Data-Driven Analysis of Student Enrollment Trends Using Machine Learning Algorithms: A Case Study of the University of Tetova

More than just numbers, data tools now help un-cover how students move through universities. From 2021 to 2024, patterns in who enrolls where at the University of Tetova were mapped using code, not guesswork. Python cleaned the records first; then came visual scans, grouping similar cases, and mapping choices like branches on a tree. Medical Sciences stands out, drawing more interest year after year than other faculties. Instead of relying on assumptions, predictions grow from what past behavior suggests might happen next. These models do not replace judgment; they sharpen it with evidence most overlook. What began in technology labs now finds purpose inside academic halls, shifting how leaders respond to change. Not limited to stores or factories, smart number crunching proves useful wherever decisions matter. Behind every trend line is a story about people, shaped quietly by mathematics working behind the scenes.