Comprehensive House Search App

This project is a web application that allows users to filter and search for houses based on various criteria such as the number of bedrooms and bathrooms, square footage, price per square foot, percentage change in price, and neighborhood. The app provides an interactive and user-friendly interface where users can easily adjust the parameters and see the filtered results in real-time.

Technologies Used

• Python: The core programming language used to develop the application.
• Pandas: A powerful data manipulation and analysis library in Python, used to load and filter the housing dataset.
• Gradio: A Python library that allows you to quickly create user interfaces for machine learning models or data applications. In this project, Gradio is used to build the interactive web interface.
• Hugging Face Spaces: The platform where the web app is hosted, providing easy access and sharing of the application.

Features

• Multiple Filters: Users can filter houses based on the number of bedrooms, bathrooms, square footage, price, and neighborhood.
• Dynamic Filtering: The app dynamically updates the filtered results based on the input parameters provided by the user.
• Interactive Interface: The interface is easy to use, allowing users to explore different combinations of filters and see the corresponding results immediately.

Usage

You can access and use the web application via the following link: Rental Filter WebApp.

Simply adjust the filters according to your preferences, and the app will display the number of available houses that match your criteria, along with a detailed table of the filtered results.

Example

For example, if you are looking for houses in the "South Loop" neighborhood with more than 2 bedrooms, 2 bathrooms, a square footage around 1000 sqft, and a price per square foot under $5.861, you can easily set these parameters in the app, and it will show you the available options.

This app is designed to be flexible and accommodate various search criteria, making it a valuable tool for anyone in the real estate market.

Conclusion

This project demonstrates the effective use of Python and its libraries to build a data-driven web application. The app is not only functional but also provides a seamless user experience, making it a useful tool for filtering rental properties.

Feel free to explore the web app and see how it can assist you in your property search!