A Comparative Study of Different Machine Learning Techniques to Predict the Result of an Individual Student Using Previous Performances

Abstract—Machine learning is a sub-field of computer science refers to a system’s ability to automatically learn from experience and predict new things using the learned knowledge. Different machine learning techniques can be used to predict the result of the students in examination using previous data. Machine learning models can recognize vulnerable students who are at risk and take early action to prevent them from failure. Here, a model was developed based on the academic performance of the students and their result in the SSC exam. This paper also shows a comparative study of different machine learning techniques for predicting student results. Five different machine learning techniques were used to demonstrate the proposed work. They are Naive Bayes, K-nearest Neighbours, Support Vector Machine, XG-boost, Multi-layer Perceptron. Data were preprocessed before fitting into these classifiers. Among the five classifiers, MLP achieved the highest accuracy of 86.25%. Other classifiers also achieved a satisfactory result as all of them were above 80% accuracy. The results showed the effectiveness of machine learning techniques to predict the performance of the students. Index Terms—Machine learning, Result, Predictio