[{'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:def PrepareFeatures(TestOrTrain):<br>    source = f\'../input/{TestOrTrain}.json\'<br>    data = pd.read_json(source)<br>    # Some noise in price feature we saw in Part 1:<br>    ulimit = np.percentile(data.price.values, 99)<br>    data[\'price\'][data[\'price\']>ulimit] = ulimit<br>    # Get Different features as in Part 1:<br>    data[\'hasDesc\'] = data[\'description\'].apply(lambda x: len(x.strip())!=0)<br>    data["nFeatures"] = data["features"].apply(len)<br>    data["nDescWords"] = data["description"].apply(lambda x: len(x.split(" ")))<br>    data[\'nPhotos\'] = data[\'photos\'].apply(lambda x: min(10, len(x)))<br>    data[\'created\'] = pd.to_datetime(data[\'created\'])<br>    data[\'month\'] = data[\'created\'].dt.month<br>    data[\'weekday\'] = data[\'created\'].apply(lambda x: x.weekday())<br>    return data<br><br># Using categorical (more sparse) data, we ispected in Part 1:<br>def CreateCategFeat(data, features_list):<br>    f_dict = {\'hasParking\':[\'parking\', \'garage\'], \'hasGym\':[\'gym\', \'fitness\', \'health club\'],<br>              \'hasPool\':[\'swimming pool\', \'pool\'], \'noFee\':[\'no fee\', "no broker\'s fees"],<br>              \'hasElevator\':[\'elevator\'], \'hasGarden\':[\'garden\', \'patio\', \'outdoor space\'],<br>              \'isFurnished\': [\'furnished\', \'fully  equipped\'], <br>              \'reducedFee\':[\'reduced fee\', \'low fee\'],<br>              \'hasAC\':[\'air conditioning\', \'central a/c\', \'a/c\', \'central air\', \'central ac\'],<br>              \'hasRoof\':[\'roof\', \'sundeck\', \'private deck\', \'deck\'],<br>              \'petFriendly\':[\'pets allowed\', \'pet friendly\', \'dogs allowed\', \'cats allowed\'],<br>              \'shareable\':[\'shares ok\'], \'freeMonth\':[\'month free\'],<br>              \'utilIncluded\':[\'utilities included\']}<br>    for feature in features_list:<br>        data[feature] = False<br>        for ind, row in data.iterrows():<br>            for f in row[\'features\']:<br>                f = f.lower().replace(\'-\', \'\')<br>                if any(e in f for e in f_dict[feature]):<br>                    data.at[ind, feature]= True'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]