[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:import pandas as pd\nimport xgboost as xgb\nimport numpy as np\nfrom sklearn import preprocessing\n\ndf = pd.read_csv("../input/train.csv")\ntest = pd.read_csv("../input/test.csv")\n\ndf[\'Date\']=pd.to_datetime(pd.Series(df[\'Original_Quote_Date\']))\ndf[\'Year\']=df[\'Date\'].apply(lambda x: int(str(x)[:4]))\ndf[\'Month\']=df[\'Date\'].apply(lambda x: int(str(x)[5:7]))\ndf[\'Date\']=df[\'Date\'].apply(lambda x: int(str(x)[8:10]))\ndf[\'Field10\'].apply(lambda x : int(x.replace(\',\',\'\')) )\n\ntest[\'Date\']=pd.to_datetime(pd.Series(test[\'Original_Quote_Date\']))\ntest[\'Year\']=test[\'Date\'].apply(lambda x: int(str(x)[:4]))\ntest[\'Month\']=test[\'Date\'].apply(lambda x: int(str(x)[5:7]))\ntest[\'Date\']=test[\'Date\'].apply(lambda x: int(str(x)[8:10]))\ntest[\'Field10\'].apply(lambda x : int(x.replace(\',\',\'\')))\n\nlabel=df[\'QuoteConversion_Flag\']\ndf.drop(\'QuoteConversion_Flag\',axis=1,inplace=True)\nnumber=test[\'QuoteNumber\']\ndrop_columns=[\'Original_Quote_Date\',\'QuoteNumber\']\nfor names in drop_columns:\n        df.drop(names,axis=1,inplace=True)\n        test.drop(names,axis=1,inplace=True)\nclf=xgb.XGBClassifier(max_depth=7,learning_rate=0.03,n_estimators=650,subsample=0.86,seed=50)\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]