Abstract:
The dynamic nature of the job market presents significant challenges for students, recent graduates, and job seekers in making informed career decisions. Despite the abundance of employment platforms, there remains a notable gap in accessing comprehensive, real- time job market trend analysis combined with direct application capabilities. This project introduces CareerLens, a web-based platform designed to bridge this gap by providing data- driven job market insights alongside streamlined job application functionalities. The system leverages web scraping technologies to extract and analyze real-time job posting data from LinkedIn, presenting users with visualization of emerging industry trends, in-demand skills, and salary insights. CareerLens features a dual-component architecture comprising a Python backend utilizing BeautifulSoup and Selenium for automated data extraction, and a React frontend with Node.js backend for user interface and data management. The platform offers personalized job filtering capabilities, bookmarking functionality, and trend analysis visualizations that assist users in making strategic career decisions. By utilizing MongoDB for data persistence and Firebase for real-time user interaction, CareerLens delivers a comprehensive solution for three primary target audiences: students seeking elective guidance, individuals exploring short-term skill development opportunities, and active job seekers. This project addresses the critical need for an integrated platform that not only facilitates job discovery but also provides contextual market intelligence to inform career pathway decisions in an increasingly competitive employment landscape
