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 first chapter, you have dealt with a classical tabular competition. By reading the notebooks and discussions of the competition, we have come up with a simple solution involving just two models that can be easily blended. In particular, we have offered an example of how to use a denoising autoencoder in order to produce alternative data processing, particularly useful when operating with neural networks for tabular data. By understanding and replicating solutions from past competitions, you can quickly build up your core competencies on Kaggle competitions and quickly become able to perform consistently higher in more recent competitions and challenges.

In the next chapter, we will explore another tabular competition from Kaggle, this time revolving around a complex prediction problem with time series.

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