In this recipe, we will learn how we can visualize 2D data that is linearly non-separable in 3D. This is typically used to explain the internal workings of the Support Vector Machines algorithm, which takes lower dimensional data to higher dimensional space so that it can find a plane that separates the data into various clusters neatly.
We will plot both 2D and 3D plots with the same data to visualize it better.