Crosstalk is one method for examining two or even more variables. It’s basically counting the amount between two or more categorical variables.
To get loan data, click here .
Loading Libraries
import numpy as np import pandas as pd import matplotlib as plt |
Loading data
data = pd.read_csv ( "loan_status.csv" ) print (data.head ( 10 )) |
Output:
Describe the data
data.describe () |
Output:
Data Information
Output:
< p> Data Types
# object / attribute data types # in data data.dtypes |
Output:
Code # 1: A contingency table showing the relationship between grades and credit status.
data_crosstab = pd.crosstab (data [ ’grade’ ], data [ ’loan_status’ ], margins = False ) print (data_crosstab) |
Output:
Code # 2: A contingency table showing the relationship between purpose and credit status.
data_crosstab = pd.crosstab (data [ ’purpose’ ], data [ ’loan_status’ ], < / p> margins = False ) print ( data_crosstab) |
Output:
Code # 3: A contingency table showing the relationship between classes + purpose and status of the loan.
data_crosstab = pd.crosstab ([data.grade, data.purpose], data.loan_status, margins = False ) print (data_crosstab) |
Output:
Like in code, contingency tables give clear correlation values between two or more variables. Thus, it is much more useful to understand the data for further information extraction.
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