XGBoost Classifier

Aim:

        To develop a fraud-detection system that learns patterns directly from transaction data without relying on labels. Captures differences between legitimate and fraudulent activity using an unsupervised representation-learning approach. It improve fraud-spotting accuracy by training simple classifiers on these learned representations instead of raw features.