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Practical Predictive Analytics

You're reading from   Practical Predictive Analytics Analyse current and historical data to predict future trends using R, Spark, and more

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Product type Paperback
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Length 576 pages
Edition 1st Edition
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Author (1):
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Ralph Winters Ralph Winters
Author Profile Icon Ralph Winters
Ralph Winters
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Predictive Analytics FREE CHAPTER 2. The Modeling Process 3. Inputting and Exploring Data 4. Introduction to Regression Algorithms 5. Introduction to Decision Trees, Clustering, and SVM 6. Using Survival Analysis to Predict and Analyze Customer Churn 7. Using Market Basket Analysis as a Recommender Engine 8. Exploring Health Care Enrollment Data as a Time Series 9. Introduction to Spark Using R 10. Exploring Large Datasets Using Spark 11. Spark Machine Learning - Regression and Cluster Models 12. 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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