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

Merging the aggregate data back into the original data

Often, you will want to augment your original data with some of the calculated data as derived previously. In these cases, you can merge the data back into the original data using a common key. Again, we will use the dplyr package to take the results just obtained ( and join them back to the original data (x), using the common key cat.

We will be using a left_join just for an example; however, we could have used a right join to obtain the same results, since was completely derived from x. After joining the two dataframes, we will end up with a new dataframe named x2:

 # Merge the summary measures back into the original data. Merge by cat.

 x2 <- %>% left_join(x, by = "cat")
 > Source: local data frame [6 x 9]
 >        cat Avg.Total.Insured Avg.People      Year Year.1 Total.People
 >     (fctr)             (dbl)      (dbl)    (fctr)  (int)        (dbl)
 > 1 ALL AGES           ...