path stringlengths 13 17 | screenshot_names listlengths 1 873 | code stringlengths 0 40.4k | cell_type stringclasses 1
value |
|---|---|---|---|
128031091/cell_21 | [
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', '... | code |
128031091/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
dataset.head() | code |
128031091/cell_20 | [
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', '... | code |
128031091/cell_2 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns | code |
128031091/cell_11 | [
"text_html_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_19 | [
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/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 |
128031091/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', '... | code |
128031091/cell_18 | [
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', '... | code |
128031091/cell_15 | [
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_16 | [
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_17 | [
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_14 | [
"text_plain_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_12 | [
"text_html_output_1.png"
] | 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)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'educa... | code |
128031091/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = '/kaggle/input/adult-dataset/adult.csv'
dataset = pd.read_csv(dataset, header=None, sep=',\\s')
dataset.shape
col_names = ['age', 'workclass', 'fnlwgt', 'education', 'education_num', 'marital_status', 'occupation', '... | code |
72072461/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
X_full = pd.read_csv('../input/housingdataset/train.csv', index_col='Id')
X_test_full = pd.read_csv('../input/housingdataset/test.csv', in... | code |
72072461/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
X_full = pd.read_csv('../input/housingdataset/train.csv', index_col='Id')
X_test_full = p... | code |
72072461/cell_2 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
X_full = pd.read_csv('../input/housingdataset/train.csv', index_col='Id')
X_test_full = pd.read_csv('../input/housingdataset/test.csv', index_col='Id')
X_full.dropna(axis=0... | code |
72072461/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
import pandas as pd
from sklearn.model_selection import train_test_split
X_full = pd.read_csv('../input/housingdataset/train.csv', index_col='Id')
X_test_full = p... | code |
2028129/cell_4 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/flights.csv')
df = df[df['MONTH'] == 1]
df.head() | code |
2028129/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot') | code |
2028129/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/flights.csv')
df = df[df['MONTH'] == 1]
airlineList = df['AIRLINE'].unique()
airlineList = airlineList.tolist()
def calculate_Airline_D_Delays(airlineName):
d = df[df['AIRLINE'] == airlineName]
d = d[d['DEPART... | code |
2028129/cell_14 | [
"text_html_output_1.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/flights.csv')
df = df[df['MONTH'] == 1]
airlineList = df['AIRLINE'].unique()
airlineList = airlineList.tolist()
def calculate_Airline_D_Delays(airlineName):
d = df[df['AIRLINE'] == airlineName]
d = d[d['DEPART... | code |
2028129/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/flights.csv')
df = df[df['MONTH'] == 1]
airlineList = df['AIRLINE'].unique()
airlineList = airlineList.tolist()
def calculate_Airline_D_Delays(airlineName):
d = df[df['AIRLINE'] == airlineName]
d = d[d['DEPART... | code |
2025927/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/SkillCraft.csv')
y = df.LeagueIndex.astype(int)
X = df.drop(['LeagueIndex', 'GameID'], axis=1) | code |
2025927/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier, GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
import ... | code |
2025927/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | set(y_train) | code |
2025927/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/SkillCraft.csv')
y = df.LeagueIndex.astype(int)
X = df.drop(['LeagueIndex', 'GameID'], axis=1)
X_train, X_test, y_train, y_t... | code |
2025927/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/SkillCraft.csv')
print(df.shape)
df.head() | code |
2025927/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import classification_report
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import time
import p... | code |
128049391/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
df = pd.read_csv('/kaggle/input/hackerearth-machine-learni... | code |
128049391/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
df = pd.read_csv('/kaggle/input/hackerearth-machine-learni... | code |
128049391/cell_29 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
df = pd.read_csv('/ka... | code |
128049391/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 |
128049391/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
df = pd.read_csv('/kaggle/input/hackerearth-machine-learni... | code |
128049391/cell_38 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn... | code |
128049391/cell_31 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
df = pd.read_csv('/ka... | code |
128049391/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
df = pd.read_csv('/kaggle/input/hackerearth-machine-learni... | code |
128049391/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
pd.set_option('display.max_columns', None)
import datetime
from sklearn.impute import SimpleImputer
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
df = pd.read_csv('/kaggle/input/hackerearth-machine-learni... | code |
320866/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from dateutil.parser import parse
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
data = pd.read_csv('../input/3-Airplane_Crashes_Since_1908.txt')
import matplotlib.pyplot as plt
from dateutil.parser import parse
years = []
for i in range(len(data)):
years.append(parse... | code |
32071213/cell_21 | [
"text_html_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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_13 | [
"text_plain_output_1.png",
"image_output_2.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 plotly.express as px
import seaborn as sns
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv(... | code |
32071213/cell_9 | [
"text_html_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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_4 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-india/covid_19_india.csv')... | code |
32071213/cell_20 | [
"text_html_output_2.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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-india/covid_19_india.csv')
covidage = pd.read_csv('/kaggle... | code |
32071213/cell_11 | [
"image_output_3.png",
"image_output_2.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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_19 | [
"text_html_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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/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 |
32071213/cell_7 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max... | code |
32071213/cell_8 | [
"text_html_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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_15 | [
"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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
import seaborn as sns
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max... | code |
32071213/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-india/covid_19_india.csv')
covid.tail() | code |
32071213/cell_17 | [
"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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_14 | [
"text_html_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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_10 | [
"text_html_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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
32071213/cell_5 | [
"text_html_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
import plotly as py
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
pd.set_option('display.max_rows', None)
covid = pd.read_csv('/kaggle/input/covid19-in-ind... | code |
90127412/cell_3 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | code | |
90127412/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from pathlib import Path
from torch import nn
from torch.utils.data import Dataset, ConcatDataset
from torchmetrics.functional import accuracy, f1_score, precision, recall
import pandas as pd
import pytorch_lightning as pl
import torch
import torch_optimizer as optim
import transformers as T
TRAIN_DATASET = '.... | code |
90127412/cell_12 | [
"text_plain_output_1.png"
] | from pathlib import Path
from torch.utils.data import Dataset, ConcatDataset
import pandas as pd
import torch
TRAIN_DATASET = '../input/starpredict-dataset/train.parquet'
VAL_DATASET = '../input/starpredict-dataset/val.parquet'
TEST_DATASET = '../input/starpredict-dataset/test.parquet'
SAMPLE_DATASET = '../input/st... | code |
73061961/cell_21 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import NearMiss
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix,classification_report
y_train.value_counts()
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=2)
X_train_res, y_trai... | code |
73061961/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
y_train.value_counts()
lr = LogisticRegression()
lr.fit(X_train, y_train) | code |
73061961/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
pd.set_option('display.max_columns', None)
data.drop(['Time', 'Amount'], axis=1, inplace=True)
data | code |
73061961/cell_20 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import NearMiss
y_train.value_counts()
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=2)
X_train_res, y_train_res = sm.fit_resample(X_train, y_train.ravel())
print("Before Undersampling, counts of label '1': {}".format(sum(y_tra... | code |
73061961/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
pd.set_option('display.max_columns', None)
data['Class'].value_counts() | code |
73061961/cell_19 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix,classification_report
y_train.value_counts()
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=2)
X_train_res, y_train_res = sm.fit_resample(X_train, y_train.ravel... | code |
73061961/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 |
73061961/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix,classification_report
y_train.value_counts()
lr = LogisticRegression()
lr.fit(X_train, y_train)
predictions = lr.predict(X_valid)
confusion_matrix(y_valid, predictions) | code |
73061961/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
pd.set_option('display.max_columns', None)
data.head(10) | code |
73061961/cell_17 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
y_train.value_counts()
print("Before OverSampling, counts of label '1': {}".format(sum(y_train == 1)))
print("Before OverSampling, counts of label '0': {} \n".format(sum(y_train == 0)))
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=2)
X_train_res, y_train_re... | code |
73061961/cell_22 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import NearMiss
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix,classification_report
y_train.value_counts()
from imblearn.over_sampling import SMOTE
sm = SMOTE(random_state=2)
X_train_res, y_trai... | code |
73061961/cell_12 | [
"text_html_output_1.png"
] | y_train.value_counts() | code |
73061961/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/creditcardfraud/creditcard.csv')
pd.set_option('display.max_columns', None)
data.info() | code |
2035023/cell_9 | [
"image_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/ehresp_2014.csv', usecols=['erbmi', 'euexfreq', 'euwgt', 'euhgt', 'ertpreat'])
df = df[df['erbmi'] > 0]
x = df[[... | code |
2035023/cell_6 | [
"image_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/ehresp_2014.csv', usecols=['erbmi', 'euexfreq', 'euwgt', 'euhgt', 'ertpreat'])
df = df[df['erbmi'] > 0]
x = df[['euexfreq', 'euwgt',... | code |
2035023/cell_2 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/ehresp_2014.csv', usecols=['erbmi', 'euexfreq', 'euwgt', 'euhgt', 'ertpreat'])
df.head() | code |
2035023/cell_11 | [
"image_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/ehresp_2014.csv', usecols=['erbmi', 'euexfreq', 'euwgt', 'euhgt', 'ertpreat'])
df = df[df['erbmi'] > 0]
x = df[[... | code |
2035023/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
import statsmodels.api as sm
from statsmodels.graphics.gofplots import ProbPlot | code |
2035023/cell_7 | [
"text_html_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/ehresp_2014.csv', usecols=['erbmi', 'euexfreq', 'euwgt', 'euhgt', 'ertpreat'])
df = df[df['erbmi'] > 0]
x = df[[... | code |
2035023/cell_8 | [
"text_plain_output_1.png"
] | from statsmodels.graphics.gofplots import ProbPlot
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/ehresp_2014.csv', usecols=['erbmi', 'euexfreq', 'euwgt', 'euhgt', 'ertpreat'])
df = df[df['erbmi'] > 0]
x = df[[... | code |
2035023/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import statsmodels.api as sm
df = pd.read_csv('../input/ehresp_2014.csv', usecols=['erbmi', 'euexfreq', 'euwgt', 'euhgt', 'ertpreat'])
df = df[df['erbmi'] > 0]
x = df[['euexfreq', 'euwgt', 'euhgt', 'ertpreat']]
y = df['erbmi']
x = sm.add_constant(x)
model = sm.OLS(y, x).fit()
model2 = sm.GLM(y,... | code |
2035023/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import statsmodels.api as sm
df = pd.read_csv('../input/ehresp_2014.csv', usecols=['erbmi', 'euexfreq', 'euwgt', 'euhgt', 'ertpreat'])
df = df[df['erbmi'] > 0]
x = df[['euexfreq', 'euwgt', 'euhgt', 'ertpreat']]
y = df['erbmi']
x = sm.add_co... | code |
72083691/cell_33 | [
"text_html_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
def compute_association_rule(rule_matrix, metric='lift', min_thresh=1):
"""
Compute the final association rule
rule_matrix: the corresponding algorithms matrix
metric: the metric to be used (default is lift)
... | code |
72083691/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import mlxtend as ml
import mlxtend as ml
print(ml.__version__) | code |
72083691/cell_40 | [
"text_html_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
def compute_association_rule(rule_matrix, metric='lift', min_thresh=1):
"""
Compute the final association rule
rule_matrix: the corresponding algorithms matrix
metric: the metric to be used (default is lift)
... | code |
72083691/cell_26 | [
"text_plain_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.preprocessing import TransactionEncoder
import pandas as pd
import time
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.shape
all_transacti... | code |
72083691/cell_48 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
def plot_metrics_relationship(rule_matrix, col1, col2):
"""
shows the relationship between the two input columns
"""
fit = np.polyfit(rule_matrix[col1], rule_matrix[col2], 1)
fit_funt = np.poly1d(fit)
def compare_time_exec(algo1=list, algo2=list... | code |
72083691/cell_41 | [
"text_html_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
import matplotlib.pyplot as plt
import numpy as np
def compute_association_rule(rule_matrix, metric='lift', min_thresh=1):
"""
Compute the final association rule
rule_matrix: the corresponding algorithms matrix
me... | code |
72083691/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.head()
data.shape | code |
72083691/cell_45 | [
"text_html_output_1.png"
] | from mlxtend.frequent_patterns import apriori, association_rules,fpgrowth,fpmax
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.preprocessing import TransactionEncoder
import pandas as pd
import time
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.shape
all_transacti... | code |
72083691/cell_28 | [
"text_html_output_1.png"
] | fpgrowth_matrix.tail() | code |
72083691/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.shape
all_transactions = [transaction[1]['itemDescription'].tolist() for transaction in list(data.groupby(['Member_number', 'Date']))]
all_transactions[0:15] | code |
72083691/cell_17 | [
"text_plain_output_1.png"
] | from mlxtend.preprocessing import TransactionEncoder
from mlxtend.preprocessing import TransactionEncoder
import pandas as pd
data = pd.read_csv('../input/groceries-dataset/Groceries_dataset.csv')
data.shape
all_transactions = [transaction[1]['itemDescription'].tolist() for transaction in list(data.groupby(['Membe... | code |
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