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

Generalized linear models

Generalized linear models (GLMs) refer to a larger framework of prediction techniques which can include linear regression, logistic regression (used to predict binary outcomes), and poisson regression (used to predict counts). They are a generalization of linear regression techniques which allow you to work with other distributions which have non-normal error terms. GLMs can be implemented in R by using the glm package, in which you supply a link function to specify which distribution you are modeling. That makes it easier to work with different types of models within a single package, using standard syntax.

Linear regression using GLM

In Chapter 1, Getting Started with Predictive Analytics, our original example that we used to predict womens' heights based upon womens' weights used the lm package. We could also have used the glm package, specifying family=Gaussian as the link function:

lm_output <-  lm(women$height ~ women$weight) 


glm_output <-  glm(women...