Resume & Job Posting Analysis using NLP, Regex, and Fuzzy Matching

Project Overview

This project focuses on analyzing job postings and resumes using web scraping, natural language processing (NLP), and fuzzy matching techniques. It extracts and compares skills, experience, education, certifications, keywords, and salary expectations between job descriptions and candidate resumes. The results are visualized through an interactive Power BI dashboard to evaluate ATS (Applicant Tracking System) compatibility.

Key Features

• Automated Job Scraping using Selenium for dynamic job portal data
• Resume Parsing and information extraction using NLP & regex
• Skill, Education, Experience, and Certification Matching
• Fuzzy Matching Algorithm to align resume qualifications with job requirements
• ATS Score Computation to quantify resume-job compatibility
• Power BI Dashboard for visual insights and analysis

Tech Stack

• Python (Selenium, BeautifulSoup, Regex, spaCy, FuzzyWuzzy)
• Power BI (Interactive dashboards for data visualization)
• Pandas, Numpy, Matplotlib (for data analysis and cleaning)

Power BI Dashboard

Dashboard Highlights

• Resume vs Job Skills Gap
• Most Common Job Requirements
• ATS Compatibility Score
• Experience Insights
• Interactive Resume Matcher View

Key Learnings

• Hands-on NLP for unstructured text
• Regex mastery for structured data extraction
• Real-time scraping with Selenium
• Resume-job matching logic using fuzzy scoring
• Power BI storytelling with data insights

Contact

For any questions, suggestions, or feedback, feel free to open an issue in this GitHub repository.