[{'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:# This Python 3 environment comes with many helpful analytics libraries installed<br># It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python<br># For example, here\'s several helpful packages to load in <br><br>import numpy as np # linear algebra<br>import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)<br><br># Input data files are available in the "../input/" directory.<br># For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory<br><br>from subprocess import check_output<br>print(check_output(["ls", "../input"]).decode("utf8"))<br><br># Any results you write to the current directory are saved as output.'}, {'role': 'assistant', 'content': "['helper_functions']"}]