text: import pandas
  import numpy as np
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import confusion_matrix
  from sklearn import preprocessing
  import sys
  import os
  path=os.getcwd()
  
  path=path.strip('with_set')
  path=path.strip('set_divide')
  sys.path.append(path)
  from helper import svm,sgd,knn,dtree,lda
  import glob
  path = os.path.split(path)[0]
  path =path.strip('ML')
  path = path+'/CSV_Data/set_experiment'
  allfiles = glob.glob(path+"/*")
  
  setlist = [['a','m','n','s','t','o','g','q','x'],['b','e','c'],['h','k','u','v'],
  ['d','r','p'],['f'],['l'],['i'],['w'],['y']]
  set_name_list = ['set_'+str(x) for x in range(len(setlist))]
  for filename in allfiles:
  
      all_data = pandas.read_csv(filename)
      for curr_set,i in zip(setlist,set_name_list):
          all_data['label'][all_data['label'].isin(curr_set)] = i
  
      sum_acc=0;
      sum_conf_matrix = [[0 for x in range(len(set_name_list))] for y in range(len(set_name_list))]
      for i in range(100):
          acc , conf ,report= knn.find_accuracy(all_data)
          sum_acc = sum_acc + acc
          sum_conf_matrix = np.add(sum_conf_matrix,conf)
      print(filename)
      print(sum_acc/100)
  