print("explain variance score ="round(sm explained_variance_score(y_testy_test_pred) )print(" score ="round(sm _score(y_testy_test_pred) )output performance of linear regressormean absolute error mean squared error median absolute error explain variance score - score - in the above codewe have used this small data if you want some big dataset then you can use sklearn dataset to import bigger dataset ,,, , , , ,,,, , ,, ,,,,, multivariable regressor firstlet us import few required packagesimport numpy as np from sklearn import linear_model import sklearn metrics as sm import matplotlib pyplot as plt from sklearn preprocessing import polynomialfeatures nowwe need to provide the input data and we have saved our data in the file named linear txt input ' :/programdata/mul_linear txtwe will load this data by using the np loadtxt function input_data np loadtxt(inputdelimiter=','xy input_data[::- ]input_data[:- the next step would be to train the modelwe will give training and testing samples training_samples int( len( )testing_samples len(xnum_training