• Sukrant Kathuria
• Anjesh Kumar
• Shikha Gupta
• Atul Mishra

Computational Advertising is the currently emerging model in the advertising industry. Web pages visited per user every day is considerably increasing day by day which results in the vast access to display advertisements (ads). The main metric facilitates the measurement of the effectiveness of an advertisement is termed as Click Through Rate (CTR) [3], it’s the rate at which the ad is clicked by the user. The placement of ads in appropriate location leads to the rise in the CTR value that influences the growth of customer access to advertisement which is beneficial for publisher and advertiser. Thus, it is very important to predict the CTR metric in order to formulate efficient ad strategy for placement. This paper proposes a predictive model that generates the click through rate based on different dimensions of ad placement for display advertisements using machine learning techniques such as decision trees, random forest, SGD-based logistic regression. The experiment result reports that SGD-based logistic regression-based click model outperforms in predicting CTR. Further this paper is divided into 5 sections, section-1 contains introduction, section-2 contains literature review, section-3 contains ctr prediction models, section-4 contains experiments and result, section-5 contains conclusion and future work and section-6 contains references.

Click Through Rate (CTR), Contextual Advertisements, Machine Learning, Web advertisements, Decision tree, random forest, SGD-logistic regression.

"Predicting the Click Through Rate Using Machine Learning Methodologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.474-478, March-2019, Available :http://www.jetir.org/papers/JETIR1903971.pdf

"Predicting the Click Through Rate Using Machine Learning Methodologies", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp474-478, March-2019, Available at : http://www.jetir.org/papers/JETIR1903971.pdf