A Comparative Analysis of Stress among Undergraduate Students Using Logistic Regression and Random Forest Techniques



Stress, Logistic regression, Random Forest, Mental illness, Predictive Analysis, Stress in Students, Machine Learning, Data Analysis, Causes & Effects of Stress


Mental illness has become a major problem for youngsters nowadays. Our project deals with calculation of stress as we know that overall collegiate performance and social obligation have created a pressurized cerebral as well as emotional state for students. With limited college seats, and high number of post metric students applying to get into the top universities and colleges, it could be difficult to get into the college one wished for. Same is the case for a student in his last year of graduation. There is lot of pressure that one undergoes like pressure of getting placed, pressure of getting into a top college for PG and many more. Lastly the stress caused due to the pandemic can be least ignored. Students weren’t able to attend online classes properly due to lack of resources which resulted students to undergo a lot of stress about their academics. We collected the data through a google survey form which was send to all the students known to us. We collected 101 responses and then converted them into numerical value and lastly implemented the logistic regression and random forest algorithm, where we got our f1 score = 0.9411 and Accuracy score (Random Forest) = .0.8571.