[{'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.manifold import TSNE\n# from sklearn.preprocessing import StandardScaler\n\n\ndef process(x, cols=None, all_stores=False):\n    x.sort_values("Date",inplace=True)\n#     scaler = StandardScaler()\n    \n    if cols is None:\n        cols = x.columns\n        \n    x = x.fillna(x.median())\n       \n#     for i in x.columns[(x.dtypes.values == np.dtype(\'float64\'))]:\n#         if i not in [\'Id\', \'Promo2SinceWeek\', \'Promo2SinceYear\',\'CompetitionOpenSinceMonth\', \'CompetitionOpenSinceYear\', \'Sales\',\n#                         \'Quarter\', \'WeekOfYear\', \'PromoOpen\', \'Promo2SinceWeek\', \'Promo2SinceYear\']:\n#             x[i] = np.round(np.log1p(x[i]),2)\n    \n    x_train = x[x["Date"]<="2015-06-12"][cols].copy()\n    x_test  = x[x["Date"]>"2015-06-12"][cols].copy()\n\n    store_test = x_test[\'Store\'].unique().tolist()\n    x_train = x_train[(x_train[\'Store\'].isin(store_test))]\n    \n    y_train = np.log(x_train[\'Sales\'])\n    \n    if all_stores:\n        rmv = [\'Date\', \'Sales\']\n    else:\n        rmv = [\'Date\', \'Sales\', \'Store\']\n  \n    x_train= x_train.drop(rmv, 1)\n    x_train = pd.get_dummies(x_train)\n    x_train_arr = x_train.values\n    x_test_arr = pd.get_dummies(x_test.drop(rmv, 1)).values\n    \n    #scaler.fit(x_train_arr)\n    #x_train_arr = scaler.transform(x_train_arr)\n    #x_test_arr = scaler.transform(x_test_arr)\n    #reduc = TSNE(n_components=2)\n    #reduc.fit(x_train_arr)\n    #x_train_arr = reduc.transform(x_train_arr)\n    #x_test_arr = reduc.transform(x_test_arr)\n   \n    return x_train.columns, x_train_arr, y_train, x_test, x_test_arr\''}, {'role': 'assistant', 'content': 'modelling'}]