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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

Description of the data


Year and Year.1 (columns 1 and 2): Year is the year for which the annual enrollment figures are taken. You will notice that year appears twice, in column 1 (as a factor) and then again in column 2 (integer). This is because the data has been previously preprocessed, and appears twice merely for convenience, since there are certain instances in which we will prefer to use a factor, and other instances in which we prefer to use an integer. The numbers in parentheses in Year refer to footnotes in the original data sources. Please refer the the reference notes at the CMS website for a full explanation of how the data was collected. While you could always create integers from factors and vice versa in the code, this saves valuable processing time if certain transformations can be made available beforehand.

Total people (column 3): Total people is the population size of the category. They may either enrolled for health coverage (total) or not (Not.Covered).

Total (column...

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