path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
32065703/cell_8 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
import nltk
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
root_path = '/kaggle/input/CORD-19-research-challenge/'
metadata_path = root_path + 'metadata.csv'
meta... | code |
32065703/cell_14 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
root_path = '/kaggle/input/CORD-19-research-challenge/'
metadata_path = root_path + 'metadata.csv'
metadata_df = pd.... | code |
32065703/cell_22 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from nltk.tokenize import word_tokenize
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import string
root_path = '/kaggle/input/CORD-19-research-challenge/'
metadata_path = root_path + 'metadata.csv'
metadata_df = pd.... | code |
32065703/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/CORD-19-research-challenge/'
metadata_path = root_path + 'metadata.csv'
metadata_df = pd.read_csv(metadata_path, dtype={'pubmed_id': str, 'Microsoft Academic Paper ID': str, 'doi': str})
metadata_df = metadata_df.fillna(0... | code |
104116798/cell_21 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8)
f.read(4)
f.seek(0) | code |
104116798/cell_25 | [
"text_plain_output_1.png"
] | help(open) | code |
104116798/cell_4 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read() | code |
104116798/cell_34 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8)
f.read(4)
f.seek(0)
f.close()
f = open('text.txt', mode='w')
f.write('THIS IS MY FIRST LINE')
f.close()
f = open('text.txt')
f.close()
f = open('... | code |
104116798/cell_44 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8)
f.read(4)
f.seek(0)
f.close()
f = open('text.txt', mode='w')
f.write('THIS IS MY FIRST LINE')
f.close()
f = open('text.txt')
f.close()
f = open('... | code |
104116798/cell_20 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8)
f.read(4) | code |
104116798/cell_6 | [
"text_plain_output_1.png"
] | print('My name is Foofoo', end=', ')
print('We are learning Python') | code |
104116798/cell_48 | [
"text_plain_output_1.png"
] | img = open('../input/cifar10-pngs-in-folders/cifar10/test/airplane/0001.png', mode='rb')
img.readline() | code |
104116798/cell_41 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8)
f.read(4)
f.seek(0)
f.close()
f = open('text.txt', mode='w')
f.write('THIS IS MY FIRST LINE')
f.close()
f = open('text.txt')
f.close()
f = open('... | code |
104116798/cell_19 | [
"text_plain_output_1.png"
] | help(open) | code |
104116798/cell_52 | [
"text_plain_output_1.png"
] | img = open('../input/cifar10-pngs-in-folders/cifar10/test/airplane/0001.png', mode='rb')
img.readline()
store = img.read()
a = open('airplane.png', mode='wb')
a.write(store) | code |
104116798/cell_7 | [
"text_plain_output_1.png"
] | help(print) | code |
104116798/cell_18 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8) | code |
104116798/cell_28 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8)
f.read(4)
f.seek(0)
f.close()
f = open('text.txt', mode='w')
f.write('THIS IS MY FIRST LINE') | code |
104116798/cell_31 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8)
f.read(4)
f.seek(0)
f.close()
f = open('text.txt', mode='w')
f.write('THIS IS MY FIRST LINE')
f.close()
f = open('text.txt')
print(f.read()) | code |
104116798/cell_14 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline() | code |
104116798/cell_10 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
print(f.read()) | code |
104116798/cell_37 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
f.readline()
f = open('../input/poetry/Kanye_West.txt', encoding='utf-8-sig')
f.close()
f.read(8)
f.read(4)
f.seek(0)
f.close()
f = open('text.txt', mode='w')
f.write('THIS IS MY FIRST LINE')
f.close()
f = open('text.txt')
f.close()
f = open('... | code |
104116798/cell_12 | [
"text_plain_output_1.png"
] | f = open('../input/poetry/Kanye_West.txt')
f.read()
f.close()
for i in f:
print(i, end='\n') | code |
130027580/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/best-movies-on-netflix/100 Best Movies on Netflix.csv')
df.tail() | code |
130027580/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/best-movies-on-netflix/100 Best Movies on Netflix.csv')
df.isnull().sum() | code |
130027580/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
130027580/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/best-movies-on-netflix/100 Best Movies on Netflix.csv')
df.isnull().sum()
df.duplicated().sum() | code |
130027580/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/best-movies-on-netflix/100 Best Movies on Netflix.csv')
df.isnull().sum()
df.duplicated().sum()
df.describe() | code |
130027580/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/best-movies-on-netflix/100 Best Movies on Netflix.csv')
df.head() | code |
130027580/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/best-movies-on-netflix/100 Best Movies on Netflix.csv')
df.isnull().sum()
df.duplicated().sum()
df[['Movie Title', 'Score']].sort_values('Score', ascending=False).head(10) | code |
130027580/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/best-movies-on-netflix/100 Best Movies on Netflix.csv')
df.isnull().sum()
df.duplicated().sum()
df[['Movie Title', 'Rank']].sort_values('Rank').head(10) | code |
130027580/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/best-movies-on-netflix/100 Best Movies on Netflix.csv')
df.info() | code |
2028522/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def sigmoid(z):
return 1.0 / (1 + np.exp(-z))
def z(theta, x):
assert theta.shape[1] == 1
assert theta.shape[0] == x.shape[1]
return np.dot(x, theta)
def hypothesis(theta, x):
... | code |
1005077/cell_4 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
hr = pd.read_csv(DIR_DATA + '/HR_comma_sep.csv')
hr.head() | code |
1005077/cell_20 | [
"text_html_output_1.png"
] | from sklearn import neighbors, svm
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier,\
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import cross_val_score, train_test_split,\
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot ... | code |
1005077/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
hr = pd.read_csv(DIR_DATA + '/HR_comma_sep.csv')
print('Percent who left: {:.2f}'.format(np.sum(hr.left) / len(hr.left) * 100)) | code |
1005077/cell_26 | [
"image_output_1.png"
] | from sklearn import neighbors, svm
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier,\
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import cross_val_score, train_test_split,\
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot ... | code |
1005077/cell_18 | [
"text_plain_output_1.png"
] | from sklearn import neighbors, svm
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier,\
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import cross_val_score, train_test_split,\
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot ... | code |
1005077/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
hr = pd.read_csv(DIR_DATA + '/HR_comma_sep.csv')
hr.sales = hr.sales.astype('category').cat.codes
hr.salary = hr.salary.astype('category').cat.codes
hr[['sales', 'salary']].head() | code |
1005077/cell_15 | [
"text_html_output_1.png"
] | from sklearn import neighbors, svm
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier,\
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import cross_val_score, train_test_split,\
from sklearn.naive_bayes import MultinomialNB
import numpy as np
impor... | code |
1005077/cell_24 | [
"text_plain_output_1.png"
] | from sklearn import neighbors, svm
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier,\
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import cross_val_score, train_test_split,\
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot ... | code |
1005077/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import neighbors, svm
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier,\
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import cross_val_score, train_test_split,\
from sklearn.naive_bayes import MultinomialNB
import matplotlib.pyplot ... | code |
1005077/cell_12 | [
"text_html_output_1.png"
] | from sklearn import neighbors, svm
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier,\
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import cross_val_score, train_test_split,\
from sklearn.naive_bayes import MultinomialNB
import numpy as np
impor... | code |
1005077/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
hr = pd.read_csv(DIR_DATA + '/HR_comma_sep.csv')
hr.describe() | code |
16123553/cell_42 | [
"image_output_1.png"
] | 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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numer... | code |
16123553/cell_21 | [
"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/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numerical = df._get_numeric_data()
df['Duration'].describe() | code |
16123553/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum() | code |
16123553/cell_9 | [
"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/travel insurance.csv')
df1 = df
df['Gender'].isnull().sum() | code |
16123553/cell_56 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
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 scipy.stats as ss
impor... | code |
16123553/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import missingno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
missingno.matrix(df) | code |
16123553/cell_2 | [
"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/travel insurance.csv')
df1 = df
df.head(5) | code |
16123553/cell_54 | [
"text_html_output_1.png"
] | from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel... | code |
16123553/cell_60 | [
"text_plain_output_1.png"
] | from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.svm import LinearSVC
from sklearn.svm import LinearSVC
lsvc = LinearSVC()
svc_model = lsvc.fit(X_train, y_train)
lsvc_pred = lsvc.predict(X_test)
from sklearn.metrics import classification_report
print... | code |
16123553/cell_50 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel... | code |
16123553/cell_52 | [
"application_vnd.jupyter.stderr_output_116.png",
"application_vnd.jupyter.stderr_output_74.png",
"application_vnd.jupyter.stderr_output_268.png",
"application_vnd.jupyter.stderr_output_362.png",
"text_plain_output_673.png",
"text_plain_output_445.png",
"text_plain_output_201.png",
"text_plain_output_2... | from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel... | code |
16123553/cell_64 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
rf = RandomForestClassifier(n_estimators=100)
rf_model = rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)
print(classification_report(y_test, rf_pred)) | code |
16123553/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import missingno
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import RFE
... | code |
16123553/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | 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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numer... | code |
16123553/cell_49 | [
"text_plain_output_1.png"
] | from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel... | code |
16123553/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numerical = df._get_numeric_data()
for i, col in enumerate(df_numer... | code |
16123553/cell_32 | [
"text_plain_output_1.png"
] | 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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numer... | code |
16123553/cell_28 | [
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numerical = df._get_numeric_data()
df.loc[df['Duration'] < 0, 'Duration'] = 49.317
df6 = df['Net Sales'] < df['Commision (... | code |
16123553/cell_16 | [
"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/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numerical = df._get_numeric_data()
df_numerical.info() | code |
16123553/cell_38 | [
"text_plain_output_1.png"
] | 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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numer... | code |
16123553/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/travel insurance.csv')
df1 = df
df.info() | code |
16123553/cell_35 | [
"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/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numerical = df._get_numeric_data()
df.loc[df['Duration'] < 0, 'Duration'] = 49.317
df.loc[df['Net Sales'] == 0.0, 'Commisi... | code |
16123553/cell_24 | [
"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/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numerical = df._get_numeric_data()
df10 = df['Duration'] < 0
df10.sum() | code |
16123553/cell_53 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. ... | code |
16123553/cell_36 | [
"image_output_1.png"
] | 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 scipy.stats as ss
import seaborn as sns
df = pd.read_csv('../input/travel insurance.csv')
df1 = df
df.fillna('Not Specified', inplace=True)
df.isnull().sum()
df_numer... | code |
89129153/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt # pyplot plotting tool
import pandas as pd # data processing
import seaborn as sns # seaborn plotting tool
winedf = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
winedf.quality.value_counts()
corr = winedf.corr()
X = winedf.iloc[:, :-1]
y = win... | code |
89129153/cell_25 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # pyplot plotting tool
import pandas as pd # data processing
import seaborn as sns # seaborn plotting tool
winedf = pd.read_csv('/... | code |
89129153/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
dtree = DecisionTreeRegressor(random_state=22)
dtree.fit(X_train, y_train)
print('Making predictions for the following 5 wines:')
print(X_test.head())
print('The predictions are')
print(dtree.predict(X_test.head())) | code |
89129153/cell_44 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import matplotli... | code |
89129153/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
winedf = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
winedf.head() | code |
89129153/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # pyplot plotting tool
import numpy as np # numeric python
import pandas as pd... | code |
89129153/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # pyplot plotting tool
import pandas as pd # data processing
import seaborn as sns # seaborn plotting tool
winedf = pd.read_csv('/... | code |
89129153/cell_41 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import matplotli... | code |
89129153/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89129153/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt # pyplot plotting tool
import pandas as pd # data processing
import seaborn as sns # seaborn plotting tool
winedf = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
winedf.quality.value_counts()
corr = winedf.corr()
plt.figure(figsize=(9, 8))
sns.h... | code |
89129153/cell_32 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
dtree = DecisionTreeRegressor(random_state=22)
dtree.fit(X_train, y_train) | code |
89129153/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
winedf = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
print('winedf shape: ', winedf.shape, '\n')
print('winedf information:')
print(winedf.info()) | code |
89129153/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
winedf = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
winedf.quality.value_counts()
winedf.iloc[:, 1:11].hist(figsize=(20, 10), bins=20, edgecolor='black', color='lightgreen') | code |
89129153/cell_38 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt # pyplot plotting tool
import panda... | code |
89129153/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
winedf = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
winedf.quality.value_counts()
winedf['quality'].hist(align='right', bins=range(3, 9), edgecolor='black', grid=False) | code |
89129153/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing
winedf = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
print(winedf.describe(), '\n')
print('The median wine quality is: ', winedf['quality'].median()) | code |
89129153/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt # pyplot plotting tool
import pandas as pd # data processing
import seaborn as sns # seaborn plotting tool
winedf = pd.read_csv('/... | code |
89129153/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing
winedf = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
winedf.quality.value_counts() | code |
89129153/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import DecisionTreeRegressor
dtree = DecisionTreeRegressor(random_state=22)
dtree.fit(X_train, y_train)
from sklearn.metrics import mean_absolute_error
predicted_wine_quality = dtree.predict(X_test)
mean_... | code |
72111473/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
import numpy as np
import cv2
import pydicom
import matplotlib
matplotlib.rcParams['animation.html'] = 'jshtml'
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from tqdm i... | code |
72111473/cell_2 | [
"text_html_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
root_dir = '../input/rsna-miccai-voxel-64-dataset/'
df = pd.read_csv('../input/training-labels/train_labels.csv')
sns.countplot(data=df, x='MGMT_value')
def full_ids(data):
zeros = 5 - len(str(data))
if zeros > 0:
prefix = ''.join(['0' for i in range(zeros)])
... | code |
72111473/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
root_dir = '../input/rsna-miccai-voxel-64-dataset/'
df = pd.read_csv('../input/training-labels/train_labels.csv')
def full_ids(data):
zeros = 5 - len(str(data))
if zeros > 0:
prefix = ''.join(['0' for i in range(zeros)])
return prefix + str(data)
df['BraTS... | code |
17109150/cell_13 | [
"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 |
17109150/cell_9 | [
"application_vnd.jupyter.stderr_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 |
17109150/cell_4 | [
"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')
num_classes = len(labels['Id'].unique())
print(num_classes) | code |
17109150/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from PIL import Image
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
data_dir = '../input'
train_dir = data_dir + '/train'
test_dir = data_dir + '/test'
pil2tensor = transforms.ToTensor()
tensor2pil = transforms.ToPILImage()
pil_image = Image.open(train_dir + '/0a750c2e8.jpg')
... | code |
17109150/cell_2 | [
"text_plain_output_1.png"
] | import torch
train_on_gpu = torch.cuda.is_available()
if not train_on_gpu:
print('CUDA is not available. Training on CPU ...')
else:
print('CUDA is available! Training on GPU ...') | code |
17109150/cell_11 | [
"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 |
17109150/cell_19 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
from io import BytesIO
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from torch.optim import lr_scheduler
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
im... | code |
17109150/cell_7 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr... | from PIL import Image
from io import BytesIO
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import torch
train_on_gpu = torch.cuda.is_available()
data_dir = '../input'
train_dir = data_dir + '/train'
test_dir = data_dir + '/test'
pil2tens... | code |
17109150/cell_18 | [
"image_output_1.png"
] | from PIL import Image
from io import BytesIO
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from torch.optim import lr_scheduler
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
im... | code |
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