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

Read the data in

Next, we will read in a few rows of the file (using the nrow parameters), and then run a str() function on the input to see which variables are contained within the file. There are several metrics in the file related to medicare enrollment. We will just concentrate on the total enrollment metrics, and not utilize some of the other sub-segments (such as military and private insurance) for this chapter:

x <- read.csv("x <- read.csv("hihist2bedit.csv", nrow = 10)"
 > 'data.frame': 10 obs. of  13 variables:
 >  $ Year        : Factor w/ 10 levels "2003","2004 (4)",..: 10 9 8 7 6 5 
 4 3 2 1
 >  $ Year.1      : int  2012 2011 2010 2009 2008 2007 2006 2005 2004 2003
 >  $ Total.People: num  311116 308827 306553 304280 301483 ...
 >  $ Total       : num  263165 260214 256603 255295 256702 ...
 >  $ pritotal    : num  198812 197323 196147 196245 202626 ...
 >  $ priemp      : num  170877 170102 169372 170762 177543 ...
 >  $ pridirect   : num...