Popular Music Data Analysis

Authors- K. Raguvaran, Professor Dr. S. Prasanna

Abstract--This paper presents a comprehensive analysis of popular music using data-driven methodologies to uncover patterns, trends, and insights in musical preferences over time. By leveraging publicly available datasets from streaming platforms, music charts, and social media, the study explores key attributes such as genre distribution, lyrical content, tempo, key, mood, and artist popularity. Using Python-based tools and libraries such as Pandas, NumPy, Matplotlib, and machine learning algorithms, the research identifies correlations between musical features and chart success. Future work may include integrating audio signal processing and real-time streaming data for more dynamic and predictive modeling.

DOI: /10.61463/ijset.vol.13.issue2.441