Correlations Between Song Popularity and Their Audio Features Using Machine Learning
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
Other Computer Engineering, Machine Learning, Classical Machine Learning, Music Data, Prediction Analysis, Data Visualization
City University of New York - Graduate Center
Networked Digital Library of Theses and Dissertations
The Graduate School and University Center of The City University of New York