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


In this chapter, we learned about a specific type of recommender engine, under the umbrella term market basket analysis.

We saw that market basket analysis enabled you to mine large quantities of transactions containing semi-structured data to derive association rules among the itemsets contained in each basket.

Some additional data cleaning techniques were used on the market basket data, in order to standardize and consolidate some of the descriptions of the purchased items. We also learned how to isolate the most powerful rules, using plotting techniques, along with metrics such as lift, support, and confidence.

Finally, we showed you how to generate clusters from your market basket data training data, and to predict cluster assignments based upon a test data set.