[{'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:# This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import os<br><br>import h2o<br>from h2o.estimators.random_forest import H2ORandomForestEstimator<br><br>import numpy as np<br>import pandas as pd<br><br>os.environ[\'http_proxy\'] = \'\'<br>os.environ[\'https_proxy\'] = \'\'<br>os.environ[\'NO_PROXY\'] = \'localhost\'<br><br>def transform(df_):<br>    """<br>    transform Date to datetime type.<br>    """<br>    df_[\'Date\']=pd.to_datetime(df_[\'Date\'])<br>    df_[\'month\']=df_[\'Date\'].dt.month<br>    df_[\'year\']=df_[\'Date\'].dt.year<br>    df_[\'Store\']=pd.to_numeric(df_[\'Store\'])<br>    return df_<br><br>store=pd.read_csv("../input/store.csv")<br>train=pd.read_csv("../input/train.csv")<br>test=pd.read_csv("../input/test.csv")<br># filter sales invalid column<br>train=train[train[\'Sales\'] > 0]<br># merge train and test with store<br>train=pd.merge(train, store, on=[\'Store\'])<br>test=pd.merge(test, store, on=[\'Store\'])<br># transform<br>train=transform(train)<br>test=transform(test)<br># train convert to logSales from log(Sales)<br>train[\'logSales\']=pd.to_numeric(np.log(train[\'Sales\']))<br>test[\'logSales\']=0<br><br># initialization of h2o<br>h2o.init(nthreads=-1, max_mem_size = "8G")<br>train_hf = h2o.H2OFrame(train)<br>test_hf = h2o.H2OFrame(test)<br>rf_v1_model = H2ORandomForestEstimator(model_id="rf_covType_v1", ntrees=200, stopping_rounds=2, max_depth = 30, nbins_cats = 1115, score_each_iteration=True, seed=1000000)<br><br># Training prepare for model<br>covtype_X=[col for col in train_hf.columns if col not in ["Id","Date","Sales","logSales","Customers"]]<br>covtype_y=train_hf.columns[-1]<br>rf_v1_model.train(x=covtype_X, y=covtype_y, training_frame=train_hf)<br>test_result_hf = rf_v1_model.predict(test_hf)<br>test_result_df = test_result_hf.as_data_frame()<br>test_result_df[\'predict\']=np.exp(test_result_df[\'predict\'])<br>test_result_df.rename(columns={\'predict\': \'Sales\'}, inplace=True)<br>test_result_df.insert(loc=0, column=\'Id\', value=test[\'Id\'])<br>test_result_df.set_index(\'Id\')<br>test_result_df.to_csv(\'python_h2o_rf.csv\', header=True, index=False)'}, {'role': 'assistant', 'content': 'helper_functions'}]