path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
72111100/cell_11 | [
"text_html_output_1.png"
] | train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(train['name'])]
train.insert(0, 'brand', names)
train = train.drop(['name', 'seller_type', 'owner', 'torque', 'fuel'], axis=1)
train.head(2) | code |
72111100/cell_18 | [
"image_output_1.png"
] | train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(train['name'])]
train.insert(0, 'brand', names)
train = train.drop(['name', 'seller_type', 'owner', 'torque', 'fuel'], axis=1)
train['engine'] = [int(x.split(' ')[0]) for x in list(train['engine'])]
train['mileage'] = [f... | code |
72111100/cell_32 | [
"text_plain_output_1.png"
] | from math import sqrt
from sklearn.linear_model import LinearRegression as LR, Perceptron
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
train = pd.read_csv('../input/car-price/train ... | code |
72111100/cell_28 | [
"text_plain_output_1.png"
] | from math import sqrt
from sklearn.linear_model import LinearRegression as LR, Perceptron
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(... | code |
72111100/cell_16 | [
"image_output_1.png"
] | train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(train['name'])]
train.insert(0, 'brand', names)
train = train.drop(['name', 'seller_type', 'owner', 'torque', 'fuel'], axis=1)
train['engine'] = [int(x.split(' ')[0]) for x in list(train['engine'])]
train['mileage'] = [f... | code |
72111100/cell_3 | [
"text_plain_output_1.png"
] | train = pd.read_csv('../input/car-price/train set.csv')
train.head(2) | code |
72111100/cell_24 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression as LR, Perceptron
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(train['name'])]
train.... | code |
72111100/cell_14 | [
"text_html_output_1.png"
] | train = pd.read_csv('../input/car-price/train set.csv')
names = [x.split(' ')[0] for x in list(train['name'])]
train.insert(0, 'brand', names)
train = train.drop(['name', 'seller_type', 'owner', 'torque', 'fuel'], axis=1)
train['engine'] = [int(x.split(' ')[0]) for x in list(train['engine'])]
train['mileage'] = [f... | code |
72111100/cell_5 | [
"text_plain_output_1.png"
] | train = pd.read_csv('../input/car-price/train set.csv')
test = pd.read_csv('../input/car-price/test set.csv')
test.head(2) | code |
74067093/cell_7 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.layers import Input, GlobalAveragePooling2D, Dense, Dropout
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow.keras.metrics import AUC
from tensorflow.keras.models import Model
from tensorflow.keras.models i... | code |
17138373/cell_4 | [
"image_output_1.png"
] | import pandas as pd
dfBlackFriday = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
dfBlackFriday.head() | code |
17138373/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
dfBlackFriday = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
dfBlackFriday.isnull().sum() | code |
17138373/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dfBlackFriday = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
dfBlackFriday.isnull().sum()
produtosmaisComprados = dfBlackFriday['Product_ID'].value_counts().head(10)
produtosmaisComprados.plot(kind='bar', title='10 Produtos mais co... | code |
17138373/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dfBlackFriday = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
dfBlackFriday.isnull().sum()
sns.violinplot(dfBlackFriday['Age'].sort_values(), dfBlackFriday['Purchase'], data=dfBlackFriday)
plt.show() | code |
17138373/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
dfBlackFriday = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
dfBlackFriday.isnull().sum()
dfBlackFridayCons = dfBlackFriday.query('Purchase > 9000')
sns.violinplot(dfBlackFridayCons['Marital_Status'], dfBlackFridayCons['Occupation'... | code |
17138373/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
dfBlackFriday = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
dfBlackFriday.isnull().sum()
dfBlackFriday['Product_ID'].value_counts() | code |
17138373/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
dfBlackFriday = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
dfBlackFriday.describe() | code |
122252864/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
death = pd.read_csv('/kaggle/input/character-deathscsv/character-deaths.csv')
death.shape | code |
122252864/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.rename(columns={'defender_1': 'primary_defender'}, inplace=True)
sns.set(rc={'figure.figsize': (13, 5)})
sns.barp... | code |
122252864/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape | code |
122252864/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.rename(columns={'defender_1': 'primary_defender'}, inplace=True)
battle.head() | code |
122252864/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.rename(columns={'defender_1': 'primary_defender'}, inplace=True)
sns.set(rc={'figure.figsize': (13, 5)})
sns.set... | code |
122252864/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.rename(columns={'defender_1': 'primary_defender'}, inplace=True)
battle['attacker_king'].value_counts() | code |
122252864/cell_18 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.rename(columns={'defender_1': 'primary_defender'}, inplace=True)
sns.set(rc={'figure.figsize': (13, 5)})
sns.set... | code |
122252864/cell_8 | [
"image_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.rename(columns={'defender_1': 'primary_defender'}, inplace=True)
battle['location'].value_counts() | code |
122252864/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
death = pd.read_csv('/kaggle/input/character-deathscsv/character-deaths.csv')
death.shape
death['Nobility'].value_counts() | code |
122252864/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
death = pd.read_csv('/kaggle/input/character-deathscsv/character-deaths.csv')
death.shape
death['Death Year'].value_counts() | code |
122252864/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.head() | code |
122252864/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.rename(columns={'defender_1': 'primary_defender'}, inplace=True)
sns.set(rc={'figure.figsize': (13, 5)})
sns.set... | code |
122252864/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
death = pd.read_csv('/kaggle/input/character-deathscsv/character-deaths.csv')
death.shape
death['Gender'].value_counts() | code |
122252864/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.rename(columns={'defender_1': 'primary_defender'}, inplace=True)
sns.set(rc={'figure.figsize': (13, 5)})
sns.set... | code |
122252864/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
death = pd.read_csv('/kaggle/input/character-deathscsv/character-deaths.csv')
death.head() | code |
122252864/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
battle = pd.read_csv('/kaggle/input/games-of-thrones/battles.csv')
battle.shape
battle.rename(columns={'attacker_1': 'primary_attacker'}, inplace=True)
battle.head() | code |
129020918/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from matplotlib import patches, patheffects
from torch.utils.data import ConcatDataset
from torch.utils.data import Dataset
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import Subset
from torchvision import datasets, transforms
from torchvision.transforms.functiona... | code |
129020918/cell_11 | [
"image_output_1.png"
] | from PIL import Image
from matplotlib import patches, patheffects
from torch.utils.data import Dataset
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
from torchvision.transforms.functional import to_tensor
from tqdm.notebook import trange, tqdm
import matplotlib.pyp... | code |
129020918/cell_15 | [
"text_plain_output_1.png"
] | from matplotlib import patches, patheffects
from torch.utils.data import ConcatDataset
from torch.utils.data import Dataset, DataLoader
from torch.utils.data import Subset
from torchvision import datasets, transforms
from tqdm.notebook import trange, tqdm
import math
import matplotlib.pyplot as plt
import numpy... | code |
90136510/cell_13 | [
"text_html_output_1.png"
] | from sqlalchemy import create_engine
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'Company... | code |
90136510/cell_9 | [
"text_html_output_1.png"
] | from sqlalchemy import create_engine
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'Company... | code |
90136510/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'CompanyName', 'Job Title': 'JobTitle', 'Salar... | code |
90136510/cell_11 | [
"text_html_output_1.png"
] | from sqlalchemy import create_engine
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'Company... | code |
90136510/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 |
90136510/cell_7 | [
"text_plain_output_1.png"
] | from sqlalchemy import create_engine
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'Company... | code |
90136510/cell_8 | [
"text_html_output_1.png"
] | from sqlalchemy import create_engine
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'Company... | code |
90136510/cell_15 | [
"text_html_output_1.png"
] | from sqlalchemy import create_engine
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'Company... | code |
90136510/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df.head() | code |
90136510/cell_10 | [
"text_html_output_1.png"
] | from sqlalchemy import create_engine
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'Company... | code |
90136510/cell_12 | [
"text_plain_output_1.png"
] | from sqlalchemy import create_engine
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'Company... | code |
90136510/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd ## import liberaries pandas
import pandas as pd
df = pd.read_csv('/kaggle/input/analytics-industry-salaries-2022-india/Salary Dataset.csv')
df = df.rename(columns={'Company Name': 'CompanyName', 'Job Title': 'JobTitle', 'Salar... | code |
90144122/cell_13 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import numpy as np
import pandas as pd
import pickle
data = pickle.load(open('../inp... | code |
90144122/cell_9 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import numpy as np
import pickle
data = pickle.load(open('../input/dog-and-cat/DOGnCA... | code |
90144122/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import numpy as np
import pickle
data = pickle.load(open('../input/dog-and-cat/DOGnCA... | code |
90144122/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Se... | code |
90144122/cell_7 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import numpy as np
import pandas as pd
import pickle
data = pickle.load(open('../inp... | code |
90144122/cell_8 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Se... | code |
90144122/cell_3 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pickle
data = pickle.load(open('../input/dog-and-cat/DOGnCAT50x50.pickle', 'rb'))
fig, axes = plt.subplots(3, 3, figsize=(15, 15))
index = 75
for i in range(3):
for j in range(3):
axes[i, j].imshow(data[index][0], cmap='gray')
index += 1
plt.show() | code |
90144122/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Se... | code |
90144122/cell_10 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import numpy as np
import pandas as pd
import pickle
data = pickle.load(open('../inp... | code |
90144122/cell_12 | [
"text_plain_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.models import Sequential
import numpy as np
import pickle
data = pickle.load(open('../input/dog-and-cat/DOGnCA... | code |
90144122/cell_5 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np
import pickle
data = pickle.load(open('../input/dog-and-cat/DOGnCAT50x50.pickle', 'rb'))
X = np.array([e[0] for e in data]).astype('float32')
y = np.array([e[1] for e in data])
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size... | code |
90102125/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/adult-census-income/adult.csv')
df.head() | code |
90102125/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/adul... | code |
90102125/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/adult-census-income/adult.csv')
X = df.drop(['income'], axis=1)
X = pd.get_dummies(X)
X.head(5) | code |
90102125/cell_22 | [
"text_html_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/adult-census-income/adult.csv')
... | code |
90102125/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/adult-census-income/adult.csv')
low = '<=50K'
y = df['income'].apply(lambda x: 0 if x == low else 1)
y.head(5) | code |
90102125/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/adult-census-income/adult.csv')
X = df.drop(['income'], axis=1)
X.head(5) | code |
34137153/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
miss_values = train.isna().sum().sort_values(ascending=False).head(... | code |
34137153/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 |
34137153/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.describe() | code |
34137153/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams['xtick.labelsize'] = 10
plt.rcParams['ytick.labelsize'] = 10
plt.figure(figsize=(4, 7))
color = sns.dark_palette('deeppink', reverse=True, n_colors=18)
ax = sns.barplot(x='y', y='x', data=miss_values, palette=color, orient='h')
plt.xticks(rotation=90)
p... | code |
74052153/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
print(train.columns) | code |
74052153/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe
train.dtypes.unique()
list(train.select_dtypes(include=['int64']).columns)
list(train.select_dtypes(include=['float64']).columns)
lis... | code |
74052153/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/home-da... | code |
74052153/cell_39 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/home-da... | code |
74052153/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer
from sklearn.impute import SimpleImputer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd
import xgboost as xgb
train = pd.read_csv('../input/home-da... | code |
74052153/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe
print(train.info) | code |
74052153/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.head() | code |
74052153/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe
train.dtypes.unique()
list(train.select_dtypes(include=['int64']).columns)
list(train.select_dtypes(include=['float64']).columns)
lis... | code |
74052153/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
print('The train dataset have the shape', train.shape)
print('The test dataset have the shape', test.shape) | code |
74052153/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe
train.dtypes.unique()
list(train.select_dtypes(include=['int64']).columns)
list(train.select_dtypes(include=['float64']).columns) | code |
74052153/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.impute import SimpleImputer
from sklearn.metrics import mean_absolute_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder
import numpy as np
from sklearn.impute import SimpleImputer
numerical_transformer = SimpleImputer(... | code |
74052153/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe
train.dtypes.unique()
list(train.select_dtypes(include=['int64']).columns)
list(train.select_dtypes(include=['float64']).columns)
lis... | code |
74052153/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe
train.dtypes.unique()
list(train.select_dtypes(include=['int64']).columns) | code |
74052153/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe
train.dtypes.unique()
list(train.select_dtypes(include=['int64']).columns)
list(train.select_dtypes(include=['float64']).columns)
lis... | code |
74052153/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe | code |
74052153/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/home-data-for-ml-course/train.csv')
test = pd.read_csv('../input/home-data-for-ml-course/test.csv')
id = test['Id']
train.describe
train.dtypes.unique() | code |
49124211/cell_9 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/drive-and-act/iccv_activities_3s/activities_3s/kinect_color/tasklevel.chunks_90.split_0.train.csv')
root_path = '../input/drive-and-act/kinect_color/kinect_color/'
sample_rate = 5
for j in range(1):
file... | code |
49124211/cell_4 | [
"text_html_output_1.png"
] | ! nvidia-smi | code |
49124211/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
df = pd.read_csv('../input/drive-and-act/iccv_activities_3s/activities_3s/kinect_color/tasklevel.chunks_90.split_0.train.csv')
df.head() | code |
49124211/cell_2 | [
"text_plain_output_1.png"
] | import psutil
import psutil
def get_size(bytes, suffix='B'):
factor = 1024
for unit in ['', 'K', 'M', 'G', 'T', 'P']:
if bytes < factor:
return f'{bytes:.2f}{unit}{suffix}'
bytes /= factor
print('=' * 40, 'Memory Information', '=' * 40)
svmem = psutil.virtual_memory()
print(f'Total:... | code |
49124211/cell_8 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/drive-and-act/iccv_activities_3s/activities_3s/kinect_color/tasklevel.chunks_90.split_0.train.csv')
root_path = '../input/drive-and-act/kinect_color/kinect_color/'
sample_rate = 5
for j in range(1):
file... | code |
49124211/cell_10 | [
"text_plain_output_1.png"
] | import cv2
import numpy as np
import os
import pandas as pd
import pandas as pd
df = pd.read_csv('../input/drive-and-act/iccv_activities_3s/activities_3s/kinect_color/tasklevel.chunks_90.split_0.train.csv')
root_path = '../input/drive-and-act/kinect_color/kinect_color/'
sample_rate = 5
for j in range(1):
file... | code |
104119293/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy.linalg
from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
def theta_m2_m1(n2=1, n1=1, a=1, b=1, c=1, mu_1=1, mu_2=1):
... | code |
104119293/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import numpy as np
import numpy.linalg
from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
def theta_m2_m1(n2=1, n1=1, a=1, b=1, c=1, mu_1=1, mu_2=1):
... | code |
73075873/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
import matplotlib.pyplot as plt
total ... | code |
73075873/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
data.head() | code |
73075873/cell_23 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
data = pd.read_csv('/kaggle/input/loan-prediction-based-on-customer-behavior/Training Data.csv')
import matplotlib.pyplot as plt
total ... | code |
73075873/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.ensemble import BalancedRandomForestClassifier
from imblearn.over_sampling import ADASYN
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score, accuracy_score, roc_auc_score, plot_roc_curve, plot_confusion_matrix
import matplotlib.pyplot as plt
import matplotlib.pyp... | code |
73075873/cell_33 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.combine import SMOTETomek
from imblearn.ensemble import BalancedRandomForestClassifier
from imblearn.over_sampling import ADASYN
from imblearn.under_sampling import TomekLinks
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score, accuracy_score, roc_auc_score, plot... | code |
73075873/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.over_sampling import ADASYN
from imblearn.over_sampling import ADASYN
print('Initial size:', X_train.shape)
ada = ADASYN(random_state=42)
X_ada, y_ada = ada.fit_resample(X_train, y_train)
print('Resampled size:', X_ada.shape) | code |
73075873/cell_2 | [
"text_plain_output_1.png",
"image_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 |
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