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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The importance of EDA

The term EDA comes from the work of John W. Tukey, one of the most prominent exponents of modern statistical methodology. In his 1977 book Exploratory Data Analysis (hence the acronym EDA), Tukey thinks of EDA as a way to explore data, uncover evidence, and develop hypotheses that can later be confirmed by statistical tests.

His idea was that how we define statistical hypotheses could be based more on observation and reasoning than just sequential tests based on mathematical computations. This idea translates well to the world of machine learning because, as we will discuss in the next section, data can be improved and pre-digested so that learning algorithms can work better and more efficiently.

In an EDA for a Kaggle competition, you will be looking for:

  • Missing values and, most importantly, missing value patterns correlated with the target.
  • Skewed numeric variables and their possible transformations.
  • Rare categories in categorical...