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Practical Predictive Analytics

You're reading from  Practical Predictive Analytics

Product type Book
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Pages 576 pages
Edition 1st Edition
Languages
Author (1):
Ralph Winters Ralph Winters
Profile icon Ralph Winters

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Getting Started with Predictive Analytics The Modeling Process Inputting and Exploring Data Introduction to Regression Algorithms Introduction to Decision Trees, Clustering, and SVM Using Survival Analysis to Predict and Analyze Customer Churn Using Market Basket Analysis as a Recommender Engine Exploring Health Care Enrollment Data as a Time Series Introduction to Spark Using R Exploring Large Datasets Using Spark Spark Machine Learning - Regression and Cluster Models Spark Models – Rule-Based Learning

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...

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