Abstract
Educational effectivity is paramount towards enhancing modernization. In our study we have taken into account various socioeconomic, psychological and academic factors to properly understand a student’s life during adolescence and their effect on academic performance. In order to build a predictive model, we have pre-processed the data using dimensionality reduction, data balancing, discretization, and normalization and then classified the data using different machine learning techniques like Artificial Neural Net, K-Nearest Neighbors and Support Vector Machine. Lastly we have discovered patterns throughout the dataset in relation with academic performance.
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Shakil Ahamed, A.T.M., Mahmood, N.T., Rahman, R.M. (2017). Prediction of Academic Performance During Adolescence Based on Socioeconomic, Psychological and Academic Factors. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_7
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DOI: https://doi.org/10.1007/978-3-319-56660-3_7
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