We got employee data from a few companies. We have data about all employees who joined from 2011/01/24 to 2015/12/13. For each employee, we also know if they are still at the company as of 2015/12/13 or they have quit. Beside that, we have general info about the employee, such as avg salary during her tenure, dept, and yrs of experience.
As said above, the goal is to predict employee retention and understand its main drivers. Specifically, you should:
- Assume, for each company, that the headcount starts from zero on 2011/01/23. Estimate employee headcount, for each company, on each day, from 2011/01/24 to 2015/12/13. That is, if by 2012/03/02 2000 people have joined company 1 and 1000 of them have already quit, then company headcount on 2012/03/02 for company 1 would be 1000. You should create a table with 3 columns: day, employee_headcount, company_id.
- What are the main factors that drive employee churn? Do they make sense? Explain your findings. If you could add to this data set just one variable that could help explain employee churn, what would that be?
Please see the Jupyter Notebook on my repository or
