I have a regression analysis project where I’m implementing the math from scratch rather than calling a library function. The goal is to understand what’s actually happening inside sklearn.linear_model.LinearRegression() before I use it as a black box.
This post is coming soon. It will cover least squares, the normal equation, gradient descent, and polynomial regression, all in raw numpy.