This document summarizes an analysis of wine classification data using machine learning models. It explores logistic regression, decision trees, and support vector machines on a dataset of 6497 red and white wines. All three models achieved over 98% accuracy in classification. The decision tree model was able to infer classification based on thresholds for chloride, total SO2, volatile acidity, and sulphates, correctly identifying wines as white. Key differences between white and red wine production are also summarized.