figure - distributions for wine quality for red and white wine samples the bar plots depicted in figure - show us the distribution of wine samples based on type and quality it is quite evident that high quality wine samples are far less as compared to low and medium quality wine samples multivariate analysis analyzing multiple feature variables and their relationships is what multivariate analysis is all about we would want to see if there are any interesting patterns and relationships among the physicochemical attributes of our wine sampleswhich might be helpful in our modeling process in the future one of the best ways to analyze features is to build pairwise correlation plot depicting the correlation coefficient between each pair of features in the dataset the following snippet helps us build correlation matrix and plot the same in the form of an easy-to-interpret heatmap fax plt subplots(figsize=( )corr wines corr(hm sns heatmap(round(corr, )annot=trueax=axcmap="coolwarm",fmt= 'linewidths subplots_adjust(top= tf suptitle('wine attributes correlation heatmap'fontsize=