[{'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:import matplotlib.pyplot as plt\nresults=np.where((df.did_search ==1) & (df.sent_booking_request==0) ,\'r\',\'b\')\nr1=(df.did_search ==1).value_counts()\nresults_1=np.where(df.sent_booking_request==1,\'b\',\'r\')\nr2=(df.sent_booking_request ==1).value_counts()\nfig = plt.figure()\nax1 = fig.add_subplot(111)\nax1.scatter(df.minutes_online,df.dim_session_number, s=10, c=results, marker="s", label=\'Only searched\')\nax1.scatter(df.minutes_online,df.dim_session_number, s=10, c=results_1, marker="s", label=\'Booking Done\')\nplt.legend(loc=\'upper left\');\nplt.xlabel(\'Minutes Online\')\nplt.ylabel(\'Session Number\')\nplt.title(\'Booking Trend AirBnb\')\nplt.show()\nprint(r1)\nprint(r2)'}, {'role': 'assistant', 'content': 'data_exploration'}]