Predicting Attrition in a Public Nutrition Education Program: A Machine Learning Approach

Key Points

• Attrition is predicted effectively using a machine learning approach, enhancing program retention efforts.
• The model achieved an accuracy rate of 85% in identifying participants likely to drop out.
• Analysis employs predictive modeling techniques to assess program engagement and retention.
• Findings highlight the need for targeted interventions to improve participant retention in nutrition programs.