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

Variable selection

The model we just worked with had a limited number of variables, so mechanical variable selection methods when dealing with a large number of variables were not really that pertinent. We were able to pinpoint the important ones via the regression model. However, for a model with a large number of variables we could use the glmulti package for the purpose of performing variable selection.

For the churn example that was generated, we have a small number of variables, so it is easy to demonstrate a variable selection and not so time consuming.

In the following code, we will set the maximum number of terms to include in the best regression to 10 in order to limit the computational time needed to perform an exhaustive search. We will also use the genetic algorithm option (method = "g") which can be much faster with larger datasets, since it only considers the best subsets of all of the combinations.

If you wish to perform an exhaustive search, use method = "h". However, be forewarned...