What are the steps involved in PCA?

Can you explain the steps involved in Principal Component Analysis?

Sure! The main steps in PCA are: 1) Standardize the data, 2) Compute the covariance matrix, 3) Calculate the eigenvalues and eigenvectors, 4) Sort the eigenvalues and eigenvectors, and 5) Project the data onto the new feature space.

Why is it important to standardize the data before PCA?

Standardizing the data ensures that each feature contributes equally to the analysis, preventing features with larger scales from dominating the results.

What do the eigenvalues and eigenvectors represent in PCA?

Eigenvalues indicate the amount of variance captured by each principal component, while eigenvectors represent the direction of these components in the feature space.

How do I choose the number of principal components to keep?

You can use the explained variance ratio to determine how many components to keep. A common approach is to choose enough components to explain a certain percentage of the total variance, like 95%.