66 feature visualize clusters method

[idanmoradarthas]

Summary

Closes #66

Adds cluster visualization utilities to the unsupervised module so users can quickly evaluate clustering results on a 2D scatter plot (with optional centroids and dimensionality reduction).

What’s included

• plot_clusters (matplotlib) – static 2D scatter plot of clustering results
• plot_clusters_plotly – interactive Plotly version with hover info
• Automatic dimensionality reduction for data with >2 features:
  • PCA (default)
  • t-SNE
  • UMAP (optional extra: pip install data-science-utils[umap])
• Optional cluster centroid overlay
• Support for existing axes / figures
• Full documentation updates (README.md, docs/source/unsupervised.rst, SKILL.md, CHANGELOG.md)
• Installation notes for the new [umap] extra
• Comprehensive unit tests + baseline images for visual regression

Motivation (from #66)

Unsupervised models (KMeans, DBSCAN, etc.) produce cluster labels, but interpreting them usually requires boilerplate plotting code. These helpers reduce that friction and integrate visualization directly into the modeling workflow.

Example usage

from sklearn.cluster import KMeans
from ds_utils.unsupervised import plot_clusters, plot_clusters_plotly
from matplotlib import pyplot as plt

estimator = KMeans(n_clusters=8, random_state=42)
estimator.fit(X)

# Matplotlib
plot_clusters(X, estimator.labels_, estimator.cluster_centers_)
plt.show()

# Plotly (interactive)
fig = plot_clusters_plotly(X, estimator.labels_, estimator.cluster_centers_)
fig.show()