house price prediction

import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import metrics
from sklearn.linear_model import LinearRegression

df = pd.read_csv("Housing.csv")
newdf = df[["price", "bedrooms"]]
X = newdf.drop(['price'], axis=1)
Y = newdf['price']

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)

model = LinearRegression()
model.fit(X_train, Y_train)
Y_predict = model.predict(X_test)

score_1 = metrics.r2_score(Y_test, Y_predict)
score_2 = metrics.mean_absolute_error(Y_test, Y_predict)
score_3 = metrics.mean_squared_error(Y_test, Y_predict)

print('Mean Squared Error:', score_3)
print('R Squared Error:', score_1)
print('Mean Absolute Error:', score_2)