PLACEMENT AND RESUME MATCHER

This paper presents a Placement and Resume Matcher system developed to automate resume screening and improve candidate-job matching for campus placement processes. Traditional recruitment systems often depend on manual resume evaluation or simple keyword-based filtering, which can be inefficient, time-consuming, and less accurate in identifying relevant candidate skills. The proposed system applies Natural Language Processing (NLP) techniques to extract meaningful information from unstructured resume documents. A custom Named Entity Recognition (NER) model built using spaCy is used to identify and classify skill-related entities from resumes. An Applicant Tracking System (ATS)-based scoring mechanism compares extracted candidate skills with job requirements to calculate an effective match score. The system is integrated with a Streamlit-based user interface, enabling users to upload resumes and receive real-time analysis and matching results. This solution enhances recruitment efficiency, reduces manual workload, and provides a scalable and intelligent approach for modern placement management systems.