About
From raw data to deployed prediction APIs — every system built, tested, and live.
Achievement-driven ML professional with a PG Diploma in Data Science from IIIT-Bangalore (3.7/4). I build complete systems — not just notebooks — covering data ingestion, EDA, feature engineering, model training, evaluation, and deployment via REST APIs.
My work spans classical ML, deep learning (CNN/RNN/Transfer Learning), NLP, computer vision, and Generative AI (RAG, Agents, LangChain). Every project here is live and interactive.
Core Competencies
Key Metrics
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Education
Skills
End-to-end capabilities — from raw data through classical ML, deep learning, NLP, computer vision, and Generative AI to production deployment.
Projects
All projects are hosted on Render free tier — first load may take ~15s to spin up.
ML Capabilities
Deduplicate rows, impute missing values with 8+ numeric strategies (Mean, Median, KNN, MICE) and 4 categorical strategies, remove outliers via IQR / Z-score / Winsorize, fix skewness, and apply Yeo-Johnson power transform. Download a clean CSV or hand off directly to AutoML.
RF, XGBoost, LightGBM, and CatBoost compete via 5-fold cross-validation. The winner is selected automatically by F1 (classification) or MAE (regression). Optional Optuna tuning and SHAP explanation run on the winner.
log1p, sqrt, Yeo-Johnson, percentile rank, outlier flag, and missing flag per numeric column. Plus binning, polynomial pairs, interaction terms, date extraction, and cyclical encoding (sin / cos). All transforms are fit on training data only — no leakage.
Four methods — Variance Threshold, Correlation Filter (drop >0.9 correlated), RFE (Random Forest), and SelectKBest (Mutual Info) — automatically prune irrelevant or redundant columns before training. Configurable top-K cutoff.
TPE sampler runs up to 30 trials on the AutoML winner to find optimal hyperparameters. Tuning is optional and runs after model selection — not before — so it never inflates the competition score.
Every prediction comes with a SHAP bar chart showing which features drove the result and by how much. FE-derived columns are grouped back to their originals so you see source-feature influence, not transform noise.
Simple voting (VotingClassifier / VotingRegressor) or stacking with a meta-learner on top of the AutoML winners. Reduces variance and improves generalization over any single model.
ML Pipeline
A complete end-to-end ML pipeline — click any stage to explore what happens there.
Click any stage to expand · stages run sequentially in a real pipeline
AI News
Latest research papers from arXiv and industry news — updated hourly.
Experience
A journey from financial services to full-stack ML engineering.
Current employer.
Technical support and ML project development. Built and deployed ML systems end-to-end.
Business development, client management, and analytics-driven sales strategy.
Technical operations and client support in a software environment.
Business development and distribution operations.
Financial services operations, process execution, and data management in a global enterprise environment.
Contact
Open to ML engineering roles, freelance projects, and collaborations. Drop a message or reach out directly.