An Efficient Ad-Click Prediction System using Machine Learning Techniques
A.Lakshmanarao1, A.Srisaila2, T. Srinivasa Ravi Kiran3
1A. Lakshmanarao*, Department of Computer Science & Engineering, Raghu Engineering College, Visakhapatnam, A.P, India.
2Dr. A.Srisaila, Department of Information Technology, V.R. Siddhartha Engineering College Vijayawada, India.
3T. Srinivasa Ravi Kiran, Department of Information Technology, V.R. Siddhartha Engineering College Vijayawada, India.
Manuscript received on February 06, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on February 30, 2020. | PP: 1269-1272 | Volume-9 Issue-3, February, 2020. | Retrieval Number: C5518029320 /2020©BEIESP | DOI: 10.35940/ijeat.C5518.029320
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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: Ad-click prediction is a learning problem that is highly related to the multi-billion-dollar ad- promoting the online advertising industry. As the number of internet users in India reached 525 million in 2019, online advertising companies are trying to influence internet usage users for promoting their business. Machine learning is a technique in which systems getting to act without any explicit programming. Currently, machine learning is pervasive today and we can use machine learning models in every research field. The accuracy of the ad-click prediction system impacts business revenue, so it is very important to build a prediction system with fewer false positives and false negatives.in this paper, we proposed an ad-click prediction system based on machine learning techniques. The dataset is taken from Kaggle. The dataset contains nine features. The goal of the model is to evaluate the probability of an online user to click on a given ad. We built a machine learning model based on these features. We applied a voting classifier on the dataset and achieved an accuracy of 98%.We used python language for implementation.
Keywords: Ad-click prediction, machine learning, python.