A HML-EVC Model for Analyzing the Risk of the Students to Predict the Success Probability in the Field of Education

Predictive analytics is one of the paramount fields in data analytics which plays an eminent role of analyzing the data and predicting the feature probabilities. In Predictive analytics Machine learning algorithms are the necessitated part to analyze the data in an accurate approach. Using the machine learning algorithms the models can be constructed for prediction according to the data. These machine models can be used to analyze various data of different sectors. In the education field, the success of the students can be predicted using the machine learning models by detecting or uncovering the risk of the students. The benefits for detecting student issues and learning difficulties early a unique opportunity to address the causal factors on time in order to prevent student failure and drop out tendencies. [2]( Taiwo Olapeju Olaleye, Olufunke Rebecca Vincent, 2020). This paper exposes a prediction algorithm developed based on the classification techniques for identifying the risk of the students on a real time foot with student’s data sets on various aspects.