Abstract:
Employee attrition is the departure of employees from the organization for any
reason (voluntary or involuntary), including resignation, termination, death, or
retirement. Attrition is widely understood to be one of the major problems affecting
organizations today. Losing employees has many direct and indirect impacts across a
company. It occurs employee attrition when an employee leaves and is not replaced at
all or for a significant amount of time, resulting in a reduction of the workforce. In this
system, Decision Tree (ID3) classifier is used to analyze the causes of employee
attrition. And then Bayes Risk Post-Pruning (PBMR) technique is applied to reduce the
condition of overfitting on decision tree. The proposed system performance is evaluated
various evaluation standards such as precision, sensitivity and F1 score values based on
IBM Human Resource Analytic Employee Attrition and Performance dataset from
Kaggle site. The proposed system compares the accuracy between before post-pruning
and after Bayes Risk post pruning was applied. The proposed approach findings help
organizations overcome employee attrition by improving the factors that cause attrition.
This system is implemented by using python programming language with Google
Collab Drive.
