[{'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:# Loop through each store, \n# train the model using the data of current store, and predict it\'s sales values.\n\nrossmann_dic = dict(list(rossmann_df.groupby(\'Store\')))\ntest_dic     = dict(list(test_df.groupby(\'Store\')))\nsubmission   = Series()\nscores       = []\n\nfor i in test_dic:\n    \n    # current store\n    store = rossmann_dic[i]\n    \n    # define training and testing sets\n    X_train = store.drop(["Sales","Store"],axis=1)\n    Y_train = store["Sales"]\n    X_test  = test_dic[i].copy()\n    \n    store_ids = X_test["Id"]\n    X_test.drop(["Id","Store"], axis=1,inplace=True)\n    \n    # Linear Regression\n    lreg = LinearRegression()\n    lreg.fit(X_train, Y_train)\n    Y_pred = lreg.predict(X_test)\n    scores.append(lreg.score(X_train, Y_train))\n\n    # Xgboost\n    # params = {"objective": "reg:linear",  "max_depth": 10}\n    # T_train_xgb = xgb.DMatrix(X_train, Y_train)\n    # X_test_xgb  = xgb.DMatrix(X_test)\n    # gbm = xgb.train(params, T_train_xgb, 100)\n    # Y_pred = gbm.predict(X_test_xgb)\n    \n    # append predicted values of current store to submission\n    submission = submission.append(Series(Y_pred, index=store_ids))\n\n# append rows(store,date) that were closed, and assign their sales value to 0\nsubmission = submission.append(Series(0, index=closed_store_ids))\n\n# save to csv file\nsubmission = pd.DataFrame({ "Id": submission.index, "Sales": submission.values})\nsubmission.to_csv(\'rossmann.csv\', index=False)\''}, {'role': 'assistant', 'content': "['modelling']"}]