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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Building your portfolio with Kaggle

Kaggle’s claim to be the “home of data science” has to be taken into perspective. As we have discussed at length, Kaggle is open to everyone willing to compete to figure out the best models in predictive tasks according to a given evaluation metric.

There are no restrictions based on where you are in the world, your education, or your proficiency in predictive modeling. Sometimes there are also competitions that are not predictive in nature, for instance, reinforcement learning competitions, algorithmic challenges, and analytical contests that accommodate a larger audience than just data scientists. However, making the best predictions from data according to a metric is the core purpose of Kaggle competitions.

Real-world data science, instead, has many facets. First, your priority is to solve problems, and the metric for scoring your model is simply a more or less exact measurement of how well it solves the problem...