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
Other Books You May Enjoy


Having arrived at the end of the chapter, we will summarize the advice we have discussed along the way so you can organize your validation strategy and reach the end of a competition with a few suitable models to submit.

In this chapter, we first analyzed the dynamics of the public leaderboard, exploring problems such as adaptive overfitting and shake-ups. We then discussed the importance of validation in a data science competition, building a reliable system, tuning it to the leaderboard, and then keeping track of your efforts.

Having discussed the various validation strategies, we also saw the best way of tuning your hyperparameters and checking your test data or validation partitions by using adversarial validation. We concluded by discussing some of the various leakages that have been experienced in Kaggle competitions and we provided advice about how to deal with them.

Here are our closing suggestions:

  • Always spend the first part of the competition...