Beyond Beats: A Recipe to Song Popularity? A machine learning approach
Niklas Sebastian Jung Florian Mayer
Music popularity prediction has garnered significant attention in both
industry and academia, fuelled by the rise of data-driven algorithms and
streaming platforms like Spotify. This study aims to explore the predictive
power of various machine learning models in forecasting song popularity using a
dataset comprising 30,000 songs spanning different genres from 1957 to 2020.
Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive
Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse
song characteristics and their impact on popularity. Results: Ordinary Least
Squares (OLS) regression analysis reveals genre as the primary influencer of
popularity, with notable trends over time. MARS modelling highlights the
complex relationship between variables, particularly with features like
instrumentalness and duration. Random Forest and XGBoost models underscore the
importance of genre, especially EDM, in predicting popularity. Despite
variations in performance, Random Forest emerges as the most effective model,
improving prediction accuracy by 7.1% compared to average scores. Despite the
importance of genre, predicting song popularity remains challenging, as
observed variations in music-related features suggest complex interactions
between genre and other factors. Consequently, while certain characteristics
like loudness and song duration may impact popularity scores, accurately
predicting song success remains elusive.