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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In this chapter, we have discussed tabular competitions on Kaggle. Since most of the knowledge applicable in a tabular competition overlaps with standard data science knowledge and practices, we have focused our attention on techniques more specific to Kaggle.

Starting from the recently introduced Tabular Playground Series, we touched on topics relating to reproducibility, EDA, feature engineering, feature selection, target encoding, pseudo-labeling, and neural networks applied to tabular datasets.

EDA is a crucial phase if you want to get insights on how to win a competition. It is also quite unstructured and heavily dependent on the kind of data you have. Aside from giving you general advice on EDA, we brought your attention to techniques such as t-SNE and UMAP that can summarize your entire dataset at a glance. The next phase, feature engineering, is also strongly dependent on the kind of data you are working on. We therefore provided a series of possible feature...