Skip to content

Latest commit

 

History

History
48 lines (27 loc) · 1.85 KB

PrincipalComponentAnalysis.md

File metadata and controls

48 lines (27 loc) · 1.85 KB
Raw

Principal Component Analysis Slides

Class Learning Objectives/Competencies (5 min)

By the end of class, you’ll be able to…

  • Describe how PCA works
  • Explain why you need dimensionality reduction

What is principal component analysis? (5 min)

  • PCA is a mathematical technique to reduce redundancy in the data
  • PCA is one of the well-known algorithm for Dimensionality Reduction

PCA Visualization (15 min)

Activity: are the countries in great UK different in terms of food? (20 min)

The last section in the Setosa article discusses eating in the UK. Let's dive deeper into that:

  • In the table, we see the average consumption of 17 types of food in grams per person per week for every country in the UK
  • It would be great if we represent difference among UK countries based on the food they eat visually
  • Write a Python script that visualizes the PCA component for the dataset

The mathematics for eigenvalue and eigenvector (20 min)

Activity: write a Python code to return PCA (30 min)

1- Subtract column mean from feature matrix

2- Calculate the covariance of centered matrix

3- Calculate the eigenvalue and eigenvector of covariance matrix

4- Return the first K (two for example) column of matrix multiplication of centered matrix with eigenvector matrix

  • Compare the result of custom function with PCA in sklearn

  • Calculate the correlation of the first two principle component

  • How much of the dataset information is preserved in the first two components?

  • Hint: use pca.explained_variance_ratio_