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Predicting song popularity based on Spotify's audio features: insights from the Indonesian streaming users

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  • Harriman Samuel Saragih

Abstract

Using regression and classification machine learning algorithms, this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia, and then evaluates the feature importance for prediction. The publicly accessible Kaggle data consists of 92,755 rows and 20 columns. Using multiple model comparisons for regression and classification, this study identifies Extra Trees Regressor and Random Forest Classifier as the two predictive approaches with the highest accuracy. This study contributes to the scientific literature on hit songs by examining the influence of audio features on a song's popularity using both classification and regression machine learning methods, with an emphasis on Indonesia based on consumer culture theory.

Suggested Citation

  • Harriman Samuel Saragih, 2023. "Predicting song popularity based on Spotify's audio features: insights from the Indonesian streaming users," Journal of Management Analytics, Taylor & Francis Journals, vol. 10(4), pages 693-709, October.
    • Handle: RePEc:taf:tjmaxx:v:10:y:2023:i:4:p:693-709
    DOI: 10.1080/23270012.2023.2239824
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