[{'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:target = df_train[\'Demanda_uni_equil\'].tolist()\n\ndef label_plot(title, x, y):\n    plt.title(title)\n    plt.xlabel(x)\n    plt.ylabel(y)\n\nplt.hist(target, bins=200, color=\'blue\')\nlabel_plot(\'Distribution of target values\', \'Demanda_uni_equil\', \'Count\')\nplt.show()\n\nprint("Looks like we have some pretty big outliers, let\'s zoom in and try again")\n\nprint(\'Data with target values under 50: \' + str(round(len(df_train.loc[df_train[\'Demanda_uni_equil\'] <= 50]) / 5000, 2)) + \'%\')\n\nplt.hist(target, bins=50, color=\'blue\', range=(0, 50))\nlabel_plot(\'Distribution of target values under 50\', \'Demanda_uni_equil\', \'Count\')\nplt.show()\n\''}, {'role': 'assistant', 'content': "['load_data']"}]