tar-ive/credit-risk

Credit Default Prediction is a web application that predicts whether a customer will default on their credit based on various financial factors. The application uses an XGBoost model to make predictions and presents the results in an easy-to-use web interface.

Credit Default Prediction

Live Demo

Overview

Credit Default Prediction is a web application that predicts whether a customer will default on their credit based on various financial factors. The application uses an XGBoost model to make predictions and presents the results in an easy-to-use web interface.

Features

• Predicts credit default using multiple financial features
• Displays the prediction (Default or No Default) without showing the probability
• Provides an explanation of the key factors influencing the prediction
• Visualizes input feature values and the relationship between age and debt ratio

Technologies Used

• Backend: Python, Flask, Gunicorn
• Frontend: HTML, CSS, JavaScript, Chart.js
• Machine Learning: Scikit-learn (XGBoost)
• Deployment: Docker, Google App Engine

Prerequisites

Ensure you have the following installed on your machine:

• Python 3.9
• Docker
• Google Cloud SDK (for deployment)

Local Setup

Follow these steps to set up the application locally:

1. Clone the Repository

   git clone https://github.com/Tar-ive/credit-risk.git
   cd credit-default-prediction

2. Create and Activate Virtual Environment

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

3. Install Dependencies

   pip install -r requirements.txt

4. Run the Flask Application

   gunicorn --bind :8080 model:app

   Access the application at http://localhost:8080.

5. Docker Setup

   Build Docker Image:

   docker build -t credit-prediction-app .

   Run Docker Container:

   docker run -p 8080:8080 credit-prediction-app

   Access the application at http://localhost:8080.

Deployment to Google App Engine

1. Authenticate with GCP

   gcloud auth login

2. Set Your GCP Project

   gcloud config set project YOUR_PROJECT_ID

3. Deploy the Application

   gcloud app deploy

4. Access the Deployed Application Navigate to https://YOUR_PROJECT_ID.uc.r.appspot.com.

Usage

1. Navigate to the Web Application
2. Input Financial Data
3. Get Prediction
4. View Explanation and Charts

File Structure

• app.yaml: Configuration file for Google App Engine
• Dockerfile: Docker configuration file
• requirements.txt: Python dependencies
• model.py: Main application code
• index.html: Frontend code

Contributing

Feel free to open issues or submit pull requests for improvements and bug fixes.

License

This project is licensed under the MIT License.

Acknowledgements

Inspired by various credit risk prediction projects and tutorials from LEARNERA.