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


In this chapter, we learned about the various structured approaches to predictive analytics and how implementing an analytics project in a methodical way can enhance the success of an analytics project through collaboration and communication. We went through the various steps of the CRISP-DM methodology and demonstrated tools that you could use to help you progress along these steps.

We discussed the benefits of sampling and how it could speed up your project. SQL was demonstrated to illustrate basic charts and plots, so that you can begin to develop insight even before you create a first model. We showed that data simulation could also be used at the data understanding phase as a preliminary modeling tool to do "what ifing", even before actual company data is obtained.

We learned about the various types of data that you will encounter, and showed some examples of independent and dependent variables and the importance of doing preliminary 1-way and 2-way variable analysis as a precursor...