Analyzing Student Performance in Engineering Placement Using Data Mining

  • Krishnanshu AgarwalEmail author
  • Ekansh Maheshwari
  • Chandrima Roy
  • Manjusha Pandey
  • Siddharth Swarup Rautray
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 28)


Data mining is the practice of mining valuable information from huge data sets. Data mining allows the users to have perceptions of the data and make convenient decisions out of the information extracted from databases. The purpose of the engineering colleges is to offer greater chances to its students. Education data mining (EDM) is a process for analyzing the student’s performance based on numerous constraints to predict and evaluate whether a student will be placed or not in the campus placement. The idea of predicting the performance of higher education students can help various institutions in improving the quality of education, identifying the pupil’s risk, upgrading the overall accomplishments, and thereby refining the education resource management for better placement opportunities for students. This research proposes a placement prediction model which predicts the chance of an undergrad student getting a job in the placement drive. This self-analysis will assist in identifying the patterns, where a comparative study between two individual methods has been made in order to predict the student’s success and a database has been generated.


Data mining Campus placement prediction Classification KNN Random forest 


  1. 1.
    Parekh S et al (2016) Results and placement analysis and prediction using data mining and dashboard. Int J Comput Appl 137(13)CrossRefGoogle Scholar
  2. 2.
    Patel T, Tamrakar A (2017) A data mining techniques for campus placement prediction in higher education. Indian J Sci Res 14(2):467–471Google Scholar
  3. 3.
    Roy C, Rautaray SS, Pandey M (2018) Big data optimization techniques: a survey. Int J Inf Eng Electr Bus 10(4):41Google Scholar
  4. 4.
    Roy C, Pandey M, Rautaray SS (2018) A proposal for optimization of horizontal scaling in big data environment. In: Kolhe M, Trivedi M, Tiwari S, Singh V (eds) Advances in data and information sciences. Lecture Notes in Networks and Systems, vol 38. Springer, SingaporeCrossRefGoogle Scholar
  5. 5.
    Adhatrao K (2013) et al Predicting students’ performance using ID3 and C4. 5 classification algorithms. arXiv preprint arXiv:1310.2071
  6. 6.
    Ramesh V, Parkavi P, Ramar K (2013) Predicting student performance: a statistical and data mining approach. Int J Comput Appl 63(8):35–39Google Scholar
  7. 7.
    Rathore RK, Jayanthi J (2017) Student prediction system for placement training using fuzzy inference system. ICTACT J Soft Comput 7(3):1443–1446CrossRefGoogle Scholar
  8. 8.
    Giri A et al A placement prediction system using k-nearest neighbors classifier. In: 2016 second international conference on cognitive computing and information processing (CCIP), IEEE, 2016Google Scholar
  9. 9.
    Sharma AS et al (2014) PPS—placement prediction system using logistic regression. MOOC. In: 2014 IEEE international conference on innovation and technology in education (MITE), IEEE, 2014Google Scholar
  10. 10.
    Sreenivasa Rao K, Swapna N, Praveen Kumar P (2018) Educational data mining for student placement prediction using machine learning algorithms. Int J Eng Technol 7(1.2):43–46. [Online]. Web 13 Aug 2018CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Krishnanshu Agarwal
    • 1
    Email author
  • Ekansh Maheshwari
    • 1
  • Chandrima Roy
    • 1
  • Manjusha Pandey
    • 1
  • Siddharth Swarup Rautray
    • 1
  1. 1.School of Computer EngineeringKalinga Institute of Industrial Technology (KIIT) Deemed to be UniversityBhubaneswarIndia

Personalised recommendations