Developing Kaggle Notebooks (Early Access)

Developing Kaggle Notebooks (Early Access) - Printable Version

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Developing Kaggle Notebooks (Early Access) - BaDshaH - 10-06-2023

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Developing Kaggle Notebooks (Early Access)

English | 2023 | ISBN: 9781805128519 | 176 pages | True EPUB, MOBI | 21.14 MB

Develop an array of effective strategies and blueprints to approach any new data analysis on Kaggle and create Notebooks with substance, style, and impact for Kaggle.

Key Features
Start from the basics to master ingestion, cleaning, and exploration of data, and prepare for building baseline models
Develop a robust approach to working with any type, modality, and size of data - from tabular, text, and image to video and sound data
Improve your style and readability and make your Notebooks more visible and impactful

Book [b]Description
[/b]Developing Kaggle Notebooks is here to introduce you to the wide world of data analysis, with a focus on using Kaggle Notebooks resources to help you achieve mastery in this field as well as rising to the top in the Kaggle Notebooks tier. The book is structured as a seven-step trip into the world of analysis, exploring the features available in Kaggle Notebooks alongside various data analysis techniques and different kinds of datasets.
For each topic, we will provide one or more Notebooks and develop reusable analysis components using the Kaggle utility scripts feature. These will be introduced gradually, first as part of a Notebook, and then extracted and used across future Notebooks to improve code reusability on Kaggle and make the Notebooks' code more structured, easy to maintain, and readable.
Although the focus of this book is on data analytics, some of the examples will also help you learn how to prepare a full machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and assessment of data quality, you'll move on to preliminary data analysis and advanced data exploration. Discover how to qualify features to build an initial model baseline and perform feature engineering and hyperparameter tuning to iteratively refine your model and prepare for a submission on Kaggle competitions.

What you will learn
Approach a new dataset or competition to perform a data analysis via a Notebook and get noticed
Start exploring a new source of data, from tools to use for ingestion to treating various issues with ingested data
Structure your code using reusable components
Perform a deep dive for both small and large datasets of various types
Differentiate yourself from the crowd with the content of your analysis
Improve the style of your Notebook: color scheme, content organization, visual effects, and theme
Use storytelling techniques to captivate your audience, improve the clarity of the presentation, and raise its impact

Who This Book Is For
This book is suitable for a wide audience with a keen interest in data science and machine learning and those who want to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks.
Beginners on Kaggle from any background will benefit
Seasoned contributors who want to improve various skills like ingestion, preparation, exploration, and visualization
Expert contributors who would like to learn from the Grandmasters to rise into the upper Kaggle rankings
Professionals who already use Kaggle for learning and competing

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