AI-Driven Smart Attendance and Academic Performance Prediction System Using Machine Learning

Gutti Reddy Prakash, Mrs. V. Vijayalakshmi

Artificial Intelligence; Attendance Management; Machine Learning; Random Forest; Student Performance Prediction.

The increasing demand for intelligent academic management systems has accelerated the integration of artificial intelligence and machine learning techniques into educational environments. Traditional attendance management systems, which rely on manual processes or basic digital tools, are inefficient, error-prone, and lack the analytical capabilities required for proactive academic monitoring. This paper presents an AI-Driven Smart Attendance and Academic Performance Prediction System, designed to automate attendance tracking while integrating predictive analytics for enhanced decision-making. The proposed system is developed using Python and the Django web framework, with SQLite as the backend database and Scikit-learn for implementing machine learning algorithms. A Random Forest Classifier is employed to analyse student data, including attendance percentage, assignment scores, quiz performance, late submissions, and study hours. Based on these features, the system predicts whether a student is academically safe or at risk, enabling early intervention by educators. The application follows a modular architecture consisting of student management, attendance tracking, AI prediction, reporting and visualisation, and dashboard modules. Interactive dashboards and graphical reports are implemented using Chart.js to provide real-time insights into student performance. Experimental results demonstrate that the system effectively identifies at-risk students and supports data-driven academic decisions. This work contributes toward transforming conventional attendance systems into intelligent academic monitoring platforms aligned with modern educational technology trends [1][2].

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