Development of a Music Recommendation System Using Machine Learning Techniques

Thesis Abstract

**Abstract
** This thesis presents the development of a music recommendation system utilizing machine learning techniques to enhance user experience in discovering music tailored to their preferences. The project aims to address the challenges faced by music streaming platforms in recommending relevant music to users based on their listening history and preferences. By leveraging machine learning algorithms and data analysis, the system will provide personalized music recommendations to users, ultimately improving user engagement and satisfaction. The study begins with an introduction that outlines the background of the project, problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The literature review in Chapter Two explores existing research on music recommendation systems, machine learning algorithms, and their applications in the music industry. This section provides a comprehensive overview of the current state-of-the-art techniques and technologies in the field. Chapter Three details the research methodology employed in developing the music recommendation system. This chapter includes the data collection process, data preprocessing techniques, feature selection methods, machine learning algorithms utilized, model training, evaluation metrics, and validation strategies. The methodology section provides a detailed insight into the steps taken to design and implement the recommendation system. Chapter Four presents a thorough discussion of the findings obtained from the experimentation and evaluation of the music recommendation system. This section highlights the performance metrics, accuracy of recommendations, user feedback, and comparison with existing systems. The discussion offers critical insights into the effectiveness and efficiency of the developed system in providing relevant music suggestions to users. In the final chapter, Chapter Five, the conclusions drawn from the study are summarized, and the implications of the research findings are discussed. The study concludes with key recommendations for future research and potential enhancements to the music recommendation system. Overall, this thesis contributes to the field of music recommendation systems by showcasing the feasibility and effectiveness of leveraging machine learning techniques to enhance user satisfaction and engagement in music discovery. Keywords Music Recommendation System, Machine Learning, Personalization, User Engagement, Data Analysis, Music Streaming Platforms.