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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

Exploring and cleaning the data

Now we move on to what might be the most important and time-consuming part of the data science workflow: exploring and cleaning the data. We'll begin by grabbing some basic statistics for the data that we have.

Type the following into another Jupyter notebook cell and run it:

df_raw.describe()

You will see the basic info across all our columns. Note in the following example I grabbed a subset just for practical purposes of displaying it here:

Figure 9.8 – Combined wine basic statistics

There are a few things we can pick out: one is that the mean quality is 5.8, so that is the number that we would want to beat, but if we are looking to be at the higher end of wine quality, we would want to shoot for something above 6, which is the 75th percentile, and nothing gets above a 9, so perhaps that could be our lofty goal.

Note that the quality is a discrete integer in the range 3-9. Should we one-hot encode it...

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