MKRF Stacking-Voting

Abstract

Data mining is most efficient when used deliberately to achieve a corporate goal, answer business or research questions, or contribute to a problem-solving solution. Data mining aids in the accurate prediction of outcomes, the recognition of patterns and anomalies, and frequently inform forecasts. Online education is becoming more popular all around the world because of the COVID-19 pandemic. The main goal of this research is to Predict Educational Satisfaction Level of Bangladeshis Students During the Pandemic using data mining approaches by only filling up with some basic questionnaires which are related to the satisfaction level of online education collected through a public survey. By surveying 1004 students from various academic institutions, schools, colleges, and universities on the quality of online education in COVID-19 pandemic scenarios, we were able to determine how productive it would be. Influence how online learning is measured and how satisfied people are with it. To achieve our aim of predicting satisfaction levels, we used a total of eight classifiers, six of which were based classifiers, which we combined with the best three top-scoring classifiers to build a novel ensemble approach called MKRF Stacking and MKRF Voting ensemble classifier. Among those classifiers, the Random Forest classifier outperforms the other six base classifiers with 97.21% accuracy. Our proposed data mining ensemble approaches MKRF Stacking and MKRF Voting outperform applied classifiers. Typically, voting ensemble classifiers outperform voting ensemble classifiers, but in this case, MKRF Stacking defeated MKRF Voting and all applied classifiers with a supreme accuracy of 97.68% (Average). The proposed method would be used in a framework where education counselors find the root causes and minor explanations for dissatisfaction in online education among students so that they can better understand all aspects and provide them with the best advice and solutions to their problem...