path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
49124084/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/twitter-train/train.txt', delimiter='\n', header=None)
data_array = data.to_numpy()
x_array = np.reshape(data_array, (-1, 3))
column = ['Twe... | code |
49124084/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 |
49124084/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/twitter-train/train.txt', delimiter='\n', header=None)
data_array = data.to_numpy()
x_array = np.reshape(data_array, (-1, 3))
column = ['Twe... | code |
49124084/cell_3 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/twitter-train/train.txt', delimiter='\n', header=None)
print(data) | code |
49124084/cell_10 | [
"text_plain_output_1.png"
] | word_to_index['sid'] | code |
49124084/cell_12 | [
"text_plain_output_1.png"
] | word_to_index['unk'] | code |
49124084/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
data = pd.read_csv('../input/twitter-train/train.txt', delimiter='\n', header=None)
data_array = data.to_numpy()
x_array = np.reshape(data_array, (-1, 3))
column = ['Twe... | code |
50212280/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import category_encoders as ce
import pandas as pd
aug_data = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv', index_col='enrollee_id')
aug_data = aug_data.sort_index()
aug_data
aug_data.isnull().sum()
aug_data.isnull().sum()
y =... | code |
50212280/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
aug_data = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv', index_col='enrollee_id')
aug_data = aug_data.sort_index()
aug_data
aug_data.isnull().sum()
aug_data.isnull().sum() | code |
50212280/cell_11 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
aug_data = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv', index_col='enrollee_id')
aug_data = aug_data.sort_index()
aug_data
aug_data.isnull().sum()
aug_data.isnull().sum()
y = aug_data.target.astype('int')
X... | code |
50212280/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | encoder_X_test = catboost_encode_x_data(X_test)
y_test_predict = model.predict(encoder_X_test)
submit_data = pd.DataFrame({'label': y_test_predict}, index=X_test.index)
submit_data.to_csv('submission.csv')
!head submission.csv | code |
50212280/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
aug_data = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv', index_col='enrollee_id')
aug_data = aug_data.sort_index()
aug_data
aug_data.isnull().sum()
print('gender：', aug_data.gender.unique(), '\n')
print('enrolled_university：', aug_data.enrolled_university.unique... | code |
50212280/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
aug_data = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv', index_col='enrollee_id')
aug_data = aug_data.sort_index()
aug_data
aug_data.isnull().sum()
def fill_null_data(df):
df.gender = df.gender.fillna('Other')
df.enrolled_university = df.enrolled_univers... | code |
50212280/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import svm
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.linear_model import SGDRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import... | code |
50212280/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import category_encoders as ce
import pandas as pd
aug_data = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv', index_col='enroll... | code |
50212280/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
aug_data = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv', index_col='enrollee_id')
aug_data = aug_data.sort_index()
aug_data | code |
50212280/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
aug_data = pd.read_csv('../input/hr-analytics-job-change-of-data-scientists/aug_train.csv', index_col='enrollee_id')
aug_data = aug_data.sort_index()
aug_data
aug_data.isnull().sum() | code |
88104935/cell_4 | [
"text_plain_output_1.png"
] | import random
face = ['BlueF', 'BlackF', 'OrangeF', 'WhiteF']
face_weights = [2, 47, 3, 48]
eyes = ['BlueE', 'BrownE', 'GreenE', 'PurpleE', 'RedE', 'YellowE']
eye_weights = [20, 50, 20, 6, 3, 1]
hair = ['BlackdevH', 'DanH', 'DevH', 'PeteH', 'SophH']
hair_weights = [22, 25, 25, 3, 25]
mouth = ['frownM', 'indiffM', 'smi... | code |
88104935/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
import random
face = ['BlueF', 'BlackF', 'OrangeF', 'WhiteF']
face_weights = [2, 47, 3, 48]
eyes = ['BlueE', 'BrownE', 'GreenE', 'PurpleE', 'RedE', 'YellowE']
eye_weights = [20, 50, 20, 6, 3, 1]
hair = ['BlackdevH', 'DanH', 'DevH', 'PeteH', 'SophH']
hair_weights = [22, 25, 25, 3, 25]
mouth = ['f... | code |
88104935/cell_5 | [
"text_plain_output_1.png"
] | import random
face = ['BlueF', 'BlackF', 'OrangeF', 'WhiteF']
face_weights = [2, 47, 3, 48]
eyes = ['BlueE', 'BrownE', 'GreenE', 'PurpleE', 'RedE', 'YellowE']
eye_weights = [20, 50, 20, 6, 3, 1]
hair = ['BlackdevH', 'DanH', 'DevH', 'PeteH', 'SophH']
hair_weights = [22, 25, 25, 3, 25]
mouth = ['frownM', 'indiffM', 'smi... | code |
128000263/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv').drop(['id'], axis=1)
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv').drop(['id'], axis=1)
df_submission = pd.read_csv('/kaggle/input/playground-series... | code |
128000263/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error, r2_score, roc_curve, confusion_matrix, classification_report, accuracy_score, auc, log_loss
from sklearn.model_selection import StratifiedKFold, GridSearchCV
from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler, RobustScaler, LabelEncoder
impo... | code |
128000263/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error, r2_score, roc_curve, confusion_matrix, classification_report, accuracy_score, auc, log_loss
from sklearn.model_selection import StratifiedKFold, GridSearchCV
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
im... | code |
128000263/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error, r2_score, roc_curve, confusion_matrix, classification_report, accuracy_score, auc, log_loss
from sklearn.model_selection import StratifiedKFold, GridSearchCV
from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler, RobustScaler, LabelEncoder
impo... | code |
128000263/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv').drop(['id'], axis=1)
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv').drop(['id'], axis=1)
df_submission = pd.read_csv('/kaggle/input/playground-series... | code |
128000263/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import xgboost as xgb
import matplotlib.pyplot as plt
import lightgbm as lgb
import optuna
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier, BaggingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tr... | code |
128000263/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv').drop(['id'], axis=1)
df_test = pd.read_csv('/kaggle/input/playground-series-s3e13/test.csv').drop(['id'], axis=1)
df_submission = pd.read_csv('/kaggle/input/playground-series... | code |
128000263/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler, RobustScaler, LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/playground-series-s3e13/train.csv').drop(['id'], axis=1)
df_test = pd.read_csv('/kaggle/input/playgroun... | code |
320335/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv', usecols=['Producto_ID', 'Demanda_uni_equil'])
train_data['log_Dem'] = np.log(np.array(train_data['Demanda_uni_equil'].tolist... | code |
320335/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv', usecols=['Producto_ID', 'Demanda_uni_equil'])
train_data['log_Dem'] = np.log(np.array(train_data['Demanda_uni_equil'].tolist... | code |
320335/cell_3 | [
"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
train_data = pd.read_csv('../input/train.csv', usecols=['Producto_ID', 'Demanda_uni_equil'])
mean_data = train_data.groupby(train_data['Producto_ID']).mean()
print(mean_data) | code |
320335/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
train_data = pd.read_csv('../input/train.csv', usecols=['Producto_ID', 'Demanda_uni_equil'])
train_data['log_Dem'] = np.log(np.array(train_data['Demanda_uni_equil'].tolist... | code |
312349/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.location.value_counts()[:30].plot(kind='bar', figsize=(12, 7))
plt.title('Number of locations repor... | code |
312349/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
age_groups = ('confirmed_age_under_1', 'confirmed_age_1-4', 'confirmed_age_5-9', 'confirmed_age_10-14'... | code |
312349/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sbn
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
312349/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
age_groups = ('confirmed_age_under_1', 'confirmed_age_1-4', 'confirmed_age_5-9', 'confirmed_age_10-14'... | code |
312349/cell_3 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df.head(3) | code |
312349/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/cdc_zika.csv', parse_dates=['report_date'], infer_datetime_format=True, index_col=0)
df[df.data_field == 'confirmed_male'].value.plot()
df[df.data_field == 'confirmed_female'].value.plot(... | code |
333414/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_9 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape | code |
333414/cell_11 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.head() | code |
333414/cell_7 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_3 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
fig, axs = plt.subplots(1, 3, sharey=True)
data.plot(kind='scatt... | code |
333414/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import statsmodels.formula.api as smf
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv', index_col=0)
data.shape
#Check assumption 2
#Test... | code |
333414/cell_5 | [
"text_plain_output_1.png"
] | 7.032594 + 0.047537 * 50 | code |
50229416/cell_13 | [
"text_html_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/ibm-hr-analy... | code |
50229416/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'Employee... | code |
50229416/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum() | code |
50229416/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum()
df.isnull().values.any()
df.describe() | code |
50229416/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df.head() | code |
50229416/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'Em... | code |
50229416/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 |
50229416/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum()
df.isnull().values.any()
categorial_col = df.select_... | code |
50229416/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum(... | code |
50229416/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve,confusion_matrix, f1_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing... | code |
50229416/cell_16 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve,confusion_matrix, f1_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing... | code |
50229416/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, roc_auc_score, roc_curve,confusion_matrix, f1_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing... | code |
50229416/cell_14 | [
"text_html_output_1.png"
] | from IPython.display import Image
from io import StringIO
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt
import pandas as pd # data proce... | code |
50229416/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum(... | code |
50229416/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Att... | code |
50229416/cell_5 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/ibm-hr-analytics-attrition-dataset/WA_Fn-UseC_-HR-Employee-Attrition.csv')
df = df.drop(['EmployeeNumber', 'EmployeeCount', 'StandardHours'], axis=1)
df.isna().sum()
df.isnull().values.any() | code |
88086811/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Generation.dtypes
dF_Generation.isnull().sum()
dF_Generation.isnull().sum()
... | code |
88086811/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.csv')
dF_Sample.dtype... | code |
88086811/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.head() | code |
88086811/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/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 |
88086811/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes | code |
88086811/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Generation.dtypes
dF_Generation.isnull().sum()
dF_Generation.isnull().sum() | code |
88086811/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.csv')
dF_Sample.head() | code |
88086811/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.... | code |
88086811/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Generation.dtypes | code |
88086811/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Sample = pd.read_csv('../input/enerjisa-enerji-veri-maraton/sample_submission.csv')
dF_Sample.dtype... | code |
88086811/cell_35 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Generation.dtypes
dF_Generation.isnull().sum()
dF_Generation.isnull().sum()
... | code |
88086811/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dF_Generation = pd.read_csv('../input/enerjisa-enerji-veri-maraton/generation.csv', sep=';')
dF_Generation.columns = ['DateTime', 'Generation']
dF_Generation.dtypes
dF_Generation.dtypes
dF_Generation.isnull().sum() | code |
34147803/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
df = pd.read_csv('../input/programs-broadcast-by-disney-csv/Kids TV Data.csv')
df['First Aired'].value_counts().plot(kind='bar') | code |
34147803/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
df = pd.read_csv('../input/programs-broadcast-by-disney-csv/Kids TV Data.csv')
df.head() | code |
34147803/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 |
34147803/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)
import matplotlib.pyplot as plt
df = pd.read_csv('../input/programs-broadcast-by-disney-csv/Kids TV Data.csv')
df['Series Type'].value_counts().plot(kind='bar') | code |
105216483/cell_13 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
105216483/cell_9 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
105216483/cell_23 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB... | code |
105216483/cell_30 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB... | code |
105216483/cell_11 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
105216483/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
105216483/cell_28 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.naive_bayes import GaussianNB... | code |
105216483/cell_8 | [
"text_html_output_1.png"
] | from sklearn.ensemble import BaggingClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.... | code |
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