Credit Score Classifier

• Web App where you can enter customers parameters and get credit scoring result

1. Machine Learning

   • Implementation of XGBoost
   • Model training and evaluation
   • Classification of high and low risk customers

2. FastAPI Backend

   • RESTful API development with FastAPI
   • Request/response handling
   • Model serving endpoints

3. PostgreSQL Database

   • Database setup and configuration
   • CRUD operations implementation
   • Data persistence for ML models
   • Query optimization

4. Containerization with Docker

   • Dockerfile creation
   • Container orchestration
   • Multi-container applications with docker-compose
   • Environment configuration

5. CI/CD with GitHub Actions [IN Progress]

   • Automated testing pipeline
   • Continuous integration workflow
   • Automated deployment
   • Code quality checks

The project demonstrates a production-ready ML system with automated testing, containerized deployment, and scalable database operations.

Project Setup and Running Instructions

1. Python: Ensure you have Python 3.x installed. You can download it from python.org.

2. Virtual Environment: It's recommended to use a virtual environment to manage dependencies. You can create one using venv or virtualenv.

3. Docker (Optional): If your project uses Docker, ensure Docker is installed and running on your machine. You can download it from docker.com.

Setup Instructions

Build project using Docker container

For pulling use this command

docker pull https://hub.docker.com/repository/docker/metaphysicist/credit-score-classifier/general

For running use this command

docker run -it -p 8000:8000 metaphysicist/credit-score-classifier

Build project using Git locally

1. Clone the Repository

   git clone https://github.com/yourusername/yourproject.git
   cd yourproject

2. Create and Activate a Virtual Environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Dependencies

   pip install -r requirements.txt

Running the Project

1. Run the Application

   If using FastAPI with Uvicorn, you can start the server with:

   uvicorn app.api.endpoints.prediction:router --reload

   This will start the server on http://127.0.0.1:8000.

2. Access the API

   Open your browser or use a tool like curl or Postman to interact with the API at http://127.0.0.1:8000.

Docker Instructions (Optional)

1. Build the Docker Image

   docker build -t yourproject .

2. Run the Docker Container

   docker run -p 8000:8000 yourproject

Additional Information

• Database Setup: If your project uses a database, ensure it is set up and running. Update the database connection settings in your configuration files as needed.
• Environment Variables: Set any necessary environment variables, such as API keys or database URLs, before running the application.

Troubleshooting

• If you encounter issues, check the logs for error messages.
• Ensure all dependencies are installed and up-to-date.
• Verify that your virtual environment is activated when running commands.