Maximizing Campus Placement Through Machine Learning

Campus placement is an essential aspect of a student's academic career, as it determines their entry into the workforce. Predicting students' campus placement can help universities and colleges identify students who are likely to be successful in their chosen career paths and provide them with the necessary support to secure a job. Application of machine learning algorithms to forecast students' placement on campuses has gained traction in recent years. This research paper will explore the different approaches to predicting students' campus placement, the factors that influence campus placement, and the benefits and limitations of using machine learning algorithms for prediction. Machine learning is capable of adaptability and with the use of statistical models and algorithms they are able to draw inferences from patterns in data. Using ML algorithms forecasting can be done about the campus placement of students. Three ML algorithms viz, Naïve Bayes, Random Forest and Decision Trees are used to forecast the job/campus placement of students and evaluation of the aforesaid algorithms are performed with respect to accuracy of the classifier11.