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Öğe Machine learning methods for prediction real estate sales prices in Turkey(Pontificia Univ Catolica Chile, Escuela Construccion Civil, 2023) Çılgın, Cihan; Gökçen, HadiOwning a house is one of the most important decisions that low and middle income people make in their lives. The real estate market is a significant factor of the national economy as much as it is important for individuals. Therefore, predicting real estate values or real estate valuation is beneficial and necessary not only for buyers, but also for real estate agents, economists and policy makers. This issue represents an active area of research, as individuals, companies and governments hold considerable assets in real estate. In this context, the aim of the study is to predict real estate prices with Machine Learning methods using the real estate sales data set in June and July 2021 belonging to the province of Ankara. In particular, it is to perform a comprehensive comparison on Machine Learning regression types methods that give suc-cessful prediction results in various but similar tasks, which are not included in the real estate literature. Real estate data obtained over the Internet was first included in a detailed data preprocessing process, and then Linear, Lasso and Ridge Regression, XGBoost and Artificial Neural Networks (ANN) methods were used on this dataset. According to empirical findings, XGBoost and ANNs appear as very important alternatives in predicting real estate sales prices.Öğe Sentiment analysis of public sensitivity to COVID-19 vaccines on Twitter by majority voting classifier-based machine learning(Gazi University, Faculty of Engineering and Architecture, 2023) Çılgın, Cihan; Gökçen ,Hadi; Gökşen, YılmazPurpose: The aim of this study is to analyze public sentiment with Machine Learning of vaccine-related tweets obtained on Twitter in order to better understand the attitudes and concerns of social media users, especially regarding COVID-19 vaccines in Turkey. For this purpose, a majority voting method has been developed with machine learning methods, which are frequently used in sentiment analysis studies.Theory and Methods: In the study, machine learning algorithms used in six different classification tasks, which are frequently used in sentiment analysis, were compared. Then, by comparing these machine learning methods, the majority voting method, which is an ensemble learning method, was developed by using the three methods with the highest accuracy. For this purpose, both soft voting and hard voting methods were used to generate majority voting in the classification task. In addition, the data used in the study were collected between 01.04.2021 and 31.08.2021, when vaccine studies accelerated in Turkey, a total of 412,588 tweets in Turkish. Results: Although the SVM algorithm among the individual methods achieved a high success rate of 89.6%, it is seen that the XGBoost model is the most successful algorithm with a accuracy rate of 89.8%. Although the Random Forest approach among other machine learning approaches has achieved remarkable success, the same is not the case for other methods. For this reason, high accuracy SVM, XGBoost and Random Forest methods are used in both hard voting and soft voting majority voting approaches. Although the hard voting method achieved a higher accuracy than the individual methods with a success rate of 88.9%, the soft voting method was the most successful classification method with a relatively high accuracy rate of 90.5%. For this reason, soft voting approach was used in the labeling of daily tweets obtained in the study.Conclusion: As a result of the analyses carried out with the soft voting method, although there are fluctuations in the sentiment polarity of the tweets about the vaccine, it is observed that the negative sentiments and therefore the opposition to the vaccine is becoming more and more popular on social media. Particularly, when compared to previous study findings, positive sentiments in vaccine-related posts in Turkey are quite low rate. For this reason, the ongoing opposition to vaccination on social media in Turkey becomes a subject that needs to be examined more carefully. As far as we know, this study is the first in Turkey to perform sentiment analysis on COVID-19 vaccines. In addition, the findings of the study show that the proposed method is a valuable and easily applied tool to monitor the sensitivity of COVID-19 vaccines with a sentiment analysis approach via social media.Öğe Dış ticaret verileri için kümeleme analizi: Türkiye, Azerbaycan ve Kazakistan örneği(SOSYOEKONOMİ SOC, 2021) Çılgın, Cihan; Kurt, Aslı SedaForeign trade is one of the most critical sources of welfare. Factors such as population, per capita income, and the distance between countries is among the crucial determinants of foreign trade. This paper aims to cluster the data set regarding the export and import of Turkey and Turkic Republics by considering other determinants of foreign trade for 2017. In this study, Kazakhstan and Azerbaijan, of which data sets are available, are considered, and Turkey. For this paper, K-means, Ward hierarchical clustering, and self-organizing maps are used. The findings of this paper present detailed evidence as to the export and import of the countries handled.
