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Certificate of Completion

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THIS ACKNOWLEDGES THAT

HAS COMPLETED THE SPRING 2022 DATA SCIENCE BOOT CAMP

Stefanie Wang

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Roman Holowinsky, PhD

JUNE 08, 2022

DIRECTOR

DATE

TEAM

IceBridge

Stefanie Wang, Hongyi Chen, Tyler Ellis, Sahinde Dogruer

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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.

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