[{'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:rec_method = input("Select recommendation method \\n(type \'User-based\', \'Item-based\', \'Hybrid\'): ")\nnum_recs = int(input("Number of products to recommend? (Input an integer:\\n )"))\nprint(\'\\n\')\nif rec_method == \'User-based\' or rec_method == \'Hybrid\':\nn = int(input("How many similar users to base rec\'s off of? (Input an integer:\\n )"))\nif random_or_input == \'Random user\':\nsimUsers = our_funcs.getUserPC(user_sim_mat,u1,n)\nelse:\nsimUsers = our_funcs.getUser(nonan_test, u1, n, similarity=our_funcs.find_sim)\nif rec_method == \'User-based\':\nprint(\'\\n\')\nour_funcs.userBasedRecs(nonan_test, simUsers, unrated, num_recs, rated, unique_products, metadata)\nelif rec_method == \'Hybrid\':\nour_funcs.hybridRec(nonan_test, simUsers, unrated, num_recs, rated, unique_products, metadata)\nelif rec_method == \'Item-based\':\nour_funcs.itemSim(nonan_test, u1, num_recs, unique_products, metadata, itm_sim_mat, unrated, rated)'}, {'role': 'assistant', 'content': "['data_exploration']"}]