When working with the data, it is important to understand all the aspects of it. Understanding the variables and the data collection process helps us better do the analysis. For example, while dealing with the missing values, if we have an understanding of data collection process we would be better off dealing with them.
This project done as a part of examination assignment.
It is a problem of employees leaving the firm in a large number. Employees maybe leaving the firm due to multiple reasons, some reasons can be directly understood, like maybe, bad work culture. And some reasons might be difficult to figure out directly. So, data might help us give better understanding of the problem. And help us understand why an employee might stay or leave the company?
The purpose of the analysis is to find which factors share a strong relationship with attrition and use them to decide what changes to be made in the workplace to retain employees.
Our analysis is focused around the fact that: if an employee leaves the company, the reason has to be significant. So instead of looking around the averages our focus was looking around the extreme cases.
For example, What is the attrition rate for those employees who quickly got promotions?
Not only this, we also found that missing values played a key role in the analysis.
How?
Let's suppose if an employee do not answer the question on work culture in an employee survey form, it's more likely that the employee is not happy with the work culture.
Hence, focus around the missing values, and extreme cases will give better understanding of the employee attrition problem which will directly result into better recommendation to the firm.