[{'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\n\nfig,ax =plt.subplots(1,2,figsize = (20,10))\nins1 = ax[0].inset_axes([0.5,0.5,0.4,0.4])\nins2 = ax[1].inset_axes([0.7,0.7,0.2,0.2])\n\nsns.distplot(train[train.Sales != 0].Sales,ax=ax[0],bins=100) \nsns.distplot(np.log1p(train[train.Sales != 0].Sales),ax=ins1,bins=100,color = 'red')\n\n\nsns.boxplot(train[train.Sales != 0].Sales,ax=ax[1])\nsns.boxplot(np.log1p(train[train.Sales != 0].Sales),ax=ins2)\n\n\n# We see that sales values shpw positive skeew, it can be fixed by applying np.log1p (embedded plot)\n# Also there are some outliers, lets define functions to perform transformation and outliers removal"}, {'role': 'assistant', 'content': "['result_visualization']"}]