[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': "Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:# Here I would like to know what is rmspe score with th emost dumb approach\n\n# we devide data on train and test\ntest_bs  = data_prep[data_prep.year == 2015]\ntrain_bs = data_prep[data_prep.year  < 2015]\n\n# I am going to use mean Sales grouped by store-month-day among previous years as predicted values for 2015\npredict_bs = (train_bs\n              .groupby(['Store','month','dayofmonth']).Sales.mean().reset_index().rename({'Sales':'predictions'},axis = 1)\n              .merge(test_bs,how='right',on = ['Store','month','dayofmonth'])\n              .fillna(train_bs.Sales.mean())\n              .sort_values('Date')\n              )\n\n# Display baseline\nprint('Baseline to overcome = {:.2f}'.format(rmspe(np.expm1(predict_bs.Sales),np.expm1(predict_bs.predictions))))\n\n# Let`s see how prediction looks like\nfig,ax = plt.subplots(1,3,figsize = (30,10))\n\nrnd_store = np.random.randint(min(predict_bs.Store),max(predict_bs.Store),3)\n\nfor idx,store in enumerate(rnd_store):\n    \n    ax[idx].plot(predict_bs[predict_bs.Store == store].Date,np.expm1(predict_bs[predict_bs.Store == store].Sales), color = 'blue'    ,label = 'Observed')\n    ax[idx].plot(predict_bs[predict_bs.Store == store].Date,np.expm1(predict_bs[predict_bs.Store == store].predictions),color = 'red',label = 'Predicted')\n\n    ax[idx].legend()\n    ax[idx].set_title('Store '+str(store))\n    \n    \n# It doesn`t look so bad"}, {'role': 'assistant', 'content': 'result_visualization'}]