# macOS / Linux
source venv/bin/activate

# Windows
venv\Scripts\activate


cd /path/to/HousePrice
source venv/bin/activate      # Windows: venv\Scripts\activate
streamlit run app.py


On the very first run, the app trains the Random Forest model on all 6 city datasets. This takes about 30–60 seconds. The trained model is saved as 

 — every subsequent launch loads from this file instantly.

To force a retrain (e.g. after changing the pipeline), delete 

HousePrice/
├── app.py                          # Streamlit UI
├── model_pipeline.py               # ML pipeline (load, preprocess, train, predict)
├── housepriceprediction.py         # Original Colab notebook (reference only)
├── dataenricherforhousepriceprediction.py  # Original data enricher (reference only)
├── requirements.txt                # Python dependencies
├── house_price_model.pkl           # Saved model (auto-generated on first run)
└── HousePricePredictionDataset/
    ├── Bangalore_updated.csv
    ├── Chennai_updated.csv
    ├── Delhi_updated.csv
    ├── Hyderabad_updated.csv
    ├── Kolkata_updated.csv
    └── Mumbai_updated.csv
