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

Running local R packages

Once you have extracted your sample, you can run normal R functions such as pairs to generate a correlation matrix, or use the reshape2 package along with ggplot to generate a correlation plot.

Using the pairs function (available in the base package)

#this takes our "collect()" data frame which we exported from Spark, and runs a basic correlation matrix

pairs(samp[,3:8], col=samp$outcome) 

Generating a correlation plot

Here is a more sophisticated visualization which uses ggplot to illustrate how to generate a correlation matrix using shading to indicate the degree of correlation for each of the intersecting variables. Again, the point is to emphasis that you can perform analysis outside of Spark if your sample size is reasonable, and the exact functionality you need is not available in the version of Spark you are running.

cormatrix <- round(cor(samp),2)
cormatrix_melt <- melt(cormatrix)
ggplot(data = cormatrix_melt...