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    Steel Product Clustering and Feature-Based Product Price Estimation for Flat Secondary Materials
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kemerci, Meryem; Özlük, Özgür
    Machine Learning replaces manual and repeatable processes every day, none of the industries can resist these developments. Older systems were rule-based which would bring some level of automation, but all had their limits. One of the goals of Machine Learning is prediction, and it can be used to obtain higher accuracy and better forecasts. Price predictions are made by hand according to market expectations and countries’ conjuncture in the past, but it is changing fast with the developments of Artificial Intelligence tools. In steel Industry, price levels are determining based on human intuition and simpler statistics. Profits are directly connected to the right pricing for the right time, machine learning algorithms may do the quotation of the steel properly to increase the company profits. This study aims to classify items as per quality and estimate the price level for the products. Feature selection preprocessing steps are used to enhance the performance and scalability of the classification method. The second part is feature-based product price estimation for the secondary products and selects the predictors of each quality under the product family.