In Chapter 1, The Python Machine Learning Ecosystem, we learned the essentials for working with data. We'll now apply that knowledge to build out our first machine learning application. We'll begin with a minimal, but highly-practical example: building an application to identify underpriced apartments.
If you've ever searched for an apartment, you will appreciate just how frustrating the process can be. Not only is it time-consuming, but even when you do find an apartment you like, how do you know whether it's the right one?
Most likely, you have a target budget and a target location. But, if you are anything like me, you are also willing to make a few trade-offs. For example, I live in New York City, and being near an amenity like the subway is a big plus. But how much is that worth? Should I trade being in a building...
