Optimization of Sales Forecasting Using Machine Learning

Businesses need to estimate sales to properly strategize and make decisions. For this, conventional statistical techniques have been primarily applied; however, they mostly fall short of perfectly modeling the complexities of sales data. This paper explores the machine learning method to improve sales forecast accuracy. We will then assess the efficiency of various machine learning algorithms in estimating future sales from such inputs as current sales and profits with the use of real sales data. In this paper, we will cover the pre-processing of data, training of the model, and finally, the performance assessment. We understand that, contrary to traditional techniques, the machine learning models-Random Forest, Decision Tree, Neural Network, and Linear Regression-have much better accuracy and flexibility in the presence of complex sales patterns. More specifically, our results have returned that trained ML models can predict future sales upon the input of pre-existing sales and profits. The findings have implications for firms seeking to enhance their skills in forecasting. In addition, our research serves to add to the literature with some empirical evidence on the efficacy of machine learning techniques in sales prediction, and some possible ways in which the utility of these techniques in this domain could be usefully improved in the future.