[{'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:from sklearn.preprocessing import StandardScaler,MinMaxScaler\nfrom sklearn.preprocessing import OneHotEncoder,LabelEncoder,OrdinalEncoder\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.pipeline import make_pipeline\n\ntransformer = make_column_transformer(\n    (StandardScaler(),['CompetitionDistance', 'Customers_enc_mean']),\n    (OneHotEncoder(),['StateHoliday', 'StoreType', 'Assortment', 'DayOfWeek', 'month']),\n    remainder = 'passthrough'\n)\n\n\nimport xgboost as xgb\n\nregressor = xgb.XGBRegressor(n_estimators = 200,\n                             max_depth    =  10\n                            )\n\n\npipeline = make_pipeline(transformer,\n                         regressor)\n\n\npipeline.fit(X_train,y_train)\n\n\nprint('TRAIN RMSPE = ',rmspe(np.expm1(pipeline.predict(X_train)),np.expm1(y_train)))\nprint('VAL   RMSPE = ',rmspe(np.expm1(pipeline.predict(X_val)),np.expm1(y_val)))"}, {'role': 'assistant', 'content': "['modelling']"}]