Retaining the Best: How AI and IoT Can Combat Employee Turnover

High employee turnover plagues businesses globally, leading to financial strain, decreased sales, and reputational damage. This study explores innovative solutions leveraging the power of the Internet of Things (IoT) and Artificial Intelligence (AI) to address employee security concerns and pave the way for growth. Utilizing IBM HR analytics models and advanced machine learning algorithms like Random Forest, K-Nearest Neighbors (KNN), XGBoost (XGB), and Gradient Boosting (GB), the research predicts employee attrition rates with remarkable accuracy. To optimize training processes, techniques like Random Forest and Recursive Feature Elimination identify the nine key factors influencing employee turnover, enabling targeted interventions. The study then trains supervised machine learning models (Random Forest, KNN, XGB, GB, and a Voting Classifier) on the curated dataset, applying the Synthetic Minority Oversampling Technique (SMOTE) to ensure balanced data representation. A detailed classification report compares model performance with and without SMOTE, highlighting the effectiveness of this data balancing technique. The research results are compelling: both the Voting Classifier and GB models achieve exceptional performance, offering promising solutions to curb employee turnover and foster a stable, thriving organization. By harnessing the power of data and AI, businesses can proactively address employee concerns, optimize training programs, and ultimately, secure their valuable talent pool for sustained growth and success.