stellazhangsy / loan-risk-prediction

Identify risk in Peer-to-Peer loan marketplace by predicting the probability default with classification method.

Loan-Risk-Prediction

This project aims to use machine learning techniques to accurately predict the default rate of an individual borrower. In the real business world, a rather accurate prediction will benefit stakeholders since they can better assess early-stage loan performance on historical data, conduct contribution analysis to identify risky segments, and measure credit expansion opportunities.

A detailed version of description and result of the project can be found here: Report

Data Source

Prosper Market Place Loan Dataset from Kaggle comprising 81 variables and 113,9794 observations from 2005-2014.

Prediction Model

Build Logistic Model, ANN, Random Forest, XGBoost for model selection and comparison.

Recommendations

Borrower from Loan with lower risks are long term employer with less delinquencies,inquiries who possess good credit history.