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Machine learning model in order to correctly predict the prices of Banglore houses based on different features.

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alessiococchieri/home-price-prediction

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home-price-prediction

The aim of the project was to develop a machine learning model in order to correctly predict the prices of Banglore houses based on:

  • sqare feet
  • number of bedrooms
  • number of bathrooms
  • region

Model

The model was developed by exploiting common python libraries such as Numpy and by using a Python Notebook. The final model has been then exported as a pickle file.

Server

The server side has been developed by using Flask. It alloed to develop the web application easily.

Client

A simple interface has been bulit using HTML, CSS and Javascript. It allows the user to test directly the app.

Deployment

The app has been deployed on Heroku. Heroku lets you deploy a Flask app online for free. This is a fantastic option to test dev environments, to make SEO tests in Google Search Console or to make a python a public app to showcase your work without paying for hosting and for a domain name.

Web Hosting

The app is hosted at the following link: https://flask-bhpp.herokuapp.com/

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Machine learning model in order to correctly predict the prices of Banglore houses based on different features.

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