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

Description of the data

Year and Year.1 (columns 1 and 2): Year is the year for which the annual enrollment figures are taken. You will notice that year appears twice, in column 1 (as a factor) and then again in column 2 (integer). This is because the data has been previously preprocessed, and appears twice merely for convenience, since there are certain instances in which we will prefer to use a factor, and other instances in which we prefer to use an integer. The numbers in parentheses in Year refer to footnotes in the original data sources. Please refer the the reference notes at the CMS website for a full explanation of how the data was collected. While you could always create integers from factors and vice versa in the code, this saves valuable processing time if certain transformations can be made available beforehand.

Total people (column 3): Total people is the population size of the category. They may either enrolled for health coverage (total) or not (Not.Covered).

Total (column...