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

Removing colors automatically

If you did not want to bother specifying colors, and you wanted to remove colors automatically, you could accomplish that as well.

The colors() function

The colors() function returns a list of colors that are used in the current palette. We can then perform a little code manipulation in conjunction with the gsub() function that we just used to replace all of the specified colors from OnlineRetail$Description with blanks.

We will also use the kable() function, which is contained within the knitr package, in order to produce simple HTML tables of the results:

# compute the length of the field before changes
 before <- sum(nchar(OnlineRetail$Description))

 # get the unique colors returned from the colors function, and remove any digits found at the end of the string

 # get the unique colors
 col2 <- unique(gsub("[0-9]+", "", colors(TRUE)))

 #Now we will filter out any colors with a length > 7. This number is somewhat arbitrary but it is just done for illustration...