ATTENDANCE PATTERN ANALYZER USING DATA ANALYTICS

Abstract: Educational institutions generate large volumes of attendance data on a daily basis. However, traditional attendance systems are primarily limited to recording presence and calculating attendance percentages, without extracting deeper insights from the collected data. This restricts the potential of attendance data as a valuable resource for understanding student engagement and academic behavior.

This research proposes an Attendance Pattern Analyzer using Data Analytics to transform raw attendance data into actionable insights. The system utilizes structured datasets and applies data preprocessing, rule-based classification, trend detection, and correlation analysis techniques. By analyzing attendance patterns across time, class, and performance metrics, the system identifies irregular behaviors, classifies students into risk categories, and evaluates the relationship between attendance and academic performance.

The system is implemented using Python and Pandas for data processing, Flask for backend development, and Chart.js for visualization. The results demonstrate that analytical processing of attendance data enables early identification of at- risk students and supports proactive intervention strategies. The proposed approach enhances traditional attendance systems by converting them into decision-support tools, contributing to improved academic monitoring and student outcomes.

Keywords: Attendance Analytics, Data Analysis, Student Engagement, Pattern Detection, Academic Performance