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 import numpy as np 

import pandas as pd 

import matplotlib.pyplot as plt 

from sklearn.datasets import load_iris 

from sklearn.decomposition import PCA 

# Load the Iris dataset 

iris = load_iris() 

data = iris.data 

labels = iris.target 

label_names = iris.target_names 

# Reduce dimensionality to 2 using PCA 

pca = PCA(n_components=2) 

reduced_data = pca.fit_transform(data) 

# Create DataFrame for visualization 

df_pca = pd.DataFrame(reduced_data, columns=['PC1', 'PC2']) 

df_pca['Label'] = labels 

# Plot PCA result 

plt.figure(figsize=(8, 6)) 

colors = ['r', 'g', 'b'] 

for i, label in enumerate(np.unique(labels)): 

subset = df_pca[df_pca['Label'] == label] 

plt.scatter(subset['PC1'], subset['PC2'], color=colors[i], 

label=label_names[label], alpha=0.7) 

plt.title('PCA on Iris Dataset') 

plt.xlabel('Principal Component 1') 

plt.ylabel('Principal Component 2') 

plt.legend() 

plt.grid(True) 

plt.tight_layout() 

plt.show() 


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