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Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
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Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn

by Tshepo Chris Nokeri
October 2021
Intermediate to advanced
128 pages
1h 59m
English
Apress
Content preview from Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022
T. C. NokeriData Science Solutions with Pythonhttps://doi.org/10.1007/978-1-4842-7762-1_4

4. Survival Analysis withPySpark and Lifelines

Tshepo Chris Nokeri1  
(1)
Pretoria, South Africa
 

This chapter describes and executes several survival analysis methods using the main Python frameworks (i.e., Lifelines and PySpark). It begins by explaining the underlying concept behind the Cox Proportional Hazards model. It then introduces the accelerated failure time method.

Exploring Survival Analysis

Survival methods are common in the manufacturing, insurance, and medical science fields. They are convenient for properly assessing risk when an independent investigation is ...

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Publisher Resources

ISBN: 9781484277621Purchase LinkPublisher Website