IBM Attrition Prediction Analysis: Factors That Can Influence the Attrition Rate


  • Rong Fan



Attrition; Classification prediction; XGBoost.


This paper aims at solving the high level of attrition rate at IBM. Data scientists would like to measure the relationships between the attrition rate and other descriptive information such as gender, age, working experience and position, to predict the attrition status and look for commons on those who have left or will probably leave the corporation in the future. Based on those predictions, appropriate summaries of their characteristics can be sketched for the manager in IBM to provide better working conditions to lower the rate. The main prediction method in this paper will be tree-based including simple decision tree and other advanced techniques, and the prediction results will be shown as scatter plots. While XGBoost shows the highest performance in this study, it will be selected as the prediction method to predict the attrition status of IBM employees. With such a model, IBM data analysts will be able to do further research on the reasons for attrition and thus lower the attrition rate.


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How to Cite

Fan, R. (2023). IBM Attrition Prediction Analysis: Factors That Can Influence the Attrition Rate. BCP Business & Management, 36, 312–321.