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"],
}- 🤖 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
|
Real-time Object Detection for Indian Traffic
Performance by Class:
Key Learning: Data quality > Model complexity Tech: YOLOv8 | PyTorch | Python | Colab 🔗 GitHub Repo | Demo Video | Writeup |
NLP Classifier — Production-Style Triage
Production Features:
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 |
|
Production-Ready REST API — 45+ Endpoints
Endpoints:
Tech: FastAPI | SQLAlchemy | SQLite | JWT | Pydantic |
Medical Classification — 92.2% Accuracy
Methodology:
Tech: Scikit-learn | Pandas | NumPy |
|
Role-Based Access Control
Capabilities:
Tech: FastAPI | SQLAlchemy | JWT | Pydantic |
30+ Hands-On ML Projects
Coverage:
Tech: Scikit-learn | Matplotlib | Seaborn | Pandas |
| 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 |
| 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 |
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%
| Platform | Link |
|---|---|
| linkedin.com/in/mukesh-k | |
| 💻 GitHub | github.com/Mukesh-2005 |
| starmukesh2005@gmail.com | |
| 🔗 Live API | Social Media API Demo |
| 🚗 Traffic Detection | GitHub Repo |
| 🎫 Ticket Routing NLP | GitHub Repo |
| Project | Type | Performance | Status |
|---|---|---|---|
| 🚗 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 |
| 🔐 Task Manager API | Backend | JWT + Role-Based | ✅ Completed |
| 📚 Data Science Labs | Educational | 30+ Projects | ✅ Completed |