[{'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:# ## Add 1-year `Weekly_Sales` lag ##\n\n# X_all = pd.concat([X_train, X_test])\n# X_all['Date2'] = pd.to_datetime(X_all['Date'], utc = True)\n# X_all['52_Week_Lag'] = X_all['Date2'] - np.timedelta64(52,'W')\n# X_all_temp = X_all[['Weekly_Sales', 'Date2', 'Store', 'Dept']]\n\n# X_all = X_all.merge(X_all_temp,\n#                     left_on=['Store', 'Dept', '52_Week_Lag'], \n#                     right_on=['Store', 'Dept', 'Date2'],\n#                     how='inner',\n#                     suffixes=('', '_y'))\n# X_all.rename(columns={'Weekly_Sales_y': 'Weekly_Sales_Lag_52_Weeks'}, inplace=True)\n# X_all = X_all[[col for col in X_all.columns if not col.endswith('_y')]]\n\n# drop_cols = ['Date2_y', '1_Year_Lag']\n# X_all.drop(['52_Week_Lag'], axis=1, inplace=True)\n\n# X_all.isna().sum()"}, {'role': 'assistant', 'content': "['helper_functions']"}]