Inspiration
Traditional resume filters are often inconsistent and miss great candidates — especially in competitive fields like data science. As someone passionate about solving hiring bottlenecks using data tools, I built this project to help hiring teams screen smarter — not harder.
What it does
The AI Resume Screener:
Accepts multi-format resumes (PDF/DOCX/TXT)
Lets you paste a job description
Uses OpenAI GPT to match skills + experience
Shows an interactive match score, missing/matched skills, and radar heatmaps
Supports batch resume uploads and CSV export
Fully deployable — built for recruiters and candidates
How I built it
Python & Pandas – Resume parsing, skill extraction
OpenAI GPT API – Skill match logic, explanation scoring
Streamlit – Clean, interactive UI with visual analytics
Plotly & Seaborn – Radar chart and heatmap visuals
Key Features
GPT-powered skill comparison and scoring
Match heatmap + radar chart
Batch analysis of multiple resumes
Filterable skill match tables
Export results to CSV Challenges I ran into Parsing different resume formats reliably
Designing a good radar heatmap for skill match
Optimizing GPT usage for fast batch scoring
What’s next?
PostgreSQL logging for historical resume scores
Admin dashboard for recruiters
Candidate ranking across time and roles
ATS-ready integrations
Why it matters
This project reflects my skills in:
Real-world problem solving with data tools
Clean UI/UX thinking with Streamlit
AI-Augmented decision support Perfectly aligned with the way top tech/data teams build products.
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