Machine Learning Model to Predict Placement Success

Abstract

Predicting whether a student will succeed in getting a good placement has become a key use of machine learning in education data analysis [8]. The old ways mainly depend on people checking manually, looking at past grades, and using opinions, which aren't always accurate or easy to scale [9]. In this study, a new approach using machine learning is suggested to forecast a student's chance of getting placed [2]. This approach uses different factors like academic results, technical abilities, behavior, and socio-economic. The system uses different machine learning methods like Logistic Regression, Random Forest, Support Vector Machine, and Gradient Boosting to find out which one works best [9]. The model is trained using organized data that includes things like grades, technical skills, projects, internships, communication skills, test scores, and participation in extra activities [2].