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

Spark SQL

Another way to explore data in Spark is by using Spark SQL. This allows analysts who may not be well-versed in language-specific APIs, such as SparkR, PySpark (for Python), and Scala, to explore Spark data.

I will describe two different ways of accessing Spark data via SQL:

  • Issuing SQL commands through the R interface:

This has the advantage of returning the results as an R dataframe, where it can be further manipulated

  • Issuing SQL queries via databricks SQL magic: directive

This method allows analysts to issue direct SQL commands, without regard to any specific language environment

Before processing an object as SQL, the object needs to be registered as a SQL table or view. Once it is registered, it can be accessed through the SQL interface of any language API.

Once registered, you can use the show tables SparkR command to get a list of registered tables for your session.

Registering tables

#register out_sd as a table