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Certificate of Completion
THIS ACKNOWLEDGES THAT
HAS COMPLETED THE MAY-SUMMER 2024 DATA SCIENCE BOOT CAMP
Claire Merriman
Roman Holowinsky, PhD
JUNE 10, 2024
DIRECTOR
DATE
TEAM
Doggy Doggy What Now?: Using Machine Learning to Predict Animal Shelter Intakes and Outcomes
John Harden, Claire Merriman, Angela Kubena, Jun Lau, Robert Young
The Humane Society states that over 3 million dogs enter animal shelters around the United States each year, and around 2 million dogs are adopted each year. Shelters are understandably busy, noisy, and fast-moving places where many challenges present themselves. The ability to correctly anticipate how the coming days, weeks, and months will go would allow shelter managers to allocate resources more effectively. Our group sought to leverage machine learning tools and 100,000s of observations over the last decade to predict animal shelter intakes, outcomes, and adoptions. We developed time series models which include macro-level features and can predict the number of intakes and outcomes per day, week, and month with over 90% accuracy. Additionally, we achieved over 70% accuracy exploring how random forest can be used to get a paw up on predicting adoption rates with shelter-level features.