🏡 House Price Prediction

A full-stack machine learning project for predicting house prices.
This project integrates Flask (backend API) and Next.js/Bun (frontend) to provide real-time house price predictions based on user inputs.

🎯 Objective

The goal of this project is to demonstrate model deployment using a modern workflow:

• Train regression models on a house dataset
• Build a Flask REST API for model inference
• Develop a Next.js/Bun frontend for interaction
• Integrate backend & frontend for real-time predictions

🚀 Features

• Machine Learning Models: Linear Regression & Random Forest
• Prediction Pipeline: Feature engineering + trained models
• Flask API: Serves predictions via REST endpoints
• Next.js/Bun Frontend: User-friendly client for input & results
• Full Deployment: Backend + frontend + models + dataset included

📂 Project Structure

house-price-prediction/
│── backend/              # Flask API code
│   ├── app.py            # Main API script
│   ├── models/           # Trained ML models
│   ├── requirements.txt  # Python dependencies
│   └── dataset/          # Cleaned dataset
│
│── frontend/             # Next.js + Bun app
│   ├── pages/            # Frontend pages
│   ├── components/       # Reusable UI components
│   └── package.json      # Dependencies
│
│── README.md             # Project documentation

⚙️ Installation & Setup

1️⃣ Backend (Flask API)

cd backend
pip install -r requirements.txt
python app.py

API will run on: http://127.0.0.1:5000/

2️⃣ Frontend (Next.js with Bun)

cd frontend
bun install
bun dev

Frontend will run on: http://localhost:3000/

🔗 API Usage

Send a POST request with house features to the Flask API:

POST /predict
Content-Type: application/json

{
  "area": 2000,
  "bedrooms": 3,
  "bathrooms": 2,
  "location": "Downtown"
}

Response:

{
  "predicted_price": 250000
}

📦 Deployment & Access

• Full code and dataset are available on GitHub:
  House Price Prediction Repository
• Both backend & frontend can be run locally by following the steps above
• Includes: source code, trained models, dataset, and documentation

📝 Notes

• Code is clean and modular following best practices
• Each commit in GitHub has a clear message for traceability
• Project README explains setup, usage, and licensing

📜 License

This project is licensed under the MIT License – free to use and modify.