Intelligent Career Path Recommendations Leveraging Blockchain and Machine Learning

Bauyrzhan Berlikozha, Azamat Serek, Nurshapagat Shapay, Tamara Kokenovna Zhukabayeva, Muhammad Shoaib Ayub, Daniyar Nurlanov

2025

Closed access Metadata (CSL-JSON)
DOI 10.1109/sist61657.2025.11139304
Data confidence 70 medium
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Abstract

This study explores the development of a personalized career recommendation system for students, leveraging machine learning models to predict career compatibility based on a comprehensive set of student attributes. The data set covers a wide range of variables, including GPA, certifications, hackathons, leadership skills, personality type, programming skills, and extracurricular activities. We employed five machine learning models— XGBoost, Neural Network (MLP), Logistic Regression, Random Forest, and Support Vector Machine (SVM)— to predict students’ preferred career fields. Each model was rigorously tuned with hyperparameters to achieve optimal performance. Our results indicate that the Neural Network (MLP) achieved the highest precision at 92%, followed by XGBoost at 91%, Random Forest at 89%, SVM at 86%, and Logistic Regression at 85%. We evaluated these models using various metrics, including accuracy, precision, recall, and F1 score, to ensure a comprehensive assessment of their performance. To improve transparency and security, we integrated blockchain technology into the recommendation system. This integration allows for secure storage of predicted recommendations along with the underlying reasoning. The immutable nature of blockchain, verified through a blockchain integrity test, ensures data trustworthiness, prevents tampering, and promotes accountability. Our findings highlight the effectiveness of combining machine learning with blockchain technology to create a transparent, reliable, and personalized career recommendation system. This integration not only improves the accuracy of career recommendations, but also ensures the integrity and security of the recommendation process.