Machine Learning Model for Prediction of Stress Levels in Students of Technical Education


Garima Verma
DIT University, Dehradun, INDIA
Sandhya Adhikari
DIT University, Dehradun, INDIA
Vaishnavi Khanduri
DIT University, Dehradun, INDIA
Shubhi Tandon
DIT University, Dehradun, INDIA
Shubhadika Rawat
DIT University, Dehradun, INDIA
Palak Singh
DIT University, Dehradun, INDIA


An alarming rate of teenagers and youths are facing depression and anxiety now a days. One of the major reasons is the mental stress. The objective of this study is to identify the factors that affects the mental condition like depression, stress, anxiety in the students studying at the college level specially in engineering colleges. Two machine learning models logistic and Support Vector Machine (SVM) are proposed to predict the stress level of the students. For this study the dataset of 513 students has been collected by some engineering colleges of the northern India studying at graduation level. The data is collected using online and offline questionnaires. Accuracy, precision, recall and AUC-ROC curve performance metrics are being used to measure the performance of the models. The accuracy achieved by the logistic regression is 67% while the SVM has achieved 86.84%.

ICAMCS19 Cover Page
August 8, 2020
Online ISSN