[{'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:import warnings\nwarnings.filterwarnings(\'ignore\')\nimport numpy as np\nimport pandas as pd\nfrom sklearn import *\nimport final_functions as our_funcs\ndata_df = pd.read_csv("Clothing_Shoes_Jewelry_5.csv")\ndata_df = data_df.drop(\'Unnamed: 0\', 1)\nunique_products = data_df.asin.unique()\nmetadata = pd.read_csv("clothing_shoes_jewelry_metadata.csv")\nmetadata = metadata.drop(\'Unnamed: 0\', 1)\ndata_df_slim = data_df[[\'asin\', \'overall\', \'reviewerID\']]\nuser_item_mat = data_df_slim.pivot(index=\'reviewerID\', columns = \'asin\', values=\'overall\')\ntest = np.array(user_item_mat)\nnonan_test = np.nan_to_num(test)'}, {'role': 'assistant', 'content': "['helper_functions', 'load_data']"}]