So instead what we can think about doing is adding more features.

So instead of just looking at the relationship between square feet and

price, we can add number of bathrooms.

And now for

each one of the listings that I looked at before, I'm gonna have to go through and

record how many square feet that house had, and the number of bathrooms.

And I'm gonna plot each of these points in the 3D space.

Okay? So it's this hyper cube of square

feet versus bathrooms versus price.

And now instead of fitting a line to the data, if I'm thinking about just a very

simple model, I can think about fitting a hyper plane.

Okay, so it's just a slice through the space.

We're here.

This is the equation of the hyper plane, and this is the equation of this plane.

So we have w0, which is our intercept, just where this plane lives up and

down on the y-axis.

And we have w1 times the number of square feet, and

w2 times the number of bathrooms.

But a question is where do we stop?

Do we just want to include the number of bathrooms as our additional feature?

There are lots of things we could think about including.

We could think about in addition to our square feet, number of bathrooms,

there's the number of bedrooms, the lot size, how old the house is, and

the list goes on and on.

In terms of different properties of the house that could be influential in

assessing it's value.

But we're gonna actually hold off on this question of looking at which features

are important for this regression task, until we get to the regression course.

So go to the regression course to learn more about this topic.

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