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Forecasting With Ensemble Methods: an Application Using Fashion Retail Sales Data
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yüzbaşıoğlu, Orkun Berk; Küçükaydın, Hande
In this project, ensemble methods of machine learning are used to predict short term store sales of a fashion retailer. Sales forecasts of various products at different stores are generated for a span of three months with bagging tree regressor, random forest regressor, and gradient boosting regressor algorithm. Algorithms are trained and evaluated with real past sales data of a Turkish fashion retailer. The predictive performance of the models is compared with linear regression. The results of the study show that random forest regressor shows the best performance

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