[{'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:computed_rating_list = []\nreal_reco_users_rating_list = []\nfor homesitter in homesitters:\nreco_user = homesitter\nuser_pref_vector = users_pref_df[users_pref_df.index == reco_user]\nindexes = users_pref_df[users_pref_df.index!= user_pref_vector.index[0]].index\nsorted_user_similarities = cos_similarity(indexes, users_pref_df, user_pref_vector)\nsimilar_users = sorted_user_similarities[-200:]\nsimilar_user_business_rating_list = business_rating(similar_users, big_df)\nsimilar_user_business_rating_list = similar_user_business_rating_list[-300:]\nreviews_latitude_list = big_df[big_df.user_id == reco_user].latitude.tolist()\nreviews_longitude_list = big_df[big_df.user_id == reco_user].longitude.tolist()\nuser_mean_latitude = np.median((reviews_latitude_list))\nuser_mean_longitude = np.median((reviews_longitude_list))\nsim_bus = distance(similar_user_business_rating_list, big_df, user_mean_latitude, user_mean_longitude)\nmost_close_businesses_list = combination_metric(sim_bus)\npref_weighted_businesses = weight_tastes(most_close_businesses_list, big_df)\nrecommended = sorted(pref_weighted_businesses.items(), key = lambda t:t[1])[-10:]\nreviwed_business_ids = big_df[big_df.user_id == reco_user].business_id.values\nrecommended_business_ids = list()\nfor reco in recommended:\nrecommended_business_ids.append(reco[0])\noverlapped_reviews = list(set(recommended_business_ids) & set(reviwed_business_ids.tolist()))\nfor item in similar_user_business_rating_list:\nfor another_item in overlapped_reviews:\nif item[0] == another_item:\ncomputed_rating = item[1]\nreal_reco_users_rating = big_df[(big_df.user_id == reco_user) & (big_df.business_id == another_item)].stars_x.values\nif len(real_reco_users_rating) != 1:\nreal_reco_users_rating = float(real_reco_users_rating.min())\nelse:\nreal_reco_users_rating = float(real_reco_users_rating[0])\ncomputed_rating_list.append(computed_rating)\nreal_reco_users_rating_list.append(real_reco_users_rating)\nelse:\ncontinue\nprint(computed_rating_list, real_reco_users_rating_list)'}, {'role': 'assistant', 'content': 'data_preprocessing'}]