Credit card fraud detection is a binary classification problem where the goal is to distinguish fraudulent transactions from legitimate ones. This repository provides the necessary data preprocessing, exploratory data analysis (EDA), feature engineering, and machine learning modeling to achieve accurate fraud detection.
The dataset used for this project contains anonymized transaction data. Key attributes include:
- Time: Seconds elapsed between this transaction and the first transaction in the dataset.
- V1, V2, ..., V28: Principal components obtained using PCA.
- Amount: Transaction amount.
- Class: Target variable (1 for fraudulent, 0 for legitimate).
You can access the dataset from Kaggle: Credit Card Fraud Detection.
-
Clone the repository:
git clone https://github.com/HMNS19/credit-fraud.git cd credit-fraud -
Install dependencies:
pip install numpy pandas matplotlib seaborn scikit-learn imbalanced-learn
Details of these results are documented in the notebooks/ directory.
Contributions are welcome! Please follow these steps:
- Fork the repository.
- Create a feature branch (
git checkout -b feature-name). - Commit your changes (
git commit -m 'Add feature'). - Push to the branch (
git push origin feature-name). - Open a pull request.
This project is licensed under the MIT License.
For questions or feedback, please reach out via the repository's issues page.