PCA (Principal Component Analysis)

Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction. It transforms a large set of variables into a smaller one that still contains most of the information in the original set. Here's a plain text explanation:

What is PCA?

PCA identifies patterns in data by finding the directions (principal components) along which the variance of the data is maximized. It projects the data onto these principal components to reduce the number of dimensions while retaining the most important features of the data.

Why Use PCA?

• Reduce Complexity: By reducing the number of features, PCA simplifies the dataset, making it easier to visualize and analyze.

• Remove Noise: PCA can help filter out noise from the data, improving the performance of machine learning algorithms.

• Prevent Overfitting: With fewer features, models are less likely to overfit, especially when dealing with small datasets.

Example Application

Imagine you have a dataset with 100 features, and you want to reduce it to 2 dimensions for visualization:

1. Standardize the Data: Ensure all features have the same scale.

2. Compute Covariance Matrix: Understand how features vary together.

3. Eigenvalues and Eigenvectors: Determine the principal components.

4. Select Principal Components: Choose the top 2 components.

5. Transform the Data: Project data onto the new 2D space.