Student Placement Chance Prediction Model using Machine Learning Techniques

Abstract

Obtaining employment upon graduation from uni-versity is one of the highest, if not the highest, priorities for students and young adults. Developing a system that can help these individuals obtain placement advice, analyze labor market trends, and assist educational institutions in assessing growing fields and opportunities would serve immense value. With the emergence of heavily refined Data Mining techniques and Machine Learning boiler plates, a model based on predictive analysis can help estimate a variety of realistic and possible placement metrics, such as the types of companies a junior year student can be placed in, or the companies that are likely to look for the specific skill sets of a student. Various attributes such as academic results, technical skills, training experiences, and projects can help predict purposes. We devised the XGBoost Technique, a structured or tabular data-focused approach that has recently dominated applied machine learning and Kaggle tournaments. XGBoost is a high-speed and high-performance implementation of gradient boosted decision trees. We created a model and ran numerous EDAs to determine whether the student will be placed or not, as well as in which type of organization he will be placed [Day Sharing, Dream, Super Dream, Marquee].