House Price Predictor

🏠 A full-stack house price prediction app. The backend serves a trained ML model via a Django REST API, and the frontend delivers a fast, responsive UI for entering property details and viewing predictions.

✨ Highlights

• 🔄 End-to-end flow: form input → API → model inference → UI result
• 🚀 Django REST API with health, model info, prediction, and history endpoints
• 💎 React + Vite frontend with Tailwind UI components
• ⚡ Model artifacts loaded once and reused per request

🌐 Live Demo

https://house-price-predictor-live.vercel.app

🛠️ Tech Stack

• 🔧 Backend: Django, Django REST Framework, scikit-learn, pandas
• 🎨 Frontend: React, Vite, Tailwind CSS, axios
• 💾 Storage: SQLite (prediction history)

📁 Project Structure

backend/
  config/           # Django settings and URL routing
  predictions/      # API + ML service
  db.sqlite3
frontend/
  src/              # React app
  public/
train and test/
  house_data.csv
  Model Training And Testing.ipynb

🔌 API Endpoints

Base URL: http://127.0.0.1:8000/api

Method	Endpoint	Description
POST	/predict/	🎯 Predict house price
GET	/model-info/	📊 Model metadata (R2, RMSE, features)
GET	/history/?limit=	📜 Recent prediction history
GET	/health/	✅ Health check

🚀 Quick Start

1️⃣ Backend (Django)

cd backend
python -m venv env
.\env\Scripts\activate
pip install -r requirements.txt
python manage.py migrate
python manage.py runserver

2️⃣ Frontend (React + Vite)

cd frontend
npm install
npm run dev

🌐 Open the UI at: http://localhost:3000

⚙️ Environment Notes

• 🔗 The frontend expects the API at http://127.0.0.1:8000/api
• 🛡️ CORS is enabled for http://localhost:3000

🤖 Model Files

The ML service loads these from backend/predictions/ml_models:

• 📦 house_price_model.pkl
• 📏 scaler.pkl
• 🎯 feature_names.pkl
• 📋 model_metadata.pkl

💡 Development Tips

• 🎨 If the UI looks unstyled, make sure Tailwind is installed and npm run dev is restarted.
• 🐛 If API calls fail, verify the Django server is running and the endpoint is reachable.

📝 License

MIT ✨