AI Resume Screening and Candidate Ranking System

AI in Recruitment Process: Resume Analysis using NLP

A production-style prototype that uses NLP and Sentence Transformers to automatically screen resumes, match candidates against job descriptions, and rank them with full explainability.

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🚀 Run on Google Colab (No Setup Required!)

Mam / Evaluator: Sirf neeche diye link pe click karein aur Runtime → Run All press karein. Koi installation nahi, sirf Google account chahiye!

Direct Link:

https://colab.research.google.com/github/rohangargjfl/AI-Resume_Screening_System/blob/main/Run_On_Google_Colab.ipynb

Steps to Run:

1. Click the link above → Opens directly in Google Colab
2. Go to Runtime → Run All
3. Wait ~5 minutes for setup (first time only)
4. Click the public URL that appears at the end of the last cell
5. Register with any email/password → Start screening resumes!

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Features

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Tech Stack

Python · Flask · SpaCy · scikit-learn · Vanilla JS · Bootstrap 5 · Chart.js

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Project Structure

AI_Resume_Screening_System/
├── resume_parser/          # PDF/DOCX/TXT extraction & preprocessing
├── nlp_processing/         # SpaCy NLP pipeline & skill extraction
├── feature_extraction/     # TF-IDF + Sentence Transformer embeddings
├── matching_engine/        # Cosine similarity scoring
├── explainable_ai/         # Skill-level explanations
├── visualization/          # Matplotlib chart generation
├── web_app/                # Flask routes, templates, static assets
│   ├── templates/
│   └── static/
├── example_resumes/        # Sample resumes & job description for testing
├── requirements.txt
├── app.py                  # Application entry point
└── README.md

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Quick Start

1. Install Dependencies

pip install -r requirements.txt
python -m spacy download en_core_web_sm

2. Run the Application

python app.py

3. Open in Browser

Visit http://127.0.0.1:5000

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Usage

1. Home Page — Overview of the system and how it works.
2. Upload — Paste or upload a job description, then upload one or more candidate resumes.
3. Analyse — Confirm and run the AI analysis pipeline.
4. Results Dashboard — View ranked candidates with:
   • Match scores
   • Matched / missing skill breakdowns
   • Detected soft skills
   • Comparison charts

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Example Data

The example_resumes/ directory contains:

• job_description.txt — Sample Senior Python Developer JD
• 5 candidate resumes with varying skill profiles

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How Scoring Works

The absolute final match score is a dynamic, recruiter-controlled blend of four core similarity measures:

Plus an autonomous mathematically bounded Extra Skills bonus (Default cap: +5%) for tech skills discovered but not mathematically requested in the JD.

All weights, metrics, and bonus caps are actively customizable by the recruiter via UI Range Sliders on the Upload Dashboard before firing the background Analysis thread!

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Author

B.Tech Final Year Project — AI in Recruitment Process