Abstract
Employability is critical in determining a country's growth. Predicting the employability rate will provide a framework for the government and economists to plan for the future, as well as education management to understand changing trends. As a result, in recent years, models to predict job admission have been developed. However, in order to increase employability, deficiencies in the educational system must be addressed. The goal of this project is to find the best-fit classifier model to assess educational data, which can then be used to understand the importance of academic parameters in a person getting a job during on-campus recruitment. This is accomplished by comparing four different types of classifier algorithms (ANN (Artificial Neural Network), AdaBoost Classifier, Random Forest, and Logistic Regression) for the created dataset.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Olinsky A, Kennedy K, Kennedy BB (2012) Assessing gradient boosting in the reduction of misclassification error in the prediction of success for actuarial majors. Case Stud Bus Ind Govern Stat (2012)
Dreiseitl S, Ohno-Machado L (2002) Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform
Dias N, Cooray TMJA, Rajapakse W (2018) Employee satisfaction of academics in Sri Lanka: a logistic regression approach. Global J Comput Sci Technol
Amjad S, Younas M, Anwar M, Shaheen Q, Shiraz M, Gani A (2022) Data mining techniques to analyze the impact of social media on academic performance of high school students. Wirel Commun Mob Comput
Wang R (2012) AdaBoost for feature selection, classification and its relation with SVM, a review. Phys Procedia
Imdad U, Ahmad W, Asif M, Ishtiaq A (2017) Classification of students results using KNN and ANN. In: 13th international conference on emerging technologies (ICET), IEEE
Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn
Alamri LH, Almuslim RS, Alotibi MS, Alkadi DK, Ullah Khan I, Aslam N (2020) Predicting student academic performance using support vector machine and random forest. In: 3rd international conference on education technology management
Petkovic D, Altman R, Wong M, Vigil A (2018) Improving the explainability of Random Forest classifier–user centered approach. In: Pacific symposium on biocomputing 2018, pp 204–215
Tsangaratos P, Ilia I (2016) Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of model’s complexity and training dataset size. CATENA 145:164–179
Stoltzfus JC (2011) Logistic regression: a brief primer. Acad Emerg Med
Wyner AJ, Olson M, Bleich J, Mease D (2017) Explaining the success of adaboost and random forests as interpolating classifiers. J Mach Learn Res 18(1):1558–1590
Liu Y, Wang Y, Zhang J (2012) New machine learning algorithm: Random Forest. In: International conference on information computing and applications. Springer, Berlin, Heidelberg, pp 246–252
Maalouf M (2011) Logistic regression in data analysis: an overview. Int J Data Anal Tech Strat 3(3):281–299
Saritas MM, Yasar A (2019) Performance analysis of ANN and Naive Bayes classification algorithm for data classification. Int J Intell Syst Appl Eng 7(2):88–91
Ying C, Qi-Guang M, Jia-Chen L, Lin G (2013) Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica 39(6):745–758
An TK, Kim MH (2010) A new diverse AdaBoost classifier. In: 2010 International conference on artificial intelligence and computational intelligence, vol 1. IEEE, pp 359–363
Collins M, Schapire RE, Singer Y (2002) Logistic regression, AdaBoost and Bregman distances. Mach Learn 48(1):253–285
Rahman HAA, Wah YB, He H, Bulgiba A (2015) Comparisons of AdaBoost, KNN, SVM and logistic regression in classification of imbalanced dataset. In: International conference on soft computing in data science, pp 54–64. Springer, Singapore
Liu M (2010) Fingerprint classification based on AdaBoost learning from singularity features. Pattern Recogn 43(3):1062–1070
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bala Dhanalakshmi, S., Rajakumar, R., Shankar, S., Sowndharya Rani, R., Deepa, N., Subha Priyadharshini, C. (2023). An Analytical Assessment of Machine Learning Algorithms for Predicting Campus Placements. In: Shakya, S., Papakostas, G., Kamel, K.A. (eds) Mobile Computing and Sustainable Informatics. Lecture Notes on Data Engineering and Communications Technologies, vol 166. Springer, Singapore. https://doi.org/10.1007/978-981-99-0835-6_16
Download citation
DOI: https://doi.org/10.1007/978-981-99-0835-6_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0834-9
Online ISBN: 978-981-99-0835-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)