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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

Plotting the data with the trend lines

Now that we have the trend coefficients, we will use ggplot to first plot enrollment for all of the 24 categories, and then create a second set of plots which adds the trend line based upon the linear coefficients we have just calculated.

Code notes: facet_wrap will order the plots by the value of variable z, which was assigned to the coefficient rank. Thus, we can get to see the categories with declining enrollment first, ending with the categories having the highest trend in enrollment from the period 1999-2012.

I like to assign the variables that I will be changing to standard variable names, such as x, y, and z, so that I can remember their usage (for example, variable x is always the x variable, and y always the x variable). But you can supply the variable names directly in the call to ggplot, or set up your own function to do the same...
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