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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

Closing Our Journey: How to Stay Relevant and on Top

We near the conclusion of our enlightening journey through the realm of data science, and we have traversed a diverse landscape of challenges, ranging from geospatial analysis and natural language processing to image classification and time-series forecasting. This expedition has enriched our understanding of how to adeptly combine various cutting-edge technologies. We’ve delved into large language models, such as those developed by Kaggle, explored vector databases, and discovered the efficiency of task chaining frameworks, all to harness the transformative potential of generative AI.

Our learning journey has also encompassed working with an array of data types and formats. We’ve engaged in feature engineering, constructed several baseline models, and acquired the skill of iteratively refining these models. This process is central to mastering the numerous tools and techniques essential for comprehensive data...

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