Loan Default Risk Predictor

This project provides a web-based tool for predicting the probability of loan default using three machine learning models: Logistic Regression, XGBoost, and a Feedforward Neural Network (FFNN). The goal is to assist in evaluating borrower risk based on financial and demographic information.

Try the Loan Default Risk Predictor on HuggingFace Spaces 🤗

Features

• Interactive Gradio Interface: Easily input borrower data through dropdowns and number fields.
• Multi-Model Predictions: View default probabilities from Logistic Regression, XGBoost, and FFNN models.
• Derived Financial Metrics: Automatically computes ratios like Affordability Ratio, Total Interest, Debt-to-Income Ratio, and Average Borrowed per Credit Line.
• Data Preprocessing: Includes standardization and Box-Cox transformation for skewed features.
• Model Interpretability: Outputs both probability scores and binary classification (Default / No Default) for each model.

Technologies Used

• Python (Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow/Keras)
• Gradio for the web interface
• Joblib / Pickle for model and transformer serialization

Getting Started Locally

Prerequisites

• Python 3.9+

Running the App

1. Clone the repository:

   git clone <repository-url>

2. Install the required packages:

   pip install -r requirements.txt

3. Navigate to the project directory:

   cd loan_default_prediction

4. Run the app:

   python app.py