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

Constructing a cross-tab

Now that we have categorized age, we can run a cross-tab which counts outcomes by the age category.

Since there are only two outcomes and two age categories, this results in a four-cell crosstab:

  1. First, display the results using the special Databricks display command.
  2. After the results appear as shown in the table below, you can click the plot button (2nd icon on the bottom left) and the Customized Plot dialogue will appear, which will allow the results to be plotted as a bar chart. The plot show that diabetes occurs more frequently in the higher age group than in the lower age group, while the reverse is true for the non-diabetes group:
        table <- crosstab(filtered, "outcome", "agecat")