An Educational based Intelligent Student Stress Prediction using ML

Prediction of Stress of a college student is a rare case scenario. Universities generally don’t have a stress predictor as a basic need but, a com-mon counsellors and therapist can be further availed if a student is in stressful state. In this paper, the implementation of Stress Prediction has been done by improving the real-time application, in which a student or a staff can use this in their particular university premises to predict whether the student is Stress or Stress_Free. Here, K Nearest Neighbor (KNN) and Naive Bayes algorithms have been implemented and as an interesting yet common fact, these both are a Machine Learning Techniques. Further, these two algorithms are compared, tested and calculated differently to obtain the result in an ease manner. The studies show that the Naive Bayes are highly efficient in this particular proposed system than a KNN and has high efficacy rate. These results are availed to this paper after a test and implementation. The proposed system itself explains the process step by step with examples and implementations.