path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
50222118/cell_24 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def create_mean_std_df(df):
means = df.mean()
stds = df.s... | code |
50222118/cell_14 | [
"image_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)
def create_mean_std_df(df):
means = df.mean()
stds = df.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref']... | code |
50222118/cell_10 | [
"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)
def create_mean_std_df(df):
means = df.mean()
stds = df.std()
ret_df = pd.concat([means, stds], axis=1).reset_index()
ret_df.columns = ['index', 'mean', 'std']
ret_df['pref']... | code |
50222118/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | train_features[['cp_type']].value_counts() | code |
73090447/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', index_col='Id')
train.shape
trainNum = train.select_dtyp... | code |
73090447/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', index_col='Id')
train.shape
train.hist(column='SalePrice', bins=50) | code |
73090447/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', index_col='Id')
train.shape
trainNum = train.select_dtyp... | code |
73090447/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', index_col='Id')
train.shape | code |
73090447/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', index_col='Id')
train.shape
train.head() | code |
73090447/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', index_col='Id')
train.shape
trainNum = train.select_dtyp... | code |
73090447/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', index_col='Id')
train.shape
train[['SalePrice']].describe() | code |
128033200/cell_9 | [
"text_html_output_1.png"
] | from itertools import chain
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggle/input/productcategorization2/new_images/new_images'
images_list = [int(iter.split('.')[0]) for iter in os.listdir(... | code |
128033200/cell_4 | [
"text_html_output_1.png"
] | from itertools import chain
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggle/input/productcategorization2/new_images/new_images'
images_list = [int(iter.split('.')[0]) for iter in os.listdir(... | code |
128033200/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from itertools import chain
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggl... | code |
128033200/cell_6 | [
"text_html_output_1.png"
] | from itertools import chain
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggle/input/productcategorization2/new_images/new_images'
images_list = [int(iter.split('.')[0]) for iter in os.listdir(... | code |
128033200/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import os
from tqdm import tqdm
from itertools import chain
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
import tensorflow.keras.backend as K
import shutil, os, time, random, copy
import imageio
import h5py
from scipy.io import loa... | code |
128033200/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from itertools import chain
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggl... | code |
128033200/cell_8 | [
"text_plain_output_1.png"
] | from itertools import chain
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggle/input/productcategorization2/new_images/new_images'
images_list = [int(iter.split('.')[0]) for iter in os.listdir(... | code |
128033200/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from itertools import chain
from tqdm import tqdm
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggle/input/productcategorization2/new_images/new_images'
... | code |
128033200/cell_14 | [
"text_html_output_1.png"
] | from itertools import chain
from tqdm import tqdm
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggle/input/productcategorization2/new_images/new_images'
... | code |
128033200/cell_22 | [
"text_plain_output_1.png"
] | from itertools import chain
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggl... | code |
128033200/cell_10 | [
"text_plain_output_1.png"
] | from itertools import chain
from tqdm import tqdm
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggle/input/productcategorization2/new_images/new_images'
... | code |
128033200/cell_12 | [
"text_html_output_1.png"
] | from itertools import chain
from tqdm import tqdm
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
DATAFRAME_PATH = '/kaggle/input/productcategorization2/image_taxonomy_and_description.csv'
IMAGE_DIRECTORY_PATH = '/kaggle/input/productcategorization2/new_images/new_images'
... | code |
1001261/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv') | code |
128035984/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[:3]:
print(os.path.join(dirname, filename))
if len(filenames) > 3:
print('...') | code |
128035984/cell_8 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import csv
import cv2
import numpy as np
import numpy as np # linear algebra
import random
import torch
import torch.nn as nn
TRAIN_PATH = '/kaggle/input/captcha-hacker-2023-spring/dataset/train'
TEST_PATH = '/kaggle/input/captcha-hacker-2023-spring/dataset/test'... | code |
34149992/cell_4 | [
"text_plain_output_1.png"
] | X_train | code |
34149992/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import os
import pandas as pd
pd.set_option('display.max_colwidth', None)
folder = '../input/nlp-getting-started'
test = pd.read_csv(os.path.join(folder, 'test.csv'), index_col='id')
train = pd.read_csv(os.path.join(folder, 'train.csv'), index_col='id')
X = train... | code |
34149992/cell_10 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import os
import pandas as pd
import spacy
pd.set_option('display.max_colwidth', None)
folder = '../input/nlp-getting-started'
test = pd.read_csv(os.path.join(folder, 'test.csv'), index_col='id')
train = pd.read_csv(os.path.join(folder, 'train.csv'), index_col='... | code |
72080311/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.mlab as mlab # some MATLAB commands
import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random # generating (pseudo)-random numbers
import numpy as np
import pandas as pd
pd.set_option('d... | code |
72080311/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
pd.set_option('display.max_colwidth', None)
import matplotlib.pyplot as plt
from glob import glob
import random
import matplotlib.mlab as mlab
from scipy.interpolate import interp1d
training_labels_path = '..... | code |
72080311/cell_23 | [
"image_output_1.png"
] | !pip -q install pycbc
import pycbc | code |
72080311/cell_30 | [
"text_plain_output_1.png"
] | import matplotlib.mlab as mlab # some MATLAB commands
import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pycbc
import random # generating (pseudo)-random numbers
import numpy as np
import pandas as pd
pd... | code |
72080311/cell_11 | [
"text_html_output_1.png"
] | training_paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
print('The total number of files in the training set:', len(training_paths)) | code |
72080311/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random # generating (pseudo)-random numbers
import numpy as np
import pandas as pd
pd.set_option('display.max_colwidth', None)
import matplotlib.pyplot as ... | code |
72080311/cell_28 | [
"image_output_1.png"
] | import matplotlib.mlab as mlab # some MATLAB commands
import matplotlib.pyplot as plt # plotting
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pycbc
import random # generating (pseudo)-random numbers
import numpy as np
import pandas as pd
pd... | code |
72080311/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
pd.set_option('display.max_colwidth', None)
import matplotlib.pyplot as plt
from glob import glob
import random
import matplotlib.mlab as mlab
from scipy.interpolate import interp1d
training_labels_path = '..... | code |
72080311/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
pd.set_option('display.max_colwidth', None)
import matplotlib.pyplot as plt
from glob import glob
import random
import matplotlib.mlab as mlab
from scipy.interpolate import interp1d
training_labels_path = '..... | code |
34134310/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_fifa = pd.read_csv('/kaggle/input/fifa19/data.csv')
columns_to_drop = ['Unnamed: 0', 'ID', 'Name', 'Photo', 'Nationality', 'Flag', 'Club', 'Club Logo', 'Valu... | code |
34134310/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_fifa = pd.read_csv('/kaggle/input/fifa19/data.csv')
columns_to_drop = ['Unnamed: 0', 'ID', 'Name', 'P... | code |
34134310/cell_33 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_fifa = pd.read_csv('/kaggle/input/fifa19/data.csv')
columns_to_drop = ['Unnamed: 0', 'ID', 'Name', 'Photo', 'Nationality', 'Flag', 'Club', 'Club Logo', 'Valu... | code |
34134310/cell_29 | [
"text_plain_output_1.png"
] | tp_componentes = tuple(zip(autovalores, autovetores))
tp_componentes
sorted(tp_componentes, reverse=True) | code |
34134310/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_fifa = pd.read_csv('/kaggle/input/fifa19/data.csv')
columns_to_drop = ['Unnamed: 0', 'ID', 'Name', 'Photo', 'Nationality', 'Flag', 'Club', 'Club Logo', 'Valu... | code |
34134310/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34134310/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_fifa = pd.read_csv('/kaggle/input/fifa19/data.csv')
columns_to_drop = ['Unnamed: 0', 'ID', 'Name', 'Photo', 'Nationality', 'Flag', 'Club', 'Club Logo', 'Valu... | code |
34134310/cell_28 | [
"text_plain_output_1.png"
] | tp_componentes = tuple(zip(autovalores, autovetores))
tp_componentes | code |
34134310/cell_31 | [
"text_plain_output_1.png"
] | total = sum(autovalores)
var_acum = [autovalor / total for autovalor in sorted(autovalores, reverse=True)]
var_acum | code |
34134310/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_fifa = pd.read_csv('/kaggle/input/fifa19/data.csv')
columns_to_drop = ['Unnamed: 0', 'ID', 'Name', 'Photo', 'Nationality', 'Flag', 'Club', 'Club Logo', 'Valu... | code |
2007135/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeature... | code |
2007135/cell_25 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.preprocessing import PolynomialFeatures
from sklearn.tree import DecisionTreeRegressor
import pandas ... | code |
2007135/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeature... | code |
2007135/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeature... | code |
2007135/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeature... | code |
2007135/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeature... | code |
2007135/cell_19 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd # data processing
import numpy as np
import pandas as pd
from sklearn.model_selection import ... | code |
2007135/cell_16 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score, cross_val_predict
import pandas as pd # data processing
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import... | code |
2007135/cell_22 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn.preprocessing import PolynomialFeatures
from sklearn.tree import DecisionTreeRegressor
import pandas as pd # data processing
import numpy as np
import p... | code |
2007135/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict
from sklearn import metrics
from sklearn.preprocessing import Imputer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeature... | code |
129033726/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #to perform different visualizations
import pandas as pd #to handle the dataframe
import seaborn as sns
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not alw... | code |
129033726/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.head() | code |
129033726/cell_25 | [
"image_output_1.png"
] | from scipy.stats import f_oneway
import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not always missing values. Missing values can be present in other ... | code |
129033726/cell_33 | [
"text_plain_output_1.png"
] | from scipy.stats import f_oneway
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
import numpy as np #to perform arithmetic operation
import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.... | code |
129033726/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt #to perform different visualizations
import pandas as pd #to handle the dataframe
import seaborn as sns
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not alw... | code |
129033726/cell_55 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, LeakyReLU
from keras.models import Sequential
from scipy.stats import f_oneway
from sklearn.compose import ColumnTransformer
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import StandardScaler... | code |
129033726/cell_29 | [
"text_plain_output_1.png"
] | from scipy.stats import f_oneway
import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not always missing values. Missing values can be present in other ... | code |
129033726/cell_65 | [
"text_plain_output_1.png"
] | """
#developing the model using Dropout
#here we will initialize dropout in each layer except the output layer as it might result into a unstable network
classifier=Sequential()
#1st hidden layer
classifier.add(Dense(units=6, kernel_initializer="uniform"))
classifier.add(LeakyReLU(alpha=0.1))
classifier.add(Dropout(p... | code |
129033726/cell_61 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, LeakyReLU
from keras.models import Sequential
from scipy.stats import f_oneway
from sklearn.compose import ColumnTransformer
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEnco... | code |
129033726/cell_67 | [
"text_plain_output_1.png"
] | """
from keras.wrappers.scikit_learn import KerasClassifier #this is used to wrap sklearn in keras
from sklearn.model_selection import GridSearchCV
def ann_classifier(optimizer):
classifier=Sequential() #this is the local classifier
classifier.add(Dense(units=6, kernel_initializer="uniform"))
classifier.add(Lea... | code |
129033726/cell_60 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, LeakyReLU
from keras.models import Sequential
from scipy.stats import f_oneway
from sklearn.compose import ColumnTransformer
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEnco... | code |
129033726/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt #to perform different visualizations
import pandas as pd #to handle the dataframe
import seaborn as sns
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not alw... | code |
129033726/cell_49 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, LeakyReLU
from keras.models import Sequential
classifier = Sequential()
classifier.add(Dense(units=6, kernel_initializer='uniform'))
classifier.add(LeakyReLU(alpha=0.1))
classifier.add(Dense(units=6, kernel_initializer='uniform'))
classifier.add(LeakyReLU(alpha=0.1))
classifier.add(... | code |
129033726/cell_32 | [
"text_plain_output_1.png"
] | from scipy.stats import f_oneway
import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not always missing values. Missing values can be present in other ... | code |
129033726/cell_51 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, LeakyReLU
from keras.models import Sequential
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
classifier = Sequential()
classifier.add(Dense(unit... | code |
129033726/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt #to perform different visualizations
import pandas as pd #to handle the dataframe
import seaborn as sns
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not alw... | code |
129033726/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt #to perform different visualizations
import pandas as pd #to handle the dataframe
import seaborn as sns
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not alw... | code |
129033726/cell_47 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, LeakyReLU
from keras.models import Sequential
classifier = Sequential()
classifier.add(Dense(units=6, kernel_initializer='uniform'))
classifier.add(LeakyReLU(alpha=0.1))
classifier.add(Dense(units=6, kernel_initializer='uniform'))
classifier.add(LeakyReLU(alpha=0.1))
classifier.add(... | code |
129033726/cell_3 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install keras #integration of tensorflow and theano. Used to build DNN in an efficient way
!pip install tensorflow
!pip install theano #powerfull library to perform mathematical operation | code |
129033726/cell_17 | [
"image_output_1.png"
] | import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not always missing values. Missing values can be present in other format also.\nFeature can also hav... | code |
129033726/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt #to perform different visualizations
import pandas as pd #to handle the dataframe
import seaborn as sns
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not alw... | code |
129033726/cell_22 | [
"image_output_1.png"
] | from scipy.stats import f_oneway
import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not always missing values. Missing values can be present in other ... | code |
129033726/cell_53 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.layers import Dense, LeakyReLU
from keras.models import Sequential
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform... | code |
129033726/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not always missing values. Missing values can be present in other format also.\nFeature can also hav... | code |
129033726/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt #to perform different visualizations
import pandas as pd #to handle the dataframe
data = pd.read_csv('/kaggle/input/churn-predictions-personal/Churn_Predictions.csv')
data.isnull().sum()
' It\'s alaways necessary to check for the missing values. "Nan" is not always missing values. Mis... | code |
326306/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plot
import pandas
import seaborn
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=... | code |
326306/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].g... | code |
326306/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].g... | code |
326306/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp'] | code |
326306/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
import seaborn
import matplotlib.pyplot as plot
seaborn.set(style='darkgrid', palette='husl') | code |
326306/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].g... | code |
326306/cell_8 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
import seaborn
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['... | code |
326306/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime) | code |
326306/cell_10 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plot
import pandas
import seaborn
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=... | code |
326306/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas
data = pandas.read_csv('../input/wowah_data.csv')
data.columns = ['char', 'level', 'race', 'charclass', 'zone', 'guild', 'timestamp']
data['time'] = data['timestamp'].apply(pandas.to_datetime)
last70 = data[data['level'] == 70].groupby('char', as_index=False).last()
ding80 = data[data['level'] == 80].g... | code |
17139134/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import itertools
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import os
import seaborn as sns
import itertools
sns.set(style='darkgrid')
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
data.ren... | code |
17139134/cell_23 | [
"image_output_1.png"
] | import itertools
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import os
import seaborn as sns
import itertools
sns.set(style='darkgrid')
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
data.ren... | code |
17139134/cell_20 | [
"text_plain_output_1.png"
] | import itertools
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import os
import seaborn as sns
import itertools
sns.set(style='darkgrid')
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
data.rename({'Runtime (Minutes)': 'Durati... | code |
17139134/cell_17 | [
"text_html_output_1.png"
] | import itertools
import pandas as pd
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
data.rename({'Runtime (Minutes)': 'Duration', 'Revenue (Millions)': 'Revenue'}, axis='columns', inplace=True)
for col in data.columns:
nans = pd.value_counts(data[col].isnull())
Nans = data[pd.isnull(data).any(axis=1)]
dat... | code |
17139134/cell_24 | [
"image_output_1.png"
] | import itertools
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import os
import seaborn as sns
import itertools
sns.set(style='darkgrid')
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
data.ren... | code |
17139134/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
data.rename({'Runtime (Minutes)': 'Duration', 'Revenue (Millions)': 'Revenue'}, axis='columns', inplace=True)
for col in data.columns:
nans = pd.value_counts(data[col].isnull())
data.describe(include='all') | code |
17139134/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/IMDB-Movie-Data.csv')
data.rename({'Runtime (Minutes)': 'Duration', 'Revenue (Millions)': 'Revenue'}, axis='columns', inplace=True)
print('The dataset contains NaN values: ', data.isnull().values.any())
print('Missing values in the dataset : ', data.isnull().values.su... | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.