LEVERAGING COUNT VECTORIZER FOR JOB TITLE PREDICTION: A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS

Job title prediction from description data is a crucial task in automating job classification and improving digital job search platforms. This study evaluates the performance of advanced machine learning models—K-Nearest Neighbors, Support Vector Machines, Gradient Boosting, and Logistic Regression—for predicting job titles based on descriptive text. Experiments were conducted using two data splits: 70-30 and 80-20 for training and testing. Results reveal that the 70-30 split consistently outperforms the 80-20 configuration in terms of prediction accuracy. Among the evaluated models, Gradient Boosting achieved the highest performance, with an accuracy of 98.05%, utilizing the Count Vectorizer method. Furthermore, Gradient Boosting recorded the highest F1-score of 0.88, along with a recall of 0.81 for the class for 70-30. These findings highlight the superior capability of Gradient Boosting in capturing complex patterns in textual data and emphasize the significance of data pre-processing and splitting strategies. The outcomes contribute to the optimization of machine learning applications in employment platforms, enhancing user experience and efficiency in matching candidates with appropriate job opportunities. This paper focuses on the gap in bringing job seekers and proper opportunities to improve a more transparent, efficient, and trustworthy job marketplace.