Tag: credit risk analytics

This project utilized a variety of machine learning algorithms (RF, KNN, DT, GB, LR, ANN, and SVM) to create credit risk modeling for efficient credit rating. Among these, the Gradient Boosting algorithm showed the highest test accuracy at 81.7%. The project also produced feature importance coefficients that reflect relevant credit ratings or probability of default,…