Description:

Hong Kong, one of the cities with the most expensive property prices in the world, makes it difficult for students to find affordable accommodation. Moreover, students have to search for homes by visiting different platforms and realtors, which is time-consuming and complicated.

The project, Home and Roommate Finder, aims to simplify home searching for students by providing an application that matches homes to users that fit the users’ preferences and requirements. The listings also includes pricing adequacy information through price prediction modeling to better inform students with the listing, preventing misinformation and fraudulent activity. The application additionally offers a roommate finding feature for students to find compatible roommates for apartment sharing.

Based on the housing data available publicly, the application utilizes machine learning, clustering techniques, price prediction models, and recommendation algorithm to provide personalized housing and roommate recommendations.