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Building Data Science Solutions with Anaconda

You're reading from  Building Data Science Solutions with Anaconda

Product type Book
Published in May 2022
Publisher Packt
ISBN-13 9781800568785
Pages 330 pages
Edition 1st Edition
Languages
Author (1):
Dan Meador Dan Meador
Profile icon Dan Meador

Table of Contents (16) Chapters

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

Technical requirements

There are just a few things that we'll need in order to go through this chapter.

The first is that the Anaconda distribution is installed. As we know, this includes Python, conda, Navigator, as well as many other packages used in data science.

We will use the following packages:

  • SHAP
  • LIME
  • pandas
  • NumPy
  • Scikit-learn

You should create a new conda environment to install all of these packages. It's recommended to install them all at the beginning, but you can also do so as you get to the relevant part of the chapter.

After you have these things in place, we can look at why we should care about the interpretation of models in the first place.

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