In the last post we had a simple stepping algorithm, and a gradient descent implementation, for fitting a line to a set of points with one variable and one 'outcome'. As I mentioned though, it's fairly straightforward to extend that to multiple variables, and even to curves, rather than just straight lines. For this example I've reorganised the code slightly into a class to make life a little easier, but the main changes are just the hypothesis and learn functions.

He restructures the learning method to make it easier to reuse and includes a "scale data" method to compensate for irregularities in the data and compute the variance.