Thomas Tai | Findigs+: A personalized apartment recommendation and prediction service

 
This project aims to design and develop a new listing service for Findigs.com, a rental technology startup based out of New York City. A combination of machine learning algorithms and techniques is used to implement a state-of-the-art hybrid-based recommendation system. Using data from past user applications, the model can determine a new renter’s chance of success for a given apartment. The baseline accuracy achieved is 75% using a random forest model. This recommendation service will increase transparency for both tenants and landlords, improving the quality of applications on the platform.