path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90153696/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y) | code |
90153696/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df.head() | code |
90153696/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_ | code |
90153696/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
y.head() | code |
90153696/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
df = df.drop(columns=['Neck', 'Chest', 'Hip'])
X = df[['BodyFat', 'Age']]
y = df['Density']
model = LinearRegression()
model.fit(X, y)
model.score(X, y)
model.intercept_
mod... | code |
90153696/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/body-fat-prediction-dataset/bodyfat.csv')
sns.lmplot(x='BodyFat', y='Density', data=df, ci=None) | code |
50214099/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import ImageOps
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray... | code |
50214099/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import ImageOps
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray... | code |
50214099/cell_20 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import ImageOps
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray... | code |
50214099/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 |
50214099/cell_7 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray(img_arr)
print(img.mode)
... | code |
50214099/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import ImageOps
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray... | code |
50214099/cell_28 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import ImageOps
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray... | code |
50214099/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from PIL import ImageOps
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray... | code |
50214099/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
print(image.format)
print(image.mode)
print(image.size) | code |
50214099/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray(img_arr)
image.save('ope... | code |
50214099/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import ImageOps
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray... | code |
50214099/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray(img_arr)
new_img = Image... | code |
50214099/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from PIL import ImageOps
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
img = Image.fromarray... | code |
50214099/cell_5 | [
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
from PIL import Image
image = Image.open('../input/pilimages/opera_house.jpg')
import matplotlib.pyplot as plt
import numpy as np
img_arr = np.asarray(image)
print(img_arr.dtype)
print(img_arr.shape)
plt.im... | code |
1003162/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_folder = '../input/'
data = pd.read_csv(base_folder + 'train.csv')
data.head() | code |
1003162/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | figure = plt.figure(figsize=(15, 8))
plt.hist([data[data['Survived'] == 1]['Age'], data[data['Survived'] == 0]['Age']], color=['g', 'r'], bins=10, label=['Survived', 'Dead'])
plt.xlabel('Age')
plt.ylabel('Number of passengers')
plt.legend() | code |
1003162/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1003162/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)
base_folder = '../input/'
data = pd.read_csv(base_folder + 'train.csv')
survived_sex = data[data['Survived'] == 1]['Sex'].value_counts()
dead_sex = data[data['Survived'] == 0]['Sex'].value_counts()
df = pd.DataFrame([survived_sex, dead_sex])
df.in... | code |
1003162/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
# dead and survived based on age of people
figure = plt.figure(figsize=(15,8))
plt.hist([data[data['Survived']==1]['Age'],data[data['Survived']==0]['Age']], color = ['g','r'],
bins = 10,label = ['Survived','Dead'])
plt.xlabel('Age')
plt.yl... | code |
1003162/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
base_folder = '../input/'
data = pd.read_csv(base_folder + 'train.csv')
data.describe() | code |
49127047/cell_4 | [
"text_plain_output_1.png"
] | print(' * ')
print(' *** ')
print(' ***** ')
print(' *******')
print(' ***** ')
print(' *** ')
print(' * ') | code |
49127047/cell_6 | [
"text_plain_output_1.png"
] | for genap in range(50, 103, 4):
if genap % 2 == 0:
print(genap) | code |
49127047/cell_2 | [
"text_plain_output_1.png"
] | A = eval(input('masukan angka'))
kuadrat = A * A * A
print('hasil kuadrat dari', A, 'adalah', kuadrat, ',', sep='') | code |
49127047/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | print('1 gram =424,000 Rupiah')
gram = input('Masukan gram emas:')
try:
gram = int(gram)
except ValueError:
exit('Input wajib bilangan bulat')
print('Emas', gram, 'gram setara', format(gram * 424000, ','), 'Rupiah') | code |
49127047/cell_5 | [
"text_plain_output_1.png"
] | n = eval(input('masukan jumlah bilangan Fibonacci = '))
n1 = 1
n2 = 1
for i in range(n):
nth = n1 + n2
n1 = n2
n2 = nth
print(n1, end='.') | code |
49127148/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
imp... | code |
49127148/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
X = pd.read_csv('/kaggle/input/modelling-ready-data/X.csv')
print(f'Shape of X= {X.shape}')
X.head() | code |
49127148/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import LinearRegression, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from ... | code |
49127148/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import LinearRegression, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_... | code |
49127148/cell_26 | [
"text_html_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import LinearRegression, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from ... | code |
49127148/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
imp... | code |
49127148/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
import numpy as np
import pandas as pd
import war... | code |
49127148/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import LinearRegression, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_... | code |
49127148/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.linear_model import LinearRegression, Lasso, ElasticNet
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from ... | code |
49127148/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
X = pd.read_csv('/kaggle/input/modelling-ready-data/X.csv')
y = pd.read_csv('/kaggle/input/modelling-ready-data/y.csv')
print(f'Shape of y= {y.shape}')
y.head() | code |
49127148/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
n_jobs = -1
random_state = 42
X = pd.read_csv('/kaggle/input/modelling-ready-data/X.csv')
y = pd.read_csv('/kaggle/input/modelling-ready-data/y.csv')
X_train, X_test, y_train, y_test = train_... | code |
1009303/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1009303/cell_3 | [
"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/Rate.csv')
df.head().T | code |
1009303/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/Rate.csv')
df.head().T
df.loc[df['IssuerId'] == 11324, 'IndividualRate'] | code |
1003644/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
from subprocess import check_output
df_train = pd.read_csv('../input/train.csv')
df_train.colu
price = df_train['SalePrice']
... | code |
1003644/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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 seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
from subprocess import check_output
df_train = pd.read_csv('../input/train.csv')
df_train... | code |
1003644/cell_2 | [
"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
import seaborn as sns
from subprocess import check_output
df_train = pd.read_csv('../input/train.csv')
df_train.colu | code |
1003644/cell_1 | [
"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
import seaborn as sns
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
df_train = pd.read_csv('../input/train.csv') | code |
1003644/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | 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 seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
from subprocess import check_output
df_train = pd.read_csv('../input/train.csv')
df_train... | code |
1003644/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
import seaborn as sns
from subprocess import check_output
df_train = pd.read_csv('../input/train.csv')
df_train.colu
price = df_train['SalePrice']
price.describe() | code |
1003644/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
from subprocess import check_output
df_train = pd.read_csv('../input/train.csv')
df_train.colu
price = df_train['SalePrice']
... | code |
34145078/cell_9 | [
"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)
DataDir = '/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv'
campus_data = pd.read_csv(DataDir)
s = campus_data.dtypes == 'object'
object_cols = list(s[s].index)
drop_data = campus_data.select_dtypes(exclude=['object... | code |
34145078/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DataDir = '/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv'
campus_data = pd.read_csv(DataDir)
campus_data.describe() | code |
34145078/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DataDir = '/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv'
campus_data = pd.read_csv(DataDir)
s = campus_data.dtypes == 'object'
object_cols = list(s[s].index)
print('categorical columns:')
print(object_cols) | code |
34145078/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34145078/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DataDir = '/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv'
campus_data = pd.read_csv(DataDir)
s = campus_data.dtypes == 'object'
object_cols = list(s[s].index)
print('unic objects in the salary: ', campus_data['sal... | code |
34145078/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DataDir = '/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv'
campus_data = pd.read_csv(DataDir)
s = campus_data.dtypes == 'object'
object_cols = list(s[s].index)
drop_data = campus_data.select_dtypes(exclude=['object... | code |
34145078/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DataDir = '/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv'
campus_data = pd.read_csv(DataDir)
print('successfuly uploading the data.') | code |
34145078/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
DataDir = '/kaggle/input/factors-affecting-campus-placement/Placement_Data_Full_Class.csv'
campus_data = pd.read_csv(DataDir)
campus_data.head() | code |
32071248/cell_21 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_env = pd.read_csv('/kaggle/input/global-environmental-factors/env.csv')
df_airpol = pd.read_csv('/kaggle/input/pm25-global-air-pollution/pm25-global-air-pollution-2017.csv')
df_pop = pd.read_csv('/kaggle/input/world-population-by-country-state/country_population.csv')
df_env... | code |
32071248/cell_9 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_env = pd.read_csv('/kaggle/input/global-environmental-factors/env.csv')
df_airpol = pd.read_csv('/kaggle/input/pm25-global-air-pollution/pm25-global-air-pollution-2017.csv')
df_pop = pd.read_csv('/kaggle/input/world-population-by-country-state/country_population.csv')
df_env... | code |
32071248/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
fr... | code |
32071248/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_env = pd.read_csv('/kaggle/input/global-environmental-factors/env.csv')
df_airpol = pd.read_csv('/kaggle/input/pm25-global-air-pollution/pm25-global-air-pollution-2017.csv')
df_pop = pd.read_csv('/kaggle/input/world-population-by-country-state/country_population.csv')
df_pop = df_pop[~df_pop['C... | code |
32071248/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
df_env = pd.read_csv('/kaggle/input/global-environmental-factors/env.csv')
print('env: ', df_env.shape)
df_airpol = pd.read_csv('/kaggle/input/pm25-global-air-pollution/pm25-global-air-pollution-2017.csv')
print('pol: ', df_airpol.shape)
df_pop = pd.read_csv('/kaggle/input/world-population-by-count... | code |
32071248/cell_11 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
fr... | code |
32071248/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics import make_scorer, me... | code |
32071248/cell_18 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_8 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_15 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_16 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df_env = pd.read_csv('/kaggle/input/global-environmental-factors/env.csv')
df_airpol = pd.read_csv('/kaggle/input/pm25-global-air-pollution/pm25-global-air-pollution-2017.csv')
df_pop = pd.read_csv('/kaggle/input/world-population-by-country-state/country_population.csv')
df_env.isna().sum() | code |
32071248/cell_14 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
fr... | code |
32071248/cell_10 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_12 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import os
import math
import pandas as pd
import numpy as np
import scipy as sp
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import TimeSeriesSplit
from sklearn.model_selection import cross_val_score
from sklearn.metrics i... | code |
32071248/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df_env = pd.read_csv('/kaggle/input/global-environmental-factors/env.csv')
df_airpol = pd.read_csv('/kaggle/input/pm25-global-air-pollution/pm25-global-air-pollution-2017.csv')
df_pop = pd.read_csv('/kaggle/input/world-population-by-country-state/country_population.csv')
df_pop = df_pop[~df_pop['C... | code |
32068979/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
by_ctry_prov = df_train.groupby(['Country_Reg... | code |
32068979/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
by_ctry_prov = df_train.groupby(['Country_Reg... | code |
32068979/cell_1 | [
"text_plain_output_1.png"
] | import os
import pandas as pd
import numpy as np
from scipy.interpolate import Rbf
from scipy.optimize import curve_fit
from scipy.stats import linregress
from datetime import timedelta
from sklearn.metrics import mean_squared_log_error
from sklearn.impute import SimpleImputer
from sklearn.ensemble import RandomForestR... | code |
32068979/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
df_test.head(3) | code |
32068979/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
by_ctry_prov = df_train.groupby(['Country_Reg... | code |
32068979/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
df_sub.head(3) | code |
32068979/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
by_ctry_prov = df_train.groupby(['Country_Reg... | code |
32068979/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
by_ctry_prov = df_train.groupby(['Country_Reg... | code |
32068979/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
by_ctry_prov = df_train.groupby(['Country_Reg... | code |
32068979/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
by_ctry_prov = df_train.groupby(['Country_Reg... | code |
32068979/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
by_ctry_prov = df_train.groupby(['Country_Reg... | code |
32068979/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
df_train.tail(3) | code |
90156125/cell_13 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggl... | code |
90156125/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import cudf as pd
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Mat... | code |
90156125/cell_6 | [
"image_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggl... | code |
90156125/cell_26 | [
"text_plain_output_1.png"
] | import cudf as pd
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Mat... | code |
90156125/cell_11 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggl... | code |
90156125/cell_7 | [
"image_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggl... | code |
90156125/cell_18 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_matc... | code |
90156125/cell_8 | [
"image_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggl... | code |
90156125/cell_15 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df_train = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Training.csv')
match_2020 = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2020.csv')
previous_matc... | code |
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