Search icon
Subscription
0
Cart icon
Close icon
You have no products in your basket yet
Arrow left icon
All Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Newsletters
Free Learning
Arrow right icon
Practical Predictive Analytics

You're reading from  Practical Predictive Analytics

Product type Book
Published in Jun 2017
Publisher Packt
ISBN-13 9781785886188
Pages 576 pages
Edition 1st Edition
Languages
Author (1):
Ralph Winters Ralph Winters
Profile icon Ralph Winters

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Predictive Analytics 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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $15.99/month. Cancel anytime}