A machine learning project for detecting fraudulent credit card transactions using data mining techniques.
This project implements various machine learning algorithms to identify fraudulent transactions in credit card data. Credit card fraud detection is a critical application of data science, helping financial institutions protect customers from unauthorized transactions.
- Analyze credit card transaction data to identify patterns associated with fraud
- Handle imbalanced datasets common in fraud detection scenarios
- Build and evaluate machine learning models for fraud classification
- Compare different algorithms to find the best performing model
- Python - Primary programming language
- Jupyter Notebook - Interactive development environment
- Pandas - Data manipulation and analysis
- NumPy - Numerical computing
- Scikit-learn - Machine learning algorithms
- Matplotlib/Seaborn - Data visualization
CCFD/
├── DM_Project_(1).ipynb # Main Jupyter notebook with analysis and models
├── DM.pdf # Project documentation/report
└── README.md # Project documentation
Make sure you have Python 3.x installed along with the following packages:
pip install pandas numpy scikit-learn matplotlib seaborn jupyter-
Clone the repository:
git clone https://github.com/AmmarAhmedl200961/CCFD.git cd CCFD -
Launch Jupyter Notebook:
jupyter notebook
-
Open
DM_Project_(1).ipynband run the cells sequentially
The project typically follows these data mining steps:
- Data Exploration - Understanding the dataset structure and features
- Data Preprocessing - Handling missing values, scaling, and encoding
- Handling Imbalanced Data - Techniques like SMOTE, undersampling, or oversampling
- Feature Engineering - Creating meaningful features for better predictions
- Model Training - Training various classification algorithms
- Model Evaluation - Using metrics like Precision, Recall, F1-Score, and AUC-ROC
For fraud detection, we focus on:
- Precision - Accuracy of positive predictions
- Recall - Ability to find all fraudulent transactions
- F1-Score - Harmonic mean of precision and recall
- AUC-ROC - Area under the ROC curve
Contributions are welcome! Feel free to:
- Fork the repository
- Create a feature branch (
git checkout -b feature/improvement) - Commit your changes (
git commit -am 'Add new feature') - Push to the branch (
git push origin feature/improvement) - Open a Pull Request
This project is open source and available for educational purposes.
Ammar Ahmed
- GitHub: @AmmarAhmedl200961
- Credit card fraud detection dataset providers
- Data Mining course resources and guidance
If you find this project useful, please consider giving it a ⭐!