FutureFit: Leveraging LinkedIn Data to Visualize In-Demand IT Skills and Generate Personalized Career Paths in Sri Lanka

Abstract

The Sri Lankan IT job market is rapidly evolving, leading to a significant skill mismatch where job seekers often lack the competencies employers require, hindering employability and career growth. This is further compounded by the absence of clear guidance on in-demand skills and an education system that fails to align with industry needs, resulting in a considerable number of underprepared IT graduates. To tackle this issue, the author developed a data-driven system that collects and analyzes real time job postings to identify in-demand IT skills for specific roles. The system includes a job recommendation model that suggests career paths based on individual skills, with tailored learning paths to bridge skill gaps. It employs an advanced algorithm to scrape job descriptions and extract key skills, then a visualization of the most sought-after skills and their relation ship to relevant online courses. This allows job seekers to access personalized learning paths aligned with the current demands of the job market. This study presents a job recommendation module leveraging Collaborative Filtering enhanced with Singular Value Decomposition (SVD) to predict optimal job roles based on in-demand skills and skill-frequency analysis. The model demonstrates high predictive accuracy, achiev ing a MeanAbsoluteError (MAE)of0.1521andRootMeanSquaredError(RMSE)of0.3479, indicating robust performance. The findings show the system’s effectiveness in providing clear insights for career growth and aligning education with industry needs.