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

Cox regression modeling

KM tests can be satisfactory in many situations, especially during preliminary analysis; however, KM tests are non-parametric, and typically are less powerful than parametric equivalents. Cox regression extends survival analysis to a parametric regression type framework under which it assumes more power. If there are several independent variables that need to be incorporated into a model, and some of them are continuous, it is advantageous to perform cox proportional hazard modeling rather than KM.

Our first model

Cox modeling also starts with creating a survival object, as we did in previous examples. Other than that, a cox model looks very similar to a standard regression model with the response variables specified to the left of the ~ and the independent variables specified to the right.

In cox regression modeling, we use the coxph() function over the surv() function to specify the dependent variable. This can be done directly in the formula, or by assigning it to...