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#

hazard

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In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

  • Updated Sep 30, 2022
  • Jupyter Notebook

Proactive methodological disclosure of a high resolution precision calibrated estimate of the Gompertz-Makeham Law of Mortality and general utilization hazard rates through lifespan interferometry against annual census data consolidated from the administrative data of all publicly funded healthcare provided in a single geopolitical jurisdiction.

  • Updated May 27, 2020
  • TSQL

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