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:
- 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
- Next, we load the Iris dataset...
