Certificate of Completion
THIS ACKNOWLEDGES THAT
HAS COMPLETED THE SPRING 2022 DATA SCIENCE BOOT CAMP
Roman Holowinsky, PhD
JUNE 08, 2022
Stefanie Wang, Hongyi Chen, Tyler Ellis, Sahinde Dogruer
Our objective in this project was to predict rental prices in Indianapolis, IN to help prospective tenants determine if a rental listing is fairly priced based on its features and to help landlords set fair prices for their units. We scraped data from www.apartments.com, cleaned the data, and looked at four models: CAT boost, gradient boost, linear regression, and random forest regression. All models shared the key features of the apartment: number of bedrooms, bathrooms, square footage, and neighborhood. CAT boost was the most effective in terms of lowest MSE and can predict if a rental listing is fair within 10% accuracy.