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. UCI wine data
  2. UCI pima indians diabetes data
  3. kaggle titanic data
  4. kaggle social network ads data