About The Project

Real Estate Price Prediction: Bangalore House Dataset

Overview

This project aims to predict house prices in Bangalore using the Bangalore House Dataset. The dataset includes information about various features of houses such as size, number of bedrooms (BHK), and number of bathrooms. The goal is to build a machine learning model that can accurately predict the price of a house based on these features.

Dataset

The dataset used for this project includes the following columns:

  • Size: The size of the house in square feet.
  • BHK: Number of bedrooms (BHK - Bedroom, Hall, Kitchen).
  • Bathroom: Number of bathrooms.

Target Variable

  • Price: The price of the house in Indian Rupees (INR).

Project Structure

This repository contains the following directories:

  • Real Estate Price Prediction: Complete project code and files.
  • Model: Contains saved machine learning models.
  • HTML, CSS, and JavaScript files for the client-side web interface.
  • Server: Python files for the Flask server.
  • Template: It contains the html files of the project that are used in the website.
  • Static: It contains the css and js file and images folder.
  • Requirements: It contains the versions and libraries that are used in this project.

Usage

  1. Setting Up the Environment:
    • Ensure you have Python installed on your machine.
    • Install the required Python packages listed in requirements.txt at our GitHub Page
      • pip install -r requirements.txt
  2. Running the Flask Server:
    • Navigate to the server directory:
      • cd server
    • Run the Flask server:
      • python server.py
    • The server will start running locally at http://localhost.
  3. Accessing the Web Interface:
    • Open your web browser and go to http://localhost.
    • You can now interact with the web interface to predict house prices.

Models

This project includes machine learning models for predicting house prices.