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

Working with categorical values with one-hot encoding

Machine learning and statistics can be quite good at determining relationships between numbers. But what if you have a feature that is categorical and doesn't have a relationship? The definition of a categorical feature is when the variable is a label or category with discrete possibilities, such as colors , the animal kingdom, or cities.

One option when you have this type of data is to use use one-hot encoding. This is the process of converting a categorical value into a set of ones and zeroes so that the model can interpret them as independent, but not infer that there is a relationship between them. This also prevents the inference that some categories are superior or inferior.

You can see an example of what this looks like in the following figure. Say you are looking at sales data for bouncy balls and one of the features is the color. There are three colors – red, blue and green. This is represented as data...