RESUME ANALYSIS USING ML FOR PERSONALIZED JOB RECOMMENDATIONS

The Resume Analysis Using ML For Personalized Job Recommendations presents an innovative and efficient approach to modern recruitment by leveraging Artificial Intelligence and Machine Learning techniques. In today’s fast-paced job market, manual resume screening is no longer practical due to the large volume of applications and the need for quick decision-making. This system effectively automates the process of analyzing resumes, extracting key information, and matching candidate profiles with suitable job roles. By using Natural Language Processing, the system converts unstructured resume data into structured and meaningful information, enabling accurate analysis. Techniques such as TF-IDF vectorization and cosine similarity help in comparing resumes with job descriptions and determining relevance scores. The integration of a supervised machine learning algorithm like Random Forest further enhances the accuracy and reliability of job recommendations. The system not only assists recruiters in selecting the best candidates but also helps job seekers identify opportunities that match their skills and qualifications. Features such as personalized job recommendations and skill gap analysis provide added value to users by guiding them toward career improvement. Automated notification functionality ensures timely updates about job openings, improving user engagement. The system reduces human bias and ensures fair and consistent evaluation of candidates. It is scalable and capable of handling large datasets efficiently, making it suitable for real-world applications. The user-friendly interface enhances accessibility and usability for both recruiters and candidates. Moreover, the system can be integrated with job portals, recruitment platforms, and campus placement systems. Continuous updates and maintenance ensure that the system remains relevant to changing industry demands. Overall, the project demonstrates how intelligent technologies can transform traditional recruitment processes into efficient and data-driven systems. It improves hiring efficiency, reduces workload, and enhances the overall experience for users. Thus, the proposed system stands as a powerful, reliable, and future-ready solution for modern recruitment challenges.

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