Interactive machine learning guide

K-Means Visualizer

K-Means groups unlabeled points around centroids. The useful part is not just the final colors: it is seeing which centroid each point selects, how the centers move, and when the assignments stop changing.

Open the interactive visualizer →

How K-Means works

Each iteration assigns points to their nearest centroid and recomputes each centroid from its assigned points. The process repeats until convergence or the configured iteration limit.

The main visualizer runs the implemented model in your browser and exposes the stored state, predictions, and metrics instead of substituting fabricated demo numbers.

What you can inspect

initial centroids and subsequent centroid positions
point-to-centroid assignments
inertia as a clustering objective rather than accuracy
the actual number of recorded iterations

Use this guide with the live model

Start with the default educational dataset, predict what will happen, run the model, then inspect one calculation or parameter at a time. For supported CSV workflows, local files are parsed and processed in the browser rather than uploaded by the application.