🏠 House Price Prediction App (FastAPI + Streamlit)

This project is a full-stack Machine Learning application that predicts house prices using a trained LightGBM model. It includes a FastAPI backend for prediction and a Streamlit frontend for user interaction and visualization.

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🚀 Live Demo

• 🌐 Frontend (Streamlit): https://house-price-prediction-streamlit-4xb6.onrender.com
• ⚡ Backend API (FastAPI): https://house-price-prediction-fastapi-7kxt.onrender.com

⚠️ Important: Please run the FastAPI backend first before using the Streamlit app.

⚠️ Otherwise, the app will not return any responses.

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📊 Features

• 🏠 House price prediction using ML model
• 📈 Interactive Streamlit UI
• 📊 Feature Importance visualization
• 🧠 SHAP Explainability (optional)
• 🔌 FastAPI REST API
• ☁️ Deployed on Render

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🧠 Model Details

• Algorithm: LightGBM Regressor
• Features used:
  • OverallQual
  • GrLivArea
  • GarageCars
  • GarageArea
  • TotalBsmtSF
  • 1stFlrSF
  • FullBath
  • YearBuilt
  • YearRemodAdd
  • LotArea

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🏗️ Project Structure

House-Price-API/
│
├── api/
      └── main.py # FastAPI app
├── dashboard/
      └── app.py # Streamlit UI
├── data
      └── dataset.csv
├── model/
      └── train.py # Training script
      └── Predict_fun.py # Prediction logic
├── saved_model/
      └── model.pkl # Trained model
├── requirements.txt
└── README.md

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⚙️ Installation

1️⃣ Clone the repository

bash
git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

2️⃣ Create virtual environment

python -m venv venv
venv\Scripts\activate   # Windows

3️⃣ Install dependencies

pip install -r requirements.txt

▶️ Run Locally

Start FastAPI
uvicorn app.main:app --reload

👉 Open: http://127.0.0.1:8000/docs

Start Streamlit streamlit run streamlit_app.py 📡 API Usage

POST /predict
Request Body (JSON)
{
  "OverallQual": 7,
  "GrLivArea": 1500,
  "GarageCars": 2,
  "GarageArea": 500,
  "TotalBsmtSF": 800,
  "FirstFlrSF": 900,
  "FullBath": 2,
  "YearBuilt": 2005,
  "YearRemodAdd": 2010,
  "LotArea": 8500
}
Response
{
  "predicted_price": 203925.23,
  "status": "success"
}

🧠 Explainability

📊 Feature Importance (LightGBM)
☁️ Deployment
Backend: FastAPI deployed on Render
Frontend: Streamlit deployed on Render

#💡 Future Improvements
🧠 SHAP values
📊 Interactive Plotly charts
🤖 Advanced SHAP visualization
📈 Model performance dashboard
🌍 Multi-dataset support

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👨‍💻 Author

Wai Phyo Ko

⭐ If you like this project

Give it a star ⭐ on GitHub!