Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Link prediction

Link Prediction is the problem of predicting which links will appear in a network. For example, we can have a friendship graph in Facebook or another social network, and functionality like people you may know is an application of Link Prediction. So, we can see Link Prediction is a recommendation system for social networks.

For this problem, we need to find a dataset that contains a graph evolving over time. Then, we can consider such a graph at some point in its evolution, calculate some characteristics between the existing links, and, based on that, predict which links are likely to appear next. Since for such graphs we know the future, we can use this knowledge for evaluating the performance of our models.

There are a number of interesting datasets available, but unfortunately, most of them do not have a time associated to the edges, so it is not possible to see how these graphs developed over time. This makes it harder to test the methods, but, of course, it is possible...