README.md

metadata

title: Loan Prediction System
emoji: 🏦
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.28.0
app_file: scripts/app.py
pinned: false
license: mit
tags:
  - machine-learning
  - deep-learning
  - loan-prediction
  - pytorch
  - streamlit
  - finance
  - classification
datasets:
  - lending-club
models:
  - pytorch

🏦 Loan Prediction System

A comprehensive machine learning system for predicting loan approval decisions using deep neural networks. This project implements an end-to-end ML pipeline with exploratory data analysis, feature engineering, model training, and deployment capabilities.

📊 Project Overview

This project uses the LendingClub dataset to build a robust loan prediction model that helps financial institutions make data-driven lending decisions. The system achieves 70.1% accuracy with 86.4% precision using a deep neural network architecture.

Key Features

• Advanced EDA: Comprehensive exploratory data analysis with feature engineering
• Deep Learning Model: Multi-layer neural network with dropout regularization
• Production Ready: Streamlit web application for real-time predictions
• Robust Pipeline: End-to-end ML pipeline with data preprocessing and model training
• Performance Monitoring: Detailed metrics and visualization tools

🎯 Performance Metrics

Metric	Score
Accuracy	70.1%
Precision	86.4%
Recall	74.5%
F1-Score	80.0%
AUC-ROC	69.0%

🏗️ Architecture

Model Architecture

• Input Layer: 9 features (after feature selection)
• Hidden Layers:
  • Layer 1: 128 neurons (ReLU, Dropout 0.3)
  • Layer 2: 64 neurons (ReLU, Dropout 0.3)
  • Layer 3: 32 neurons (ReLU, Dropout 0.2)
  • Layer 4: 16 neurons (ReLU, Dropout 0.1)
• Output Layer: 1 neuron (Sigmoid activation)

Project Structure

loan_prediction/
├── README.md                 # Main project documentation
├── requirements.txt          # Python dependencies
├── src/                      # Source code
│   ├── model.py             # Neural network architecture
│   ├── train.py             # Training pipeline
│   └── inference.py         # Inference and prediction
├── scripts/                  # Utility scripts
│   └── app.py               # Streamlit web application
├── notebooks/               # Jupyter notebooks
│   └── EDA.ipynb           # Exploratory data analysis
├── docs/                    # Documentation
│   ├── EDA_README.md       # EDA decisions and methodology
│   └── MODEL_ARCHITECTURE.md # Model design details
├── data/                    # Data files
│   ├── lending_club_loan_two.csv
│   ├── lending_club_info.csv
│   └── processed/          # Processed data files
├── bin/                     # Model checkpoints
│   └── best_checkpoint.pth
└── __pycache__/            # Python cache files

🚀 Quick Start

Prerequisites

• Python 3.8+
• PyTorch 1.12+
• Streamlit 1.28+

Installation

1. Clone the repository

   git clone <repository-url>
   cd loan_prediction
   

2. Install dependencies

   pip install -r requirements.txt
   

3. Run the web application

   streamlit run scripts/app.py
   

Training the Model

python src/train.py

Making Predictions

# Interactive single prediction
python src/inference.py --single

# Batch prediction
python src/inference.py --batch input.csv output.csv

# Sample prediction
python src/inference.py --sample

📋 Usage Examples

Web Application

Launch the Streamlit app for an interactive loan prediction interface:

streamlit run scripts/app.py

Command Line Inference

# Single prediction with interactive input
python src/inference.py --single

# Batch processing
python src/inference.py --batch data/test_file.csv results/predictions.csv

Training Custom Model

python src/train.py --epochs 200 --batch_size 1536 --learning_rate 0.012

📈 Data & Features

Dataset

• Source: LendingClub loan data
• Size: ~400,000 loan records
• Features: 23 original features reduced to 9 after feature selection

Selected Features

1. loan_amnt: Loan amount requested
2. int_rate: Interest rate on the loan
3. installment: Monthly payment amount
4. grade: LC assigned loan grade
5. emp_length: Employment length in years
6. annual_inc: Annual income
7. dti: Debt-to-income ratio
8. open_acc: Number of open credit accounts
9. pub_rec: Number of derogatory public records

📚 Documentation

• EDA Analysis & Decisions - Detailed explanation of exploratory data analysis and feature engineering decisions
• Model Architecture - Deep dive into neural network design and training methodology

🔧 Configuration

Training Configuration

{
  "learning_rate": 0.012,
  "batch_size": 1536,
  "num_epochs": 200,
  "early_stopping_patience": 30,
  "weight_decay": 0.0001,
  "validation_split": 0.2
}

📊 Model Performance

Training History

• Best Epoch: Achieved at epoch 112
• Training Loss: Converged to ~0.32
• Validation Loss: Stabilized at ~0.34
• Early Stopping: Activated after 30 epochs without improvement

Class Distribution

• Default Rate: ~22% (imbalanced dataset)
• Handling: Weighted loss function and class balancing techniques

🤝 Contributing

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

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• LendingClub for providing the dataset
• PyTorch team for the deep learning framework
• Streamlit for the web application framework

📞 Contact

For questions or support, please open an issue in the repository.

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Note: This model is for educational and research purposes. Always consult with financial experts before making actual lending decisions.