unsupervised-learning

Unsupervised work gets much better when you compare clustering assumptions instead of treating one algorithm as truth. KMeans prefers spherical clusters, DBSCAN handles noise, and hierarchical clustering is useful when you want a multi-resolution view

I use dimensionality reduction both as a modeling tool and as an investigative lens. PCA is good for compression and signal inspection; t-SNE is useful when I need to see whether latent clusters or label separation exist at all. I never present those