Abstract:
Medical professionals diagnose depression based on patient self-reporting and mental status
questionnaires. Mentally ill people are reluctant to seek mental health treatment, in addition to the
fact that the approaches vary greatly depending on the patient's state of mind at the time. Academic
fields typically offer their students bright futures. But among the many other things that could
cause a student to experience depression are peer pressure, academic competition, loneliness, and
many other things. This study aims to identify depression in college students using a big data
analytics template. It is asserted once more that the framework models the relationship between
depression and factors like isolation and separation, which are thought to have the most profound
effect on students. In summary, the journal assesses how well the suggested framework performs
using a sizable real dataset gathered from various Bangladeshi university students, and it
demonstrates that machine learning models detect depression in universities more accurately than
traditional methods. I will apply six pre-trained models (Logistic Regression, Decision Tree,
Random Forest, Gradient Boost, KNN, and Naive Bayes) to classify and identify this issue. Among
them, Logistic Regressiongave performed better than any other proposed model. In comparison to
other models, the model provided good detection accuracy
