ONE CLICK CAREER ASSISTANT: AN INTEGRATED RESUME-CENTRIC FRAMEWORK FOR CAREER GUIDANCE, JOB MATCHING AND ASSISTED APPLICATION

The increasing use of Applicant Tracking Systems (ATS) in recruitment has created challenges for students and fresh graduates in presenting their qualifications effectively and identifying suitable career opportunities. Existing solutions address resume parsing, ATS scoring, job recommendation, skill-gap analysis, and application automation as separate tasks, resulting in a fragmented job-search process. This paper presents One Click Career Assistant, an integrated web-based platform that combines resume analysis, career guidance, job recommendations, and application assistance within a unified workflow. The system extracts text from PDF, DOCX, and text files using PyPDF2 and python-docx and applies Natural Language Processing (NLP) techniques, including text preprocessing, keyword identification, and skill extraction, to convert unstructured resume content into a structured candidate profile. A transparent, rule-based ATS-oriented scoring mechanism evaluates keyword relevance, skills, experience, education, formatting, and resume completeness. The extracted profile further supports personalized career-path suggestions, skill-gap identification, job recommendations, and project and certification guidance by comparing candidate competencies with relevant job requirements. Additionally, a Selenium-based automation module assists with supported application-form fields and resume uploads while retaining user control over final submission. Unlike fragmented approaches, the proposed system reuses a single resume-derived profile throughout the candidate workflow, reducing repetitive data entry and improving the consistency of career-related recommendations. The paper presents the system methodology, architecture, implementation, illustrative scoring calculations, and evaluation scenarios. As no labeled benchmark dataset or controlled user study is currently available, the reported calculations are illustrative rather than evidence of measured performance. The proposed platform provides an interpretable and extensible approach to integrating NLP-assisted resume analysis with early-career guidance and responsible application assistance.