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🎯 About Me

Mukesh = {
    "role"         : "Data Scientist & ML Engineer",
    "location"     : "Tenkasi, Tamil Nadu 🇮🇳",
    "education"    : "B.Sc Data Science @ MSU (2023–2026)",
    "email"        : "starmukesh2005@gmail.com",
    "linkedin"     : "linkedin.com/in/mukesh-k-6899902b7/",
    "github"       : "github.com/Mukesh-2005",
    
    "expertise"    : [
        "Object Detection (YOLO)",
        "NLP / Text Classification",
        "Ensemble Learning",
        "Feature Engineering",
        "Data Science Pipeline",
        "REST APIs"
    ],
    
    "learning"     : ["Computer Vision", "Transformers / LLMs", "PyTorch", "Production ML"],
    "open_to"      : ["Full-time Data Science", "ML Internships", "Collaborations"],
}

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🏆 Highlights

• 🏥 Canonical Medical Record FHIR Pipeline — 113 FHIR R4 resources | 100% validation pass | End-to-end medical data structuring
• 🤖 Traffic Vehicle Detection — mAP50: 0.749 | Real-time inference (4.2ms) | 6.9x improvement
• 🎫 Automated Ticket Routing System — TF-IDF + Logistic Regression NLP classifier | 80% test accuracy | production-style confidence triage
• 🎓 IBM Professional Data Science Certificate — 12-course specialization (Coursera) ✅
• 🎯 End-to-End Projects — Data prep → Model training → Production deployment

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🚀 Featured Projects

🏥 Canonical Medical Record FHIR Pipeline ⭐ LATEST End-to-End FHIR R4 Structuring for Multi-Document Medical Records Result: 113 FHIR R4 resources Validation: 100% FHIR R4 compliant Extraction: 98 medical facts (45 conditions + 17 meds + 36 labs) Stages: 7-stage pipeline with provenance tracking Achievement: Production-grade data integrity + reproducibility 7 Stages: Page classification Content extraction Entity extraction (Claude AI) Terminology normalization (100% mapped) FHIR construction + validation SQLite persistence + 5 queries Provenance tracking Tech: Python | Claude API | FHIR R4 | SQLite | Pydantic | PyPDF 🔗 GitHub Repo	🚗 Traffic Vehicle Detection ⭐ FEATURED Real-time Object Detection for Indian Traffic Model: YOLOv8s mAP50: 0.749 ✓ Inference: 4.2ms (real-time!) Dataset: 1,300 perfectly balanced images Achievement: 6.9x improvement by fixing data imbalance (6.2:1 → 1:1) Performance by Class: Car: 0.762 ✓ Bus: 0.880 ⭐ (Best) Truck: 0.634 Motorcycle: 0.677 Auto-rickshaw: 0.795 Key Learning: Data quality > Model complexity Tech: YOLOv8 | PyTorch | Python | Colab 🔗 GitHub Repo | Demo Video | Writeup
🎫 Automated Ticket Routing System ⭐ FEATURED NLP Classifier — Production-Style Triage Model: TF-IDF + Logistic Regression Test Accuracy: 80% | Macro F1: 0.79 Classes: 4 departments (Billing, Technical, HR, General) Diagnosed Failure: Fixed an HR-class 0% recall bug by identifying vocabulary overlap and rewriting training data Production Features: Confidence-based "needs human review" threshold (0.60) Keyword-based urgent/normal priority tagging Interactive CLI demo for live ticket testing Feedback loop scaffolding for future retraining Key Learning: Model architecture was correct from the start — the real fix was diagnosing why a class had zero signal in the data, not just tuning hyperparameters Tech: Scikit-learn | TF-IDF | Python | Pandas | Joblib 🔗 GitHub Repo	💬 Social Media API Production-Ready REST API — 45+ Endpoints Status: 🟢 Live & Running URL: https://social-media-api-ispt.onrender.com/docs Features: Posts, comments, followers, smart feeds, trends Database: 5-table relational schema with complex SQL Deployment: Render (production-grade, $0 cost) Endpoints: User management (CRUD) Post creation & engagement Follow system Personalized feeds Trending algorithms Tech: FastAPI | SQLAlchemy | SQLite | JWT | Pydantic 🔗 GitHub Repo
🧬 Diabetes Risk Prediction Medical Classification — 92.2% Accuracy Accuracy: 92.2% Classes: 7 risk categories Dataset: 2,000+ medical records Best Model Log-Loss: 0.427 Methodology: Compared OvA, OvO, Multinomial Stratified cross-validation Feature scaling & encoding Class imbalance handling Tech: Scikit-learn | Pandas | NumPy 🔗 GitHub Repo	📚 Coursera Data Science Labs 30+ Hands-On ML Projects Topics: Supervised & Unsupervised Learning Algorithms: Classification, Regression, Clustering Metrics: Accuracy, Precision, Recall, F1, AUC-ROC, MSE, RMSE Models: Decision Trees, SVM, KNN, Linear Regression, K-Means, PCA, DBSCAN Coverage: Data preprocessing Feature scaling & selection Model evaluation Hyperparameter tuning Cross-validation Tech: Scikit-learn | Matplotlib | Seaborn | Pandas 🔗 GitHub Repo

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🛠️ Tech Stack

🐍 Languages & ML Frameworks

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⚙️ Backend & APIs

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🗄️ Databases

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🧰 Tools & Environment

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📈 GitHub Stats

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🎓 Certifications & Education

Certification	Organization	Status
🏅 IBM Professional Data Science Certificate	IBM / Coursera	✅ Completed
🏅 Machine Learning with Python	IBM / Coursera	✅ Completed
🏅 Databases & SQL for Data Science	IBM / Coursera	✅ Completed
📚 B.Sc Data Science	Manonmaniam Sundaranar University	✅ Completed

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📚 Currently Learning

Area	Focus
👁️ Computer Vision	Advanced YOLO, OpenCV, Image Processing
🗣️ NLP	Transformers, LLMs, RAG Systems
🧠 Deep Learning	Advanced PyTorch, Neural Architecture Design
🐳 DevOps	Docker, Kubernetes, CI/CD Pipelines

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💡 Data Science Philosophy

Data Quality > Model Complexity

My core principles:

• ✅ EDA First — Understand data before modeling
• ✅ Fix Imbalance Early — Balance classes before upgrading models
• ✅ Feature Engineering > Model Size — Smart features beat complex models
• ✅ End-to-End Thinking — Production-ready from day one
• ✅ Iterate & Validate — Cross-validation, not lucky splits

Real Example 1 — Traffic Detection:

• Initial: 0.108 mAP50 (broken model)
• Root Cause: 6.2:1 class imbalance, not model
• Solution: Fixed data (1:1 balance)
• Result: 0.749 mAP50 (6.9x improvement!)

Real Example 2 — Ticket Routing NLP:

• Initial: HR class at 0% precision and recall
• Root Cause: Generic training phrasing overlapping with other classes, not the model
• Solution: Rewrote HR examples with domain-distinctive vocabulary
• Result: HR precision 0% → 85.7%

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🌐 Connect With Me

Platform	Link
💼 LinkedIn	linkedin.com/in/mukesh-k
💻 GitHub	github.com/Mukesh-2005
📧 Email	starmukesh2005@gmail.com
🔗 Live API	Social Media API Demo
🚗 Traffic Detection	GitHub Repo
🎫 Ticket Routing NLP	GitHub Repo
🏥 Medical FHIR Pipeline	GitHub Repo

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📊 Project Quality Metrics

Project	Type	Performance	Status
🏥 Medical Record FHIR Pipeline	Healthcare ML	113 FHIR resources, 100% valid	✅ Production
🚗 Traffic Vehicle Detection	Computer Vision	mAP50: 0.749	✅ Production
🎫 Ticket Routing NLP	NLP / Text Classification	Accuracy: 80%	✅ Completed
💬 Social Media API	Backend	45+ Endpoints	🟢 Live
🧬 Diabetes Prediction	Classification	Accuracy: 92.2%	✅ Completed
📚 Data Science Labs	Educational	30+ Projects	✅ Completed

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