[{'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:columns = [\'DayOfWeek\',\'Open\',\'Promo\',\'SchoolHoliday\',\'Year\',\'Month\',\'Day\',\'StateHoliday\']\n# Customers are not present in test, not worth using\nfor store in stores: # takes *FOREVER* to run\n    # pandas throws "IndexingError: Unalignable boolean Series key provided" if I index df directly\n    # well, it must think it\'s so clever\n    train_store_indices = (train.Store.values==store)\n    \n    y_train = train.Sales.values[train_store_indices]\n    X_train = train[columns].values[train_store_indices]\n    \n    model = LinearRegression(normalize=True,n_jobs=-1).fit(X_train,y_train)\n    \n    test_store_indices = (test_nona.Store==store)\n    X_test = test_nona[columns].values[test_store_indices]\n    \n    test_nona.Sales.values[test_store_indices] = model.predict(X_test)\''}, {'role': 'assistant', 'content': "['modelling']"}]