Correlations Between Song Popularity and Their Audio Features Using Machine Learning

<p>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</p>

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Master

Discipline thesis:degree_discipline

Data Analysis & Visualization

Grantor

The Graduate School and University Center of The City University of New York

Year dc:date.available

2025

Author dc:creator

• Chen, Rong

Advisor dc:contributor.advisor

• Kevin Ferguson

• Other Computer Engineering
• Machine Learning
• Classical Machine Learning
• Music Data
• Prediction Analysis
• Data Visualization

Repository record dc:identifier

https://academicworks.cuny.edu/gc_etds/6182

OAI identifier oai:identifier

oai:academicworks.cuny.edu:gc_etds-7277