A complex pipeline

In this section, we will determine the best classifier to predict the species of an Iris flower using its four different features. We will use a combination of four different data preprocessing techniques along with four different ML algorithms for the task. The following is the pipeline design for the job:

We will proceed as follows:

1. We start with importing the various libraries and functions that are required for the task:

from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn import svm
from sklearn import tree
from sklearn.pipeline import Pipeline

2. Next, we load the Iris dataset and split it into train and test datasets. The X_train and Y_train dataset will be used for training the different...