Perform multiclass classification of white wine quality using a support vector
machine and feature union as follows:
a) Using the data/winequality-white.csv file, build a pipeline to standardize
data, then create a feature union between interaction terms and a feature selection
method of your choice from the sklearn.feature_selection module,
followed by an SVM (use the SVC class).
b) Run grid search on your pipeline with 85% of the data to find the best values for
the include_bias parameter (PolynomialFeatures) and the C parameter
(SVC) in the search space of your choosing with scoring='f1_macro'.
c) Look at the classification report for your model.
d) Create a confusion matrix using the confusion_matrix_visual() function
from the ml_utils.classification module.
e) Plot a precision-recall curve for multiclass data using the plot_multiclass_
pr_curve() function from the ml_utils.classification module.