Use supervised machine learning to predict employee attrition and enhance retention strategies by analyzing workforce data trends and patterns

Use principal component and exploratory factor analysis to identify core factors in performance appraisals, enhancing HR insights and decision-making

Use k-means and hierarchical clustering to analyze employee absenteeism, improving productivity and reducing costs with data-driven insights

Use association rule mining and apriori algorithm to analyze job satisfaction factors, enhancing employee contentment and productivity insights

Use t-tests and ANOVA to assess training effectiveness, comparing demographic impacts and program success in employee development

Optimizing Employee Satisfaction Through Strategic Benefits Analysis

Use KNIME for HR analytics with data visualization techniques like graphs, plots, and charts to enhance employee metric insights and decision-making

Use KNIME for HR analytics: Apply statistical techniques, data manipulation, and string operations to analyze and understand employee data