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ABSTRACT
In education system evaluation and prediction of student performance is a challenging task. In this paper, a model is proposed to predict the performance of students in an academic organization. The algorithm employed is a machine learning technique called Naïve Bayes and KNN. Further, the importance of several different attributes, or “features” is considered, in order to determine which of these are
correlated with student performance. Finally, the results of an experiment follow, showcasing the power of machine learning in such an application. In perspective of this project we are going to predict the student development and examine the greater result through machine learning algorithm. We foresee the student performance by scanning their previous academic details. To execute this prediction we have created a dataset, by using this we can predict student details.
INTRODUCTION
OVERVIEW
There are many studies in the learning field that investigated the ways of applying machine learning techniques for various educational purposes. One of the focuses of these studies is to identify high-risk students, as well as to identify features which affect the performance of students. Students are the major strength for numerous universities. Universities and students play a significant part in producing graduates of superior calibers with its academic performance accomplishment. However, academic performance achievement changes as various sort of students may have diverse degree of performance achievement. Machine learning is the ability of a system to consequently gain from past experience and
improve performance. Nowadays machine learning for education gains more attention. Machine learning is used for analyzing information based on past experience and predicting future performance.
