Stay one step ahead of your competitors with proven tips, strategies, and insights from over 30 Kaggle Masters and Grandmasters and become a better data scientist.

This new edition features updated content and new chapters on Kaggle Models, time series, and Generative AI competitions.

Key Features

• Learn how Kaggle works to make the most of every competition with winning strategies from 30+ expert Kagglers
• Sharpen your modeling skills with feature engineering, adversarial validation, gradient boosting, tabular deep learning, ensembling, and AutoML
• Master data handling techniques for smarter modeling and parameter tuning

Book Description

Kaggle has become the proving ground for millions of data enthusiasts worldwide, offering what no classroom tutorial can match: battle-tested skills built through real-world challenges and the hands-on experience that employers seek. Every competition sharpens your data analysis skills, expands your network within the data scientist community, and gives compelling proof of expertise to unlock career opportunities.

The first book of its kind, The Kaggle Book brings together everything you need to excel in competitions, data science projects, and beyond. This new edition includes fresh content and new chapters on Kaggle Models, time series, and Generative AI competitions, with three Kaggle Grandmasters guiding you through modeling strategies and sharing hard-earned insights accumulated over years of competition.

The book extends far past competition tactics, revealing techniques for tackling image, tabular, and textual data as well as reinforcement learning tasks. You’ll also discover tips for designing better validation schemes and working confidently with both standard and unconventional evaluation metrics.

Whether you want to climb the Kaggle leaderboard, accelerate your data science career, or improve the accuracy of your models, this book is for you.

Join our Discord community of over 1,000 members to learn, share, and grow together!

What you will learn

• Get acquainted with Kaggle as a competition platform
• Make the most of Kaggle Notebooks, Datasets, Models and Discussion forums
• Build a compelling portfolio of projects and ideas to advance your career
• Understand binary and multi-class classification, as well as object detection
• Approach NLP and time series problems with greater efficiency
• Design k-fold and probabilistic validation schemes and experiment with multiple approaches
• Get to grips with common and never-before-seen evaluation metrics
• Handle simulation, optimization, and the new Generative AI competitions on Kaggle

Who this Book is for

This book is for anyone interested in Kaggle, whether you’re just starting out, a veteran user, or somewhere in between. Data analysts and data scientists looking to improve their performance in Kaggle competitions and improve their job prospects with tech giants will find this book useful.

A basic understanding of machine learning concepts will help you get the most out of this book.

Table of Contents

Part 1: Your Kaggle Launchpad: Mastering the Essentials

Chapter 1: Introducing Kaggle and Other Data Science Competitions

Chapter 2: Organizing Data with Datasets

Chapter 3: Working and Learning with Kaggle Notebooks

Chapter 4: Kaggle Models

Chapter 5: Leveraging Discussion Forums

Part 2: Elevating Your Game: Advanced Techniques for Competitive Success

Chapter 6: Competition Tasks and Metrics

Chapter 7: Designing Good Validation

Chapter 8: Modeling for Tabular Competitions

Chapter 9: Hyperparameter Optimization

Chapter 10: Ensembling with Blending and Stacking Solutions

Chapter 11: Modeling for Computer Vision

Chapter 12: Modeling for NLP

Chapter 13: Generative Al in Kaggle Competitions

Chapter 14: Simulation and Optimization Competitions

Part 3: Kaggle for Your Career: Building Your Profile and Finding Opportunities

Chapter 15: Creating Your Portfolio of Projects and Ideas

Chapter 16: Finding New Professional Opportunities

Chapter 17: Unlock Your Exclusive Benefits

Review

“The Kaggle Book distills what really wins: structured problem framing, reproducible pipelines, and an honest treatment of feature engineering and validation. The first edition is the closest thing to a field manual I recommend to data teams—useful whether you’re aiming for gold medals or production-grade models.”

Fahrettin Firat Gonen, PhD, Deputy General Manager & Executive Vice President at GTech

“I missed the first edition, but I won't miss the second. It's Data Science, Machine learning, and AI from the perspective and experience of three Kaggle Competition Grandmasters. Any plans to climb the Kaggle rankings? This is the book.”

Marília Prata, retired Dental Doctor and Kaggle Legacy Grandmaster (mpwolke)

“A practical and comprehensive guide by Kaggle pioneers, paving the way to Grandmaster level.”

Shotaro Ishihara, Senior Research Scientist at Japanese Media Company

“I remember reading The Kaggle Book when it was published, and I think that many parts are still relevant nowadays. I believe that the chapters about the metrics and the validation setup are the most important ones. Whether you participate in an ML competition or work on a project at your job, it is crucial to set up the validation approach. You need to be able to evaluate your approach and measure the improvements from the incremental experiments. The book does a great job at describing this and provides enough links to the materials for further study.”

Andrey Lukyanenko, Kaggle GDE, MLE @ Meta

“Participating in Kaggle competitions has been an invaluable step in my journey to mastering data science and machine learning topics - and it has had a significant impact on my career. Luca, Bojan, and Konrad are among the most knowledgeable and respected Kaggle Grandmasters in the community. Starting from foundational elements like proper validation patterns and scoring metrics, to more advanced topics such as stacking, the authors demonstrate a deep understanding of machine learning and Kaggle's inner workings, providing valuable insights to both the beginner and the experienced data scientist.”

Alberto Danese, Head of Data Science & Advanced Analytics at Nexi, Kaggle Competitions Grandmaste

“The Kaggle Book not only offers a detailed guide to tackle and participate in Kaggle competitions, but its insights and learnings can easily be applied to real-world industry problems.”

Parul Pandey, ML Consultant, Prev H2O.ai and Weights & Biases

“As a Kaggle Grandmaster with over a decade of competition experience, I found The Kaggle Book to be an invaluable resource that I wish I had when starting out. The practical competition strategies and technical approaches shared here compress years of trial and error into actionable insights that will accelerate any data scientist's journey from beginner to medalist.”

Dmitry Larko, 3x Kaggle Competition Grandmaster

“One of Kaggle's greatest gifts to the community is the opportunity to learn from the very best. The Kaggle Book distills this wisdom into a treasure trove of winning strategies and expert advice for machine learning practitioners.”

Martin (aka Head or Tails), Staff Data Scientist at Crunchbase

About the Authors

Luca Massaron is a data scientist with over a decade of experience in transforming data into high-impact, innovative artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of numerous bestselling books on AI, machine learning, and algorithms. Luca is also a 3x Kaggle Grandmaster who reached number 7 in the worldwide user rankings for his performance in data science competitions. Additionally, he is recognized as a Google Developer Expert (GDE) in AI, Kaggle, and the cloud.

Bojan Tunguz is the founder and CEO of TabulAI, a start-up focused on applying machine learning and AI to structured-data problems. Before founding TabulAI, he worked at three other machine learning start-ups and most recently at NVIDIA. He holds a PhD in theoretical physics from the University of Illinois and has taught as a professor at three liberal arts colleges.

Konrad Banachewicz holds a PhD in statistics from Vrije Universiteit Amsterdam. His academic work focused on extreme dependency modeling in credit risk. In addition to his research activities, he was a tutor and supervised master's students. He transitioned from classical statistics to data mining and machine learning before “data science” became a buzzword. Over the next decade, he tackled quantitative analysis problems in various financial institutions, becoming an expert in the full life cycle of a data product. His work spanned high-frequency trading to credit risk, predicting potato prices, and analyzing anomalies in the performance of large-scale industrial equipment. He is a believer in knowledge sharing and also competes on Kaggle.