[{'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:# Libraries\nimport numpy as np\nimport pandas as pd\npd.set_option('max_columns', None)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nplt.style.use('seaborn')\n%matplotlib inline\nimport copy\nimport datetime\nimport lightgbm as lgb\nfrom scipy import stats\nfrom sklearn.model_selection import train_test_split, StratifiedKFold, KFold, cross_val_score, GridSearchCV, RepeatedStratifiedKFold\nfrom sklearn.preprocessing import StandardScaler\nimport os\nimport plotly.offline as py\npy.init_notebook_mode(connected=True)\nimport plotly.graph_objs as go\nimport plotly.tools as tls\nimport xgboost as xgb\nimport lightgbm as lgb\nfrom sklearn import model_selection\nfrom sklearn.metrics import accuracy_score, roc_auc_score, log_loss, classification_report, confusion_matrix\nimport json\nimport ast\nimport time\nfrom sklearn import linear_model\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nimport os\nimport glob\n\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.preprocessing import LabelEncoder"}, {'role': 'assistant', 'content': "['helper_functions']"}]