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

Time-based variables

Up until now, we have treated all of our variables as static, that is, they maintained their original values measured from the beginning of the measurement period.

In reality, values such as age and marital status change over time, and these changes can be accounted for by the model. In the marketing context, surveys might be administered after the study has begun. Based upon changes in some of these variables, coupons and other incentives might be offered (interventions) with the purpose of changing customer behavior. In the model, these interventions can also be accounted for.

In our example, we will introduce a hypothetical second survey, which was introduced 6 months into the measurement period and measured the effect of treating some of the unsatisfied customers.

Changing the data to reflect the second survey

The following code uses the survSplit function to create a new record a time period 6 that will reflect the response to a second hypothetical customer survey administered...