This is the code repository for Java Data Science Cookbook, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
classVals = new ArrayList<String>();
for (int i = 0; i < 5; i++){
classVals.add("class" + (i + 1));
}
We have used Java to solve real-world data science problems. Our focus was to deliver content that can be effective for anyone who wants to know how to solve problems with Java. A minimum knowledge of Java is required, such as classes, objects, methods, arguments and parameters, exceptions, and exporting Java Archive (JAR) files. The code is well supported with narrations, information, and tips to help the readers understand the context and purpose. The theories behind the problems solved in this book, on many occasions, are not thoroughly discussed, but references for interested readers are provided whenever necessary.
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