[{'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:def model(case):\n    state = df['Province_State'].unique()\n    confirmed = []\n    for s in state:\n        train_ts = df[df['Province_State'] == s][:57]\n        pred_ts = df[df['Province_State'] == s][57:]\n        a = 9999\n        \n        for pdq in pdq_results:\n            try:\n                model = ARIMA(train_ts[case], order = pdq, dates = train_ts['Date'], freq = 'D')\n                model_fit = model.fit()\n                aicval = model_fit.aic\n            \n                if aicval < a:\n                    a = aicval\n                    param = pdq\n            except:\n                pass\n        \n        try:\n            model = ARIMA(train_ts[case], order = param, dates = train_ts['Date'], freq = 'D')\n            model_fit = model.fit()\n            pred = model_fit.forecast(steps = int(len(pred_ts)))[0]\n            confirmed = np.append(confirmed, pred.tolist())\n        except:\n            confirmed = np.append(confirmed, np.repeat(0, 43))\n            continue\n            \n    test[case] = confirmed"}, {'role': 'assistant', 'content': "['evaluation', 'prediction']"}]