Mobile advertisements click through rate prediction using machine learning

• Main Authors: Jacob, Jacinta Ann, Gnanavel, S.
• Format: Conference Proceeding
• Language: English
• Subjects: Advertising; Decision trees; Forecasting; Information sources; Machine learning; Online advertising; Platforms; Prediction models; Regression models
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0217006

Online advertising has a big impact on whether your business succeeds or fails. Because of this, it is crucial to assess your advertisement’s effectiveness before posting it online. Finding the Click-Through Rate (CTR) allows for this. Unfortunately, because you must gather user clicks before determining Click-Through Rate, this method is not environmentally friendly. In this situation, CTR prediction is helpful. For forecasting ad Click-Through Rate, user click data is a crucial source of information. Accurate Click-Through Rate prediction for contemporary e-advertising platforms is a challenging and crucial undertaking. Click-Through Rate prediction employs machine learning methods to determine how many times a potential consumer has clicked on an online ad. The more clicks an advertisement receives, the more successful it is. In this paper, we create a machine learning-based Click-Through Rate prediction model. Finding the Click-Through Rate allows for this. Unfortunately, because you must gather user clicks before determining Click-Through Rate, this method is not environmentally friendly. In this situation, CTR prediction is helpful. For forecasting ad Click-Through Rate, user click data is a crucial source of information. Accurate CTR prediction for contemporary e-advertising platforms is a challenging and crucial undertaking. Click-Through Rate prediction employs machine learning methods to determine how many times a potential consumer has clicked on an online ad. The more clicks an advertisement receives, the more successful it is. In this paper, we create a machine learning-based Click-Through Rate prediction model. The proposed study defines a model that produces accurate results with minimal use of computational resources. Three classification methods were used namely logistic regression, decision tree classifier and random forest classifier. Awasu dataset was used for analysis. The click data is generated over a 10-day period and sorted chronologically. This study answers the following question: Considering a user and the page they visit. What is the likelihood that they will click on a particular ad? The Random Forest classifier proved to be the best model with an accuracy score of 96%.