types of machine learning table how pca calculates the original variablescorrelation with latent variables fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density ph sulphates alcohol - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - giving useable name to each new variable is bit trickier and would probably require consultation with an actual wine expert for accuracy howeveras we don' have wine expert on handwe'll call them the following (table table interpretation of the wine quality pca-created variables latent variable possible interpretation persistent acidity sulfides volatile acidity chlorides lack of residual sugar we can now recode the original data set with only the five latent variables doing this is data preparation againso we revisit step three of the data science processdata preparation as mentioned in the data science process is recursive one and this is especially true between step threedata preparation and step data exploration table shows the first three rows with this done table the first three rows of the red wine quality data set recoded in five latent variables persistent acidity sulfides volatile acidity chlorides lack of residual sugar - - - - - - already we can see high values for wine in volatile aciditywhile wine is particularly high in persistent acidity don' sound like good wines at all