Song Longevity on Music Chart Prediction Using Machine Learning Models

A music chart is one way to measure the success and popularity of a song. One of the companies that presents music charts is Billboard Publication which serves as a critical reference point. However, many popular songs struggle to maintain longevity on the Billboard chart. This study focuses on predicting song longevity on the music charts, specifically the Billboard chart. The model incorporates characteristic data from previously charted popular songs on the Top 100 Billboard Chart and additional attributes from Spotify to ensure accurate predictions. The findings of this research will offer valuable insights to upcoming artists and producers by identifying the attributes they must focus on improving to enhance the popularity’s longevity of their music. Four machine learning models were utilized: Random Forest, Logistic Regression, Neural Network, and XGBoost. The tuned Random Forest model achieved an overall metric average of approximately 91.3%, followed by XGBoost with around 89.9%. These results demonstrate the effectiveness of decision tree models for this prediction task. Furthermore, artist-popularity, loudness, song-duration-ms, instrumentalness, and speechiness proved significant in this context.