Smart Resume Analyzer & Job Match Recommender: A Machine Learning and NLP-Based Approach for Automated Resume Screening

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Recruiters today are buried under resumes the moment a job opening goes live, and sorting through them by hand is slow, tiring, and rarely consistent from one reviewer to the next. Qualified applicants sometimes get missed simply because no one had time to read carefully, while candidates on the other end are left guessing why they were turned down. We built a Smart Resume Analyzer & Job Match Recommender to take some of that guesswork out of the process. Resumes uploaded as PDF or DOCX are read using PyMuPDF and python-docx, cleaned up, and compared against a job description supplied by the user. The comparison itself is done through TF-IDF vectorization over unigrams and bigrams, feeding into a Logistic Regression model that was trained to separate "good" resume-to-job fits from "poor" ones and to report how confident it is in that call. On top of the match prediction, the tool checks the resume's text against a list of common technical skills, flags whatever the job asks for that the resume doesn't mention, and turns that gap into a short list of things worth learning. Everything runs through a Streamlit interface, so a recruiter or a job seeker can use it without touching any code. In testing against a labelled set of resume-job pairs, the pipeline held up well: it stayed fast, its reasoning was easy to trace back to actual keywords, and it gave applicants something concrete to work on instead of a plain rejection.