[{'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:q = np.percentile(simulations, 1)\nfig = go.Figure()\nfig[\'data\'].append(go.Histogram(x = simulations, nbinsx = 100,\nmarker = dict(line = dict(color = \'white\', width = 1.3))))\nfig[\'layout\'].update(title = \'Distribution of Final Results\',\nannotations = [# Starting Price\ndict(x = 0.9, y = 0.9,\nshowarrow = False,\nxref = \'paper\', yref = \'paper\',\ntext = "Start Price: ${:.2f}".format(start_price)),\ndict(x = 0.9, y = 0.8,\nshowarrow = False,\nxref = \'paper\', yref = \'paper\',\ntext = "Mean Final Price: ${:.2f}".format(simulations.mean())),\ndict(x = 0.9, y = 0.7,\nshowarrow = False,\nxref = \'paper\', yref = \'paper\',\ntext = "VaR(0.99): ${:.2f}".format(start_price - q)),\ndict(x = 0.07, y = 0.8,\nshowarrow = False,\nxref = \'paper\', yref = \'paper\',\ntext = "q(0.99): ${:.2f}".format(q))],\nshapes = [# Plot a line at the 1% quantile result\ndict(type = \'line\',\nx0 = q,\nx1 = q,\ny0 = 0,\ny1 = 500,\nline = dict(color = \'red\', width = 2))])\nplotly.offline.iplot(fig)'}, {'role': 'assistant', 'content': 'result_visualization'}]