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I share what I learn as I build — RAG systems, ML experiments, career reflections, and the occasional late-night AI rabbit hole.

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Detecting anomalies in fraud datasets — methods, metrics, and what actually works.

How I approach anomaly detection in large-scale datasets with multiple algorithm comparison.

Classification on 1M+ IEEE-CIS transactions — EDA, feature engineering, and XGBoost results.

Forecasting supermarket prices across 1.7M rows — LightGBM, Prophet, and the features that matter.

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I post regularly about AI engineering, RAG systems, ML insights, and lessons from building production systems. Join the conversation!