💳 Credit Card Fraud Detection (CCFD)

A machine learning project for detecting fraudulent credit card transactions using data mining techniques.

📋 Project Overview

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.

🎯 Objectives

• 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

🛠️ Technologies Used

• 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

📁 Project Structure

CCFD/
├── DM_Project_(1).ipynb    # Main Jupyter notebook with analysis and models
├── DM.pdf                   # Project documentation/report
└── README.md                # Project documentation

🚀 Getting Started

Prerequisites

Make sure you have Python 3.x installed along with the following packages:

pip install pandas numpy scikit-learn matplotlib seaborn jupyter

Running the Project

1. Clone the repository:

   git clone https://github.com/AmmarAhmedl200961/CCFD.git
   cd CCFD

2. Launch Jupyter Notebook:

   jupyter notebook

3. Open DM_Project_(1).ipynb and run the cells sequentially

📊 Methodology

The project typically follows these data mining steps:

1. Data Exploration - Understanding the dataset structure and features
2. Data Preprocessing - Handling missing values, scaling, and encoding
3. Handling Imbalanced Data - Techniques like SMOTE, undersampling, or oversampling
4. Feature Engineering - Creating meaningful features for better predictions
5. Model Training - Training various classification algorithms
6. Model Evaluation - Using metrics like Precision, Recall, F1-Score, and AUC-ROC

📈 Evaluation Metrics

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

🤝 Contributing

Contributions are welcome! Feel free to:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/improvement)
3. Commit your changes (git commit -am 'Add new feature')
4. Push to the branch (git push origin feature/improvement)
5. Open a Pull Request

📝 License

This project is open source and available for educational purposes.

👤 Author

Ammar Ahmed

• GitHub: @AmmarAhmedl200961

⭐ Acknowledgments

• Credit card fraud detection dataset providers
• Data Mining course resources and guidance

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