Tutorials

End-to-end walkthroughs on public datasets. Each tutorial loads and standardizes the data, trains a SOM, and reads the result through the visualization suite. Every figure on these pages is an actual output of the matching notebook in the notebooks/ directory, so you can reproduce them end to end.

Iris — Classification

The classic 4-feature, 3-class dataset. Train a SOM and read the classification map and component planes.

Iris — Classification

Wine — Classification

13 chemical features, 3 cultivars. A higher-dimensional classification example.

Wine — Classification

Boston Housing — Regression

Map a continuous target with mean, std, score, and rank maps.

Boston Housing — Regression

Energy Efficiency — Multi-target

Two regression targets (heating and cooling load) on one map.

Energy Efficiency — Multi-target Regression

Clustering — Synthetic blobs

Cluster the neurons and use the elbow, silhouette, and comparison diagnostics.

Clustering — Synthetic Blobs

Datasets at a glance

Tutorial	Task	Features	Visualizations highlighted
Iris — Classification	Classification	4	Classification map, component planes
Wine — Classification	Classification	13	Classification map, U-matrix
Boston Housing — Regression	Regression	13	Mean / std / score / rank maps
Energy Efficiency — Multi-target Regression	Regression (×2)	8	Per-target metric maps
Clustering — Synthetic Blobs	Clustering	4	Cluster map, elbow, silhouette, comparison