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A Reduced feature based neural network approach to classify the category of students
To ensure more effectiveness in the learning process in educational institutions, categorization of students is a very interesting method to enhance student's learning capabilities by identifying the factors that affect their performance and use their categories to design targeted inventions for improving their quality. Many research works have been conducted on student performances, to improve their grades and to stop them from dropping out from school by using a data driven approach [1] [2]. In this paper, we have proposed a new model to categorize students into 3 categories to determine their learning capabilities and to help them to improve their studying techniques. We have chosen the state of the art of machine learning approach to classify student's nature of study by selecting prominent features of their activity in their academic field. We have chosen a data driven approach where key factors that determines the base of student and classify them into high, medium and low ranks. This process generates a system where we can clearly identify the crucial factors for which they are categorized. Manual construction of student labels is a difficult approach. Therefore, we have come up with a student categorization model on the basis of selected features which are determined by the preprocessing of Dataset and implementation of Random Forest Importance; Chi2 algorithm; and Artificial Neural Network algorithm. For the research we have used Python's Machine Learning libraries: Scikit-Learn [3]. For Deep Learning paradigm we have used Tensor-Flow, Keras. For data processing Pandas library and Matplotlib and Pyplot has been used for graph visualization purpose.
Artificial Neural Network, Random Forest Importance, Chi2, Classification, Predictive Model, Student Category.
Mirza Mohtashim Alam, Karishma Mohiuddin, Amit Kishor Das, Md. Kabirul Islam, Md. Shamsul Kaonain, Md. Haider Ali
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ACM International Conference Proceeding Series
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