Analysis of Machine Learning Model for Predicting Sales Forecasting

The purpose of the study is to investigate the possibilities of machine learning methods for predicting revenue for a retail company. The significance of precise sales projections and the difficulties companies encounter in attaining it are covered in the opening section of the paper. The different machine learning techniques used in the research are then described, including neural networks (NN), decision trees, RF and linear regression. The machine learning algorithms are trained and tested using past sales data from a retail company. The outcomes demonstrate that the Random Forest (RF) algorithm worked good as compare to other models in terms of precision and accuracy. The research also finds crucial elements that have a big effect on purchases, like timing, marketing campaigns, and economic signs. The paper's conclusion highlights the benefits of machine learning for sales predictions, including improved precision, speed, and scale. The study's findings offer practical guidance for businesses seeking to enhance their capacity for sales planning and streamline their operations.