Thesis Abstract

Abstract
This thesis presents a comprehensive study on the analysis and prediction of music genre trends utilizing machine learning algorithms. The music industry is constantly evolving, with new genres emerging and existing genres shifting in popularity. Understanding these trends is crucial for music professionals, including artists, producers, and marketers, to make informed decisions and effectively target their audience. Machine learning, a subset of artificial intelligence, offers powerful tools for analyzing large volumes of music data and identifying patterns that can help predict genre trends. Chapter One provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The chapter sets the stage for the subsequent chapters by outlining the rationale and context for the study. Chapter Two consists of a detailed literature review that examines existing research and studies related to music genre analysis, trend prediction, and machine learning applications in the music industry. The review synthesizes key findings and identifies gaps in the literature that this research aims to address. Chapter Three describes the research methodology employed in this study, including data collection, preprocessing, feature selection, model development, and evaluation. The chapter outlines the steps taken to analyze music data and build predictive models using machine learning techniques. Chapter Four presents the findings of the study, including the analysis of music genre trends, the performance of the predictive models, and the identification of key features influencing genre popularity. The chapter discusses the implications of the findings and provides insights for music professionals looking to leverage machine learning for trend analysis. Chapter Five concludes the thesis by summarizing the key findings, discussing the contributions of the study, and outlining potential future research directions. The conclusion highlights the significance of using machine learning algorithms for analyzing music genre trends and emphasizes the importance of data-driven decision-making in the music industry. Overall, this thesis contributes to the growing body of research on music genre analysis and trend prediction by demonstrating the effectiveness of machine learning algorithms in understanding and forecasting genre trends. The findings provide valuable insights for music professionals seeking to stay ahead of evolving music landscapes and make strategic decisions based on data-driven analytics.

Thesis Overview