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RealEstatePricePrediction

REAL ESTATE PRICE PREDICTION

This Project is a Real Estate Price Prediction using Machine Learning.

Table of Contents

Project Introduction

The Aim of the Project is to provide the best areas in the Bangalore to invest in for a national real estate developer, individual buyers looking for a place to develop a new apartment building or to purchase one. The main goal of project is to predict the efficient house pricing for real estate in Bangalore. We have developed a website that will show price prediction based on land size, land location, no of bedrooms and bathrooms in Bangalore.

Technologies Used

  • Python as Programming Language
  • Pandas for Data Cleaning
  • matplotlib for Data Visualization
  • sklearn for Model Building
  • HTML, CSS & JS for Frontend
  • Flask for Backend Server

Project Description

I have built a Real Estate Price Prediction using fundamentals of Data Scicence and Machine Learning like Feature Engineering, Data Cleaning, One Hot Encoding, Outlier Detection, Dimensionality Reduction, and Model Evaluation. I have implemented a Linear Regression Model to predict the Real Estate Price.

The Project has a Website Made using HTML, CSS and JavaScript as frontend and a Flask Server as backend where inputs are taken and predictions are made.

In the Website the User Must Enter Required Area Value in sqft.Select BHK and Bathroom values. Choose desired Location and Click on ESTIMATE button.

frontend

Getting Started

To Setup the Project, you need to install the following:

Pre-Requisites:

  1. Install Git Version Control [ https://git-scm.com/ ]

  2. Install Python Latest Version [ https://www.python.org/downloads/ ]

Installation

1. Create a Folder where you want to save the project

2. Create a Virtual Environment and Activate

Install Virtual Environment First

$  pip install virtualenv

Create Virtual Environment

$  virtualenv env

Activate Virtual Environment

source env/scripts/activate

3. Clone this project

git clone 

Then, Enter the project

cd Real-Estate-Price-Prediction

4. Install Requirements from 'requirements.txt'

pip install -r requirements.txt

5. Run the project

  • Start Flask Backend Server:
python server.py
  • Open the index.html Page in your Browser.

The dataset used in this project is from Kaggle which is the Bengaluru House Price Data.


License

MIT License

Contributing Guidlines

Please refer to CODE_OF_CONDUCT.md and CONTRIBUTING.md before contributing.

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