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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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 Winters Winters
Author Profile Icon Winters
Winters
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Table of Contents (19) Chapters Close

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 FREE CHAPTER 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

Summary


In this chapter, we learned about what survival analysis is, and how two main techniques, Kaplan-Meir and Cox Regression, can be used to explain and predict customer churn.

We also learned how we can generate our own data to test assumptions and test the robustness of the models.

Finally, we included some coding techniques to help us reproduce and save our generated code and images.

In the next chapter, we will not be concerned with a customer leaving, but will cover how to keep customers happy by predicting what they will purchase next using a technique known as Market Basket Analysis.

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