Emerging Scholars

Song popularity is an influential subject within the modern music streaming industry. It determines which artists can gain media attraction, gather loyal fans, and ultimately succeed. Analyzing song popularity with ML algorithms contributes to demystifying success within the music industry. Two datasets, datasets 1 and 2, collected from the Spotify Web API contain audio information on respectively 2000 songs and 240,057 songs. Ordinary Least Squares Linear Regression (OLS LR) and Neural Network (NN) algorithms were used on each dataset to predict song popularity. The most complex NN structure used in this study contains three hidden layers, achieving the best regression performances on both datasets; however, it was superior to other models by a small margin. Overall, models trained with dataset 2 achieved superior results, particularly in the R^2 metrics, but were unimpressive due to low regression metrics.