path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
17109150/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from torchvision import datasets, models, transforms
model = models.resnet152(pretrained=True)
for param in model.parameters():
param.requires_grad = False
print(model) | code |
17109150/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
labels = pd.read_csv('../input/train.csv')
labels.head()
print(type(labels)) | code |
17109150/cell_14 | [
"text_plain_output_1.png"
] | from PIL import Image
from io import BytesIO
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
... | code |
33104188/cell_13 | [
"text_html_output_1.png"
] | from math import sqrt
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
import lightgbm as lgb
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/electrical-consumption/train_6BJx641.csv')
test = pd.read_csv('/k... | code |
33104188/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/electrical-consumption/train_6BJx641.csv')
test = pd.read_csv('/kaggle/input/electrical-consumption/test_pavJagI.csv')
train.head() | code |
33104188/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 |
33104188/cell_18 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/electrical-consumption/train_6BJx641.csv')
test = pd.read_csv('/kaggle/input/electrical-consumption/test_pavJagI.csv')
train['datetime'] = pd.to_datetime(train['datetime'])
test['datetime'] = pd.to_datetime(test[... | code |
33104188/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/electrical-consumption/train_6BJx641.csv')
test = pd.read_csv('/kaggle/input/electrical-consumption/test_pavJagI.csv')
train['datetime'] = pd.to_datetime(train['datetime'])
test['datetime'] = pd.to_datetime(test[... | code |
33104188/cell_12 | [
"text_html_output_1.png"
] | from math import sqrt
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold
import lightgbm as lgb
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/electrical-consumption/train_6BJx641.csv')
test = pd.read_csv('/k... | code |
74067618/cell_9 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.index = pd.DatetimeIndex(netflix['Date'])
netflix.drop(['Date'], axis=1, inplace=True)
netflix = netflix.asfreq('d')
netflix.index
netflix = netflix.fillna(method='ffill')... | code |
74067618/cell_4 | [
"text_html_output_1.png"
] | !pip install pycaret-ts-alpha | code |
74067618/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.index = pd.DatetimeIndex(netflix['Date'])
netflix.drop(['Date'], axis=1, inplace=True)
netflix.head() | code |
74067618/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.head() | code |
74067618/cell_11 | [
"text_plain_output_1.png"
] | from sktime.utils.plotting import plot_series
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.index = pd.DatetimeIndex(netflix['Date'])
netflix.drop(['Date'], axis=1, inplace=True)
netfl... | code |
74067618/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix.head() | code |
74067618/cell_7 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.index = pd.DatetimeIndex(netflix['Date'])
netflix.drop(['Date'], axis=1, inplace=True)
netflix = netflix.asfreq('d')
netflix.index | code |
74067618/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.index = pd.DatetimeIndex(netflix['Date'])
netflix.drop(['Date'], axis=1, inplace=True)
netflix = netflix.asfreq('d')
netflix.index
netflix.head() | code |
74067618/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.info() | code |
74067618/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.index = pd.DatetimeIndex(netflix['Date'])
netflix.drop(['Date'], axis=1, inplace=True)
netflix = netflix.asfreq('d')
netflix.index
netflix = netflix.fillna(method='ffill')... | code |
74067618/cell_12 | [
"text_plain_output_1.png"
] | from pycaret.internal.pycaret_experiment import TimeSeriesExperiment
import pandas as pd
import pandas as pd
netflix = pd.read_csv('../input/tcs-share/TCS.NS (1).csv')
netflix = netflix[['Date', 'Close']]
netflix.index = pd.DatetimeIndex(netflix['Date'])
netflix.drop(['Date'], axis=1, inplace=True)
netflix = netfl... | code |
74067618/cell_5 | [
"text_html_output_1.png"
] | from pycaret.internal.pycaret_experiment import TimeSeriesExperiment
from sktime.utils.plotting import plot_series | code |
18140030/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader
import matplotlib.image as Image
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import numpy as n... | code |
18140030/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '../input'
train_dir = data_dir + '/train/train'
test_dir = data_dir + '/test/test'
labels = pd.read_csv(data_dir + '/train.csv')
balance = labels['has_cactus'].value_counts()
balance | code |
18140030/cell_6 | [
"text_plain_output_1.png"
] | import torch
num_epochs = 25
num_classes = 2
batch_size = 128
learning_rate = 0.0001
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
device | code |
18140030/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as Image
import os
print(os.listdir('../input'))
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoa... | code |
18140030/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_dir = '../input'
train_dir = data_dir + '/train/train'
test_dir = data_dir + '/test/test'
labels = pd.read_csv(data_dir + '/train.csv')
labels.head() | code |
18140030/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset, DataLoader
import matplotlib.image as Image
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torchvision.transforms as transforms
import numpy as n... | code |
18140030/cell_12 | [
"text_html_output_1.png"
] | from torch.utils.data import Dataset, DataLoader
import matplotlib.image as Image
import os
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as Image
import os
from sklearn.model_selection import train_test_split
import torch
import t... | code |
122256475/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
Bbox.rename(columns={'Finding Label': 'Diagnosis'}, inplace=T... | code |
122256475/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
path = '/kaggle/input/data/'
file = path + 'Data_Entry_2017.csv'
Data_entry = pd.rea... | code |
122256475/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
Bbox.rename(columns={'Finding Label': 'Diagnosis'}, inplace=T... | code |
122256475/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)
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
print(file)
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox | code |
122256475/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
Bbox.rename(columns={'Finding Label': 'Diagnosis'}, inplace=T... | code |
122256475/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)
import seaborn as sns
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
Bbox.rename(columns={'Finding Label': 'Diagnosis'}, inplace=T... | code |
122256475/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
path = '/kaggle/input/data/'
file = path + 'Data_Entry_2017.csv'
Data_entry = pd.rea... | code |
122256475/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)
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
Bbox.rename(columns={'Finding Label': 'Diagnosis'}, inplace=True)
Bbox.head(5) | code |
122256475/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)
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
path = '/kaggle/input/data/'
file = path + 'Data_Entry_2017.csv'
Data_entry = pd.rea... | code |
122256475/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
Bbox.rename(columns={'Finding Label': 'Diagnosis'}, inplace=T... | code |
122256475/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
path = '/kaggle/input/data/'
file = path + 'BBox_List_2017.csv'
Bbox = pd.read_csv(file)
Bbox = Bbox.drop(columns=['Unnamed: 6', 'Unnamed: 7', 'Unnamed: 8'])
Bbox
path = '/kaggle/input/data/'
file = path + 'Data_Entry_2017.csv'
Data_entry = pd.rea... | code |
89142178/cell_4 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recomm... | code |
89142178/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recomm... | code |
89142178/cell_3 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recomm... | code |
89142178/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recomm... | code |
128042254/cell_21 | [
"text_html_output_1.png"
] | from sklearn.ensemble import GradientBoostingRegressor, VotingRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv'... | code |
128042254/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
df.isnull().sum()
df.isnull().sum() | code |
128042254/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LassoCV, RidgeCV, LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
d... | code |
128042254/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
df.info() | code |
128042254/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
df.isnull().sum()
df.isnull().sum()
plt.figure(figsize=(16, 16))
sns.heatmap(df.corr(), annot=True, fmt='.1f') | code |
128042254/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, RepeatedKFold
from sklearn.ensemble import GradientBoostingRegressor, VotingRegressor
from sklearn.linear_model import Lasso... | code |
128042254/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
df.isnull().sum() | code |
128042254/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
df | code |
128042254/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
df.isnull().sum()
df.isnull().sum()
df.columns | code |
128042254/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LassoCV, RidgeCV, LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import train_test_split, RepeatedKFold
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = p... | code |
128042254/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
df.isnull().sum()
df.isnull().sum()
df.describe() | code |
128042254/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
df.isnull().sum()
df.isnull().sum()
sns.barplot(df) | code |
128042254/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/boston-housing-dataset/HousingData.csv')
for col in df.columns:
print(col + '\n------')
print(df[col].value_counts())
print('---------------------') | code |
73069551/cell_19 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
import plotly.express as px
class CustomLinearRegression:
def __init__(self, learning_rate=... | code |
73069551/cell_15 | [
"text_html_output_2.png"
] | from sklearn.metrics import mean_squared_error
import pandas as pd
import plotly.express as px
df = pd.DataFrame(columns=['Number of iterations', 'MSE', 'Regularization', 'Average Weights'])
for n_iters in range(1, 51):
model = CustomLinearRegression(n_iters=n_iters, regularization=None)
model.fit(X_train, y... | code |
73069551/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error
import pandas as pd
import plotly.express as px
df = pd.DataFrame(columns=['Number of iterations', 'MSE', 'Regularization', 'Average Weights'])
for n_iters in range(1, 51):
model = CustomLinearRegression(n_iters=n_iters, regularization=None)
model.fit(X_train, y... | code |
72117495/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from functools import partial
from sklearn.feature_selection import f_regression, f_classif
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data_x = train.loc[:, [col for col in train.columns if co... | code |
72117495/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from functools import partial
from sklearn.feature_selection import f_regression, f_classif
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data_x = train.loc[:, [col for col in train.columns if co... | code |
72117495/cell_17 | [
"text_plain_output_1.png"
] | from functools import partial
from sklearn.feature_selection import f_regression, f_classif
from sklearn.preprocessing import OneHotEncoder
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data_x = train.loc[:, [col for col in train.columns if co... | code |
72117495/cell_12 | [
"text_plain_output_1.png"
] | from functools import partial
import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data_x = train.loc[:, [col for col in train.columns if col not in ['Survived']]]
data_y = train.loc[:, ['Survived']]
test_x = test.loc[:, [col for col in train.columns i... | code |
72117495/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/titanic/train.csv')
test = pd.read_csv('../input/titanic/test.csv')
data_x = train.loc[:, [col for col in train.columns if col not in ['Survived']]]
data_y = train.loc[:, ['Survived']]
test_x = test.loc[:, [col for col in train.columns if col not in ['Survived']]]
da... | code |
129007856/cell_9 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input, Embedding, Dot, Flatten, Dense, Dropout
from tensorflow.keras.models import Model
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/flicktime/rating.csv')
import pandas as pd
import numpy as np
import tensorf... | code |
129007856/cell_4 | [
"text_plain_output_1.png"
] | history = model.fit([user_ids, movie_ids], ratings, batch_size=128, epochs=5, validation_split=0.2) | code |
129007856/cell_6 | [
"text_plain_output_1.png"
] | import gc
import gc
gc.collect() | code |
129007856/cell_11 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input, Embedding, Dot, Flatten, Dense, Dropout
from tensorflow.keras.models import Model
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
import pickle
data = pd.read_csv('/kaggle/input/flicktime/rating.csv')
import pandas as pd
import numpy as np... | code |
129007856/cell_7 | [
"image_output_1.png"
] | import ctypes
import ctypes
libc = ctypes.CDLL('libc.so.6')
libc.malloc_trim(0) | code |
129007856/cell_3 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input, Embedding, Dot, Flatten, Dense, Dropout
from tensorflow.keras.models import Model
import numpy as np
import numpy as np
import pandas as pd
import pandas as pd
data = pd.read_csv('/kaggle/input/flicktime/rating.csv')
import pandas as pd
import numpy as np
import tensorf... | code |
129007856/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
plt.plot(history.history['loss'], label='train_loss')
plt.plot(history.history['val_loss'], label='val_loss')
plt.title('Model Learning Curve')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()
plt.show() | code |
49117653/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_55 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_48 | [
"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)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/... | code |
49117653/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import folium
from folium.plugins import MarkerCluster
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
49117653/cell_50 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_52 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_45 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_49 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_51 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/... | code |
49117653/cell_8 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_38 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_53 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_27 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
49117653/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df1 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020-OldRMS-09292020 (Corrected 11_25_20)/COBRA-2020-OldRMS-09292020.csv')
df2 = pd.read_csv('../input/atlanta-crime-data2020/COBRA-2020 (Updated 12_10_2020)/COBRA-2020.csv')
df3 = pd.read_cs... | code |
73082264/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=Tr... | code |
73082264/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.info() | code |
73082264/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/broadband-customers-base-churn-analysis/bbs_cust_base_scfy_20200210.csv')
df = data.copy()
df
df.drop('Unnamed: 19', axis=1, inplace=True)
df.churn.replace('N', '0', inplace=True)
df.churn.replace('Y', '1', inplace=Tr... | code |
73082264/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import missingno as msno
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report, confusion_matrix
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filen... | code |
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