A personalized apartment recommendation and prediction service
Applying for an apartment rental involves a lot of uncertainty in addition to a steep application fee. With Findigs+, you can know your chance of getting approved before submitting an application.
Project Video
Abstract
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.
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