[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118059-en":3,"doc-seo-118059-105":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},118059,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","AN AUTOMATED MACHINE LEARNING APPROACH FOR MORE EFFICIENT MARINE CORPS RECRUITER PROSPECTING - read online","The military recruiting environment faces significant challenges that make recruiting goals harder to achieve. This thesis proposes a proof of concept for recruiting by evaluating whether automated machine learning can accurately prioritize public high schools using publicly available data. It contrasts Marine Corps Recruiting Command’s largely unsystematic current prioritization with a more data-driven approach. Using Microsoft Azure, the study finds that AutoML predictions are effective for identifying which public high schools to prioritize and can generate more contracts than recruiters’ chosen priority schools, while also producing closely aligned yet more granular, pattern-level insights.","Calhoun: The NPS Institutional Archive  \nDSpace Repository  \n\n| 2024-03\u003Cbr>AN AUTOMATED MACHINE LEARNING APPROACH FOR MORE EFFICIENT MARINE CORPS RECRUITER PROSPECTING\u003Cbr>Born, Andrew A.\u003Cbr>Monterey, CA; Naval Postgraduate School |\n| --- |\n| [https://hdl.handle.net/10945/72685](https://hdl.handle.net/10945/72685) |\n\nNPS Scholarship Theses  \nThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.  \nDownloaded from NPS Archive: Calhoun  \nNAVAL POSTGRADUATE  \nSCHOOL MONTEREY, CALIFORNIA  \nTHESIS  \nAN AUTOMATED MACHINE LEARNING APPROACH FOR MORE EFFICIENT MARINE CORPS RECRUITER PROSPECTING  \nby  \nAndrew A. Born  \nMarch 2024  \nThesis Advisor: Maxim Massenkoff  \nSecond Reader: Sae Young Ahn  \nApproved for public release. Distribution is unlimited.  \nTHIS PAGE INTENTIONALLY LEFT BLANK  \n\n| REPORT DOCUMENTATION PAGE |  |  |  | Form Approved OMB No. 0704–0188 |  |  |\n| --- | --- | --- | --- | --- | --- | --- |\n| Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302, and to the Office of Management and Budget, Paperwork Reduction Project (0704- 0188) Washington, DC, 20503. |  |  |  |  |  |  |\n| 1. AGENCY USE ONLY (Leave blank) |  | 2. REPORT DATE\u003Cbr>March 2024 | 3. REPORT TYPE AND DATES COVERED\u003Cbr>Master’s thesis |  |  |  |\n| 4. TITLE AND SUBTITLE\u003Cbr>AN AUTOMATED MACHINE LEARNING APPROACH FOR MORE EFFICIENT MARINE CORPS RECRUITER PROSPECTING |  |  |  |  | 5. FUNDING NUMBERS |  |\n| 6. AUTHOR(S) Andrew A. Born |  |  |  |  |  |  |\n| 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)\u003Cbr>Naval Postgraduate School\u003Cbr>Monterey, CA 93943-5000 |  |  |  |  | 8. PERFORMING\u003Cbr>ORGANIZATION REPORT NUMBER |  |\n| 9. SPONSORING / MONITORING AGENCY NAME(S) AND\u003Cbr>ADDRESS(ES)\u003Cbr>N/A |  |  |  |  | 10. SPONSORING / MONITORING AGENCY REPORT NUMBER |  |\n| 11. SUPPLEMENTARY NOTES The views expressed in this thesis are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S. Government. |  |  |  |  |  |  |\n| 12a. DISTRIBUTION / AVAILABILITY STATEMENT\u003Cbr>Approved for public release. Distribution is unlimited. |  |  |  |  | 12b. DISTRIBUTION CODE\u003Cbr>A |  |\n| 13. ABSTRACT (maximum 200 words)\u003Cbr>The military recruiting environment is facing significant challenges, making recruitment goals more difficult to obtain. Due to these difficulties, the Marine Corps must find new ways to target the right demographics effectively. This thesis serves as a proof of concept for recruiting: can we employ automated machine learning to accurately prioritize public high schools using publicly available data? Current methods by Marine Corps Recruiting Command to prioritize high schools are largely unsystematic, potentially leading to inefficient allocation of recruiting resources. This study employs Microsoft Azure to demonstrate how we can use automated machine learning to enhance the efficiency of recruiting efforts.\u003Cbr>I find that automated machine learning using publicly available data may be an effective tool for predicting which public high schools to prioritize. Additionally, the automated machine learning predictions produced more contracts than the Marine Corps’ choices of priority schools. I recommend that the Marine Corps and other branches of service further explore the use of automated machine learning and open-source data to enhance their recruitment strategies. 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