[{'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:def plot_rreg_basic_country(data, country_name, dates_list, day_start, shift):\n    \n    data_country = data[data[\'Country_Region\']==country_dict[country_name]]\n    data_country = data_country.loc[data_country[\'Day_num\']>=day_start]\n    X_train, Y_train_1, Y_train_2, X_test = split_data(data_country)\n    model, pred = ridge_reg(X_train, Y_train_1, X_test)\n\n    # Create a df with both real cases and predictions (predictions starting on March 12th)\n    X_train_check = X_train.copy()\n    X_train_check[\'Target\'] = Y_train_1\n\n    X_test_check = X_test.copy()\n    X_test_check[\'Target\'] = pred\n\n    X_final_check = pd.concat([X_train_check, X_test_check])\n\n    # Select predictions from March 1st to March 25th\n    predicted_data = X_final_check.loc[(X_final_check[\'Day_num\'].isin(list(range(day_start, day_start+len(dates_list)))))].Target\n    real_data = train.loc[(train[\'Country_Region\']==country_name) & (train[\'Date\'].isin(dates_list))][\'ConfirmedCases\']\n    dates_list_num = list(range(0,len(dates_list)))\n\n    # Plot results\n    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15,6))\n\n    ax1.plot(dates_list_num, np.expm1(predicted_data))\n    ax1.plot(dates_list_num, real_data)\n    ax1.axvline(17-shift, linewidth=2, ls = \':\', color=\'grey\', alpha=0.5)\n    ax1.legend([\'Predicted cases\', \'Actual cases\', \'Train-test split\'], loc=\'upper left\')\n    ax1.set_xlabel("Day count (from March " + str(1+shift) + " to March 25th)")\n    ax1.set_ylabel("Confirmed Cases")\n\n    ax2.plot(dates_list_num, predicted_data)\n    ax2.plot(dates_list_num, np.log1p(real_data))\n    ax2.axvline(17-shift, linewidth=2, ls = \':\', color=\'grey\', alpha=0.5)\n    ax2.legend([\'Predicted cases\', \'Actual cases\', \'Train-test split\'], loc=\'upper left\')\n    ax2.set_xlabel("Day count (from March " + str(1+shift) + " to March 30th)")\n    ax2.set_ylabel("Log Confirmed Cases")\n\n    plt.suptitle(("ConfirmedCases predictions based on Log-Lineal Regression for "+country_name))\n    \n    \n    \n# Filter Spain, run the Linear Regression workflow\ncountry_name = "Spain"\nmarch_day = 0\nday_start = 39+march_day\ndates_list2 = dates_list[march_day:]\nplot_rreg_basic_country(data, country_name, dates_list2, day_start, march_day)\''}, {'role': 'assistant', 'content': 'result_visualization'}]