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

Applying feature engineering

In real-world projects, what can make the difference between a successful machine learning model and a mediocre one is often the data, not the model. When we talk about data, the differentiator between bad, good, and excellent data is not just the lack of missing values and the reliability of the values (its “quality”), or the number of available examples (its “quantity”). In our experience, the real differentiator is the informational value of the content itself, which is represented by the type of features.

The features are the real clay to mold in a data science project, because they contain the information that models use to separate the classes or estimate the values. Every model has an expressiveness and an ability to transform features into predictions, but if you are lacking on the side of features, no model can bootstrap you and offer better predictions. Models only make apparent the value in data. They are not...