The machine_learning_workflow.ipynb notebook in this chapter's folder of the book's GitHub repository contains several examples that illustrate the machine learning workflow using a dataset of house prices.

We will use the fairly straightforward k-nearest neighbors (KNN) algorithm that allows us to tackle both regression and classification problems.

In its default sklearn implementation, it identifies the k nearest data points (based on the Euclidean distance) to make a prediction. It predicts the most frequent class among the neighbors or the average outcome in the classification or regression case, respectively.