
sex = pd.get_dummies(X... | code |
2044953/cell_5 | [
"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('../input/train.csv')
test_df = pd.read_csv('../input/test.csv')
test_y = pd.read_csv('../input/gender_submission.csv')
X_train = train_df[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
sex = pd.get_dummies(X... | code |
16168012/cell_21 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
OUTPUT_FILE = 'potential_energy_upd'
R... | code |
16168012/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
OUTPUT_FILE = 'potential_energy_upd'
RANDOM_STATE = 123
N_SPLITS = 3
SHUFFLE = True
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
d... | code |
16168012/cell_25 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import KFold
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
OUTPUT_FILE = 'potential_energy_upd'
R... | code |
16168012/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import KFold
import numpy as np
import pandas as pd
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
OUTPUT_FILE = 'potential_energy_upd'
RANDOM_STATE = 123
N_SPLITS = 3
SH... | code |
16168012/cell_2 | [
"text_plain_output_1.png"
] | import os
import warnings
import warnings
import numpy as np
warnings.simplefilter(action='ignore', category=FutureWarning)
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.linear_model import LinearRegr... | code |
16168012/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
OUTPUT_FILE = 'potential_energy_upd'
RANDOM_STATE = 123
N_SPLITS = 3
SHUFFLE = True
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
d... | code |
16168012/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd
FIGSIZE = (10, 6)
SAVE_PICKLE = True
FREE_MEMORY = True
OUTPUT_FILE = 'potential_energy_upd'
RANDOM_STATE = 123
N_SPLITS = 3
SHUFFLE = True
VERBOSE = False
DATA_PATH = '../input'
def csv_path(dataset='train', data_path=DATA_PATH):
"""
"""
return '{}/{}.csv'.format(data_path, dataset)
d... | code |
16166680/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
num_vars = [var for var in data.columns i... | code |
16166680/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
print(data.shape)
data.head() | code |
16166680/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
for var in vars_with_na:
print(var, np... | code |
16166680/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
num_vars = [var for var in data.columns i... | code |
16166680/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
num_vars = [var for var in data.columns i... | code |
16166680/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
num_vars = [var for var in data.columns i... | code |
16166680/cell_35 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_31 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
16166680/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.pandas.set_option('display.max_columns', None)
data = pd.read_csv('houseprice.csv')
vars_with_na = [var for var in data.columns if data[var].isnull().sum() > 1]
def anal... | code |
88093938/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_9 | [
"image_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fa... | code |
88093938/cell_6 | [
"image_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_39 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kag... | code |
88093938/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_41 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kag... | code |
88093938/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_17 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_43 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kag... | code |
88093938/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_22 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
88093938/cell_36 | [
"text_plain_output_1.png"
] | import pandas as pd
articles_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/articles.csv')
customers_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/customers.csv')
submission_data = pd.read_csv('/kaggle/input/h-and-m-personalized-fashion-recommendations/s... | code |
106206518/cell_21 | [
"text_plain_output_1.png"
] | from heapq import nlargest
from spacy.lang.en.stop_words import STOP_WORDS
from string import punctuation
import spacy
text = '"In an attempt to build an AI-ready workforce, Microsoft announced Intelligent Cloud Hub which has been lanched to empower the next generation of students with AI-ready skills. Envisioned a... | code |
106206518/cell_15 | [
"text_plain_output_1.png"
] | text = '"In an attempt to build an AI-ready workforce, Microsoft announced Intelligent Cloud Hub which has been lanched to empower the next generation of students with AI-ready skills. Envisioned as a three-year collaborative program, Intelligent Cloud Hub will support around 100 institutions with AI infrastructure, co... | code |
72086844/cell_4 | [
"text_html_output_1.png"
] | 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 |
72086844/cell_6 | [
"text_html_output_1.png"
] | 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 |
72086844/cell_8 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OrdinalEncoder
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... | code |
50216735/cell_9 | [
"text_plain_output_1.png"
] | X_train | code |
50216735/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train.Sentiment.unique() | code |
50216735/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='... | code |
50216735/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50216735/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='... | code |
50216735/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='... | code |
50216735/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train.head() | code |
50216735/cell_17 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test = pd.read... | code |
50216735/cell_14 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.naive_bayes import MultinomialNB
MNB = MultinomialNB()
MNB.fit(X_train, Y_train)
from sklearn import metrics
predicted = MNB.predict(X_test)
accuracy_score = metrics.accuracy_score(predicted, Y_test)
print(str('{:04.2f}'.format(accuracy_score * 100)) + '%') | code |
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