Smart Job-Seeking Assistant and Web Scraping
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Abstract
Finding suitable job opportunities is often challenging and time-consuming for candidates due to limited search capabilities and unstructured information available across job portals. Most existing platforms provide only basic keyword matching and fail to align job recommendations with an individual’s real skills or resume content. To overcome this limitation, this work proposes a Smart Job-Seeking Assistant that integrates Web Scraping, Natural Language Processing (NLP), and intelligent recommendation techniques to automate and personalize the job search process. The system’s backend is developed using FastAPI (Python) and utilizes web scraping tools such as BeautifulSoup, Selenium, and Apify to fetch up-to-date job listings from platforms including Naukri and LinkedIn. A resume parser powered by NLP extracts key information such as skills, education, and experience from uploaded resumes. The extracted profile data is then matched with scraped job postings to compute relevance scores and generate suitable job recommendations. In addition, the system identifies missing or weak skills by performing skill-gap analysis and suggests appropriate online courses from platforms like Coursera, Udemy, and freeCodeCamp. The frontend, developed using ReactJS, provides an interactive dashboard that displays resume insights, job matches, readiness scores, and personalized learning suggestions. Overall, the proposed system offers an end-to-end, intelligent job recommendation and career guidance solution that enables users to discover relevant opportunities, understand their skill gaps, and improve their employability through data-driven insights.
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