🏦 Smart Loan Approval Prediction System

This project is a complete Machine Learning pipeline for predicting whether a loan application will be approved based on applicant details.

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📌 Features

• ✅ Trained XGBoost model with full preprocessing
• 🚀 FastAPI backend for model predictions
• 💻 Streamlit frontend for user interaction
• 🧠 SHAP-based model explainability
• 🧰 Modular code with deployment readiness
• 📝 Well-structured and documented project

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🚀 Quick Start

1. Clone the Repo

git clone https://github.com/your-username/loan-approval-project.git
cd loan-approval-project

2. Install Requirements

pip install -r requirements.txt

3. Run FastAPI Backend

uvicorn api.main:app --reload

4. Run Streamlit Frontend

streamlit run frontend/app.py

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🔍 Model Explainability

We used SHAP to explain model predictions and identify key features affecting loan approvals.
Refer to: notebooks/explainability.ipynb for visualizations.

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🧪 Sample API Request

POST /predict

{
  "Gender": "Male",
  "Married": "Yes",
  "Dependents": "0",
  "Education": "Graduate",
  "Self_Employed": "No",
  "ApplicantIncome": 5000,
  "CoapplicantIncome": 0,
  "LoanAmount": 150,
  "Loan_Amount_Term": 360.0,
  "Credit_History": 1.0,
  "Property_Area": "Urban"
}

✅ Response

{
  "prediction": 1,
  "result": "Approved ✅"
}

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📁 Project Structure

loan_approval_project/
├── api/                  # FastAPI backend
├── frontend/             # Streamlit frontend
├── models/               # Trained model and preprocessor
├── data/                 # Cleaned dataset
├── notebooks/            # EDA, training, explainability
├── requirements.txt
├── README.md

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🧠 Tech Stack

• Python
• XGBoost
• scikit-learn
• FastAPI
• Streamlit
• SHAP

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📄 License

This project is licensed under the MIT License.

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🙌 Acknowledgements

• Kaggle Loan Prediction Dataset
• Open-source tools that made this possible 🎉