Book Image

Machine Learning in Java - Second Edition

By : AshishSingh Bhatia, Bostjan Kaluza
Book Image

Machine Learning in Java - Second Edition

By: AshishSingh Bhatia, Bostjan Kaluza

Overview of this book

As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level. 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.
Table of Contents (13 chapters)

Market basket analysis

Since the introduction of electronic points of sale, retailers have been collecting an incredible amount of data. To leverage this data in to produce business value, they first developed a way to consolidate and aggregate the data to understand the basics of the business.

Recently, the focus shifted to the lowest level of granularity—the market basket transaction. At this level of detail, the retailers have direct visibility into the market basket of each customer who shopped at their store, understanding not only the quantity of the purchased items in that particular basket, but also how these items were bought in conjunction with one another. This can be used to drive decisions about how to differentiate store assortment and merchandise, as well as to effectively combine offers of multiple products, within and across categories, to drive higher...