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

Target time series variable

The variable that we will begin to look at initially will be the variable Not.Covered. We will be interested in examining any possible enrollment trends using this variable. Since the population size will differ depending upon the category, we will calculate the percentage of people not covered in a given year by dividing the raw number corresponding to this variable by the total in the population for that category. This will give us a new variable named Not.Covered.Pct. This will also standardize the metric across the different-sized categories, large and small, and enable us to compare.

After calculating the variable, we can print the first few records, and also print some summary statistics for this one variable:


Note that the average non covered percentage was 14.5% of all of the years, but you can see that there is a considerable able of variation by just looking at the difference between the 1st and 3rd quartile (.15 - .11) = .04. That can translate to...