A Comprehensive Analysis of Predicting Future Sale and Forecasting Using Random Forest Regression

Abstract

In the realm of sales prediction, accurately forecasting future sales is a critical challenge for businesses seeking to optimize marketing strategies and resource allocation. The conventional methodology for sales prediction often involves linear regression, which may not capture the intricate, non-linear relationships between advertising expenditures and sales. Consequently, the algorithm proposed here is an innovative solution utilizing random forest regression. Random forest is a versatile ensemble learning technique that can effectively model complex interactions among advertising channels and their impact on sales. By harnessing the collective wisdom of multiple decision trees, this method can offer superior predictive accuracy compared to traditional linear approaches. The results demonstrate that this random forest regression model outperforms existing methodologies, providing a more robust framework for future sales prediction.