Principal Component Analysis

Original made in Colabotory, it will have three version:

• Colaboratory
• Jupyter notebook
• Python script

In this repository we will show how to apply PCA to reduce dimensionality in datasets.

Steps involved in PCA

• Standardize the data
• Compute covariance matrix
• Obtain the Eigenvectors and Eigenvalues from the covariance matrix
• Sort eigenvalues in descending order and choose the top k Eigenvectors that correspond to the k largest eigenvalues
• nstruct the projection matrix W from the selected k Eigenvectors
• Transform the original data set X via W to obtain the new k-dimensional feature subspace Y

Structure

.
├── .gitignore 
├── README.md
├── requirements.txt
├── jupyter/
│   └── pca.ipynb
├── script/
│   └── pca.py
│   └── pca.png

Runing Project

Installing Dependecies

• pip install -r requirements.txt

Running Script

• python script/pca.py

Running Jupyter Notebook

• jupyter notebook jupyter/pca.ipynb

Running Colaboratory

• See in Colaboratory