Machine Learning Approaches for Predicting Song Popularity: A Case Study in Music Analytics

Abstract

Comprehending the aspects that impact song popularity has become crucial in the always changing music industry. This study explores the field of music popularity predictive modeling using the cutting-edge algorithms XGBoost and LightGBM. Predictive models 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 in 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. Keywords — Music Popularity Prediction; Machine Learning; XGBoost; LightGBM; Predictive Modeling; Feature Engineering; Hyperparameter Optimization; Data Analytics; Comparative Analysis; Song Characteristics; Genre Classification; Ensemble Models; Cross-Validation; Optuna; Data Preprocessing; Feature Importance; Regression; Music Analytics.