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


Spark itself is written in a programming language called Scala and runs in a Java environment. However, you are not restricted to using Scala. Spark has several interfaces which are exposed through an API, which allows Spark programs to be written in these other languages:

  • R
  • Scala
  • Java
  • Python
  • Clojure

We will be demonstrating some of the examples in this chapter using SparkR. SparkR is an R package that provides a frontend to use Apache Spark from R. This allows SparkR to allow data scientists to interactively run jobs from R on a cluster. One big advantage of using SparkR, for the traditional R programmer, is that it uses some of the techniques that they already know such as the concept of dataframes is also available within SparkR.


Spark RDDs can be a bit difficult to work with, so in recent versions of Spark, Dataframe abstractions have been built on top of RDDs, which allows analysts to view data in ways that they are used to looking at them, for example, being able to view...