Predicting Dog Adoptions Using a Machine Learning Model

Publication Date

Spring 4-22-2026

Presentation Length

Poster/Gallery presentation

College

College of Sciences & Mathematics

Department

Math and Computer Science, Department of

Student Level

Undergraduate

Faculty Mentor

Dr. Christina Davis

Presentation Type

Poster

Summary

Approximately 5.7 million dogs and cats are admitted to animal shelters each year. Of that number, dogs comprise nearly half. Some dogs spend extended periods of time within these shelters. This decreases their chances of being adopted. Dogs that are not adopted from animal shelters are at risk of euthanasia. This project uses a LightGBM model to analyze dog adoption to determine how quickly a dog will be adopted. The features that are used to determine this are the age, breed, and size of the dog. This analysis will show which of these features is most important to determining this.Some of this may be logical, such as puppies being adopted quickly,while other factors may be less intuitive. This could be a useful tool for animal shelters to determine which dogs will be adopted quickly and which will not. This could allow them to make more informed decisions on how to utilize their resources.

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