AI Enhanced Skill Matcher

In today's competitive job market, matching candidates to job descriptions efficiently and accurately is crucial for recruiters and HR professionals.This research introduces a web application, termed the Resume Match Predictor, designed to predict the percentage match between a candidate's resume and a given job description.Leveraging natural language processing (NLP) techniques and machine learning algorithms, the application aims to streamline the candidate screening process and enhance decision-making.This paper begins by outlining the challenges faced by recruiters in manually assessing resumes against job descriptions, including time constraints, subjectivity, and inconsistency.It then presents the architecture and key functionalities of the Resume Match Predictor, detailing the process of text preprocessing, feature extraction, and model training for predicting the match percentage.Furthermore, the paper discusses the NLP techniques employed in the application, such as word2Vec, and text classification algorithms.Special attention is given to the integration of pre-trained language models, Ethical considerations surrounding algorithmic decision-making in recruitment are addressed, including bias mitigation, fairness, and transparency.Resume Match Predictor across various industries and job roles.Results demonstrate its ability to significantly reduce the time and effort required for candidate screening while maintaining accuracy and fairness.conclusion, the Resume Match Predictor emerges as a valuable tool for modernizing the recruitment process, offering tangible benefits in terms of efficiency, objectivity, and candidate quality.