[{'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:def business_rating(similar_users, big_df):\nbus_rating_dict = dict()\nfor similar_user in similar_users:\nsimilar_user_id = similar_user[0]\nsimilar_user_cos_sim = similar_user[1]\nsimilar_user_sq_sim = similar_user_cos_sim**2\nsimilar_user_data = big_df[big_df.user_id == similar_user_id]\nfor business_id in similar_user_data.business_id.unique():\nstars = similar_user_data[similar_user_data.business_id == business_id].stars_x.values\nfor star in stars:\nquadratic_business_rating = star * similar_user_sq_sim\nif business_id not in bus_rating_dict.keys():\nbus_rating_dict.setdefault(business_id, []).append(similar_user_sq_sim)\nbus_rating_dict.setdefault(business_id, []).append(quadratic_business_rating)\nbus_rating_dict.setdefault(business_id, []).append(quadratic_business_rating/similar_user_sq_sim)\nelse:\nbus_rating_dict[business_id][0] = bus_rating_dict[business_id][0] + similar_user_sq_sim\nbus_rating_dict[business_id][1] = bus_rating_dict[business_id][1] + quadratic_business_rating\nbus_rating_dict[business_id][2] = float(bus_rating_dict[business_id][1] / bus_rating_dict[business_id][0])\nsimilar_user_business_rating_dict = dict() # transforming bus_rating_dict, retriving the 3rd value in the value field for each key\nfor key in bus_rating_dict.keys():\nvalue = bus_rating_dict[key][2]\nsimilar_user_business_rating_dict[key] = value\nsimilar_user_business_rating_dict\nsimilar_user_business_rating_list = sorted(similar_user_business_rating_dict.items(), key = lambda t:t[1])\nreturn similar_user_business_rating_list'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]