Problem_10: Perform Principal Component Analysis (PCA) on a dataset using Scikit-learn

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Problem_10:
Perform Principal Component Analysis (PCA) on a dataset using
Scikit-learn

#pythoncode :

from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
import pandas as pd

Load dataset
iris = load_iris()
X = iris.data

Perform PCA
pca = PCA(n_components=2)
principalComponents = pca.fit_transform(X)
df = pd.DataFrame(data=principalComponents, columns=['principal component 1', 'principal component 2'])

print(df.head())

Explanation: This code performs PCA on the Iris dataset, reducing its dimensionality to 2 components.

#sample_output

principal component 1 principal component 2
0 -2.684207 0.326607
1 -2.715391 -0.169557
2 -2.889820 -0.137346
3 -2.746437 -0.311124
4 -2.728593 0.333925

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