Design and Evaluation of a Machine Learning Framework for Anticipating Workforce Attrition Trends

For businesses employee retention is a major issue, and forecasting attrition can assist HR departments to put in place proactive measures to lower turnover. Using methods including Random Forest, XGBoost, Decision Tree, Support Vector Classifier (SVC), Logistic Regression, KNearest Neighbors (KNN), and Naive Bayes, this project uses machine learning approaches to study important factors affecting employee departure. The model discovers trends in job satisfaction, workload, career development, and worklife balance trained on the IBM Analytics dataset with 35 characteristics and 1,500 records. Deployed as an interactive Flask based web application, the system includes capabilities for data upload, forecasting, and model performance visualization. This AI driven solution helps HR staff to find early at-risk employees, manage issues efficiently, and enhance staff stability by offering practical insights. By using predictive analytics in HR management, businesses can lower attrition expenses, improve staff engagement, and create a more resilient setting.