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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632 bootstrap 172


accuracy 114

acquisition function 261, 295

adaptive overfitting 150

adversarial testing 25

adversarial validation 160, 180

example 181, 182

using 179-181

AI Ethics

reference link 74

Akiyama, Osamu 472, 473

albumentations 346, 347

characteristics 346

Alibaba Cloud 11

reference link 11


reference link 431

Analytics competitions 19

Analytics Vidhya 11, 462

reference link 11

Annuals competitions 18

attention 288

AUC metric 105

augmentation techniques

for image 335

for text 414

auto-correlation 166


denoising, with 226-230

AutoGluon 330


reference link 204

average precision 118

average precision at k (AP@K) 134

averaging ensembling technique 307-309


bagging technique 305

basic optimization techniques 242

grid search 243-245

halving search 246, 247

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