Resume Ranking Using Python

The process of evaluating resumes for job applications is a time-consuming
and complex task. To simplify this process, we propose a resume ranker system using
Python. The system uses natural language processing (NLP) techniques to extract and
analyze the contents of resumes. It then assigns a rank to each resume based on its
relevance to the job description.The proposed system uses various NLP techniques such as
tokenization, part-of-speech tagging, and named entity recognition to extract relevant
information from the resumes. We also utilize machine learning algorithms, such as
support vector machines and random forests, to train the model and predict the rank of each
resume.To evaluate the performance of our system, we conduct experiments on a large
dataset of resumes and job descriptions. The results show that the proposed resume ranker
system achieves high accuracy in predicting the rank of the resumes. The system can help
recruiters and hiring managers to quickly identify the most qualified candidates for a job,
reducing the time and effort required for the hiring process.