Analyzing Ed Sheeran's Musical Characteristics and Popularity Using Spotify Data
Year: 2025
Type: article
Abstract: In this article, we will be using a sample data set of songs to find correlations between users and songs so that a new song will be recommended to them. We will analyze Ed Sheeran's songs' musical characteristics and popularity using Spotify data. By analyzing audio features such as dance ability, energy, acousticness, instrumentalness, and release date, the research identifies patterns in his music, investigates the correlation between musical features and popularity, and examines how his style has evolved across albums. By leveraging libraries such as NumPy, Pandas, Seaborn, Plotly, and Scikit-learn, and applying statistical analysis, visualization techniques, and machine learning, K-Nearest Neighbors (KNN). We aim to extract insights from his music metrics, identify trends, and determine the most "average" Ed Sheeran song based. The results provide insights into what makes a song popular, how featured artists impact track success, and how Ed Sheeran’s music has changed over time.
Authors: V. Sri Harshitha, R. Sanjana, C. Harini, Syeda Hifsa Naaz
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6930659806
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