Using Machine Learning Model : Logistic Regression
AI System tries to predict the people who can be granted loans or need to be rejected
We see that the most correlate variables are:
ApplicantIncome with LoanAmount
Credit_History with Loan_Status.
LoanAmount is also correlated with CoapplicantIncome
Feature Engineering and and using models LR, Decision Tree, Random Forest
After using Feature engineering with the introduction of some new features like
- Total Income: As discussed during bivariate analysis we will combine the Applicant Income and Coapplicant Income. If the total income is high, chances of loan approval might also be high.
- EMI :is the monthly amount to be paid by the applicant to repay the loan. Idea behind making this variable is that people who have high EMI’s might find it difficult to pay back the loan. We can calculate the EMI by taking the ratio of loan amount with respect to loan amount term.
- Balance Income :This is the income left after the EMI has been paid. Idea behind creating this variable is that if this value is high, the chances are high that a person will repay the loan and hence increasing the chances of loan approval.
We can see that:
- Credit_History is the most important feature followed by
- Balance Income,
- Total Income = ApplicantInc + Co-AppIncome
To know more about the data analysis look at this project on
Project on Github