Book Image

Machine Learning with R

By : Brett Lantz
Book Image

Machine Learning with R

By: Brett Lantz

Overview of this book

Machine learning, at its core, is concerned with transforming data into actionable knowledge. This fact makes machine learning well-suited to the present-day era of "big data" and "data science". Given the growing prominence of R—a cross-platform, zero-cost statistical programming environment—there has never been a better time to start applying machine learning. Whether you are new to data science or a veteran, machine learning with R offers a powerful set of methods for quickly and easily gaining insight from your data. "Machine Learning with R" is a practical tutorial that uses hands-on examples to step through real-world application of machine learning. Without shying away from the technical details, we will explore Machine Learning with R using clear and practical examples. Well-suited to machine learning beginners or those with experience. Explore R to find the answer to all of your questions. How can we use machine learning to transform data into action? Using practical examples, we will explore how to prepare data for analysis, choose a machine learning method, and measure the success of the process. We will learn how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data. "Machine Learning with R" will provide you with the analytical tools you need to quickly gain insight from complex data.
Table of Contents (19 chapters)
Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
9
Finding Groups of Data – Clustering with k-means
Index

Summary


Association rules are one solution to the Big Data problem. As an unsupervised learning algorithm, they are capable of extracting knowledge from large databases without any prior knowledge of what patterns to seek. The catch is that it takes some effort to reduce the wealth of information into a smaller and more manageable set of results. The Apriori algorithm, which we studied in this chapter, does so by setting minimum thresholds of interestingness, and reporting only the associations meeting these criteria.

We put the Apriori algorithm to work while performing a market basket analysis for a month's worth of transactions at a moderately-sized supermarket. Even in this small example, a wealth of associations were identified. Among these, we noted several patterns that may be useful for future marketing campaigns. The same methods applied here are used at much larger retailers on databases many times this size.

In the next chapter, we will examine another unsupervised learning algorithm...