Student graduation rates has always taken prominence in academic studies since it is considered a major factor to reflect the performance of any university. Accurate models for predicting student retention plays a major role is universities strategic planning and decision making. Studentsβ enrollment behavior and retention rate are also relevant factors in the measurement of the effectiveness of universities. This thesis provides a comparison of predictive models for predicting student retention at Saint Cloud State University. The models are trained and tested using a set of features reflecting the readiness of students for college education, their academic capacities, financial situation, and academic results during their freshman year. Principle Component Analysis (PCA) were used for feature selection. Six predictive models has been built. A comparison of the prediction results have been conducted using all features and selected features using PCA analysis
