This document summarizes an analysis of wine quality data. It describes the steps taken which include data exploration, cleaning, examining relationships between variables, modeling and prediction. The data was explored, cleaned by imputing missing values, removing outliers, and scaling quantitative variables. Correlations between variables and the output quality variable were examined. The data was divided into train and test sets, and regression modeling was performed on the train set to determine important predictors of quality. The model was then used to predict quality on the test set.