The objective of this project is to determine the risk of default that a client presents and assign a risk rating to each client. The risk rating will determine if the company will approve (or reject) the loan application

This project analyzes LendingClub loan data to identify risk factors and predict loan defaults. The project uses feature engineering, exploratory data analysis, and machine learning models (Logistic Regression, LightGBM, XGBoost) to classify loans and uncover key insights for credit risk management.

Credit Risk Modeling project using Python and LightGBM. Built a predictive classifier on customer, loan, and bureau datasets with advanced preprocessing, feature engineering, and evaluation through decile analysis. Achieved AUC: 0.98, Gini: 0.96, and KS: 85.98%, ensuring strong rank ordering and reliable loan default prediction.