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Developing Kaggle Notebooks

Developing Kaggle Notebooks

By : Gabriel Preda
5 (29)
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Developing Kaggle Notebooks

Developing Kaggle Notebooks

5 (29)
By: Gabriel Preda

Overview of this book

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. It aims to make the notebooks' code more structured, easy to maintain, and readable. 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 qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne 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.
Table of Contents (14 chapters)
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12
Other Books You May Enjoy
13
Index

Summary

In this last chapter, we reviewed a few of the “secrets” of the authors of great notebook content on Kaggle. They have in common a few qualities: they have a constant presence on the platform, start early working on a new dataset or competition dataset, continuously improve their work, recognize and appreciate quality content created by others, are continuous learners, are humble, share their knowledge, and constantly work outside their comfort zone. These are not aims on their own, but merely symptoms of the passion and constant interest in all there is to know about analyzing data and creating great predictive models.

As we wrap up this book and leave you to embark on your Kaggle Notebook adventures, I wish you a safe journey. I hope you enjoyed reading, and please remember that the world of data science is continuously changing. Keep experimenting, stay curious, and dive into data with confidence and skill. May your future Kaggle Notebook be filled with...

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