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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

Building connections with other competition data scientists

Connections are essential for finding a job, as they help you get into contact with people who may know about an opportunity before it becomes public and the search for potential candidates begins. In recent years, Kaggle has increasingly become a place where you can connect with other data scientists, collaborate, and make friends. In the past, competitions did not give rise to many exchanges on forums, and teams were heavily penalized in the global rankings because competition points were split equally among the team members. Improved rankings (see https://www.kaggle.com/general/14196) helped many Kagglers see teaming up in a more favorable light.

Teaming up in a Kaggle competition works fine if you already know the other team members and you already have an established approach to assigning tasks and collaborating remotely. In these situations, each team member already knows how to collaborate by:

  • Taking...
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