[{'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:from sklearn.linear_model import Ridge\nfrom sklearn.preprocessing import PolynomialFeatures \nfrom sklearn.pipeline import make_pipeline\nfrom tqdm import tqdm\n\nplt.rcParams.update({\'font.size\': 12})\nfig,(ax0,ax1) = plt.subplots(1,2,figsize=(20, 8))\ncountries_europe = [\'Italy\', \'France\', \'Spain\', \'Germany\', \'United Kingdom\']\n\n# Take the 1st day as 2020-02-23\ndf = train_data.loc[train_data.Date >= \'2020-02-23\']\nn_days_europe = df.Date.nunique()\nrainbow_col= plt.cm.jet(np.linspace(0,1,len(countries)))\n\nfor country, c in tqdm(zip(countries,rainbow_col)): \n    df_country_train = df_max_fatality_country[df_max_fatality_country[\'Country_Region\']==country] \n    df_country_test = test_data[test_data[\'Country_Region\']==country]  \n    df_country_train = df_country_train.reset_index()[df_country_train.reset_index().Date > \'2020-02-22\']\n    n_days_sans_China = df.Date.nunique() - df_country_train.Date.nunique() \n    \n    x_train = np.arange(1, n_days_europe+1).reshape((-1,1))\n    x_test  = (np.arange(1,n_days_europe+n_test_days+1-overlap_days)).reshape((-1,1)) \n    y_train_f = df_country_train[\'Fatalities\']\n    #print (x_train, y_train_f)\n    model_f = make_pipeline(PolynomialFeatures(degree=3), Ridge(fit_intercept=False)) \n    model_f = model_f.fit(x_train, y_train_f)\n    y_predict_f = model_f.predict(x_test) \n    #print (x_test[-n_test_days:], y_predict_f[-n_test_days:])\n    y_train_c = df_country_train[\'ConfirmedCases\'] \n    model_c = make_pipeline(PolynomialFeatures(degree=3), Ridge(fit_intercept=False)) \n    model_c = model_c.fit(x_train, y_train_c)\n    y_predict_c = model_c.predict(x_test)\n    \n    extend_days_test = [i+len(x_test) for i in range(n_days_sans_China)]\n    x_test      = np.append(x_test, extend_days_test) \n    y_predict_c = np.pad(y_predict_c, (n_days_sans_China, 0), \'constant\')\n    y_predict_f = np.pad(y_predict_f, (n_days_sans_China, 0), \'constant\')\n    \n    ax0.plot(x_test[-n_test_days:], y_predict_c[-n_test_days:],linewidth=2, label=\'predict_\'+country, color=c)\n    ax0.plot(x_train, y_train_c, linewidth=2, color=c, linestyle=\'dotted\', label=\'train_\'+country)\n    ax0.set_title("Prediction vs Training for Confirmed Cases")\n    ax0.set_xlabel("Number of days")\n    ax0.set_ylabel("Confirmed Cases")\n    #ax0.legend(loc=\'center left\',bbox_to_anchor=(1.0, 0.5))\n    #ax0.set_yscale(\'log\')\n    \n    ax1.plot(x_test[-(n_test_days):], y_predict_f[-(n_test_days):],linewidth=2, label=\'predict_\'+country, color=c)\n    ax1.plot(x_train, y_train_f, linewidth=2, color=c, linestyle=\'dotted\', label=\'train_\'+country)\n    ax1.set_title("Prediction vs Training for Fatalities")\n    ax1.set_xlabel("Number of days")\n    ax1.set_ylabel("Fatalities")\n    ax1.legend(loc=\'center left\',bbox_to_anchor=(1.0, 0.5))\n    #ax1.set_yscale(\'log\')\''}, {'role': 'assistant', 'content': 'result_visualization'}]