Harnessing Behavioral Analytics for Fraud Detection and Credit Card Personalization

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This project focuses on the application of behavioral analytics in the banking sector, aiming to enhance fraud detection and customer segmentation. It covers data preparation and feature engineering, emphasizing data cleaning and the creation of significant features like 'Is_Night'. The project employs supervised learning with XGBoost for detecting fraudulent activities, comparing performance metrics to other models. Additionally, unsupervised learning using K-Means clustering helps in...