Skill Based Course Recommendation System
Viddhesh Sankhe, Janice Shah, Tejas Paranjape, Radha Shankarmani · 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON) · 2020
In today's world, students face an immense repertoire of options relating to the number of courses that they may choose from. To make this seemingly massive choice relatively easy to make, many authors have created their own recommender systems to map students to the courses that are best suited for them. However, they are not widely used as they give good results only for the dataset that they consider. In our paper, we have mapped the current students to their alumni based on multiple criterion. Afterwards, unlike other papers that used k-means, we used c-means and fuzzy clustering to arrive at a better solution to predict an elective course for the student. Since all of this is done on a broad actual dataset, the results can be applied anywhere in a real world scenario.