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Developing Kaggle Notebooks
Developing Kaggle Notebooks

Developing Kaggle Notebooks: Pave your way to becoming a Kaggle Notebooks Grandmaster

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Profile Icon Gabriel Preda
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Full star icon Full star icon Full star icon Full star icon Full star icon 5 (29 Ratings)
Paperback Dec 2023 370 pages 1st Edition
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Arrow left icon
Profile Icon Gabriel Preda
Arrow right icon
Free Trial
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (29 Ratings)
Paperback Dec 2023 370 pages 1st Edition
eBook
NZ$40.99 NZ$58.99
Paperback
NZ$73.99
Subscription
Free Trial
eBook
NZ$40.99 NZ$58.99
Paperback
NZ$73.99
Subscription
Free Trial

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Table of content icon View table of contents Preview book icon Preview Book

Developing Kaggle Notebooks

Models

Models are the newest section introduced on the platform, at the time of writing this book being less than one month old. Models started to be contributed quite often by the users in several ways and for few purposes. Most frequently, models were saved as output of Notebooks (Code) after being trained using a custom code and related to a competition. Then the model will be optionally included in a dataset or used directly (one can add to a code either a dataset or directly the output of another code). Also, sometime models build outside the platform were uploaded as datasets and then included in the pipeline of users to prepare a solution for a competition. Meantime models repositories were available either through a public cloud, like Google Cloud, AWS, or Azure or from a company specialized in such a service, like HuggingFace. With the concept of downloadable models, ready to be used or easy to fine-tune for a custom task, Kaggle choose to include the Models in his platform....

The content of next chapters

We learned what Kaggle is and how we can use the resources and features of the platform. Let’s take a quick look into the content of the next chapters that focus on how to create original, insightful, and recognizable content in the Notebooks space.

Getting ready for Kaggle environment

You learn here more details about Code features on Kaggle, with information about the computing environments, how to use the online editor, how to fork and modify an existing example, how to use the source control facilities on Kaggle to either save or save and run a new Notebook.

Starting our travel – how to survive on Titanic?

Most of the Kagglers will start their journey on the platform with this competition. Although is using a small and simple dataset, it has some hidden insights that we will explore together. Here we start to build the skills that we will further develop in the book. We introduce some tools for data analysis in Python (pandas and numpy...

Summary

In this chapter we learned about the Kaggle platform resources and capabilities and introduced the content of the following chapters. It’s now the time to get ready for your trip. In the next chapter, you will learn how to use the full capacity of the platform to code, get familiar with the development environment, learn how to use it at its maximum potential. Let’s get ready.

Exploring notebook capabilities

Notebooks serve as powerful tools for data exploration, model training, and running inferences. In this section, we will examine the various capabilities that Kaggle Notebooks have to offer.

We will start off with the most frequently used features of notebooks. We will go through the options to add various resources to a notebook (data and models) and to modify the execution environment. Then, we continue with more advanced features, which will include setting up utility scripts, adding or using secrets, using Google Cloud services, or upgrading a notebook to a Google Cloud AI Notebook. Let’s get started!

Basic capabilities

On the right-side panel, we have quick menu actions for access to frequently used features of notebooks. In the following screenshot, we take a more detailed look at these quick menu actions.

Figure 2.6: Zoomed-in view of the right-side panel with quick menus

As you can see, the first quick menu actions...

Using the Kaggle API to create, update, download, and monitor your notebooks

The Kaggle API is a powerful tool that extends the functionality available in the Kaggle user interface. You can use it for various tasks: define, update, and download datasets, submit to competitions, define new notebooks, push or pull versions of notebooks, or verify a run status.

There are just two simple steps for you to start using the Kaggle API. Let’s get started:

  1. First, you will need to create an authentication token. Navigate to your account, and from the right-side icon, select the menu item Account. Then go to the API section. Here, click on the Create new API token button to download your authentication token (it is a file named kaggle.json). If you will be using the Kaggle API from a Windows machine, its location is C:\Users\<your_name>\.kaggle\kaggle.json. On a Mac or Linux machine, the path to the file should be ~/.kaggle/kaggle.json.
  2. Next, you will have to...

Summary

In this chapter, we learned what Kaggle Notebooks are, what types we can use, and with what programming languages. We also learned how to create, run, and update notebooks. We then visited some of the basic features for using notebooks, which will allow you to start using notebooks in an effective way, to ingest and analyze data from datasets or competitions, to start training models, and to prepare submissions for competitions. Additionally, we also reviewed some of the advanced features and even introduced the use of the Kaggle API to further extend your usage of notebooks, allowing you to build external data and ML pipelines that integrate with your Kaggle environment.

The more advanced features give you more flexibility in using Kaggle Notebooks. With Utility scripts, you can create modular code, with specialized Python modules for ingesting data, performing statistical analysis on it, preparing visualizations, generating features, and building models. You can reuse...

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Key benefits

  • Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models
  • Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound
  • Improve the style and readability of your Notebooks, making them more impactful and compelling

Description

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.

Who is this book for?

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

What you will learn

  • Approach a dataset or competition to perform data analysis via a notebook
  • Learn data ingestion and address issues arising with the ingested data
  • Structure your code using reusable components
  • Analyze in depth both small and large datasets of various types
  • Distinguish yourself from the crowd with the content of your analysis
  • Enhance your notebook style with a color scheme and other visual effects
  • Captivate your audience with data and compelling storytelling techniques

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Publication date : Dec 27, 2023
Length: 370 pages
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Language : English
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Product Details

Publication date : Dec 27, 2023
Length: 370 pages
Edition : 1st
Language : English
ISBN-13 : 9781805128519
Category :
Languages :
Concepts :
Tools :

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Table of Contents

13 Chapters
Introducing Kaggle and Its Basic Functions Chevron down icon Chevron up icon
Getting Ready for Your Kaggle Environment Chevron down icon Chevron up icon
Starting Our Travel – Surviving the Titanic Disaster Chevron down icon Chevron up icon
Take a Break and Have a Beer or Coffee in London Chevron down icon Chevron up icon
Get Back to Work and Optimize Microloans for Developing Countries Chevron down icon Chevron up icon
Can You Predict Bee Subspecies? Chevron down icon Chevron up icon
Text Analysis Is All You Need Chevron down icon Chevron up icon
Analyzing Acoustic Signals to Predict the Next Simulated Earthquake Chevron down icon Chevron up icon
Can You Find Out Which Movie Is a Deepfake? Chevron down icon Chevron up icon
Unleash the Power of Generative AI with Kaggle Models Chevron down icon Chevron up icon
Closing Our Journey: How to Stay Relevant and on Top Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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Sonia Pipa Jan 18, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book introduces you to data analytics, focusing on how to use Kaggle Notebooks. Various data types are used, from tabular data to text, images, video, text and measurements signal data. Special attention is given to how to build your narrative around data and use custom graphical elements to maximize your analysis impact. Some of the examples in the book helps you to prepare an end-to-end machine learning pipeline. It also contains a chapter on how to use LLM resources available on Kaggle platform to prototype applications that leverages Generative AI.
Amazon Verified review Amazon
KGM Feb 12, 2024
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Having read "Developing Kaggle Notebooks" by Gabriel Preda, I can confidently say it's a cornerstone resource for anyone serious about mastering Kaggle. Preda, a triple Kaggle Grandmaster, distills his vast experience into this guide, covering everything from basic data analysis to the nuances of machine learning pipelines and generative AI applications. The book's practical approach, coupled with examples across various data types, makes it an invaluable asset for both beginners and advanced practitioners alike. Its emphasis on building reusable components and enhancing notebook readability is particularly commendable. Additionally, the exploration of Large Language Models and the book's visually appealing format, complete with color plots and photos, significantly enrich the learning experience. Whether you're aiming to climb the ranks of Kaggle competitions or simply broaden your data science expertise, this book is a must-read.
Amazon Verified review Amazon
Jeong-Yoon Lee Feb 10, 2024
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TL;DR; Kaggle Grandmaster Gabriel Preda’s new book, “Developing Kaggle Notebooks”, is a great resource to upskill your data science skills across data analytics, visualization, ML modeling, NLP, CV, and LLM, all without expensive server or GPU machine, but just using Kaggle Notebooks. Highly recommended.Since 2011, when I first started Kaggle, at every company where I worked, I have encouraged data scientists to try Kaggle and formed multiple teams to join Kaggle competitions together. I also organized a panel discussion with Kaggle Grandmaster at an academic conference, KDD, in 2018.In recent years, I no longer need to pitch hard for Kaggle as most data scientists know Kaggle and understand its value. Instead, aspiring data scientists and Kagglers often ask me where to start Kaggle and how to get better at it.Konrad and Luca’s “The Kaggle Book,” published in 2022, has been my answer to the “where to start” question, and now Gabriel’s new book, “Developing Kaggle Notebooks,” will be my answer to the “how to get better” question.The best way to get better at Kaggle is to learn from the top Kagglers, and Kaggle Notebooks are a great way to do so because, in contrast to write-ups or code repos, which are valuable resources as well, Kaggle Notebooks provide complete packages, including insights, code, and reproducible outputs.In this book, Gabriel shared his master class on Kaggle Notebooks with various data science applications such as data analytics, data visualization, predictive modeling, NLP, CV, and LLM. He also added great Kaggle Notebooks shared by other Kagglers as references.If you’re an aspiring data scientist, who’d like to upskill your data science skills, I highly recommend this book. Even if you don’t plan to compete at Kaggle competitions, you will learn much from this book.
Amazon Verified review Amazon
Ram Seshadri Mar 01, 2024
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I have a confession to make: Kaggle has always been a flickering passion for me though I suck at it.I have developed over 200 notebooks on Kaggle yet many of my notebooks barely get a single upvote (LOL). While a few have snagged (about 50) bronze medals, I have rarely won any Gold or Silver medals.With this kind of track record, it is no surprise that I found the book “Developing Kaggle Notebooks” by Gabriel Preda extremely interesting.After reading the book, I must say that this book exceeds my expectations because the author has collected a fantastic set of Kaggle notebooks for both beginners and experts alike. Just to give you an example: for beginners, there is the ubiquitous Titantic notebook but with a twist: the EDA and feature engineering are top class. For the experts, there is even a chapter on using LLM’s for building a multi-task application using Langchain. The rest of the book is a varied mix of offerings for beginners to advanced users.The author starts off with a classic notebook on tabular datasets (chapter 3: titanic) and then moves to a voluminous analysis of geospatial data (chapter 4: English pubs and Starbucks). This chapter alone consumes over 50 pages of the 300+ page volume and is well worth the time and effort that the author spends on it. This kind of data has not been expertly analyzed in most books on data science but would be a difficult task for any medium level data scientist.The next two chapters focus on text and image data. Both are well analyzed and handled very well with useful utility scripts that you can use in your future datasets. These utility scripts are mind blowingly simple yet provide elegant results that you would want to book and use in your Kaggle kernels.The chapter on Acoustic signal data is very much a class on handling time series data and the author has once again provided very useful tips and tricks to not only analyze the data but to build a very elegant model with boosting regressions. I highly recommend this chapter for forecasting aficionados as well.Chapter 9 focuses on video data and how to detect deepfakes using object detection models. This is of course a hot topic which will be made all the more difficult in the OpenAI SoRA and Google Lumiere era.The nice thing about the last chapter of the book is how the author weaves transformers throughout the book to focus on langchaining multiple tasks to an LLM for code generation as well as a RAG based QnA system. All in all a fitting end to a very recent and worthwhile topic.One added bonus of this fantastic book is that it is in full color and very easy to read. I highly recommend the book for new and experienced Kagglers like me who want to continuously learn from others.
Amazon Verified review Amazon
Radu Orghidan Jan 23, 2024
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I recently had the pleasure of reading "Developing Kaggle Notebooks" by my colleague, Gabriel Preda. This book is a gem in the field of data science, particularly for those keen on mastering Kaggle Notebooks. Gabriel expertly guides readers through the essentials of data analysis, offering invaluable insights on handling various data types and improving notebook presentation.What sets this book apart is its focus on building reusable analysis components and enhancing notebook readability. It's not just about data analytics; it's a comprehensive guide to developing complete machine learning pipelines in Kaggle. This book is an essential read for anyone aspiring to climb the ranks in Kaggle's competitive environment.
Amazon Verified review Amazon
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