Machine Learning Based Supplementary Prediction System Using K Nearest Neighbour Algorithm

Abstract

In this paper we have proposed a model for prediction of supplementary of a certain subject using machine learning approach. It is a basically classification system that spots whether a student would get supplementary or not. Individual or subject wise chances of getting supplementary can be manifested. The model is using K Nearest Neighbour Algorithm, which is a lazy algorithm a providing a good level of accuracy. The proposed method will help in analysis of supplementary students along with the faculty can analyze probably how much students are going to get supplementary and decide how much extra attention he/she should give while teaching that paper.