[{'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:df_train_temp = df_train.groupby([\'Hour\',\'Weekday\']).mean().reset_index()\ndic = {\'Weekday\':[\'Monday\',\'Tuesday\',\'Wednesday\',\'Thursday\',\'Friday\',\'Saturday\',\n                  \'Sunday\']}\ndic1 = {\'registered\':\'Average count of registered poeple commuting.\',\n        \'count\': \'Average people commuting\',\'Hour\':\'Hour of the day\',\n        \'Weekday\':\'Day of the week\'}\nfig = px.line(x = \'Hour\', y = "registered", data_frame = df_train_temp.reset_index(),\n              color = \'Weekday\',hover_data = [\'count\'],category_orders = dic,\n              title = \'Interactive LinePlot of the registered separated by weekday(Hover for more details)\',\n              labels = dic1,range_y = [0,550],height = 670,width = 980)\nfig.show()\''}, {'role': 'assistant', 'content': 'result_visualization'}]