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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

By : Meador
5 (12)
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Building Data Science Solutions with Anaconda

Building Data Science Solutions with Anaconda

5 (12)
By: Meador

Overview of this book

You might already know that there's a wealth of data science and machine learning resources available on the market, but what you might not know is how much is left out by most of these AI resources. This book not only covers everything you need to know about algorithm families but also ensures that you become an expert in everything, from the critical aspects of avoiding bias in data to model interpretability, which have now become must-have skills. In this book, you'll learn how using Anaconda as the easy button, can give you a complete view of the capabilities of tools such as conda, which includes how to specify new channels to pull in any package you want as well as discovering new open source tools at your disposal. You’ll also get a clear picture of how to evaluate which model to train and identify when they have become unusable due to drift. Finally, you’ll learn about the powerful yet simple techniques that you can use to explain how your model works. By the end of this book, you’ll feel confident using conda and Anaconda Navigator to manage dependencies and gain a thorough understanding of the end-to-end data science workflow.
Table of Contents (16 chapters)
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1
Part 1: The Data Science Landscape – Open Source to the Rescue
6
Part 2: Data Is the New Oil, Models Are the New Refineries
11
Part 3: Practical Examples and Applications

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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