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

Evaluating the accuracy of a rule

Three main metrics have been developed that measure the importance, or accuracy of an association rule: support, confidence, and lift.


Support measures how frequently the items occur together. Imagine having a shopping cart in which there can be a very large number of combinations of items. Some items that occur rarely could be excluded from the analysis. When an item occurs frequently you will have more confidence in the association among the items, since it will be a more popular item. Often your analysis will be centered around items with high support.

Calculating support

Calculating support is simple. You first calculate a proportion by counting the number of times that the items in the rule appear in the basket divided by the total number of occurences in the itemsets:


  • We can see that for the first rule (index #63), {bottled water} and {tropical fruit} appear together in the same transaction in two different transactions (2 and 3), therefore...