Project overview
This project focuses on identifying fraudulent credit card transactions using advanced predictive modeling and insightful visualizations. Leveraging Python, I developed machine learning models to accurately classify transactions as fraudulent or legitimate. The predictive modeling process involved data preprocessing, feature engineering, and training using various algorithms to achieve optimal accuracy. To complement the analysis, I utilized Power BI to create dynamic visualizations, enabling stakeholders to explore transaction patterns, fraud trends, and key metrics interactively. This comprehensive approach not only improves fraud detection efficiency but also empowers decision-makers with actionable insights through data-driven storytelling.
