code: 
  from sklearn.datasets import load_iris
  from ravop.ml.knn import KNN
  from sklearn.model_selection import train_test_split
  from sklearn.neighbors import KNeighborsClassifier
  import pandas as pd
  import numpy as np
  import sys
  
  
  dataset = load_iris()
  
  X = dataset.data
  X = X[:10]
  print("\n New X array is \n", X)
  y = dataset.target
  
  y = y[:10]
  print(type(y))
  print("\n New Y array is \n", y)
  
  # sys.exit(0)
  mu = np.mean(X, 0)
  sigma = np.std(X, 0)
  X = (X - mu ) / sigma
  
  X_train, X_test, y_train, y_test = train_test_split(\
                  X, y, test_size=0.1, random_state=45)
  
  our_classifier = KNN(X_train, y_train, n_neighbours=1, n_classes=3, weights="distance")
  sklearn_classifier = KNeighborsClassifier(n_neighbors=1).fit(X_train, y_train)
  
  our_accuracy = our_classifier.score(X_test, y_test)
  sklearn_accuracy = sklearn_classifier.score(X_test, y_test)
  
  print(" \n\n DISPLAY ACUURACY COMPARISON CHART \n\n", pd.DataFrame([[our_accuracy, sklearn_accuracy]],
               ['Accuracy'],
               ['Our Implementation', 'Sklearn\'s Implementation']))
  
  print("DONE -> Let's Move to Ravop")
apis: sklearn.neighbors.KNeighborsClassifier sklearn.model_selection.train_test_split sklearn.datasets.load_iris