04. Enstitüler / Lisansüstü Eğitim Enstitüsü

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Sentiment Analysis of Hürriyet Emlak
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Korkmaz, Alev; Özlük, Özgür; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
Sentiment analysis refer to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, whether the attitude behind a text is positive, negative or neutral.
Risk Parameter Calculation Using Princpal Component Analysis of Yield Curves: the Case of Borsa İstanbul Fixed Income Market
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Konuk, Hayrettin; Güntay, Levent; 04.03. Department of Business Administration; 04. Faculty of Economics, Administrative and Social Sciences; 01. MEF University
To enable a trustworthy clearing operation, clearinghouses require conservative margins to avoid the risk of incurring a loss in case one counterparty defaults. When margin requirements for fixed income instruments are calculated, yield curves of each instrument are stressed using their first three principal components. All instruments in an account are then evaluated against each stressed yield curve and the margin requirement is calculated as the difference of the combined value of these instruments calculated with the worst of the stressed yield curves between their combined values calculated with related unstressed curves. The aim of this project is to construct a tool for applying principle component analysis (PCA) on daily zero coupon yield curve of Turkish Treasury Securities. The analysis employs a yield curve panel data set obtained consisting historical zero coupon yield curves. The data set includes interest rates of 60 different maturities varying between overnight and 15 years and 1250 daily observations between December 2010 and December 2015. The result of this analysis provides a method that could be run at the end of each clearing day to determine the major components of the yield curve such as level/height, slope and curvature that describes at least 95% of the variation in interest changes and subject to stress shocks
Hotel Recommendation for Online Travel Agencis
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Kılıçlı, Cem; Küçükaydın, Hande; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
Since the early 2000s, online travel agencies (OTAs) have become a central online market source, used by millions of users in all over the world. Recommendation systems became one of the essential tools for them to increase their profit.
The Passanger Load Factor Prediction of Airline Transport
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Karakoç, Kalender; Arslan, Şuayb Ş.; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
Turkish Airlines is one of the most preferred leading European air carriers with global network coverage thanks to its strict compliance with flight safety, reliability, product line, service quality and competitiveness. Turkish Airlines maintains its identity as the flag carrier of Turkey.
Order Management Performance Study
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Güneş, Vedat; Akbari, Vahid; 01. MEF University
Vodofone Türkiye'de bütün işlemler (order) adı verilen talepler üzerinden gerçekleştirilir.
Understandng Emotion Fluctuations Using Social Media
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Ceran, Serkan; Akpınar, Ezgit; 01. MEF University
During the last years, the importance of social media is increasing in an amazing way. In this paper, we looked at one such popular microblog platform called Twitter and build models for classifying “tweets” into some specific emotion. We used Turkey’s twitter data in order to explore the change in emotions over time using sentiment analysis. Using LIWC dictionary database, we conducted an emotion analysis of approximately 2.2 million tweets. We tracked how emotions evolve over time based on the prominent events in and or related to Turkey. Our results showed that there is a significant relationship between emotions and prominent events. We also analyzed the correlation between these emotions and the dollar exchange and made a predictive modeling experiment.
Development and Comparison of Prediction Models for Estimating Short Term Energy Demand of a Hotel Building
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Yılmaz, Selimcan; Özlük, Özgür; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
This project presents a machine learning model building approach to developing a model for predicting next hour electricity consumption of a hotel complex in Cyprus, with the aim of improving existing prediction accuracy due to comparing different models to choose best performing. Model building process in this project includes three main steps.
Underlying the Bias for Human Music Evaluation
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Yıldırım, Burak; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
Predictive analysis is the process of using data analytics to predict the future over historical data. Data analytics is the use of statistical modelling and / or machine learning methods to measure the future. In short, it is one of the data mining techniques for predictive analysis that focuses on creating a predictive model for the future by extracting relationships from the data.
Predictive Cahce Management
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Baltaoğlu, Olcay Gürsel; Akbari, Vahid; 01. MEF University
Major dependency of a mobile application performance is the response time of backend services. Building a cache layer can be a solution in architectural way to provide better experience to user but it cannot affects when the cache is empty for the first usages.
Fraud Detection In the Bitcoin Exchange Market
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Namlı, Hüseyin; Güntay, Levent; 04.03. Department of Business Administration; 04. Faculty of Economics, Administrative and Social Sciences; 01. MEF University
The trading volume and financial assets of Bitcoin are growing up, while the popularity of Bitcoin world increasing continuously in recent years. In parallel, the market becomes an attraction center for malicious people.
Smart Precision Agriculture With Autonomous Irrigation System Using Rnn-Based Techniques
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Anuşlu, Timuçin; Özlük, Özgür; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
The study presents a solution to improve freshwater usage for irrigation in the agriculture by building a neural network model to predict soil moisture at 20 cm level with time series data over longer periods of time.
Chuen Analysis of Gittigidiyor Customers
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Kantarcı, Özlem Hazal; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
In this project, it is aimed to estimate the loyalty of the customers of the e-commerce company named GittiGidiyor by analzying the customer movements and examined which movements affected the customer loyalty positively / negtively. In the dataset studied, it was seen that the number of active customers is much higher than that of passive customers. Several methods have been tried to solve this "Class imbalance" problem and it has been decided to replicate some lines of passive customers. Rows of smaller classes are duplicated to compensate classes with generated code. The data set was divided into training, validation and test and different algorithms were used. One of the innovative approaches was training and validating models in an earlier time window and testing the model with samples from a later time window. As a result of the studies, it was decided to use "Linear Discriminant Analysis" considering its short training time and especially the success of predicting passive customers.
Churn Prediction of a Deal E-Commerce Website Customers
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Çevik, Müge; Küçükaydın, Hande; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
Today, there is a lot of deal e-commerce sites which are essentially marketplaces. They provide deals which are offered by merchandisers. Because of the nature of these sites there is no subscription model; customers continue because of price or interest or quality not because of subscription. It is normal to have some customers who stop buying, which is defined by "churn". Data mining is now a new technique to define "churned" customers and to have prediction who will churn and what should be against. In this project customers are clustered via unsupervised clustering technique for clusters as "newly purchased", "frequently purchased" and "mostly payed" and "churned". Random Forest Classifier is used to prove that the "churned" customer clusters have homogeneous character and also it has been proved that the "churned" labelled customers have actually no deal order after the observed time period. To recommend what should be done to regain the churned customers to the site the deal order history of these customers have been explored and the deal categories from which they have bought have been found.
Gittigidiyor Basket Analysis
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Yılmaz, Kerem; 01. MEF University
Data Mining is becoming more important for lots of sector and companies worlwide. Because, it can find patterns, correlations, anomalies in the databases which can help us to make accurate future decision. Data Mining contains of various statistical analyses that reveal unknown aspect of the data. Data Mining encompasses a huge variety of statistical and computational technigues such as; Market Basket Analysis, Clustering, Classification and Regression Analyses.
Duplicate Record Detection: a Rule-Based Approach
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Malkaralı, Gülce; Özgür Özlük; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
The study presents a rule based algorithm to detect dublicate and near-dublicate rocords within a dataset that is extracted from a leading online reality platform.
Market Analysis - Aydınlı Group
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Öney, Çağlayan Özgür; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
In this paper, we have analyzed the purchase transaction data of Aydınlı Group. Aydınlı offers their customers diverse set of products by providing Polo, Cacharel and Pierre Cardin brands on both retail and online store. The million dollar question that we seek an answer in our research is "can we determine the purchase pattern of customers?".
Churn Prediction in Vodafone Turkey
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Genel, Gökhan; Ağralı, Semra; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
This Capstone Project focuses on finding a churn pattern in Vodafone postpaid consumer subscribers. The churn value refers to disconnection from subscription via port-out / Mobile number portability (MNP). It is one of the most important items that demonstrate revenue-loss. The subscriber who churned with MNP switches to a rival GSM operator. The cost of keeping an existing customer is generally cheaper than the cost of acquisition of a new customer. Focusing on customer retention is one of the most profitable strategy for growth. Statistical analysis and machine learning can help analyze churn activities and they can even alert companies when their existing customers are likely to churn. By using machine-learning algorithms, this project aims to detect Vodafone postpaid consumer subscribers who are likely to churn. This project will help the company to decrease its revenue loss.
Online Check-In Likelihood of Hotel Guests
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Tiryaki, Yusuf; Özlük, Özgür; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
Hotel operators benefit from current technological developments in order to provide the best experience for their guests to stay. In the case of an enterprise which providing guest hospitality service, the flow is composed of 4 steps: booking, check-in, accommodating and check-out. Online reservation systems have been in use for a long time and are services that offer room reservations for date ranges that guests will stay with. Online check-in applications are a new type of service that has just begun to be implemented in the hospitality sector. The advanced online hotel check-in systems enable users to save time by creating an entry log on the internet, specifying floor and room selection, assigning additional services, notifying the check-in time during the process, and reducing waiting times for hotel help desk during check-in. In the online check-in forecasting process, a data analytics application was implemented that computes the score of the user's proximity to online check-in after the booking step and the booking information was obtained. The score calculation process uses statistical learning algorithms. Within the scope of the study, the guests were classified according to closeness to service reception with Random Forest and DNN(Deep Neural Networks) methods using a dataset in which the guests had hotel booking and provided online information. The trained model for classification was presented as a web service to return the likelihood score of new booking guests.
Benchmarking of Recommendation Models for an On-Line Fast Fashion Retailer
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tilkat, Mustafa; Küçükaydın, Hande; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
This project studies the usage of the recommendation engines to improve the sales in an online fashion retailer. Fashion retailers sale variety of products throughout their online channels. Since the number of products can be huge compared to an in-line shop, customers may miss some of them while shopping online. Hence, it is crucial to display products that are more likely to be purchased by a customer when the customer is surfing on the website. Our problem is motivated by practice at an online fashion retailer in Turkey. Four collaborative filtering-based algorithms and a random recommender are utilized to design a recommendation engine. 80% of the data is used for training while the other 20% is to used test the designed method. Based on our experiments, User Based Collaborative Filtering (UBCF) using Pearson correlation outperform the other algorithms based on Receiver Operating Characteristic (ROC) curve.
Market Basket Analysis Using Apriori Algorithm
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Şimşek, Yıldırım Murat; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
Predictive analysis is a branch of data engineering that predicts some occurrence or probabilities depend on the data. To make predictions about future events, predictive analytics uses data mining techniques. The process of these techniques involves an analysis of historic data and predicts the future events based on that analysis. Also using predictive analytics modelling techniques, a model can be created to predict. Depending on the data that they are using these predictive models can be varied. Predictive analytics is made of various statistical and analytical techniques used to develop models that will predict future occurrence, events or probabilities. Market basket analysis is one of the data mining techniques that focusing on discovering purchasing pattern by extracting associations from a store’s transactional data. The electronic commerce point-of-sale expanded the utilization and application of transactional data in Market Basket Analysis. The needs of the customers have to be known and adapted to them from the retailers. The retailers collect information about their customers and what they purchase with the help of the advanced technology. Analysing this information is extremely valuable for understanding purchasing behaviour in retail commerce. Market basket analysis is one possible way to discover which items can be sold together. This analysis gives retailer valuable information about related sales on a group of goods basis customers who buy bread often also buy several products related to bread like milk or butter. It makes sense that these groups are placed side by side in a store so that customers can reach them quickly. Market basket analysis is very useful technique for the related group of products that are bought together, and to reorganize the supermarket layout, and also to design promotional campaigns such that products’ purchase can be improved. The main aim of this capstone project is to find the co-occurring items in consumer shopping baskets in the data set that provided by GittiGidiyor E-Commerce Company with the help of the association rule mining algorithm; apriori. Mining association rules from transactional data will provide us with valuable information about co-occurrences and copurchases of products. Such information can be used as a basis for decisions about marketing activity such as promotional support, inventory control and cross-sale campaigns.

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