ML-FinFraud-Detector is a machine learning project for detecting financial transaction fraud. Utilizing XGBoost, precision-recall, and ROC curves, it provides accurate fraud detection. Explore feature importance, evaluate model performance, and enhance financial security with this comprehensive fraud detection solution.
Financial transaction fraud poses a significant threat to organizations and individuals. ML-FinFraud-Detector offers an effective solution to identify fraudulent activities, helping enhance financial security. By leveraging machine learning techniques, this project can analyze transaction data and classify transactions as either fraudulent or legitimate.
- Utilizes the XGBoost algorithm for robust fraud detection
- Incorporates precision-recall and ROC curves for performance evaluation
- Feature importance analysis to identify influential factors
- Helps organizations prevent financial losses and enhance security
To get started with ML-FinFraud-Detector, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/ML-FinFraud-Detector.git - Install the required dependencies.
- Prepare your dataset and ensure it follows the required format.
- Run the
ML-FinFraud-Detector.ipynbnotebook to preprocess the data, train the model, and perform fraud detection on your dataset.
Contributions to the project are welcome! If you'd like to contribute, please follow these steps:
- Fork the repository on GitHub.
- Create a new branch from the 'main' branch to work on your changes.
- Make your modifications and commit your changes.
- Push your changes to your forked repository.
- Open a pull request on the main repository to submit your changes for review.
Please ensure that your contributions align with the project's coding style and guidelines.
This project is licensed under the MIT License. See the LICENSE file for more information.
We would like to acknowledge the creators of the Bank Account Fraud (BAF) suite of datasets, which served as the foundation for this project. Their contribution to the field of fraud detection is highly appreciated.
- Robert Rusev - robertrusev
If you have any questions or suggestions, feel free to contact me at robert.rusev@yahoo.com.