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Forecasting the Daily Sales of a Franchise
Abstract
Historically, restaurant managers used either historical data or simple logical methods to estimate customer numbers or sales volume. These techniques usually consist of an intuitive prediction based on the experience of the manager. However, restaurant sales forecasts are a complex task because they are influenced by numerous factors that can be classified as time, weather conditions, economic factors, and random events. In this case, old techniques may give insufficient results. It is aimed to compare the estimation Simit which is one of the most consumed daily snacks in Turkey sales accuracy of the learning methods and determine the model that provides the highest accuracy and determine the factors affecting the buying behavior of one of the leading Simit chain stores in Turkey in the food sector by using popular machine learning algorithms.
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