[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:colors = ['red','green','blue','purple', 'black']\nk_means = KMeans(n_clusters=5)\nk_means.fit(X)\nlabels = k_means.labels_\ncolors_1 = [colors[labels[i]] for i in range(len(labels))]\nfig = plt.figure(1, figsize=(5, 4))\nplt.clf()\nax = Axes3D(fig, rect=[0, 0, .95, 1], elev=50)#, azim=134)\nplt.cla()\nax.scatter(X[:, 3], X[:, 0], X[:, 2], c=colors_1)\nax.w_xaxis.set_ticklabels([])\nax.w_yaxis.set_ticklabels([])\nax.w_zaxis.set_ticklabels([])\nax.set_xlabel('Petal width')\nax.set_ylabel('Sepal length')\nax.set_zlabel('Petal length')\nplt.show()\nprint(k_means.inertia_)"}, {'role': 'assistant', 'content': 'result_visualization'}]