Process Mining & Optimization

This comprehensive process mining project analyzes student enrollment event logs to understand and optimize educational processes within academic institutions. Using advanced process mining techniques, the study reveals hidden patterns, bottlenecks, and inefficiencies in student enrollment workflows.

The research employs multiple process discovery algorithms including Alpha Miner and Heuristic Miner to reconstruct process models from event logs. These algorithms help visualize the actual enrollment processes, identify deviations from intended procedures, and highlight areas for improvement.

The project includes extensive exploratory data analysis (EDA) to understand the characteristics of enrollment data, followed by process conformance checking to compare actual processes with ideal models. Advanced statistical analysis and data visualization techniques are used to communicate findings effectively to stakeholders.

This research demonstrates the practical application of process mining in educational settings, showcasing how data-driven insights can be used to improve administrative processes and enhance student experience in academic institutions.