Credit Card Fraud Detection
Introduction : Credit card fraud is a significant issue in the digital age, affecting consumers and financial institutions worldwide. This project aims to develop robust fraud detection systems to safeguard electronic payment systems and ensure customer trust.
Team Members • Brandon Sharp • Manpreet Kaur • Shivani Shrivastav • Mohammed Sarshaar • Tyler Keating
Motivation: The motivation behind this project is to combat credit card fraud effectively, mitigating financial losses and enhancing the security of credit card transactions.
Techniques and Trends: We explore common fraud techniques such as Identity Theft, Account Takeover, and Card-Not-Present Fraud, aiming to understand and detect fraudulent activities better.
Technical Overview: • Data Analysis: Utilization of PCA for feature dimensionality reduction. • Machine Learning Models: Implementation of Random Forest and XGBoost for fraud detection. • Hyperparameter Optimization: Techniques like Random Grid Search and Out-of-Bag Error estimation for model enhancement. • Model Interpretability: Usage of Partial Dependence Plots (PDP) to understand the influence of variables on predictions.
Key Achievements: • Developed robust fraud detection models using Random Forest and XGBoost. • Conducted extensive hyperparameter optimization to enhance model accuracy. • Applied model interpretability techniques for better understanding and trust in predictions.
Future Scope: • Integrate real-time data monitoring to enhance detection capabilities. • Explore advanced techniques like deep learning for complex pattern recognition. • Collaborate with industry partners for practical deployment and continuous improvement.
How to Use:
- Clone the repository.
- Install required dependencies:
pip install -r requirements.txt - Run the main script to train the model and predict:
python main.py
Acknowledgments: A special thanks to everyone who supported this project and contributed to its success.
Conclusion : This project highlights the importance of advanced fraud detection systems in today's digital economy, demonstrating effective strategies to bolster security for consumers and financial institutions alike.
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