machine learning table the first three rows of the red wine quality data set fixed volatile acidity acidity citric acid residual chlorides sugar free sulfur dioxide total sulfur dioxide density ph sulfates alcohol quality principal component analysis-- technique to find the latent variables in your data set while retaining as much information as possible scikit-learn--we use this library because it already implements pca for us and is way to generate the scree plot part one of the data science process is to set our research goalwe want to explain the subjective "wine qualityfeedback using the different wine properties our first job then is to download the data set (step twoacquiring data)as shown in the following listingand prepare it for analysis (step threedata preparationthen we can run the pca algorithm and view the results to look at our options listing data acquisition and variable standardization is matrix of predictor variables these variables are wine properties such as density and alcohol presence import pandas as pd from sklearn import preprocessing from sklearn decomposition import pca import pylab as plt from sklearn import preprocessing downloads location of wine-quality data set url winequality-red csv data pd read_csv(urlsep";" data[[ 'fixed acidity' 'volatile acidity' 'citric acid'reads in the csv 'residual sugar' 'chlorides' 'free sulfur dioxide'data it' separated by semi-colon 'total sulfur dioxide' 'density' 'ph' 'sulphates' 'alcohol'] data quality xpreprocessing standardscaler(fit(xtransform(xy is vector and when standardizing datathe following formula is applied to every data pointz ( -)/where is the new observation valuex the old oneis the meanand the standard deviation the pca of data matrix is easier to interpret when the columns have first been centered by their means represents the dependent variable (target variabley is the perceived wine quality