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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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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

The Meta Kaggle dataset

The Meta Kaggle dataset (https://www.kaggle.com/kaggle/meta-kaggle) is a collection of rich data about Kaggle’s community and activity, published by Kaggle itself as a public dataset. It contains CSV tables filled with public activity from Competitions, Datasets, Notebooks, and Discussions. All you have to do is to start a Kaggle Notebook (as you saw in Chapters 2 and 3), add to it the Meta Kaggle dataset, and start analyzing the data. The CSV tables are updated daily, so you’ll have to refresh your analysis often, but that’s worth it given the insights you can extract.

We will sometimes refer to the Meta Kaggle dataset in this book, both as inspiration for many interesting examples of the dynamics in a competition and as a way to pick up useful examples for your learning and competition strategies. Here, we are going to use it in order to figure out what evaluation metrics have been used most frequently for competitions in the last...

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