Career Mapping and Enhancing Personalized Education through Machine Learning-Based Recommendation Systems

DOI

10.2991/978-94-6463-754-0_56How to use a DOI?

Keywords

Machine learning; Recommendation system; SMOTE; Cat-Boost; Classification; Bayesian Optimization

Abstract

A machine learning-based predictive model was developed to deliver personalized career recommendations based on student data, including academic performance and personal attributes. The dataset includes information such as gender, absenteeism, extracurricular activities, part-time work status, study habits, and subject scores. Machine learning techniques such as Logistic Regression, Support Vector Classifier, Random Forest, K-Nearest Neighbors, CatBoost, LightGBM, and XGBoost — were trained and tested to determine the most effective approach for career prediction. To improve model performance, Bayesian optimization was used to optimize algorithm parameters. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) was utilized to ensure a balanced representation of career aspirations. The system accepts user inputs, processes them through the trained model, and returns the top five career recommendations with associated probability. Feature scaling was implemented to normalize input data, further improving prediction accuracy. This study evaluates the use of machine learning in career guidance to assist students in making informed decisions. Future additions could incorporate dynamic features and adapt the model to evolving educational landscapes, further improving its usefulness in career counseling.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - R. Angeline
AU  - R. Charanya
AU  - Konduru Sri Abhinaya
AU  - Lingutla Shasank Chowdary
PY  - 2025
DA  - 2025/06/30
TI  - Career Mapping and Enhancing Personalized Education through Machine Learning-Based Recommendation Systems
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 638
EP  - 648
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_56
DO  - 10.2991/978-94-6463-754-0_56
ID  - Angeline2025
ER  -