A Comparative Analysis on Machine Learning Based Student Placement Prediction

Improving student placement success is the foremost important concerns facing education institutions in the present days. The prediction of placement becomes more intricate when the complexity of the educational entities increases. As a result, educational institutions are seeking more efficient technology that can help helping them to create new strategies or better manage and support decision-making processes. Introducing fresh information about educational processes and entities to the management system is an efficient technique to handle the challenges for improving qualitv. Machine learning techniques can extract this knowledge from the operational and historical data contained within the databases of the educational organization using the dataset for system implementation. This data is utilized to train the model for the identification of rules and for the testing of the model for categorization, This model will provide a recommendation system that will predict a student's placement level. With the help of this approach, the organization's recruitment department can better recognize promising students and pay attention to and develop both their technical and interpersonal skills. They may concentrate their efforts on landing jobs in organizations with greater hierarchies now that they have this information.