Predicting Price of Daily Commodities using Machine Learning

Daily commodities are necessities that take up a large part of the product market. The fluctuation of the price in daily commodities is an apparent phenomenon that has a high bearing on the cost of living. Early prediction of price can help to control prices by monitoring and adjusting the price in the market beforehand so that the commodity market runs in a stable way. Suppliers and manufacturers can choose to produce or supply commodities accordingly. It will help to balance inventory and profitability as well as improve availability to consumers. Thus, it is directly related to the interest of consumers and producers. The selection of suitable and favorable algorithms is one of the most practiced studies in the field of data forecasting. It takes the advantage of the empirical evidence available at hand to choose the most appropriate model since no model can be contemplated as the best. In this study, We performed an extensive and comprehensible experimental evaluation to predict commodity price using state-of-the-art Machine Learning algorithms.