This project addresses the rising challenge of credit card fraud by building a machine learning-based fraud detection system. Using a dataset of 1.85 million transactions, the system identifies fraudulent activities with high accuracy while minimizing customer inconvenience. The project includes data analysis, model building, cost-benefit analysis, and actionable insights for the banking sector.
- Load and inspect transactional data of ~1.85 million records.
- Understand data features like transaction amounts, merchant details, and fraud labels.
- Handle missing values and imbalanced data.
- Transform skewed data for better model performance.
- Create derived features to enhance predictions.
- Experiment with various models, including Logistic Regression, Random Forest, and XGBoost.
- Perform hyperparameter tuning for optimal results.
- Address data imbalance using oversampling (SMOTE) and undersampling techniques.
- Evaluate model performance using metrics like precision, recall, and F1-score.
- Calculate monthly savings for the bank by comparing costs incurred before and after model deployment.
- Include a second-layer authentication mechanism for flagged transactions.
- Generate insightful plots to identify trends in fraudulent transactions.
- Highlight model performance through ROC curves and other visual metrics.
- Python 🐍
- pandas: Data manipulation and preprocessing.
- numpy: Numerical computations.
- matplotlib & seaborn: Data visualization.
- scikit-learn: Model building and evaluation.
- imbalanced-learn: Handling class imbalance.
- xgboost: Advanced machine learning algorithms.
- fraud_detection.ipynb: Jupyter Notebook containing the Python code, visualizations, and results.
- transactions_dataset.csv: The dataset used for analysis.
- README.md: Documentation for the project.
- presentation.pdf: Business presentation showcasing insights, savings, and recommendations.
- Detected fraudulent transactions with high accuracy and minimal false positives.
- Identified patterns in fraudulent transactions, such as time of occurrence and transaction amounts.
- Calculated substantial monthly cost savings by implementing the fraud detection model.
- Highlighted the importance of a second-layer authentication mechanism for flagged transactions.
The Credit Card Fraud Detection System provides a robust solution to mitigate unauthorized transactions, saving financial institutions millions in losses. By leveraging machine learning and a cost-effective authentication mechanism, the system ensures secure transactions while maintaining a seamless customer experience.
Contributions are welcome! Feel free to fork this repository, open issues, or submit pull requests to improve the project.