en/learn/tutorials/machine_learning/pca

Principal Component Analysis (PCA) Tutorial 🧠📊

Principal Component Analysis (PCA) is a statistical technique used to reduce the dimensionality of data while preserving as much variance as possible. It transforms correlated variables into a set of linearly uncorrelated variables called principal components.

Key Concepts

• Objective: Simplify complex datasets by identifying patterns and reducing redundancy.
• Mathematical Foundation: PCA relies on eigenvalues and eigenvectors of the covariance matrix.
• Applications: Feature extraction, data visualization, noise reduction.

Steps to Perform PCA

1. Standardize the Data
   Normalize features to have zero mean and unit variance.
   Tip: Use StandardScaler in scikit-learn for this step.

   

2. Compute Covariance Matrix
   Understand relationships between variables.

3. Calculate Eigenvalues and Eigenvectors
   Sort components by eigenvalues to prioritize importance.

4. Select Principal Components
   Choose top n components to retain most variance.

5. Transform Data
   Project original data onto the new component space.

Use Cases

• Visualizing high-dimensional data in 2D/3D (e.g., iris dataset).
• Improving model performance by removing redundant features.
• Compressing data for storage or transmission.

For a deeper dive into PCA implementation, check out our PCA in Python tutorial. Want to explore related topics like feature selection? Learn more here. 📚