Understand & interpret result of pca MATLAB | Part 2 | Machine Learning

Code:

clc
clear all
close all
warning off
t=randn(1,1000);
x=0.2*randn(1,1000);
a=-pi/4;
z=[cos(a) -sin(a);sin(a) cos(a)];
m=[t;x];
k=z*m;
r=k(1,:);
y=k(2,:);
figure;
plot(r,y,'b.');
title('Data after rotation');
k=k';
[coeff,score,latent,tsquared,explained,mu]=pca(k);
Prerequisite:
Principal Component Analysis (PCA) Intuition | Machine Learning
https://youtu.be/xMTyAL4f6S4
PCA (Practical) | MATLAB
https://youtu.be/PfROP0VQ3wU
Principal Component Analysis (PCA) | MATLAB | Machine Learning
https://youtu.be/zBFno27oeTg
Secret message extraction from high dimensional space using PCA | MATLAB
https://youtu.be/IrQJoSi3dpE
How many dimensions are needed?
https://youtu.be/iMHTgwTFJjQ
Understand & interpret result of pca MATLAB | Machine Learning
https://youtu.be/u1kUB4Cs9nI

Learn Complete Machine Learning & Data Science using MATLAB:
https://www.youtube.com/playlist?list=PLjfRmoYoxpNoaZmR2OTVrh-72YzLZBlJ2

Learn Digital Signal Processing using MATLAB:
https://www.youtube.com/playlist?list=PLjfRmoYoxpNr3w6baU91ZM6QL0obULPig

Learn Complete Image Processing & Computer Vision using MATLAB:
https://www.youtube.com/playlist?list=PLjfRmoYoxpNostbIaNSpzJr06mDb6qAJ0

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