[{'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:%matplotlib inline\nimport numpy as np\nimport statistics as stat\nimport matplotlib.pyplot as plt\nimport scipy.stats\ndata_file = open("MCAS.csv","r")\ndata_list = []\nfor ii in data_file:\ndata_list.append(ii)\ndel data_list[0];\ndist_code = []\nmunicipal = []\ndist_name = []\ncomb_score_4 = [] #scores are math + eng + sci\ncomb_score_8 = []\nreg_day_spend = [] #all spending is per day\nspec_need_spend = []\nbiling_spend = []\noccup_therapy_spend = []\ntotal_spend = []\nstud_teach = [] #students per teacher\nstud_computer = [] #students per computer\nspec_ed = [] #% special ed students\nfree_lunch = [] #%eligilbe for reduced price lunch\navg_salary = [] #avg teacher salary\nincome = [] # per capita income\neng_learn = [] #% english learners\nfor jj in range(len(data_list)):\ndata_list[jj] = data_list[jj].replace("\\n", "");\ndata_list[jj] = data_list[jj].split(",");\ndist_code.append(float(data_list[jj][1]))\nmunicipal.append(data_list[jj][2])\ndist_name.append(data_list[jj][3])\nreg_day_spend.append(float(data_list[jj][4]))\nspec_need_spend.append(float(data_list[jj][5]))\nbiling_spend.append(float(data_list[jj][6]))\noccup_therapy_spend.append(float(data_list[jj][7]))\ntotal_spend.append(float(data_list[jj][8]))\ntry:\nstud_computer.append(float(data_list[jj][9]))\nexcept:\nstud_computer.append(0.0)\nspec_ed.append(float(data_list[jj][10]))\nfree_lunch.append(float(data_list[jj][11]))\nstud_teach.append(float(data_list[jj][12]))\nincome.append(float(data_list[jj][13]))\ntry:\ncomb_score_4.append(float(data_list[jj][14]))\nexcept:\ncomb_score_4.append(0.0)\ntry:\ncomb_score_8.append(float(data_list[jj][15]))\nexcept:\ncomb_score_8.append(0.0)\ntry:\navg_salary.append(float(data_list[jj][16]))\nexcept:\navg_salary.append(0.0)\neng_learn.append(float(data_list[jj][17]))\nbig_list = [reg_day_spend, spec_need_spend, biling_spend, occup_therapy_spend, total_spend,\nstud_computer, spec_ed, free_lunch, stud_teach, income, comb_score_4, comb_score_8,\navg_salary, eng_learn]\nbig_list_str = ["reg_day_spend", "spec_need_spend", "biling_spend", "occup_therapy_spend", "total_spend",\n"stud_computer", "spec_ed", "free_lunch", "stud_teach", "income", "comb_score_4", "comb_score_8",\n"avg_salary", "eng_learn"]\npearson_r = [] #array to store pearson correlation coefficient\nelement_1 = [] #array to store what the first input array was to the pearson r function (as str)\nelement_2 = [] #array to store what the first input array was to the pearson r function (as str)\nfor kk in range(len(big_list)):\nfor ll in range(len(big_list)):\nif kk != ll:\npearson_r.append(scipy.stats.pearsonr(big_list[kk],big_list[ll])[0])\nelement_1.append(big_list_str[kk])\nelement_2.append(big_list_str[ll])\ncomb_score_4_removed = []\ncomb_score_8_removed = []\nfor pp in range (len(comb_score_8)):\nif comb_score_8[pp] != 0:\ncomb_score_4_removed.append(comb_score_4[pp])\ncomb_score_8_removed.append(comb_score_8[pp])\ncomb_score_4_removed2 = []\navg_salary_removed = []\nfor qq in range (len(comb_score_8)):\nif avg_salary[qq] != 0:\ncomb_score_4_removed2.append(comb_score_4[qq])\navg_salary_removed.append(avg_salary[qq])\ncomb_score_4_removed3 = []\nstud_computer_removed = []\nfor rr in range (len(comb_score_8)):\nif stud_computer[rr] != 0:\ncomb_score_4_removed3.append(comb_score_4[rr])\nstud_computer_removed.append(avg_salary[rr])\nr_input = [] #stores correlation coefficients of all variables compared\nr_input_4 = [] #stores correlation coefficients of 4th scores compared w other variables\nfor mm in range(len(pearson_r)):\ntuple1 = (element_1[mm], element_2[mm], pearson_r[mm])\nr_input.append(tuple1)\nif element_1[mm] == "comb_score_4":\nr_input_4.append(tuple1)\nr_input.append(("comb_score_4_removed", "comb_score_8_removed", scipy.stats.pearsonr(comb_score_4_removed, comb_score_8_removed)[0]))\nr_input_4.append(("comb_score_4_removed", "comb_score_8_removed", scipy.stats.pearsonr(comb_score_4_removed, comb_score_8_removed)[0]))\nr_input.append(("comb_score_4_removed2", "avg_salary_removed", scipy.stats.pearsonr(comb_score_4_removed2, avg_salary_removed)[0]))\nr_input_4.append(("comb_score_4_removed2", "avg_salary_removed", scipy.stats.pearsonr(comb_score_4_removed2, avg_salary_removed)[0]))\nr_input.append(("comb_score_4_removed3", "stud_computer_removed", scipy.stats.pearsonr(comb_score_4_removed3, stud_computer_removed)[0]))\nr_input_4.append(("comb_score_4_removed3", "stud_computer_removed", scipy.stats.pearsonr(comb_score_4_removed3, stud_computer_removed)[0]))\nsort_r_input = []\nsort_r_input_4 = []\nsort_r_input = sorted(r_input, key=lambda s: -abs(s[2]))\nsort_r_input_4 = sorted(r_input_4, key=lambda s: -abs(s[2]))\nprint("Variables Most Correlated and their Pearson R Correlation Coefficient\\n")\nprint(sort_r_input[0][0],"\\t\\t", sort_r_input[0][1],"\\t\\t", sort_r_input[0][2])\nprint(sort_r_input[2][0],"\\t", sort_r_input[2][1],"\\t", sort_r_input[2][2])\nprint(sort_r_input[3][0],"\\t\\t", sort_r_input[3][1],"\\t\\t", sort_r_input[3][2])\nprint("\\n")\nprint("Variables Most Correlated with 4th Grade Test Scores and their Pearson R \\nCorrelation Coefficient\\n")\nprint(sort_r_input_4[0][0],"\\t", sort_r_input_4[0][1],"\\t", sort_r_input_4[0][2])\nprint(sort_r_input_4[1][0],"\\t\\t", sort_r_input_4[1][1],"\\t\\t", sort_r_input_4[1][2])\nprint(sort_r_input_4[2][0],"\\t\\t", sort_r_input_4[2][1],"\\t\\t", sort_r_input_4[2][2])'}, {'role': 'assistant', 'content': "['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing']"}]