[{'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:# Risk Analysis\n# Analyze the risk of a store; Risk(std) Vs Expected(mean)\n\n# .... countiue using store_data\nstore_average = store_data["Sales"].mean()\nstore_std     = store_data["Sales"].std()\n\n# Plot\nplt.scatter(store_average, store_std,alpha = 0.5,s =np.pi*20)\n\n# Get min & max mean and std of store sales\n# Remember that store_df["Sales"] has the average sales for a store\nstd_sales = rossmann_df.groupby(\'Store\')["Sales"].std()\n\nmin_average = store_df["Sales"].min()\nmax_average = store_df["Sales"].max()\nmin_std     = std_sales.min()\nmax_std     = std_sales.max()\n\n# Set the x and y limits of the plot\nplt.ylim([min_std, max_std])\nplt.xlim([min_average, max_average])\n\n# Set the plot axis titles\nplt.xlabel(\'Expected Sales\')\nplt.ylabel(\'Risk\')\n\n# Set label\nlabel, x, y = "Store {}".format(store_id), store_average, store_std\nplt.annotate(\n        label, \n        xy = (x, y), xytext = (50, 50),\n        textcoords = \'offset points\', ha = \'right\', va = \'bottom\',\n        arrowprops = dict(arrowstyle = \'-\', connectionstyle = \'arc3,rad=-0.3\'))\''}, {'role': 'assistant', 'content': "['result_visualization']"}]