INTALLIGENT SALES PRIDICTION

Predictions and decision analysis must be integrated in an intelligent decision analysis system. The majority of business organizations rely heavily on a knowledge base and demand forecasts of trends in sales. The exactness in deals gauge gives a major effect in business. To improve forecasting accuracy and efficiency, data mining methods are very effective tools for uncovering buried information in a large dataset. This research focuses on the in-depth investigation and evaluation of understandable predictive models with the goal of improving predictions regarding future sales. With the volume of data and precision required for sales forecasting, traditional forecasting systems struggle. Various data mining methods could be used to solve these issues. The concepts of sales data and sales forecast are the subject of a brief discussion in this paper. The research work’s concluding section provides a description of the various sales prediction methods and metrics. On the premise of an exhibition assessment, a most ideal prescient model is proposed for the deals pattern gauge. The reliability and accuracy of effective methods used for prediction and forecasting are summed up in the findings. The Gradient Boost Algorithm was found to be the best-fitting model, providing the highest level of forecasting and future sales prediction accuracy.