On Using Artificial Intelligence to Predict Music Playlist Success

The emergence of digital music platforms has fundamentally transformed the way we engage with and organize music. As playlist creation has gained widespread popularity, there is an increasing desire among music aficionados and industry experts to comprehend the factors that drive playlist success. This paper presents a machine learning-based approach designed to predict the success of music playlists. By analyzing various musical characteristics of songs, our model achieves an impressive accuracy of 89.6% in predicting playlist success. Notably, it exhibits a remarkable 92.0% accuracy in forecasting the success of popular playlists, while also effectively identifying unpopular playlists with an accuracy of 89.4%. These findings provide invaluable insights into playlist creation, ultimately enhancing the overall music-listening experience. By harnessing the power of machine learning, our proposed approach unlocks new prospects for optimizing playlist design strategies and delivering personalized music recommendations. This has significant ramifications for music enthusiasts and industry professionals seeking to elevate playlist creation and enrich the music consumption experience.