A Framework for Machine Learning Based Support System for Post-graduation Admission with the Case Study Conducted on D.K.M. College for Women, Vellore

India and other developing nations face difficulties building effective higher education systems, particularly when it comes to female students. Although the government made an effort, our nation did not benefit from its innovative and excellent educational policies. Indians still have a lot of issues with our educational system. The Indian government is aware that the current state of the world presents unique difficulties for the higher education sector. The UGC noted that a broad range of abilities will be expected of graduates in the humanities, social sciences, natural sciences, and business, as well as in a variety of professional fields like hospitality, tourism, agriculture, law, management, medical, and engineering. This knowledge cannot be adequately imparted during graduation, which ultimately leads to inadequate job. These include inadequate facilities and infrastructure, open seats in academic fields and poor faculty members thereof, low student enrollment rates, outdated and ineffective teaching strategies, falling standards for research, unmotivated students, crammed and cramped classrooms, and pervasive geographic, economic, gender, and racial imbalances. The post-graduation admission rate has significantly decreased in the past year at D.K.M. college for women in Vellore. A machine learning-based predictive analysis system is proposed to provide recommendations to improve PG admission. Taking into account the aforementioned scenario, the proposed work is primarily focused on the identification of issues, challenges, and decline factors of post-graduation admission from the perspectives of students, parents, teaching staffs, and management.