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

Health insurance coverage dataset

We will start by reading in a dataset which contains health care enrollment data over a period for several categories. This data has been sourced from Table HIB-2, health insurance coverage status and type of coverage all persons by age and sex: 1999 to 2012, and it is available from the CMS website at

This table shows the number of people covered by government and private insurance, as well as the number of people not covered.

This table has several embedded time series across all the 14 years represented. 14 data points would not be considered an extremely long time series; however, we will use this data to demonstrate how we can comb through many time series at once. Since it is small, it will be easy enough to verify the results via visual inspection and printing subsets of the data. As you become familiar with the methodology, it will enable you to expand to...