[{'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:country = "Vietnam"\ndf_val = df_val_1\ndf_country = df_val[df_val[\'Country_Region\'] == country].groupby([\'Date\',\'Country_Region\']).sum().reset_index()\ndf_train = train[(train[\'Country_Region\'].isin(df_country[\'Country_Region\'].unique())) & (train[\'ConfirmedCases\'] > 0)].groupby([\'Date\']).sum().reset_index()\n\nidx = df_country[((df_country[\'ConfirmedCases\'].isnull() == False) & (df_country[\'ConfirmedCases\'] > 0))].shape[0]\nfig = px.line(df_country, x="Date", y="ConfirmedCases_hat", title=\'Forecast Total Cases of \' + df_country[\'Country_Region\'].values[0])\nfig.add_scatter(x=df_train[\'Date\'], y=df_train[\'ConfirmedCases\'], mode=\'lines\', name="Actual train", showlegend=True)\nfig.add_scatter(x=df_country[\'Date\'][0:idx], y=df_country[\'ConfirmedCases\'][0:idx], mode=\'lines\', name="Actual test", showlegend=True)\nfig.show()\n\nfig = px.line(df_country, x="Date", y="Fatalities_hat", title=\'Forecast Total Fatalities of \' + df_country[\'Country_Region\'].values[0])\nfig.add_scatter(x=df_train[\'Date\'], y=df_train[\'Fatalities\'], mode=\'lines\', name="Actual train", showlegend=True)\nfig.add_scatter(x=df_country[\'Date\'][0:idx], y=df_country[\'Fatalities\'][0:idx], mode=\'lines\', name="Actual test", showlegend=True)\n\nfig.show()\''}, {'role': 'assistant', 'content': "['result_visualization']"}]