Cost Optimized Hybrid System in Digital Advertising using Machine Learning
Avinash Sharma1, Swati V. Kulkarni2, Dhanajay Kalbande3, Surekha Dholay4
1Avinash Sharma, Maharishi Markandeshwar Engineering College, Campus, India.
2Swati V. Kulkarni, Sardar Patel Institute of Technology, Andheri, Mumbai, India.
3Dhanajay Kalbande, Sardar Patel Institute of Technology, Andheri, Mumbai, India.
4Surekha Dholay, Sardar Patel Institute of Technology, Andheri, Mumbai, India.
Manuscript received on 10 June 2019 | Revised Manuscript received on 17 June 2019 | Manuscript Published on 22 June 2019 | PP: 934-939 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H11580688S219/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Digital advertising or Internet marketing is the term used to describe the process of advertising a product or brand through digital medium. It includes promotional advertisements and messages delivered through email, social media websites, search engines, mobile applications, web sites and affiliates programs. This work presents a system that solves the challenge of reaching correct people and optimizing the cost problem using various machine learning techniques. It also explains various research trends in predictive analytics, product pricing and targeting audience for digital advertising. Digital Advertising has captured wide attention from market. It is very powerful tool to reach correct people at correct time. Also it reduces the cost of broadcasting advertisement as the ad is displayed only to people who might be interested in the content. The interest prediction for audience targeting provides 89.44% accuracy using Naive Bayes classifier.
Keywords: Digital Advertising, Predictive Analytics, Cost Optimizing, Audience Expansion, Machine Learning, Logistic Regression, Naive Bayes.
Scope of the Article: Machine Learning