Book Image

Building Data Science Solutions with Anaconda

By : Dan Meador
5 (1)
Book Image

Building Data Science Solutions with Anaconda

5 (1)
By: Dan 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)
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

Finding bias in an example

In the following example, there is a significant business impact to finding bias in data.

The housing data company Zillow recently backed out of the iBuying business. Zillow is a US-based company that lists housing information for average consumers to look at. iBuying is the term used for instant buying and involves Zillow buying properties directly and then selling them for a profit (in theory). Zillow found that their estimations (or zestimates) were off by a large factor, which led to the company pulling out of that area. Maybe we can find out why.

In this scenario, we will try to find where bias could have entered the system in something such as a zestimate. To give you a framework to work through, we'll walk through steps and each type of bias discussed earlier to think through it. This is important, as you might not instantly jump to a certain type of bias unless you see it. This is an issue, as everyone has a bias toward looking for something...