Correlations Between Song Popularity and Their Audio Features Using Machine Learning
type: CreativeWork
description: 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: 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
author: Chen, Rong
keywords: Other Computer Engineering, Machine Learning, Classical Machine Learning, Music Data, Prediction Analysis, Data Visualization
date published: 2025-02-01T08:00:00Z
date modified: 2026-07-24T01:58:31Z
provider: City University of New York - Graduate Center
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The Graduate School and University Center of The City University of New York