Understanding why and when employees are most likely to leave can lead to actions to improve employee retention as well as possibly planning new hiring in advance. I will be usign a step-by-step systematic approach using a method that could be used for a variety of ML problems. This project would fall under what is commonly known as "HR Anlytics", "People Analytics".
In this study, we will attempt to solve the following problem statement is:
What is the likelihood of an active employee leaving the company?
What are the key indicators of an employee leaving the company?
What policies or strategies can be adopted based on the results to improve employee retention?
Given that we have data on former employees, this is a standard supervised classification problem where the label is a binary variable, 0 (active employee), 1 (former employee). In this study, our target variable Y is the probability of an employee leaving the company.
In this case study, a HR dataset was sourced from IBM HR Analytics Employee Attrition & Performance which contains employee data for 1,470 employees with various information about the employees. I will use this dataset to predict when employees are going to quit by understanding the main drivers of employee churn.
As stated on the IBM website "This is a fictional data set created by IBM data scientists". Its main purpose was to demonstrate the IBM Watson Analytics tool for employee attrition.