📚 Loan Approval Classifier with PyTorch

📖 Overview

This project predicts loan approval outcomes (Approved/Rejected) using a neural network built with PyTorch.

• 🧠 Neural Network with multiple hidden layers using LeakyReLU activation function and Dropout
• ⚖️ Binary Cross-Entropy (BCEWithLogitsLoss) for training
• 🚀 Adam optimizer for gradient updates
• 🔀 Mini-batch training with DataLoader
• 📊 Train/Validation/Test split for robust evaluation
• 📈 Live training & validation loss monitoring
• ✅ Sigmoid activation on the output to produce probabilities, with a threshold for Approved/Rejected decision
• 🎨 Interactive Gradio Interface for real-time prediction

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🖼️ Application Screenshot

Below is a preview of the Gradio Interface used for real-time classification:

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🧩 Libraries

• PyTorch – model, training, and inference
• pandas – data handling
• matplotlib – loss visualization
• pickle – saving/loading normalization params and trained model
• Gradio — interactive web interface for real-time model demos

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⚙️ Requirements

• Python 3.13+
• Recommended editor: VS Code

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📦 Installation

• Clone the repository

git clone https://github.com/hurkanugur/Loan-Approval-Classifier.git

• Navigate to the Loan-Approval-Classifier directory

cd Loan-Approval-Classifier

• Install dependencies

pip install -r requirements.txt

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🔧 Setup Python Environment in VS Code

1. View → Command Palette → Python: Create Environment
2. Choose Venv and your Python version
3. Select requirements.txt to install dependencies
4. Click OK

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📂 Project Structure

assets/
└── app_screenshot.png                # Screenshot of the application

data/
└── loan_data.csv                     # Raw dataset

model/
├── loan_approval_classifier.pth      # Trained model (after training)
├── feature_transformer.pkl           # Fitted preprocessing transformer
└── statistics.pkl                    # Preprocessing stats

src/
├── config.py                         # Paths, hyperparameters, split ratios
├── dataset.py                        # Data loading & preprocessing
├── device_manager.py                 # Selects and manages compute device
├── train.py                          # Training pipeline
├── inference.py                      # Inference pipeline
├── model.py                          # Neural network definition
└── visualize.py                      # Training/validation plots

main/
├── main_train.py                     # Entry point for training
└── main_inference.py                 # Entry point for inference

requirements.txt                      # Python dependencies

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📂 Model Architecture

Input → Linear(128) → LeakyReLU(0.01) → Dropout(0.2)
      → Linear(64)  → LeakyReLU(0.01) → Dropout(0.1)
      → Linear(32)  → LeakyReLU(0.01)
      → Linear(8)   → LeakyReLU(0.01)
      → Linear(1)   → Sigmoid(Output)

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📂 Train the Model

Navigate to the project directory:

cd Loan-Approval-Classifier

Run the training script:

python -m main.main_train

or

python3 -m main.main_train

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📂 Run Inference / Make Predictions

Navigate to the project directory:

cd Loan-Approval-Classifier

Run the app:

python -m main.main_inference

or

python3 -m main.main_inference