[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:if Make_submission==True:\n    results=[]\n\n    for d in days_to_predict:\n        print("Predicting {}".format(d))\n        if d in known:\n            print("Data Known")\n        \n            X=big_train.drop(columns=[\'Province_State\',\'Country_Region\',\'ConfirmedCases\',\'Fatalities\',\'location\',\'Date\',\n                                  \'day_of_year\',\'day_of_week\',\'d_ConfirmedCases\',\'d_Fatalities\'])\n\n            y=big_train[\'d_ConfirmedCases\']\n            y_2=big_train[\'d_Fatalities\']\n        \n            mask_train=big_train[\'Date\']<d\n            mask_val=big_train[\'Date\']==d\n        \n            X_train=X[mask_train]\n            y_train=y[mask_train]\n            y_train_2=y_2[mask_train]\n        \n            X_val=X[mask_val]\n            y_val=y[mask_val]\n            y_val_2=y_2[mask_val]\n        \n            reg = xgb.XGBRegressor(n_estimators=n_estimators,max_depth=max_depth,random_state=42)\n            reg_2 = xgb.XGBRegressor(n_estimators=n_estimators,max_depth=max_depth,random_state=42)\n        \n            reg.fit(X_train,np.log1p(y_train))\n        \n            y_pred = reg.predict(X_val)\n            print("MSLE {}".format(mean_squared_error(y_pred,np.log1p(y_val))))\n        \n            X_train_2=X_train.copy()\n            X_train_2[\'d_ConfirmedCases\']=np.log1p(y_train)  #0.4412899060661785 <- without , with - 0.4463  \n            X_val_2=X_val.copy()\n            X_val_2[\'d_ConfirmedCases\']=y_pred\n        \n            reg_2.fit(X_train_2,np.log1p(y_train_2))\n        \n            y_pred_2 = reg_2.predict(X_val_2)\n        \n            print("MSLE {}".format(mean_squared_error(y_pred_2,np.log1p(y_val_2))))\n        \n        #result=X_test[[\'\']]\n        elif d-np.timedelta64(86400000000000,\'ns\') in known:\n            print("Data Known")\n        \n            X=big_train.drop(columns=[\'Province_State\',\'Country_Region\',\'ConfirmedCases\',\'Fatalities\',\'location\',\'Date\',\n                                  \'day_of_year\',\'day_of_week\',\'d_ConfirmedCases\',\'d_Fatalities\'])\n\n            y=big_train[\'d_ConfirmedCases\']\n            y_2=big_train[\'d_Fatalities\']\n        \n            mask_train=big_train[\'Date\']<d\n        \n            X_train=X[mask_train]\n            y_train=y[mask_train]\n            y_train_2=y_2[mask_train]\n        \n        \n            reg = xgb.XGBRegressor(n_estimators=n_estimators,max_depth=max_depth,random_state=42)\n            reg_2 = xgb.XGBRegressor(n_estimators=n_estimators,max_depth=max_depth,random_state=42)\n        \n            reg.fit(X_train,np.log1p(y_train))\n        \n            X_train_2=X_train.copy()\n            X_train_2[\'d_ConfirmedCases\']=np.log1p(y_train)  #0.4412899060661785 <- without , with - 0.4463  \n            \n            reg_2.fit(X_train_2,np.log1p(y_train_2))\n        \n        \n        \n        X_test=test[test[\'Date\']==d]\n    \n        day=X_test[\'day_of_year\'].iloc[0]\n    \n        country_info=big_train[big_train[\'day_of_year\']==day-1].groupby([\'Country_Region\'])[\'ConfirmedCases\'].sum()\n    \n        border_cases=border_info.merge(country_info, how=\'inner\', \n                left_on=[\'country_border_name\'],right_on=[\'Country_Region\'])\n    \n        border_cases=border_cases.groupby([\'country_name\'])[\'ConfirmedCases\'].sum()\n        border_cases=border_cases.rename(\'ConfirmedCases_neighbors\')\n    \n        X_test=X_test.merge(border_cases, how=\'left\', left_on=[\'Country_Region\'],right_on=[\'country_name\'])\n        X_test[\'ConfirmedCases_neighbors\']=X_test[\'ConfirmedCases_neighbors\'].fillna(0)\n    \n        X_test = pd.concat([X_test,pd.get_dummies(X_test[\'location\'], prefix=\'loc\')],axis=1)\n        X_test[\'ConfirmedCases_neighbors\']=np.log1p(X_test[\'ConfirmedCases_neighbors\'])\n        \n        X_test=X_test.merge(big_train[big_train[\'day_of_year\']==day-1][[\'location\',\'ConfirmedCases\',\'Fatalities\']], how=\'left\', \n                 left_on=[\'location\'],right_on=[\'location\'])\n        X_test.rename(columns={\'ConfirmedCases\':\'prev_ConfirmedCases\',\'Fatalities\':\'prev_Fatalities\'},inplace=True)\n        \n        X_test[\'prev_ConfirmedCases\']=np.log1p(X_test[\'prev_ConfirmedCases\'])\n        X_test[\'prev_Fatalities\']=np.log1p(X_test[\'prev_Fatalities\'])\n        \n    \n        X_test.set_index(\'Id\',inplace=True)\n    \n    #print(X_test.head(5))\n    \n        y_test=reg.predict(X_test.drop(columns=[\'Province_State\',\'Country_Region\',\'location\',\'Date\',\'day_of_year\',\'day_of_week\']))\n    \n    #print(y_test)\n    \n        X_test[\'d_ConfirmedCases\']=y_test\n    \n        y_test=reg_2.predict(X_test.drop(columns=[\'Province_State\',\'Country_Region\',\'location\',\'Date\',\n                                            \'day_of_year\',\'day_of_week\']))\n    \n        X_test[\'d_Fatalities\']=y_test\n    \n    #print(X_test.shape)\n    \n        X_test[\'Id\']=X_test.index\n    \n        X_test=X_test.merge(big_train[big_train[\'day_of_year\']==day-1][[\'location\',\'ConfirmedCases\',\'Fatalities\']], how=\'left\', \n                 left_on=[\'location\'],right_on=[\'location\'])\n    \n    #print(X_test.head(5))\n    \n    #X_test.set_index(\'Id\',inplace=True)\n    \n    #print(X_test.shape)\n    \n        X_test.set_index(\'Id\',inplace=True)\n    \n        print(X_test.head(5))\n        \n        X_test[\'d_ConfirmedCases\']=np.expm1(X_test[\'d_ConfirmedCases\'])\n        X_test[\'d_Fatalities\']=np.expm1(X_test[\'d_Fatalities\'])\n    \n        X_test[\'ConfirmedCases\']+=X_test[\'d_ConfirmedCases\']\n        X_test[\'Fatalities\']+=X_test[\'d_Fatalities\']\n       \n    \n    \n        results.append(X_test[[\'ConfirmedCases\',\'Fatalities\']])\n    \n        if not d in known: #Needed to correctly get data on neighbors         \n            big_train=pd.concat([big_train,X_test])\n    \n    \n    \n    \n    \n    \n    \n    \n        \n        \n        \n        \''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]