[{'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 == \'Spain\'][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[:len(X)-6], X[len(X)-6:len(X)]\\nprint(len(train))\\n# train autoregression\\nmodel = AR(train)\\nmodel_fit = model.fit()\\nwindow = model_fit.k_ar\\ncoef = model_fit.params\\n# walk forward over time steps in test\\nhistory = train[len(train)-window:]\\nhistory = [history[i] for i in range(len(history))]\\npredictions = list()\\nfor t in range(len(test)+31):\\n\\tlength = len(history)\\n\\tlag = [history[i] for i in range(length-window,length)]\\n\\tyhat = coef[0]\\n\\tfor d in range(window):\\n\\t\\tyhat += coef[d+1] * lag[window-d-1]\\n\\tif t >= len(test):\\n\\t\\ttest.append(yhat)\\n\\tobs = test[t]\\n\\tpredictions.append(yhat)\\n\\thistory.append(obs)\\n\\tprint(\'predicted=%f, expected=%f\' % (yhat, obs))\\nerror = mean_squared_error(test, predictions)\\nprint(\'Test MSE: %.3f\' % error)\\n# plot\\npyplot.plot(train+test)\\npyplot.plot(train+predictions, color=\'red\')\\npyplot.show()"'}, {'role': 'assistant', 'content': 'data_preprocessing'}]