Published Paper Details:

The growing number of job listings and the wider range of skill requirements have made it difficult for job seekers to find opportunities that match their qualifications. This project introduces an AI-driven Job Recommendation System aimed at simplifying the process of connecting candidates with suitable jobs. The system uses Natural Language Processing (NLP) and Machine Learning (ML) techniques to effectively analyze resumes and job descriptions. Resumes are processed with PyResparser, which extracts key details such as skills, experience, and education. At the same time, job data are prepared and transformed using the TF-IDF (Term Frequency-Inverse Document Frequency) method, allowing the system to assess how similar candidate profiles are to job requirements. The Nearest Neighbors algorithm is then used to find the most relevant job suggestions. A Flask-based web interface enables users to upload their resumes and receive personalized job recommendations in real time. The system also includes automated data scraping to keep the job database current, ensuring that recommendations are relevant and accurate. This approach removes the need for manual browsing through many job portals and offers a more efficient, user-friendly solution. Experimental results show that the system greatly improves the speed and accuracy of job matching, making it a useful tool for job seekers and recruitment platforms alike.