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The model achieves **99.97% accuracy** on real transaction data.\n\n## Dataset Information 📊\n\n**Source:** [Kaggle - Credit Card Fraud Detection Dataset 2023](https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023)  \n**Size:** 57,000+ transactions  \n\n**Features:**\n- Transaction amount\n- Time of transaction\n- 28 anonymized features (V1-V28)\n- Target: Binary classification (Fraud/Non-Fraud)\n\n**Data Quality:**\n- No missing values\n- Preprocessed and anonymized for privacy\n- Standardized numerical features\n- Reflects real-world transaction patterns\n\n---\n\n## Key Features\n\n### 📊 Data Analysis\n- Comprehensive data exploration\n- Advanced feature engineering\n- Robust data quality checks\n\n### 📈 Visualizations\n- Interactive correlation matrices\n- Distribution analysis\n- Pattern recognition plots\n\n### 🤖 Machine Learning Model\n- **99.97% accuracy rate**\n- Only **18-19 errors** per 57,000 transactions\n- SMOTE implementation for class balancing\n\n### 📉 Performance Metrics\n- Cross-validation scores: [0.9996 - 0.9997]\n- Balanced precision and recall\n- Minimal false positives/negatives\n\n---\n\n## Technical Stack\n\n- 🐍 Python 3.12\n- 📝 Scikit-learn\n- 📊 Pandas \u0026 NumPy\n- 🔄 Matplotlib \u0026 Seaborn\n- 🔄 SMOTE for imbalance handling\n\n---\n\n## Installation 🔧\n\n1. Clone the repository:\n```bash\n$ git clone https://github.com/camille-maslin/SecureCard-AI.git\n$ cd SecureCard-AI\n```\n\n2. Create a virtual environment and activate it:\n```bash\n$ python3 -m venv venv\n$ source venv/bin/activate  # Linux/MacOS\n$ .\\venv\\Scripts\\activate  # Windows\n```\n\n3. Install dependencies:\n```bash\n$ pip install -r requirements.txt\n```\n\n---\n\n## Usage\n\n1. Run the Jupyter Notebook:\n```bash\n$ jupyter notebook\n```\n\n2. Open `SecureCard-AI.ipynb` in your Jupyter environment.\n\n3. Follow the instructions and run each cell to:\n   - Load data\n   - Analyze and preprocess the dataset\n   - Train the fraud detection model\n   - Evaluate performance and visualize results\n\n---\n\n## License 💼\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n\n## Contributions 🛠️\n\nContributions are welcome! Please submit a pull request or open an issue for suggestions or bug reports.\n\n---\n\n## Acknowledgments\n\n- Kaggle for the dataset.\n- Open-source libraries and contributors for tools used.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcamille-maslin%2Fsecurecard-ai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcamille-maslin%2Fsecurecard-ai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcamille-maslin%2Fsecurecard-ai/lists"}