The Kaggle Workbook : Self-Learning Exercises and Valuable Insights for Kaggle Data Science Competitions / Konrad Banachewicz and Luca Massaron.

Format:

Book

Author/Creator:

Banachewicz, Konrad, author.

Massaron, Luca, author.

Language:

English

Subjects (All):

Machine learning--Problems, exercises, etc.

Machine learning.

Big data--Problems, exercises, etc.

Big data.

Physical Description:

1 online resource (173 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2023]

Biography/History:

Banachewicz Konrad: Konrad Banachewicz is the author of the bestselling, The Kaggle Book and The Kaggle Workbook. He is a data science manager with experience stretching longer than he likes to ponder on. He holds a PhD in statistics from Vrije Universiteit Amsterdam, where he focused on problems of extreme dependency modeling in credit risk. He slowly moved from classic statistics towards machine learning and into the business applications world. Massaron Luca: Having joined Kaggle over 10 years ago, Luca Massaron is a Kaggle Grandmaster in discussions and a Kaggle Master in competitions and notebooks. In Kaggle competitions he reached no. 7 in the worldwide rankings. On the professional side, Luca 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 a Google Developer Expert(GDE) in machine learning and the author of best-selling books on AI, machine learning, and algorithms.

Summary:

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.

Contents:

Cover

Copyright

Table of Contents

Preface

Chapter 1: The Most Renowned Tabular Competition - Porto Seguro's Safe Driver Prediction

Understanding the competition and the data

Understanding the evaluation metric

Examining the top solution ideas from Michael Jahrer

Building a LightGBM submission

Setting up a denoising autoencoder and a DNN

Ensembling the results

Summary

Chapter 2: The Makridakis Competitions - M5 on Kaggle for Accuracy and Uncertainty

Understanding the Evaluation Metric

Examining the 4th place solution's ideas from Monsaraida

Computing predictions for specific dates and time horizons

Assembling public and private predictions

Chapter 3: Vision Competition: Cassava Leaf Disease Competition

Understanding the data and metrics

Building a baseline model

Learning from top solutions

Pretraining

Test time augmentation

Transformers

Ensembling

A complete solution

Chapter 4: NLP Competition - Google Quest Q&amp

A Labeling

The baseline solution

Packt page

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Index.

Notes:

Includes index.

Description based on print version record.

ISBN:

9781804610114

1804610119

OCLC:

1369598031