[{'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:#Creating a flag for each type of restaurant\ndata['Type_IL'] = np.where(data['Type'] == 'IL', 1, 0)\ndata['Type_FC'] = np.where(data['Type'] == 'FC', 1, 0)\ndata['Type_DT'] = np.where(data['Type'] == 'DT', 1, 0)\n\n#Creating a flag for 'Big Cities'\ndata['Big_Cities'] = np.where(data['City Group'] == 'Big Cities', 1, 0)\n\n#Converting Open_Date into day count\n#Considering the same date the dataset was made available\ndata['Days_Open'] = (pd.to_datetime('2015-03-23') - pd.to_datetime(data['Open Date'])).dt.days\n\n#Removing unused columns\ndata = data.drop('Type', axis=1)\ndata = data.drop('City Group', axis=1)\ndata = data.drop('City', axis=1)\ndata = data.drop('Open Date', axis=1)\n\n#Adjusting test data as well\ntest_data['Type_IL'] = np.where(test_data['Type'] == 'IL', 1, 0)\ntest_data['Type_FC'] = np.where(test_data['Type'] == 'FC', 1, 0)\ntest_data['Type_DT'] = np.where(test_data['Type'] == 'DT', 1, 0)\ntest_data['Big_Cities'] = np.where(test_data['City Group'] == 'Big Cities', 1, 0)\ntest_data['Days_Open'] = (pd.to_datetime('2015-03-23') - pd.to_datetime(test_data['Open Date'])).dt.days\ntest_data = test_data.drop('Type', axis=1)\ntest_data = test_data.drop('City Group', axis=1)\ntest_data = test_data.drop('City', axis=1)\ntest_data = test_data.drop('Open Date', axis=1)"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]