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

Creating the market basket transaction file

We are almost there! There is an extra step that we need to do in order to prepare our data for market basket analysis.

The association rules package requires that the data be in transaction format. Transactions can either be specified in two different formats:

  1. One transaction per itemset with an identifier and this shows the entire basket in one line, just as we saw with the Groceries data.
  2. One single item per line with an identifier.

Additionally, you can create the actual transaction file in two different ways, by either:

  1. Physically writing a transactions file.
  2. Coercing a dataframe to transaction format.

For smaller amounts of data, coercing the dataframe to a transaction file is simpler, but for large transaction files, writing the transaction file first is preferable, since append files can be fed from large operational transaction systems. We will illustrate both ways.

Method one Coercing a dataframe to a transaction file

Now we are ready to coerce...