Machine Learning

When Data Feels the Music.

This project demonstrates how clustering algorithms can power intelligent recommendation systems. Using Spotify’s API data from The Rolling Stones discography as a case study, I explored how audio features and popularity metrics can be used to group songs with similar characteristics. Through exploratory data analysis, feature engineering, and unsupervised learning, the notebook showcases how platforms like Spotify identify and recommend “songs you might also like.”