Abstract
At the beginning of the university, students often face confusion in career choices. Most of the students cannot understand what field they are interested. There are many existing systems where some researchers have worked with fresh graduates and some with selected tenth or twelfth-grade students to help them get an idea about one career option. However, those research works failed to help them choose a domain of interest from many opportunities in their field. Therefore, this research aims to propose a system for predicting interest of beginner-level engineering students for better career planning at an early age. This analysis gives a chance of finding students interest in four specific demanding fields (web development field, graphics design field, Android development field, and data science field). This research starts with collecting primary sources of data using structured questionnaires. The data are preprocessed for creating a well-formed dataset. Then, the research is done in two parts: statistical analysis (Chi-square test and binary logistic regression analysis) and machine learning analysis (decision tree, SVM, and multinomial logistic regression). In machine learning analysis, appropriate features are selected first, then build the models. The results from both parts are then evaluated using a confusion matrix and choose the best prediction. The research shows noteworthy results with respect to existing works.
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Ullah, M.A., Alam, M.M., Sheuli, S.A., Mumu, J.A. (2023). Predicting Interest of Beginner-Level Engineering Students for Better Career Using Classification Method. In: Hossain, M.S., Majumder, S.P., Siddique, N., Hossain, M.S. (eds) The Fourth Industrial Revolution and Beyond. Lecture Notes in Electrical Engineering, vol 980. Springer, Singapore. https://doi.org/10.1007/978-981-19-8032-9_5
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DOI: https://doi.org/10.1007/978-981-19-8032-9_5
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