AI-Driven Resume Ranking Ensuring Fair and Efficient Hiring Decision.

ABSTRACT AI-driven Resume Ranking helps companies deal with the huge number of resumes they receive. The usual way of going through resumes takes a lot of time and can often be unfair. This project focuses on building a machine learning model that can automatically read, sort, and rank resumes using natural language processing (NLP). It pulls out important details like education, work experience, skills, and qualifications using techniques like Named Entity Recognition (NER) and TF-IDF Term Frequency-Inverse Document Frequency. By doing this, the system makes hiring faster, reduces bias, cuts down on manual work, and improves how well candidates match job openings. In the end, each resume is scored and ranked based on how well the candidate fits the role. Keywords: Named Entity Recognition (NER), Natural Language Processing (NLP),Term Frequency-Inverse Document Frequency, Machine Learning(ML).