Now, let's train the model using the training portion of the dataset:

1. Let's start by importing the linear regression package:

from sklearn.linear_model import LinearRegression

2. Then, let's instantiate the linear regression model and train it using the training dataset:

regressor = LinearRegression()
regressor.fit(X_train, y_train)

3. Now, let's predict the results using the test portion of the dataset:

y_pred = regressor.predict(X_test)
from sklearn.metrics import mean_squared_error
from math import sqrt
sqrt(mean_squared_error(y_test, y_pred))

4. The output generated by running the preceding code will generate the following:

As discussed in the preceding section, RMSE is the standard deviation of the error. It indicates that 68.2% of predictions will fall within 4.36 of the value of the target variable.