Credit-Risk-Predictor-using-ML

A web app built with React and Flask to predict loan approval using machine learning. Evaluates user inputs (income, loan amount, CIBIL score) and provides predictions, probability scores, and feature importance.

Table of Contents

1. Project Overview
2. Features
3. Technologies Used
4. Installation
5. Usage
6. API Endpoints
7. Frontend Integration
8. Contributing

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Project Overview

This project is a Machine Learning based Loan Approval Prediction System. It predicts whether a loan application should be approved or rejected based on various features such as income, loan amount, CIBIL score, and asset values. The system consists of:

• A Flask backend that serves the machine learning model and provides a REST API.
• A React frontend that allows users to input data and view the prediction results.

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Features

• Loan Approval Prediction: Predicts whether a loan application will be approved or rejected.
• Probability Score: Provides the probability of loan approval.
• Feature Importance: Displays the importance of each feature in the prediction.
• User-Friendly Interface: A React-based frontend for easy interaction.
• REST API: A Flask-based backend that serves predictions via API.

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Technologies Used

Backend

• Python: Primary programming language.
• Flask: Web framework for building the REST API.
• XGBoost: Machine learning model for loan approval prediction.
• Pandas: Data manipulation and preprocessing.
• Joblib: For saving and loading the trained model.

Frontend

• React: JavaScript library for building the user interface.
• Axios: For making HTTP requests to the backend.
• Recharts: For visualizing feature importance and probability.

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Installation

Prerequisites

• Python 3.8+
• Node.js 16+
• Flask
• React

Backend Setup

1. Clone the repository:

   git clone https://github.com/NixonTX/Credit-Risk-Predictor-using-ML.git
   cd Credit-Risk-Predictor-using-ML/backend

2. Create 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 Flask app:

   python app.py

Frontend Setup

1. Navigate to the frontend directory:

   cd ../frontend

2. Install dependencies:

   npm install

3. Start the React app:

   npm start

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Usage

1. Backend:

   • The Flask backend runs on http://127.0.0.1:5000.
   • Use the /predict endpoint to get loan approval predictions.

2. Frontend:

   • The React frontend runs on http://localhost:3000.
   • Fill in the form with the required details and click Predict to see the results.

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API Endpoints

POST /predict

• Description: Predicts loan approval based on input data.
• Request Body:

  {
      "no_of_dependents": 2,
      "education": 1,
      "self_employed": 0,
      "income_annum": 9600000,
      "loan_amount": 29900000,
      "loan_term": 12,
      "cibil_score": 778,
      "residential_assets_value": 2400000,
      "commercial_assets_value": 17600000,
      "luxury_assets_value": 22700000,
      "bank_asset_value": 8000000
  }

• Response:

  {
      "prediction": "Approved",
      "probability": 0.99909866,
      "feature_importance": {
          "no_of_dependents": 0.012247798,
          "education": 0.013926751,
          "self_employed": 0.011063362,
          "income_annum": 0.023593476,
          "loan_amount": 0.025837155,
          "loan_term": 0.15287666,
          "cibil_score": 0.703851,
          "residential_assets_value": 0.016562847,
          "commercial_assets_value": 0.013460946,
          "luxury_assets_value": 0.008908062,
          "bank_asset_value": 0.017671902
      }
  }

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Frontend Integration

The React frontend interacts with the Flask backend to:

1. Collect user inputs via a form.
2. Send the data to the /predict endpoint.
3. Display the prediction, probability, and feature importance.

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Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/YourFeatureName).
3. Commit your changes (git commit -m 'Add some feature').
4. Push to the branch (git push origin feature/YourFeatureName).
5. Open a pull request.