Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback


In this chapter, we started with a discussion of supervised and unsupervised learning and emphasized the difference between pure predictive and exploratory analytics. We were then introduced to the first of the core algorithms (general linear models) which are important in the predictive analytics world. We then discussed various regression methods, along with its pros and cons, and noted that regression can be an extremely flexible and well researched statistical based modeling tool. We then used a pain threshold study to show examples of logistic regression and regularized regression, along with discussing important regressions concepts such as interaction, p-values and effect sizes.

In the next chapter, we will resume our discussion of the core predictive analytics algorithms by discussing three additional algorithms, that is, decision trees, clustering, and support vector machines.