Data Analysis and Machine Learning with Kaggle

Get a step ahead of your competitors with a concise collection of smart data handling and modeling techniques

Key Features

Learn how Kaggle works and how to make the most of competitions from two expert Kagglers

Sharpen your modeling skills with ensembling, feature engineering, adversarial validation, AutoML, transfer learning, and techniques for parameter tuning

Discover tips, tricks, and best practices for winning on Kaggle and becoming a better data scientist

Book Description

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 the rest of the community, and gain valuable experience to help grow your career.

The first book of its kind, Data Analysis and Machine Learning with Kaggle assembles the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two masters of Kaggle walk you through modeling strategies you won’t easily find elsewhere, and the tacit 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 data, tabular data, 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.

What you will learn

Get acquainted with Kaggle and other competition platforms

Make the most of Kaggle Notebooks, Datasets, and Discussion forums

Understand different modeling tasks including binary and multi-class classification, object detection, NLP (Natural Language Processing), and time series

Design good validation schemes, learning about k-fold, probabilistic, and adversarial validation

Get to grips with evaluation metrics including MSE and its variants, precision and recall, IoU, mean average precision at k, as well as never-before-seen metrics

Handle simulation and optimization competitions on Kaggle

Create a portfolio of projects and ideas to get further in your career

Who This Book Is For

This book is suitable for Kaggle users and data analysts/scientists of all experience levels who are trying to do better in Kaggle competitions and secure jobs with tech giants.

Table of Contents

Introducing Data Science competitions

Organizing Data with Datasets

Working and learning with kaggle notebooks

Leveraging Discussion forums

Detailing competition tasks and metrics

Designing good validation schemes

Ensembling and stacking solutions

Modelling for tabular competitions

Modeling for image classification and segmentation

Modeling for Natural Language Processing

Handling simulation and optimization competitions

Creating your portfolio of projects and ideas

Finding new professional opportunities

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.

Having joined Kaggle over 10 years ago, Luca Mass...

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