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This paper presents the development of placement predictor system (PPS) using logistic regression model. Based on the student scores in matriculation, senior secondary, subjects in various semesters of technical education and demographics, PPS predicts the placement of a student in upcoming recruitment session. The steps involved in designing and building logistic regression model is stated using the past academic and in-house placement data of Guru Nanak Dev Engineering College (GNDEC), Ludhiana. Machine learning parameterized approach is used to support research and analyze the students performance in previous sessions. The results are generated from an open source GNU Octave programming tool. The developed model has been applied to predict the placement of students at training and placement office (TPO). The testing of PPS brings about promising 83.33% accuracy. The learned parameters of the model gave insights into the placement process. Hence, the TPO decided to adopt this system to help them in informed decision making. This application endows the targeted group of students to boost their placement probability.
Sharma et al. (Mon,) studied this question.