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

Plotting outside of Spark

If you wish to use other tools to plot the data, you can first take a sample of the Spark data and plot it using another package such as ggplot. Note that some versions of Spark may now have ggplot integrated and available for use within Spark. However this example will show another example of extracting data which can be used by other packages.

Collecting a sample of the results

We will take a 2% sample of all of the predictions and then print some of the results. Note that the Spark sample function has a different syntax from the base R sample function we used earlier. You could also specify this as SparkR::sample to make sure you are invoking the correct function:

local = collect(sample(preds, F,.02))


Examining the distributions by outcome

Next, you can run ggplot to graphically display the errors grouped by outcome. The resulting boxplots show that the three quartiles for diabetes are above the non-diabetic patients. This demonstrates that the model...