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 scores back into the original dataframe

We will augment the original x2 dataframe with this new information by merging back by category, and then by sorting the dataframe by the rank of the coefficient. This will allow us to use this as a proxy for trend:

x2x <- x2 %>% left_join(xx4, by = "cat") %>% arrange(coef.rank, cat)

# exclude some columns so as to fit on one page
head(x2x[, c(-2, -3, -4, -8)]) 
> Source: local data frame [6 x 7]
>                   cat Year.1 Total.People    Total Not.Covered.Pct
>                (fctr)  (int)        (dbl)    (dbl)           (dbl)
> 1 MALE 18 to 24 YEARS   2012     15142.04 11091.86       0.2674787
> 2 MALE 18 to 24 YEARS   2011     15159.87 11028.75       0.2725034
> 3 MALE 18 to 24 YEARS   2010     14986.02 10646.88       0.2895460
> 4 MALE 18 to 24 YEARS   2010     14837.14 10109.82       0.3186139
> 5 MALE 18 to 24 YEARS   2008     14508.04 10021.66       0.3092339
> 6 MALE 18 to 24 YEARS   2007...