What is PCA in machine learning and how is it applied in Python?

What is PCA in machine learning and how is it applied in Python?

PCA, or Principal Component Analysis, is a dimensionality reduction technique used in machine learning to reduce the number of features in a dataset while preserving as much variance as possible. In Python, you can apply PCA using libraries like scikit-learn.

Can you show me a simple example of PCA in Python?

Sure! Here's a basic example using scikit-learn:

from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt

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

# Apply PCA
pca = PCA(n_components=2)
X_reduced = pca.fit_transform(X)

# Plot the results
plt.scatter(X_reduced[:, 0], X_reduced[:, 1], c=iris.target)
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('PCA of Iris Dataset')
plt.show()

What are the benefits of using PCA?

PCA helps in reducing the dimensionality of data, which can lead to improved model performance, reduced computation time, and better visualization of data. It also helps in removing noise and redundancy.

Are there any limitations to PCA?

Yes, PCA assumes linear relationships among features, which may not always hold true. It can also be sensitive to outliers and may not perform well with non-linear data.