Preprocess: LDA and Kernel PCA in Python

Principal component analysis (PCA) is an unsupervised linear transformation technique that is widely used across different fields, most prominently for dimensionality reduction. We talked about it here: We use the data from sklearn library, and the IDE is Python3. Most of the code comes from Sebastian Raschka's book:


NBA Winning Estimator with Decision Tree in Python

It would be interesting to conduct prediction to understand the trend of NBA winning teams. We will use data from and follow workflow. More details can be found in Robert Layton's book here:

Clustering Application in Face Recognition in Python

We used face datasets for PCA application here: It also will be interesting to see how clustering algorithms assign images into different clusters and visualize them. We use the data from sklearn library(need to download face datasets separately), and the IDE is sublime text3. Most of the code comes from the book: