Predicting Information Technology Career Paths Using Stacking Ensemble Learning Model

Abstract

Selecting a profession route in the fast-changing Information Technology (IT) industry is now an even more complicated issue among students and job applicants, and this has resulted to job dissatisfaction and skill gaps. The conventional counselling practices are often based/ on subjectivity and are not capable of examining the large number of modern tech positions and the individual ability of a person. This paper outlines the creation of an IT Career Prediction System that was developed to deliver recommendations based on personal data, using technical skills, including Database Fundamentals and AI/ML, and psychological traits, including Openness and Conscientiousness. A stacking technique was employed to implement an ensemble machine learning approach in order to achieve high predictive reliability by combining four base classifiers, such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest Classifier (RFC), and Naive Bayes (NB). The developed ensemble learning method combined the computational powers of the four based classifiers used with a performance accuracy of 99.1%, and was effective by applying interpretability and balancing the risk of overfitting when giving practical advice. Hence, the paper concludes that the automated system provides a scalable, objective, and accurate alternative to conventional career guidance, which ends up enhancing more effective career alignment and longterm professional success in the IT industry.