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Building Data Science Solutions with Anaconda

You're reading from   Building Data Science Solutions with Anaconda A comprehensive starter guide to building robust and complete models

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Length 330 pages
Edition 1st Edition
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Author (1):
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Dan Meador Dan Meador
Author Profile Icon Dan Meador
Dan Meador
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Table of Contents (16) Chapters Close

Preface 1. Part 1: The Data Science Landscape – Open Source to the Rescue
2. Chapter 1: Understanding the AI/ML landscape FREE CHAPTER 3. Chapter 2: Analyzing Open Source Software 4. Chapter 3: Using the Anaconda Distribution to Manage Packages 5. Chapter 4: Working with Jupyter Notebooks and NumPy 6. Part 2: Data Is the New Oil, Models Are the New Refineries
7. Chapter 5: Cleaning and Visualizing Data 8. Chapter 6: Overcoming Bias in AI/ML 9. Chapter 7: Choosing the Best AI Algorithm 10. Chapter 8: Dealing with Common Data Problems 11. Part 3: Practical Examples and Applications
12. Chapter 9: Building a Regression Model with scikit-learn 13. Chapter 10: Explainable AI - Using LIME and SHAP 14. Chapter 11: Tuning Hyperparameters and Versioning Your Model 15. Other Books You May Enjoy

Cleaning data with pandas

One of the most important aspects that come into play when working with data is ensuring that it's in the correct format that you need. Along with getting enough data, this might be the most vital component to training an accurate model. In this section, we're going to walk through the steps of importing a CSV file and then seeing how to analyze and clean it to make sure that it's prepped for us.

The example that we are going to look at is the data for various US university majors and how it relates to pay. Having a general sense of the domain we are looking into is critical, and this is an area that you might already have a grasp of. This dataset is provided by the excellent FiveThirtyEight site, and more information can be found here: https://github.com/fivethirtyeight/data/tree/master/college-majors.

Our goal is to see whether we can figure out whether we should have chosen another major using this data. We might even find out that...

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