xy make_moons(n_samples= noise= random_state= kmeans kmeans(n_clusters= random_state= kmeans fit(xy_pred kmeans predict(xplt scatter( [: ] [: ] =y_preds= cmap='paired'plt scatter(kmeans cluster_centers_[: ]kmeans cluster_centers_[: ] = marker='^' =range(kmeans n_clusters)linewidth= cmap='paired'plt xlabel("feature "plt ylabel("feature "print("cluster memberships:\ {}format(y_pred)out[ ]cluster memberships[ figure - using many -means clusters to cover the variation in complex dataset unsupervised learning and preprocessing