Prediction of Finest Department Assortment on Undergraduate Admission Through Machine Learning Approach
Year: 2021
Type: article
Abstract: Students are feeling too much confusion to select the right department at undergraduate level after completing the higher secondary in Bangladesh. This study is emphasis on the perspective of students to choose a suitable department in a smart way. However, a system is developed where the students provide their prior data to find out which department is best for them in regarding the future profession. This article uses SPSS (Statistical Package for the Social Sciences) statistical platform for analyzing data and used WEKA tool to find out the best algorithm for finding the optimum outcome according to the data structure. The KNN (k-Nearest Neighbors) model has been selected for this study because it works well with numerical data. The KNN has the stunning accuracy among other classification algorithms with the accuracy of 90% or above with proper training and labeling. The major purpose of this research work is to making e-system that can predict the suitable department for under graduate students and also verified that KNN is the paramount algorithm model for the data classifier.
Institution: Daffodil International University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W3130615841