When Data Feels the Music
Turning listener behavior into smart recommendations
About the project
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.
- PCA + K-Means
- Modeling approach
- 5+
- Insight views
- 0 black box
- Explainable output
Project Type
AI & Machine Learning
Data Engineering
Analytics & BI
Tech Stack / Toolbox
Python
Pandas
NumPy
scikit-learn
Matplotlib
Seaborn
Plotly
Reporting
SQL / ETL
How jordan_digital Delivered Value
jordan_digital partnered with the client to turn raw Spotify API data into a clear, decision-ready view of song similarity and clustering—demonstrating how modern recommendation systems can be designed and explained with confidence.
- Engineered a clean, scalable dataset from Spotify’s API to support repeatable modeling and reporting.
- Mapped feature signals (audio traits + popularity metrics) to the business question: “What content is meaningfully similar?”
- Developed a cohorting approach using PCA + K-Means to surface natural groupings and reduce noise.
- Visualized similarity patterns to make recommendations explainable—not a black box.
- Translated model findings into a personalization-ready narrative that stakeholders can act on.