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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

Chapter 9: Building a Regression Model with scikit-learn

So far, we have covered everything from how to install packages with conda to determining which modeling approach to use. In this chapter, we are going to put all that we've learned to use by walking through a real-world situation to see how all the pieces fit together.

In this scenario, we'll pretend that we own a winery, and we want to predict how our newest wine would score in a quality test to find out whether we should adjust our growing methods in any way. This will require a few things from us.

First, we'll look at the problem space that we're working in, which in this case is making wine. Then we'll dig into the data to understand it better and to see whether there could be any issues with it, and what we can learn at a high level. After that, we'll learn how to quickly evaluate some popular regression algorithms that we saw in Chapter 7, Choosing the Best AI Algorithm, using common...

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