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Machine Learning · Major Project

AI Resume Screening System

NLP pipeline that ranks candidates against a job description

3-4 weeks build

Overview

An end-to-end recruitment assistant that parses resumes, extracts structured entities, and scores each candidate against a job description using semantic similarity. Includes a recruiter dashboard, a bias-audit report and an explainability panel that shows exactly why a candidate ranked where they did.

What makes it stand up

  • 01Resume parsing for PDF and DOCX with layout-aware extraction
  • 02Custom named-entity model for skills, degrees and experience
  • 03TF-IDF baseline plus sentence-transformer semantic scoring
  • 04Explainability panel showing per-skill match contribution
  • 05Bias audit notebook comparing outcomes across cohorts

Module breakdown

  1. 01

    Ingestion

    Bulk upload, OCR fallback and document normalisation.

  2. 02

    Entity extraction

    Trained spaCy pipeline with a custom skills gazetteer.

  3. 03

    Scoring engine

    Hybrid lexical and embedding similarity with tunable weights.

  4. 04

    Recruiter dashboard

    Ranked shortlist, filters, notes and CSV export.

  5. 05

    Evaluation

    Precision@k, nDCG and fairness metrics with plots.

After this, you will be able to

  • Explain how transformer embeddings beat keyword matching
  • Defend your evaluation methodology in a viva
  • Show a working demo on live resume uploads

Technology stack

  • Python
  • spaCy
  • scikit-learn
  • FastAPI
  • React
  • PostgreSQL

Complete kit

₹4,499₹7,999
-44%

Delivered in under 48 hours

Level
Major Project
Domain
Machine Learning
Build time
3-4 weeks
Difficulty
Rating
4.9 / 5 · 214

You receive

  • Complete, commented source code
  • 60-90 page project report (IEEE format)
  • Editable presentation deck
  • Architecture, ER and UML diagrams
  • Local setup and deployment guide
  • Sample dataset or seed data
  • Viva question bank with answers
  • One 45-minute walkthrough call
  • 30 days of doubt-clearing support
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