Principal Component Analysis for Visualization
Principal component analysis (PCA) is an unsupervised machine learning technique. Perhaps the most popular use of principal component analysis is dimensionality reduction. Besides using PCA as a data preparation technique, we can also use it to help visualize data. A picture is worth a thousand words. With the data visualized, it is easier for us to get some insights and decide on the next step in our machine learning models.
In this tutorial, you will discover how to visualize data using PCA, as well as using visualization to help determining the parameter for dimensionality reduction.
After completing this tutorial, you will know:
- How to use visualize a high dimensional data
- What is explained variance in PCA
- Visually observe the