Fraud Detection System

Overview

This project is a Django-based fraud detection system that leverages real-time data processing to identify potentially fraudulent transactions. The system integrates with Fluvio for real-time data streaming, and a machine learning model is used to predict fraud.

Contain

• Features
• Demo Video
• Technologies Used
• Installation
• Usage
• Project Structure
• Fluvio Integration
• Data Visualization
• Contributing
• License
• Acknowledgments

Demo Video

Fruad.dectation.mp4

Features

• Real-time Fraud Detection: Integrates with Fluvio to process and analyze transaction data in real-time.
• Django Web Interface: Provides a web interface for creating and viewing transactions.
• Fraud Alerts: Flags and notifies about potentially fraudulent transactions.
• Data Visualization: Visualizes transaction statistics and fraud detection results.

Technologies Used

• Python
• Django
• Fluvio
• PostgreSQL
• Rust compiler (for building Fluvio)
• Other Python packages: pandas, joblib, matplotlib, psycopg2, etc.

Installation

Setting Up Your Environment

1. Clone the Repository:

   git clone https://github.com/rupacesigdel/Fraud_Detection_.git
   cd fraud-detection-system

2. Create a Virtual Environment:

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

3. Upgrade pip (if needed):

   pip install --upgrade pip

4. Install Dependencies:

   pip install -r requirements.txt

5. Install Fluvio:

   • If you encounter issues with Fluvio installation, ensure you have Rust installed. Follow the steps below if needed:

     • Install Rust using rustup:

       curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

     • Restart your terminal and ensure Rust is in your PATH.

     • Try installing Fluvio again:

       pip install fluvio

6. Database Setup:

   • Configure your database settings in settings.py.

   • Run migrations to set up the database schema:

     python manage.py migrate

7. Load Initial Data (Optional):

   If you have initial data, you can load it using:

   python manage.py loaddata initial_data.json

Project Structure

• fraud_detection_project/
• │
• ├── fraud_detection/
• │ ├── init.py
• │ ├── admin.py
• │ ├── apps.py
• │ ├── forms.py
• │ ├── models.py
• │ ├── tests.py
• │ ├── views.py
• │ ├── fraud_detection.py
• │ ├── templates/
• │ │ └── transactions/
• │ │ ├── create_transaction.html
• │ │ ├── transaction_detail.html
• │ │ ├── transaction_list.html
• │ │ ├── statics_view.html
• │ │ └── home.html
• │ │ └── base.html
• │ ├── static/
• │ │ └── transactions/
• │ │ └── styles.css
• │ └── migrations/
• │ └── init.py
• │
• ├── fraud_detection_project/
• │ ├── init.py
• │ ├── asgi.py
• │ ├── settings.py
• │ ├── urls.py
• │ ├── wsgi.py
• │
• ├── manage.py
• ├── model.pkl
• ├── README.md
• └── requirements.txt

Usage

1. Start the Development Server:

   python manage.py runserver

2. Access the Web Application:

   Open your web browser and navigate to http://127.0.0.1:8000/ to access the application.

3. Create Transactions:

   • Use the "Create Transaction" page to add new transactions.
   • Transactions will be processed in real-time, and potentially fraudulent transactions will be flagged.

4. View Transactions:

   • Navigate to the "Transactions" page to view a list of all transactions.
   • Click on a transaction to view its details and fraud status.

Fluvio Integration

1. Set Up Fluvio:

   • Ensure Fluvio is properly set up and running. Refer to the Fluvio documentation for setup instructions.

2. Start Fluvio Streams:

   • Use the Fluvio CLI to create and manage streams. Ensure your Django application is correctly configured to interact with Fluvio.

Data Visualization

1. Run Data Visualization Script:

   To generate a pie chart or other visualizations, use the provided scripts:

   python visualize_data.py

Contributing

1. Fork the Repository:

   • Create a fork of the repository on GitHub.

2. Create a Pull Request:

   • Open a pull request from your forked repository to the main repository.

License

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

Acknowledgments

• Fluvio Team: For providing the powerful real-time streaming platform that enables our fraud detection system to operate efficiently.
• Django Community: For the robust web framework that serves as the foundation of our application.
• OpenAI: For their research and technologies that inspired parts of the fraud detection algorithms and overall approach.
• Stack Overflow and GitHub: For community support and code examples that helped resolve technical challenges during development.
• Rust Community: For their tools and libraries that made it possible to build and integrate Fluvio into our project.

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