[{'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:b"dfa = df[df.Country_Region == \'Italy\'][df.Province_State.isnull()][df.ConfirmedCases>10]\\nprint(dftesta.shape)\\nprint(dfa.shape)\\n\\nfrom matplotlib import pyplot\\nfrom statsmodels.tsa.ar_model import AR\\nfrom sklearn.metrics import mean_squared_error\\n\\nX = list(dfa.GrowthRate.values)\\n\\nX = [x for x in X if not np.isnan(x) and not np.isinf(x)]\\nprint(len(X))\\n\\ntrain, test = X[10:len(X)-6], X[len(X)-6:len(X)]\\nprint(len(train))\\n# train autoregression\\nmodel = AR(train)\\nmodel_fit = model.fit()\\nprint(\'Lag: %s\' % model_fit.k_ar)\\nprint(\'Coefficients: %s\' % model_fit.params)\\n# make predictions\\npredictions = model_fit.predict(start=len(train), end=len(train)+len(test)-1, dynamic=False)\\nfor i in range(len(predictions)):\\n\\tprint(\'predicted=%f, expected=%f\' % (predictions[i], test[i]))\\nerror = mean_squared_error(test, predictions)\\nprint(\'Test MSE: %.3f\' % error)\\n# plot results\\npyplot.plot(train[:15]+test)\\npyplot.plot(train[:15]+list(predictions), color=\'red\')\\npyplot.show()"'}, {'role': 'assistant', 'content': 'modelling'}]