Program
3. Develop a program to implement Principal Component Analysis (PCA) for reducing the dimensionality of the Iris dataset from 4 features to 2.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
iris = load_iris()
X = iris.data
y = iris.target
target_names = iris.target_names
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X_scaled)
df_pca = pd.DataFrame(X_pca, columns=["PC1", "PC2"])
df_pca["Target"] = y
plt.figure(figsize=(8, 6))
sns.scatterplot(x=df_pca["PC1"], y=df_pca["PC2"], hue=df_pca["Target"], palette="viridis", style=df_pca["Target"])
plt.xlabel("Principal Component 1")
plt.ylabel("Principal Component 2")
plt.title("PCA Visualization of Iris Dataset (2D)")
plt.legend(labels=target_names)
plt.grid(True)
plt.show()
Output:
