- Size of the House
- Number of Rooms in the house
- Garden present or not
- Orientation of house (House in which part of Geography)
- FastAPI
- Python
- Streamlit
- Create a virtual environment with python3
python3 -m venv House Price Prediction
- Activate the virtual environment:
cd House Price Prediction source /bin/activate
- Install dependencies
pip install -r requirements.txt
Please read the following guidelines for the Streamlit Setup: https://docs.streamlit.io/library/get-started/installation
- Navigate to the
/webApp
directory of application - Run streamlit application as:
streamlit run frontend.py
FastAPI Please read the following guidelines for the FastAPI Setup: https://fastapi.tiangolo.com/tutorial/
- Run Unicorn Server
uvicorn api.app:app --reload
The web app has been deployed on Streamlit Cloud. You can go ahead and check it out on the following link: https://share.streamlit.io/jacer7/paris_house_price_prediction/webapp/webApp/frontend.py
The Model has been exposed to API and deployed as Model As Service (MAS) for the user who wants to check at:
https://predict-price-house-paris.herokuapp.com/docs
Happy to receive feedback !!