[{'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:import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport random as rnd\n\n# visualization\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom nltk.classify import SklearnClassifier\nfrom sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier\nfrom sklearn.svm import SVR,NuSVR,LinearSVR,SVC #support vector regression\nfrom sklearn.linear_model import LinearRegression,Ridge,Lasso#Ridge() and Lasso()\nfrom sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn.model_selection import StratifiedKFold\ntest = pd.read_csv("../input/restaurant-revenue-prediction/test.csv")\ntrain = pd.read_csv("../input/restaurant-revenue-prediction/train.csv")\ntrain.head()'}, {'role': 'assistant', 'content': 'load_data'}]