  This research presents an innovative and unique way of solving the
advertisement prediction problem which is considered as a learning problem over
the past several years. Online advertising is a multi-billion-dollar industry
and is growing every year with a rapid pace. The goal of this research is to
enhance click through rate of the contextual advertisements using Linear
Regression. In order to address this problem, a new technique propose in this
paper to predict the CTR which will increase the overall revenue of the system
by serving the advertisements more suitable to the viewers with the help of
feature extraction and displaying the advertisements based on context of the
publishers. The important steps include the data collection, feature
extraction, CTR prediction and advertisement serving. The statistical results
obtained from the dynamically used technique show an efficient outcome by
fitting the data close to perfection for the LR technique using optimized
feature selection.
