Machine Learning, coursework, 2025
Spotify Music Data Analysis
Exploratory analysis and ML-based recommendations on Spotify track and artist data for a decision support system.
The problem
Producers and curators need to know which audio features relate to popularity and how to group and recommend tracks.
What it does
- Merged Kaggle tracks and artists datasets on artist ID.
- EDA on popularity, audio features, explicit content and trends since 2000.
- KMeans clustering to group songs into stylistic clusters.
- KNN recommender driven by danceability, energy, release year and artist followers.
Built with
- Python
- Pandas
- scikit-learn
- Matplotlib
- Seaborn
From the repository
Screenshots and charts as they appear in the project's code and notebooks.
