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FixProbs Technologies
AI-Based Resume Analyzer
AcademicAI & Machine Learning•2024

AI-Based Resume Analyzer

An AI-powered tool that analyzes resumes against job descriptions, providing match scores, skill gap analysis, and improvement suggestions. Built as a demonstration of practical NLP applications in HR technology.

Project Overview

An AI-powered tool that analyzes resumes against job descriptions, providing match scores, skill gap analysis, and improvement suggestions. Built as a demonstration of practical NLP applications in HR technology.

Problem Statement

Recruiters spend excessive time manually screening resumes, leading to inconsistency, bias, and delayed hiring processes. Small teams lack resources for sophisticated ATS systems.

Proposed Solution

An intelligent resume analyzer that uses NLP to parse resumes, extract skills and experience, and match them against job requirements with explainable scoring.

Key Features

Resume parsing with PDF/DOCX support
Skill extraction and normalization using NLP
Job description analysis and requirement extraction
Match scoring with weighted criteria
Skill gap identification and learning recommendations
Bias detection in job descriptions
Batch processing for high-volume screening
Exportable reports with visual analytics
API for integration with existing ATS
Privacy-first design with local processing option

Technology Stack

PythonFastAPIspaCyscikit-learnTransformers (HuggingFace)ReactNext.jsTypeScriptTailwind CSSPostgreSQLRedisDocker

Project Screenshots

AI-Based Resume Analyzer - Screenshot 1
AI-Based Resume Analyzer - Screenshot 2

Development Process

01

Research

3 weeks
  • Literature review on resume parsing techniques
  • Dataset collection and annotation
  • Model selection and benchmarking
02

Prototyping

4 weeks
  • NLP pipeline development with spaCy/Transformers
  • Matching algorithm design
  • API development with FastAPI
03

UI Development

3 weeks
  • React/Next.js frontend development
  • Results visualization components
  • Batch upload interface
04

Evaluation

2 weeks
  • Accuracy testing on annotated dataset
  • User testing with HR professionals
  • Bias audit and mitigation

Challenges

  • Handling diverse resume formats and layouts
  • Accurate skill extraction from unstructured text
  • Balancing precision and recall in matching
  • Mitigating bias in automated screening

Results & Outcomes

  • 85%+ accuracy in skill extraction on test dataset
  • Reduced screening time by 70% in simulated workflows
  • Open-sourced core NLP pipeline for community use