Predictive Analysis of Student Stress Level Using Naïve Bayesian Classification Algorithm

Overall academic performance and social compulsion have created a pressurized psychological state for students. Earlier, the competition level used to be less. As the science evolves, the knowledge and workload of the faculty and the school students have conjointly been evolving to view with the society. Students find it tough at times after they had completed their standards in SSLC (Secondary School Leaving Certificate) and HSC (Higher Secondary School Certificate). Associating with the NEET exam, the result influences a crucial-end on countless students' life. Naive Bayes technique is an emerging discipline, used to analyze the accurate facts and helps in organizing the most stress-causing factors based on the probabilistic parameters. Abstract Reference Object characteristics and correlation are the two main attributes based on which the stress accuracy level is predicted. Information gathered from the survey is analyzed using Bayes classifier and the outcomes are inferred using visualization tools. Based on the output, the socioeconomic and physical stress causing factors have been encoded and prioritized. Frequently stated stress factors should be reduced to help the students to excel in their academic performance and activate the social activities, thereby decreasing individual health issues like migraine headaches, spectacle- wearing and so on.