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accelerated-failure-time

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This repository explores the integration of Machine Learning with the Weibull Distribution to improve the accuracy of Remaining Useful Life (RUL) estimations. By treating the Weibull scale parameter ( λ ) as a dynamic target for regression, these projects bridge the gap between statistical reliability analysis and modern data-driven maintenance.

  • Updated Jun 18, 2026
  • Jupyter Notebook

A comprehensive Python survival analysis workflow using statsmodels. Covers Kaplan-Meier estimation, log-rank tests, Cox proportional hazards regression, and alternative approaches like Nelson-Aalen and Accelerated Failure Time models with practical code examples.

  • Updated Dec 17, 2025
  • Jupyter Notebook

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