from sklearn.linear_model import SGDRegressor # Create the SGDRegressor object sgd = SGDRegressor(max_iter=1000) # Fit the model using training data sgd.fit(X_train, y_train) # Predict the target values on test data y_pred = sgd.predict(X_test)In the above example, we’ve imported the SGDRegressor class from the sklearn.linear_model module. We’ve created an object sgd of the SGDRegressor class with the max_iter set to 1000. Then, we’ve fitted the model using training data (X_train and y_train) and predicted the target values on test data (X_test). Overall, `sklearn.linear_model.SGDRegressor` is a powerful and useful tool for implementing linear regression using the stochastic gradient descent algorithm in Python.