https://github.com/camille-maslin/securecard-ai
🛡️ SecureCard-AI: A high-performance credit card fraud detection system implemented in a Jupyter Notebook, achieving 99.97% accuracy.
https://github.com/camille-maslin/securecard-ai
classification credit-card-fraud-detection data-analysis data-science fraud-detection jupyter-notebook machine-learning matplotlib numpy pandas python scikit-learn
Last synced: 4 months ago
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🛡️ SecureCard-AI: A high-performance credit card fraud detection system implemented in a Jupyter Notebook, achieving 99.97% accuracy.
- Host: GitHub
- URL: https://github.com/camille-maslin/securecard-ai
- Owner: Camille-Maslin
- License: mit
- Created: 2025-01-02T13:32:00.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-02T21:20:52.000Z (over 1 year ago)
- Last Synced: 2025-04-03T01:51:10.349Z (about 1 year ago)
- Topics: classification, credit-card-fraud-detection, data-analysis, data-science, fraud-detection, jupyter-notebook, machine-learning, matplotlib, numpy, pandas, python, scikit-learn
- Language: HTML
- Homepage:
- Size: 5.52 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SecureCard-AI: Credit Card Fraud Detection System 🛡️
## Project Overview
**Author:** Camille Maslin
**Contact:**
- [LinkedIn](https://www.linkedin.com/in/camille-maslin/)
- [GitHub](https://github.com/camille-maslin)
- [Email](mailto:camillemaslin@gmail.com)
- [Portfolio](https://camille-maslin.github.io/Portfolio/)
**Description:**
This project implements a high-performance credit card fraud detection system using advanced machine learning techniques. The model achieves **99.97% accuracy** on real transaction data.
## Dataset Information 📊
**Source:** [Kaggle - Credit Card Fraud Detection Dataset 2023](https://www.kaggle.com/datasets/nelgiriyewithana/credit-card-fraud-detection-dataset-2023)
**Size:** 57,000+ transactions
**Features:**
- Transaction amount
- Time of transaction
- 28 anonymized features (V1-V28)
- Target: Binary classification (Fraud/Non-Fraud)
**Data Quality:**
- No missing values
- Preprocessed and anonymized for privacy
- Standardized numerical features
- Reflects real-world transaction patterns
---
## Key Features
### 📊 Data Analysis
- Comprehensive data exploration
- Advanced feature engineering
- Robust data quality checks
### 📈 Visualizations
- Interactive correlation matrices
- Distribution analysis
- Pattern recognition plots
### 🤖 Machine Learning Model
- **99.97% accuracy rate**
- Only **18-19 errors** per 57,000 transactions
- SMOTE implementation for class balancing
### 📉 Performance Metrics
- Cross-validation scores: [0.9996 - 0.9997]
- Balanced precision and recall
- Minimal false positives/negatives
---
## Technical Stack
- 🐍 Python 3.12
- 📝 Scikit-learn
- 📊 Pandas & NumPy
- 🔄 Matplotlib & Seaborn
- 🔄 SMOTE for imbalance handling
---
## Installation 🔧
1. Clone the repository:
```bash
$ git clone https://github.com/camille-maslin/SecureCard-AI.git
$ cd SecureCard-AI
```
2. Create a virtual environment and activate it:
```bash
$ python3 -m venv venv
$ source venv/bin/activate # Linux/MacOS
$ .\venv\Scripts\activate # Windows
```
3. Install dependencies:
```bash
$ pip install -r requirements.txt
```
---
## Usage
1. Run the Jupyter Notebook:
```bash
$ jupyter notebook
```
2. Open `SecureCard-AI.ipynb` in your Jupyter environment.
3. Follow the instructions and run each cell to:
- Load data
- Analyze and preprocess the dataset
- Train the fraud detection model
- Evaluate performance and visualize results
---
## License 💼
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
## Contributions 🛠️
Contributions are welcome! Please submit a pull request or open an issue for suggestions or bug reports.
---
## Acknowledgments
- Kaggle for the dataset.
- Open-source libraries and contributors for tools used.