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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Example discussion approaches

It is a completely normal thing to feel lost in a competition at some point: you came in, tried a few ideas, got some traction on the leaderboard, and then you hit the Kaggle version of a runner’s wall. This is the moment when discussion forums are the place to consult.

As an example, we will look at the Optiver Realized Volatility Prediction competition (, characterized by the organizers like this:

In the first three months of this competition, you’ll build models that predict short-term volatility for hundreds of stocks across different sectors. You will have hundreds of millions of rows of highly granular financial data at your fingertips, with which you’ll design your model forecasting volatility over 10-minute periods. Your models will be evaluated against real market data collected in the three-month evaluation period after training.