Fraud dectection system

I developed a sophisticated fraud detection system for an e-commerce platform, incorporating various stages of the data science workflow. This project involved comprehensive data collection, meticulous cleaning, and exploratory data analysis (EDA) to uncover patterns and anomalies. Using Python, along with libraries like NumPy and Pandas, I efficiently preprocessed and managed transaction data, leveraging SQL databases for optimal storage and retrieval.

To enhance the dataset for modeling, I applied feature engineering techniques and constructed a Random Forest model for detecting fraudulent activities. The model was rigorously evaluated using performance metrics such as precision, recall, and F1-score. Additionally, I designed a Power BI dashboard to provide real-time insights into transaction patterns and fraud activities, enabling dynamic monitoring and visualization.

The implementation of this end-to-end fraud detection system significantly improved platform security, reduced financial losses, and bolstered customer trust. Through actionable insights and real-time monitoring, the system has contributed to more effective fraud prevention and detection.