Using Machine Learning models to predict whether a loan application is likely to be approved or denied based on various factors. – Features that significantly impact loan approval decisions are selected. Common features include credit score, income, employment history, debt levels, and the purpose of the loan.
The director of SZE bank identified that going through the loan applications to filter the people who can be granted loans or need to be rejected is a tedious and time-consuming process. He wants to automate it and increase his bank’s efficiency. After talking around a bit, your name pops up as one of the few data scientists who can make this possible within a limited time. Will you help the director out?
The idea behind this ML project is to build an ML model and web application that the bank can use to classify if a user can be granted a loan or not.
- Loan_ID: A unique ID assigned to every loan applicant
- Gender: Gender of the applicant (Male, Female)
- Married: The marital status of the applicant (Yes, No)
- Dependents: No. of people dependent on the applicant (0,1,2,3+)
- Education: Education level of the applicant (Graduated, Not Graduated)
- Self_Employed: If the applicant is self-employed or not (Yes, No)
- ApplicantIncome: The amount of income the applicant earns
- CoapplicantIncome: The amount of income the co-applicant earns
- LoanAmount: The amount of loan the applicant has requested for
- Loan_Amount_Term: The no. of days over which the loan will be paid
- Credit_History: A record of a borrower's responsible repayment of debts (1- has all debts paid, 0- not paid)
- Property_Area : The type of location where the applicant’s property lies (Rural, Semiurban, Urban)
- Target:
- Loan_Status: Loan granted or not (Y, N)