Computational Statistics and Machine Learning Techniques for Effective Decision Making on Student’s Employment for Real-Time

The present study accentuated a hybrid approach to evaluate the impact, association and discrepancies of demographic characteristics on student’s job placement. extracted several significant academic features that determine Master Business Administration (MBA) student placement confirm placed gender. This paper recommended novel futuristic roadmap for students, parents, guardians, institutions, companies benefit at certain level. Out seven experiments, first five experiments were conducted with deep statistical computations, last two performed supervised machine learning approaches. On one hand, Support Vector Machine (SVM) outperformed others uppermost accuracy 90% predict employment status. other Random Forest (RF) attained maximum 88% recognize gender students. Further, are also identify A t-test 0.05 significance level proved did not influence their offered salary during MBA specializations Marketing Finance (Mkt&Fin) Human Resource (Mkt&HR) (p > 0.05). Additionally, result showed affect test percentage scores 0.05) degree streams such as Science Technology (Sci&Tech), Commerce Management (Comm&Mgmt). Others χ2 revealed between course specialization status < It there is no current automatic prediction impact identification higher educational universities institutions will help human communities (students, teachers, institutions) prepare future accordingly.