I build end-to-end ML pipelines that go beyond model accuracy — validating on real data, simulating business impact honestly, and deploying production-grade systems with clear ROI.
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🔭 I'm currently working on End-to-end Data Science projects covering the full lifecycle: (EDA → Feature Engineering → Modeling → Deployment → Honest Business Impact).
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🚀 What makes my work different Every project ships with clearly disclosed simulations — real metrics validated on actual data, business value projected with explicit assumptions. No inflated claims.
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👯 I'm looking to collaborate on Problem-driven projects that require rigorous interpretation — NLP pipelines, time-series forecasting, or unsupervised pattern discovery with real operational stakes.
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🌱 I'm currently learning Deep Learning through fast.ai's Practical Deep Learning for Coders and the D2L.ai book — building intuition for PyTorch from the ground up.
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💬 Ask me about Applied ML, Feature Engineering, translating technical metrics into business recommendations, and building production-ready FastAPI deployments.
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⚡ Fun fact I believe mathematics is the language of the universe, and physics unlocks its power — a mindset that directly shapes how I engineer features and model systems.
Core Data Science & ML
Visualization & Deployment
Databases & Tools
