Smart AI Resume Analyzer

Abstract

The objective of resume screening is to identify the most suitable candidates for a job role while also providing applicants with a score for their resumes along with suggestions for improvement. A review of existing methods indicates that conventional approaches, such as manual resume screening, often lead to biased assumptions and the possible loss of valuable human talent. Additionally, these traditional methods lack efficiency, accuracy, and scalability in handling large volumes of applications. To obtain more reliable results, modern systems employ machine learning and natural language processing (NLP) techniques to automatically analyze, match, and rank candidates’ resumes in real time. In such a system, the input consists of applicants’ resumes, while the output includes a ranked list of candidate resumes for administrators and improvement suggestions for users. Real-time results are achieved by applying NLP methods for text processing and information extraction. In the proposed framework, techniques such as Cosine Similarity, TF-IDF, and other NLP-based matching approaches are utilized for effective string comparison and ranking. The system offers several advantages, including enhanced security, interpretability, high accuracy, lightweight implementation, and fast processing speed. This solution can be applied in multinational corporations, government institutions, and administrative organizations where a large number of resumes must be evaluated daily for multiple job openings. Experimental results indicate that the system achieves a text parsing accuracy of approximately 85% and a ranking accuracy of around 92%.