Performed end-to-end Exploratory Data Analysis (EDA) on real-world datasets, handling missing data, outliers, feature distributions, and delivering business-driven insights through structured data storytelling.

Built and optimized machine learning models using feature engineering, ensemble methods, and hyperparameter tuning, with rigorous validation, error analysis, and bias–variance trade-off evaluation.

Designed production-oriented ML pipelines, including model selection, experiment tracking, data leakage prevention, and KPI-aligned performance evaluation.

Deployed machine learning models via FastAPI, applying model serialization, input validation, and MLOps concepts such as versioning, drift awareness, and monitoring strategies.