Car Price Prediction Using Machine Learning

Abstract

Because of new computing technologies,
machine learning today is not like machine
learning of the past. It was born from pattern
recognition and the theory that computers can
learn without being programmed to perform
specific tasks; researchers interested in artificial
intelligence wanted to see if computers could learn
from data. The iterative aspect of machine learning
is important because as models are exposed to
new data, they are able to independently adapt.
They learn from previous computations to produce
reliable, repeatable decisions and results. It’s a
science that’s not new – but one that has gained
fresh momentum. While there is an end number
of applications of machine learning in real life
one of the most prominent application is the
prediction problems. There are various topics on
which the prediction can be applied. One such
application is what this project is focused upon.
Websites recommending items you might like based
on previous purchases are using machine learning
to analyze your buying history – and promote other
items you'd be interested in. This ability to capture
data, analyze it and use it to personalize a
shopping experience (or implement a marketing
campaign) is the future of retail