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)
Preface
1
Part I: Introduction to Competitions
6
Part II: Sharpening Your Skills for Competitions
15
Part III: Leveraging Competitions for Your Career
18
Other Books You May Enjoy
19
Index

The Tabular Playground Series

Due to the large demand for tabular problems, Kaggle staff started an experiment in 2021, launching a monthly contest called the Tabular Playground Series. The contests were based on synthetic datasets that replicated public data or data from previous competitions. The synthetic data was created thanks to a deep learning generative network called CTGAN.

You can find the CTGAN code at https://github.com/sdv-dev/CTGAN. There’s also a relevant paper explaining how it works by modeling the probability distribution of rows in tabular data and then generating realistic synthetic data (see https://arxiv.org/pdf/1907.00503v2.pdf).

Synthetic Data Vault (https://sdv.dev/), an MIT initiative, created the technology behind CTGAN and quite a number of tools around it. The result is a set of open-source software systems built to help enterprises generate synthetic data that mimics real data; it can help data scientists to create anonymous datasets...