A machine learning-based fraud detection system that preprocesses data, manages outliers, handles missing values, and mitigates multi-collinearity. It utilizes predictive modeling techniques (Logistic Regression, Random Forest, Gradient Boosting) and evaluates performance using precision, recall, F1-score, and ROC-AUC.