[{'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 import numpy as np from sklearn.preprocessing import LabelEncoder from xgboost.sklearn import XGBClassifier\n\n#Loading the Data again train= pd.read_csv('../input/airbnb-recruiting-new-user-bookings/train_users_2.csv.zip') test = pd.read_csv('../input/airbnb-recruiting-new-user-bookings/test_users.csv.zip') train['first_affiliate_tracked'] = train['first_affiliate_tracked'].fillna('Unknown') test['first_affiliate_tracked'] = test['first_affiliate_tracked'].fillna('Unknown') train['date_account_created'] = pd.to_datetime(train['date_account_created']) train['timestamp_first_active'] = pd.to_datetime(train['timestamp_first_active'], format='%Y%m%d%H%M%S') train['timestamp_first_active'] = pd.to_datetime(train['timestamp_first_active']).dt.date test['date_account_created'] = pd.to_datetime(test['date_account_created']) test['timestamp_first_active'] = pd.to_datetime(test['timestamp_first_active'], format='%Y%m%d%H%M%S') test['timestamp_first_active'] = pd.to_datetime(test['timestamp_first_active']).dt.date train.drop(['date_first_booking'], axis=1,inplace=True) test.drop(['date_first_booking'], axis=1,inplace=True)\n\n#date_account_created\n\ndac = np.vstack(train.date_account_created.astype(str).apply(lambda x: list(map(int, x.split('-')))).values) train['dac_year'] = dac[:,0] train['dac_month'] = dac[:,1] train['dac_day'] = dac[:,2] train.drop(['date_account_created'], axis=1,inplace=True)\n\n#timestamp_first_active tfa = np.vstack(train.timestamp_first_active.astype(str).apply(lambda x: list(map(int, x.split('-')))).values) train['tfa_year'] = tfa[:,0] train['tfa_month'] = tfa[:,1] train['tfa_day'] = tfa[:,2] train.drop(['timestamp_first_active'], axis=1,inplace=True)\n\n#date_account_created\n\ndac = np.vstack(test.date_account_created.astype(str).apply(lambda x: list(map(int, x.split('-')))).values) test['dac_year'] = dac[:,0] test['dac_month'] = dac[:,1] test['dac_day'] = dac[:,2] test.drop(['date_account_created'], axis=1,inplace=True)\n\n#timestamp_first_active tfa = np.vstack(test.timestamp_first_active.astype(str).apply(lambda x: list(map(int, x.split('-')))).values) test['tfa_year'] = tfa[:,0] test['tfa_month'] = tfa[:,1] test['tfa_day'] = tfa[:,2] test.drop(['timestamp_first_active'], axis=1,inplace=True) import numpy as np train[(train.age < 14) & (train.age > 110)]=np.nan train['age'].fillna(train['age'].mean(), inplace=True)\n\ntest[(test.age < 14) & (test.age > 110)]=np.nan test['age'].fillna(train['age'].mean(), inplace=True)\n\n#Converting below columns as categories for plotting in graphs categorical_features = [ 'affiliate_channel', 'affiliate_provider', 'first_affiliate_tracked', 'first_browser', 'first_device_type', 'gender', 'language', 'signup_app', 'signup_method', 'signup_flow' ]\n\nfor categorical_feature in categorical_features: train[categorical_feature] = train[categorical_feature].astype('category') for categorical_feature in categorical_features: test[categorical_feature] = test[categorical_feature].astype('category')\n\n#One-hot-encoding features ohe_feats = ['gender', 'signup_method', 'signup_flow', 'language', 'affiliate_channel', 'affiliate_provider', 'first_affiliate_tracked', 'signup_app', 'first_device_type', 'first_browser'] for f in ohe_feats: train_dummy = pd.get_dummies(train[f], prefix=f) train_cont= train.drop([f], axis=1) train = pd.concat((train_cont, train_dummy), axis=1)\n\n#One-hot-encoding features ohe_feats = ['gender', 'signup_method', 'signup_flow', 'language', 'affiliate_channel', 'affiliate_provider', 'first_affiliate_tracked', 'signup_app', 'first_device_type', 'first_browser'] for f in ohe_feats: test_dummy = pd.get_dummies(test[f], prefix=f) test_cont= test.drop([f], axis=1) test = pd.concat((test_cont, test_dummy), axis=1)\n\n#Splitting train and test\n\nfrom sklearn.model_selection import train_test_split y=train['country_destination'] X=train.drop(['country_destination','id'],axis=1) from imblearn.combine import SMOTETomek\n\ntransform the dataset\nsmotetomek = SMOTETomek(sampling_strategy='auto')\n\nsplit the dataset into train and test sets\nX_train1, X_test1, y_train1, y_test1 = train_test_split(X, y, test_size=0.30, random_state=1,shuffle=True) X_train, y_train = smotetomek.fit_resample(X_train1, y_train1)\n\n#Classifier\n\nxgb = XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bynode=1, colsample_bytree=0.5, gamma=0, gpu_id=-1, importance_type='gain', interaction_constraints=None, learning_rate=0.3, max_delta_step=0, max_depth=6, min_child_weight=1, monotone_constraints=None, n_estimators=25, n_jobs=0, num_parallel_tree=1, objective='multi:softprob', random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=None, seed=0, subsample=0.5, tree_method=None, validate_parameters=False, verbosity=None) xgb.fit(X_train, y_train) target_names = ['NDF', 'US', 'other', 'FR', 'CA', 'GB', 'ES', 'IT', 'PT', 'NL','DE', 'AU'] y_predxgb=xgb.predict(X_test1)\n\nfrom sklearn.metrics import classification_report print(classification_report(y_test1, y_predxgb, target_names=target_names))"}, {'role': 'assistant', 'content': 'modelling'}]