Streamlining Talent Acquisition: A Machine Learning Approach to Automated Resume Screening

The paper presents an Automated Resume Screening System that leverages machine learning and natural language processing (NLP) to enhance the efficiency and objectivity of the recruitment process. Traditional resume screening methods are often labor-intensive and prone to bias, leading to suboptimal hiring decisions. This system addresses these challenges by automating the extraction and evaluation of candidate information, such as skills, experience, and education, thereby improving accuracy and reducing processing time. By utilizing data-driven criteria, the system minimizes human bias and ensures a fair assessment of applicants. Additionally, it incorporates robust security measures to protect sensitive candidate data, aligning with privacy regulations. The findings demonstrate significant improvements in recruitment efficiency and candidate matching accuracy, highlighting the system's potential to transform hiring practices in various industries.