Sklearn Linear Regression

import numpy as np
import pandas as pd
import sklearn
import matplotlib.pyplot as plt
from matplotlib import style
from mpl_toolkits.mplot3d import Axes3D
from sklearn import linear_model, preprocessing

style.use('dark_background')

DATA = pd.read_csv('CarPrice.csv', sep=',')
X = np.array(DATA[['wheelbase', 'horsepower', 'curbweight', 'enginesize']])
Y = np.array(DATA['price'])

X_Train, X_Test, Y_Train, Y_Test = sklearn.model_selection.train_test_split(X, Y, test_size=0.1)
LinearModel = linear_model.LinearRegression()
LinearModel.fit(X_Train, Y_Train)
Accuracity = LinearModel.score(X_Test, Y_Test)
print(Accuracity)

R = LinearModel.predict([[88.6, 111, 2548, 130]])
print(R)