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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
Languages
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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

Predicting cluster assignments


The goal in this exercise is to score the test dataset, by assigning clusters based upon the predict method for the training dataset.

Using flexclust to predict cluster assignment

The standard kmeans function does not have a prediction method. However, we can use the flexclust package which does. Since the prediction method can take a long time to run, we will illustrate it only on a sample number of rows and columns. In order to compare the test and training results, they also need to have the same number of columns. For illustration purposes, we will set the number at 10.

To begin, take a sample from the OnlineRetail training data:

set.seed(1)
 sample.size <- 10000
 max.cols <- 10

library("flexclust") OnlineRetail <- OnlineRetail[1:sample.size, ]

Next, create the document term matrix from the description column in the sampled dataset. We will use the create_matrix function from the RTextTools package, which can create a TDM first without having a separate...

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