From the course: Applied AI for Human Resources

Employee attrition - Python Tutorial

From the course: Applied AI for Human Resources

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Employee attrition

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- [Instructor] The first featured use case that we will solve in this course is predicting employee attrition. Employee attrition, especially those of key and star employees is a major concern for an HR organization. When employees leave, it has many side effects. There is loss of organizational and product-specific expertise, loss of productivity due to new hires taking time to onboard. Sometimes employees have great relationships with customers, and that is hard to rebuild. There are also hiring costs and training costs associated. Employees leave due to various reasons. They include compensation, work satisfaction, performance, and issues with their supervisors. The online world had made it easy for outside recruiters to approach employees with better job offers, which can make a content employee leave. How can AI help in this employee attrition? First, we need to collect 360-degree data about the employee's past and present. This include, but not limited to the tenure in the company, performance ratings, compensation and promotions. Relationships the employee has with their supervisor and peers also play a key part. 360 reviews will help understand this. Once the data is collected and associated with both past and present employees, it provides input to build an ML model to predict attrition. Then, HR can take preventive action that is needed. In this chapter, I will show you how to build a simple model to predict attrition.

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