path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32068790/cell_11 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_16 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train = pd.read_csv('../inp... | code |
32068790/cell_14 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_10 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_12 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train =... | code |
32068790/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import os, gc, pickle, copy, datetime, warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import lightgbm as lgb
from sklearn import metrics
pd.set_option('display.max_columns', 100)
warnings.filterwarnings('ignore')
df_train = pd.read_csv('../inp... | code |
17113130/cell_25 | [
"text_plain_output_1.png"
] | from keras.layers import Input, Embedding, Flatten, Dot, Dense
from keras.models import Model
import numpy as np
import pandas as pd
def import_data():
dataset = pd.read_csv('../input/ratings.csv')
books = pd.read_csv('../input/books.csv')
return (dataset, books)
def extract_book_and_user(dataset):
... | code |
17113130/cell_1 | [
"text_plain_output_1.png"
] | ## importing necessary packges
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
try:
!pip install tensorflow-gpu
import tensorflow as tf
except:
!pip install tensorflow
import tensorflow as tf | code |
17113130/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
def import_data():
dataset = pd.read_csv('../input/ratings.csv')
books = pd.read_csv('../input/books.csv')
return (dataset, books)
def extract_book_and_user(dataset):
n_user = len(dataset.user_id.unique())
n_books = len(dataset.book_id.unique())
return (n_user, n_books)
pr... | code |
17113130/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
def get_unique_value():
book_data = np.array(list(set(datasets.book_id)))
return book_data
book_data = get_unique_value()
def setting_user(user_id):
user = np.array([user_id for i in range(len(book_data))])
return user
user = setting_user(1)
user | code |
17113130/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
def import_data():
dataset = pd.read_csv('../input/ratings.csv')
books = pd.read_csv('../input/books.csv')
return (dataset, books)
datasets, book = import_data()
book = book[['id', 'original_title', 'authors', 'isbn', 'original_publication_year']]
book.head() | code |
17113130/cell_24 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Input, Embedding, Flatten, Dot, Dense
from keras.models import Model
import numpy as np
import pandas as pd
def import_data():
dataset = pd.read_csv('../input/ratings.csv')
books = pd.read_csv('../input/books.csv')
return (dataset, books)
datasets, book = import_data()
book = b... | code |
17113130/cell_10 | [
"text_html_output_1.png"
] | from keras.layers import Input, Embedding, Flatten, Dot, Dense
from keras.models import Model
import pandas as pd
def import_data():
dataset = pd.read_csv('../input/ratings.csv')
books = pd.read_csv('../input/books.csv')
return (dataset, books)
def extract_book_and_user(dataset):
n_user = len(datase... | code |
16154407/cell_13 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import gensim
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.shape
def read_questions(row, column_name):
return gensim.utils.simple_preprocess(str(row[column_name]).encode('utf-8'))
documents = []
for index, row in df.iterrows():
documents.ap... | code |
16154407/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.shape | code |
16154407/cell_11 | [
"text_plain_output_1.png"
] | import gensim
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.shape
def read_questions(row, column_name):
return gensim.utils.simple_preprocess(str(row[column_name]).encode('utf-8'))
documents = []
for index, row in df.iterrows():
documents.ap... | code |
16154407/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import gensim
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.shape
def read_questions(row, column_name):
return gensim.utils.simple_preprocess(str(row[column_name]).encode('utf-8'))
documents = []
for index, row in df.iterrows():
documents.ap... | code |
16154407/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.head() | code |
16154407/cell_10 | [
"text_html_output_1.png"
] | import gensim
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.shape
def read_questions(row, column_name):
return gensim.utils.simple_preprocess(str(row[column_name]).encode('utf-8'))
documents = []
for index, row in df.iterrows():
documents.ap... | code |
16154407/cell_12 | [
"text_plain_output_1.png"
] | import gensim
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/train.csv')
df.shape
def read_questions(row, column_name):
return gensim.utils.simple_preprocess(str(row[column_name]).encode('utf-8'))
documents = []
for index, row in df.iterrows():
documents.ap... | code |
32065814/cell_42 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
age_groups = age_groups.drop(['Sno', 'TotalCases'], axis=1)
sns.barplot(x='AgeGroup', y='D... | code |
32065814/cell_25 | [
"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 matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
print('People in India died due to COVID-19 as of 11th April 2019, 05:00 pm :', age_groups.TotalCases.sum()) | code |
32065814/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_48 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_2 | [
"text_html_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 |
32065814/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
age_groups = age_groups.drop(['Sno', 'TotalCases'], axis=1)
age_groups | code |
32065814/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_32 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_35 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
32065814/cell_37 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns
age_groups = pd.read_csv('/kaggle/input/covid19-in-india/AgeGroupDetails.csv')
std = pd.read_csv('/kaggle/input/covid19-in-india/StatewiseTestingDetails.csv')
std = std.drop(['Date'], axis=1)
... | code |
90108705/cell_21 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.boxplot(x='Pclass', y='Age', data=train) | code |
90108705/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
train.head() | code |
90108705/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
def impute_age(cols):
Age = cols[0]
Pclass = cols[1]
if pd.isnull(Age):... | code |
90108705/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
90108705/cell_29 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
... | code |
90108705/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.heatmap(train.isnull(), yticklabels=False, cbar=False) | code |
90108705/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.distplot(train['Fare'].dropna(), kde=False, bins=40) | code |
90108705/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
train.drop('Cabin', axis=1, inplace=True)
test.drop('Cabin', axis=1, inplace=True)
... | code |
90108705/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.countplot(x='Survived', data=train, palette='RdBu_r') | code |
90108705/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.countplot(x='SibSp', data=train) | code |
90108705/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.distplot(train['Age'].dropna(), kde=False, bins=30) | code |
90108705/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.countplot(x='Survived', hue='Sex', data=train, palette='RdBu_r') | code |
90108705/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.set_style('whitegrid')
sns.countplot(x='Survived', data=train, hue='Pclass') | code |
74051946/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, Pipeline
from skle... | code |
74051946/cell_13 | [
"text_html_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.p... | code |
74051946/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd #To work with dataset
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
train.head() | code |
74051946/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, Pipeline
from skle... | code |
74051946/cell_6 | [
"image_output_1.png"
] | import pandas as pd #To work with dataset
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
cat_columns = train.drop(['id', 'target'], axis=1).select_dtypes(exclude=['int64', 'float64']).columns
num_columns = train.drop(['id', 'target'], axis=1).select_dtype... | code |
74051946/cell_2 | [
"image_output_1.png"
] | import warnings
import warnings
import pandas as pd
import numpy as np
import matplotlib.gridspec as gridspec
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessi... | code |
74051946/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, Pipeline
from skle... | code |
74051946/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.p... | code |
74051946/cell_16 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.p... | code |
74051946/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.preprocessing import OneHotEncoder
from sklearn.p... | code |
90120308/cell_9 | [
"text_html_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from torch import nn
from torch.utils.data import DataLoader, TensorDataset, SubsetRandomSampler
import numpy as np
import pandas as pd
import random
import torch
import pandas as pd
import numpy as np
import torch
from tor... | code |
90120308/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
from torch import nn
from torch.utils.data import DataLoader, TensorDataset, SubsetRandomSampler
import numpy as np
import pandas as pd
import random
import torch
import pandas as pd
import numpy as np
import torch
from tor... | code |
130017473/cell_13 | [
"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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='weight', hue='status', multiple='stack') | code |
130017473/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='futime') | code |
130017473/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)
import seaborn as sns
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.countplot(x='male', data=data) | code |
130017473/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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='weight') | code |
130017473/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)
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
data.head() | code |
130017473/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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.countplot(data=data, x='male', hue='status') | code |
130017473/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 |
130017473/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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='height') | code |
130017473/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)
import seaborn as sns
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='bmi') | code |
130017473/cell_15 | [
"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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='bmi', hue='status', multiple='stack') | code |
130017473/cell_16 | [
"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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=alldata, x='futime', hue='status', multiple='stack') | code |
130017473/cell_14 | [
"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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='height', hue='status', multiple='stack') | code |
130017473/cell_12 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='age', hue='status', multiple='stack') | code |
130017473/cell_5 | [
"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
data = pd.read_csv('/kaggle/input/non-alcohol-fatty-liver-disease/nafld1.csv')
import seaborn as sns
sns.kdeplot(data=data, x='age') | code |
72116744/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
kfold_df = pd.read_csv('../input/braintumor-sampling/brain_tumor_kfold.csv')
kfold_df.head(4) | code |
72116744/cell_7 | [
"application_vnd.jupyter.stderr_output_27.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_20.png",
"application_vnd.jupyter.stderr_output_26.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_pla... | from torch.utils.data import DataLoader, Dataset
import pandas as pd
kfold_df = pd.read_csv('../input/braintumor-sampling/brain_tumor_kfold.csv')
train_df = kfold_df[kfold_df.fold != 0]
train_ds = DataRetriever(train_df['BraTS21ID'].values, '../input/rsna-miccai-brain-tumor-radiogenomic-classification/train', 'FLAIR... | code |
72116744/cell_3 | [
"text_html_output_1.png"
] | from pathlib import Path
import pytorch_lightning as pl
class Config:
seed = 42
img_size = 256
num_imgs = 64
lr = 2e-08
data_dir = Path('/kaggle/input/rsna-miccai-brain-tumor-radiogenomic-classification')
pl.utilities.seed.seed_everything(Config.seed, workers=True) | code |
72116744/cell_12 | [
"text_plain_output_1.png"
] | from IPython.core.magic import register_cell_magic
from efficientnet_pytorch_3d import EfficientNet3D
from pathlib import Path
from pytorch_lightning.core.memory import ModelSummary
from sklearn.metrics import roc_auc_score, roc_curve, auc
from time import time
from torch.utils.data import DataLoader, Dataset
im... | code |
74058231/cell_11 | [
"image_output_2.png",
"image_output_1.png"
] | from sklearn.metrics import classification_report, explained_variance_score, r2_score, max_error
from sklearn.model_selection import train_test_split
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.... | code |
74058231/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 |
74058231/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report, explained_variance_score, r2_score, max_error
from sklearn.model_selection import train_test_split
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)
import torch
trai... | code |
74058231/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report, explained_variance_score, r2_score, max_error
from sklearn.model_selection import train_test_split
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)
import torch
trai... | code |
89139379/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
df.quality.value_counts()
... | code |
89139379/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.describe() | code |
89139379/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
d... | code |
89139379/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
df.quality.value_counts()
df.quality.value_count... | code |
89139379/cell_29 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
d... | code |
89139379/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.info() | code |
89139379/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
df.quality.value_counts() | code |
89139379/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 |
89139379/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.head(7) | code |
89139379/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique() | code |
89139379/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
d... | code |
89139379/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique() | code |
89139379/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import MinMaxScaler
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique()
df.Id.unique()
df = df.drop('Id', axis=1)
df.quality.unique()
d... | code |
89139379/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/wine-quality-dataset/WineQT.csv')
df.shape
df.isnull().sum()
df.Id.nunique() | code |
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