[{'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:features = [x for sublist in trainingData[\'features\'] for x in sublist]<br><br>for x in features:<br>    if "*" in x: features.remove(x)<br><br>features = set(features)<br><br>features.discard(\'<null>\')<br><br>has_ac = [ s for s in features if any(ac_name in s for ac_name in [\'A/C\', "AC", "Air Conditioning"] ) and not(any(wrong in s for wrong in ["FIRE",\'ACT\',\'APT\', \'SPACE\',\'YARD\'])) ]<br><br>trainingData[\'has_ac\'] = [any(ac in feature for ac in has_ac) for feature in trainingData[\'features\'] ]<br>  <br>free_included = [ s for s in features if any(ac_name in s for ac_name in ["free","FREE","Free", "Gift", "gift", \'1/2 Month fee\', "included", "INCLUDED","Included"] ) ]<br>    <br>trainingData["included_offer"] = [any(free in feature for free in free_included) for feature in trainingData[\'features\']]<br><br>doorman = [ s for s in features if any(ac_name in s for ac_name in ["doorman","DOORMAN","Doorman",\'doormen\',\'Doormen\', \'full-service\', \'concierge\',\'Concierge\',\'Attended Lobby\', \'Attended lobby\', \'attended lobby\'] ) ]<br>   <br>trainingData["concierge"] = [any(door in feature for door in doorman) for feature in trainingData[\'features\']]<br><br>Washer = [ s for s in features if any(ac_name in s for ac_name in [\'Washer\', "Dryer",\'Washer\',\'Dryer\',\'washer\',\'dryer\',\'laundry\',\'LAUNDRY\',\'Laundry\'] ) and not(any(notname in s for notname in [\'dish\',\'DISH\',\'Dish\', \'Disw\'] )) ]<br><br>trainingData["laundry"] = [any(laundry in feature for laundry in Washer) for feature in trainingData[\'features\']]'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]