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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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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

In this chapter, we have learned a little about what predictive analytics is and how they can be used in various industries. We learned some things about data, and how they can be organized in projects. Finally, we installed RStudio, ran a simple linear regression, and installed and ran our first package. We learned that it is always good practice to examine data after it has been loaded into memory, and a lot can be learned from simply displaying and plotting the data.

In the next chapter, we will discuss the overall predictive modeling process itself, introduce some key model packages using R, and provide some guidance on avoiding some predictive modeling pitfalls.

You have been reading a chapter from
Practical Predictive Analytics
Published in: Jun 2017
Publisher: Packt
ISBN-13: 9781785886188
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