Employee Attrition

Losing experienced employees is costly to a company. When an employee leaves the company, not only do they take valuable knowledge and contacts that may be lost to the company, but the loss of the employee directly costs the company from increased workloads on the remaining employees and the cost to recruit and train the replacement. Many times, the decision of the employee can be detected ahead of time and actions taken to improve their satisfaction and convince them to stay by recognition, pay, job modifications or moves within the organization. More broadly, understanding why employees are choosing to leave the organization can help identify problems and issues within the organization that, when fixed, may improve employee morale and productivity.


Understanding if and why an employee is likely to leave an organization allows that organization to take steps to retain that employee and to improve the experience for all employees.


Will a given employee voluntarily leave the organization in the next period of time.


While the core HR database is the key to understanding the employee’s demographics, job role, manager, etc. along with determining when an employee voluntarily leaves the organization, information regarding the level of business travel, training, stock options and overtime or schedule information will prove vital. Unfortunately this data may exist in multiple different datasets and may need significant transformations before it can be used. All this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.

Modeling techniques and libraries

Machine learning analysis

Build machine learning models to predict the employee churn as a function of the independent variables. Use model interpretability packages to evaluate the impact of the independent variables on the prediction.

  • Sklearn
  • ELI5
  • LIME
  • SHAP

Related accelerators

No items found.

No-code/low-code data prep and visualization