Recruiters and job seekers often face challenges in matching resumes to job descriptions, resulting in inefficient hiring processes and missed opportunities. This project addresses these issues by developing a machine learning-based system that automates resume-job description matching, benefiting both recruiters and candidates. The system’s objective is to rank resumes according to their relevance to specific job descriptions and recommend suitable job roles for candidates based on their qualifications. It streamlines recruitment by automating the evaluation process, enabling recruiters to quickly identify top candidates, while also allowing job seekers to assess the effectiveness of their resumes.
Using Natural Language Processing (NLP), the system extracts key information from both resumes and job descriptions. It then applies Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and cosine similarity to compute a matching score that ranks resumes. Additionally, machine learning classifiers analyze resumes to recommend job roles that best align with the candidates' skills and experience. The expected outcome is a user-friendly tool that serves both recruiters and job seekers. Recruiters can reduce the time spent manually reviewing resumes, while candidates can evaluate how well their resumes match specific job descriptions and receive suggestions for roles that suit their profiles. This system will improve the accuracy and efficiency of recruitment processes, leading to better hiring decisions and more informed job searches, benefiting both sides of the job market.