[{'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:print("Fatalities / Private")\nplt.figure(figsize=(30,30))\nfor i in range(30):\n    plt.subplot(5,6,i+1)\n    idx = i * 10\n    df_interest = df_preds_pri[df_preds_pri[\'place_id\']==places_sort[idx]].reset_index(drop=True)\n    tmp = df_interest[\'fatal/day\'].values\n    tmp = np.cumsum(tmp)\n    sns.lineplot(x=df_interest[\'day\'], y=tmp, label=\'pred\')\n    df_interest2 = df_traintest10[(df_traintest10[\'place_id\']==places_sort[idx]) & (df_traintest10[\'day\']<=day_before_private)].reset_index(drop=True)\n    sns.lineplot(x=df_interest2[\'day\'].values, y=df_interest2[\'Fatalities\'].values, label=\'true\')\n    plt.title(places_sort[idx])\nplt.show()\''}, {'role': 'assistant', 'content': 'result_visualization'}]