This award-winning business analytics project tackles the complex challenge of credit risk analysis through advanced fraud detection. By leveraging machine learning techniques on a massive dataset containing over one million transactions, the project successfully identifies the needle-in-a-haystack problem of fraudulent activities that represent less than 1% of all cases.
The project demonstrates innovative approaches to handling extremely imbalanced data, employing specialized sampling techniques, supervised learning algorithms, and ensemble methods to overcome the inherent challenges of rare event prediction. Through careful feature engineering and model optimization, the solution achieves high precision and recall metrics despite the severe class imbalance.