[{'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:def main():\nx_col = \'sleep_time\'\n(X,Y) = read_data(x_col)\n(slope, intercept) = do_linear_regression(X,Y)\ndraw_chart(X, Y, slope, intercept, x_col)\ndef read_data(col_name):\nstudent_data = []\nwith open(\'student.txt\') as fp:\ncolumns = fp.readline().strip().split(\' \')\nfor line in fp:\nline_data = line.strip().split(\' \')\nline_data_numeric = [float(x) for x in line_data]\nstudent_data.append(line_Data_numeric)\nstudent_data = np.array(student_data)\ncol_index = columns.index(col_name)\nX = student_data[:, col_index]\nY = student_data[:,-1]\nreturn(X, Y)\ndef do_linear_regression(X,Y):\nslope, intercept, r_value, p_value, std_err = linregress(X,Y)\nreturn (slope, intercept)\ndef draw_chart(X, Y, slope, intercept, x_col):\nfig = plt.figure()\nfig.suptitle("Linear Reg. for Class Data")\nax = fig.add_subplot(111)\nax.set_xlabel(x_col)\nax.set_ylabel(\'GPA\')\nplt.scatter(X,Y)\nmin_X = min(X)\nmax_X = max(X)\nmin_Y = min_X * slope + intercept\nmax_Y = max_X * slope + intercept\nplt.plot([min_X, max_X], [min_Y, max_Y], color = \'red\', linestyle = \'-\',\nlinewidth = 3.0)\nax.text(min_X, min_Y + 0.1, r\'$y = %.2lfx + %.2lf$\' % (slope, intercept), fontsize=15)\nplt.savefig(\'chart.svg\')\nelice_utils.send_image(\'chart.svg\')\nif __name__ == "__main__":\nmain()'}, {'role': 'assistant', 'content': "['load_data', 'data_exploration', 'modelling']"}]