[{'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 seaborn as sns\nimport matplotlib.pyplot as plt\n\n# check distribution of sales in train set\nfig = plt.figure(figsize=(12,5))\nax1 = fig.add_subplot(121)\nax2 = fig.add_subplot(122)\ng1 = sns.distplot(train['Sales'],hist = True,label='skewness:{:.2f}'.format(train['Sales'].skew()),ax = ax1)\ng1.legend()\ng1.set(xlabel = 'Sales', ylabel = 'Density', title = 'Sales Distribution')\ng2 = sns.distplot(np.log1p(train['Sales']),hist = True,label='skewness:{:.2f}'.format(np.log1p(train['Sales']).skew()),ax=ax2)\ng2.legend()\ng2.set(xlabel = 'log(Sales+1)',ylabel = 'Density', title = 'log(Sales+1) Distribution')\nplt.show()"}, {'role': 'assistant', 'content': "['result_visualization']"}]