Predicting Employee Attrition with Deep Learning and Ensemble Techniques for Optimized Workforce Management

Employee attrition is a substantial problem for many organizations,which leads to disruption and high costs. The capability of understanding the reason behind employee departures is necessary for creating an optimistic work environment and enhancing recruitment strategies. This study delves deeper into the prediction of employee attrition using a comprehensive comparison of models, including traditional models, deep learning algorithms, and ensemble methods. Standard models like KNN, SVM, and Logistic Regression are used as baseline systems. while advanced deep learning models including FNNs, CNNs, LSTM networks, and DNN are tested alongside robust ensemble models like Random Forest, Gradient Boosting, and XGBoost.Results demonstrate that the Random Forest model, particularly when combined with Synthetic Minority Over-sampling Technique (SMOTE), achieves as the highest accuracy at approximately 98.3 %, surpassing both traditional and deep learning approaches. This study enhances the effectiveness of ensemble models particularly RF with SMOTE in improving predictive accuracy, augmentation in labor force stability and effective management of resources through strategic analytics in revealing the complex interrelations inherent in large, high-dimensional data that will allow the identification of employee risks before developing policies for effective retention.