Employee turnover is an important aspect that all businesses must attend to. Its continued success depends heavily on the ability to retain employees and establish a healthy working environment.
But how can companies identify who is at risk of leaving and who isn't? It can prove to be a quite harrowing task to make these predictions as there can be so many factors that affect an employee's decision.
We want to provide a machine learning model that performs well in identifying and predicting employees who are at risk of leaving a company.
We are showcasing various information and a number of machine learning models that will give insight into employee retention while demonstrating some of the different methods we have learned in the bootcamp.
This database is from a large US company (no name given for privacy reasons). The management department is worried about the relatively high turnover. They want to find ways to reduce the number of employees leaving the company and to better understand the situation: which employees are more likely to leave, and why.
The HR department has assembled data on almost 10,000 employees who left the company between 2016-2020. They used information from exit interviews, performance reviews, and employee records.
The data is defined:
We have a dedicated team with members who have compiled a number of models and methods that help analyze and understand the data and the models as well.
