• TALAT KHAN
• Prof Dr. RACHANA KAMBLE

Depression has become a serious problem in this current generation and the number of students affected by depression is increasing day by day. However, some of the students manage to acknowledge that they are facing depression while some of them do not know it. On the other hand, the vast progress of social media is becoming their “diary” to share their state of mind. Several kinds of research had been conducted to detect depression through the user post on social media using machine learning algorithms. Through the data available on social media, the researcher can know whether the users are facing depression or not. ML algorithm enables to classify the data into correct groups and identify the depressive and non-depressive data. The proposed research work aims to detect the depression of the student by their data, which is shared on social media. The student data is then fed into different types of classifiers, which are Naïve Bayes and a hybrid model NB Tree, SVM, Decision Tree, Random forest. The results will be differentiate based on the highest accuracy value to determine the best algorithm to detect depression.

Depression, Students, Social Media, Classification, Hybrid, NB Tree, Naïve Bayes, SVM, Decision Tree, Random forest (RF)

"Depression Detection using Machine learning techniques on student’s social media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.244-251, August-2025, Available :http://www.jetir.org/papers/JETIRHA06035.pdf

"Depression Detection using Machine learning techniques on student’s social media", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. pp244-251, August-2025, Available at : http://www.jetir.org/papers/JETIRHA06035.pdf