Book Image

Developing Kaggle Notebooks

By : Gabriel Preda
Book Image

Developing Kaggle Notebooks

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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Introducing Kaggle and Its Basic Functions

Kaggle is currently the main platform for competitive predictive modeling. Here, those who are passionate about machine learning, both experts and beginners, have a collaborative and competitive environment to learn, win recognition, share knowledge, and give back to the community. The company was launched in 2010, offering only machine learning competitions. Currently, it is a data platform that includes sections titled Competitions, Datasets, Code, Discussions, Learn, and, most recently, Models.

In 2011, Kaggle went through an investment round, valuing the company above $25 million. In 2017, it was acquired by Google (now Alphabet Inc.), becoming associated with Google Cloud. The most notable key persons from Kaggle are co-founders Anthony Goldbloom (long-time CEO until 2022) and Ben Hammer (CTO). Recently, D. Sculley, the legendary Google engineer, became Kaggle’s new CEO, after Anthony Goldbloom stepped down to become involved in the development of a new start-up.

In this first chapter, we’ll explore the main sections that the Kaggle platform offers its members. We will also learn how to create an account, how the platform is organized, and what its main sections are. In short, this chapter will cover the following topics:

  • The Kaggle platform
  • Kaggle Competitions
  • Kaggle Datasets
  • Kaggle Code
  • Kaggle Discussions
  • Kaggle Learn
  • Kaggle Models

If you are familiar with the Kaggle platform, you probably know about these features already. You can choose to continue reading the following sections to refresh your knowledge about the platform or you can skip them and go directly to the next chapter.