Improving Employee Retention with Machine Learning for Proactive Turnover Risk Detection and Strategic Interventions

Employee retention is a significant challenge that faces organizations today due to its influence on workforce stability, employee productivity, and business success. The existing approaches employed by organizations, such as engagement surveys and interviews in case of exit, are not effective in predicting employee retention accurately based on the principle of reactivity. The weaknesses in machine learning (ML) enable companies to formulate prediction models on employee retention risks in advance. The acquired data will offer a science-driven staff retention framework that forecasts the likelihood of employees leaving and the most critical factors causing a worker to quit their job based on ML methodologies. Human resource (HR) records are used to evaluate three monitored learning models, such as logistic regression, random forests, and gradient boosting machine (GBM), and to assess their capacity to predict employee departures. A large company provided 10,248 records of its employees with 35 variables to analyze. Preprocessing of the data involved normalization, feature selection, and exploratory analysis. The training made use of an 80-20 split between training and testing data, and they utilized cross-validation techniques to guarantee their resistance to errors. Accuracy, precision, recall, F1-score, and AUC-ROC were the measures that were used in the model evaluation. The analysis of feature importance has identified three key factors that contribute to turnover: remuneration, tenure, and engagement scores. GBM achieved better performance than other models with an 87.7% F1 Score and a 0.94 AUC-ROC score. Risky employees have been identified, and thus, appropriate HR interventions have been done. The ML framework assists organizations in engaging in proactive steps to employee retention by using data-driven strategies to mitigate the risks of employee turnover. Models will be refined and improved with real-time data to enhance the prediction of the future.