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

Chapter 9. Introduction to Spark Using R

Data! Data! Data! I can't make bricks without clay!

- Sir Arthur Conan Doyle

So far, we have learned how to perform analytics on what can be referred to as "small data". However, as the amount of data increases, so does the size and the problem of how to analyze the vast amounts of data that is produced arises. When that occurs, we begin to approach "big data" and new approaches to solving problems develop and sometimes, new tools are needed as well.

To some extent, nothing changes. You still want high quality data. You still want to be able to examine the relationships and cast the problem within a predictive analytic framework.

What does change are the steps needed to achieve that end, bearing in mind that the data is more difficult to manage and as a result new tools have evolved to help you do that.

One of the tools that has evolved in recent years is Apache Spark.

In this chapter, we will cover some basics of Spark. We will start with a known small...