Who's Next: Evaluating Attrition with Machine Learning Algorithms and Survival Analysis

Every business deals with employees who voluntarily resign, retire, or are let go. In other words, they have employee turnover. Employee turnover, also known as attrition can be detrimental if highly valued employees decide to leave at an unexpected time. This paper aims to find the employee(s) that are most at risk of attrition by first identifying them as someone who will leave. Second, identify if their department increases the probability of them leaving. And third, identify the individual probability of the employee leaving at a given time. This paper found Logistic regression to consistently perform well in attrition classification compared to other Machine Learning models. Kaplan-Meier survival function is applied to identify the department with the highest risk. An attempt is also made to identify the individual risk of an employee leaving using Cox proportional hazard. Using these methods, we were able to achieve two of the three goals identified.