An Outlier Detection System to Enhance Decision-Making for Akperan Orshi Polytechnic Yandev

In the era of data-driven governance, institutions like Akperan Orshi Polytechnic Yandev are increasingly relying on digital systems to streamline operations and enhance decision-making. However, hidden anomalies or outliers in institutional data—such as irregular staff attendance, student performance, or financial transactions—can distort analyses and lead to suboptimal decisions. This study presents the design and implementation of an Outlier Detection System tailored for Akperan Orshi Polytechnic Yandev. The system employs statistical and machine learning techniques to automatically identify and flag unusual patterns across various administrative and academic datasets. By integrating this system into the Polytechnic's existing data infrastructure, stakeholders can proactively detect inconsistencies, improve data integrity, and make more informed and timely decisions. The results demonstrate the system’s effectiveness in revealing hidden anomalies, thereby supporting strategic planning and policy formulation across departments.