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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Text augmentation strategies

We discussed augmentation strategies for computer vision problems extensively in the previous chapter. By contrast, similar approaches for textual data are a less well-explored topic (as evidenced by the fact there is no single package like albumentations). In this section, we demonstrate some of the possible approaches to addressing the problem.

Basic techniques

As usual, it is informative to examine the basic approaches first, focusing on random changes and synonym handling. A systematic study of the basic approaches is provided in Wei and Zou (2019) at

We begin with synonym replacement. Replacing certain words with their synonyms produces text that is close in meaning to the original, but slightly perturbed (see the project page at if you are interested in more details, like where the synonyms are actually coming from):

def get_synonyms(word):
    synonyms = set()...