I recommend that you get down and dirty with this stuff. Try different orders of polynomials. Go back up to where we ran the polyfit() function and try different values there besides 4. You can use 1, and that would go back to a linear regression, or you could try some really high amount like 8, and maybe you'll start to see overfitting. So see what effect that has. You're going to want to change that. For example, let's go to a third-degree polynomial.

x = np.array(pageSpeeds)
y = np.array(purchaseAmount)
      
p4 = np.poly1d(np.polyfit(x, y, 3))  

Just keep hitting run to go through each step and you can see the it's effect as...

Our third-degree polynomial is definitely not as good a fit as the fourth-degree polynomial. If you actually measure the r-squared error, it would actually turn out worse, quantitatively; but if I go too high, you might start to see overfitting. So just have some fun with that, play around different values, and get a sense of what different orders of polynomials do to your regression. Go get your hands dirty and try to learn something.

So that's polynomial regression. Again, you need to make sure that you don't put more degrees at the problem than you need to. Use just the right amount to find what looks like an intuitive fit to your data. Too many can lead to overfitting, while too few can lead to a poor fit... so you can use both your eyeballs for now, and the r-squared metric, to figure out what the right number of degrees are for your data. Let's move on.