Finally, we are there—we are going to do some real data science now. In the last two chapters, I am going to introduce some of the most popular advanced data mining and machine learning algorithms. I will show you how to use them to get in-depth knowledge from your data.

The most common separation of the algorithms is separation into two groups: the unsupervised, or undirected, and the supervised, or directed algorithms. The unsupervised ones have no target variable. You just try to find some interesting patterns, for example, some distinctive groups of cases, in your data. Then you need to analyze the results to make the interpretation possible. Talking about groups of cases, or clusters – you don't know the labels of those clusters in advance. Once you determine them, you need to check the characteristics of input variables in the clusters in order to get an understanding of the meaning of the clusters.

Before starting with the advanced algorithms...