[{'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 numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\nnegsentdf = pd.DataFrame()\nnegsentdf[\'Month\'] = [\'Jan\',\'Feb\',\'Mar\',\'Apr\', \'May\', \'Jun\', \'Jul\', \'Aug\', \'Sep\', \'Oct\', \'Nov\', \'Dec\']\nnegsentdf[\'Sentiment Score\'] = [nor_add_negjan,nor_add_negfeb,nor_add_negmar,nor_add_negapr,nor_add_negmay,nor_add_negjun,nor_add_negjul,nor_add_negaug,\nnor_add_negsep,nor_add_negoct,nor_add_negnov,nor_add_negdec]\nfig, ax = plt.subplots()\nnegsentdf.plot(ax=ax, kind=\'bar\', grid = True)\nax.set_xticklabels(negsentdf[\'Month\'])\nax.set_title("Normalized Sentiment Score per Month in 2001", size =14)\nax.set_xlabel(\'Month\')\nax.set_ylabel(\'Sentiment Score\')'}, {'role': 'assistant', 'content': "['helper_functions', 'result_visualization']"}]