[{'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:sns.set()\nfig, ax = plt.subplots()                               # Create a figure\nfig.set_size_inches(12, 6)                             # Set figure size\nsns.kdeplot(crime['Hour'], ax=ax)                      # Create a kernel density plot\nax.hist(crime['Hour'], bins=24, alpha=0.25,            # Create a histogram by hour with 24 bins\nnormed=True, label='Hour')\nfig.suptitle('Kernel Density with Histogram',          # Title of graph\nfontsize=16)\nax.set_xlabel('Hour of Day', fontsize = 14)            # Label x-axis\nax.legend()                                            # Add a legend\nplt.show()\narson = crime['Category'] == 'ARSON'                   # Removes the arson cases from the dataFrame\nfig, ax = plt.subplots()                               # Create a figure\nfig.set_size_inches(12, 6)                             # Set figure size\nsns.violinplot(x='Offense Description', y='Hour',      # Creates a violin plot of arson cases by hour of day\ndata=crime[arson])\nplt.xticks(rotation=90)                                # Rotate the axis labels so they are legible\nplt.show()"}, {'role': 'assistant', 'content': "['data_exploration']"}]