The first step is to generate some training data. For this, we will use NumPy's random number generator. As discussed in the previous section, we will fix the seed of the random number generator, so that re-running the script will always generate the same values:
In [3]: np.random.seed(42)
Alright, now let's get to it. What should our training data look like exactly?
In the previous example, each data point is a house on the town map. Every data point has two features (that is, the x and y coordinates of its location on the town map) and a class label (that is, a blue square if a Blues fan lives there and a red triangle if a Reds fan lives there).
The features of a single data point can, therefore, be represented ...
