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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 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

Introduction to Regression Algorithms

"It ain't ignorance causes so much trouble; it's folks knowing so much that ain't so."
-Billings

Every day, the number of predictive analytics techniques seems to be increasing. There is a constant debate about whether we need better algorithms, or whether more or better data is needed in order to attack a predictive analytics problem.

Whatever the case, it is always good to keep abreast of all of the available algorithms at your disposal. It is equally important to hone your skills on the top three or four algorithms that can tackle 90% of the problems that you will face, and be able to understand the situations in which it makes sense to use them.

This chapter is the first of two chapters which will present four basic algorithms which cover a lot of typical business situations that you will encounter. In fact, many...

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