path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
90130223/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
90130223/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklear... | code |
105205397/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
x = dat... | code |
105205397/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/iris/Iris.csv')
x = data.drop(['Species', 'Id'], axis=1)
y = data.Species
sns.countplot(y) | code |
105205397/cell_19 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
x = dat... | code |
105205397/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 |
105205397/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
data.info() | code |
105205397/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
data.describe() | code |
105205397/cell_16 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
x = data.drop(['Species', 'Id'], axis=1)
y = data.Species
model = RandomF... | code |
105205397/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/iris/Iris.csv')
x = data.drop(['Species', 'Id'], axis=1)
y = data.Species
sns.heatmap(data.corr(), annot=True) | code |
105205397/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/iris/Iris.csv')
data.head() | code |
2013560/cell_42 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2013560/cell_21 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived... | code |
2013560/cell_25 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived... | code |
2013560/cell_34 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2013560/cell_23 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived... | code |
2013560/cell_30 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2013560/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2013560/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
gender_submission.head() | code |
2013560/cell_40 | [
"text_html_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2013560/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr_html', True)
import seaborn as sns
sns.set(style='whitegrid')
f... | code |
2013560/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
train.info() | code |
2013560/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived... | code |
2013560/cell_45 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2013560/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
train['Sex'].value_counts().plot(kind='bar') | code |
2013560/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(train['Embarked'] == 'C')... | code |
2013560/cell_38 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2013560/cell_47 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2013560/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
train['Age'].hist(width=6) | code |
2013560/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape) | code |
2013560/cell_27 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
def survival_stacked_bar(variable):
Died = train[train['Survived'] == 0][variable].value_counts() / len(train['Survived... | code |
2013560/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
test.info() | code |
2013560/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
train.head() | code |
2013560/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv')
(train.shape, test.shape)
labels = ('Cherbourg', 'Queenstown', 'Southampton')
sizes = [sum(tr... | code |
2022050/cell_13 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2022050/cell_9 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=T... | code |
2022050/cell_23 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2022050/cell_20 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2022050/cell_11 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2022050/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 |
2022050/cell_7 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.concat(deserialize_cr, ignore_index=T... | code |
2022050/cell_16 | [
"text_plain_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2022050/cell_22 | [
"text_html_output_1.png"
] | from pandas.io.json import json_normalize
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
deserialize_cr = [json_normalize(eval(r1)) for r1 in CR[0:10000]]
df_cr = pd.... | code |
2022050/cell_5 | [
"text_html_output_1.png"
] | with open('../input/matches.txt') as file:
CR = [x.strip() for x in file.readlines()]
len(CR) | code |
34137894/cell_21 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
cat_data = train_data.select_dtypes(include='object')
cat_cols = cat_data.columns
num_data = train_d... | code |
34137894/cell_9 | [
"image_output_11.png",
"image_output_24.png",
"image_output_25.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_28.png",
"image_output_23.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_21.png",
"image_output_7.p... | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_data.shape | code |
34137894/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize... | code |
34137894/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape | code |
34137894/cell_18 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
... | code |
34137894/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
train_data.info() | code |
34137894/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize... | code |
34137894/cell_14 | [
"text_plain_output_1.png"
] | from scipy import stats
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(fi... | code |
34137894/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
test_data.shape
test_data.info() | code |
34137894/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.shape
fig = plt.figure(figsize=(10, 5))
sns.distpl... | code |
34137894/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
test_data = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
train_data.head() | code |
128047114/cell_20 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from tqdm import tqdm
import json
import matplotlib.pyplot as plt
import os
import torch
import torchvision
import torchvision.transforms as T
import torch
from torch.utils.data import DataLoader... | code |
128047114/cell_6 | [
"image_output_1.png"
] | from PIL import Image
import json
import os
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm import tqdm
from PIL import ... | code |
128047114/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
ds_path = '/kaggle/input/raw-mmimdb/mmimdb/dataset/'
ids = list(set([x.split('.')[0] for x in os.listdir(ds_path)]))
print('Some image ids:', ids[:5]) | code |
128047114/cell_19 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from tqdm import tqdm
import json
import os
import torch
import torchvision
import torchvision.transforms as T
import torch
from torch.utils.data import DataLoader
import torchvision
import torchv... | code |
128047114/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from tqdm import tqdm
import json
import os
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm impor... | code |
128047114/cell_16 | [
"image_output_1.png"
] | import torchvision
model = torchvision.models.resnet50(weights=torchvision.models.resnet.ResNet50_Weights.IMAGENET1K_V1)
model | code |
128047114/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
from tqdm import tqdm
import json
import os
import torch
import torchvision.transforms as T
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as ... | code |
128047114/cell_5 | [
"application_vnd.jupyter.stderr_output_27.png",
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_25.png",
"text_pla... | from PIL import Image
import os
import torch
from torch.utils.data import DataLoader
import torchvision
import torchvision.transforms as T
from torchvision.io import read_image, ImageReadMode
from sklearn.model_selection import train_test_split
import os
import json
from tqdm import tqdm
from PIL import Image
import ... | code |
73088112/cell_21 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
test.select_dtypes(exclu... | code |
73088112/cell_25 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
def missing_values_table... | code |
73088112/cell_34 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
train.Price.describe() | code |
73088112/cell_23 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
display(train[train.Buil... | code |
73088112/cell_20 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
train.select_dtypes(excl... | code |
73088112/cell_29 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
train.Price.describe() | code |
73088112/cell_39 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', in... | code |
73088112/cell_26 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
def missing_values_table... | code |
73088112/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
print(duplicate_row) | code |
73088112/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
test.describe() | code |
73088112/cell_7 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
train.head(5) | code |
73088112/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
train.describe() | code |
73088112/cell_8 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
print(f'Kích thước tập train: {train.shape}')
print(f'Kích thước tập test: {test.shape}') | code |
73088112/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts() | code |
73088112/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', in... | code |
73088112/cell_31 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.dtypes.value_counts()
s... | code |
73088112/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', index_col='id')
duplicate_row = train[train.duplicated()]
train.info() | code |
73088112/cell_36 | [
"text_plain_output_1.png"
] | from scipy import stats
from scipy.stats import norm
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/massp-housing-prices-in-melbourne/train.csv', index_col='id')
test = pd.read_csv('../input/massp-housing-prices-in-melbourne/test.csv', in... | code |
128014397/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | autoencoder.fit(X_train, X_train, epochs=100, batch_size=32, shuffle=True, validation_data=(X_test, X_test), verbose=0) | code |
128014397/cell_20 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from pathlib import Path
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
import pandas as pd
INPUT_DIR = Path('/kaggle/input/playground-series-s3e13/')
train_data = pd.read_csv(INPUT_DIR / 'train.csv')
train_data['data_type'] = 0
train_data['prognosis'] = ... | code |
128014397/cell_26 | [
"text_plain_output_1.png"
] | from colorama import Fore, Back, Style
from keras.utils import to_categorical
from pathlib import Path
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import log_loss
from sklearn.model_selection import StratifiedKFold, KFold
from sk... | code |
128014397/cell_19 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from pathlib import Path
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
import pandas as pd
INPUT_DIR = Path('/kaggle/input/playground-series-s3e13/')
train_data = pd.read_csv(INPUT_DIR / 'train.csv')
train_data['data_type'] = 0
train_data['prognosis'] = ... | code |
128014397/cell_18 | [
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from pathlib import Path
from sklearn.preprocessing import OrdinalEncoder
import numpy as np
import pandas as pd
INPUT_DIR = Path('/kaggle/input/playground-series-s3e13/')
train_data = pd.read_csv(INPUT_DIR / 'train.csv')
train_data['data_type'] = 0
train_data['prognosis'] = ... | code |
128014397/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.utils import to_categorical
from pathlib import Path
from sklearn.preprocessing import OrdinalEncoder
from tensorflow import keras
from tensorflow.keras import layers, models, Sequential
import numpy as np
import pandas as pd
import tensorflow as tf
INPUT_DIR = Path('/kaggle/input/playground-series-s... | code |
128014397/cell_3 | [
"text_plain_output_1.png"
] | import math
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', None)
from pathlib import Path
from plotnine import *
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, models, Sequential
from tensorflow.keras... | code |
105186076/cell_13 | [
"image_output_1.png"
] | import numpy as np
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
step_size = loss_slope * lr
b2 = b1 - step_size
b2 | code |
105186076/cell_9 | [
"image_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit... | code |
105186076/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit(X, y) | code |
105186076/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = LinearRegression()
reg.fit... | code |
105186076/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1 | code |
105186076/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
X.ravel()[:5]
m = 78.35
b1 = 100
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
lr = 0.1
step_size = loss_slope * lr
b1 = b1 - step_size
b1
loss_slope = -2 * np.sum(y - m * X.ravel() - b1)
step_size = loss_slope * lr
b2 = b1 - step_size
b2
loss_slope = -2 * np.sum(y - m * X.ravel() - b2)
step_s... | code |
105186076/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.datasets import make_regression
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import numpy as np
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
from sklearn.linear_model import LinearRegression
reg = Linear... | code |
105186076/cell_3 | [
"image_output_1.png"
] | from sklearn.datasets import make_regression
import matplotlib.pyplot as plt
X, y = make_regression(n_samples=4, n_features=1, n_informative=1, n_targets=1, noise=80, random_state=13)
plt.scatter(X, y)
plt.show() | code |
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