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scikit-learn Cookbook , Second Edition

You're reading from   scikit-learn Cookbook , Second Edition Over 80 recipes for machine learning in Python with scikit-learn

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Length 374 pages
Edition 2nd Edition
Languages
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Authors (2):
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Trent Hauck Trent Hauck
Author Profile Icon Trent Hauck
Trent Hauck
Julian Avila Julian Avila
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Julian Avila
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Toc

Table of Contents (13) Chapters Close

Preface 1. High-Performance Machine Learning – NumPy FREE CHAPTER 2. Pre-Model Workflow and Pre-Processing 3. Dimensionality Reduction 4. Linear Models with scikit-learn 5. Linear Models – Logistic Regression 6. Building Models with Distance Metrics 7. Cross-Validation and Post-Model Workflow 8. Support Vector Machines 9. Tree Algorithms and Ensembles 10. Text and Multiclass Classification with scikit-learn 11. Neural Networks 12. Create a Simple Estimator

Viewing the Pima Indians diabetes dataset with pandas

How to do it...

  1. You can view the data in various ways. View the top of the dataframe:
all_data.head()
  1. Nothing seems amiss here, except possibly an insulin level of zero. Is this possible? What about the skin_mm variable? Can that be zero? Make a note about it as a comment in your IPython:
#Is an insulin level of 0 possible? Is a skin_mm of 0 possible?
  1. Get a rough overview of the dataframe with the describe() method:
all_data.describe()
  1. Make a note again in your notebook about additional zeros:
#The features plasma_con, blood_pressure, skin_mm, insulin, bmi have 0s as values. These values could be physically impossible.
  1. Draw a histogram of the pregnancy_x variable...
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