May 2024
Beginner to intermediate
360 pages
9h 2m
English
Content preview from Predictive Analytics for the Modern Enterprise
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Chapter 3. The Mathematics and Algorithms Behind Predictive Analytics
As humans, we are constantly learning. Our experiences are the teachers that shape the way we think and how we live our lives. This process of continuous learning extends beyond one individual, and as a species we continue to evolve from a biological and an intellectual standpoint. This collective learning has allowed us to find cures for deadly diseases, build airplanes, explore interstellar space, and observe far-off galaxies.
As part of this intellectual evolution, we built computers to help us in our daily lives. Today we use computers in almost every aspect of our lives. But even before computers, there was math all around us. Mathematics is everywhere, and with enough understanding and effort you can arguably represent any scenario as a mathematical problem. Hence, mathematics is often referred to as a universal language.
It is no surprise, then, that mathematics forms the foundation of computer science, from binary data representation at the hardware level to software programming and machine learning (ML) algorithms. In this chapter, we will explore statistics and linear algebra in relation to predictive data analytics. Then, we will extend this discussion to develop a better understanding of regression, decision trees, and a few other models used in machine learning and predictive analytics.
Statistics and Linear Algebra
Statistics is the art (or science) of collecting numeric data, understanding and ...
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