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This research presents an innovative and unique way of solving theadvertisement prediction problem which is considered as a learning problem overthe past several years. Online advertising is a multi-billion-dollar industryand is growing every year with a rapid pace. The goal of this research is toenhance click through rate of the contextual advertisements using LinearRegression. In order to address this problem, a new technique propose in thispaper to predict the CTR which will increase the overall revenue of the systemby serving the advertisements more suitable to the viewers with the help offeature extraction and displaying the advertisements based on context of thepublishers. The important steps include the data collection, featureextraction, CTR prediction and advertisement serving. The statistical resultsobtained from the dynamically used technique show an efficient outcome byfitting the data close to perfection for the LR technique using optimizedfeature selection.

Click Through Rate Prediction for Contextual Advertisment Using Linear Regression