The ability to learn from observations accompanied by marked targets or labels, usually in order to make predictions about unseen data, is known as supervised machine learning. If the targets are categories, the problem is one of classification and if they are numeric values, it is called regression. In effect, what is being attempted is to infer the function that maps the data to the target. Supervised machine learning is used extensively in a wide variety of machine learning applications, whenever labeled data is available or the labels can be added manually.
The core assumption of supervised machine learning is that the patterns that are learned from the data used in training will manifest themselves in yet unseen data.
In this chapter, we will discuss the steps used to explore, analyze, and pre-process the data before proceeding to training models. We will then introduce different modeling techniques ranging from simple linear...