In this chapter, we examined how PCA works. We briefly discussed how to deal with a dataset in cases where most values are missing on some attributes. We examined how to determine the adequate number of components and the proportion of variance they explain. We also saw how to give a meaningful name to the component. Finally, we began examining linear relationships between attributes using correlations. In the next chapter, we will discuss association rules with apriori
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You're reading from Learning Predictive Analytics with R
Product typeBook
Published inSep 2015
Reading LevelIntermediate
PublisherPackt
ISBN-139781782169352
Edition1st Edition
Languages
Concepts
Author (1)
Eric Mayor
Eric Mayor
Eric Mayor is a senior researcher and lecturer at the University of Neuchatel, Switzerland. He is an enthusiastic user of open source and proprietary predictive analytics software packages, such as R, Rapidminer, and Weka. He analyzes data on a daily basis and is keen to share his knowledge in a simple way.
Read more about Eric Mayor
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Learning Predictive Analytics with RPublished in: Sep 2015Publisher: PacktISBN-13: 9781782169352
© 2015 Packt Publishing Limited All Rights Reserved
Author (1)
Eric Mayor
Eric Mayor is a senior researcher and lecturer at the University of Neuchatel, Switzerland. He is an enthusiastic user of open source and proprietary predictive analytics software packages, such as R, Rapidminer, and Weka. He analyzes data on a daily basis and is keen to share his knowledge in a simple way.
Read more about Eric Mayor