Predicting AI job market dynamics: a data mining approach to machine learning career trends on glassdoor

Abstract In today’s highly competitive job market, many qualified candidates face significant difficulties in securing positions that align with their skills and career aspirations. This challenge is further compounded by the dynamic nature of the global economy, which continually reshapes the demand for specific job roles and skill sets. Simultaneously, employers encounter obstacles in efficiently identifying and recruiting applicants who possess the most relevant competencies for their organizational needs. To address these dual challenges, this study introduces a predictive system based on an ensemble learning model, which demonstrates superior performance over traditional machine learning algorithms in forecasting both job titles and salary ranges. The proposed model leverages key job-related features, including company size, ratings, income levels, and skill requirements, to provide accurate predictions. Utilizing a curated dataset comprising 956 software job postings, the study conducts a comprehensive analysis to uncover patterns and insights that inform the prediction process. Beyond prediction, the system is designed to offer personalized career guidance to job seekers by analyzing their individual profiles, technical skills, and professional preferences. By integrating data-driven analytics with user-centric recommendations, the platform aims to bridge the gap between job seekers and employers, enhance employment outcomes, and support more strategic decision-making for both parties in the hiring process.