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Java for Data Science

You're reading from   Java for Data Science Examine the techniques and Java tools supporting the growing field of data science

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Product type Paperback
Published in Jan 2017
Publisher Packt
ISBN-13 9781785280115
Length 386 pages
Edition 1st Edition
Languages
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Authors (2):
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Jennifer L. Reese Jennifer L. Reese
Author Profile Icon Jennifer L. Reese
Jennifer L. Reese
Richard M. Reese Richard M. Reese
Author Profile Icon Richard M. Reese
Richard M. Reese
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Data Science 2. Data Acquisition FREE CHAPTER 3. Data Cleaning 4. Data Visualization 5. Statistical Data Analysis Techniques 6. Machine Learning 7. Neural Networks 8. Deep Learning 9. Text Analysis 10. Visual and Audio Analysis 11. Mathematical and Parallel Techniques for Data Analysis 12. Bringing It All Together

Regression analysis


Regression analysis is useful for determining trends in data. It indicates the relationship between dependent and independent variables. The independent variables determine the value of a dependent variable. Each independent variable can have either a strong or a weak effect on the value of the dependent variable. Linear regression uses a line in a scatterplot to show the trend. Non-linear regression uses some sort of curve to depict the relationships.

For example, there is a relationship between blood pressure and various factors such as age, salt intake, and Body Mass Index (BMI). The blood pressure can be treated as the dependent variable and the other factors as independent variables. Given a dataset containing these factors for a group of individuals we can perform regression analysis to see trends.

There are several types of regression analysis supported by Java. We will be examining simple linear regression and multiple linear regression. Both approaches take a dataset...

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