### Using Singular Value Decomposition (SVD) to factorize data on car properties

This file has data on five
cars: Chevrolet Matiz, Chevrolet Evanda, Chevrolet Tacuma, Chevrolet Nubira, Chevrolet
Kalos. The properties considered are engine displacement, length, width, cargo
volume, number of cylinders, power, fuel consumption and CO2 amount.

SVD reveals that 93,4%
and 5,4% of information is contained in the first two singular vectors,
respectively – leaving only 1,2% for the rest of the decomposition. The first
singular vector can be called general assessment – it shows a basic relationship
between all the cars across the entire set of variables. Our main focus is to
try to understand the contribution that the second singular vector has. As
shown above, the most influential contributions of the second singular vector
are in engine displacement, cargo volume and power (kW) – this is the
fine-tuning, which shows that the cars can’t simply be described with a rank
one matrix.

This is the second
file on this blog, that was not originally created by me (the first was the
model of bird flocks), but rather replicated and expanded.

## No comments:

Post a Comment