TVS Credit E.P.I.C Competition - Loan Defaulter Prediction

Overview

This project secured the second runner-up position nationwide in the TVS Credit E.P.I.C competition. The objective was to predict loan defaulters, utilizing machine learning techniques. The project consists of a Machine Learning (ML) backend built with Flask and a Node.js frontend.

Project Structure

The project submission contains two main components:

1. ML Backend:

   • The models folder holds serialized machine learning models used for prediction.
   • api.py: This Python Flask application serves as the backend for making predictions. It provides two endpoints:
     • /predict/personal: Predicts loan defaulters for personal loans using a combination of machine learning models.
     • /predict/two_wheeler_loan: Predicts loan defaulters for two-wheeler loans using a specific machine learning model.
   • The backend utilizes models including Gradient Boosting Classifier, Isolation Forest, K-Nearest Neighbors, Logistic Regression, One-Class SVM, Random Forest, and XGBoost Classifier.

2. Node Frontend:

   • The frontend application, developed using Node.js, allows users to interact with the prediction system.
   • It communicates with the Flask backend to request predictions for loan defaulters.
   • The frontend provides a user-friendly interface for inputting data and viewing predictions.

Model Evaluation

• Personal Loan Prediction:
  • Classification Report:

    precision    recall  f1-score   support
    0.0       0.98      1.00      0.99     35058
    1.0       0.21      0.01      0.03       801
    accuracy                           0.98     35859
    macro avg       0.59      0.51      0.51     35859
    weighted avg    0.96      0.98      0.97     35859
    

• Two Wheeler Loan Prediction:
  • Test Accuracy: 93.48%
  • 

Usage

ML Backend:

1. Navigate to the ml_backend folder.
2. Ensure Python and the required dependencies listed in requirements.txt are installed.
3. Run the Flask API using python api.py.
4. The API will start running locally, and it will be accessible at http://localhost:5000.

Node Frontend:

1. Navigate to the node_frontend folder.
2. Ensure Node.js and the required dependencies listed in package.json are installed.
3. Start the frontend application using npm start.
4. Open your web browser and go to http://localhost:3000 to access the frontend.

Contributors

• Rohit Shinde (rohitshinde333)
• Vivek Patil

Lisence

Proprietary License

Copyright (c) 2024 Rohit Vishwas Shinde

All rights reserved. This work may not be reproduced, modified, distributed, or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without the prior written permission of the copyright owner.

Acknowledgements

We would like to express our gratitude to TVS Credit for organizing the E.P.I.C competition and providing us with the opportunity to showcase our skills. Additionally, we would like to thank the entire team for their hard work and dedication throughout the project.