Giving students a chance to select a subject of their choice is becoming popular day by day. Elective subjects provide this chance and are increasingly a key part of the progress of a student in their academics. Various universities offer different subjects which belong to various areas of studies. Opting for the best field of study definitely plays a driving role in every student’s career. The proposed system titled “Elective Subject Recommendation System” is a web application for suggesting the best elective subject, among all their academic elective subjects, in which that particular student could have a scope of scoring more. It mainly focuses on the tests that will be taken to analyze the student’s basic knowledge in the respective field. Then the elective subject is recommended using the random forest algorithm. The objective of the project is to let every student opt the elective subjects based on their capability and knowledge but not by the choice of their fellow students.

Randomforest, collaborative filtering

[1]. Rishi Kumar Dubey, Umesh Kumar Pandey, “Elective Subject Selection Recommender System”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol.5, Issue.7,pp.306 – 310, 2017.
[2]. Narimel Bendakir and Esma A ̈ımeur, “Using Association Rules for Course Recommendation”, American Association for artificial intelligence, USA, 2006.
[3]. Pawan S. Wasnik, S.D.Khamitkar, Parag Bhalchandra, S. N. Lokhande, Ajit S. Adte, “An Observation of Different Algorithmic Technique of Association Rule and Clustering”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Special Issue.1, pp.28-30, 2018.
[4]. Michael P. O’Mahony,Barry Smyth, “A Recommender System for On-line Course Enrolment:An Initial Study”, RecSys `07: Proceedings of the 2007 ACM conference on Recommender systems, USA, pp.133-136, 2007.
[5]. Khairil Imran Ghauth, Nor Aniza Abdullah, “Learning materials recommendation using good learners’ ratings and content-based filtering”, Education Tech Research Dev, pp.58:711–727, 2010.
[6]. Michael J. Pazzani and Daniel Billsus, “Content-based Recommendation Systems”, The Adaptive Web, Berlin, pp. 325-341, 2007.
[7]. Dou Gui-Qin, Zhu Yan-Song, Han Yu-Min, “Research On Selection System Based on Bayesian Recommendation Model”, Proceedings of the 2011 International Conference on Advanced Mechatronic Systems, Zhengzhou, China, pp. 35 – 38, 2011.
[8]. SongJie Gong, “A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering”, Journal of software vol. 5, Issue. 7, pp. 745 – 752, 2010.
[9]. Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms”, Proceedings of the 10th international conference on World Wide Web, Hong Kong, pp. 285 – 295, 2001.
[10]. Grewal DS and Kaur K, “Developing an Intelligent Recommendation System for Course Selection by Students for Graduate Courses”, Business and economics journal, Vol. 7, Issue. 2, pp. 1 – 9, 2016.
[11]. Oyelade, O. J, Oladipupo, O. O, Obagbuwa, I. C, “Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance”,(IJCSIS) International Journal of Computer Science and Information Security,Vol.7, Issue.1, pp. 292 – 295, 2010.
[12]. Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B.Kantor, “Recommender Systems Handbook”, Springer New York Dordrecht Heidelberg London, pp. 64-66, 2011.
[13]. Sanjog Ray and Anuj Sharma,”A Collaborative Filtering Based Approach for Recommending Elective Courses” ,ICISTM 2011 © Springer-Verlag Berlin Heidelberg, pp. 330–339, 2011.
[14]. Kumar R., “Candidate Job Recommendation System”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.12-15, 2018.
[15]. S.N. Patil, S.M. Deshpande, Amol D. Potgantwar, “Product Recommendation using Multiple Filtering Mechanisms on Apache Spark”, Vol.5, Issue.3, 2017.