Enhancing Sales Forecasting and Prediction with Cutting-Edge Machine Learning Methods

Accurate sales prediction is crucial for the success or failure of modern retail corporations that manage a vast network of businesses. As of now, general stores and large shops monitor every person’s product information in order to expect potential shopper interest and update stock administration. Sales forecasting enables these corporations to allocate resources efficiently and make informed business plans. In this paper, a machine learning-based sales forecasting model is proposed to compare their efficiencies in predicting product sales. Our study highlights the effectiveness of the new model by involving feature engineering to extract features from historical sales data, training and testing the data, followed by the use of a few machine learning models to forecast future sales amounts. Among the four major algorithmic models, RandomForest and XGBoost stood out the best, surpassing Linear Regression and Support Vector Machines. Notably, RandomForest showed slightly better results with a lower RMSE value compared to the rest of them. This study contributes to the ongoing discussion about using machine learning and artificial intelligence in the concept of sales forecasting, to make more accurate predictions in retail.