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)

A complete solution

Earlier in this chapter, we described how to get started with a baseline solution for an image classification competition. In this section, we show how an ensemble team involving rockstars, such as Abhishek (https://www.kaggle.com/abhishek) and Tanul (https://www.kaggle.com/tanulsingh077), achieved a silver medal zone solution (36th ) using a cleverly structured application of the components discussed above. The post summarizing their solution can be found here: https://www.kaggle.com/competitions/cassava-leaf-disease-classification/discussion/220628.

Their final solution was a combination of three models: EfficientNet-B7, EfficientNet-B3a, and SE-ResNext50. The models followed the pipeline described in the notebook: https://www.kaggle.com/code/abhishek/tez-faster-and-easier-training-for-leaf-detection/. A notable thing about this notebook is that it utilizes tez: a PyTorch trainer developed by Abhishek (https://github.com/abhishekkrthakur/tez). The idea behind...