USING Logistic Regression Model

Credit Risk Assessment System

Enter borrower and loan details to estimate the probability of loan default, classify the risk level, and generate a loan recommendation.

Applicant Details

fill in the details below

Age

Annual Income

Loan Amount

Months Employed

Interest Rate (%)

Employment Type

Marital Status

Has Dependents

Loan Purpose

Model Equation Used

Z = -1.121 - 0.039(Age) - 0.010(MonthsEmployed) + 0.069(InterestRate) + categorical effects + 0.042(LoanAmount/10000) - 0.087(Income/10000).

Months Employed =number of months a borrower has been working in their current job or employment.

Default Probability(Probability of borrower fails to repay the loan as agreed.) = 1 / (1 + e^-Z)

This web application was developed for educational and portfolio purposes using a publicly available Loan Default Prediction Dataset from Kaggle. The model output should not be considered as a real banking decision, and further validation would be required before using it in a real financial institution. (Dataset link)

Developed by Chamod Himesha.