Online Advertising Dataset Using ANN (Artificial Neural Networks) and LR (Linear Regression Techniques)
Asadi Srinivasulu, Kummara Bala Chowdappa, M. Deena Babu, L. Venkateswara Reddy, Ashok V. Kumar
Abstract
Advanced displaying provides online sentiment digital advertisers with information into their sales. Sales are critical in increasing the business when sentimental digital marketing methods are used. Sentiment digital marketing is one approach for providing information about their company, gadgets, and products. Utilizing web-based advertising metrics like aggregation are designed to save you time when you need the most recent information for a show or report you're dealing with against a cutoff time. Business members can use our unique time span to expand their business. The current system has a high error rate, a poor business visualization strategy, and a high time complexity. AI technology has been employed in a variety of ways to improve the reach of target audiences in online targeted digital advertising. Recent study shows that improved computational force promotes capacity-focused granular crowds. This study explores and identifies several AI technologies used to improve specific web-based advertising. These three categories widely recognize and divide the word-of-mouth, client-driven, and content-driven promotion via radio, television, and newspapers methodologies that compose AI-based Internet designed promoting strategies. The proposed AI computation accurately predicts information at 94.50% using linear regression and neural network methods.