Thoughtful Machine Learning with Python

Chapter 3. K-Nearest Neighbors

Have you ever bought a house before? If you’re like a lot of people around the world, the joy of owning your own home is exciting, but the process of finding and buying a house can be stressful. Whether we’re in a economic boom or recession, everybody wants to get the best house for the most reasonable price.

But how would you go about buying a house? How do you appraise a house? How does a company like Zillow come up with their Zestimates? We’ll spend most of this chapter answering questions related to this fundamental concept: distance-based approximations.

First we’ll talk about how we can estimate a house’s value. Then we’ll discuss how to classify houses into categories such as “Buy,” “Hold,” and “Sell.” At that point we’ll talk about a general algorithm, K-Nearest Neighbors, and how it can be used to solve problems such as this. We’ll break it down into a few sections of what makes something near, as well as what a neighborhood really is (i.e., what is the optimal K for something?).

How Do You Determine Whether You Want to Buy a House?

This question has plagued many of us for a long time. If you are going out to buy a house, or calculating whether it’s better to rent, you are most likely trying to answer this question implicitly. Home appraisals are a tricky subject, and are notorious for drift with calculations. For instance on Zillow’s website they explain that their famous Zestimate is flawed. They state that based on where you are looking, ...