[{'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:# dataframe\ndf = train.copy()\n\n# Min-Max((1, 0))\nscaler=MinMaxScaler()\ndf[\'revenue\']=scaler.fit_transform(df[[\'revenue\']])\n\n# \nfig = plt.figure(figsize=(10, 4))\nplt.subplots_adjust(wspace=0.4)\n\n# \nax = fig.add_subplot(1, 2, 1)\nsns.distplot(df[\'revenue\'], ax=ax)\n\n# QQ\nax2 = fig.add_subplot(1, 2, 2)\nstats.probplot(df[\'revenue\'], plot=ax2)\n\nplt.show()\n\n# \nprint(df[\'revenue\'].describe())\nprint("------------------------------")\nprint(": %f" % df[\'revenue\'].skew())\nprint(": %f" % df[\'revenue\'].kurt())\''}, {'role': 'assistant', 'content': "['result_visualization']"}]