The fraud-detection-system is a powerful tool designed to help you detect fraud in real-time. It uses advanced machine learning techniques to spot unusual patterns in data through ensemble models. With this software, you can analyze streaming data efficiently and understand model predictions better thanks to explainable AI using SHAP. You can easily run this application on your system using FastAPI and Docker.

Follow these steps to get started with the fraud detection system:

To get the latest version of the fraud-detection-system, please visit the following link:

Download Here

Before downloading, ensure your system meets these requirements:

• Operating System: Windows 10 or later, macOS, or a recent version of Linux.
• Memory: At least 4 GB of RAM.
• Storage: Minimum 500 MB of free disk space.
• Dependencies: Docker, FastAPI, and Python 3.x installed.

1. Visit the Docker website.
2. Download and install Docker Desktop for your operating system.
3. Follow the on-screen instructions to complete the installation.

Return to this link to download the latest version:

Download Here

1. Choose the appropriate file for your operating system.
2. Start the download.

Once you have Docker and the application downloaded, follow these steps to run it:

1. Open your command line interface (Command Prompt for Windows, Terminal for macOS and Linux).

2. Navigate to the folder where you downloaded the application.

3. Use the following command to start the Docker container:

   docker run -p 8000:8000 <your_docker_image_name>

4. You should see output indicating that the application is running.

To access the fraud detection system:

1. Open your web browser.
2. Type in http://localhost:8000 and hit Enter.
3. You will see the application interface where you can upload data and start analyzing it.

1. In the web interface, look for the upload section.
2. Select the data file you want to analyze. Ensure the data format matches expected inputs (CSV format is recommended).
3. Click the upload button to start the analysis.

After analyzing the data, the application will provide results in a clear format. You will see alerts for potential fraud cases, along with relevant details. Use this information to guide your decision-making.

The fraud-detection-system comes equipped with several features:

• Real-Time Analysis: Process and analyze data streams as they come in.
• Ensemble ML Models: Combine multiple machine learning models to improve accuracy.
• Explainable AI: Understand how predictions are made using SHAP values.
• User-Friendly Interface: Navigate easily through the web app to upload and analyze data.

If you encounter issues or have questions while using the fraud-detection-system, please join our community:

• GitHub Issues: Report any problems you face or ask questions directly on the repository.
• Discussion Forum: Engage with other users to share tips and best practices.

This project is licensed under the MIT License. For more details, please see the LICENSE file in the repository.

• FastAPI Documentation
• Docker Documentation
• SHAP Documentation