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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

Santa competition 2020

Over the last few years, a sort of tradition has emerged on Kaggle: in early December, there is a Santa-themed competition. The actual algorithmic side varies from year to year, but for our purposes, the 2020 competition is an interesting case: https://www.kaggle.com/c/santa-2020.

The setup was a classical multi-armed bandit (MAB) trying to maximize reward by taking repeated action on a vending machine, but with two extras:

  • Reward decay: At each step, the probability of obtaining a reward from a machine decreases by 3 percent.
  • Competition: You are constrained not only by time (a limited number of attempts) but also by another player attempting to achieve the same objective. We mention this constraint mostly for the sake of completeness, as it is not crucial to incorporate explicitly in our demonstrated solution.

For a good explanation of the methods for approaching the general MAB problem, the reader is referred to https...

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