Introduction

The previous chapters introduced you to very popular and extremely powerful machine learning algorithms. They all have one thing in common, which is that they belong to the same category of algorithms: supervised learning. This kind of algorithm tries to learn patterns based on a specified outcome column (target variable) such as sales, employee churn, or class of customer.

But what if you don't have such a variable in your dataset or you don't want to specify a target variable? Will you still be able to run some machine learning algorithms on it and find interesting patterns? The answer is yes, with the use of clustering algorithms that belong to the unsupervised learning category.

Clustering algorithms are very popular in the data science industry for grouping similar data points and detecting outliers. For instance, clustering algorithms can be used by banks for fraud detection by identifying unusual clusters from the data. They can also be used by e...