A Placement Prediction System for Polytechnic Students

Abstract

Abstract: The placement prediction system makes predictions about a candidate's likelihood of being hired based on several factors, including talents, backlogs, CGPA, and more. Here, previous placement information is analyzed to identify success parameters and create a machine-learning model that forecasts placement results in the future. It is designed to encourage students to improve their academic performance, enhance their skill set, and develop additional soft skills to increase their likelihood of securing successful job placements. This system guarantees that educational establishments are adaptable to the changing demands of the job market and can proficiently equip learners for prosperous career prospects. This study aims to predict the placement outcomes of polytechnic students in Kerala using three different classification algorithms: Knearest neighbors (KNN), Gaussian Naïve Bayes Classifier, and Support Vector Classifier (SVC). By employing these algorithms, the most effective method for predicting whether a student will be placed or not after completing their polytechnic education is identified. Student’s CGPA and scores obtained from skill tests have been utilized for placement prediction.