[{'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:lx = np.arange(0,45000,1000)\nly = lx\npl.figure(figsize = (6,6))\npl.plot(maleall, femaleall, \'or\', markersize = 10, alpha = 0.5, label = \'All\')\npl.plot(malewhite, femalewhite, \'ob\', markersize = 10, alpha = 0.5, label = \'White\')\npl.plot(maleblack, femaleblack, \'og\', markersize = 10, alpha = 0.5, label = \'Black\')\npl.plot(maleasian, femaleasian, \'oy\', markersize = 10, alpha = 0.5, label = \'Asian\')\npl.plot(malehis, femalehis, \'oc\', markersize = 10, alpha = 0.5, label = \'Hispanic\')\npl.plot(lx,ly)\npl.plot(all_males, all_males * result.x[0] + result.x[1] * 1e4, label="best fit line")\npl.plot(all_males, all_males * beta1 + beta0, label="analytical fit line")\npl.plot(all_males, all_males * lmallpar , \'k-\', label="sm.OLS fit all income")\npl.xlabel(\'Male median income\', fontsize = 16)\npl.ylabel(\'Female median income\', fontsize = 16)\npl.xlim(0,45000)\npl.ylim(0,45000)\nplt.legend()'}, {'role': 'assistant', 'content': "['data_exploration', 'result_visualization']"}]