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

Picking out the top groups in terms of average population size

In many instances, we will only want to look at the top categories, especially when there are many demographical categories that have been subsetted. In this example, there are only 24 categories but in other examples, there may be a much larger number of categories.

The dataframe x2 is already sorted by Avg.People. Since we know that there are 14 enrollment records for each category, we can get the top 10 categories based upon the highest base population by selecting the first 14*10 (or 140) rows. We will store this in a new dataframe, x3, and save this to disk.

Since we know each group has 14 years, extracting the top 10 groups is easy to calculate. After assigning x2, print the first 15 records and observe that the category break after the first 14 records:

x3 <- x2[1:(14 * 10), ] 
  cat Avg.Total.Insured Avg.People Year Year.1 Total.People Total Not.Covered
 <fctr> <dbl> <dbl> <fctr> &lt...