The Kaggle Workbook : Self-Learning Exercises and Valuable Insights for Kaggle Data Science Competitions

Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises.

Purchase of the print or Kindle book includes a free eBook in PDF format.

Key Features

• Challenge yourself to start thinking like a Kaggle Grandmaster
• Fill your portfolio with impressive case studies that will come in handy during interviews
• Packed with exercises and notes pages for you to enhance your skills and record key findings

Book Description

More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they've come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist.

In this book, you'll get up close and personal with four extensive case studies based on past Kaggle competitions. You'll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering.

You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor.

What you will learn

• Take your modeling to the next level by analyzing different case studies
• Boost your data science skillset with a curated selection of exercises
• Combine different methods to create better solutions
• Get a deeper insight into NLP and how it can help you solve unlikely challenges
• Sharpen your knowledge of time-series forecasting
• Challenge yourself to become a better data scientist

Who this book is for

If you're new to Kaggle and want to sink your teeth into practical exercises, start with The Kaggle Book, first. A basic understanding of the Kaggle platform, along with knowledge of machine learning and data science is a prerequisite.

This book is suitable for anyone starting their Kaggle journey or veterans trying to get better at it. Data analysts/scientists who want to do better in Kaggle competitions and secure jobs with tech giants will find this book helpful.

Table of Contents

1. The Most Renowned Tabular Competition – Porto Seguro's Safe Driver Prediction
2. The Makridakis Competitions – M5 on Kaggle for Accuracy and Uncertainty
3. Vision Competition: Cassava Leaf Disease Competition
4. NLP Competition – Google Quest Q&A Labeling

Konrad Banachewicz holds a PhD in statistics from Vrije Universiteit Amsterdam. He is a lead data scientist at eBay and a Kaggle Grandmaster. He worked in a variety of financial institutions on a wide array of quantitative data analysis problems. In the process, he became an expert on the entire lifetime of a data product cycle.

Luca Massaron is a data scientist with more than a decade of experience in transforming data into smarter artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of bestselling books on AI, machine learning, and algorithms. Luca is also a Kaggle Grandmaster who reached no. 7 in the worldwide user rankings for his performance in data science competitions, and a Google Developer Expert (GDE) in machine learning. He was part of books like The Kaggle Book, Machine Learning For Dummies, Algorithms for Dummies, Artificial Intelligence For Dummies. My warmest thanks go to my family, Yukiko and Amelia, for their support and loving patience.