Credit-Default-Approximation-for-Unsecured-Lending

In this project, I've addressed the challenge of imbalance that occurs due to a limited subset of credit defaults by deploying the Synthetic Minority Oversampling Technique and applied multiple machine learning classification models. On the basis of the results obtained, it was concluded that the Light Gradient Boosting Machine (LGBM) Classifier outperformed the other models and attested that it is better equipped to deliver higher efficiencies and manage larger data volumes.