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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
Development and Comparison of Prediction Models for Estimating Short Term Energy Demand of a Hotel Building
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Yılmaz, Selimcan; Özlük, Özgür
This project presents a machine learning model building approach to developing a model for predicting next hour electricity consumption of a hotel complex in Cyprus, with the aim of improving existing prediction accuracy due to comparing different models to choose best performing. Model building process in this project includes three main steps.

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