Project detail
Employee Turnover with PyMC
Bayesian model for estimating HR turnover risk using tenure, satisfaction, salary, and workload.
Highlights
- Turnover prediction with Bayesian logistic regression
- Key signals: tenure, satisfaction, salary, and workload
- Early identification of at-risk employees
- Foundation for more precise retention strategies
No public repository or demo link is published for this project yet.
Architecture
Applied notebook/guide with data simulation, probabilistic modeling, and individual turnover risk prediction
Challenges
- Choosing variables that truly explain employee exits
- Defining useful risk thresholds without too many false positives
- Keeping the model interpretable for HR
Outcome
A practical approach to prioritizing retention actions using probabilistic evidence instead of intuition
Overview
Problem
Employee turnover is often detected too late, using tools that are too rigid to capture uncertainty.
Approach
I built a Bayesian model to estimate each employee's likelihood of leaving based on work-context variables.
Outcome
The system makes it possible to detect turnover risk before the problem escalates and guide more informed retention actions.