House-Price-Prediction-Using-ML

Predicting House Prices in Pune using Random Forest

Quickstart (Demo-ready)

1. Create and activate a virtual environment (optional but recommended)

• Windows PowerShell
  • python -m venv venv
  • ./venv/Scripts/Activate.ps1

2. Install dependencies

pip install -r requirements.txt

3. Train or reuse the model

• The repo already includes house_price_model.pkl. If you want to retrain:

python train_model.py

4. Run the web app

python app_with_reverse_geocoding.py

Then open your browser to http://127.0.0.1:5000/.

Features

• Great UI with a price prediction form and interactive map
• Predicts price (in Lakhs) from inputs: Area, Area (sq.ft.), BHK, Bathrooms, Furnishing Status, Age of Property (years), and distances to School/Hospital/Metro
• Optional AI-powered location summaries on map clicks

Optional: Enable AI location summaries (Gemini)

The app can summarize clicked map locations using Gemini. This is optional.

1. Get an API key
   • Create a key and set it as an environment variable GEMINI_API_KEY.
2. Run the app with the environment variable set

$env:GEMINI_API_KEY="YOUR_KEY_HERE"; python app_with_reverse_geocoding.py

If no key is provided, the app will still run and show a simple fallback summary.

Notes

• The frontend calls relative endpoints (/predict and /summarize-location), so it works as long as the HTML is served by Flask (default behavior in app_with_reverse_geocoding.py).
• CORS is enabled; you can also host the HTML separately if needed.