🚀 ResumeIQ AI

Intelligent Resume Analyzer

Your resume, decoded — before the recruiter even sees it.

An AI-powered resume analysis platform that extracts resume insights, evaluates ATS compatibility, identifies skill gaps, and provides personalized improvement recommendations.

🔗 Live Demo  •  📁 GitHub Repository

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📚 Table of Contents

• Overview
• Why ResumeIQ?
• Problem Statement
• Features
• System Architecture
• Tech Stack
• AI Engineering Concepts Demonstrated
• Project Structure
• Installation & Setup
• Deployment
• Screenshots
• Future Improvements
• Key Highlights
• License
• Author

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📌 Overview

ResumeIQ AI is an AI-powered Resume Analyzer built using Python, Streamlit, and open-source NLP technologies.

Every resume tells a story — but not every resume tells it in a way that an Applicant Tracking System (ATS) or a busy recruiter can quickly understand. ResumeIQ AI acts as a first-pass reviewer: parsing the document, understanding its content, and scoring it against real-world expectations, all before it ever reaches a human.

The application helps job seekers understand how well their resume matches industry expectations by analyzing:

• 🧱 Resume structure
• 🛠️ Technical skills
• 🎯 Job-role compatibility
• 🤖 ATS readiness
• 🕳️ Missing skills
• 📈 Improvement opportunities

The project combines Natural Language Processing (NLP), rule-based scoring systems, and machine learning techniques to create an explainable resume evaluation system — no black-box scores, just clear, actionable feedback.

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🌟 Why ResumeIQ?

Most resume checkers give a single opaque score and little else. ResumeIQ AI is designed to be explainable end-to-end: every score comes with a "why," every gap comes with a "what to do about it." It's built for job seekers who want to understand their resume the way a hiring pipeline actually sees it — structurally, semantically, and competitively.

A resume isn't just a document — it's the first algorithm you have to pass before you meet a human.

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🎯 Problem Statement

Many candidates submit resumes without knowing:

• ❓ Whether their resume is ATS-friendly
• ❓ Which skills they are missing for a target role
• ❓ How closely their resume matches job requirements
• ❓ What improvements can increase their chances

ResumeIQ AI solves this by providing instant AI-assisted feedback — turning uncertainty into a clear, prioritized action plan.

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✨ Features

📄 Resume Processing

• 📤 Upload PDF resumes
• 📝 Extract resume text using PyMuPDF
• ⚙️ Process and analyze resume content automatically

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🧠 Information Extraction

Extracts important resume information:

Field	Extracted
👤 Name	✅
📧 Email	✅
📱 Phone number	✅
🛠️ Skills	✅
🎓 Education	✅
💼 Experience	✅
📁 Projects	✅

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📊 ATS Score Analysis

Generates an ATS compatibility score based on:

• 📋 Resume completeness
• 📧 Contact information
• 🛠️ Technical skills
• 🎓 Education
• 💼 Experience
• 📁 Projects

Includes visual score representation using Plotly — so the score isn't just a number, it's a picture.

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🔍 Skill Gap Detection

Analyzes missing skills based on selected career roles.

Supported roles:

• 🤖 AI Engineer
• 📊 Data Scientist
• 🧠 Machine Learning Engineer
• 🐍 Python Developer
• 📈 Data Analyst

Provides:

• ✅ Existing skills
• ❌ Missing skills
• 📚 Recommended learning areas

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🎯 Job Match Prediction

Measures similarity between:

• 📄 Resume content
• 📋 Target job description

Using:

• 🧬 Sentence Transformers embeddings
• 🔢 TF-IDF similarity fallback

Outputs:

• 📊 Job compatibility percentage
• 🎯 Matching areas

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💡 Resume Improvement Suggestions

Provides actionable recommendations, such as:

• ➕ Add missing projects
• 🐙 Include GitHub profile
• 🏅 Add certifications
• 🛠️ Improve technical skills section
• ✍️ Strengthen resume summary
• 📊 Include measurable achievements

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🏗️ System Architecture

graph TD
    A[👤 User] --> B[📤 Upload Resume PDF]
    B --> C[📄 PDF Parser<br/>PyMuPDF]
    C --> D[📝 Resume Text Processing]
    D --> E[🧠 Information Extraction]
    D --> F[📊 ATS Score]
    D --> G[🔍 Skill Gap Analysis]
    E --> H[🎯 Job Match Model<br/>Sentence Transformers]
    F --> H
    G --> H
    H --> I[🌐 Streamlit Dashboard]
    I --> J[💡 Resume Insights & Suggestions]

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🛠️ Tech Stack

Category	Tools
Programming	Python
Frontend	Streamlit
NLP & Machine Learning	spaCy, Sentence Transformers, scikit-learn
Data Processing	Pandas, NumPy
Visualization	Plotly
Document Processing	PyMuPDF

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🧠 AI Engineering Concepts Demonstrated

This project demonstrates practical AI engineering skills:

• ✅ PDF document processing
• ✅ Natural Language Processing pipeline
• ✅ Information extraction
• ✅ Semantic similarity matching
• ✅ Hybrid AI systems (rule-based logic + Machine Learning)
• ✅ Explainable scoring systems
• ✅ Model fallback strategies
• ✅ Deployment of AI applications

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📂 Project Structure

ResumeIQ-AI/
├── app.py                          # Streamlit application
├── requirements.txt                 # Dependencies
├── README.md                       # Documentation
├── assets/
│   └── screenshots/                 # App screenshots
├── components/
│   └── ui.py                       # UI components
├── parsers/
│   └── pdf_parser.py                # PDF extraction logic
├── analysis/
│   ├── ats_score.py                 # ATS scoring logic
│   ├── information_extraction.py    # Resume field extraction
│   ├── job_match.py                 # Job similarity matching
│   ├── skill_gap.py                 # Skill gap detection
│   └── suggestions.py               # Improvement recommendations
├── data/
│   └── job_descriptions.py          # Reference job descriptions
└── utils/                          # Helper utilities

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⚙️ Installation & Setup

1️⃣ Clone Repository

git clone https://github.com/SHALINISAURAV/ResumeIQ-AI.git

2️⃣ Navigate to Project Folder

cd ResumeIQ-AI

3️⃣ Create Virtual Environment

python3 -m venv .venv

4️⃣ Activate Environment

Mac/Linux

source .venv/bin/activate

Windows

.venv\Scripts\activate

5️⃣ Install Dependencies

pip install -r requirements.txt

6️⃣ Run Application

streamlit run app.py

7️⃣ Open in Browser

http://localhost:8501

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🚀 Deployment

The application is deployed using Streamlit Community Cloud.

Live Application: 🔗 https://resumeiq-ai-nssmnqpuwft6tcrjsiz6py.streamlit.app

Deployment Workflow

graph LR
    A[💻 Local Development] --> B[🐙 GitHub Repository]
    B --> C[☁️ Streamlit Cloud]
    C --> D[🌐 Live AI Application]

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📸 Screenshots

(Add screenshots after deployment)

assets/screenshots/
├── home.png
├── analysis-results.png
└── skill-gap.png

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🔮 Future Improvements

• 🤖 Resume improvement suggestions powered by LLMs
• 🖨️ OCR support for scanned resumes
• 📑 Multiple resume comparison
• 🌐 Real-time job scraping
• 🗺️ Personalized learning roadmap
• 🔐 User authentication
• 🏆 Resume ranking system

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📌 Key Highlights

• ⭐ Built an end-to-end AI application from scratch
• ⭐ Implemented NLP-based resume understanding
• ⭐ Designed hybrid AI architecture using rules + ML
• ⭐ Developed explainable ATS scoring system
• ⭐ Integrated semantic similarity models
• ⭐ Deployed a production-ready Streamlit application

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📜 License

This project is created for educational and portfolio purposes.

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👩‍💻 Author

Shalini Saurav

AI & Data Science Enthusiast

Interested in: 🤖 Artificial Intelligence  •  📊 Machine Learning  •  ✨ Generative AI  •  ⚙️ AI Engineering

⭐ If you found this project useful, consider giving it a star on GitHub!