[{'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:logmeans_prod = (x_train.groupby(["product_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_store = (x_train.groupby(["product_id", "store_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_depot = (x_train.groupby(["product_id", "depot_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_chan = (x_train.groupby(["product_id", "channel_id"]))["log_demand"].mean().to_dict()\n\'\'\'logmeans_prod_store_depot = (x_train.groupby(["product_id", "store_id", "depot_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_store_chan = (x_train.groupby(["product_id", "store_id", "channel_id"]))["log_demand"].mean().to_dict()\'\'\'\nlogmeans_prod_depot_store = (x_train.groupby(["product_id", "depot_id", "store_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_chan_store = (x_train.groupby(["product_id", "channel_id", "store_id"]))["log_demand"].mean().to_dict()\n\'\'\'logmeans_prod_store_depot_chan = (x_train.groupby(["product_id", "store_id", "depot_id",\n"channel_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_store_chan_depot = (x_train.groupby(["product_id", "store_id", "channel_id",\n"depot_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_depot_store_chan = (x_train.groupby(["product_id", "depot_id", "store_id",\n"channel_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_depot_chan_store = (x_train.groupby(["product_id", "depot_id", "channel_id",\n"store_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_chan_depot_store = (x_train.groupby(["product_id", "channel_id", "depot_id",\n"store_id"]))["log_demand"].mean().to_dict()\nlogmeans_prod_chan_store_depot = (x_train.groupby(["product_id", "channel_id", "store_id",\n"depot_id"]))["log_demand"].mean().to_dict()\'\'\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]