[{'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:# predict test data in public\ndf_preds_pri = []\nfor i, place in enumerate(places[:]):\n    df_interest = copy.deepcopy(df_traintest10[df_traintest10[\'place_id\']==place].reset_index(drop=True))\n    df_interest[\'cases/day\'][(pd.isna(df_interest[\'ForecastId\']))==False] = -1\n    df_interest[\'fatal/day\'][(pd.isna(df_interest[\'ForecastId\']))==False] = -1\n    len_known = (df_interest[\'day\']<=day_before_private).sum()\n    len_unknown = (day_before_private<df_interest[\'day\']).sum()\n    for j in range(len_unknown): # use predicted cases and fatal for next days\' prediction\n        X_valid = df_interest[col_var].iloc[j+len_known]\n        X_valid2 = df_interest[col_var2].iloc[j+len_known]\n        pred_f = model_pri.predict(X_valid)\n        pred_c = model2_pri.predict(X_valid2)\n        pred_c = (np.exp(pred_c)-1).clip(0, 1e10)\n        pred_f = (np.exp(pred_f)-1).clip(0, 1e10)\n        df_interest[\'fatal/day\'][j+len_known] = pred_f\n        df_interest[\'cases/day\'][j+len_known] = pred_c\n        df_interest[\'Fatalities\'][j+len_known] = df_interest[\'Fatalities\'][j+len_known-1] + pred_f\n        df_interest[\'ConfirmedCases\'][j+len_known] = df_interest[\'ConfirmedCases\'][j+len_known-1] + pred_c\n#         print(df_interest[\'ConfirmedCases\'][j+len_known-1], df_interest[\'ConfirmedCases\'][j+len_known], pred_c)\n        df_interest = df_interest.drop([\n            \'cases/day_(1-1)\', \'cases/day_(1-7)\', \'cases/day_(8-14)\', \'cases/day_(15-21)\', \n            \'fatal/day_(1-1)\', \'fatal/day_(1-7)\', \'fatal/day_(8-14)\', \'fatal/day_(15-21)\',\n            \'days_since_1cases\', \'days_since_10cases\', \'days_since_100cases\',\n            \'days_since_1fatal\', \'days_since_10fatal\', \'days_since_100fatal\',\n\n                                       ],  axis=1)\n        df_interest = do_aggregations(df_interest)\n    if (i+1)%10==0:\n        print("{:3d}/{}  {}, len known: {}, len unknown: {}".format(i+1, len(places), place, len_known, len_unknown), df_interest.shape)\n    df_interest[\'fatal_pred\'] = np.cumsum(df_interest[\'fatal/day\'].values)\n    df_interest[\'cases_pred\'] = np.cumsum(df_interest[\'cases/day\'].values)\n    df_preds_pri.append(df_interest)\ndf_preds_pri = pd.concat(df_preds_pri)\''}, {'role': 'assistant', 'content': 'modelling'}]