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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

Frequently used features

On the right-side panel we have quick menu actions for access to frequently used features of notebooks. The first actions are grouped under Data section. Here you have buttons for adding or removing dataset to the notebook. By pressing Add Data button you can add one existing dataset. You have search text box, and quick buttons to select from your datasets, competition datasets and from your notebooks. When you select your notebooks, you can include output of notebooks as data sources for the current notebook. You have also an upload button next to the Add Data button, and you can use it to upload a new dataset, before adding it to the notebook. In the same Data section on the panel, you have input and output folder browser, and buttons next each item so that you can copy the path to either folders or files.Right under the Data section we have the Models section. Here we can add models to the Notebook. Models are a new feature on the platform, and this allows...

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