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


Most of the code examples in this book are written in R. As a prerequisite to this book, it is presumed that you will have some basic R knowledge, as well as some exposure to statistics. If you already know about R, you may skip this section, but I wanted to discuss it a little bit for completeness.

The R language is derived from the S language which was developed in the 1970s. However, the R language has grown beyond the original core packages to become an extremely viable environment for predictive analytics.

Although R was developed by statisticians for statisticians, it has come a long way since its early days. The strength of R comes from its package system, which allows specialized or enhanced functionality to be developed and linked to the core system.

Although the original R system was sufficient for statistics and data mining, an important goal of R was to have its system enhanced via user-written contributed packages. At the time of writing, the R system contains more than 10,000 packages. Some are of excellent quality, and some are of dubious quality. Therefore, the goal is to find the truly useful packages that add the most value.

Most, if not all, R packages in use address most common predictive analytics tasks that you will encounter. If you come across a task that does not fit into any category, the chances are good that someone in the R community has done something similar. And of course, there is always a chance that someone is developing a package to do exactly what you want it to do. That person could be eventually be you!


The Comprehensive R Archive Network (CRAN) is a go-to site which aggregates R distributions, binaries, documentation, and packages. To get a sense of the kind of packages that could be valuable to you, check out the Task Views section maintained by CRAN here:

R installation

R installation is typically done by downloading the software directly from the Comprehensive R Archive Network (CRAN) site:

  1. Navigate to
  2. Install the version of R appropriate for your operating system. Please read any notes regarding downloading specific versions. For example, if you are a Mac user may need to have XQuartz installed in addition to R, so that some graphics can render correctly.

Alternate ways of exploring R

Although installing R directly from the CRAN site is the way most people will proceed, I wanted to mention some alternative R installation methods. These methods are often good in instances when you are not always at your computer:

  • Virtual environment: Here are a few ways to install R in the virtual environment:
    • VirtualBox or VMware: Virtual environments are good for setting up protected environments and loading preinstalled operating systems and packages. Some advantages are that they are good for isolating testing areas, and when you do not wish to take up additional space on your own machine.
    • Docker: Docker resembles a virtual machine, but is a bit more lightweight since it does not emulate an entire operating system, but emulates only the needed processes.
  • Cloud-based: Here are a few methods to install R in the cloud-based environment. Cloud based environments as perfect for working in situations when you are not working directly on your computer:
    • AWS/Azure: These are three environments which are very popular. Reasons for using cloud based environments are similar to the reasons given for virtual environment, but also have some additional advantages: such as the additional capability to run with very large datasets and with more memory. All of the previously mentioned require a subscription service to use, however free tiers are offered to get started. We will explore Databricks in depth in later chapters, when we learn about predictive analytics using R and SparkR
  • Web-based: Web-based platforms are good for learning R and for trying out quick programs and analysis. R-Fiddle is a good choice, however there are other including: R-Web, Jupyter, Tutorialspoint, and Anaconda Cloud.
  • Command line: R can be run purely from a command line. When R is run this way, it is usually coupled with other Linux tools such as curl, grep, awk, and various customized text editors, such as Emacs Speaks Statistics (ESS). Often R is run this way in production mode, when processes need to be automated and scheduled directly via the operating system