Jacer7/Paris_House_Price_Prediction

Paris House Prediction using Machine learning Model

House Price Prediction in Paris

Using Linear Regression Model

Created a web app for a user to input the parameter such as

• Size of the House
• Number of Rooms in the house
• Garden present or not
• Orientation of house (House in which part of Geography)

Streamlit Web App and Heroku for model deployment

This would be the model as a service web app for a Data Science Project.

Used technologies

1. FastAPI
2. Python
3. Streamlit

Steps for Installation and Setup to run this project after clone !!

1. Install Dependencies
2. Run API
3. Run Airflow
4. Run Frontend

Install Dependencies

1. Create a virtual environment with python3

   python3 -m venv House Price Prediction

2. Activate the virtual environment:

   cd House Price Prediction
   source /bin/activate

3. Install dependencies

   pip install -r requirements.txt

Run Frontend

Please read the following guidelines for the Streamlit Setup: https://docs.streamlit.io/library/get-started/installation

1. Navigate to the /webApp directory of application
2. Run streamlit application as:

   streamlit run frontend.py

Run Fast API Server

FastAPI Please read the following guidelines for the FastAPI Setup: https://fastapi.tiangolo.com/tutorial/

1. Run Unicorn Server

uvicorn api.app:app --reload

Streamlit Cloud Deployment

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

Heroku Deployment

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 !!