Milk Quality Prediction
Classifying milk grade from physicochemical measurements
01The problem
Dairy QC labs catch contamination at the end of the line, after bad batches are already produced. Earlier prediction from upstream sensor data would catch issues before a batch is ruined.
02Approach & architecture
Built a multi-class classifier predicting milk quality grade (low / medium / high) from chemical sensor features (pH, fat, protein, lactose, somatic cell count). Compared tree ensembles against linear baselines and tuned class weights to handle imbalanced grade distributions.
03Results
Measured What the work shows
Predictive model identifying milk grade from upstream sensor data alone, with feature importance pinpointing which chemical signals matter most. Useful pattern for any process-quality prediction problem.
04Lessons
Domain context matters: a feature that looks weak on its own (e.g. color alone) can become highly informative once combined with chemistry features the way an expert would combine them mentally.