A Regression Model to Predict Key Performance Indicators in Higher Education Enrollments

Key Performance Indicators (KPIs) are essential factors for the success of an organization. KPIs measure current performance and identify ongoing progress specified business objectives. The Ministry Higher Education (MoHE) in Palestine used established formulas to predict KPI. These vital charting organization aims. This study applies regression models student enrollment data sets accurate that can be adapted any higher education system. predictive engine will determine KPI based on linear techniques such as Lasso, Elastic Net, non-linear Support Vector Regression (SVR), K-Nearest Neighbor (KNN). provided datasets related enrollments graduations different Institutions (HEIs). algorithms were evaluated by mean absolute error, square error (MSE), root (RMSE) R Squared. experiment demonstrates 40% training with 60% testing splitting using shows best result.