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The Kaggle Book

You're reading from   The Kaggle Book Data analysis and machine learning for competitive data science

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801817479
Length 534 pages
Edition 1st Edition
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Authors (2):
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Luca Massaron Luca Massaron
Author Profile Icon Luca Massaron
Luca Massaron
Konrad Banachewicz Konrad Banachewicz
Author Profile Icon Konrad Banachewicz
Konrad Banachewicz
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Toc

Table of Contents (20) Chapters Close

Preface
1. Part I: Introduction to Competitions
2. Introducing Kaggle and Other Data Science Competitions FREE CHAPTER 3. Organizing Data with Datasets 4. Working and Learning with Kaggle Notebooks 5. Leveraging Discussion Forums 6. Part II: Sharpening Your Skills for Competitions
7. Competition Tasks and Metrics 8. Designing Good Validation 9. Modeling for Tabular Competitions 10. Hyperparameter Optimization 11. Ensembling with Blending and Stacking Solutions 12. Modeling for Computer Vision 13. Modeling for NLP 14. Simulation and Optimization Competitions 15. Part III: Leveraging Competitions for Your Career
16. Creating Your Portfolio of Projects and Ideas 17. Finding New Professional Opportunities 18. Other Books You May Enjoy
19. Index

Organizing Data with Datasets

In his story The Adventure of the Copper Beeches, Arthur Conan Doyle has Sherlock Holmes shout “Data! Data! Data! I cannot make bricks without clay.” This mindset, which served the most famous detective in literature so well, should be adopted by every data scientist. For that reason, we begin the more technical part of this book with a chapter dedicated to data: specifically, in the Kaggle context, leveraging the power of the Kaggle Datasets functionality for our purposes.

In this chapter, we will cover the following topics:

  • Setting up a dataset
  • Gathering the data
  • Working with datasets
  • Using Kaggle Datasets in Google Colab
  • Legal caveats
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