Date of Graduation


Document Type


Degree Name

Bachelor of Science in Industrial Engineering

Degree Level



Industrial Engineering


Chimka, Justin

Committee Member/Reader

Liu, Xiao


Understanding music popularity and what drives it is important not only for artists but for other individuals who are financially tied to music sales including producers, writers, and record labels. Studies have been done to define how a song’s popularity can be measured, what attributes or features are drivers for popularity, and to what extent can a song’s popularity even be predicted. This paper takes two linear regression approaches to predicting the popularity of a Taylor Swift song on Spotify based on auditory features the Spotify API estimates and historic popularity of songs on Spotify. One model takes into consideration interacting predictors to divide the data into four different subsets. Another model uses backward elimination to generate one model that describes the whole dataset. Based on Bayesian Information Criteria, the collection of four models does a better job predicting a song’s popularity compared to the backward elimination model. Additionally, both models found a song’s acousticness and release year as the two most important predictors of popularity.


linear regression, statistics, music