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
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface

References


You can refer to the following articles:

  • Determine the Root Cause: 5 Whys. Retrieved from https://www.isixsigma.com/tools-templates/cause-effect/determine-root-cause-5-whys/
  • Gettysburg Address, (1863, November 19). Retrieved from Abraham Lincoln Online: http://www.abrahamlincolnonline.org/lincoln/speeches/gettysburg.html
  • Stevens, S. (1946). On the Theory of Scales of Measurement. Science
  • Wikipedia. Bias-variance tradeoff. Retrieved from Wikipedia: https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

Notes

Random Forests (tm) is a trademark of Leo Breiman and Adele Cutler and is licensed exclusively to Salford Systems for the commercial release of the software.