XGBoost

A two-notebook study on the Home Credit Default Risk dataset — the canonical real-world imbalanced classification problem (91% non-defaulters vs 9% defaulters). The interesting question wasn’t “can a model hit 99% accuracy” (a constant predict(0) already does that — and is useless). It was: for a problem where the minority class is the one that matters, which combination of techniques actually moves the needle? The work splits into a companion EDA notebook (missing-value handling, feature engineering, demographic-level defaulter analysis) and this modeling notebook. ...

Context At Bayer, I interned with the Solutions team under Engineering and Data Science, working on machine learning, data engineering, and operational analytics for seed processing and field equipment operations. What I Built I built XGBoost and LightGBM regression models to predict machine-level seed processing times from historical throughput and runtime data, achieving RMSE = 35. These models supported capacity planning and maintenance scheduling for upcoming planting cycles. I also engineered ETL pipelines over 10GB+ of agronomic and equipment data from BigQuery and SQL Server, consolidating multi-year data from multiple sources and reducing retrieval latency by 40%. ...