Introduction

In the previous chapter, we learned how to represent data in a tabular format, created features and target matrices, pre-processed data, and learned how to choose the algorithm that best suits the problem at hand. We also learned how the scikit-learn API works and why it is easy to use, as well as the difference between supervised and unsupervised learning.

This chapter focuses on the most important task in the field of unsupervised learning: clustering. Consider a situation in which you are a store owner wanting to make a targeted social media campaign to promote selected products to certain customers. Using clustering algorithms, you would be able to create subgroups of your customers, allowing you to profile those subgroups and target them accordingly. The main objective of this chapter is to solve a case study, where you will implement three different unsupervised learning solutions. These different applications serve to demonstrate the uniformity of the scikit-learn API, as well as to explain the steps taken to solve machine learning problems. By the end of this chapter, you will be able to understand the use of unsupervised learning to comprehend data in order to make informed decisions.