A Python pipeline for segmenting financial assets using unsupervised learning models like K-Means and GMM. This project evaluates clustering configurations not only on performance (Silhouette Score) but also on their statistical stability across train, test, and validation sets using the Wasserstein distance.
python machine-learning unsupervised-learning clustering-evaluation time-series-segmentation financial-clustering model-stability
-
Updated
Jan 5, 2026 - Python