Abstract:
In modern society, depression affects the majority of people. Many even commit suicide
as a result of inadequate care. If depression is present in the patient in the early stages, it
is simple to identify and cure. Since we don't know the depression level, we can't make
the best choice at the appropriate time. A whole nation is built on its students. By
teaching and improving the country, students represent it to the outside world. Depression
is caused by a variety of factors, including challenges Bangladeshi adolescents have in
their education. Determining the prevalence of depressive symptoms, their contributing
variables, and strategies for reducing depression of university students are the objectives
of our study. We analyzed the dataset with different samples from university students.
We provide some question by a Google from, students are chosen the answer then set a
range to find their depression level. About 1049 people's data were obtained from a
Google form. In essence, the test was the data. In essence, the student has provided the
data. We were able to determine the depression level through the analysis of that data. On
this data, several algorithms have been applied. And have achieved the highest accuracy.
With the help of this project, we can detect depression levels and administer the
appropriate care or therapy. We're using some kind of algorithm to detect their depression
level. They are five algorithms are chosen for this research. There are Decision Tree
classification, Random Forest classifier, SVM, KNeighbors Classifier, and GaussianNB.
Overall, the SVM algorithm prediction has the best performance. It gives 93% which is
the best algorithm to prefer for this research.
