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

Some tips for using Spark

Take a look at the following tips:

  • Sample when possible. Use the sample_bin methodology and filter command liberally. Sampling will speed up analysis both for the analysis phase and for the development/testing phase.

  • Once testing has been completed on a smaller segment, it can be scaled up to a much larger population with confidence.

  • Preprocess the data so that you can subselect potentially interesting sub segments.

  • Cache analysis when it makes sense.

  • If performance becomes a factor, try a larger number of partitions in your data.

  • For larger number crunching, bring back a representative sample to local R.