KNN Algorithm

import sklearn
from sklearn.utils import shuffle
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
import numpy as np
from sklearn import linear_model, preprocessing

CarsData = pd.read_csv("car.data")
le = preprocessing.LabelEncoder()
buying = le.fit_transform(list(CarsData["buying"]))
maint = le.fit_transform(list(CarsData["maint"]))
door = le.fit_transform(list(CarsData["door"]))
persons = le.fit_transform(list(CarsData["persons"]))
lug_boot = le.fit_transform(list(CarsData["lug_boot"]))
safety = le.fit_transform(list(CarsData["safety"]))
cls = le.fit_transform(list(CarsData["class"]))

MyPredict = "class"

X = list(zip(buying, maint, door, persons, lug_boot, safety))  # Features
y = list(cls)  # Labels
#n = 7
Max_Ac = 0
KNN = None
for n in range (1,10):
    for i in range (10):
        x_train, x_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.1)
        KNN_Model = KNeighborsClassifier(n_neighbors=n)

        KNN_Model.fit(x_train, y_train)
        Accuracity = KNN_Model.score(x_test, y_test)
        if Accuracity > Max_Ac:
            Max_Ac = Accuracity
            KNN = n
Accuracity = str("- Max Accuracity: ") + str(round(Max_Ac*100, 2)) + str("%") + str("\n") + str("- Neighbors: ") + str(KNN)
print(Accuracity)