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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
Languages
Tools
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 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

Summary

This concludes this chapter, and this book. I started off by saying that this was a different kind of predictive analytics book, and I covered many different kinds of topics, from both a technical and conceptual viewpoint. I hope that you have learned a lot from this and that it has given you some new algorithms to use, and has taught you something about some 'older' tools, such as SQL, which are very capable of doing some of the heavy lifting that is sometimes needed. I also tried to emphasis 'small data', metadata, and sampling in the hopes that that will aid you in understand your data better, just by virtue of being able to look at individual pieces separately. I also hope that some of the material in the book will enable you to work collaboratively with different team members having different skill sets. That could be anything from someone who...

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