Prediction of HSC examination performance using socioeconomic, psychological and academic factors

Understanding how to make education effective is a critical step in educational data mining. We have considered various socioeconomic, psychological and academic factors to fully understand what a person's life is during adolescence and how those factors impact their academic performance. Using pre-processing techniques such as feature selection, data balancing, discretization and normalization, and classification techniques such as Random Forest, Artificial Neural Net (ANN), and Naïve Bayes we have developed different models to predict academic performance and also investigated prominent patterns in this study.

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