[{'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:plt.scatter(comb_score_4_removed, comb_score_8_removed)\nplt.xlabel("Combined English, Reading and Math Scores (4th Grade)")\nplt.ylabel("Combined English, Reading and Math Scores (8th Grade)")\nplt.title("8th Grade Scores vs 4th Grade Scores (R = 0.859)")\nsecondPlot = plt.figure()\nax2 = secondPlot.add_subplot(111)\nax2.scatter(free_lunch, comb_score_4) #most negatively correlated\nplt.xlabel("Percent of Students Recieving Free or Reduced Lunch")\nplt.ylabel("Combined English, Reading and Math Scores (4th Grade)")\nplt.title("Free Lunch % vs 4th Grade Scores (R = -0.784)")\nsecondPlot = plt.figure()\nax2 = secondPlot.add_subplot(111)\nax2.scatter(income, comb_score_4) #second most positively correlated\nplt.xlabel("Income per Capita")\nplt.ylabel("Combined English, Reading and Math Scores (4th Grade)")\nplt.title("Average Income vs 4th Grade Scores (R = 0.623)")\nprint("income", "free_lunch", scipy.stats.pearsonr(income, free_lunch)[0])\nsecondPlot = plt.figure()\nax2 = secondPlot.add_subplot(111)\nax2.scatter(income, free_lunch) #second most positively correlated\nplt.xlabel("Income per Capita")\nplt.ylabel("Percentage of Students Recieving Free Lunch")\nplt.title("Average Income vs % Free Lunch (R = -0.563)")'}, {'role': 'assistant', 'content': 'data_exploration'}]