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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)

Abstract

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.

Keywords

Data mining Campus placement prediction Classification KNN Random forest 

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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

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