In this chapter, we introduced data mining using Python. If you could 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 Jupyter Notebook to run our code, which allows us to immediately view the results of a small section of the code. Jupyter Notebook is a useful tool 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, find the next big medical breakthrough, or create 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.
To expand on the outcomes of this chapter, think about how you would implement a variant of OneR that can take multiple feature/value pairs into consideration. Take a shot at implementing your new algorithm and evaluating it. Remember to test your algorithm on a separate dataset to the training data. Otherwise, you run the risk of over fitting your data.
Over the next few chapters, we will expand on the concepts of classification and affinity analysis. We will also introduce classifiers in the scikit-learn package and use them to do our machine learning, rather than writing the algorithms ourselves.
