sis 20(26): e9

Research Article

An Automated Recommender System for Educational Institute in India

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  • @ARTICLE{10.4108/eai.13-7-2018.163155,
        author={Mamata Garanayak and Sipra Sahoo and Sachi Nandan Mohanty and Alok Kumar Jagadev},
        title={An Automated Recommender System for Educational Institute in India},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={7},
        number={26},
        publisher={EAI},
        journal_a={SIS},
        year={2020},
        month={2},
        keywords={Recommender system, Random Forest Classification, K-Nearest Neighbor (KNN)},
        doi={10.4108/eai.13-7-2018.163155}
    }
    
  • Mamata Garanayak
    Sipra Sahoo
    Sachi Nandan Mohanty
    Alok Kumar Jagadev
    Year: 2020
    An Automated Recommender System for Educational Institute in India
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.163155
Mamata Garanayak1,*, Sipra Sahoo2, Sachi Nandan Mohanty3, Alok Kumar Jagadev4
  • 1: Department of Computer Science, Centurion University of Technology & Management, State Private University Bhubaneswar, Odisha, 752054, India
  • 2: Department of Computer Science & Engineering, Siksha o Anusandhan Deemed to be University, Bhubaneswar Odisha, 751030, India
  • 3: Department of Computer Science & Engineering, ICFAI Foundation for Higher Education, Hyderabad, Telangana, 50120, India
  • 4: School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, 751024, India
*Contact email: mamatagaranayak@gmail.com

Abstract

This study aims to suggest a recommender System for undergraduate students who desire to seek admission into engineering courses in different Indian Institute of Technology (IITs) in India. Initially, the focus is to purpose a recommender system for admission into the top 10 IIT on a pilot basis in four common branches such as Electrical Engineering, Computer Science and Engineering, Mechanical Engineering, Civil Engineering. Data were collected from different authentic sources from 2016 to 2018. A model was built to predict the ranks for 2019 for each branch of every IITs. This paper illustrates prediction using Time Series Forecasting and recommendation algorithm using classification techniques. A comparative study of Random Forest Classification and K-Nearest Neighbor classification has been done. Finally, the recommendation algorithm shown reliable results with high accuracy in prediction model. It can be diversify and implement other streams as part of future work.