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Scoring Neighborhoods for Locating Atm Using Machine Learning
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Yıldırım, Oğuzhan; Küçükaydın, Hande
Facility location is a general problem that is important for many different sectors and it is even more important when building the facility costs too much. In this project we analyzed the neighborhoods of Turkey and built two different models to estimate the good and bad neighborhoods for locating an ATM, which has significant costs for banks to build one. We used demographic and socio-economic data of 4,504 neighborhoods in Turkey and built models using Linear Regression and Decision Tree techniques of Machine Learning to find the best neighborhoods for locating a new ATM for a new bank entering the market. We compared the results of two machine learning methods and the results showed that we can make successful predictions of the neighborhoods by using machine learning methods which are good to locate an ATM without classical optimization techniques that requires complex calculations and machine learning methods.

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