Explainable Machine Learning for Predicting Employee Attrition: A Human Resource Analytics Framework for Strategic Workforce Management
Abstract
Employee attrition presents a major challenge to workforce stability, productivity, and organisational continuity. This study developed an explainable human resource analytics framework for predicting employee attrition and identifying the principal factors associated with employee departure. A quantitative, cross-sectional secondary-data design was adopted using the IBM HR Analytics Employee Attrition dataset, comprising 1,470 employees and 35 variables. Microsoft Excel was used for data cleaning, descriptive analysis, statistical testing, logistic regression modelling, visualisation, and model evaluation. The overall attrition rate was 16.1%. Employees who left were generally younger, earned lower monthly incomes, lived farther from work, and had shorter organisational and managerial tenure. Over time, frequent business travel, single marital status, distance from home, number of previous companies worked, and years since the last promotion significantly increased attrition likelihood. In contrast, job satisfaction, environmental satisfaction, job involvement, work–life balance, age, job level, training, tenure in the current role, and years with the current manager reduced attrition risk. The model explained approximately 30.0% of attrition variation and achieved an area under the ROC curve of 0.810, indicating good discrimination. Lowering the classification threshold from 0.50 to 0.25 improved sensitivity from 34.0% to 63.8% and increased the F1-score from 44.4% to 55.0%. The findings support ethical, targeted, and proactive retention strategies based on interpretable workforce evidence.
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