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The Use of Artifi cial Neural Networks (ANN) in Forecasting Housing Prices in Ankara, Turkey

100%

Kitapci O. , Tosun Ö. , Tuna M. F. , Turk T.

Journal of Marketing and Consumer Behaviour in Emerging Markets

2017

nr 1(5)

4-14

The purpose of this paper is to forecast housing prices in Ankara, Turkey using the artificial neural networks (ANN) approach. The data set was collected from one of the biggest real estate web pages during April 2013. A three-layer (input layer – one hidden layer – output layer) neural network is designed with 15 different inputs to forecast the future housing prices. The proposed model has a success rate of 78%. The results of this paper would help property investors and real estate agents in developing more effective property pricing management in Ankara. We believe that the artifi cial neural networks (ANN) proposed here will serve as a reference for countries that develop artifi cial neural networks (ANN) method-based housing price determination in future. Applying the artifi cial neural networks (ANN) approach for estimation of housing prices is relatively new in the field of housing economics. Moreover, this is the fi rst study that uses the artificial neural networks (ANN) approach for analyzing the housing market in Ankara/Turkey.

Flight delay prediction based with machine learning

100%

Hatıpoğlu I. , Tosun Ö. , Tosun N.

tom Vol. 18, no. 1

97--107

Background: The delay of a planned flight causes many undesirable situations such as cost, customer satisfaction, environmental pollution. There is only one way to prevent these problems before they occur, and that is to know which flights will be delayed. The aim of this study is to predict delayed flights. For this, the use of machine learning techniques, which have become widespread with the development of computer capacities and data storage systems, is preferred. Methods: Estimations are made with three up-to-date techniques XGBoost, LightGBM, and CatBoost techniques based on Gradient Boosting from machine learning techniques. The bayesian technique is used for hyper-parameter settings. In addition, the Synthetic Minority Over-Sampling Technique (SMOTE) technique is also used, as the majority of flights are on time and delayed flights, which constitute a minority class, may adversely affect the results. The results are analyzed and shared with and without SMOTE. Results: As a consequence of the application, which was run on a data set containing all of an international airline's flights [18148 flights] for a year, it was discovered that flights may be predicted with high accuracy. Conclusions: The application of machine learning techniques to anticipate flight delays is new, but it has a lot of potential. Companies will be able to avert problems before they develop if delays are correctly estimated, which can generate plenty of issues. As a result, concrete advantages such as lower costs and higher customer satisfaction will emerge. Improvements will be made at the most vulnerable place in the aviation business.