Developing Kaggle Notebooks

Developing Kaggle Notebooks

eBook Details:

  • Paperback: 370 pages
  • Publisher: WOW! eBook (December 27, 2023)
  • Language: English
  • ISBN-10: 1805128515
  • ISBN-13: 978-1805128519

eBook Description:

Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster and develop an array of effective strategies and blueprints to approach any new data analysis on the Kaggle platform and create Notebooks with substance, style and impact

Developing Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques.

For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle’s Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. Developing Kaggle Notebooks book aims to make the notebooks’ code more structured, easy to maintain, and readable.

  • Approach a dataset or competition to perform data analysis via a notebook
  • Learn data ingestion and address issues arising with the ingested data
  • Structure your code using reusable components
  • Analyze in depth both small and large datasets of various types
  • Distinguish yourself from the crowd with the content of your analysis
  • Enhance your notebook style with a color scheme and other visual effects
  • Captivate your audience with data and compelling storytelling techniques

Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you’ll move on to preliminary data analysis, advanced data exploration, feature qualification to build a model baseline, and feature engineering. You’ll also delve into hyperparameter tuning to iteratively refine your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.


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