Article’s

The goal of resume screening is to identify the top applicants for a position and to inform users of their resume score and areas for improvement. The literature on existing approaches has been analysed, and it has been discovered that the traditional systems like manual screening may result in false assumptions and the wasting of human potential, but they lack robustness in terms of processing, accuracy and efficiency. To acquire accurate results, software must use machine learning and natural language processing techniques to match and rate the candidates in real-time by ranking their resumes. The input would be the applicant’s resumes and output would be a ranked candidate’s resumes and output would be a ranked candidate’s resume list on the admin side and suggestions on the user side. Instantaneous real-time output results are acquired by employing natural language processing techniques. In the proposed system authors used Cosine similarity, TF-IDF and Mong Techniques of NLP for string matching. According to experimental finding, this system has a text parsing accuracy of 85% and a ranking accuracy of 92%.