Enhancing Academic Decision-Making using Semantic Analysis based Course Recommendation System

The existing course recommendation systems around the world rely on student's past performances, grades and CGPA when giving personalized suggestions. The goal is to provide a customized course recommendation system using Machine Learning (ML) techniques for students. In this project, Machine Learning techniques are used to analyze student feedback about teachers teaching skills, teacher friendliness and course difficulty from the students who already finished said course, this feedback data will be analyzed using semantic analysis. The software developed will be able to give alternative suggestions to students based on their academic performance. By combining the past students' feedback and making real-time suggestions the program will be able to give a clear perspective about the course and the teachers. The final purpose is to give the students a better academic experience by giving them more choices and better course recommendations that will guide them to follow their college career.