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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 using lattice

The lattice package is a useful package to learn, especially for analysts who like to work in formula notation (y~x).

In this example, we will run a lattice plot in order to plot Not.Covered.Pct on the y-axis, Year on the x-axis, and produce separate plots by category.

The main call is specified by the following:

xyplot(Not.Covered.Pct ~ Year | cat, data = x3)

Since we are plotting the top 10 groups, we can specify layout=c(5,2) to indicate we want to arrange the 10 plots in a 5*2 matrix. Not.Covered.Pct is to be arranged on the y axis (left side of the ~ sign), and Year is arranged along the x-axis (right side of ~ sign). The bar (|) indicates that the data is to be plotted separately by each category:

library(lattice)
x.tick.number <- 14
at <- seq(1, nrow(x3), length.out = x.tick.number)
labels <- round(seq(1999, 2012, length.out = x.tick...
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