aysecnkci/banking-risk-analysis-imbalanced-data

The project focuses on handling imbalanced data using techniques like RandomUnderSampler and TomekLinks, while exploring various models such as CART, Random Forest, GBM, and LightGBM. The BalancedRandomClassifier, optimized through hyperparameter tuning, achieved an 80% recall on high-risk customers with an accuracy of 74%.