Summary

In this chapter, we introduced data mining using Python. If you were able to run the code in this section (note that the full code is available in the supplied code package), then your computer is set up for much of the rest of the book. Other Python libraries will be introduced in later chapters to perform more specialized tasks.

We used the IPython Notebook to run our code, which allows us to immediately view the results of a small section of the code. This is a useful framework that will be used throughout the book.

We introduced a simple affinity analysis, finding products that are purchased together. This type of exploratory analysis gives an insight into a business process, an environment, or a scenario. The information from these types of analysis can assist in business processes, finding the next big medical breakthrough, or creating the next artificial intelligence.

Also, in this chapter, there was a simple classification example using the OneR algorithm. This simple algorithm simply finds the best feature and predicts the class that most frequently had this value in the training dataset.

Over the next few chapters, we will expand on the concepts of classification and affinity analysis. We will also introduce the scikit-learn package and the algorithms it includes.