"A Comparative Study of Behaviour Predictors for School Students in Indore Using Machine Learning Algorithms".

Abstract

Predicting student academic performance has become a key area in educational data mining, with machine learning techniques offering powerful tools for early intervention and decision-making. This study explores the application of classification models to forecast student success and behavioural outcomes, with the goal of improving academic support systems and reducing dropout rates. Two distinct datasets of student information were utilized, and three boosting-based machine learning algorithms - XGBoost, AdaBoost, and an Artificial Neural Network (DenseNet) - were implemented. Feature engineering techniques were applied to optimize input variables and enhance model effectiveness.

The results demonstrate that it is feasible to predict student behaviour and academic performance with significant accuracy using machine learning models. Among the evaluated methods, XGBoost and AdaBoost achieved the best predictive performance with an accuracy rate of approximately 88%. Conversely, the DenseNet-based neural network model produced the lowest accuracy, around 49%. These findings underscore the effectiveness of boosting methods for educational prediction tasks and highlight the role of machine learning as a practical approach to advancing educational research and institutional planning.