Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation

Provides steps for carrying out principal component analysis in r and use of principal components for developing a predictive model.
R code: https://github.com/bkrai/Top-10-Machine-Learning-Methods-With-R
Timestamps:
00:00 Introduction - Principal Component Analysis in R
00:05 Iris Data
01:16 Partition Data
02:06 Scatter Plots Correlation Coefficients
05:02 Principal Component Analysis
10:17 Orthogonality of Principal Component
11:38 Bi - Plot interpretation
18:31 Prediction with Principal Components
19:50 Multinomial Logistic Regression Model with First Two PCs
21:07 Confusion Matrix & Misclassification Error ‘Training Data’
22:25 Confusion Matrix & Misclassification Error ‘Testing Data’
22:48 PCA Advantage
23:24 PCA Disadvantage

principal component analysis is an important statistical tool related to analyzing big data or working in data science field.

Machine Learning videos: https://goo.gl/WHHqWP
Becoming Data Scientist: https://goo.gl/JWyyQc
Introductory R Videos: https://goo.gl/NZ55SJ
Deep Learning with TensorFlow: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
Text mining: https://goo.gl/7FJGmd
Data Visualization: https://goo.gl/Q7Q2A8
Playlist: https://goo.gl/iwbhnE

R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Видео Principal Component Analysis in R: Example with Predictive Model & Biplot Interpretation канала Dr. Bharatendra Rai