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.

Real Estate Project Repositories

• This repositorie only consist of the frontend assets for the full resources visit this repositorie

Responsiveness Screenshot

Showcasing the responsiveness of the website

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 contain the html files of the project that are used in the website.
• static: It contain the css and js file and images folder.
• requirements: it contain 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:

     pip install -r requirements.txt

2. Running the Flask Server:

   • Navigate to the server directory:

     cd real_estate_price_prediction_project

   • Run the Flask server:

     python main.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.

Contact Information

For any inquiries or feedback regarding this project, please feel free to contact:

Arman Qureshi

• Email: [email protected]
• LinkedIn: Connect with me on LinkedIn
• Github: Follow me on Github

Ansh Arora

• Email: [email protected]
• LinkedIn: Connect with me on LinkedIn
• Kaggle: Follow me on Kaggle

Contributers

Arman Qureshi

Developed a "Website UI" for accessing the "real_estate_price_prediction" model and can be viewed by anyone. he deployed the model on the website, users can try prediction on it.

Ansh Arora

Created a prediction model using "Bangalore house dataset" performed data cleaning, EDA and others functions to build a accurate prediction model. used a python Flask Server to deploy model gateway, so it can be used in Website UI.

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Disclaimer: This project is for educational purposes and should not be used for real-world applications without proper validation and testing. The developers are not liable for any misuse or misinterpretation of the results.