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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

Merging the results back into the original data


We will want to retain the number of total items for each invoice on the original data frame. That will involve joining the number of items contained in each invoice back to the original transactions, using the merge() function, and specifying Invoicenum as the key.

If you count the number of distinct invoices before and after the merge, you can see that the invoice count is lower than prior to the merge:

#first take a 'before' snapshot 
 
nrow(OnlineRetail) 
> [1] 541909 
 
#count the number of distinct invoices 
 
sqldf("select count(distinct InvoiceNo) from OnlineRetail")  

The output shows a total of 25900 distinct invoices:

>   count(distinct InvoiceNo) 
> 1                     25900  

Now merge the counts back with the original data:

OnlineRetail <- merge(OnlineRetail, x2, by = "InvoiceNo") 

Check the new number of rows, and the new count of distinct invoices (20059 versus 25900). Note these counts compared to the original. The reduction...

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