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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

Gathering the data

Apart from legal aspects, there is no real limit on the kind of content you can store in the datasets: tabular data, images, text; if it fits within the size requirements, you can store it. This includes data harvested from other sources; tweets by hashtag or topic are among the popular datasets at the time of writing:

Obraz zawierający tekst  Opis wygenerowany automatycznie

Figure 2.6: Tweets are among the most popular datasets

Discussion of the different frameworks for harvesting data from social media (Twitter, Reddit, and so on) is outside the scope of this book.

Andrew Maranhão

https://www.kaggle.com/andrewmvd

We spoke to Andrew Maranhão (aka Larxel), Datasets Grandmaster (number 1 in Datasets at time of writing) and Senior Data Scientist at the Hospital Albert Einstein in São Paulo, about his rise to Datasets success, his tips for creating datasets, and his general experiences on Kaggle.

What’s your favourite kind of competition...

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