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

The sample market basket

Each transaction numbered 1-10 listed previously represents a basket of items purchased by a shopper. These are typically all items that are associated with a particular transaction or invoice. Each basket is enclosed within braces {}, and is referred to as an itemset. An itemset is a group of items that occur together.

Market basket algorithms construct rules in the form of:

Itemset{x1,x2,x3 ...} --> Itemset{y1,y2,y3...}. 

This notation states that buyers who have purchased items on the left-hand side of the formula (lhs) have a propensity to purchase items on the right-hand side (rhs). The association is stated using the à symbol, which can be interpreted as implies.


The lhs of the notation is also known as the antecedent, and the rhs is known as the consequence. If nothing appears on either the left-hand side or right-hand side there is no specific association rule for those items; however, it also means that those items have appeared in the basket.