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 environments

Spark runs in three different modes:

  • Standalone
  • YARN

For initial deployments (and for learning), it is best to start with standalone, which allocates clusters specifically for Spark use only. Furthermore, you can run standalone mode in local mode (your computer) or via cloud computing (an example would be using Amazon AWS).

Cluster computing

Cluster computing allows Spark to process and distribute data over many computers at once, in parallel. A cluster manager allocates resources for the cluster depending upon user requests. An important aspect of Spark is that it attempts to keep as much data in memory as needed, so that data is available for the various analyses as quickly as possible rather than having to wait to retrieve data from storage every time a query or model is specified.

Spark data is stored as RDDs, which allow different kinds of objects to be spread out over the cluster.

Parallel computing

Parallel computing refers to the ability to perform separate computing...