Implementing PCA in Python with Scikit-Learn on Iris dataset.

Step 01: importing required libraries

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

from sklearn import datasets

Step 02: importing or loading the datasets

dataset = datasets.load_iris()

Step 03: distributing the dataset into two components X and Y

X = dataset.data ; y = dataset.target

Step 04: Splitting the X and Y into the Training set and Testing set

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 75)

Step 05: performing preprocessing part

from sklearn.preprocessing import StandardScaler

sc = StandardScaler()

X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

Step 06: Applying PCA function on training and testing set of X component

from sklearn.decomposition import PCA

pca = PCA(n_components = 2)

X_train = pca.fit_transform(X_train)

X_test = pca.transform(X_test)

Step 07: Fitting Logistic Regression To the training set

from sklearn.linear_model import LogisticRegression

classifier = LogisticRegression(random_state = 0)

classifier.fit(X_train, y_train)

Step 08: Predicting the test set result using predict function under LogisticRegression

y_pred = classifier.predict(X_test)

Step 09: making confusion matrix between test set of Y and predicted value.

from sklearn.metrics import confusion_matrix

cm = confusion_matrix(y_test, y_pred)

Step 10: Predicting the training set result through scatter plot

from matplotlib.colors import ListedColormap

X_set, y_set = X_train, y_train

X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() — 1,

stop = X_set[:, 0].max() + 1, step = 0.01),

np.arange(start = X_set[:, 1].min() — 1,

stop = X_set[:, 1].max() + 1, step = 0.01))

plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),

X2.ravel()]).T).reshape(X1.shape), alpha = 0.75,

cmap = ListedColormap((‘yellow’, ‘white’, ‘aquamarine’)))

plt.xlim(X1.min(), X1.max())

plt.ylim(X2.min(), X2.max())

for i, j in enumerate(np.unique(y_set)):

plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],

c = ListedColormap((‘red’, ‘green’, ‘blue’))(i), label = j)

plt.title(‘Logistic Regression (Training set)’)

plt.xlabel(‘PC1’) # for Xlabel

plt.ylabel(‘PC2’) # for Ylabel

plt.legend() # to show legend

Step 11: show scatter plot

plt.show()

Step 12: Visualising the Test set results through scatter plot