A Python-based simple rules engine to flag potential fraudulent personal spending transactions. Topics: Python, rules-engine, anomaly-detection, personal-finance, fraud-detection, data-science, matplotlib, pandas, numpy, seaborn, Google Colab
Fraud Detector Rules Engine is a rule-based fraud detection system that analyzes personal spending patterns to identify potentially fraudulent transactions. This project demonstrates how simple rules can be created and tested using transaction data (date, amount, category).
- Statistical Anomaly Detection: Identifies transactions that deviate from normal patterns
- Category-Based Rules: Flags unusual spending in specific categories
- Frequency Analysis: Detects unusual transaction patterns
- Visual Analytics: Comprehensive charts and graphs for rule performance
- False Positive Analysis: Measures and visualizes rule effectiveness
- Google Colab Ready: Run instantly in your browser without installation
Click the badge below to open the notebook directly in Google Colab:
Feel free to fork this project and adapt it for your own use case. Pull requests are welcome!
This project is open source and available under the MIT License.
- Inspired by real-world fraud detection systems
- Built with Python, Pandas, and Matplotlib
- Special thanks to the open-source community
📧 Contact email: liz21atang@gmail.com
Elizabeth (Epse Kombe) Atang:
