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Healthcare Analytics Made Simple

You're reading from   Healthcare Analytics Made Simple Techniques in healthcare computing using machine learning and Python

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Product type Paperback
Published in Jul 2018
Publisher Packt
ISBN-13 9781787286702
Length 268 pages
Edition 1st Edition
Languages
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Authors (2):
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Vikas (Vik) Kumar Vikas (Vik) Kumar
Author Profile Icon Vikas (Vik) Kumar
Vikas (Vik) Kumar
Shameer Khader Shameer Khader
Author Profile Icon Shameer Khader
Shameer Khader
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Toc

Table of Contents (11) Chapters Close

Preface 1. Introduction to Healthcare Analytics 2. Healthcare Foundations FREE CHAPTER 3. Machine Learning Foundations 4. Computing Foundations – Databases 5. Computing Foundations – Introduction to Python 6. Measuring Healthcare Quality 7. Making Predictive Models in Healthcare 8. Healthcare Predictive Models – A Review 9. The Future – Healthcare and Emerging Technologies 10. Other Books You May Enjoy

Introduction to scikit-learn

Entire books have been written on scikit-learn (http://scikit-learn.org/stable/). The scikit-learn library has numerous submodules. Only a few of these submodules will be used in this book (in Chapter 7, Making Predictive Models in Healthcare). These include the sklearn.linear_model and sklearn.ensemble submodules, for example. Here we will give an overview of some of the more commonly used submodules. For convenience, we have grouped the relevant modules into various segments of the data science pipeline discussed in Chapter 1, Introduction to Healthcare Analytics.

Sample data

scikit-learn includes several sample datasets in the sklearn.datasets submodule. At least two of these datasets, sklearn...

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