Educational institutions require efficient systems to manage and analyze large volumes of academic data. Traditional methods for calculating SGPA, CGPA, and tracking arrears are often manual and prone to errors. This paper presents an Edu Metrics Automation System that processes multi-semester student data using Python and structured Excel inputs. The system automates SGPA and CGPA calculation based on grade points and credits, while also detecting arrears and tracking their clearance across semesters. A key feature is the automatic update of academic performance when arrears are cleared. Additionally, the system evaluates staff performance by computing subject-wise pass percentages. Furthermore, the system generates outputs in both HTML and Excel formats, providing flexibility for visualization and documentation. The Excel output includes multiple sheets for SGPA, CGPA, arrear lists, staff performance, and ranking, enabling easy interpretation and reporting. It reduces manual effort, improves accuracy, and provides comprehensive insights into student and faculty performance. The proposed system is scalable and can be extended for advanced academic analytics using data analysis techniques supported by Python and Pandas libraries [1], [2].

The Edu Metrics Automation System successfully automates the calculation of SGPA, CGPA, arrear detection, and staff performance analysis, ensuring accuracy, efficiency, and reduced manual effort. The system dynamically updates academic records through automatic arrear clearance detection and provides structured outputs in both HTML and Excel formats, making it practical for real-world academic use. In the future, the system can be enhanced by integrating a web-based interface and database for better accessibility and data management. Additional improvements such as graphical dashboards, performance visualization, and machine learning-based prediction can further extend its capabilities, making it a comprehensive academic analytics platform.

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