Unsupervised Clustering for Portfolio Analysis

Summary

Development of an unsupervised clustering workflow for internal project portfolio analysis at Italdesign.

Highlights

• Clustering of hundreds of internal projects
• Dimensionality reduction (UMAP / PCA)
• Model explainability for cluster interpretation
• Delivered as internal analytics tool

Technologies

Python, scikit-learn, UMAP, SHAP, Pandas