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

Examining the groceries transaction file

Critical to the understanding of MBA are the concepts of support, confidence, and lift. These are the measures that evaluated the goodness of fit for a set of association rules. You will also learn some specific definitions that are used in MBA, such as consequence, antecedent, and itemsets.

To introduce these concepts, we will first illustrate these terms through a very simplistic example. We will use only the first 10 transactions contained in the Groceries transaction file, which is contained in the arules package:


After the arules library is loaded, you can see a short description of the Groceries dataset by entering ?Groceries at the command line. The following description appears in the help window:

"The Groceries data set contains 1 month (30 days) of real-world point-of-sale transaction data from a typical local grocery outlet. The data set contains 9835 transactions and the items are aggregated to 169 categories".

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