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)

Examining the 4th place solution’s ideas from Monsaraida

There are many solutions available for the competition, mostly found on the competition Kaggle discussions pages. The top five methods of both challenges have also been gathered and published (except one because of proprietary rights) by the competition organizers themselves: https://github.com/Mcompetitions/M5-methods (by the way, reproducing the results of the winning submissions was a prerequisite for the collection of a competition prize).

Noticeably, all the Kagglers that placed in the higher ranks of the competitions have used, as their unique model type or in blended/stacked in ensembles, LightGBM because of its lesser memory usage and speed of computations, which gave it an advantage in the competition because of the large amount of times series to process and predict. But there are also other reasons for its success. Contrary to classical methods based on ARIMA, it doesn’t require relying on the analysis...