not-so-lucky / Bengaluru-Housing-Prediction

End-to-end machine learning regression model for predicting housing prices in Bengaluru, with Heroku deployment.

Bengaluru Housing Prediction:

Table of Contents

• Demo
• Overview
• Motivation
• Installation
• Deployement on Heroku
• Directory Tree
• Future scope of project

Demo

Link: https://bengaluru-housing-prediction.herokuapp.com/

Overview

This is a Flask web app which predicts housing prices in the Bengaluru city.

Motivation

What to do when you are at home due to this pandemic situation? I started to learn Machine Learning model to get most out of it. I came to know mathematics behind all supervised models. Finally it is important to work on application (real world application) to actually make a difference.

Installation

The Code is written in Python 3.6.10. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. To install the required packages and libraries, run this command in the project directory after cloning the repository:

pip install -r requirements.txt

Deployement on Heroku

Login or signup in order to create virtual app. You can either connect your github profile or download heroku-cli to manually deploy this project.

Our next step would be to follow the instruction given on Heroku Documentation to deploy a web app.

Directory Tree

├── Dataset
├── templates
│   ├── index.html
├── Procfile
├── README.md
├── app.py
├── BengaluruHousing.ipynb
├── RidgeModel.pkl
├── requirements.txt

Technologies Used

Future Scope

• Use multiple Algorithms
• Optimize Flask app.py
• Front-End