[{'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:def scrape_url(url):\n    \'\'\'\n    get the title of the wikipedia page and replace "_" with white space\n    \'\'\'\n    return url[29:].lower().replace("_"," ")\n\ndef check_name_in_string(name,string):\n    \'\'\'\n    check whether the name string is a substring of another string (i.e. wikipedia title)\n    \'\'\'\n\n    return name.lower() in string\n\n\n\ndef predict_coref(df):\n    pred =[]\n    for index, row in df.iterrows():\n        wiki_title = scrape_url(row["URL"])\n        if (check_name_in_string(row["A"],wiki_title)):\n            pred.append("A")\n        else:\n            if (check_name_in_string(row["B"],wiki_title)):\n                pred.append("B")\n            else:\n                pred.append("NEITHER")\n    return pred\n\ntrain_pred = predict_coref(train_df)\ntest_pred = predict_coref(test_df)\''}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]