[{'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 numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import KFold\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import make_scorer, accuracy_score, confusion_matrix\nfrom sklearn import tree\nfrom sklearn.tree import DecisionTreeClassifier\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom imblearn.over_sampling import SMOTE\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import auc\n%matplotlib inline\ndf = pd.read_csv("../data_for_student_case.csv")\ndf[\'cvcresponsecode\'].ix[df[\'cvcresponsecode\']>3] = 3\ndf[\'amount\'].ix[df[\'currencycode\'] == \'MXN\'] = df[\'amount\'].ix[df[\'currencycode\'] == \'MXN\'] * 0.052131\ndf[\'amount\'].ix[df[\'currencycode\'] == \'AUD\'] = df[\'amount\'].ix[df[\'currencycode\'] == \'AUD\'] * 0.7349\ndf[\'amount\'].ix[df[\'currencycode\'] == \'NZD\'] = df[\'amount\'].ix[df[\'currencycode\'] == \'NZD\'] * 0.68966\ndf[\'amount\'].ix[df[\'currencycode\'] == \'GBP\'] = df[\'amount\'].ix[df[\'currencycode\'] == \'GBP\'] * 1.292625\ndf[\'amount\'].ix[df[\'currencycode\'] == \'SEK\'] = df[\'amount\'].ix[df[\'currencycode\'] == \'SEK\'] * 0.112851\ndf[\'currencycode\'].ix[df[\'currencycode\'] == \'MXN\'] = \'MX\'\ndf[\'currencycode\'].ix[df[\'currencycode\'] == \'AUD\'] = \'AU\'\ndf[\'currencycode\'].ix[df[\'currencycode\'] == \'NZD\'] = \'NZ\'\ndf[\'currencycode\'].ix[df[\'currencycode\'] == \'GBP\'] = \'GB\'\ndf[\'currencycode\'].ix[df[\'currencycode\'] == \'SEK\'] = \'SE\'\ndf[\'issuercountrycode\'].fillna(\'other\',inplace=True)\ndf[\'shoppercountrycode\'].fillna(\'other\',inplace=True)\ndf[\'bin\'].fillna(1,inplace=True)\ndf = df[df[\'simple_journal\'] != \'Refused\']\ndf[\'simple_journal\'].ix[df[\'simple_journal\'] == \'Settled\'] = 1\ndf[\'simple_journal\'].ix[df[\'simple_journal\'] == \'Chargeback\'] = 0\ndf[\'cardverificationcodesupplied\'].ix[df[\'cardverificationcodesupplied\'] == True] = 1\ndf[\'cardverificationcodesupplied\'].ix[df[\'cardverificationcodesupplied\'] == False] = 0\ndf[\'cardverificationcodesupplied\'].fillna(2,inplace=True)\ndf[\'creationdate\'] = pd.to_datetime(df[\'creationdate\'])\ndf[\'cweekday\'] = df[\'creationdate\'].dt.weekday_name\ndf[\'chour\'] = df[\'creationdate\'].dt.hour\ndf[\'chour\'].apply(pd.to_numeric)\ndf[\'issue_shopper\'] = df[\'issuercountrycode\'] == df[\'shoppercountrycode\']\ndf[\'issue_shopper\'].ix[df[\'issue_shopper\'] == True] = 1.0\ndf[\'issue_shopper\'].ix[df[\'issue_shopper\'] == False] = 0.0\ndf[\'issue_currency\'] = df[\'issuercountrycode\'] == df[\'currencycode\']\ndf[\'issue_currency\'].ix[df[\'issue_currency\'] == True] = 1.0\ndf[\'issue_currency\'].ix[df[\'issue_currency\'] == False] = 0.0\ndf[\'shopper_currency\'] = df[\'shoppercountrycode\'] == df[\'currencycode\']\ndf[\'shopper_currency\'].ix[df[\'shopper_currency\'] == True] = 1.0\ndf[\'shopper_currency\'].ix[df[\'shopper_currency\'] == False] = 0.0\ndf = df.drop([\'bin\', \'shoppercountrycode\', \'issuercountrycode\', \'txid\', \'bookingdate\', \'creationdate\', \'mail_id\', \'ip_id\', \'card_id\'], axis=1)\ndummy_features = [\'txvariantcode\', \'currencycode\', \'shopperinteraction\', \'cardverificationcodesupplied\', \'cvcresponsecode\', \'accountcode\', \'cweekday\']\nfor feature in dummy_features:\njust_dummies = pd.get_dummies(df[feature],prefix=feature+\'_\')\ndf = pd.concat([df, just_dummies], axis=1)\ndf.drop([feature], inplace=True, axis=1)\ndf = df.reset_index(drop=True)\ndf.sample(5)'}, {'role': 'assistant', 'content': 'data_preprocessing'}]