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This thesis investigates predicting voluntary employee attrition using survival analysis, comparing the Cox Proportional Hazard model with novel machine learning methods like Random Survival Forest, DeepSurv, and DeepHit. The findings indicate that DeepSurv outperforms the traditional Cox model in predicting attrition based on synthetic workforce data, with key predictors identified including career length and overtime hours. The research aims to provide insights that can help organizations proactively redu

Author

Daimer Higuita Lopez

Language

EN