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Building Data Science Solutions with Anaconda

You're reading from   Building Data Science Solutions with Anaconda A comprehensive starter guide to building robust and complete models

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Length 330 pages
Edition 1st Edition
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Author (1):
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Dan Meador Dan Meador
Author Profile Icon Dan Meador
Dan Meador
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Table of Contents (16) Chapters Close

Preface 1. Part 1: The Data Science Landscape – Open Source to the Rescue
2. Chapter 1: Understanding the AI/ML landscape FREE CHAPTER 3. Chapter 2: Analyzing Open Source Software 4. Chapter 3: Using the Anaconda Distribution to Manage Packages 5. Chapter 4: Working with Jupyter Notebooks and NumPy 6. Part 2: Data Is the New Oil, Models Are the New Refineries
7. Chapter 5: Cleaning and Visualizing Data 8. Chapter 6: Overcoming Bias in AI/ML 9. Chapter 7: Choosing the Best AI Algorithm 10. Chapter 8: Dealing with Common Data Problems 11. Part 3: Practical Examples and Applications
12. Chapter 9: Building a Regression Model with scikit-learn 13. Chapter 10: Explainable AI - Using LIME and SHAP 14. Chapter 11: Tuning Hyperparameters and Versioning Your Model 15. Other Books You May Enjoy

Creating a scikit-learn pipeline

If there is one thing that you may have noticed by now in this book, it's that there are many common steps for every problem we have looked at. We're now going to ensure that we can more easily iterate on the data and model creation steps by leveraging the scikit-learn pipeline to put together an easy, repeatable process. In this section, we are going to take a previous workflow that would ordinarily need to be repeated many times and turn it into a single unit, which will allow you much greater flexibility and save time compared to the previous process. If you are starting with this chapter or jumping to it before going through the others, you need to know that the underlying concepts covered previously are still incredibly important to understand.

To visualize what the process is going to look like, you can refer to the following diagram. On the left, you will see normal data input being passed into the pipeline object. In that pipeline...

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