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

Cleaning up and caching the table in memory

Since Spark excels at processing in-memory data, we will first remove our intermediary data and then cache our out_sd dataframe, so that subsequent queries run much faster. Caching data in memory works best when similar types of queries are repeated. In that way, Spark is able to know how to juggle memory so that most of what you need resides in memory.

However, this is not foolproof. Good Spark query and table design will help with optimization, but out-of-the-box caching usually gives some benefit. Often, the first queries will not benefit from memory caching, but subsequent queries will run much faster.

Since we will no longer use the intermediary dataframes we created, we will remove them with the rm function, and then use the cache() function on the full dataframe:

#cleanup and cache df