Project Abstract

Abstract
The rapid growth of digital music consumption has led to an overwhelming amount of music available to users, creating a need for effective music recommendation systems to help users discover new music that aligns with their preferences. In response to this need, this research project aims to develop a Music Recommendation System utilizing Machine Learning Algorithms. The system will leverage the power of machine learning to analyze user preferences and behavior, providing personalized music recommendations to enhance the user experience. Chapter One Introduction 1.1 Introduction 1.2 Background of Study 1.3 Problem Statement 1.4 Objective of Study 1.5 Limitation of Study 1.6 Scope of Study 1.7 Significance of Study 1.8 Structure of the Research 1.9 Definition of Terms Chapter Two Literature Review 2.1 Evolution of Music Recommendation Systems 2.2 Machine Learning in Music Recommendation 2.3 Collaborative Filtering Techniques 2.4 Content-Based Filtering Techniques 2.5 Hybrid Recommendation Systems 2.6 Evaluation Metrics for Recommendation Systems 2.7 Challenges in Music Recommendation Systems 2.8 Current Trends in Music Recommendation Chapter Three Research Methodology 3.1 Research Design 3.2 Data Collection 3.3 Data Preprocessing 3.4 Feature Selection 3.5 Machine Learning Algorithms Selection 3.6 Model Training and Evaluation 3.7 Cross-Validation Techniques 3.8 Performance Metrics Evaluation Chapter Four Discussion of Findings 4.1 Data Analysis Results 4.2 Evaluation of Recommendation System Performance 4.3 Comparison of Machine Learning Algorithms 4.4 User Feedback and Satisfaction 4.5 System Scalability and Efficiency 4.6 Addressing Cold Start Problem 4.7 Future Enhancements and Recommendations Chapter Five Conclusion and Summary In conclusion, the development of a Music Recommendation System utilizing Machine Learning Algorithms holds great promise in enhancing user satisfaction and engagement in the digital music landscape. By leveraging the power of machine learning, personalized music recommendations can be provided to users, improving their music discovery experience. The findings of this research contribute to the advancement of music recommendation systems and provide valuable insights for future research in this domain.

Project Overview