KNN Algorithm

from pandas import read_csv
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import preprocessing
from matplotlib import style
import sklearn
import numpy as np

style.use('bmh')

DATA = read_csv('Iris_Classifier.csv')
X = np.array(DATA[['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm']])
Y = np.array(DATA['Species'])
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=5)
KNN_Model.fit(X_Train, Y_Train)
Accuracity = KNN_Model.score(X_Test, Y_Test)
print('Accuracity:', str(Accuracity*100)[:5] + str('%'))

Prediction = KNN_Model.predict([[5.1, 3.5, 1.4, 0.2]])[0]
print(Prediction)

labelEncoder = preprocessing.LabelEncoder()
numericSpecies = labelEncoder.fit_transform(DATA['Species'])
ax = plt.axes(projection='3d')
for i in range(len(DATA)):
    ax.scatter3D(DATA['SepalLengthCm'][i], DATA['SepalWidthCm'][i], numericSpecies[i], color=('red', 'green', 'blue')[numericSpecies[i]], alpha=1, edgecolors='black')
plt.show()