Book Image

Java Data Analysis

By : John R. Hubbard
Book Image

Java Data Analysis

By: John R. Hubbard

Overview of this book

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the aim of discovering useful information. Java is one of the most popular languages to perform your data analysis tasks. This book will help you learn the tools and techniques in Java to conduct data analysis without any hassle. After getting a quick overview of what data science is and the steps involved in the process, you’ll learn the statistical data analysis techniques and implement them using the popular Java APIs and libraries. Through practical examples, you will also learn the machine learning concepts such as classification and regression. In the process, you’ll familiarize yourself with tools such as Rapidminer and WEKA and see how these Java-based tools can be used effectively for analysis. You will also learn how to analyze text and other types of multimedia. Learn to work with relational, NoSQL, and time-series data. This book will also show you how you can utilize different Java-based libraries to create insightful and easy to understand plots and graphs. By the end of this book, you will have a solid understanding of the various data analysis techniques, and how to implement them using Java.
Table of Contents (20 chapters)
Java Data Analysis
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Index

Implementing user ratings


Many online vendors ask their customers to rate the products that they purchase, typically on a scale of one to five stars. We can modify our previous Recommender2 program to incorporate these numerical ratings. To test the new version, we'll also modify our DataGenerator and Filter programs.

The modified DataGenerator program is shown in Listing 9.18:

Listing 9.18: Program to generate random ratings

(The folded code is the same as in the DataGenerator1 program in Listing 9.2.) It creates random ratings from the set {1.0, 1.5, 2.0, 2.5, …, 5.0} that are normally distributed with mean 3.0 and standard deviation 1.0.

The only modifications needed for the Filter program are changing int to double where necessary. The results from a sample run are shown in Figure 9.10 and Figure 9.11:

Figure 9.10: Utility file from DataGenerator3

Figure 9.11: Similarity file from DataGenerator3

Item #9 has not been bought by anyone—all five of its entries in the utility matrix are 0.0. Consequently...