path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
88086039/cell_46 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(1... | code |
88086039/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(1... | code |
88086039/cell_14 | [
"text_plain_output_1.png"
] | train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
train.head(2) | code |
88086039/cell_22 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
print('<< % of missing data >>')
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head() | code |
88086039/cell_36 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
train['Pclass'].uniqu... | code |
106205745/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a
b = np.array([1, 2, 3])
b
d = np.array([[1], [0], [1]])
d
e = np.array([1, 2, 3])
e
f = e[:, np.newaxis]
f
e + f | code |
106205745/cell_4 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a
b = np.array([1, 2, 3])
b | code |
106205745/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a
b = np.array([1, 2, 3])
b
d = np.array([[1], [0], [1]])
d | code |
106205745/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a
b = np.array([1, 2, 3])
b
d = np.array([[1], [0], [1]])
d
e = np.array([1, 2, 3])
e
f = e[:, np.newaxis]
f
h = np.array([1, 1, 0])
g = np.array([[1], [2], [1]])
h + g | code |
106205745/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a
b = np.array([1, 2, 3])
b
d = np.array([[1], [0], [1]])
d
e = np.array([1, 2, 3])
e | code |
106205745/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a
b = np.array([1, 2, 3])
b
d = np.array([[1], [0], [1]])
d
e = np.array([1, 2, 3])
e
f = e[:, np.newaxis]
f | code |
106205745/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a | code |
106205745/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
a
b = np.array([1, 2, 3])
b
c = a + b
c | code |
90118434/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import warnings
warnings.filterwarnings('ignore')
raw_data = pd.read_csv... | code |
90118434/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
raw_data = pd.read_csv('../input/housedata/data.csv')
raw_data.describe(include='all').T | code |
90118434/cell_23 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import warnings
warnings.filterwarnings('ignore')
raw_data = pd.read_csv... | code |
90118434/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import warnings
warnings.filterwarnings('ignore')
raw_data = pd.read_csv('../input/housedata... | code |
90118434/cell_26 | [
"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
import statsmodels.api as sm
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import warnings
warnings.filterwarnings('ignore')
raw_data = pd.read_csv... | code |
90118434/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
raw_data = pd.read_csv('../input/housedata/data.csv')
raw_data.head() | code |
90118434/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
raw_data = pd.read_csv('../input/housedata/data.csv')
raw_data.describe(include='all').T
x1 = raw_data.drop(['price', 'date', 'street', 'city', 'statezip', 'country'], axis=1)
y = raw_data['price']
x = sm.add_constant(x1)
results = sm.OLS(y, x).fit()
results.summary(... | code |
90118434/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
raw_data = pd.read_csv('../input/housedata/data.csv')
raw_data.describe(include='all').T
x1 = raw_data.drop(['price', 'date', 'street', 'city', 'statezip', 'country'], axis=1)
y = raw_data['price']
x = sm.add_constant(x1)
results = sm.OLS(y, x).fit()
results.summary(... | code |
90118434/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
raw_data = pd.read_csv('../input/housedata/data.csv')
raw_data.describe(include='all').T
x1 = raw_data.drop(['price', 'date', 'street', 'city', 'statezip', 'country'], axis=1)
y = raw_data['price']
x = sm.add_constant(x1)
results = sm.OLS(y, x).fit()
results.summary(... | code |
90118434/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import warnings
warnings.filterwarnings('ignore')
raw_data = pd.read_csv... | code |
90118434/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
raw_data = pd.read_csv('../input/housedata/data.csv')
raw_data.describe(include='all').T
x1 = raw_data.drop(['price', 'date', 'street', 'city', 'statezip', 'country'], axis=1)
y = raw_data['price']
x = sm.add_constant(x1)
results = sm.OLS(y, x).fit()
results.summary(... | code |
90118434/cell_22 | [
"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
import statsmodels.api as sm
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
import warnings
warnings.filterwarnings('ignore')
raw_data = pd.read_csv... | code |
90118434/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
raw_data = pd.read_csv('../input/housedata/data.csv')
raw_data.info() | code |
74052486/cell_6 | [
"text_plain_output_1.png"
] | from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import Compose, CenterCrop, ToTensor,ColorJitter
import glob
import os
import shutil
import os
import glob
import shutil
train_data_dir = '/kaggle/input/1056lab-covid19-chest-xray-recognit/train'
worki... | code |
74052486/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import torch
import torch
use_cuda = torch.cuda.is_available()
print('Use CUDA:', use_cuda)
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu') | code |
74052486/cell_8 | [
"text_html_output_1.png"
] | !pip install efficientnet_pytorch
from efficientnet_pytorch import EfficientNet
from torch import nn, optim
model_ft = EfficientNet.from_pretrained('efficientnet-b0', num_classes=len(class_names))
print("======== Fine-funing netowrk architecutre ========\n")
print(model_ft)
model_ft = model_ft.to(device)
criterion = nn... | code |
74052486/cell_16 | [
"text_plain_output_1.png"
] | from time import time
from torch.nn.functional import softmax
from torch.utils.data import DataLoader
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.datasets import ImageFolder
from torchvision.transforms import Compose, CenterCrop, ToTensor
from torchvision... | code |
74052486/cell_10 | [
"text_plain_output_1.png"
] | from time import time
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import Compose, CenterCrop, ToTensor,ColorJitter
import glob
import os
import shutil
import torch
import torch
use_cuda = torch.cuda.is_available()
if torch.cuda.is_available():... | code |
105182335/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/salary-prediction-polynomial-linear-regression/Position_Salaries.csv')
X = data.iloc[:, 1:2].values
y = data.iloc[... | code |
105182335/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/salary-prediction-polynomial-linear-regression/Position_Salaries.csv')
X = data.iloc[:, 1:2].values
y = data.iloc[:, 2].values
from sklearn.linear... | code |
105182335/cell_6 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/salary-prediction-polynomial-linear-regression/Position_Salaries.csv')
X = data.iloc[:, 1:2].values
y = data.iloc[... | code |
105182335/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 |
105182335/cell_7 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/salary-prediction-polynomial-linear-regression/Position_Sala... | code |
105182335/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/sal... | code |
105182335/cell_3 | [
"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/salary-prediction-polynomial-linear-regression/Position_Salaries.csv')
print(data)
X = data.iloc[:, 1:2].values
y = data.iloc[:, 2].values | code |
105182335/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/sal... | code |
105182335/cell_5 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/salary-prediction-polynomial-linear-regression/Position_Salaries.csv')
X = data.iloc[:, 1:2].... | code |
34133814/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKi... | code |
34133814/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes', 'Joined', 'Wage', 'Preferred Foot', '... | code |
34133814/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes', 'Joined', 'Wage', 'Preferred Foot', '... | code |
34133814/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKi... | code |
34133814/cell_20 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKi... | code |
34133814/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.head(10) | code |
34133814/cell_2 | [
"text_html_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34133814/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes', 'Joined', 'Wage', 'Preferred Foot', '... | code |
34133814/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioni... | code |
34133814/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes', 'Joined', 'Wage', 'Preferred Foot', '... | code |
34133814/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes', 'Joined', 'Wage', 'Preferred Foot', '... | code |
34133814/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKi... | code |
34133814/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKi... | code |
34133814/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes', 'Joined', 'Wage', 'Preferred Foot', '... | code |
34133814/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.drop(df.columns.difference(['ID', 'Name', 'Age', 'Photo', 'Nationality', 'Position', 'Overall', 'Potential', 'Club', 'Composure', 'Dribbling', 'GKDiving', 'GKHandling', 'GKKicking', 'GKPositioning', 'GKReflexes', 'Joined', 'Wage', 'Preferred Foot', '... | code |
34133814/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/fifa19/data.csv')
df.head(10) | code |
34148707/cell_15 | [
"text_html_output_1.png"
] | val_fold | code |
88085268/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from mlxtend.preprocessing import minmax_scaling
from sklearn.cluster import KMeans
from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd
import seaborn as s... | code |
88085268/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
from xgboost import XGBRegressor
import pandas as pd
import seaborn as sns
import pandas as pd
import seaborn as sns
from xgboost import XGBRegressor
from sk... | code |
16158815/cell_13 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
heart = pd.read_csv('../input/heart.csv')
heart.nunique()
heart.columns = ['Age', 'Gender', 'ChestPain', 'RestingBloodPressure', 'Cholestrol', 'FastingBloodSugar', 'RestingECG', 'MaxHeartRat... | code |
16158815/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
heart = pd.read_csv('../input/heart.csv')
heart.nunique()
heart.columns = ['Age', 'Gender', 'ChestPain', 'RestingBloodPressure', 'Cholestrol', 'FastingBloodSugar', 'RestingECG', 'MaxHeartRateAchivied', 'ExerciseIndusedAngin... | code |
16158815/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
heart = pd.read_csv('../input/heart.csv')
print('unique entries in each column')
heart.nunique() | code |
16158815/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
heart = pd.read_csv('../input/heart.csv')
print(heart.shape)
heart.head() | code |
16158815/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
heart = pd.read_csv('../input/heart.csv')
heart.nunique()
heart.columns = ['Age', 'Gender', 'ChestPain', 'RestingBloodPressure', 'Cholestrol', 'FastingBloodSugar', 'RestingECG', 'MaxHeartRateAchivied', 'ExerciseIndusedAngina', 'Oldpeak', 'Slope',... | code |
16158815/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import os
print(os.listdir('../input')) | code |
16158815/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)
heart = pd.read_csv('../input/heart.csv')
heart.nunique()
heart.columns = ['Age', 'Gender', 'ChestPain', 'RestingBloodPressure', 'Cholestrol', 'FastingBloodSugar', 'RestingECG', 'MaxHeartRateAchivied', 'ExerciseIndusedAngina', 'Oldpeak', 'Slope',... | code |
16158815/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
heart = pd.read_csv('../input/heart.csv')
heart.info() | code |
16158815/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
heart = pd.read_csv('../input/heart.csv')
heart.nunique()
heart.columns = ['Age', 'Gender', 'ChestPain', 'RestingBloodPressure', 'Cholestrol', 'FastingBloodSugar', 'RestingECG', 'MaxHeartRat... | code |
16158815/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 seaborn as sns
heart = pd.read_csv('../input/heart.csv')
heart.nunique()
heart.columns = ['Age', 'Gender', 'ChestPain', 'RestingBloodPressure', 'Cholestrol', 'FastingBloodSugar', 'RestingECG', 'MaxHeartRateAchivied', 'ExerciseIndusedAngin... | code |
128049382/cell_4 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png",
"text_html_output_3.png"
] | from torchsummary import summary
import torch
from torchvision import models
from torchsummary import summary
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RN_BCNN().to(device)
summary(model, (3, 224, 224)) | code |
128049382/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score,cohen_kappa_score
from torchsummary import summary
from tqdm import tqdm
import numpy as np
import torch
import torch
import torch.nn as nn
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as tr... | code |
128049382/cell_7 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_1.png"
] | from torchsummary import summary
import torch
import torch
from torchvision import models
from torchsummary import summary
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RN_BCNN().to(device)
summary(model, (3, 224, 224))
device = torch.device('cuda:0' if torch.cuda.is_available() else... | code |
128049382/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score,cohen_kappa_score
from torchsummary import summary
from tqdm import tqdm
import numpy as np
import torch
import torch
import torchvision.transforms as transforms
import tqdm
import wandb
from torchvision import models
from to... | code |
128049382/cell_3 | [
"text_plain_output_1.png"
] | pip install torchsummary | code |
1008057/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
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 numpy as np
import pandas as pd
from subprocess import check_outp... | code |
1008057/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import VotingClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from subprocess import check_output
import matplotlib.pyplot as plt
import numpy as ... | code |
1008057/cell_2 | [
"text_html_output_1.png",
"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
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')... | code |
1008057/cell_7 | [
"text_plain_output_1.png"
] | """
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier , GradientBoostingClassifier, AdaBoostClassifier, VotingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sk... | code |
1008057/cell_3 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
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 numpy as np
import pandas as pd
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd... | code |
1008057/cell_5 | [
"image_output_1.png"
] | from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from subprocess import check_output
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e... | code |
1009991/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
type_1 = os.listdir('../input/train/Type_1')
type_1.shape | code |
1009991/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/train/']).decode('utf8')) | code |
34144217/cell_4 | [
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dates = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/calendar.csv')
data = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/sales_train_validation.csv')
sale_data = pd.read_csv(... | code |
34144217/cell_6 | [
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dates = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/calendar.csv')
data = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/sales_train_validation.csv')
sale_data = pd.read_csv(... | code |
34144217/cell_2 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dates = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/calendar.csv')
data = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/sales_train_validation.csv')
sale_data = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/sell_prices.csv')
submis... | code |
34144217/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
34144217/cell_8 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dates = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/calendar.csv')
data = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/sales_train_validation.csv')
sale_data = pd.read_csv(... | code |
34144217/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dates = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/calendar.csv')
data = pd.read_csv('/kaggle/input/m5-forecasting-accuracy/sales_train_validation.csv')
sale_data = pd.read_csv(... | code |
106213616/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/split-experiment-aggregated-data/ab_test_results_aggregated_views_clicks_5.csv')
df.info() | code |
106213616/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/split-experiment-aggregated-data/ab_test_results_aggregated_views_clicks_5.csv')
df.shape | code |
106213616/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/split-experiment-aggregated-data/ab_test_results_aggregated_views_clicks_5.csv')
df.head() | code |
106213616/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/split-experiment-aggregated-data/ab_test_results_aggregated_views_clicks_5.csv')
df.shape
df.isnull().sum() | code |
106213616/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/split-experiment-aggregated-data/ab_test_results_aggregated_views_clicks_5.csv')
df.shape
df.isnull().sum()
df.describe() | code |
106213616/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/split-experiment-aggregated-data/ab_test_results_aggregated_views_clicks_5.csv')
df['clicks'].hist() | code |
106213616/cell_5 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/split-experiment-aggregated-data/ab_test_results_aggregated_views_clicks_5.csv')
print('First 3 rows of data:\n')
df.head() | code |
32063375/cell_8 | [
"image_output_1.png"
] | import pandas as pd
hp = pd.read_csv('../input/london-house-prices/hpdemo.csv')
hp
scaler = SS()
scaler.fit(hp[['east', 'north', 'fl_area']])
hp_sc = scaler.transform(hp[['east', 'north', 'fl_area']])
mod1 = NN(n_neighbors=6, weights='uniform', p=2)
price = hp['price'] / 1000.0
mod1.fit(hp_sc, price) | code |
32063375/cell_15 | [
"text_plain_output_1.png"
] | from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plot
import numpy as np
import pandas as pd
import sklearn as sk
def print_summary(opt_reg_object):
params = opt_reg_object.best_estimator_.get_params()
score = -opt_reg_object.best_score_
return
hp = pd.read_csv('../input/london-hous... | code |
32063375/cell_16 | [
"image_output_1.png"
] | from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plot
import numpy as np
import pandas as pd
import sklearn as sk
def print_summary(opt_reg_object):
params = opt_reg_object.best_estimator_.get_params()
score = -opt_reg_object.best_score_
return
hp = pd.read_csv('../input/london-hous... | code |
32063375/cell_14 | [
"text_plain_output_1.png"
] | from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plot
import numpy as np
import pandas as pd
import sklearn as sk
def print_summary(opt_reg_object):
params = opt_reg_object.best_estimator_.get_params()
score = -opt_reg_object.best_score_
return
hp = pd.read_csv('../input/london-hous... | code |
32063375/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import sklearn as sk
def print_summary(opt_reg_object):
params = opt_reg_object.best_estimator_.get_params()
score = -opt_reg_object.best_score_
return
hp = pd.read_csv('../input/london-house-prices/hpdemo.csv')
hp
scaler = SS()
scaler.fit(hp[['east', 'north', 'fl_area']])
hp_sc = sc... | code |
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