The Study of Machine Learning Classification Algorithm for Student Placement Prediction

Students are the primary contributors to the educational system. Every student anticipates finding employment prior to graduation. Numerous factors affect a student’s academic performance and employment prospects. Students can better comprehend the fundamental qualities required to be put in this position with the use of an effective placement predictor system. The proposed system works on past placement history to examine the chances of student placement on the basis of various attributes. In this study, different classification algorithms were used to generate the perfect trained model. The accuracy of the classification algorithms is 0.88, 0.87, 0.72, 0.78, and 0.87 for decision tree, random forest, logistic regression, gaussian naive bayes, and KNN, respectively. Additionally, a user interface web portal is developed and connected with a trained model. This web portal collects the user’s input parameters and determines the likelihood of placement. The entire process of the proposed work is represented in the form of a model using the big data modelling software KNIME. The prediction system is highly correlated to the student’s CGPA, internships, and backlogs.