AI Resume Analyzer
An NLP system that scores a resume against a job description, extracts skills and returns specific, actionable improvements.
Project overview
The AI Resume Analyzer takes a candidate resume as a PDF and a target job description as text, then reports how well the two match. It extracts skills, education and experience from unstructured text, compares them against the requirements in the job description, and produces a score with a list of missing or weakly evidenced skills.
It is a good final year project because the natural language processing is substantial but tractable, the interface is simple to demonstrate, and the output is immediately understandable to a panel that has never seen the project before.
Problem statement
Applicant tracking systems reject a large share of resumes before a human reads them, and students have no way to see why. Generic advice does not help, because the gap is usually between one specific resume and one specific job description.
Proposed solution
Parse the resume into structured sections, extract a skill set using a curated skill dictionary combined with named entity recognition, embed both documents, and compute a similarity score. Present the score alongside matched skills, missing skills and section-level suggestions so the feedback is concrete rather than generic.
Key features
- PDF and DOCX resume upload with text extraction
- Section detection for education, experience, skills and projects
- Skill extraction against a maintained technical skill dictionary
- Semantic similarity scoring between resume and job description
- Missing-skill report with prioritisation by frequency in the job description
- Readability and formatting checks such as length, bullet density and contact completeness
- History of past analyses per user account
Technology stack
NLP
Parsing
Backend
Front end
System architecture
The flow below is what goes into your architecture diagram and the corresponding chapter of the report.
- Upload layer accepts the resume file and stores it temporarily
- Extraction layer converts the document to plain text and splits it into sections
- NLP layer runs tokenisation, entity recognition and skill matching
- Scoring layer embeds resume and job description and computes cosine similarity
- Recommendation layer diffs required skills against found skills and ranks the gaps
- Presentation layer renders the score, the matched and missing skills, and the suggestions
Expected output
- A match percentage with a breakdown by section
- A table of matched skills and missing skills
- Written suggestions tied to specific resume sections
- Downloadable analysis summary
Suitable branches, difficulty and duration
Documentation, review, demo and viva support
Documentation support
Abstract, problem statement, literature survey guidance, system architecture, ER and UML diagrams, module descriptions, test cases and screenshots — prepared in your department format.
Project review support
Delivery is split across your review calendar so review one has a design and plan, review two has working modules, and the final review has the integrated, tested system.
Demo support
A rehearsed demo path, seeded sample data, a reset script, and a recorded walkthrough kept as a backup in case something fails in the review room.
Viva preparation
A written question bank for this domain, a code walkthrough session, and practice at explaining each module in your own words — because the examiner will ask you.
Customisation options
Colleges reject duplicate topics, so most projects are adjusted before we start. These are the usual directions for this one.
- Restrict the skill dictionary to one domain such as data science or embedded roles
- Add a role recommendation module that suggests job titles from the resume alone
- Add recruiter-side bulk upload that ranks multiple resumes for one opening
- Swap the embedding model for a larger transformer if compute allows
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Enquire about the AI Resume Analyzer project
Send your branch, college and first review date. You will get confirmation of scope, what the customisation would look like, and a delivery plan mapped to your reviews.