Book Image

Practical Predictive Analytics

By : Ralph Winters
Book Image

Practical Predictive Analytics

By: Ralph Winters

Overview of this book

This is the go-to book for anyone interested in the steps needed to develop predictive analytics solutions with examples from the world of marketing, healthcare, and retail. We'll get started with a brief history of predictive analytics and learn about different roles and functions people play within a predictive analytics project. Then, we will learn about various ways of installing R along with their pros and cons, combined with a step-by-step installation of RStudio, and a description of the best practices for organizing your projects. On completing the installation, we will begin to acquire the skills necessary to input, clean, and prepare your data for modeling. We will learn the six specific steps needed to implement and successfully deploy a predictive model starting from asking the right questions through model development and ending with deploying your predictive model into production. We will learn why collaboration is important and how agile iterative modeling cycles can increase your chances of developing and deploying the best successful model. We will continue your journey in the cloud by extending your skill set by learning about Databricks and SparkR, which allow you to develop predictive models on vast gigabytes of data.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

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 thing:

.df <- data.frame(x = x2x$Year.1, y = x2x$Not.Covered.Pct, z = x2x$coef.rank, slope...