[{'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:sns.jointplot(\'GOOG\',\'MSFT\', data = tech_rets, kind=\'scatter\',\ncolor=\'seagreen\', edgecolor="w", s = 50,\nmarginal_kws=dict(bins=20), xlim = (-0.06, 0.06))\nfig = plt.gcf()\nplotly_fig = plotly.tools.mpl_to_plotly(fig)\nfor trace in plotly_fig[\'data\']:\ntrace[\'opacity\'] = 0.7\ntrace[\'marker\'][\'line\'][\'color\'] = \'white\'\ntrace[\'marker\'][\'line\'][\'width\'] = 0.65\nplotly_fig[\'layout\'].update(# control x and y axes of the top marginal histogram\nyaxis2 = dict(nticks = 4, showgrid = False),\nxaxis2 = dict(ticks = "", showticklabels = False, showgrid = True),\nyaxis3 = dict(ticks = "", showticklabels = False),\nxaxis3 = dict(nticks = 5, showgrid = False),\nwidth = 900,\nheight = 450)\nplotly.offline.iplot(plotly_fig)'}, {'role': 'assistant', 'content': "['data_exploration']"}]