AI/MLFRONTEND DEMO

SkillSync AI: Intelligent Talent Match & Skill Gap Analytics

Predictive career intelligence platform utilizing NLP and vector cosine similarity to diagnose individual skill deficits.

Python 3.11Scikit-learnTF-IDF VectorizationCosine SimilarityStreamlit / Web UIPandasNumPy

Source Code (GitHub)Jump to Interactive Demo

Interactive Frontend EnvironmentLive Preview

Interactive Client-Side Demonstration • Explores TF-IDF similarity vectors & delta classification directly in-browser.

Check Code

The Problem Statement

What real-world challenge does this system address?

Traditional job boards present static, opaque requirements without informing candidates precisely which skills they lack or how close they are to role qualification. Job seekers waste months applying for mismatched positions without actionable gap feedback.

The Engineering Solution

System Design & Approach

SkillSync AI takes a candidate's verified skill inventory, maps it against a vectorized industry corpus of job roles, calculates mathematical match percentages, and breaks down the delta into verified strengths, critical action plans, and domain skills.

Key Capabilities:

Target Role Analyzer: Instant match score calculation against user-selected job profiles.
Three-Tier Skill Breakdown: Automated categorization into Verified, Action Plan (missing), and Irrelevant skills.
AI Role Discovery: Reverse-engineers highest-probability target roles based on the candidate's existing strengths.
Enterprise CSV Export: Action plans and roadmap checkpoints exportable for offline tracking and portfolio building.

System Architecture Flow

System Architecture & Dataflow

User Skills Input -> TF-IDF Vectorizer -> Industry Job Corpus Matrix -> Cosine Similarity Calculation -> Skill Delta Classifier -> Visual Dashboard & Export

Phase 01:Skill Normalization

Standardizes raw text skills, removes noise, and aligns synonyms against standard tech taxonomy.

Input ProcessingPython String Preprocessing & Tokenizer

Phase 02:Vector Representation

Converts candidate skill profiles and industry role specifications into sparse numerical vectors.

Embedding & Matrix EngineScikit-Learn TfidfVectorizer

Phase 03:Similarity Engine

Computes dot product normalized distance to score semantic proximity between candidate and target profile.

Mathematical ScoringCosine Distance Metric

Phase 04:Delta Classifier & UI

Partitions missing skills into actionable learning priorities and renders comparative charts.

Presentation & Export LayerStreamlit & Pandas

Technical Implementation

Stack Specification & Components

Language & Runtime

Python 3.11

Framework

Streamlit / Next.js Demo UI

Database / Storage

Vectorized Job Dataset (JSON/CSV)

Deployment Target

Local Streamlit Server / Static Interactive Web Sandbox

Key Libraries

scikit-learnpandasnumpymatplotlib

Interactive Demonstration Sandbox

Client-Side Simulation

Interactive Skill Gap Simulation

Simulates client-side TF-IDF similarity vectors & delta classification

Target Role:

Toggle Candidate Skills in Inventory:

Calculated Match Score

43%

Cosine vector similarity against industry profile

Verified Skills (3):

✓ Python✓ Scikit-Learn✓ SQL

Action Plan / Skills to Learn (4):

! TensorFlow/PyTorch! Docker! Feature Engineering! FastAPI

Architectural Rationale & Tradeoffs

Engineering Decisions

Key design choices, constraints, and solutions formulated during development

Deterministic TF-IDF vs. Heavy LLM for Core Scoring

Decision:

Used Scikit-Learn TF-IDF vectorization with Cosine Similarity rather than calling an external LLM API for basic matching.

Engineering Rationale:

Ensures sub-second latency, deterministic score reproducibility, zero API costs, and transparent auditability without hallucinated scores.

Client-Side CSV Export Architecture

Decision:

Generated data transformations in memory via Pandas and streamed directly to browser blobs.

Engineering Rationale:

Preserves user privacy by avoiding unnecessary server-side storage of sensitive resume and skill data.

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