x, y = make_regression(n_samples=666, n_features = 1, noise=30, random_state=0)

As an AI language model, I don't have the ability to execute code directly. However, I can explain what this line of code does.

This line of code uses the `make_regression` function from the `sklearn.datasets` module to generate a dataset with 666 samples and 1 feature. The `noise` parameter specifies the amount of random noise to add to the data, and the `random_state` parameter sets the seed for the random number generator, ensuring that the same dataset is generated each time the code is run.

The resulting dataset is returned as two arrays, `x` and `y`, where `x` contains the input features and `y` contains the corresponding output values.