“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

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.