ATS Tools: AI-Powered Resume Analysis System

May 2024 – August 2024

NLP Machine Learning Python spaCy Flask

Overview

ATS Tools is an intelligent Applicant Tracking System that leverages AI and Natural Language Processing to automate resume screening, candidate ranking, and job-resume matching. The system helps HR teams streamline recruitment processes and identify the best candidates efficiently.

Key Features

  • Resume Parsing: Automatic extraction of candidate information including education, experience, skills, and contact details from various resume formats (PDF, DOCX).
  • Job-Resume Matching: Intelligent matching algorithm that compares job descriptions with candidate profiles using semantic similarity.
  • Candidate Ranking: ML-powered ranking system that scores candidates based on relevance to job requirements.
  • Skill Extraction: Automated identification and categorization of technical and soft skills from resumes.
  • ATS Score Generation: Provides ATS compatibility scores and suggestions for resume optimization.
  • Keyword Analysis: Identifies missing keywords and suggests improvements to increase ATS compatibility.
  • Batch Processing: Ability to process and analyze multiple resumes simultaneously.

Technical Implementation

  • Document Processing:
    • Implemented PDF and DOCX parsing using PyPDF2 and python-docx
    • Built robust text extraction pipeline with OCR support for scanned documents
    • Developed regex-based and ML-based named entity recognition for information extraction
  • NLP Pipeline:
    • Utilized spaCy for entity recognition, POS tagging, and dependency parsing
    • Implemented custom NER models for resume-specific entities (skills, certifications, etc.)
    • Applied TF-IDF and BERT embeddings for semantic text analysis
    • Built skill taxonomy and matching using hierarchical classification
  • Matching Algorithm:
    • Developed hybrid matching system combining keyword matching and semantic similarity
    • Implemented cosine similarity on sentence embeddings (Sentence-BERT)
    • Created weighted scoring system considering experience level, education, and skills
    • Built explanation module to provide matching rationale
  • API & Frontend:
    • Built RESTful API using Flask with multi-threading for concurrent processing
    • Developed responsive web interface for resume upload and analysis
    • Implemented real-time progress tracking for batch processing
    • Created dashboard with analytics and visualizations (D3.js)

Results & Impact

  • Reduced resume screening time by 70% through automation.
  • Achieved 92% accuracy in skill extraction from diverse resume formats.
  • Improved candidate-job matching precision by 45% compared to keyword-only methods.
  • Processed 10,000+ resumes with average processing time of 3 seconds per resume.
  • Helped HR teams reduce time-to-hire by 35%.

Use Cases

  • For Job Seekers: Analyze resume ATS compatibility and get optimization suggestions
  • For Recruiters: Quickly screen large volumes of resumes and identify top candidates
  • For HR Departments: Streamline recruitment pipeline and reduce manual effort
  • For Job Portals: Provide intelligent job recommendations based on candidate profiles

Technologies Used

NLP: spaCy, NLTK, Sentence-BERT, Transformers
ML: Scikit-learn, TensorFlow
Document Processing: PyPDF2, python-docx, Tesseract OCR
Backend: Flask, Python
Frontend: HTML, CSS, JavaScript, Bootstrap, D3.js
Database: MongoDB
Deployment: Docker, AWS

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