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A Statistical Approach to Graduate Admissions’ Chance Prediction

  • Navoneel ChakrabartyEmail author
  • Siddhartha Chowdhury
  • Srinibas Rana
Chapter
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Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 103)

Abstract

In the current scenario, grad students often experience difficulty in choosing a proper institution for pursuing masters based on their academic performances. Although there are many consultancy services and Web applications suggesting students, institutions in which they are most likely to get admitted. But, not always the decisions are staunch since there are different kinds of students with different portfolios and performances in their academic careers and institution selection is done on the basis of historical admissions’ data. This study aims to analyze a student’s academic achievements as well as university rating and give the probability of getting admission in that university, as output. The gradient boosting regressor model is deployed, which accomplished a \({R^2}\)-score of 0.84 eventually surpassing the performance of the state-of-the-art model. In addition to \({R^2}\)-score, other performance error metrics like mean absolute error, mean square error, and root mean square error are computed and showcased.

Keywords

Gradient boosting regressor \({R^2}\)-score Mean absolute error Mean square error Root mean square error 

References

  1. 1.
    Acharya MS, Armaan A, Antony AS (2019) A comparison of regression models for prediction of graduate admissions. In: 2019 IEEE International conference on computational intelligence in data science (ICCIDS). IEEEGoogle Scholar
  2. 2.
    Gupta N, Sawhney A, Roth D (2016) Will I get in? modeling the graduate admission process for American universities. In: 2016 IEEE 16th international conference on data mining workshops (ICDMW). IEEEGoogle Scholar
  3. 3.
  4. 4.
  5. 5.
    Roa, Annam Mallikharjuna, et al. “College Admission Predictor.” Journal of Network Communications and Emerging Technologies (JNCET) www.jncet.org 8.4 (2018)
  6. 6.

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Navoneel Chakrabarty
    • 1
    Email author
  • Siddhartha Chowdhury
    • 1
  • Srinibas Rana
    • 1
  1. 1.Jalpaiguri Government Engineering CollegeJalpaiguriIndia

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