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from sklearn.decomposition import PCA from mpl_toolkits.mplot3d import Axes3D # Выполняем PCA для уменьшения размерности до 3D pca_3d = PCA(n_components=3) X_reduced_3d = pca_3d.fit_transform(X_scaled) # Визуализируем результаты в 3D fig = plt.figure(figsize=(10, 7)) ax = fig.add_subplot(111, projection='3d') # Визуализация каждого кластера for i in range(k): ax.scatter(X_reduced_3d[y_kmeans == i, 0], X_reduced_3d[y_kmeans == i, 1], X_reduced_3d[y_kmeans == i, 2], s=50, label=f'Cluster {i}') ax.set_title('3D PCA Visualization of Clients') ax.set_xlabel('PCA1') ax.set_ylabel('PCA2') ax.set_zlabel('PCA3') ax.legend() plt.show()
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