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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

Preparing the raw data file for analysis


Now that we have had a short introduction to the association rules algorithm, we will illustrate applying association rules to a more meaningful example.

We will be using the online retail dataset, which can be obtained from the UCI machine learning repository at:

https://archive.ics.uci.edu/ml/datasets/Online+Retail.

As described by the source, the data is:

"A transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail. The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers".

For more information about how the dataset was created, please refer to the original journal article (Daqing Chen, 2012).

Reading the transaction file

We will input the Groceries data using the read.csv() function.

We can use the file.show() function to directly examine the input file if needed. This is sometimes needed if you find that there are...

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