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Arşivlenen Tez
Şehir dışı harcamaların ve turist gezilerinin kredi kartı işlemsel verileri kullanılarak analizi
Credit card transaction data contains a vast amount of valuable information that can indicate consumer behaviour patterns and mark out human mobility. In this study we analyse the transactions carried out by a sample of 10.000 Istanbul-based customers of a Turkish bank to scrutinize expenditures incurred out of Istanbul. In our preliminary descriptive analysis, we examine the relation between demographic attributes and spending measures, as well as investigate the extent to which the population and the number of points of interest imply higher or lower credit card expenditure by visitors. We develop a methodology to extract tourist trips from consecutive credit card transactions. Subsequently, we implement a hierarchical clustering method to evaluate what the purpose of these trips might have been. Our results indicate 5 clusters of purpose: 'Leisure', 'Business', 'Acquisition', 'Visiting Friends and Relative' and 'Package Holiday'. The same clustering method is applied to segment provinces of Turkey based on which product and service categories visitors prefer. We deploy a number of predictive models to estimate tourist expenditure and whether a person would embark on a trip in the upcoming months. The predictive power of these models are generally moderate; nevertheless, several of the most useful predictors are behavioural or are related to previous trips, factors that have not been considered in literature.
Finansal refahın tahminlenmesinde kestirimci unsur olarak büyük beşli kişilk özelliklerinin kullanılması: Büyük veri yaklaşımı
Research has posited credit card transactions as highly probable to be grounded on the personality of the card holder. In this research, we investigate whether the big five personality traits of customers derived from credit card transactions predict their financial wellbeing. Our approach uses real data from a private Turkish bank, which contain both the demographic and financial records of 10,172 consumers located in Istanbul with 911,280 transactions. We filter purchasing categories related to the big five personality traits from Matz, Gladstone, and Stillwell's study (2016). First, we link spending categories to the big five personality traits by considering Matz et al.'s study (2016). Then we calculate the big five factor scores of customers by monthly aggregating the individual big five scores of their transactions. Next, we investigate the relationship between the monthly big five personality scores and payment behavior of their credit card statements. In our main model, we estimated customers' on-time payment behavior of the full amount due 8.8 % better than a random prediction (with 54.4 % AUROC value) by using their monthly big five personality scores and yearly and six-month based trends as independent variables.
Müzik duygusu tanıma: Çok-modlu makine öğrenmesi yaklaşımı
Music emotion recognition (MER) is an emerging domain of the Music Information Retrieval (MIR) scientific community, and besides, music searches through emotions are one of the major preferences utilized by web users. As the world goes to digital, the musical contents in online databases, such as Last.fm have expanded exponentially, which require substantial manual efforts for managing them and also keeping them updated. Therefore, the demand for advanced and flexible search mechanisms, which can be personalized according to the emotional state of users, has received increasing attention in recent years. This thesis concentrates on addressing music emotion recognition problem by presenting several classification models, which were fed by textual features, as well as audio attributes extracted from the music. In this study, we build both supervised and semi-supervised classification designs under four research experiments, that addresses the emotional role of audio features, such as tempo, acousticness, and energy, and also the impact of textual features extracted by two different approaches, which are TF-IDF and Word2Vec. Furthermore, we proposed a multi-modal approach by using a combined feature-set consisting of the features from the audio content, as well as from context-aware data. For this purpose, we generated a ground truth dataset containing over 1500 labeled song lyrics and also unlabeled big data, which stands for more than 2.5 million Turkish documents, for achieving to generate an accurate automatic emotion classification system. The analytical models were conducted by adopting several algorithms on the cross-validated data by using Python. As a conclusion of the experiments, the best-attained performance was 44.2% when employing only audio features, whereas, with the usage of textual features, better performances were observed with 46.3% and 51.3% accuracy scores considering supervised and semi-supervised learning paradigms, respectively. As of last, even though we created a comprehensive feature set with the combination of audio and textual features, this approach did not display any significant improvement for classification performance.
Zengin araç rotalama: Bir lojistik firması için veri odaklı sezgisel uygulama
Changing online shopping behaviors have resulted in the emergence of different product and services that aim high customer satisfaction. In this thesis, we develop an alternative approach to solve problem of a logistics company, which operates solely for e-commerce transactions, using an Adaptive Large Neighborhood Search (ALNS) heuristic. To understand the nature of the distribution system and for the development of the solution procedure, we create, preprocess and analyze a dataset constructed from company's database that is used for daily operations. The proposed solution provides a prioritization mechanism for the deliveries based on certain specifications related to deliveries. To evaluate the performance of the proposed ALNS, we perform computational experiments using scenarios with real-life instances extracted from the dataset. Our results show that, the proposed ALNS can produce solutions with high quality regarding customer satisfaction.
Ürün ağlarında betimleyici ve ön görücü analitiklerin etkileşimi: Sam's club vakası
Due to the fact that there are massive amounts of available data all around the world, big data analytics has become an extremely important phenomenon in many disciplines. As the data grows, the need for businesses to achieve more reliable and accurate data-driven management decisions and to create value with big data applications grows as well. That is the reason why big data analytics become a primary tech priority today. In this thesis, initially we use a two-stage clustering algorithms in the customer segmentation setting. After the clustering stage, the customer lifetime value (CLV) of clusters are calculated based on the purchasing behaviors of the customers in order to reveal managerial insights and develop marketing strategies for each segment. At the second stage, we used HITS algorithm in product network analysis to achieve valuable insights from generated patterns, with the aim of discovering cross-selling effects, identifying recurring purchasing patterns, and trigger products within the networks. This is important for practitioners in real-life application in terms of emphasizing the relatively important transactions by ranking them with corresponding item sets. From practical point of view, we foresee that our proposed methodology is adaptable and applicable to other similar businesses throughout the world, providing a road map for the potential applications.
Teşhise yönelik tetkik istemi öneri sistemi tasarlama: Bir veri analitiği yaklaşımı
In the thesis, we propose a frequent itemset detection based on a diagnostic test order set recommendation by ICD code for internal medicine physicians. In order to carry out this study, we used an examination data from the internal medicine department of a state hospital in Ankara, Turkey, which included 68,033 unique visits and 46,314 unique patients in the closed interval of 2015-2016. In the study, we calculated how using the test sets that we determined with the Apriori algorithm in the training set might affect the test selection effort in the ongoing period. As an evaluation criterion, we used the percentage change in the total number of clicks that the physician will use when choosing a test on HIMS if the test request group is used. In addition, we calculated the percentage of the visit that the recommendation set could be used by looking at the intersection of the examination request of the physician and the test set we recommended.
Türkiye'de kurumsal yönetim, 2000-2018: Çeviri literatürüne göre bir süreç analizi
Institutional theory in organization studies is concerned with social contexts of organizations. Recently, how these social contexts can be better understood and how organizational responses may contribute to the shaping of these contexts have become a subject of interest in studies centering the concept of translation. In addition, process organization studies that gained popularity as a methodologically different perspective aimed to understand organizational phenomena within a process that can be traced with successive phases. These two approaches, albeit similar in their approaches, have not been fully leveraged in tandem. This dissertation takes corporate governance as an empirical material and attempts to understand how corporate governance as an idea and/or a set of practices have been translated into Turkey, a context that is very much different than the context of which the idea originated. In doing so, the process through which corporate governance has been translated from the concept's initiation in Turkey in 2000 to 2018 when the latest available data was collected is analyzed qualitatively by employing Røvik (2007)'s "rules of translation" approach as adopted by Wæraas and Sataøen (2014). Taking actors of translation as government and public authorities, business associations, consulting firms and corporations, the study reveals that corporate governance has been transformed into a more contextualized version of the concept in three phases and through narratives and practices of the actors who perform different translations depending on their agentic capacities and interests.
Kitapların çıkış tarihlerinin, satış sıralamasına olan etkisi
In this study, we examined the effect of a book's publishing date on its sales ranking with a Linear Regression model by using Amazon's daily book ranking and price data for 67 days. We found out that the release date of a book is an important factor in evaluating the book rankings. We also studied the prediction of rankings using the lagged variables of price and ranking. To transform this time series prediction problem into a supervised learning problem, we used the sliding window approach. We used four machine learning and one deep learning approach to predict the rankings. To compare the results, two evaluation criterias; $R^2$ and root mean squared error were used. When tuning the hyperparameters, we used k-fold Cross Validation. We found out that linear regression outperformed the rest of the models, which are Ridge Regression, Random Forest, Light Gradient Boosting Machine, and Neural Network.
Rüzgar santrallerindeki operasyonel iyileştirmelerin tahminlenmesi
The operational optimizations, referring to the upgrades on wind turbines, can be very expensive; on the other hand, it is very complicated to assess the level of improvement they provide. Because of the inability to make reliable estimates on improvement levels, the plant owners are often reluctant to invest in upgrades. Like the OEM power curves, the improvement percentages for the upgrades, represent merely a reference and might differ for better or worse in the actual environmental conditions of the plant. The evaluations can not be done with a simple comparison of the pre-upgrade and post-upgrade performance, due to the complexity of the variables affecting power production and high levels of uncertainty of the environmental variables. In this research, we aim to study a machine learning approach implemented on wind farm level to evaluate the impact of operational improvements. Our approach consists of modeling the power output of the farm using a group of turbines referred to as the control turbines. The control group will not be upgraded to form the baseline for the pre-upgrade conditions. This baseline is later used to make a reliable comparison with the conditions after improvements are implemented.
Yatırımcıların forum paylaşımlarındaki duygular Bitcoin getirisini tahmin edebilir mi?
This study aims to investigate if the investors' sentiment expressed on contentspecific online forum affects the return of Bitcoin. We use a large dataset consisting of 2.8 million forum posts sourced from "Bitcointalk.org" for a period between Jan 2016 and May 2020. The sentiment is derived daily with the Hu Liu lexical model after a detailed investigation of different lexicons. Using time-series data, we test for the relationship between the investors' sentiment and Bitcoin price along with other financial variables that may inform about the direction of Bitcoin price. Our results show that sentiment derived from online forums do not present an autoregressive relationship with return of Bitcoin.