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)

Summary

In this second chapter, we took on quite a complex time series competition, hence the easiest top solution we tried is actually fairly complex, and it requires coding quite a lot of processing functions. After going through the chapter, you should have a better idea of how to process time series and have them predicted using gradient boosting. Favoring gradient-boosting solutions over traditional methods when you have enough data, as with this problem, should help you create strong solutions for complex problems with hierarchical correlations, intermittent series, and availability of covariates such as events, prices, or market conditions.

In the following chapters, you will tackle even more complex Kaggle competitions, dealing with images and text. You will be amazed at how much you can learn by recreating top-scoring solutions and understanding their inner workings.

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