You're reading from Data Science Projects with Python - Second Edition

Product typeBook

Published inJul 2021

Reading LevelIntermediate

PublisherPackt

ISBN-139781800564480

Edition2nd Edition

Languages

Python

Concepts

Data Science

Author (1)

Stephen Klosterman

Summary

In this chapter, we've learned some of the most cutting-edge techniques for building machine learning models with tabular data. While other types of data, such as image or text data, warrant exploration with different types of models such as neural networks, many standard business applications leverage tabular data. XGBoost and SHAP are some of the most advanced and popular tools you can use to build and understand models with this kind of data. Having gained familiarity and practical experience using these tools with synthetic data, in the following activity, we return to the dataset for the case study and see how we can use XGBoost to model it, including the samples with missing feature values, and use SHAP values to understand the model.

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Data Science Projects with Python - Second Edition

Published in: Jul 2021Publisher: PacktISBN-13: 9781800564480

Author (1)

Stephen Klosterman

Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a Ph.D. in Biology from Harvard University, where he was an assistant teacher of the Data Science course. His professional experience includes work in the environmental, health care, and financial sectors. At work, he likes to research and develop machine learning solutions that create value, and that stakeholders understand. In his spare time, he enjoys running, biking, paddleboarding, and music.
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You're reading from Data Science Projects with Python - Second Edition

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Data Science Projects with Python

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