path
stringlengths 13
17
| screenshot_names
sequencelengths 1
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stringlengths 0
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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_
model.coef_
model.predict([[6000, 3]])
model.predict([[10000, 3]])
model.predict([[6000, 4]])
y_hat = model.predict(X)
y_hat | 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(img_arr)
image.save('opera_house.png', format='PNG')
new_img = Image.open('./opera_house.png')
from PIL import ImageOps
gray_img = ImageOps.grayscale(new_img)
gray_img_arr = np.asarray(gray_img)
"""
Current image dim width x height (640,360). thumbnail will resize the bigger dim i.e 640 to 100
and other dim will be rescaled to maintain aspect ratio.
Standard resampling algorithms are used to invent or remove pixels when resizing, and you can specify
a technique, although default is a bicubic resampling algorithm that suits most general applications
"""
new_img.thumbnail((100, 100))
hoz_flip = image.transpose(Image.FLIP_LEFT_RIGHT)
ver_flip = image.transpose(Image.FLIP_TOP_BOTTOM)
"""
In both rotations, the pixels are clipped to the original dimensions of the image
and the empty pixels are filled with black color.
"""
"""
here we created a cropped square image of 100 pixels starting at 100,100 and
extending down and left to 200,200.
"""
cropped_img = image.crop((100, 100, 200, 200))
image = Image.open('../input/bridge-image/sydney_bridge.png')
img_arr = np.asarray(image)
print('Type %s' % img_arr.dtype)
print('min pixel value %s and max pixel value %s' % (img_arr.min(), img_arr.max()))
img_arr = img_arr.astype('float32')
print('Type : %s' % img_arr.dtype)
img_arr = img_arr / 255.0
print('min pixel value %.3f and max pixel value %.3f' % (img_arr.min(), img_arr.max())) | 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(img_arr)
image.save('opera_house.png', format='PNG')
new_img = Image.open('./opera_house.png')
from PIL import ImageOps
gray_img = ImageOps.grayscale(new_img)
gray_img_arr = np.asarray(gray_img)
"""
Current image dim width x height (640,360). thumbnail will resize the bigger dim i.e 640 to 100
and other dim will be rescaled to maintain aspect ratio.
Standard resampling algorithms are used to invent or remove pixels when resizing, and you can specify
a technique, although default is a bicubic resampling algorithm that suits most general applications
"""
new_img.thumbnail((100, 100))
hoz_flip = image.transpose(Image.FLIP_LEFT_RIGHT)
ver_flip = image.transpose(Image.FLIP_TOP_BOTTOM)
"""
In both rotations, the pixels are clipped to the original dimensions of the image
and the empty pixels are filled with black color.
"""
"""
here we created a cropped square image of 100 pixels starting at 100,100 and
extending down and left to 200,200.
"""
cropped_img = image.crop((100, 100, 200, 200))
image = Image.open('../input/bridge-image/sydney_bridge.png')
plt.imshow(image) | 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(img_arr)
image.save('opera_house.png', format='PNG')
new_img = Image.open('./opera_house.png')
from PIL import ImageOps
gray_img = ImageOps.grayscale(new_img)
gray_img_arr = np.asarray(gray_img)
"""
Current image dim width x height (640,360). thumbnail will resize the bigger dim i.e 640 to 100
and other dim will be rescaled to maintain aspect ratio.
Standard resampling algorithms are used to invent or remove pixels when resizing, and you can specify
a technique, although default is a bicubic resampling algorithm that suits most general applications
"""
new_img.thumbnail((100, 100))
hoz_flip = image.transpose(Image.FLIP_LEFT_RIGHT)
ver_flip = image.transpose(Image.FLIP_TOP_BOTTOM)
"""
In both rotations, the pixels are clipped to the original dimensions of the image
and the empty pixels are filled with black color.
"""
"""
here we created a cropped square image of 100 pixels starting at 100,100 and
extending down and left to 200,200.
"""
cropped_img = image.crop((100, 100, 200, 200))
plt.imshow(cropped_img) | 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)
print(img.size)
print(img.format) | 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(img_arr)
image.save('opera_house.png', format='PNG')
new_img = Image.open('./opera_house.png')
from PIL import ImageOps
gray_img = ImageOps.grayscale(new_img)
gray_img_arr = np.asarray(gray_img)
"""
Current image dim width x height (640,360). thumbnail will resize the bigger dim i.e 640 to 100
and other dim will be rescaled to maintain aspect ratio.
Standard resampling algorithms are used to invent or remove pixels when resizing, and you can specify
a technique, although default is a bicubic resampling algorithm that suits most general applications
"""
new_img.thumbnail((100, 100))
hoz_flip = image.transpose(Image.FLIP_LEFT_RIGHT)
ver_flip = image.transpose(Image.FLIP_TOP_BOTTOM)
"""
In both rotations, the pixels are clipped to the original dimensions of the image
and the empty pixels are filled with black color.
"""
plt.subplot(3, 1, 1)
plt.imshow(image)
plt.subplot(3, 1, 2)
plt.imshow(image.rotate(45))
plt.subplot(3, 1, 3)
plt.imshow(image.rotate(90)) | 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(img_arr)
image.save('opera_house.png', format='PNG')
new_img = Image.open('./opera_house.png')
from PIL import ImageOps
gray_img = ImageOps.grayscale(new_img)
gray_img_arr = np.asarray(gray_img)
"""
Current image dim width x height (640,360). thumbnail will resize the bigger dim i.e 640 to 100
and other dim will be rescaled to maintain aspect ratio.
Standard resampling algorithms are used to invent or remove pixels when resizing, and you can specify
a technique, although default is a bicubic resampling algorithm that suits most general applications
"""
new_img.thumbnail((100, 100))
hoz_flip = image.transpose(Image.FLIP_LEFT_RIGHT)
ver_flip = image.transpose(Image.FLIP_TOP_BOTTOM)
"""
In both rotations, the pixels are clipped to the original dimensions of the image
and the empty pixels are filled with black color.
"""
"""
here we created a cropped square image of 100 pixels starting at 100,100 and
extending down and left to 200,200.
"""
cropped_img = image.crop((100, 100, 200, 200))
image = Image.open('../input/bridge-image/sydney_bridge.png')
img_arr = np.asarray(image)
img_arr = img_arr.astype('float32')
img_arr = img_arr / 255.0
image = Image.open('../input/pilimages/opera_house.jpg')
img_arr = np.asarray(image)
img_arr = img_arr.astype('float32')
mean = img_arr.mean()
print('Mean : %.3f' % mean)
print('Min : %.3f and Max: %.3f' % (img_arr.min(), img_arr.max()))
print('\nAfter applying global centering\n')
img_arr = img_arr - mean
mean = img_arr.mean()
print('Mean : %.3f' % mean)
print('Min: %.3f and Max: %.3f' % (img_arr.min(), img_arr.max())) | 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(img_arr)
image.save('opera_house.png', format='PNG')
new_img = Image.open('./opera_house.png')
from PIL import ImageOps
gray_img = ImageOps.grayscale(new_img)
gray_img_arr = np.asarray(gray_img)
"""
Current image dim width x height (640,360). thumbnail will resize the bigger dim i.e 640 to 100
and other dim will be rescaled to maintain aspect ratio.
Standard resampling algorithms are used to invent or remove pixels when resizing, and you can specify
a technique, although default is a bicubic resampling algorithm that suits most general applications
"""
new_img.thumbnail((100, 100))
hoz_flip = image.transpose(Image.FLIP_LEFT_RIGHT)
ver_flip = image.transpose(Image.FLIP_TOP_BOTTOM)
plt.subplot(3, 1, 1)
plt.imshow(image)
plt.subplot(3, 1, 2)
plt.imshow(hoz_flip)
plt.subplot(3, 1, 3)
plt.imshow(ver_flip) | 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('opera_house.png', format='PNG')
new_img = Image.open('./opera_house.png')
"""
Current image dim width x height (640,360). thumbnail will resize the bigger dim i.e 640 to 100
and other dim will be rescaled to maintain aspect ratio.
Standard resampling algorithms are used to invent or remove pixels when resizing, and you can specify
a technique, although default is a bicubic resampling algorithm that suits most general applications
"""
new_img.thumbnail((100, 100))
print('old image dim {} and new img dim {}'.format(image.size, new_img.size)) | 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(img_arr)
image.save('opera_house.png', format='PNG')
new_img = Image.open('./opera_house.png')
from PIL import ImageOps
gray_img = ImageOps.grayscale(new_img)
gray_img_arr = np.asarray(gray_img)
"""
Current image dim width x height (640,360). thumbnail will resize the bigger dim i.e 640 to 100
and other dim will be rescaled to maintain aspect ratio.
Standard resampling algorithms are used to invent or remove pixels when resizing, and you can specify
a technique, although default is a bicubic resampling algorithm that suits most general applications
"""
new_img.thumbnail((100, 100))
hoz_flip = image.transpose(Image.FLIP_LEFT_RIGHT)
ver_flip = image.transpose(Image.FLIP_TOP_BOTTOM)
"""
In both rotations, the pixels are clipped to the original dimensions of the image
and the empty pixels are filled with black color.
"""
"""
here we created a cropped square image of 100 pixels starting at 100,100 and
extending down and left to 200,200.
"""
cropped_img = image.crop((100, 100, 200, 200))
image = Image.open('../input/bridge-image/sydney_bridge.png')
print(image.format)
print(image.mode)
print(image.size) | 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.open('./opera_house.png')
print(new_img.format)
print(new_img.size)
print(new_img.mode) | 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(img_arr)
new_img = Image.open('./opera_house.png')
from PIL import ImageOps
gray_img = ImageOps.grayscale(new_img)
gray_img_arr = np.asarray(gray_img)
plt.imshow(gray_img_arr) | 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.imshow(img_arr) | 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.index = ['Survived', 'Dead']
df.plot(kind='bar', figsize=(15, 8)) | 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.ylabel('Number of passengers')
plt.legend()
base_folder = '../input/'
data = pd.read_csv(base_folder + 'train.csv')
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_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
import numpy as np
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_test_split(X, y, test_size=0.33, random_state=random_state)
def rmse_cv(model):
"""
Get Root Mean Square Error. Using KFold, split the data into 5 folds
Finding the best score using cross_val_score()
"""
score = cross_val_score(model, X.values, y, scoring='neg_mean_squared_error', cv=5)
rmse = np.sqrt(-score)
return rmse
def rmse_old(y, y_pred):
"""
Get Root Mean Square Error without using cross_val_score
"""
return np.sqrt(mean_squared_error(y, y_pred))
models_scores = []
def rmse(model):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
return mean_squared_error(y_test, y_pred, squared=False)
linear_regression = make_pipeline(LinearRegression())
score = rmse(linear_regression)
models_scores.append(['LinearRegression', score])
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=random_state))
score = rmse(lasso)
models_scores.append(['Lasso', score])
elastic_net = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=0.9, random_state=random_state))
score = rmse(elastic_net)
models_scores.append(['ElasticNet', score])
print(f'ElasticNet Score= {score}') | 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 sklearn.preprocessing import RobustScaler
import numpy as np
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_test_split(X, y, test_size=0.33, random_state=random_state)
def rmse_cv(model):
"""
Get Root Mean Square Error. Using KFold, split the data into 5 folds
Finding the best score using cross_val_score()
"""
score = cross_val_score(model, X.values, y, scoring='neg_mean_squared_error', cv=5)
rmse = np.sqrt(-score)
return rmse
def rmse_old(y, y_pred):
"""
Get Root Mean Square Error without using cross_val_score
"""
return np.sqrt(mean_squared_error(y, y_pred))
models_scores = []
def rmse(model):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
return mean_squared_error(y_test, y_pred, squared=False)
linear_regression = make_pipeline(LinearRegression())
score = rmse(linear_regression)
models_scores.append(['LinearRegression', score])
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=random_state))
score = rmse(lasso)
models_scores.append(['Lasso', score])
elastic_net = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=0.9, random_state=random_state))
score = rmse(elastic_net)
models_scores.append(['ElasticNet', score])
kernel_ridge = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
score = rmse(kernel_ridge)
models_scores.append(['KernelRidge', score])
print(f'KernelRidge Score= {score}') | 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_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
import numpy as np
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_test_split(X, y, test_size=0.33, random_state=random_state)
def rmse_cv(model):
"""
Get Root Mean Square Error. Using KFold, split the data into 5 folds
Finding the best score using cross_val_score()
"""
score = cross_val_score(model, X.values, y, scoring='neg_mean_squared_error', cv=5)
rmse = np.sqrt(-score)
return rmse
def rmse_old(y, y_pred):
"""
Get Root Mean Square Error without using cross_val_score
"""
return np.sqrt(mean_squared_error(y, y_pred))
models_scores = []
def rmse(model):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
return mean_squared_error(y_test, y_pred, squared=False)
linear_regression = make_pipeline(LinearRegression())
score = rmse(linear_regression)
models_scores.append(['LinearRegression', score])
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=random_state))
score = rmse(lasso)
models_scores.append(['Lasso', score])
elastic_net = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=0.9, random_state=random_state))
score = rmse(elastic_net)
models_scores.append(['ElasticNet', score])
kernel_ridge = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
score = rmse(kernel_ridge)
models_scores.append(['KernelRidge', score])
models = (linear_regression, lasso, elastic_net, kernel_ridge)
for model in models:
model.fit(X_train, y_train)
predictions = np.column_stack((model.predict(X_test) for model in models))
y_pred = np.mean(predictions, axis=1)
rmse_val = mean_squared_error(y_test, y_pred, squared=False)
models_scores.append(['Bagging', rmse_val])
gradient_boosting_regressor = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05, max_depth=4, max_features='sqrt', min_samples_leaf=15, min_samples_split=10, loss='huber', random_state=random_state)
score = rmse(gradient_boosting_regressor)
models_scores.append(['GradientBoostingRegressor', score])
print(f'GradientBoostingRegressor Score= {score}') | 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 sklearn.preprocessing import RobustScaler
import numpy as np
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_test_split(X, y, test_size=0.33, random_state=random_state)
def rmse_cv(model):
"""
Get Root Mean Square Error. Using KFold, split the data into 5 folds
Finding the best score using cross_val_score()
"""
score = cross_val_score(model, X.values, y, scoring='neg_mean_squared_error', cv=5)
rmse = np.sqrt(-score)
return rmse
def rmse_old(y, y_pred):
"""
Get Root Mean Square Error without using cross_val_score
"""
return np.sqrt(mean_squared_error(y, y_pred))
models_scores = []
def rmse(model):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
return mean_squared_error(y_test, y_pred, squared=False)
linear_regression = make_pipeline(LinearRegression())
score = rmse(linear_regression)
models_scores.append(['LinearRegression', score])
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=random_state))
score = rmse(lasso)
models_scores.append(['Lasso', score])
elastic_net = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=0.9, random_state=random_state))
score = rmse(elastic_net)
models_scores.append(['ElasticNet', score])
kernel_ridge = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
score = rmse(kernel_ridge)
models_scores.append(['KernelRidge', score])
models = (linear_regression, lasso, elastic_net, kernel_ridge)
for model in models:
model.fit(X_train, y_train)
predictions = np.column_stack((model.predict(X_test) for model in models))
y_pred = np.mean(predictions, axis=1)
rmse_val = mean_squared_error(y_test, y_pred, squared=False)
models_scores.append(['Bagging', rmse_val])
print(f'rmse= {rmse_val}') | 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
import numpy as np
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_test_split(X, y, test_size=0.33, random_state=random_state)
def rmse_cv(model):
"""
Get Root Mean Square Error. Using KFold, split the data into 5 folds
Finding the best score using cross_val_score()
"""
score = cross_val_score(model, X.values, y, scoring='neg_mean_squared_error', cv=5)
rmse = np.sqrt(-score)
return rmse
def rmse_old(y, y_pred):
"""
Get Root Mean Square Error without using cross_val_score
"""
return np.sqrt(mean_squared_error(y, y_pred))
models_scores = []
def rmse(model):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
return mean_squared_error(y_test, y_pred, squared=False)
linear_regression = make_pipeline(LinearRegression())
score = rmse(linear_regression)
models_scores.append(['LinearRegression', score])
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=random_state))
score = rmse(lasso)
models_scores.append(['Lasso', score])
print(f'Lasso Score= {score}') | 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 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_test_split(X, y, test_size=0.33, random_state=random_state)
def rmse_cv(model):
"""
Get Root Mean Square Error. Using KFold, split the data into 5 folds
Finding the best score using cross_val_score()
"""
score = cross_val_score(model, X.values, y, scoring='neg_mean_squared_error', cv=5)
rmse = np.sqrt(-score)
return rmse
def rmse_old(y, y_pred):
"""
Get Root Mean Square Error without using cross_val_score
"""
return np.sqrt(mean_squared_error(y, y_pred))
models_scores = []
def rmse(model):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
return mean_squared_error(y_test, y_pred, squared=False)
linear_regression = make_pipeline(LinearRegression())
score = rmse(linear_regression)
models_scores.append(['LinearRegression', score])
print(f'LinearRegression Score= {score}') | 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_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
import numpy as np
import pandas as pd
import warnings
import xgboost as xgb
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_test_split(X, y, test_size=0.33, random_state=random_state)
def rmse_cv(model):
"""
Get Root Mean Square Error. Using KFold, split the data into 5 folds
Finding the best score using cross_val_score()
"""
score = cross_val_score(model, X.values, y, scoring='neg_mean_squared_error', cv=5)
rmse = np.sqrt(-score)
return rmse
def rmse_old(y, y_pred):
"""
Get Root Mean Square Error without using cross_val_score
"""
return np.sqrt(mean_squared_error(y, y_pred))
models_scores = []
def rmse(model):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
return mean_squared_error(y_test, y_pred, squared=False)
linear_regression = make_pipeline(LinearRegression())
score = rmse(linear_regression)
models_scores.append(['LinearRegression', score])
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=random_state))
score = rmse(lasso)
models_scores.append(['Lasso', score])
elastic_net = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=0.9, random_state=random_state))
score = rmse(elastic_net)
models_scores.append(['ElasticNet', score])
kernel_ridge = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
score = rmse(kernel_ridge)
models_scores.append(['KernelRidge', score])
models = (linear_regression, lasso, elastic_net, kernel_ridge)
for model in models:
model.fit(X_train, y_train)
predictions = np.column_stack((model.predict(X_test) for model in models))
y_pred = np.mean(predictions, axis=1)
rmse_val = mean_squared_error(y_test, y_pred, squared=False)
models_scores.append(['Bagging', rmse_val])
gradient_boosting_regressor = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05, max_depth=4, max_features='sqrt', min_samples_leaf=15, min_samples_split=10, loss='huber', random_state=random_state)
score = rmse(gradient_boosting_regressor)
models_scores.append(['GradientBoostingRegressor', score])
xgb_regressor = xgb.XGBRegressor(colsample_bytree=0.4603, gamma=0.0468, learning_rate=0.05, max_depth=3, min_child_weight=1.7817, n_estimators=2200, reg_alpha=0.464, reg_lambda=0.8571, subsample=0.5213, verbosity=0, nthread=-1, random_state=random_state)
score = rmse(xgb_regressor)
models_scores.append(['XGBRegressor', score])
print(f'XGBRegressor Score= {score}') | 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 sklearn.preprocessing import RobustScaler
import numpy as np
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_test_split(X, y, test_size=0.33, random_state=random_state)
def rmse_cv(model):
"""
Get Root Mean Square Error. Using KFold, split the data into 5 folds
Finding the best score using cross_val_score()
"""
score = cross_val_score(model, X.values, y, scoring='neg_mean_squared_error', cv=5)
rmse = np.sqrt(-score)
return rmse
def rmse_old(y, y_pred):
"""
Get Root Mean Square Error without using cross_val_score
"""
return np.sqrt(mean_squared_error(y, y_pred))
models_scores = []
def rmse(model):
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
return mean_squared_error(y_test, y_pred, squared=False)
linear_regression = make_pipeline(LinearRegression())
score = rmse(linear_regression)
models_scores.append(['LinearRegression', score])
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=random_state))
score = rmse(lasso)
models_scores.append(['Lasso', score])
elastic_net = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=0.9, random_state=random_state))
score = rmse(elastic_net)
models_scores.append(['ElasticNet', score])
kernel_ridge = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
score = rmse(kernel_ridge)
models_scores.append(['KernelRidge', score])
pd.DataFrame(models_scores).sort_values(by=[1], ascending=True) | 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_test_split(X, y, test_size=0.33, random_state=random_state)
print(f'Training set--> X_train shape= {X_train.shape}, y_train shape= {y_train.shape}')
print(f'Holdout set--> X_test shape= {X_test.shape}, y_test shape= {y_test.shape}') | 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']
sns.distplot(price) | 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.colu
price = df_train['SalePrice']
correlation = df_train.corr()
k = 10
cols = correlation.nlargest(k, 'SalePrice')['SalePrice'].index
coef = np.corrcoef(df_train[cols].values.T)
sns.heatmap(coef, yticklabels=cols.values, annot=True, xticklabels=cols.values) | 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.colu
price = df_train['SalePrice']
correlation = df_train.corr()
k = 10
cols = correlation.nlargest(k, 'SalePrice')['SalePrice'].index
coef = np.corrcoef(df_train[cols].values.T)
cols_low = correlcation.nsmallest(k, 'SalePrice')['SalePrice'].index
coef_low = np.corrcoef(df_train[cols].values.T)
sns.heatmap(coef_low, yticklabels=cols_low.values) | 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']
correlation = df_train.corr()
sns.heatmap(correlation, vmin=-0.8, vmax=0.8, cmap='YlGnBu', square=True) | 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'])
index_names = campus_data[campus_data['salary'] == 'NaN'].index
campus_data.drop(index_names, inplace=True)
campus_data.head() | 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['salary'].unique()) | 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'])
drop_data.head() | 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.isna().sum()
df_env.loc[df_env['Country_Region'].isin(['Norway', 'Finland', 'Iceland', 'Estonia']), 'wind'] = 4.689151
df_env.loc[df_env['Country_Region'].isin(['Maldives']), 'wind'] = np.mean([2.698925, 3.494908])
df_env.loc[df_env['Country_Region'].isin(['Bahrain']), 'wind'] = np.mean([3.728877, 3.173667, 4.525724])
df_env.loc[df_env['Country_Region'].isin(['Antigua and Barbuda']), 'wind'] = np.mean([3.586282, 3.378886, 2.749947])
df_env.loc[df_env['Country_Region'].isin(['Saint Vincent and the Grenadines']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Malta']), 'wind'] = np.mean([3.078635, 2.648621])
df_env.loc[df_env['Country_Region'].isin(['Seychelles']), 'wind'] = 2.736786
df_env.loc[df_env['Country_Region'].isin(['Saint Kitts and Nevis']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Grenada']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Saint Lucia']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Barbados']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Monaco']), 'wind'] = np.mean([5.745106, 3.369222])
cols_na = ['accessibility_to_cities', 'elevation', 'aspect', 'slope', 'tree_canopy_cover', 'isothermality', 'rain_coldestQuart', 'rain_driestMonth', 'rain_driestQuart', 'rain_mean_annual', 'rain_seasonailty', 'rain_warmestQuart', 'rain_wettestMonth', 'rain_wettestQuart', 'temp_annual_range', 'temp_coldestQuart', 'temp_diurnal_range', 'temp_driestQuart', 'temp_max_warmestMonth', 'temp_mean_annual', 'temp_min_coldestMonth', 'temp_seasonality', 'temp_warmestQuart', 'temp_wettestQuart', 'cloudiness']
for c in cols_na:
country = df_env.loc[df_env[c].isna(), 'Country_Region'].unique()
if 'Maldives' in country:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'India'][c].mean()
else:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'Denmark'][c].mean()
df_pop = df_pop[~df_pop['Country_Region'].isna()]
df_pop.drop(['quarantine', 'schools', 'restrictions'], axis=1, inplace=True)
df_pop.isna().sum()
df_pop.dtypes
cols_na = ['pop', 'tests', 'testpop', 'density', 'medianage', 'urbanpop', 'hospibed', 'smokers', 'sex0', 'sex14', 'sex25', 'sex54', 'sex64', 'sex65plus', 'sexratio', 'lung', 'femalelung', 'malelung']
for c in cols_na:
df_pop[c] = df_pop.groupby(['Country_Region'])[c].transform(lambda x: x.fillna(x.mean()))
for c in cols_na:
df_pop[c].fillna(df_pop[c].mean(), inplace=True)
df_pop.columns.values | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
date_valid_start = df_test_raw[df_test_raw['Date'].isin(df_train_raw['Date'])]['Date'].unique().min()
date_valid_end = df_test_raw[df_test_raw['Date'].isin(df_train_raw['Date'])]['Date'].unique().max()
print('valid start: ', date_valid_start)
print('valid end: ', date_valid_end) | 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.isna().sum()
df_env.loc[df_env['Country_Region'].isin(['Norway', 'Finland', 'Iceland', 'Estonia']), 'wind'] = 4.689151
df_env.loc[df_env['Country_Region'].isin(['Maldives']), 'wind'] = np.mean([2.698925, 3.494908])
df_env.loc[df_env['Country_Region'].isin(['Bahrain']), 'wind'] = np.mean([3.728877, 3.173667, 4.525724])
df_env.loc[df_env['Country_Region'].isin(['Antigua and Barbuda']), 'wind'] = np.mean([3.586282, 3.378886, 2.749947])
df_env.loc[df_env['Country_Region'].isin(['Saint Vincent and the Grenadines']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Malta']), 'wind'] = np.mean([3.078635, 2.648621])
df_env.loc[df_env['Country_Region'].isin(['Seychelles']), 'wind'] = 2.736786
df_env.loc[df_env['Country_Region'].isin(['Saint Kitts and Nevis']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Grenada']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Saint Lucia']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Barbados']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Monaco']), 'wind'] = np.mean([5.745106, 3.369222])
cols_na = ['accessibility_to_cities', 'elevation', 'aspect', 'slope', 'tree_canopy_cover', 'isothermality', 'rain_coldestQuart', 'rain_driestMonth', 'rain_driestQuart', 'rain_mean_annual', 'rain_seasonailty', 'rain_warmestQuart', 'rain_wettestMonth', 'rain_wettestQuart', 'temp_annual_range', 'temp_coldestQuart', 'temp_diurnal_range', 'temp_driestQuart', 'temp_max_warmestMonth', 'temp_mean_annual', 'temp_min_coldestMonth', 'temp_seasonality', 'temp_warmestQuart', 'temp_wettestQuart', 'cloudiness']
for c in cols_na:
country = df_env.loc[df_env[c].isna(), 'Country_Region'].unique()
print(c, country)
if 'Maldives' in country:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'India'][c].mean()
else:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'Denmark'][c].mean() | 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
from sklearn.metrics import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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()
df_env.loc[df_env['Country_Region'].isin(['Norway', 'Finland', 'Iceland', 'Estonia']), 'wind'] = 4.689151
df_env.loc[df_env['Country_Region'].isin(['Maldives']), 'wind'] = np.mean([2.698925, 3.494908])
df_env.loc[df_env['Country_Region'].isin(['Bahrain']), 'wind'] = np.mean([3.728877, 3.173667, 4.525724])
df_env.loc[df_env['Country_Region'].isin(['Antigua and Barbuda']), 'wind'] = np.mean([3.586282, 3.378886, 2.749947])
df_env.loc[df_env['Country_Region'].isin(['Saint Vincent and the Grenadines']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Malta']), 'wind'] = np.mean([3.078635, 2.648621])
df_env.loc[df_env['Country_Region'].isin(['Seychelles']), 'wind'] = 2.736786
df_env.loc[df_env['Country_Region'].isin(['Saint Kitts and Nevis']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Grenada']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Saint Lucia']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Barbados']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Monaco']), 'wind'] = np.mean([5.745106, 3.369222])
cols_na = ['accessibility_to_cities', 'elevation', 'aspect', 'slope', 'tree_canopy_cover', 'isothermality', 'rain_coldestQuart', 'rain_driestMonth', 'rain_driestQuart', 'rain_mean_annual', 'rain_seasonailty', 'rain_warmestQuart', 'rain_wettestMonth', 'rain_wettestQuart', 'temp_annual_range', 'temp_coldestQuart', 'temp_diurnal_range', 'temp_driestQuart', 'temp_max_warmestMonth', 'temp_mean_annual', 'temp_min_coldestMonth', 'temp_seasonality', 'temp_warmestQuart', 'temp_wettestQuart', 'cloudiness']
for c in cols_na:
country = df_env.loc[df_env[c].isna(), 'Country_Region'].unique()
if 'Maldives' in country:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'India'][c].mean()
else:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'Denmark'][c].mean()
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
base_date = pd.to_datetime('2020-01-01')
base_date
df_test_clean['days_since'] = (pd.to_datetime(df_test_clean['Date']) - base_date).dt.days
df_test_clean['days_since'].unique()
df = pd.concat([df_train_clean, df_test_clean], sort=False).reset_index(drop=True)
df = pd.merge(df, df_airpol.drop_duplicates(subset=['Country_Region']), how='left')
df.shape
df = pd.merge(df, df_env.drop_duplicates(subset=['Country_Region']), how='left')
df.shape
df.dtypes | 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['Country_Region'].isna()]
df_pop.drop(['quarantine', 'schools', 'restrictions'], axis=1, inplace=True)
df_pop.isna().sum()
df_pop.dtypes | 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-country-state/country_population.csv')
print('pop: ', df_pop.shape) | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
base_date = pd.to_datetime('2020-01-01')
base_date | 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
from sklearn.metrics import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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()
df_env.loc[df_env['Country_Region'].isin(['Norway', 'Finland', 'Iceland', 'Estonia']), 'wind'] = 4.689151
df_env.loc[df_env['Country_Region'].isin(['Maldives']), 'wind'] = np.mean([2.698925, 3.494908])
df_env.loc[df_env['Country_Region'].isin(['Bahrain']), 'wind'] = np.mean([3.728877, 3.173667, 4.525724])
df_env.loc[df_env['Country_Region'].isin(['Antigua and Barbuda']), 'wind'] = np.mean([3.586282, 3.378886, 2.749947])
df_env.loc[df_env['Country_Region'].isin(['Saint Vincent and the Grenadines']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Malta']), 'wind'] = np.mean([3.078635, 2.648621])
df_env.loc[df_env['Country_Region'].isin(['Seychelles']), 'wind'] = 2.736786
df_env.loc[df_env['Country_Region'].isin(['Saint Kitts and Nevis']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Grenada']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Saint Lucia']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Barbados']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Monaco']), 'wind'] = np.mean([5.745106, 3.369222])
cols_na = ['accessibility_to_cities', 'elevation', 'aspect', 'slope', 'tree_canopy_cover', 'isothermality', 'rain_coldestQuart', 'rain_driestMonth', 'rain_driestQuart', 'rain_mean_annual', 'rain_seasonailty', 'rain_warmestQuart', 'rain_wettestMonth', 'rain_wettestQuart', 'temp_annual_range', 'temp_coldestQuart', 'temp_diurnal_range', 'temp_driestQuart', 'temp_max_warmestMonth', 'temp_mean_annual', 'temp_min_coldestMonth', 'temp_seasonality', 'temp_warmestQuart', 'temp_wettestQuart', 'cloudiness']
for c in cols_na:
country = df_env.loc[df_env[c].isna(), 'Country_Region'].unique()
if 'Maldives' in country:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'India'][c].mean()
else:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'Denmark'][c].mean()
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
base_date = pd.to_datetime('2020-01-01')
base_date
df_test_clean['days_since'] = (pd.to_datetime(df_test_clean['Date']) - base_date).dt.days
df_test_clean['days_since'].unique()
df = pd.concat([df_train_clean, df_test_clean], sort=False).reset_index(drop=True)
df = pd.merge(df, df_airpol.drop_duplicates(subset=['Country_Region']), how='left')
df.shape
df = pd.merge(df, df_env.drop_duplicates(subset=['Country_Region']), how='left')
df.shape | 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, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
base_date = pd.to_datetime('2020-01-01')
base_date
df_test_clean['days_since'] = (pd.to_datetime(df_test_clean['Date']) - base_date).dt.days
df_test_clean['days_since'].unique()
df = pd.concat([df_train_clean, df_test_clean], sort=False).reset_index(drop=True)
df = pd.merge(df, df_airpol.drop_duplicates(subset=['Country_Region']), how='left')
df.shape | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
df_test_clean['ConfirmedCases'] = df_test_clean['ConfirmedCases'].astype('float')
df_test_clean['Fatalities'] = df_test_clean['Fatalities'].astype('float')
df_test_clean['Fatalities'].dtype | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
base_date = pd.to_datetime('2020-01-01')
base_date
df_test_clean['days_since'] = (pd.to_datetime(df_test_clean['Date']) - base_date).dt.days
df_test_clean['days_since'].unique()
df = pd.concat([df_train_clean, df_test_clean], sort=False).reset_index(drop=True)
print(df.shape)
print(df.columns.values) | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
print('cc skew: {}'.format(df_train_clean['ConfirmedCases'].skew()))
print('ft skew: {}'.format(df_train_clean['Fatalities'].skew())) | 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
from sklearn.metrics import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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()
df_env.loc[df_env['Country_Region'].isin(['Norway', 'Finland', 'Iceland', 'Estonia']), 'wind'] = 4.689151
df_env.loc[df_env['Country_Region'].isin(['Maldives']), 'wind'] = np.mean([2.698925, 3.494908])
df_env.loc[df_env['Country_Region'].isin(['Bahrain']), 'wind'] = np.mean([3.728877, 3.173667, 4.525724])
df_env.loc[df_env['Country_Region'].isin(['Antigua and Barbuda']), 'wind'] = np.mean([3.586282, 3.378886, 2.749947])
df_env.loc[df_env['Country_Region'].isin(['Saint Vincent and the Grenadines']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Malta']), 'wind'] = np.mean([3.078635, 2.648621])
df_env.loc[df_env['Country_Region'].isin(['Seychelles']), 'wind'] = 2.736786
df_env.loc[df_env['Country_Region'].isin(['Saint Kitts and Nevis']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Grenada']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Saint Lucia']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Barbados']), 'wind'] = 3.515223
df_env.loc[df_env['Country_Region'].isin(['Monaco']), 'wind'] = np.mean([5.745106, 3.369222])
cols_na = ['accessibility_to_cities', 'elevation', 'aspect', 'slope', 'tree_canopy_cover', 'isothermality', 'rain_coldestQuart', 'rain_driestMonth', 'rain_driestQuart', 'rain_mean_annual', 'rain_seasonailty', 'rain_warmestQuart', 'rain_wettestMonth', 'rain_wettestQuart', 'temp_annual_range', 'temp_coldestQuart', 'temp_diurnal_range', 'temp_driestQuart', 'temp_max_warmestMonth', 'temp_mean_annual', 'temp_min_coldestMonth', 'temp_seasonality', 'temp_warmestQuart', 'temp_wettestQuart', 'cloudiness']
for c in cols_na:
country = df_env.loc[df_env[c].isna(), 'Country_Region'].unique()
if 'Maldives' in country:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'India'][c].mean()
else:
df_env.loc[df_env[c].isna(), c] = df_env[df_env['Country_Region'] == 'Denmark'][c].mean()
df_pop = df_pop[~df_pop['Country_Region'].isna()]
df_pop.drop(['quarantine', 'schools', 'restrictions'], axis=1, inplace=True)
df_pop.isna().sum()
df_pop.dtypes
cols_na = ['pop', 'tests', 'testpop', 'density', 'medianage', 'urbanpop', 'hospibed', 'smokers', 'sex0', 'sex14', 'sex25', 'sex54', 'sex64', 'sex65plus', 'sexratio', 'lung', 'femalelung', 'malelung']
for c in cols_na:
df_pop[c] = df_pop.groupby(['Country_Region'])[c].transform(lambda x: x.fillna(x.mean()))
for c in cols_na:
df_pop[c].fillna(df_pop[c].mean(), inplace=True)
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
base_date = pd.to_datetime('2020-01-01')
base_date
df_test_clean['days_since'] = (pd.to_datetime(df_test_clean['Date']) - base_date).dt.days
df_test_clean['days_since'].unique()
df = pd.concat([df_train_clean, df_test_clean], sort=False).reset_index(drop=True)
df = pd.merge(df, df_airpol.drop_duplicates(subset=['Country_Region']), how='left')
df.shape
df = pd.merge(df, df_env.drop_duplicates(subset=['Country_Region']), how='left')
df.shape
df.dtypes
df_pop.columns.values
df = pd.merge(df, df_pop.drop_duplicates(subset=['Country_Region', 'Province_State']), how='left')
df.shape | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
print('train shape: ', df_train_clean.shape)
print('test shape: ', df_test_clean.shape) | 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 import make_scorer, mean_squared_error
import lightgbm as lgb
from lightgbm import LGBMRegressor
from hyperopt import hp, tpe
from hyperopt.fmin import fmin
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')
dirname = '/kaggle/input'
train_enriched_filename = 'covid19-forecasting-data-with-containment-measures/train-enriched-with-containment_v4.csv'
train_filename = 'covid19-global-forecasting-week-4/train.csv'
test_filename = 'covid19-global-forecasting-week-4/test.csv'
df_train_enriched = pd.read_csv(os.path.join(dirname, train_enriched_filename))
df_train_raw = pd.read_csv(os.path.join(dirname, train_filename))
df_test_raw = pd.read_csv(os.path.join(dirname, test_filename))
df_train_raw.shape
df_train_clean = df_train_enriched.drop(['Id'], axis=1)
df_test_clean = df_test_raw[~df_test_raw['Date'].isin(df_train_enriched['Date'])]
df_test_clean = df_test_clean.drop(['ForecastId'], axis=1)
base_date = pd.to_datetime('2020-01-01')
base_date
df_test_clean['days_since'] = (pd.to_datetime(df_test_clean['Date']) - base_date).dt.days
df_test_clean['days_since'].unique() | 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['Country_Region'].isna()]
df_pop.drop(['quarantine', 'schools', 'restrictions'], axis=1, inplace=True)
df_pop.isna().sum() | 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_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']]
df_train[['NewCases', 'NewFatalities']] = by_ctry_prov.transform(lambda x: x.diff().fillna(0))
df_train[['NewCasesPct', 'NewFatalitiesPct']] = by_ctry_prov.transform(lambda x: x.pct_change().fillna(0))
df_train.sort_values('NewCases', ascending=False).head()
df_train[df_train.NewCases < 0].sort_values('NewCases')
df_train[df_train.NewFatalities < 0].sort_values('NewFatalities')
df_train[df_train.Fatalities > df_train.ConfirmedCases] | 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_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']]
df_train[['NewCases', 'NewFatalities']] = by_ctry_prov.transform(lambda x: x.diff().fillna(0))
df_train[['NewCasesPct', 'NewFatalitiesPct']] = by_ctry_prov.transform(lambda x: x.pct_change().fillna(0))
df_train.sort_values('NewCases', ascending=False).head()
df_train[df_train.NewCases < 0].sort_values('NewCases') | 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 RandomForestRegressor
from sklearn.linear_model import ElasticNet
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.preprocessing import OrdinalEncoder
import category_encoders as ce
import xgboost
from catboost import Pool, CatBoostRegressor
import matplotlib.pyplot as plt
plt.style.use('ggplot')
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | 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_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']]
df_train[['NewCases', 'NewFatalities']] = by_ctry_prov.transform(lambda x: x.diff().fillna(0))
df_train[['NewCasesPct', 'NewFatalitiesPct']] = by_ctry_prov.transform(lambda x: x.pct_change().fillna(0))
df_train.sort_values('NewCases', ascending=False).head()
df_train[df_train.NewCases < 0].sort_values('NewCases')
df_train[df_train.NewFatalities < 0].sort_values('NewFatalities')
df_train[df_train.Fatalities > df_train.ConfirmedCases]
df_train[(df_train.NewCasesPct > 0.4) & (df_train.NewCases > 1000)]
df_train[(df_train.NewFatalitiesPct > 0.8) & (df_train.NewFatalities > 50)]
df_train[(df_train['Country_Region'] == 'China') & (df_train['Province_State'] == 'Hubei') & (df_train.Date > '2020-02-8')].head(8) | 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_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']]
df_train[['NewCases', 'NewFatalities']] = by_ctry_prov.transform(lambda x: x.diff().fillna(0))
df_train[['NewCasesPct', 'NewFatalitiesPct']] = by_ctry_prov.transform(lambda x: x.pct_change().fillna(0))
df_train.sort_values('NewCases', ascending=False).head()
df_train[df_train.NewCases < 0].sort_values('NewCases')
df_train[df_train.NewFatalities < 0].sort_values('NewFatalities')
df_train[df_train.Fatalities > df_train.ConfirmedCases]
df_train[(df_train.NewCasesPct > 0.4) & (df_train.NewCases > 1000)]
df_train[(df_train.NewFatalitiesPct > 0.8) & (df_train.NewFatalities > 50)] | 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_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']]
df_train[['NewCases', 'NewFatalities']] = by_ctry_prov.transform(lambda x: x.diff().fillna(0))
df_train[['NewCasesPct', 'NewFatalitiesPct']] = by_ctry_prov.transform(lambda x: x.pct_change().fillna(0))
df_train.sort_values('NewCases', ascending=False).head()
df_train[df_train.NewCases < 0].sort_values('NewCases')
df_train[df_train.NewFatalities < 0].sort_values('NewFatalities')
df_train[df_train.Fatalities > df_train.ConfirmedCases]
df_train[(df_train.NewCasesPct > 0.4) & (df_train.NewCases > 1000)]
df_train[(df_train.NewFatalitiesPct > 0.8) & (df_train.NewFatalities > 50)]
df_train[(df_train['Country_Region'] == 'China') & (df_train['Province_State'] == 'Shandong') & (df_train.Date > '2020-02-18')].head(5) | 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_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']]
df_train[['NewCases', 'NewFatalities']] = by_ctry_prov.transform(lambda x: x.diff().fillna(0))
df_train[['NewCasesPct', 'NewFatalitiesPct']] = by_ctry_prov.transform(lambda x: x.pct_change().fillna(0))
df_train.sort_values('NewCases', ascending=False).head()
df_train[df_train.NewCases < 0].sort_values('NewCases')
df_train[df_train.NewFatalities < 0].sort_values('NewFatalities')
df_train[df_train.Fatalities > df_train.ConfirmedCases]
df_train[(df_train.NewCasesPct > 0.4) & (df_train.NewCases > 1000)] | 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_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']]
df_train[['NewCases', 'NewFatalities']] = by_ctry_prov.transform(lambda x: x.diff().fillna(0))
df_train[['NewCasesPct', 'NewFatalitiesPct']] = by_ctry_prov.transform(lambda x: x.pct_change().fillna(0))
df_train.sort_values('NewCases', ascending=False).head() | 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_Region', 'Province_State'])[['ConfirmedCases', 'Fatalities']]
df_train[['NewCases', 'NewFatalities']] = by_ctry_prov.transform(lambda x: x.diff().fillna(0))
df_train[['NewCasesPct', 'NewFatalitiesPct']] = by_ctry_prov.transform(lambda x: x.pct_change().fillna(0))
df_train.sort_values('NewCases', ascending=False).head()
df_train[df_train.NewCases < 0].sort_values('NewCases')
df_train[df_train.NewFatalities < 0].sort_values('NewFatalities') | 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('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
previous_match.head() | 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/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
previous_match.shape
previous_match.isnull().sum()
previous_match.dropna()
previous_match.season.unique()
plt.subplots(figsize=(15, 6))
sns.countplot(x=previous_match['season'], data=previous_match)
plt.show() | 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('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
df_train.info() | 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/Matches IPL 2020.csv')
previous_match = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
previous_match.shape
previous_match.isnull().sum()
previous_match.dropna()
previous_match.season.unique()
order = previous_match.city.value_counts().iloc[:10].index
order = previous_match.winner.value_counts().iloc[:10].index
order = previous_match.player_of_match.value_counts().iloc[:3].index
sns.countplot(x='player_of_match', data=previous_match, palette='rainbow', order=order)
plt.show() | 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('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
df_train.isnull().sum()
df_train.describe().T
df_train['Players'].value_counts() | 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('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
df_train.isnull().sum() | 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_match = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
previous_match.shape
previous_match.isnull().sum()
previous_match.dropna()
previous_match.season.unique() | 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('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
df_train.isnull().sum()
df_train.describe().T | 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_match = pd.read_csv('/kaggle/input/ipl-2020-player-performance/Matches IPL 2008-2019.csv')
players = pd.read_csv('../input/ipl-2020-player-performance/IPL 2020 Squads.csv', encoding='windows-1254')
previous_match.shape
sns.heatmap(previous_match.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
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