featureunion tool at lasta logistic regression model will be createdand the pipeline will be evaluated using -fold cross validation firstimport the required packages as followsfrom pandas import read_csv from sklearn model_selection import kfold from sklearn model_selection import cross_val_score from sklearn pipeline import pipeline from sklearn pipeline import featureunion from sklearn linear_model import logisticregression from sklearn decomposition import pca from sklearn feature_selection import selectkbest nowwe need to load the pima diabetes dataset as did in previous examplespath " :\pima-indians-diabetes csvheadernames ['preg''plas''pres''skin''test''mass''pedi''age''class'data read_csv(pathnames=headernamesarray data values nextfeature union will be created as followsfeatures [features append(('pca'pca(n_components= ))features append(('select_best'selectkbest( = ))feature_union featureunion(featuresnextpipeline will be creating with the help of following script linesestimators [estimators append(('feature_union'feature_union)estimators append(('logistic'logisticregression())model pipeline(estimatorsat lastwe are going to evaluate this pipeline and output its accuracy as followskfold kfold(n_splits= random_state= results cross_val_score(modelxycv=kfoldprint(results mean()