[{'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:card_counts_win = Counter()\ncard_counts_lose = Counter()\nfor hand in winning_cards:\nfor card in hand:\ncard_counts_win[card] += 1\nfor hand in losing_cards:\nfor card in hand:\ncard_counts_lose[card] += 1\nwin_labels, win_values = zip(*card_counts_win.items())\nlose_labels, lose_values = zip(*card_counts_lose.items())\nbar_width = 0.4\nopacity = 0.5\nwin_plt = plt.bar(win_labels, [value / num_win for value in win_values], bar_width, alpha=opacity, color='r', \\\nlabel='winning hands')\nlose_plt = plt.bar([label + bar_width for label in lose_labels], [value / num_lose for value in lose_values], \\\nbar_width, alpha=opacity, color='b', label='losing hands')\nplt.xlabel('card')\nplt.ylabel('percentage of hands')\nplt.title('Percentage of hands containing each card')\nplt.xlim((2,37))\nplt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\nplt.show()"}, {'role': 'assistant', 'content': "['data_exploration', 'modelling', 'result_visualization']"}]