Student Performance Prediction: A UI based Performance Predictor

As the competitive environment prevails among educational organizations, the challenge is to elevate the quality of education through data mining. Student's performance is a concerning factor to the higher education. In this project, one of a machine learning technique have been used to build a regressor that can predict the upcoming semester marks of students. A model is put forward to predict in which the algorithm implemented is a Supervised machine learning algorithm called Decision tree. The importance of different attributes is considered, in order to determine which of these are correlated with student marks prediction. Some of the features are selected to predict the immediate next semesters marks of a student. Features like semester scores, attendance, marks obtained from 10th and 12th exams, study time and other aspects were selected to conduct this work. Due to early prediction and solution, better results can be expected in the finals. Students can view their accuracy of passing in further studies.