> A machine-learning project built around a disciplined evaluation pipeline — k-fold cross-validation, model optimization, and reproducible results.
// pipeline
Built a robust evaluation harness using k-fold cross-validation to estimate generalization honestly and avoid overfitting to a single train/test split.
Swept hyperparameters and compared model families, tuning for the metric that mattered rather than raw accuracy on an imbalanced set.
Cleaned, encoded, and engineered features from raw data so the model had signal to learn from — often the highest-leverage step in the pipeline.
Wrapped preprocessing, training, and evaluation into a scripted pipeline so results could be regenerated end-to-end on demand.