[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120808-en":3,"doc-seo-120808-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120808,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Comparative Analysis of Classification Performance for U.S. College Enrollment Predictive Modeling Using Four Machine Learning Algorithms","The dissertation compares classification performance for U.S. college enrollment predictive modeling using four machine learning algorithms: Artificial Neural Network, Decision Tree, Support Vector Machine, and Logistic Regression. It develops a structured methodology including data transformation for categorical variables, careful data splitting with k-fold and stratified cross-validation, and systematic model evaluation through confusion matrix metrics plus ROC/AUC and precision-recall analysis. Results and comparisons across training/validation and selected test applications focus on predictive accuracy, sensitivity, specificity, precision, F1 score, and feature importance to support actionable enrollment probability interpretation.","Loyola University Chicago  \nLoyola eCommons  \n\n| Dissertations | Theses and Dissertations |\n| --- | --- |\n| 2023\u003Cbr>Comparative Analysis of Classification Performance for U.S. College Enrollment Predictive Modeling Using Four Machine Learning Algorithms (Artificial Neural Network, Decision Tree, Support Vector Machine, Logistic Regression)\u003Cbr>Anna Kye\u003Cbr>Follow this and additional works at: [https://ecommons.luc.edu/luc_diss](https://ecommons.luc.edu/luc_diss)\u003Cbr> Part of the Education Commons |  |\n\nRecommended Citation  \nKye, Anna, \"Comparative Analysis of Classification Performance for U.S. College Enrollment Predictive Modeling Using Four Machine Learning Algorithms (Artificial Neural Network, Decision Tree, Support Vector Machine, Logistic Regression)\" (2023) . Dissertations. 4030.  \n[https://ecommons.luc.edu/luc_diss/4030](https://ecommons.luc.edu/luc_diss/4030)  \nThis Dissertation is brought to you for free and open access by the Theses and Dissertations at Loyola eCommons. It has been accepted for inclusion in Dissertations by an authorized administrator of Loyola eCommons. For more information, please [contact ecommons@luc.edu](contact ecommons@luc.edu).  \nThis work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. Copyright © 2023 Anna Kye  \nLOYOLA UNIVERSITY CHICAGO  \nCOMPARATIVE ANALYSIS OF CLASSIFICATION PERFORMANCE FOR U. S. COLLEGE ENROLLMENT PREDICTIVE MODELING USING FOUR MACHINE LEARNING ALGORITHMS (LOGISTIC REGRESSION, DECISION TREE, SUPPORT VECTOR MACHINE, ARTIFICIAL NEURAL NETWORK)  \nA DISSERTATION SUBMITTED TO  \nTHE FACULTY OF THE GRADUATE SCHOOL  \nIN CANDIDACY FOR THE DEGREE OF  \nDOCTOR OF PHILOSOPHY  \nPROGRAM IN RESEARCH METHODOLOGY  \nBY  \nANNA KYE  \nCHICAGO, IL  \nMAY 2023  \nCopyright by Anna Kye, 2023 All rights reserved.  \nACKNOWLEDGMENTS  \nFirst and foremost, thank you, God, who is always with me every step of the way with countless blessings. Allow me to have a stronger heart whenever I fall and stand up. Amen.  \nI want to thank my advisor, Dr. Meng-jia Wu, sincerely. I couldn’t complete this long journey without her strong support. Additionally, I would like to express my deepest gratitude tomy dissertation committees, Dr. Eilene Edejer and Dr. Ken Fujimoto. Thank you so much. I also want to thank Timothy Heuer, assistant vice president of the Enrollment Systems Research and Reporting Department. Without his advice and support, I couldn’t have considered pursuing a doctoral degree. Finally, I would like to thank my friends and family, who prayed for me with strong encouragement.  \nTo my family.  \nThe heart of man plans his way, but the Lord establishes his steps.  \n􀁿 Proverbs 16:9  \nTABLE OF CONTENTS  \nACKNOWLEDGMENTS iii LIST OF TABLES ix  \nLIST OF FIGURES x  \nLIST OF ABBREVIATIONS xii  \nABSTRACT xiv  \nCHAPTER ONE: INTRODUCTION 1  \nImplementation of Machine Learning for College Enrollment Prediction Model 2  \nProposed Analysis 5  \nResearch Questions 7  \nSignificance of Study 8  \nCHAPTER TWO: LITERATURE REVIEW 9  \nBackground of College Choice Model 9  \nCollege Choice Models 12  \nInfluential Factors of College Choice/Enrollment 20  \nStudents’Habitus Characteristic Context Factors 21  \nHigh School Community Context Factors 24  \nHigher Education Institution Context Factors 26  \nSocial, Economic, and Policy Context Factors 28  \nEmpirical and Predictive Modeling of College Enrollment 31  \nConcept of Machine Learning 33  \nLogistic Regression (LR) 35  \nDecision Tree (DT) 37  \nSupport Vector Machine (SVM) 40  \nArtificial Neural Network (ANN) 42  \nModel Evaluation and Comparison 45  \nImplementation of Machine Learning Algorithms for College Enrolment  \nPrediction Modeling 46  \nSummary 48  \nCHAPTER THREE: METHODOLOGY 51  \nData Description 51  \nData Transformation for Categorical Variables 56  \nRace 56  \nSex 57  \nParental Education Level (PEL) 58  \nHigh School Academic Climate (HSAC) 59  \nEnrollment Decision 60  \nData Description of Numerical Variables 60  \nHigh Sch","cbCaiojGxZVSWQ20","https://ap.wps.com/l/cbCaiojGxZVSWQ20","pdf",5480428,1,206,"English","en",105,"# Table of Contents\n## Acknowledgments\n## List of Tables\n## List of Figures\n## List of Abbreviations\n## Abstract\n## Chapter One: Introduction\n## Chapter Two: Literature Review\n## Chapter Three: Methodology\n## Chapter Four: Results\n## Chapter Five: Discussion\n## Appendix A: Data Variables\n## Appendix B: Descriptions of High School Academic Climate Clusters\n## Appendix C: Python Code Open Source\n## Appendix D: Proof of IRB Exempt\n## Bibliography\n## Vita","[{\"question\":\"Which machine learning algorithms are compared for college enrollment prediction?\",\"answer\":\"The study compares Artificial Neural Network, Decision Tree, Support Vector Machine, and Logistic Regression as four classification approaches for predicting U.S. college enrollment.\"},{\"question\":\"How is model evaluation conducted in the methodology?\",\"answer\":\"Evaluation uses confusion-matrix based metrics including classification accuracy, sensitivity, specificity, precision, and F1 score, along with ROC/AUC and precision-recall curve analysis.\"},{\"question\":\"What preprocessing and validation strategy supports the modeling workflow?\",\"answer\":\"Categorical variables are transformed, and experiments use data splitting with k-fold and stratified k-fold cross-validation to test model performance consistently.\"}]","Comparative Analysis of 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