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

Open domain Q&A

In this section, we will be looking at the Google QUEST Q&A Labeling competition (https://www.kaggle.com/c/google-quest-challenge/overview/description). In this competition, question-answer pairs were evaluated by human raters on a diverse set of criteria, such as “question conversational,” “question fact-seeking,” or “answer helpful.” The task was to predict a numeric value for each of the target columns (corresponding to the criteria); since the labels were aggregated across multiple raters, the objective was effectively a multivariate regression output, with target columns normalized to the unit range.

Before engaging in modeling with advanced techniques (like transformer-based models for NLP), it is frequently a good idea to establish a baseline with simpler methods. As with the previous section, we will omit the imports for brevity, but you can find them in the Notebook in the GitHub repo.

We begin by defining...