Sales prediction for media platforms advertising expenditure using Linear Regression / Nur Athirah Abdurahman

Sales Prediction in Media Platform Advertising Expenditure Using Linear Regression is a critical research endeavor addressing the challenges associated with forecasting sales in the context of media platform advertising expenditure. The study focuses on the application of the Linear Regression algorithm to predict sales outcomes based on advertising spending patterns. Accurate sales predictions are essential for businesses to optimize their advertising strategies and maximize return on investment. The primary goal of this research is to develop a robust prediction system capable of aiding businesses in the media industry by providing insights into potential sales outcomes. Early and accurate sales predictions can significantly impact decision-making processes, allowing organizations to allocate resources effectively and enhance overall marketing strategies. The methodology employed in this project involves the implementation of the Linear Regression algorithm, a statistical modeling technique that analyzes the relationship between advertising expenditure and sales. The algorithm calculates a linear equation that represents the best-fit line through the data points, enabling the prediction of future sales based on advertising investment.