df = df.dropna()

Now, we can split the dataset into training set and test set.

X_train, X_test, y_train, y_test = train_test_split(df[["horsepower", "weight", "acceleration"]], df["mpg"], train_size=0.8, shuffle=True, random_state=1)

Please note that the shuffle=True parameter indicates that we are shuffling the dataset while splitting. And the random_state=1 parameter controls the random number generator that is used for shuffling the data. We are keeping 80% of the data as training set and the rest are test set.

Now, we execute the following Python statements:

ElasticNet_regressor = ElasticNet()
ElasticNet_regressor.fit(X_train, y_train)

We first initialize the ElasticNet regressor. After that, we train the model using the training set. Now, the model can be run on the test set and the results can be compared with the actual values and the performance of the model can be evaluated.

y_test_predicted = ElasticNet_regressor.predict(X_test)
 
r2 = r2_score(y_test, y_test_predicted)
rmse = mean_squared_error(y_test, y_test_predicted, squared=False)
 
print("R2: ", r2)
print("RMSE: ", rmse)

The R-squared score and the Root Mean Square Error (RMSE) of the model will be like the following:

R2:  0.7215906258518425
RMSE:  4.393173257934207