Efficient approach for Predicting Sales using Supervised Machine Learning Algorithms

Abstract

Abstract The significant impact in businesses is generally affected by manufacturing, planning, supply chain, marketing, warehousing, logistics, and resource management, usually managed by sales forecasting. Casual forecasting techniques and the correlations between factors are used to anticipate future sales behaviour without relying on historical data and trends. Despite the wide usage in research and application, there are severe drawbacks regarding the forecasting techniques related to classic time series. The sales related to supermarkets, along with association rules, regression techniques, time series algorithms, etc., are estimated by numerous available methods. This paper explains constructing a prediction model based on a supervised machine learning algorithm known as Ada Boost to estimate possible sales for 45 Walmart stores in various locations. It is a great opportunity for researchers to predict sales for Walmart, as it is the largest store existing in the world. The sales will be affected on a periodic basis during an event or holidays. This affect might also extend on a daily basis.