path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
122251329/cell_33 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', ... | code |
122251329/cell_44 | [
"image_output_1.png"
] | from scipy import stats
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, KFold
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDiseas... | code |
122251329/cell_29 | [
"image_output_1.png"
] | from IPython.display import display
import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attri... | code |
122251329/cell_39 | [
"image_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_att... | code |
122251329/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
hrz.info() | code |
122251329/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
print(hrz[hrz['Cholesterol'] == 0].shape[0])
print(hrz[hrz['RestingBP'] == 0].shape[0]) | code |
122251329/cell_32 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', ... | code |
122251329/cell_15 | [
"text_plain_output_1.png"
] | from IPython.display import display
import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_attribs = ['ChestPainType', 'RestingECG', 'ST_Slope']
cat_bin_attri... | code |
122251329/cell_17 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
hrz.describe() | code |
122251329/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
print('MaxHR values for F, M:')
print('HeartDisease=0', round(hrz.loc[(hrz['Sex'] == 'F') & (hrz['HeartDisease'] == 0)]['MaxHR'].mean(), 2), round(hrz.loc[(hrz['Sex'] == 'M') & (hrz['HeartDisease'] == 0)]['Ma... | code |
122251329/cell_37 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
csv_path = '/kaggle/input/heart-failure-prediction/heart.csv'
hrz = pd.read_csv(csv_path)
target = ['HeartDisease']
num_attribs = ['Age', 'RestingBP', 'Cholesterol', 'MaxHR', 'Oldpeak']
cat_nom_att... | code |
1009964/cell_4 | [
"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')
combine = [train_df, test_df]
train_df.head(10) | code |
1009964/cell_6 | [
"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')
combine = [train_df, test_df]
NAs = pd.concat([train_df.isnull().sum(), test_df.isnull().sum()], axis=1, keys=['Train', 'Test'])
NAs[NAs.sum(axis=1) > 1] | code |
1009964/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import random as rnd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors imp... | code |
1009964/cell_7 | [
"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')
combine = [train_df, test_df]
NAs = pd.concat([train_df.isnull().sum(), test_df.isnull().sum()], axis=1, keys=['Train', 'Test'])
NAs[NAs.sum(axis=1) > 1]
trai... | code |
1009964/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')
combine = [train_df, test_df]
train_df.info() | code |
1010160/cell_4 | [
"text_plain_output_1.png"
] | from scipy.misc import imread
import cv2 as cv
import glob
import numpy as np
import os
import random
species = ['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT']
select = 1000
ROWS = 90
COLS = 160
CHANNELS = 3
PATH = './input/'
def get_image(file):
pos1 = file.rfind('/img_')
return file[pos1 + ... | code |
1010160/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Convolution2D,MaxPooling2D,Flatten,Activation
from keras.layers import Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from scipy.misc import imread
import cv2 as cv
import glob
import numpy as np
import os
import random
species = ['ALB', 'BET', 'DO... | code |
1010160/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2 as cv
import glob
import random
import numpy as np
from scipy.misc import imread
import os
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense, Convolution2D, MaxPooling2D, Flatten, Activation
from keras.optimizers import Adam
from sklearn.cross_validation impo... | code |
1010160/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Convolution2D,MaxPooling2D,Flatten,Activation
from keras.layers import Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from scipy.misc import imread
import cv2 as cv
import glob
import numpy as np
import os
import random
species = ['ALB', 'BET', 'DO... | code |
1010160/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense,Convolution2D,MaxPooling2D,Flatten,Activation
from keras.layers import Dropout
from keras.models import Sequential
from keras.optimizers import Adam
from scipy.misc import imread
import cv2 as cv
import datetime
import glob
import numpy as np
import os
import pandas as pd
impor... | code |
121150047/cell_13 | [
"text_plain_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error... | code |
121150047/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from colorama import Style, Fore
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
from xgboost import XGBRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import p... | code |
121150047/cell_6 | [
"text_plain_output_1.png"
] | from colorama import Style, Fore
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.compose import ColumnTransformer
from s... | code |
121150047/cell_2 | [
"image_output_1.png"
] | from colorama import Style, Fore
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error
from sklearn.compose import ColumnTransformer
from sklearn.pipeline impor... | code |
121150047/cell_12 | [
"text_html_output_1.png"
] | from colorama import Style, Fore
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import warnings
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from xgboost import XGBRegressor
from sklearn.metrics import mean_squared_error... | code |
121150047/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
old_df = pd.read_csv('/kaggle/input/regression-with-neural-networking/concrete_data.csv')
original_df = pd.read_csv('/kaggle/input/playground-series-s3e9/train.csv').drop(columns=['id'])
test_df = pd.read_csv('/kaggle/input/playground-series-s3e9/test.csv').drop(columns=['id'])
sample = pd.read_csv... | code |
90108212/cell_13 | [
"text_html_output_1.png"
] | data | code |
90108212/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
women = train_data.loc[train_data.Sex == 'female']['Survived']
rate_women = sum(women) / len(women)
men = train... | code |
90108212/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_data.tail() | code |
90108212/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
women = train_data.loc[train_data.Sex == 'female']['Survived']
rate_women = sum(women) / len(women)
print('% of ... | code |
90108212/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
women = train_data.loc[train_data.Sex == 'female']['Survived']
rate_women = sum(women) / len(women)
men = train... | code |
90108212/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_data.head() | code |
90108212/cell_12 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
women ... | code |
90108212/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('/kaggle/input/titanic/train.csv')
test_data = pd.read_csv('/kaggle/input/titanic/test.csv')
gender = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
gender.head() | code |
90148441/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, n... | code |
90148441/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, n... | code |
90148441/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieI... | code |
90148441/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieI... | code |
90148441/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieI... | code |
90148441/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 |
90148441/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)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieI... | code |
90148441/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieI... | code |
90148441/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_movies = pd.read_csv('../input/movielens/movies.dat', sep='::', header=None, names=['MovieId', 'Title', 'Genres'], encoding='latin 1')
data_ratings = pd.read_csv('../input/movielens/ratings.dat', sep='::', header=None, names=['UserId', 'MovieI... | code |
322568/cell_13 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', '... | code |
322568/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_8.png",
"application_... | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', '... | code |
322568/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic.head()) | code |
322568/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
titanic = pd.read_csv('../input/train.csv')
print(titanic.describe()) | code |
322568/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
print(titanic['Sex']) | code |
322568/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic ... | code |
322568/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic.describe()) | code |
322568/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.cross_validation import KFold
predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Embarked', 'Fare'] | code |
322568/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', '... | code |
322568/cell_12 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0
titanic.loc[titanic['Sex'] == 'female', '... | code |
322568/cell_5 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
titanic = pd.read_csv('../input/train.csv')
print(titanic['Cabin'].count()) | code |
34147377/cell_63 | [
"text_plain_output_1.png"
] | from scipy.special import boxcox1p
import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='ob... | code |
34147377/cell_21 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_d... | code |
34147377/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_data.shape | code |
34147377/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize... | code |
34147377/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize... | code |
34147377/cell_33 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_28.png",
"image_output_23.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.p... | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize... | code |
34147377/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize... | code |
34147377/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
n... | code |
34147377/cell_61 | [
"image_output_1.png"
] | from scipy.special import boxcox1p
import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='ob... | code |
34147377/cell_54 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
n... | code |
34147377/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape | code |
34147377/cell_18 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
... | code |
34147377/cell_32 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13.png",
"image_output_40.png",
"image_output_5.... | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_d... | code |
34147377/cell_51 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
fig = p... | code |
34147377/cell_59 | [
"text_html_output_1.png"
] | from scipy.special import boxcox1p
import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
test_data.shape
cat_data = train_data.select_dtypes(include='ob... | code |
34147377/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
train_data.info() | code |
34147377/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize... | code |
34147377/cell_35 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize... | code |
34147377/cell_14 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(fi... | code |
34147377/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_data.shape
test_data.info() | code |
34147377/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_d... | code |
34147377/cell_37 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_d... | code |
34147377/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10, 5))
sns.distpl... | code |
34147377/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.head() | code |
106191058/cell_8 | [
"image_output_1.png"
] | import json
import matplotlib.pyplot as plt
import pandas as pd
import requests
import seaborn as sns
token_address = '0xd7efb00d12c2c13131fd319336fdf952525da2af'
base_url = 'https://api.ethplorer.io'
url = base_url + f'/getTokenInfo/{token_address}?apiKey=freekey'
response = requests.get(url)
if response.status_c... | code |
106191058/cell_5 | [
"text_plain_output_1.png"
] | import json
import requests
token_address = '0xd7efb00d12c2c13131fd319336fdf952525da2af'
base_url = 'https://api.ethplorer.io'
url = base_url + f'/getTokenInfo/{token_address}?apiKey=freekey'
response = requests.get(url)
if response.status_code == 200:
token_info_response = json.loads(response.text)
token_info_re... | code |
106194498/cell_2 | [
"text_plain_output_1.png"
] | a = 'Functions'
len(a) | code |
106194498/cell_5 | [
"text_plain_output_1.png"
] | def my_first_function():
pass
my_first_function() | code |
17096880/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
import numpy as np
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param ac... | code |
17096880/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from ... | code |
17096880/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.author.nunique() | code |
17096880/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import ... | code |
17096880/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import ... | code |
17096880/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
import os
import pandas as pd
import numpy as np
import xgboost as xgb
from tqdm import tqdm
from sklearn.svm import SVC
from keras.models import Sequential
from keras.layers.recurrent import LSTM, GRU
from keras.layers.core import Dense, Activation, Dropout
from keras.layers.embeddi... | code |
17096880/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import ... | code |
17096880/cell_16 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import SVC
import numpy as np
import ... | code |
17096880/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
sample = pd.read_csv('../input/sample_submission.csv')
train.head(3) | code |
17096880/cell_10 | [
"text_html_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.linear_model import LogisticRegression
import numpy as np
def multiclass_logloss(actual, predicted, eps=1e-15):
"""Multi class version of Logarithmic Loss metric.
:param actual: Array containing the actual target classe... | code |
122264561/cell_42 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practic... | code |
122264561/cell_63 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_con... | code |
122264561/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_83 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_location = pd.read_csv('/kaggle/input/a360-internship-practice/Location.csv')
df_content = pd.read_csv('/kaggle/input/a360-internship-practice/Content.csv')
df_rty... | code |
122264561/cell_87 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_profile = pd.read_csv('/kaggle/input/a360-internship-practice/Profile.csv')
df_profile = df_profile.drop('Unnamed: 0', axis=1)
type(df_profile['Interests'][2]) | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.