Resume Gap Analyzer

An explainable Python-based system that analyzes skill gaps between resumes and job descriptions using transparent text processing, rule-based NLP, and deterministic scoring.

Unlike black-box AI resume tools, this project focuses on clarity, correctness, and interview-readiness.

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🎯 Problem Statement

Recruiters and hiring managers often need to quickly understand:

• Whether a candidate meets job requirements
• Which skills are missing
• Why a candidate is (or isn’t) a good fit

Most existing tools rely on opaque AI models that provide little insight into their decisions.

This project solves that by building an explainable decision pipeline that clearly shows:

• Extracted skills
• Matched vs missing requirements
• Exact reasoning behind the match score

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🧠 System Design Overview

The system follows a clean, step-by-step pipeline:

Resume / Job Description
↓
Text Cleaning & Normalization
↓
Skill Extraction (Rule-Based)
↓
Gap Analysis (Matched / Missing / Extra)
↓
Scoring Engine (Explainable Math)
↓
Human-Readable Explanations & Recommendations

Each stage is independent, testable, and transparent.

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🧩 Key Components

1. Text Cleaner

Normalizes raw text by:

• Lowercasing
• Removing punctuation
• Normalizing whitespace

This ensures consistent and deterministic skill matching.

2. Skill Extractor

Uses a predefined skill dictionary to:

• Detect single-word and multi-word skills
• Categorize skills (languages, frameworks, tools, cloud, etc.)
• Avoid false positives and black-box inference

3. Gap Analyzer

Compares resume skills with job requirements to identify:

• Matched skills
• Missing required skills
• Extra / bonus skills

4. Scoring Engine

Calculates a match percentage using transparent logic:

Match Score = (Matched Required Skills / Total Required Skills) * 100

Every score is fully explainable.

5. Explanation & Recommendation Engine

Converts raw analysis into:

• Clear strengths
• Concrete gaps
• Actionable improvement recommendations

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🛠 Tech Stack

• Python
• Regex-based text processing
• Rule-based NLP (no black-box ML)

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✅ Why This Project Stands Out

• Deterministic & explainable logic
• Interview-safe design decisions
• Clean separation of concerns
• Easy to extend with ML or LLMs later

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🚀 Future Enhancements

• Skill importance weighting
• Required vs nice-to-have classification
• Semantic similarity matching
• LLM-assisted explanation refinement
• REST API or Web UI

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📌 Project Status

✅ Core pipeline complete
🚧 Enhancements planned