[{'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:# train model to predict fatalities/day\ncol_target2 = 'cases/day'\ncol_var2 = [\n    'Lat', 'Long',\n    'days_since_1cases', \n#     'days_since_10cases', \n#     'days_since_100cases',\n#     'days_since_1fatal', \n#     'days_since_10fatal', 'days_since_100fatal',\n#     'cases/day_(1-1)', \n#     'cases/day_(1-7)', \n#     'cases/day_(8-14)',  \n#     'cases/day_(15-21)', \n    \n#     'fatal/day_(1-1)', \n#     'fatal/day_(1-7)', \n#     'fatal/day_(8-14)', \n#     'fatal/day_(15-21)', \n    'SmokingRate',\n    'Gross_domestic_product__constant_prices',\n    'Gross_domestic_product__current_prices',\n    'Gross_domestic_product__deflator',\n    'Gross_domestic_product_per_capita__constant_prices',\n    'Gross_domestic_product_per_capita__current_prices',\n    'Output_gap_in_percent_of_potential_GDP',\n    'Gross_domestic_product_based_on_purchasing_power_parity__PPP__valuation_of_country_GDP',\n    'Gross_domestic_product_based_on_purchasing_power_parity__PPP__per_capita_GDP',\n    'Gross_domestic_product_based_on_purchasing_power_parity__PPP__share_of_world_total',\n    'Implied_PPP_conversion_rate', 'Total_investment',\n    'Gross_national_savings', 'Inflation__average_consumer_prices',\n    'Inflation__end_of_period_consumer_prices',\n    'Six_month_London_interbank_offered_rate__LIBOR_',\n    'Volume_of_imports_of_goods_and_services', 'Volume_of_Imports_of_goods',\n    'Volume_of_exports_of_goods_and_services', 'Volume_of_exports_of_goods',\n    'Unemployment_rate', \n    'Employment', 'Population',\n    'General_government_revenue', 'General_government_total_expenditure',\n    'General_government_net_lending_borrowing',\n    'General_government_structural_balance',\n    'General_government_primary_net_lending_borrowing',\n    'General_government_net_debt', 'General_government_gross_debt',\n    'Gross_domestic_product_corresponding_to_fiscal_year__current_prices',\n    'Current_account_balance', \n    'LifeExpectancy',\n#     'pop',\n    'density', \n    'medianage', \n    'urbanpop', \n    'hospibed', \n'sex0',\n'sex14',\n'sex25',\n'sex54',\n'sex64',\n'sex65plus',\n'sexratio',\n'lung',\n'femalelung',\n'malelung',\n'gdp2019',\n'healthexp',\n'healthperpop',  \n]\ncol_cat = []\ndf_train = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (df_traintest8['day']<=day_before_valid)]\ndf_valid = df_traintest8[(pd.isna(df_traintest8['ForecastId'])) & (day_before_valid<df_traintest8['day']) & (df_traintest8['day']<=day_before_public)]\nX_train = df_train[col_var2]\nX_valid = df_valid[col_var2]\ny_train = np.log(df_train[col_target2].values.clip(0, 1e10)+1)\ny_valid = np.log(df_valid[col_target2].values.clip(0, 1e10)+1)\ntrain_data = lgb.Dataset(X_train, label=y_train, categorical_feature=col_cat)\nvalid_data = lgb.Dataset(X_valid, label=y_valid, categorical_feature=col_cat)\nmodel2 = lgb.train(params, train_data, num_round, valid_sets=[train_data, valid_data],\n                  verbose_eval=100,\n                  early_stopping_rounds=150,)\nbest_itr2 = model2.best_iteration"}, {'role': 'assistant', 'content': 'data_preprocessing'}]