Credit Card Fraud Detection System

A machine learning-based web application for detecting fraudulent credit card transactions in real-time.

Features

• Real-time fraud detection for single transactions
• Batch processing of transactions via CSV upload
• Transaction history tracking
• User-friendly interface with visual feedback
• Responsive design for all devices
• Secure data handling

Tech Stack

• Frontend: HTML, CSS (Tailwind CSS), JavaScript
• Backend: Python (Flask)
• Machine Learning: scikit-learn
• Database: SQLite
• Other Libraries: pandas, numpy

Team Members

1. Suraj Srivasatv - Backend and ML Developer

   • GitHub: @surajsri23
   • LinkedIn: Suraj Srivastav

2. Sunny Kumar Yadav - Frontend Developer

   • GitHub: @sunnyadavji
   • LinkedIn: Sunny Kumar Yadav

3. Shamshad Ali - Research Lead

   • GitHub: @shamshad45481
   • LinkedIn: Shamshad Ali

4. Suhani Singh - Documentation lead

   • GitHub: @Suhanisingh13
   • LinkedIn: Suhani Singh

Setup Instructions

1. Clone the repository:

git clone https://github.com/surajsri23/SwiftSecure.git
cd credit-card-fraud-detection

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the application:

python app.py

5. Open your browser and navigate to:

http://localhost:5000

Deployment

The application is deployed on Render.com. To deploy your own instance:

1. Create a Render account at https://render.com

2. Connect your GitHub repository:

   • Click "New +" and select "Web Service"
   • Connect your GitHub repository
   • Select the repository
   • Configure the service:
     • Name: swiftsecure
     • Environment: Python
     • Build Command: pip install -r requirements.txt
     • Start Command: gunicorn app:app
     • Python Version: 3.10.0

3. Add environment variables:

   • FLASK_ENV=production
   • PYTHON_VERSION=3.10.0

4. Click "Create Web Service"

The application will be automatically deployed and available at:

https://swiftsecure.onrender.com

Project Structure

credit-card-fraud-detection/
├── app.py                 # Main Flask application
├── model.py              # Machine learning model
├── requirements.txt      # Python dependencies
├── render.yaml          # Render deployment configuration
├── static/              # Static files
│   ├── css/            # CSS styles
│   ├── js/             # JavaScript files
│   └── images/         # Image assets
└── templates/          # HTML templates
    └── index.html      # Main page template

Usage

1. Single Transaction Check:

   • Fill in the transaction details
   • Click "Analyze" to check for fraud
   • View the result with color-coded feedback

2. Batch Upload:

   • Prepare a CSV file with transaction data
   • Click "Choose File" to select the CSV
   • Click "Upload & Scan" to process all transactions
   • View the results summary

3. Transaction History:

   • View all processed transactions
   • Filter by status (Safe/Fraudulent)
   • Clear history when needed

Security Features

• Input validation
• File type verification
• Secure data handling
• Error handling
• User confirmation for critical actions

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Credit Card Fraud Detection Dataset from Kaggle
• Team members for their contributions
• Open-source community for tools and libraries