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HomePriceXpert is a web application that estimates the price of homes in Bangalore based on the estate's various features. (PS: The dataset used for this project is outdated, so the estimated prices might not reflect current market trends.)

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atandritC/HomePriceXpert-Bangalore-Home-Price-Estimator

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HomePriceXpert - Bangalore Home Price Estimator

Estimate the price of a home in Bangalore

Welcome to HomePriceXpert, a web application that allows users to estimate the price of a home in Bangalore based on various features such as total square footage, number of bedrooms (BHK), number of bathrooms, and the property's location.

What This Project Does

HomePriceXpert leverages machine learning to provide accurate home price estimates for properties in Bangalore. Users can quickly get an estimated price in Lakhs by entering key property details. This tool is ideal for home buyers, real estate agents, and anyone interested in the Bangalore housing market.

Demonstration

HomePriceXpert Demo

Installation and Usage Instructions (For End-Users)

To use the HomePriceXpert application, visit the following Streamlit site where it is hosted: HomePriceXpert on Streamlit

Installation and Usage Instructions (For Developers)

If you would like to run the application locally or contribute to its development, follow these steps:

Prerequisites

Ensure you have Python 3.x installed on your machine. You can download it from python.org.

Clone the Repository

git clone https://github.com/yourusername/HomePriceXpert-Bangalore-Home-Price-Estimator.git
cd HomePriceXpert-Bangalore-Home-Price-Estimator

Install Dependencies

Install the necessary Python packages using the requirements.txt file:

pip install -r requirements.txt

Run the Application

Start the Streamlit application:

streamlit run web.py

File Descriptions

  • bangalore_home_prices_model.pickle: The pre-trained model used for price estimation.
  • columns.json: JSON file containing the locations used in the model.
  • real_estate_price_prediction.ipynb: Jupyter notebook with the code for training the model.
  • requirements.txt: List of Python dependencies required for the project.
  • web.py: Main script for running the Streamlit web application.

Contributor Expectations

If you would like to contribute to HomePriceXpert, please follow these guidelines:

  1. Fork the repository and create your feature branch (git checkout -b feature/AmazingFeature).
  2. Commit your changes (git commit -m 'Add some AmazingFeature').
  3. Push to the branch (git push origin feature/AmazingFeature).
  4. Open a pull request.

Before contributing, ensure your code adheres to the existing style and includes tests where appropriate. Contributions that improve the application's functionality, performance, or usability are highly appreciated.

Thank you for using HomePriceXpert!

About

HomePriceXpert is a web application that estimates the price of homes in Bangalore based on the estate's various features. (PS: The dataset used for this project is outdated, so the estimated prices might not reflect current market trends.)

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