machine learning one is at position zero on the axis)two variables will capture approximately more or totaland so on table shows you the full read-out table the findings of the pca number of variables extra information captured total data captured an elbow shape in the plot suggests that five variables can hold most of the information found inside the data you could argue for cut-off at six or seven variables insteadbut we're going to opt for simpler data set versus one with less variance in data against the original data set at this pointwe could go ahead and see if the original data set recoded with five latent variables is good enough to predict the quality of the wine accuratelybut before we dowe'll see how we might identify what they represent interpreting the new variables with the initial decision made to reduce the data set from original variables to latent variableswe can check to see whether it' possible to interpret or name them based on their relationships with the originals actual names are easier to work with than codes such as lv lv and so on we can add the line of code in the following listing to generate table that shows how the two sets of variables correlate listing showing pca components in pandas data frame pd dataframe(results components_columns=list[ 'fixed acidity' 'volatile acidity' 'citric acid' 'residual sugar' 'chlorides' 'free sulfur dioxide' 'total sulfur dioxide' 'density' 'ph' 'sulphates' 'alcohol'])the rows in the resulting table (table show the mathematical correlation orin englishthe first latent variable lv which captures approximately of the total information in the sethas the following formula lv (fixed acidity (volatile acidity - (alcohol -