Automated Classification of Musical Genres: A Machine Learning Approach with Random Forest and Feature Engineering

A key element of contemporary music recommendation and content organizing systems is the classification of musical genres. This research delves into the realm of automated genre classification, employing advanced machine learning techniques and feature engineering. It explores the significance of data preprocessing, outlier removal, and the application of a Random Forest Classifier in achieving accurate genre pre-dictions. The research highlights the vital role of well-engineered audio features and offers an in-depth analysis of model performance through standard evaluation metrics. The study also shows how the model may be used to forecast the genre of newly discovered, unlabeled audio tracks, adding a useful component. This useful application raises the research’s profile and establishes it as a significant contribution to the fields of recommendation systems and music analysis. The findings and methodology presented in this research not only deepen our understanding of music genre classification but also offer insights for future developments in music-related applications.