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

Our customer satisfaction dataset

In this chapter, we will be looking at a dataset of hypothetical customers who are subscribed to an online service, and who have responded to a customer satisfaction survey prior to the beginning of the study. This survey was then matched to transactional as well as demographic data to produce this simple analysis dataset, consisting of an event variable (churn), which will represent whether or not a customer unsubscribed from the service. We will also include some transaction data (number of purchases last month), as well as some demographic data (gender, educational level), as well as an overall satisfaction survey administered prior to the start of the study:




Average dollar amount of previous purchases


Number of purchases in the month before the study begins


Overall satisfaction with the service supplied on a Likert scale


Follow-up satisfaction score


Male or female

Education Level...