[{'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:# lets check few random stores \n\nrnd_store = np.random.randint(min(data_prep.Store),max(data_prep.Store),3)\n\nfig,ax = plt.subplots(3,2,figsize = (15,10))\n\nfor idx,store in enumerate(rnd_store):\n    df_store = data_prep.copy().loc[data_prep.Store == store,['Date','Sales']].set_index('Date')\n    df_store = df_store.resample('D').fillna('bfill')\n    plot_acf(df_store,lags = 60,ax = ax[idx,0],label = store) \n    plot_pacf(df_store,lags = 60,ax = ax[idx,1], label = store)\n    ax[idx,0].set_title('Autocorelation for store {}'.format(store))\n    ax[idx,1].set_title('Partial Autocorelation for store {}'.format(store))\n    plt.tight_layout()\n    \n    \n# By running this part few times we can notice that almost for all stores there is hogh corelation with following lags:\n# 1 14,28,42, 49\n# Therefore lets use this values to create new features\n# But we need to preduct 48 days in future, threre fore we cannot use something lower 48"}, {'role': 'assistant', 'content': "['result_visualization']"}]