I am a statistical consultant and R enthusiast.

I believe in the use of data science for social good and have worked with several non-profit organizations.

## How to implement PCA from scratch in R

What is PCA ? Principal component analysis (PCA) is an approach based on the singular value decomposition of the data. The goal of PCA is reduce the dimension of the data you are using by projecting this data into the sub-space The french school of ‘Analyse des données’ It focuses on geometrical summary(cars)## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15. [Read More]

## Hello world

A small test

summary(cars)
##      speed           dist
##  Min.   : 4.0   Min.   :  2.00
##  1st Qu.:12.0   1st Qu.: 26.00
##  Median :15.0   Median : 36.00
##  Mean   :15.4   Mean   : 42.98
##  3rd Qu.:19.0   3rd Qu.: 56.00
##  Max.   :25.0   Max.   :120.00

Regression test

fit = lm(dist ~ speed, data = cars)
b = coef(summary(fit))
plot(fit)