[{'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:def majorities(clust_name):\ncount = 1\ntotal_agrees_among_clust = []\ntotal_disagree_among_clust = []\nfor name in clust_name:\nprint("CLUSTER:", count)\ncommon_agrees_among_clust = []\ncommon_disagree_among_clust = []\nfor col in name.columns:\nall_values=(name[str(col)].value_counts() / (len(name))*100)\nif \'Yes\' in all_values:\ntotal_true = all_values[\'Yes\']\nif total_true > 50.0:\nprint("Column Name:", str(col), \'Total Yes\', total_true)\nif \'No\' in all_values:\ntotal_false = all_values[\'No\']\nif total_false > 50.0:\nprint("Column Name:", str(col), \'Total No\', total_false)\nif "Radio" in all_values:\nif all_values[\'Radio\'] > 50.0:\nprint("Column Name:", str(col), "Total Radio", all_values[\'Radio\'])\nif "Internet" in all_values:\nif all_values[\'Internet\'] > 50.0:\nprint("Column Name:", str(col), "Total Internet", all_values[\'Internet\'])\nif "Television" in all_values:\nif all_values[\'Television\'] > 50.0:\nprint("Column Name:", str(col), "Total Television", all_values[\'Television\'])\nif "Newspaper" in all_values:\nif all_values[\'Newspaper\'] > 50.0:\nprint("Column Name:", str(col), "Total Newspaper", all_values[\'Newspaper\'])\nif \'Positive\' in all_values and \'Very Positive\' in all_values:\ntotal_positive = all_values[\'Positive\'] + all_values[\'Very Positive\']\nif total_positive > 50.0:\nprint("Column Name:", str(col), \'Total Positive\', total_positive)\nif \'Negative\' in all_values and \'Very Negative\' in all_values:\ntotal_negative = all_values[\'Negative\'] + all_values[\'Very Negative\']\nif total_negative > 50.0:\nprint("Column Name:", str(col), \'Total Negative\', total_negative)\nif \'Agree\' in all_values and \'Strongly Agree\' in all_values:\ntotal_agree = all_values[\'Agree\'] + all_values[\'Strongly Agree\']\nif total_agree > 50.0:\nprint("Column Name:", str(col), \'Total Agree\', total_agree)\ncommon_agrees_among_clust.append(str(col))\nif \'Disagree\' in all_values and \'Strongly Disagree\' in all_values:\ntotal_disagree = all_values[\'Disagree\'] + all_values[\'Strongly Disagree\']\nif total_disagree > 50.0:\nprint("Column Name:", str(col), \'Total Disagree\', total_disagree)\ncommon_disagree_among_clust.append(str(col))\nif "Aid" in all_values:\nif all_values[\'Aid\'] > 50.0:\nprint("Column Name:", str(col), "Total Aid", all_values[\'Aid\'])\nif "Country" in all_values:\nif all_values[\'Country\'] > 50.0:\nprint("Column Name:", str(col), "Total Country", all_values[\'Country\'])\nif "Drones" in all_values:\nif all_values[\'Drones\'] > 50.0:\nprint("Column Name:", str(col), "Total Drones", all_values[\'Drones\'])\nif "Iraq" in all_values:\nif all_values[\'Iraq\'] > 50.0:\nprint("Column Name:", str(col), "Total Iraq", all_values[\'Iraq\'])\nif "Palestinian Israeli Crisis" in all_values:\nif all_values[\'Palestinian Israeli Crisis\'] > 50.0:\nprint("Column Name:", str(col), "Total Palestinian Israeli Crisis", all_values[\'Palestinian Israeli Crisis\'])\nif "Presence" in all_values:\nif all_values[\'Presence\'] > 50.0:\nprint("Column Name:", str(col), "Total Presence", all_values[\'Presence\'])\nif "Spies" in all_values:\nif all_values[\'Spies\'] > 50.0:\nprint("Column Name:", str(col), "Total Spies", all_values[\'Spies\'])\nif "Drone Strike" in all_values:\nif all_values[\'Drone Strike\'] > 50.0:\nprint("Column Name:", str(col), "Total Spies", all_values[\'Drone Strike\'])\nif "Taiz burnings" in all_values:\nif all_values[\'Taiz burnings\'] > 50.0:\nprint("Column Name:", str(col), "Total Taiz burnings", all_values[\'Taiz burnings\'])\nif "Hospital" in all_values:\nif all_values[\'Hospital\'] > 50.0:\nprint("Column Name:", str(col), "Total Hospital", all_values[\'Hospital\'])\nif "Gas Crisis" in all_values:\nif all_values[\'Gas Crisis\'] > 50.0:\nprint("Column Name:", str(col), "Total Gas Crisis", all_values[\'Gas Crisis\'])\nif "Electricity Crisis" in all_values:\nif all_values[\'Electricity Crisis\'] > 50.0:\nprint("Column Name:", str(col), "Total Electricity Crisis", all_values[\'Electricity Crisis\'])\nif "Terrorism" in all_values:\nif all_values[\'Terrorism\'] > 50.0:\nprint("Column Name:", str(col), "Total Terrorism", all_values[\'Terrorism\'])\nif "Water Crisis" in all_values:\nif all_values[\'Water Crisis\'] > 50.0:\nprint("Column Name:", str(col), "Total Water Crisis", all_values[\'Water Crisis\'])\nif "Food Crisis" in all_values:\nif all_values[\'Food Crisis\'] > 50.0:\nprint("Column Name:", str(col), "Total Food Crisis", all_values[\'Food Crisis\'])\nif "Civil War" in all_values:\nif all_values[\'Civil War\'] > 50.0:\nprint("Column Name:", str(col), "Total Civil War", all_values[\'Civil War\'])\nif "Religion" in all_values:\nif all_values[\'Religion\'] > 50.0:\nprint("Column Name:", str(col), "Total Religion", all_values[\'Religion\'])\nif "Family" in all_values:\nif all_values[\'Family\'] > 50.0:\nprint("Column Name:", str(col), "Total Family", all_values[\'Family\'])\nif "Work or Employment" in all_values:\nif all_values[\'Work or Employment\'] > 50.0:\nprint("Column Name:", str(col), "Total Work or Employment", all_values[\'Work or Employment\'])\nif "Health" in all_values:\nif all_values[\'Health\'] > 50.0:\nprint("Column Name:", str(col), "Total Health", all_values[\'Health\'])\nif "Education" in all_values:\nif all_values[\'Education\'] > 50.0:\nprint("Column Name:", str(col), "Total Education", all_values[\'Education\'])\nif "Leisure" in all_values:\nif all_values[\'Leisure\'] > 50.0:\nprint("Column Name:", str(col), "Total Leisure", all_values[\'Leisure\'])\nif col == \'q34_choice6\':\ntotal_agrees_among_clust.append(common_agrees_among_clust)\ntotal_disagree_among_clust.append(common_disagree_among_clust)\ncount += 1\nprint("\\n")\nprint("AGREE INTERSECTIONS:", set(total_agrees_among_clust[0]).intersection(*total_agrees_among_clust))\nprint("DISAGREE INTERSECTIONS:", set(total_disagree_among_clust[0]).intersection(*total_disagree_among_clust))\nmajorities(cluster_list)'}, {'role': 'assistant', 'content': 'prediction'}]