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
","abstract_html":"<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>","abstract_has_math":false,"creators":["Chen, Rong"],"institution":"The Graduate School and University Center of The City University of New York","degree_name":"Master of Science","degree_level":"Master","degree_discipline":"Data Analysis & Visualization","degree_department":null,"school":null,"contributors":[],"advisors":["Kevin Ferguson"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-02-01T08:00:00Z","date_published":"2025-02-01T08:00:00Z","updated_at":"2026-07-24T01:58:31Z","subjects":["Other Computer Engineering","Machine Learning","Classical Machine Learning","Music Data","Prediction Analysis","Data Visualization"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://academicworks.cuny.edu/gc_etds/6182","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kevin Ferguson"]},{"key":"dc:creator","label":"Author","values":["Chen, Rong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-08-01T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Data Analysis & Visualization"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The Graduate School and University Center of The City University of New York"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Other Computer Engineering","Machine Learning","Classical Machine Learning","Music Data","Prediction Analysis","Data Visualization"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://academicworks.cuny.edu/gc_etds/6182"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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
"]},{"key":"dc:title","label":"Title","values":["Correlations Between Song Popularity and Their Audio Features Using Machine Learning"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kevin Ferguson"],"dc:creator":["Chen, Rong"],"dc:date.available":["2025-08-01T07:00:00Z"],"dc:description.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
"],"dc:identifier":["https://academicworks.cuny.edu/gc_etds/6182"],"dc:subject":["Other Computer Engineering","Machine Learning","Classical Machine Learning","Music Data","Prediction Analysis","Data Visualization"],"dc:title":["Correlations Between Song Popularity and Their Audio Features Using Machine Learning"],"thesis:degree_discipline":["Data Analysis & Visualization"],"thesis:degree_level":["Master"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["The Graduate School and University Center of The City University of New York"]},"updated_at":"2026-07-24T01:58:31Z"}