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)

Understanding the competition and the data

The competition (https://www.kaggle.com/competitions/m5-forecasting-accuracy) ran from March to June 2020 and over 7,000 participants took part in it on Kaggle. The organizers arranged it into two separate tracks, one for point-wise prediction (accuracy track) and another one for estimating reliable values at different confidence intervals (uncertainty track). Our focus in this chapter will be to try to replicate one of the best submissions for the accuracy track and also pave the way for the uncertainty track (since it is based on the predictions of the accuracy one).

Walmart provided the data. It consisted of 42,840 daily sales time series of items hierarchically arranged into departments, categories, and stores spread in three U.S. states (the time series are somewhat correlated with each other). Along with the sales, Walmart also provided accompanying information (exogenous variables, usually not often provided in forecasting problems...