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

About Spark

At the time of writing, Spark is probably the most popular very large dataset architecture for predictive analytics. Spark is a distributed architecture which helps you manage your large data and makes it easier to analyze. Spark is built upon Hadoop and they share the same filesystem.

However, Spark is not based upon the MapReduce paradigm, and uses the resilient distributed dataset (RDD) structure in order to implement in-memory analytics and manage the parallel processing cluster across all of the nodes of the environment. What that means for analysts is that queries can be very quick, since data is retrieved from memory, which offers much quicker retrieval than disk access. Quicker access means more time for analysis, and less time waiting for results.

Here are some advantages of Spark:

  • Spark overcomes some of the limitations of memory-bound analytics, since it manages memory, and is able to optimize data access and querying.
  • Spark has its own machine learning library known as...