[{'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:#We will devide the train dataset in seasons and working days vs non working days. The idea is to group the results by hour and observe if the ratio \n#mean standard deviation for each group is better or not for the overall group.\nX_workingday_1 = X_train[(X_train.workingday == 1) & (X_train.season == 1)]\nX_holiday_1    = X_train[(X_train.workingday == 0) & (X_train.season == 1)]\nX_total_1      = X_train[(X_train.season == 1)]\n\nX_workingday_2 = X_train[(X_train.workingday == 1) & (X_train.season == 2)]\nX_holiday_2    = X_train[(X_train.workingday == 0) & (X_train.season == 2)]\nX_total_2      = X_train[(X_train.season == 2)]\n\nX_workingday_3 = X_train[(X_train.workingday == 1) & (X_train.season == 3)]\nX_holiday_3    = X_train[(X_train.workingday == 0) & (X_train.season == 3)]\nX_total_3      = X_train[(X_train.season == 3)]\n\n\nX_workingday_4 = X_train[(X_train.workingday == 1) & (X_train.season == 4)]\nX_holiday_4    = X_train[(X_train.workingday == 0) & (X_train.season == 4)]\nX_total_4      = X_train[(X_train.season == 4)]'}, {'role': 'assistant', 'content': 'data_preprocessing'}]