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

Association rule algorithms

Without an association rule algorithm, you are left with the computationally very expensive task of generating all possible pairs of itemsets, and then trying to mine the data in order to identify the best ones yourself. Associate rule algorithms help with filtering this.

The most popular algorithm for MBA is the apriori algorithm, which is contained within the arules package (the other popular algorithm is the eclat algorithm).

Running apriori is fairly simple. We will demonstrate this using our demo 10 transaction itemset that we just printed.

The apriori algorithm is based upon the principle that if a particular itemset is frequent, then all of its subsets must also be frequent. That principle itself is helpful for reducing the number of itemsets that need to be evaluated, since it only needs to look at the largest items sets first, and then be able to filter down:

  • First, some housekeeping. Fix the number of printable digits to 2:
         options(digits = 2)
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