Analyzing job roles and skill descriptions using NLP techniques (part 1: data cleaning and pre-processing and part 4: front-end visualization/user interface) - collaboration with ARISE and SSG

• Main Author: Tan, Kenneth Meng Hwee
• Other Authors: S Supraja
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2024
• Subjects: Engineering
• Online Access: https://hdl.handle.net/10356/176337
• Institution: Nanyang Technological University

The primary goal of this project is to devise an innovative approach to assist researchers at Ageing Research Institute for Society and Education (ARISE) with their research to help job seekers in recognizing and addressing skill gaps for their desired roles, while concurrently recommending similar job positions. The project is a collaboration between ARISE and SkillsFuture Singapore (SSG), who will be providing the necessary datasets. By utilizing Python programming and Natural Language Processing (NLP) techniques, the project unfolds in four distinct phases. The initial phase emphasizes data cleaning and pre-processing to fine-tune job descriptions and associated skill sets. The subsequent phase employs NLP-driven Machine Learning methodologies to align job seekers with suitable roles and identify any potential skill gap. In the third phase, an NLP-based statistical model will be deployed to identify similar job roles in the prevailing economic environment. Concluding the project, a user-centric dashboard will be introduced, granting researchers the ability to dynamically navigate through various job roles, requisite skills, and potential upskilling opportunities. Through this project we aim to improve the job-seeking experience by enhancing career path clarity for individuals in the current dynamic job market.