Predictive Analytics for the Modern Enterprise

Chapter 5. Python and scikit-learn for Predictive Analytics

We started our journey with a brief history of data analytics. We discussed the importance of predictive analytics in the modern enterprise, and we covered some industry use cases to appreciate the real-world implications of its implementation. We then took a slightly deep dive into the statistics and mathematics behind different predictive analytics algorithms (if you are a diver, you can think of that as a 10-meter dive rather than a 100-meter deep sea exploration). I am a big proponent of strong foundations. I believe that once you have a strong grasp of the foundation, you can learn and understand the details much more easily, even though they can evolve over time. Now that we have the analytics foundation established, in this chapter we will get our hands dirty with some actual predictions.

Anaconda and Jupyter Notebooks

This is a hands-on chapter. If you are a data science professional or student, the content should be familiar to you. However, even if you are new to data science, the material and sample code should be clear enough for you to understand, so long as you have a basic grasp of computer programming.

We’ll need a few prerequisites in place before we begin. These are shown in Table 5-1.

Table 5-1. Prerequisites

Serial #	Name	Description	Version used in this chapter	URL
1	Python	Python is a high-level programming language used heavily in data science.	V3.9.13	https://www.python.org
2	Anaconda ...