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Hands-On Data Science and Python Machine Learning

You're reading from   Hands-On Data Science and Python Machine Learning Perform data mining and machine learning efficiently using Python and Spark

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781787280748
Length 420 pages
Edition 1st Edition
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Author (1):
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Frank Kane Frank Kane
Author Profile Icon Frank Kane
Frank Kane
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Table of Contents (11) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Statistics and Probability Refresher, and Python Practice 3. Matplotlib and Advanced Probability Concepts 4. Predictive Models 5. Machine Learning with Python 6. Recommender Systems 7. More Data Mining and Machine Learning Techniques 8. Dealing with Real-World Data 9. Apache Spark - Machine Learning on Big Data 10. Testing and Experimental Design

Polynomial regression

We've talked about linear regression where we fit a straight line to a set of observations. Polynomial regression is our next topic, and that's using higher order polynomials to fit your data. So, sometimes your data might not really be appropriate for a straight line. That's where polynomial regression comes in.

Polynomial regression is a more general case of regression. So why limit yourself to a straight line? Maybe your data doesn't actually have a linear relationship, or maybe there's some sort of a curve to it, right? That happens pretty frequently.

Not all relationships are linear, but the linear regression is just one example of a whole class of regressions that we can do. If you remember the linear regression line that we ended up with was of the form y = mx + b, where we got back the values m and b from our linear regression...

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