I'm Yash Gajjar ! 😎
I'm an IT student focused on building end-to-end Machine Learning systems and modular ML pipelines. 🤖
📃 Skills I use:
🌱 Currently Learning:
⭐ Here are some projects that I've built:
- Unified ML Pipelines: Modular ML framework organizing 15+ algorithms by mathematical families.
- ASPIRELY (GitHub): AI-Powered Virtual Career Advisor featuring voice-based mock interviews. 🏆 1st Prize Winner at SAL College (out of 300+ teams).
- Fake News Detection System: NLP classification system using Ridge Classifier and TF-IDF vectorization.
- 📝 Published Paper: When More Data Hurts: Empirical study on noise sensitivity and performance saturation in ML models (IJCSPUB | ISSN: 2250-1770).
🏆 Github Stats & Trophies
📊 Education & Recent Activity
- Pursuing B.E. in Information Technology at SAL College of Engineering (CGPA: 8.83)
- Active Kaggle Contributor focusing on feature engineering and end-to-end data science workflows
- Built ASPIRELY proof-of-concept at the Tic Tech Toe 2025 Hackathon, and later carried it forward to win 🏆 1st Prize at SAL College's Project & Poster Presentation (among 300+ teams)
- Published "When More Data Hurts" in the International Journal of Current Science (IJCSPUB | ISSN: 2250-1770)
- Certified in Machine Learning and Data Analysis with Python (FreeCodeCamp)
