Most college kids find their heads spinning when school work piles up, friendships shift, or home feels too far away. When life gets like that, tension builds - sometimes tipping into worry or sadness strong enough to mess with sleep, focus, meals.Spotting those shifts fast matters because reaching out early tends to lighten the load before it grows heavier. Help at the right moment changes how things unfold later on.

Nowadays more schools turn to number-focused methods. Computers try spotting kids who might struggle using math tools hidden inside programs. One way uses past choices to guess future ones another draws lines between types of behavior one sorts outcomes by closeness while others grow like trees from decisions made before. Some work better than others depending on what you need found out. How well each does its job shows which fits best when results matter.

One way this research worked began by testing several prediction methods for student mental health concerns. Not until the data passed close review did any modeling start - only then could analysis move forward. Effectiveness of every method came into view through common evaluation metrics, each revealing different results.

Surprisingly, Logistic Regression stood out with better accu-racy than fancier models. That hints at how straightforward approaches might work just as well when spotting possible mental health issues. With an eye on obvious trends instead of heavy math, the method stays quick and easy to follow.

One thing stands clear: machines might help spot trouble before it grows. Spotting signs earlier means schools can step in faster - less guesswork, more care where it counts. Help arrives when needed, easing strain on student minds everywhere

Index Terms—College students; Mental health; Machine learn-ing; Logistic Regression; Early detection; Predictive modeling