Nahian Arnob

This project analyses credit card transaction data to identify and understand fraudulent behaviour using exploratory data analysis and machine learning techniques. The analysis explores transaction patterns across fraud and non-fraud cases, including class imbalance, transaction amounts, and time-based trends. Multiple visualizations are used to highlight differences in spending behavior, and baseline classification models are developed to evaluate fraud detection performance using precision-focused metrics. The project serves as a prototype fraud detection system and demonstrates practical approaches used in real-world financial risk analytics.

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