In this project, I analyzed the prosper load data, studied the trends and concluded that monthly income, loan amount and borrower's rate significantly affect the prosper rating and a good predictors of delinquency.

This project tackles the crucial challenge of assessing Credit Risk Management in banking. Using Supervised Machine learning, the goal is to predict the probability of default, providing insights into customers' creditworthiness by analyzing variables like account details, purchases, and delinquency information.

Python model to predict likely loan defaulters

Forecasting U.S. Delinquency Rates using Economic Data

Analyzing the economic impacts that influence the multiple job holder data.