[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': "Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:colors = ['red','green','blue','purple', 'black']\ncolors_1 = [colors[labels[i]] for i in range(len(labels))]\nfig = plt.figure(1, figsize=(5, 4))\nplt.clf()\nax = Axes3D(fig, elev=50)\nplt.cla()#clear axes\nax.scatter(X[:, 3], X[:, 0], X[:, 2], c=colors_1)# Plotting just three columns, because we can't plot 4!\nax.w_xaxis.set_ticklabels([]) # Removes markings along the axes\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': "['data_exploration']"}]