Book Image

Java Data Science Cookbook

By : Rushdi Shams
Book Image

Java Data Science Cookbook

By: Rushdi Shams

Overview of this book

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. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.
Table of Contents (16 chapters)
Java Data Science Cookbook
About the Author
About the Reviewer
Customer Feedback

Generating linear regression models

Most of the linear regression modelling follows a general pattern--there will be many independent variables that will collectively produce a result, which is a dependent variable. For instance, we can generate a regression model to predict the price of a house based on different attributes/features of a house (mostly numeric, real values) such as its size in square feet, number of bedrooms, number of washrooms, importance of its location, and so on.

In this recipe, we will use Weka's linear regression classifier to generate a regression model.

How to do it...

  1. In this recipe, the linear regression model we will be creating is based on the cpu.arff dataset, which can be found in the data directory of the Weka installation directory.

    Our code will have two instance variables: the first variable will contain the data instances of the cpu.arff file, and the second variable will be our linear regression classifier:

            Instances cpu = null;