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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

Evaluating potential models using MSE and R2 scores

There will always be a large number of potential models that you can attempt to train, and you can spend a large amount of time tweaking each of them to optimize them. It's valuable to understand which ones could give you the best outcome before you spend a large amount of time on any option. We're going to use k-fold validation to check how we trained the model. This will take our training data and create k sections. You can think of this as folding a piece of paper k times, and then taking turns using one of the k sections as the testing data, and the rest as the training data:

  1. First, we want to import what we need for this exercise. The next bit of code will do the training so we can see which model would be a nice fit. We'll start as usual by importing what we need:
    from sklearn.model_selection import cross_val_score
    from sklearn.model_selection import StratifiedKFold
    from sklearn.linear_model import ...
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