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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

Comparing the Test and Training for the "UNDER 18 YEARS" group

We can also examine the plots for the training and test groups for this sub segment to see if anything is really going on:

par(mfrow = c(1, 2))
plot(forecast(fit))
plot(forecast(fit2))

From the following plots we can see that although there is a decline in the not-covered percentage for this group, there is probably not enough data to discern trend so that the projections are set at the mean values.

For the test data plot we can also see why the absolute residual plot dropped in xxx as it transitioned from a local peak in 2010 (when the Affordable Care Act was enacted) to the start of a decline in 2011:

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