preprocessing

A lot of computer vision performance comes from cleaner inputs rather than larger models. I use OpenCV for resizing, denoising, thresholding, and contour extraction when preparing images for OCR or downstream classification. These classical steps ofte

Real data arrives dirty. I usually start with missing-value audits, duplicate removal, explicit type conversion, and canonical text cleanup. The trick is to make each cleanup rule reproducible rather than burying it in notebook state. I prefer small,

Categoricals are where good intentions become leakage. I use one-hot encoding for low-cardinality stable fields, ordinal encoders only when order is real, and frequency or target encoders with strict cross-validation boundaries. The encoder strategy s

I push nearly all preprocessing into a Pipeline so training and inference paths share exactly the same logic. ColumnTransformer is the workhorse here because real-world tables mix numeric, categorical, boolean, and text fields. It gives you reproducib