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 the basics of exploring Spark data, using some Spark-specific commands that allowed us to filter, group, and summarize our Spark data.

We also learned about the ability to visualize data directly in Spark, along with learning how to run R functions such as ggplot against data.

We learned about some strategies for working with Spark data, such as performing intelligent filtering and sampling.

Finally, we demonstrated that often we need to extract some Spark data back into local R if we want the flexibility to use some of our usual tools that may not be supplied natively in the Spark environment.

In the next chapter, we will delve into the various predictive models that you can use that are specific to large datasets.