| Abstrakt: |
In today’s competitive job market, effective candidate evaluation is paramount for an organization to identify the most suitable candidate. This research study examines automated resume parsing and evaluation, offering a thorough methodology to speed up the hiring process. The research focuses on the automatic extraction of essential resume components, such as contact details (phone numbers, Gmail IDs), professional networking accounts (LinkedIn, GitHub), academic standing (CGPA), and abilities. Carefully extract and organize these crucial data points using a combination of regular expressions, natural language processing libraries (Spacy, NLTK), and PDF parsing tools (PDF Miner). Used mathematical methods for resume scoring to evaluate candidate fitness and to facilitate effective candidate ranking. In an era of digital transformation and fierce competition, this research provides practical, scalable ways to automate and enhance candidate evaluation, empowering recruiters to make better hiring decisions. This paper is an important step towards modernizing recruitment processes, improving candidate assessment, and fostering superior talent acquisition in an ever-changing job market. [ABSTRACT FROM AUTHOR] |
