Accelerating University Admission System using Machine Learning Techniques

In this paper, we proposes a solution to enhance university admission system using different machine learning techniques like K-Nearest Neighbour(KNN), Decision Tree(DT) and Random Forest(RF). A deterministic algorithm is used to create a basis for the analysis, followed by several probabilistic algorithms, KNN, decision tree, and random forest. The outcomes are compared and a consensus is reached in categorising each student whether they got accepted, placed in foundation or rejected in the desired institute. Processing university applications is time-consuming for admission staff members, it is not efficient in terms of cost and time, thus it can sometimes be an overwhelming task, the chances of bribes occurring, or discrimination is highly likely to occur. The solution for this problem is to automate the application process thus using the help of a software with several embedded AI algorithm that improves the efficiency and reliability of the application process.