Book Image

The Kaggle Book

By : Konrad Banachewicz, Luca Massaron
5 (2)
Book Image

The Kaggle Book

5 (2)
By: Konrad Banachewicz, Luca Massaron

Overview of this book

Millions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics. Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!
Table of Contents (20 chapters)
Part I: Introduction to Competitions
Part II: Sharpening Your Skills for Competitions
Part III: Leveraging Competitions for Your Career
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Modeling for Tabular Competitions

Until 2017, there was no need to distinguish too much between competition types and, since the vast majority of competitions were based on tabular data, you could not even find mention of “tabular competitions” on Kaggle forums. Suddenly, something changed. After a relative shortage of competitions (see, deep learning competitions took the upper hand and tabular competitions became rarer, disappointing many. They became so rare that Kaggle recently had to launch a series of tabular competitions based on synthetic data. What happened?

By 2017-2018, data science had grown to full maturity and many companies had initiated their data journeys. Data science was still a hot topic, but no longer such an uncommon one. Solutions to problems similar to those that had populated Kaggle for years at the time had become standard practice in many companies. Under these circumstances, sponsors were less motivated...