[{'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:b"# Revenue is right skewed, taking the log will make it more normally distributed for the linear models\\n# Remember to use expm1 on predictions to transform back to dollar amount\\n(mu, sigma) = norm.fit(np.log1p(df[\'revenue\']))\\nf, (ax1, ax2) = plt.subplots(1, 2, figsize=(19, 5))\\nax1 = sns.distplot(np.log1p(df[\'revenue\']) , fit=norm, ax=ax1)\\nax1.legend([f\'Normal distribution ($\\\\mu=$ {mu:.3f} and $\\\\sigma=$ {sigma:.3f})\'], loc=\'best\')\\nax1.set_ylabel(\'Frequency\')\\nax1.set_title(\'Log(1+Revenue) Distribution\')\\nax2 = stats.probplot(np.log(df[\'revenue\']), plot=plt)\\nf.show();"'}, {'role': 'assistant', 'content': 'result_visualization'}]