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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

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Colour book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

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Sonia Pipa Jan 18, 2024

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

KGM Feb 12, 2024

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

Jeong-Yoon Lee Feb 10, 2024

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

Ram Seshadri Mar 01, 2024

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

Radu Orghidan Jan 23, 2024

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

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

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