Machine Learning Approaches for Predicting Song Popularity: A Case ‎Study in Music Analytics - Soundharya , Uppuluri Lakshmi; Gaddam, Sasidhar Reddy; Chaitanya, Gogineni Krishna; Roy , T. L. Deepika; Madhuri , Uppuluri Naga Lakshmi (International Journal of Basic and Applied Sciences, 2025)
Comprehending the aspects that impact song popularity has become crucial in the ever-changing music industry. This ‎study explores the field of music popularity predictive modeling using the cutting-edge algorithms XGBoost and ‎LightGBM. Predictive models were developed by the study using a large dataset that includes a variety of musical variables, ‎such as song duration, tempo, lyrical content, and release year. To improve the models’ predictive capacity, the study ‎approach includes extensive work. To provide a thorough assessment of the algorithms’ performance, the dataset is ‎divided into training and testing sets. Additionally, the effectiveness of XGBoost and LightGBM forecasting music ‎popularity is evaluated by a comparison analysis. To increase the prediction models’ accuracy, hyperparameter ‎optimization methods—specifically, Optuna—are used to fine-tune them. In addition, the study looks at feature ‎importance, illuminating elements of music that, in the eyes of each algorithm, greatly add to its appeal. Using a rigorous ‎cross-validation approach, the models are validated, and their generalization capabilities are shown. The performance ‎metrics, which provide a comprehensive picture of the models’ predicted accuracy, include mean absolute error, mean ‎squared error, median absolute error, and R-squared. By providing a comparative analysis of two well-known machine ‎learning methods for forecasting music popularity, this paper advances the rapidly developing field of music analytics. The ‎results offer significant perspectives for professionals in the field and data scientists who are looking for efficient ‎approaches to forecast music popularity across various genres‎.