Inspiration
As someone passionate about tech and hiring efficiency, I recognized how time-consuming and inconsistent resume screening can be especially when handling a large volume of applications. Recruiters often struggle to quickly identify the most suitable candidates, while job seekers are left guessing whether their resumes align with the job requirements. I wanted to build a tool that leverages AI to streamline this process on both sides: helping recruiters make faster, smarter decisions and enabling job seekers to better understand how well their resume matches a specific job description.
What it does
Resume Ranker is an AI-powered web application that:
Accepts a job description and multiple resumes (.pdf, .docx, .doc)
Extracts relevant skills from both the resumes and job description
Computes semantic similarity using Sentence-BERT (SBERT)
Generates a match score combining skill overlap and text similarity
Ranks candidates and highlights matching/missing skills
Provides resume previews with an interactive and secure interface
How I built it
Frontend: HTML, CSS (Bootstrap), JavaScript
Backend: Python with Flask
NLP: Sentence-BERT for semantic similarity, spaCy for skill extraction
Parsing: PyPDF2 and python-docx for reading resume files
Challenges I ran into
Parsing and extracting consistent text from diverse resume formats
Ensuring the semantic similarity model worked accurately across industries
Managing file uploads securely without overwhelming the session memory
Designing a ranking logic that fairly balances skills and similarity
Accomplishments that I'm proud of
Building a working end-to-end resume screening tool from scratch
Successfully integrating NLP models into a real-world use case
Creating a responsive and intuitive user interface
Making the system flexible to support multiple file formats
What I learned
How to apply Sentence-BERT and spaCy in production use cases
Practical experience with Flask session management and routing
Insights into ATS (Applicant Tracking System) logic and hiring workflows
Real-world NLP challenges like skill ambiguity and synonym handling
What's next for Resume Ranker
Deploying to a cloud platform (Heroku or AWS)
Adding user login and recruiter dashboards
Expanding to analyze cover letters and LinkedIn URLs
Adding analytics and filtering tools for recruiters
Making the app mobile-friendly
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