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Practical Predictive Analytics

You're reading from  Practical Predictive Analytics

Product type Book
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Pages 576 pages
Edition 1st Edition
Languages
Author (1):
Ralph Winters Ralph Winters
Profile icon Ralph Winters

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Predictive Analytics 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. Spark Models – Rule-Based Learning

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:

Variable

Description

Monthly.Charges

Average dollar amount of previous purchases

Purch.last.Month

Number of purchases in the month before the study begins

Satisfaction

Overall satisfaction with the service supplied on a Likert scale

Satisfaction2

Follow-up satisfaction score

Gender

Male or female

Education Level...

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