Correlations Between Song Popularity and Their Audio Features Using Machine Learning

Author

Rong Chen, The Graduate Center, City University of New YorkFollow

Date of Degree

2-2025

Document Type

Capstone Project

Degree Name

M.S.

Program

Data Analysis & Visualization

Advisor

Kevin Ferguson

Subject Categories

Other Computer Engineering

Keywords

Machine Learning, Classical Machine Learning, Music Data, Prediction Analysis, Data Visualization

Abstract

This project is an interactive visual project that explores the relationship between audio features and song popularity on Spotify using machine learning techniques. Through the collection of nearly half a million songs and implementation of seven different machine learning models, including Linear Regression, Random Forest, Decision Trees, and Gradient Boosting, I investigated how audio characteristics correlate with a song's popularity ranking. The project utilized MongoDB for data storage, Spotipy for API integration, and Streamlit with Plotly for visualization. This work provides insights into the practical challenges of large-scale music analysis and the relationship between technical audio characteristics and commercial success, while highlighting areas for future research with more comprehensive data access and enterprise-level deployment solutions. Stable link of the project source code:https://github.com/rongchengit/SongPopularityPredictorML

Recommended Citation

Chen, Rong, "Correlations Between Song Popularity and Their Audio Features Using Machine Learning" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/6182