PeerLoanKart is an NBFC(Non-banking Financial Company) that facilitates peer-to-peer loan.It connects people who need money(borrowers) with people who have money(investors). An investor would want to invest in people who showed a profile of having a high probability of paying you back.I am creating a model that will help predict whether a borrower will pay the loan or not.Here, I am trying to increase profits up to some extent as NPA (Non-Performing Asset) will be reduced due to loan disbursal for only creditworthy borrowers.
Features: credit.policy purpose int.rate installment log.annual.inc dti fico days.with.cr.line revol.bal revol.util inq.last.6mths delinq.2yrs pub.rec not_fully_paid
Completed Steps:
- Data Cleaning
- Exploratory Data Analysis
- Cross validation
- Selection of Models: Decision Tree, Random Forest, and Gradient Boosting
- Evaluation of Models
Outcome: I am being able to reduce the False Positive Errors of such loan prediction problem completely using Gradient Boosting Model, which is a great advantage for the PeerLoanKart financial company and its customers as well.