End-to-end ML modelling process with compressed features
  1. Data reading and preprocessing: missing data, visualization, scaling
  2. Algorithm selection: PCA, Kernel PCA, LDA

Algorithms
  1. Principal Component Analysis (PCA)
  2. Linear Discriminant Analysis (LDA)
  3. Kernal PCA
 
Examples
  1. kaggle social network ads data (PCA,KPCA)
  2. UCI pima indians diabetes data (PCA,KPCA)
  3. UCI wine data (PCA,KPCA,LDA)