[{'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:print("Welcome to the Amazon clothing, shoes & jewelry recommender")\nprint(\'\\n\')\nrandom_or_input = input("Would you like to review some products and get recommendations, or see recommendations for a random user? \\n (type \'User input\' or \'Random user\'): ")\nprint(\'\\n\')\nprint("You selected: ", random_or_input)\nif random_or_input == \'User input\':\nuser_rev = [0]*np.shape(nonan_test)[1]\nfor i in range(5):\ni += 1\nrandom_prod = np.random.randint(0, np.shape(nonan_test)[1])\nasin_pd = unique_products[random_prod]\nproduct = metadata[metadata[\'asin\']==asin_pd].title.item()# \'categories\']]\nproduct = str(product)\nuser_review = float(input("Please rate " + product + ": "))\nuser_rev[random_prod] = user_review\nu1 = np.shape(nonan_test)[0]\nnonan_test = np.append(nonan_test, np.array([user_rev]), axis = 0)\nelse:\nrandom_row = np.random.randint(0, np.shape(nonan_test)[0])\nu1 = random_row\nprint(\'\\n\')\nunrated, rated = our_funcs.getUnrated(nonan_test,u1)\nfor item in rated:\nasin_pd = unique_products[item]\nproduct = metadata[metadata[\'asin\']==asin_pd].title.item()# \'categories\']]\nproduct = str(product)\nprint("User Rated:" + product)'}, {'role': 'assistant', 'content': 'data_exploration'}]