Book Image

The Kaggle Workbook

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

The Kaggle Workbook

5 (1)
By: Konrad Banachewicz, Luca Massaron

Overview of this book

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.
Table of Contents (7 chapters)



4th place solutions ideas

examining 48-51


accuracy 41, 50, 90, 100

Allstate Claim Prediction Challenge

reference link 5

auto-regressive (AR) 48


baseline model

building 92-99

baseline solution 111-136

building 124


classical tabular competition, in motor insurance modeling 2, 3

features 3

reference link 3

complex time series competition 42-45


data 89

dates and time horizons prediction

computing 52-79

denoising autoencoder (DAE) 17

setting up 17-35

using 8

DNN model

setting up 18-35


EfficientNet 94, 101-103

ensembling 35-38, 90, 101-102

evaluation metric 5-7, 45-47, 90-92


Gini coefficient 6

Google Quest Q&A Labeling contest

reference link 109

gradient boosting 72

Gradient-Based One-Side Sampling (GOSS) 72

Gradient Boosting Decision Tree (GBDT) 72


Hugging Face (HF) 111


Inverse Document Frequency...