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Java Data Analysis

You're reading from   Java Data Analysis Data mining, big data analysis, NoSQL, and data visualization

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787285651
Length 412 pages
Edition 1st Edition
Languages
Concepts
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Author (1):
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John R. Hubbard John R. Hubbard
Author Profile Icon John R. Hubbard
John R. Hubbard
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Table of Contents (20) Chapters Close

Java Data Analysis
Credits
About the Author
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Preface
1. Introduction to Data Analysis FREE CHAPTER 2. Data Preprocessing 3. Data Visualization 4. Statistics 5. Relational Databases 6. Regression Analysis 7. Classification Analysis 8. Cluster Analysis 9. Recommender Systems 10. NoSQL Databases 11. Big Data Analysis with Java Java Tools Index

Summary


In this chapter, we have investigated several examples of regression analysis, including linear regression, polynomial regression, multiple linear regression, and more general curve fitting. In each case, the objective is to derive from a given dataset a function that can then be used to extrapolate from the given data to predict unknown values of the function.

We've seen that these regression algorithms work by solving a system of linear equations, called the normal equations, for the problem. That part of the solution can be done by various algorithms, such as Cramer's Rule, Gaussian Elimination, or LU decomposition.

We used several approaches to implement these algorithms, including Windows Excel, direct Java implementations, and the Apache Commons Math library.

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