Introduction
Unsupervised learning tutorials usually ship as disconnected scripts: K-Means in one folder, PCA in another, hierarchical clustering in a third. That is fine for learning algorithms in isolation. It fails when you need a single service that trains segmentations, serves predictions, exposes lesson demos over HTTP, and runs in Docker without eight separate virtual environments.
This project unifies Days 85–92 (plus stub health routes for the 93–98 review window) into one stack:
A learning layer that exposes each lesson’s logic through structured HTTP routes.
A composition layer that selects canonical implementations when multiple days solve the same problem differently.
A product layer that runs customer segmentation, taxonomy building, and PCA workflows end-to-end.
A React dashboard behind nginx for operators and learners.
You will walk away knowing how clustering theory, optimal-K selection, hierarchical taxonomies, and production PCA become modules inside one engine—not eight apps stitched together.
System Overview
Runtime shape is deliberately small:
