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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

Method two Creating a physical transactions file


Now that you know how to run association rules using the coerce to dataframe method, we will now illustrate the write to file method:

  • In the write to file method, each item is written to a separate line, along with the identifying key, which in our case is the InvoiceId

  • The advantage to the write to file method is that very large data files can be accumulated separately, and then combined together if needed

  • You can use the file.show function to display the contents of the file that will be input to the association rules algorithm:

setwd("C:/PracticalPredictiveAnalytics/Data")
load("OnlineRetail.full.Rda")
OnlineRetail <- OnlineRetail[1:100,]
nrow(OnlineRetail)
> [1] 268034 
head(OnlineRetail) 
> InvoiceNo StockCode  Description                Quantity
 > 5   6365     71053  METAL LANTERN                     6
 > 6   536365   21730  GLASS STAR FROSTED T-LIGHT HOLDER 6
 > 2   536365   22752  SET 7 BABUSHKA NESTING BOXES      2...
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