Efficient Resume Screening Tool

In the evolving landscape of recruitment, traditional manual resume screening methods are increasingly inadequate due to the sheer volume and diversity of applications. This paper introduces an automated resume screening tool that leverages Machine Learning (ML) and Natural Language Processing (NLP) to enhance the efficiency and accuracy of candidate evaluation. The system employs NLP techniques such as named entity recognition and part-of-speech tagging to extract pertinent information from resumes. Subsequently, ML classifiers, including K-Nearest Neighbors and Support Vector Machines, are utilized to assess and rank candidates based on their alignment with job descriptions. By automating the extraction and interpretation of resume data, the proposed tool significantly reduces the time and effort required in the initial stages of recruitment, while also mitigating human biases inherent in manual screening processes. Experimental results demonstrate the tool’s effectiveness in accurately identifying suitable candidates, thereby streamlining the hiring process and contributing to more objective and data-driven recruitment decisions.