Book Image

The Kaggle Book

By : Konrad Banachewicz, Luca Massaron
5 (1)
Book Image

The Kaggle Book

5 (1)
By: Konrad Banachewicz, Luca Massaron

Overview of this book

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 an amazing community of data scientists, and gain valuable experience to help grow your career. The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the 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, tabular, 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. Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!

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Table of Contents (20 chapters)
Preface
Preface
1
Part I: Introduction to Competitions
Part I: Introduction to Competitions
Free Chapter
2
Introducing Kaggle and Other Data Science Competitions
Introducing Kaggle and Other Data Science Competitions
The rise of data science competition platforms
Introducing Kaggle
Summary
3
Organizing Data with Datasets
Organizing Data with Datasets
Setting up a dataset
Gathering the data
Working with datasets
Using Kaggle Datasets in Google Colab
Legal caveats
Summary
4
Working and Learning with Kaggle Notebooks
Working and Learning with Kaggle Notebooks
Setting up a Notebook
Running your Notebook
Saving Notebooks to GitHub
Getting the most out of Notebooks
Kaggle Learn courses
Summary
5
Leveraging Discussion Forums
Leveraging Discussion Forums
How forums work
Example discussion approaches
Netiquette
Summary
6
Part II: Sharpening Your Skills for Competitions
Part II: Sharpening Your Skills for Competitions
7
Competition Tasks and Metrics
Competition Tasks and Metrics
Evaluation metrics and objective functions
Basic types of tasks
The Meta Kaggle dataset
Handling never-before-seen metrics
Metrics for regression (standard and ordinal)
Metrics for classification (label prediction and probability)
Metrics for multi-class classification
Metrics for object detection problems
Metrics for multi-label classification and recommendation problems
Optimizing evaluation metrics
Summary
8
Designing Good Validation
Designing Good Validation
Snooping on the leaderboard
The importance of validation in competitions
Trying different splitting strategies
Tuning your model validation system
Using adversarial validation
Handling leakage
Summary
9
Modeling for Tabular Competitions
Modeling for Tabular Competitions
The Tabular Playground Series
Setting a random state for reproducibility
The importance of EDA
Reducing the size of your data
Applying feature engineering
Pseudo-labeling
Denoising with autoencoders
Neural networks for tabular competitions
Summary
10
Hyperparameter Optimization
Hyperparameter Optimization
Basic optimization techniques
Key parameters and how to use them
Bayesian optimization
Summary
11
Ensembling with Blending and Stacking Solutions
Ensembling with Blending and Stacking Solutions
A brief introduction to ensemble algorithms
Averaging models into an ensemble
Blending models using a meta-model
Stacking models together
Creating complex stacking and blending solutions
Summary
12
Modeling for Computer Vision
Modeling for Computer Vision
Augmentation strategies
Classification
Object detection
Semantic segmentation
Summary
13
Modeling for NLP
Modeling for NLP
Sentiment analysis
Open domain Q&A
Text augmentation strategies
Summary
14
Simulation and Optimization Competitions
Simulation and Optimization Competitions
Connect X
Rock-paper-scissors
Santa competition 2020
The name of the game
Summary
15
Part III: Leveraging Competitions for Your Career
Part III: Leveraging Competitions for Your Career
16
Creating Your Portfolio of Projects and Ideas
Creating Your Portfolio of Projects and Ideas
Building your portfolio with Kaggle
Arranging your online presence beyond Kaggle
Monitoring competition updates and newsletters
Summary
17
Finding New Professional Opportunities
Finding New Professional Opportunities
Building connections with other competition data scientists
Participating in Kaggle Days and other Kaggle meetups
Getting spotted and other job opportunities
Summary (and some parting words)
18
Other Books You May Enjoy
Other Books You May Enjoy
19
Index
Index

Introducing Kaggle and Other Data Science Competitions

Data science competitions have long been around and they have experienced growing success over time, starting from a niche community of passionate competitors, drawing more and more attention, and reaching a much larger audience of millions of data scientists. As longtime competitors on the most popular data science competition platform, Kaggle, we have witnessed and directly experienced all these changes through the years.

At the moment, if you look for information about Kaggle and other competition platforms, you can easily find a large number of meetups, discussion panels, podcasts, interviews, and even online courses explaining how to win in such competitions (usually telling you to use a variable mixture of grit, computational resources, and time invested). However, apart from the book that you are reading now, you won’t find any structured guides about how to navigate so many data science competitions and how to get the most out of them – not just in terms of score or ranking, but also professional experience.

In this book, instead of just packaging up a few hints about how to win or score highly on Kaggle and other data science competitions, our intention is to present you with a guide on how to compete better on Kaggle and get back the maximum possible from your competition experiences, particularly from the perspective of your professional life. Also accompanying the contents of the book are interviews with Kaggle Masters and Grandmasters. We hope they will offer you some different perspectives and insights on specific aspects of competing on Kaggle, and inspire the way you will test yourself and learn doing competitive data science.

By the end of this book, you’ll have absorbed the knowledge we drew directly from our own experiences, resources, and learnings from competitions, and everything you need to pave a way for yourself to learn and grow, competition after competition.

As a starting point, in this chapter, we will explore how competitive programming evolved into data science competitions, why the Kaggle platform is the most popular site for such competitions, and how it works.

We will cover the following topics:

  • The rise of data science competition platforms
  • The Common Task Framework paradigm
  • The Kaggle platform and some other alternatives
  • How a Kaggle competition works: stages, competition types, submission and leaderboard dynamics, computational resources, networking, and more
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