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

Determining all of the subset groups

Since we have only looked at parts of the file (via head() or tail() functions), we do not know how many categories there are and how they differ in terms of health care coverage. So we will start off by looking at some of the groupings.

In previous chapters, we have used sql() and the aggregate() function to group data. For this example, we will use the dplyr package. One advange of the dplyr() package is that it can also be used with pipe syntax, which allows the result of one function to be passed to the next function without intermediate assignments:

> Attaching package: 'dplyr' 
> The following objects are masked from 'package:stats':
>     filter, lag 
> The following objects are masked from 'package:base':
>     intersect, setdiff, setequal, union 
# str(x)

The object will show the average number insured, and the average total population for each category. Remember, this data is also grouped...