PCA Tutorial with scikit-learn
Join us on TekMinded In this quick tutorial as we dive into Principal Component Analysis (PCA) using the scikit-learn library. PCA is a powerful dimensionality reduction technique that helps us uncover patterns and relationships within high-dimensional datasets. We'll cover the step-by-step implementation, exploring the explained variances and cumulative variances to understand the significance of each principal component. Additionally, we'll visualize the transformed data to gain insights into the compressed representation. Whether you're new to PCA or looking to refresh your knowledge, this tutorial provides a detailed understanding of PCA's concepts and practical applications. Join us on TekMinded and expand your data science toolkit today!
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CODE
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import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.decomposition import PCA
Load the Iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
Perform PCA
pca = PCA()
X_pca = pca.fit_transform(X)
Explained variances and cumulative explained variances
explained_variances = pca.explained_variance_ratio_
cumulative_variances = np.cumsum(explained_variances)
Plot explained variances and cumulative variances
plt.figure(figsize=(8, 5))
plt.plot(range(1, len(explained_variances) + 1),
explained_variances, marker='o', linestyle='-', label='Explained Variance')
plt.plot(range(1, len(cumulative_variances) + 1),
cumulative_variances, marker='o', linestyle='--',
label='Cumulative Explained Variance')
plt.xlabel('Number of Components')
plt.ylabel('Variance Explained')
plt.title('Explained Variances and Cumulative Explained Variances')
plt.legend()
plt.grid(True)
plt.show()
Visualize the transformed data
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y, edgecolor='k')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.title('PCA Visualization of Iris Dataset')
plt.show()
Perform PCA with 2 components
pca = PCA(n_components=2)
X_pca = pca.fit_transform(X)
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