AI-POWERED JOB RECOMMENDATION AND SKILL GAP ANALYZER USING NLP AND MACHINE LEARNING

In today’s competitive job market, candidates often apply for positions without fully un-
derstanding whether their qualifications and skills align with employer expectations. This mismatch

can lead to repeated rejections and uncertainty regarding career direction. To address this challenge,
the AI-Powered Job Recommendation and Skill Gap Analyzer is a web-based system designed to
provide personalized career guidance by leveraging Artificial Intelligence (AI) and Natural Language
Processing (NLP) techniques. The system allows users to upload resumes in PDF or DOCX formats,

extracting textual information using libraries such as PyPDF2 and python-docx. NLP techniques, im-
plemented through SpaCy, identify technical skills, educational qualifications, work experience, and

relevant keywords. Real-time job listings are fetched from job portals via APIs such as Adzuna, and
a matching algorithm compares the user’s skills with job descriptions to calculate similarity scores,
recommending the most suitable job roles. In addition, the system performs a skill gap analysis by
identifying missing or underdeveloped skills required for specific positions. Users receive actionable

suggestions to enhance their qualifications and improve employment prospects. The platform is de-
veloped using Python and Flask for backend processing, with HTML and CSS for a responsive and

user-friendly interface, allowing candidates to upload resumes and receive structured feedback effi-
ciently. By combining AI-driven job matching and skill gap analysis, the system reduces uncertainty

in career planning, streamlines the job search process, and empowers users with data-driven insights.
This project demonstrates how intelligent systems can support smarter career development strategies
and enhance employment outcomes in the modern workforce.