Confused about polynomial regression with multiple variables I'm trying to create a multivariable polynomial regression model from scratch but I'm getting kind of confused by how to structure it.
So, I have an array of feature vectors such that each vector can be displayed like so:
[height, weight, age]

I know with multivariable linear regression I would create an algorithm like so:
y=B0+B1*x0+...Bn*xn
Where x0 would be the first element of each in the feature vector.
So for multiple variable polynomial regression would it go something like this:
y = B0+B1*x0+B2*x1**2+...Bn*Xn**d
Where d is the degree of the polynomial. Apologies if this is painstakingly obvious and formatted badly, I'm just a small bit lost.
Not exactly,
Polynomial regression means that the dataset is not linear and we have to transform it to a specific polynomial degree based on the dataset, so that we may map the Linear model
 Decide a polynomial degree first, let's say 2
$y=b_0+b_1*x_0^2+b_2*x_1^2+...b_n*x_n^2$
If we want to add feature interaction,
$y=b_0+b_1*x_0^2+b_2*x_1^2+b_3*x_0*x_1....$
Basically, you have to implement this Class of Scikit-Learn
PolynomialFeatures