NLP-Based resume screening and resume generation system

Resume screening is an important part of recruitment, but in many organizations, it is still done manually. This makes the process slow, repetitive, and sometimes unfair because suitable candidates may be missed if their resumes do not contain the exact expected keywords. At the same time, many job seekers find it difficult to create a professional resume in a proper format.

To address these issues, this project proposes an NLP-based Resume Screening System with an integrated Resume Generation module. The system uses Natural Language Processing techniques to extract useful information such as skills, qualifications, and experience from resumes and job descriptions. TF-IDF and semantic similarity methods are then used to calculate a matching score that shows how closely a resume fits a particular job role. The system also identifies missing skills and common resume issues, and it provides feedback for improvement. In addition, the resume generation feature allows users to create a professional resume from keywords and basic details. The proposed system reduces manual effort, improves accuracy, and makes the recruitment process more efficient for both recruiters and job seekers.