Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785
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Master Term Project A Study on Churn Prediction in Telecommunication and Pay Tv Area(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2020) Şayık, Murat; …...Master Term Project Duplicate Record Detection: a Rule-Based Approach(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Malkaralı, Gülce; Özgür ÖzlükThe 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.Master Term Project Forecasting With Ensemble Methods: an Application Using Fashion Retail Sales Data(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yüzbaşıoğlu, Orkun Berk; Küçükaydın, HandeIn this project, ensemble methods of machine learning are used to predict short term store sales of a fashion retailer. Sales forecasts of various products at different stores are generated for a span of three months with bagging tree regressor, random forest regressor, and gradient boosting regressor algorithm. Algorithms are trained and evaluated with real past sales data of a Turkish fashion retailer. The predictive performance of the models is compared with linear regression. The results of the study show that random forest regressor shows the best performanceMaster Term Project Alternative Credit Scoring Model for Thin File Customers(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Korkmaz, İstem Akça; Taş Küten, DuyguCredit scoring is a widely used tool for banks, financial institutions or corporations. Traditional credit score models are calculated from past financial history of users, and this may lead to exclude some people who have limited financial history from the credit system. Alternative credit scoring allows sector players to access to a larger portion of these customers. The credit scoring industry has expanded with an "all data is credit data" approach that combines traditional credit scoring systems with new data points. In this study, we aim to build an alternative credit scoring model for customers who have limited financial historical data (thin file) by using alternative data points for a national bank in Turkey. Some of the alternative data points and variables have been gathered from one of the bank’s products: the authorized card for Turkish national league football tickets (Passolig). Using alternative data points combining with demographical and geographical information, we perform a comparison between the machine-learning approaches. We use logistic regression approach as a base model and perform a comparison between tree-based approaches: decision tree, random forest and XGBoost to select the most effective modelling approachMaster Term Project Predicttion of Brent Oil Spot Prices Using Country Based Inventory and Trading Data(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Usta, İsmail Batur; Ağralı, SemraCrude oil price forecasting has been the focus of numerous authorities, yet the task still persists on being a challenging one. The extremely volatile nature of oil market and high number of active players in it makes establishing a solid forecasting model that is constantly relevant to time very difficult. Recent advancements on data technologies, mainly ever-increasing computing power and trending big data technologies allowed new approaches to be born. From online learners to natural language processing, advanced data analytics models were employed with the help of easily accessible and diverse data. This project is an attempt on making use of such available data in order to forecast Brent oil spot price. By using monthly country by country inventory, trading and economic data, strong drivers of crude price was explored. The data used in this project comes from various sources and in multiple formats, with the final merged data frame has over 17000 observations and contains information on 86 countries. To enhance prediction power, a specialized learner is fit on each country individually and then the predictions are accumulated and filtered before outputting a single prediction. Compared to a single predictor, this approach enhanced the predictive power of the algorithm by adapting to dynamics of each country.Master Term Project A Comprasion of Ensemble Learning Methods in Retail Sales Forecasting(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Süer, Serhan; Güney, EvrenForecasting has always been an essential skill which companies try to have and implement in various areas. Sales forecasting is one of the major usage areas of forecasting which is used in almost all sectors. This study refers to forecasting sales of Walmart Stores based on several features such as store id, department id, date, and store size. Walmart sales data which was used in this study contains information of stores between 2010 and 2012. At the beginning of the study, the introduction of the dataset and exploratory data analysis were made to identify dependent/independent variables and their characteristics. To apply machine learning algorithms, data preprocessing methods such as missing value treatment, outlier treatment, and feature selection was applied. Ensemble learning methods in machine learning algorithms were applied in the modeling stage. These methods were addressed in three parts such as Bootstrap Aggregation, Boosting, and Stacked Generalization and these parts consist of six different algorithms in total. The models were compared based on four regression metrics as Root Mean Square Error, Mean Absolute Error, R-Squared, and runtime. After selecting the main metric which models were evaluated, cross-validation was applied to achieve unbiased estimates. Finally, parameters of the model which have the highest score in cross-validation were tuned in the hyperparameter optimization stage and a machine learning model which can be used in forecasting sales of Walmart stores and its success score were obtained.Master Term Project Machine Learning Applications To Increase Customer Satisfaction In Finance Sector(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yiğit, Leyla; Çakar, TunaIn this project, consumers’ complaints about financial data are analyzed. After the analysis, we aim to provide a tool for financial companies such as banks, Lenders that will help them in managing communication with the consumers. Our main aim is to answer the question “How do consumers feel?” This analysis will give a complete picture of consumers’ feedback. We start the project by clustering the customers into different groups. In order to classify customers, we use classification algorithms XGBOOST and Random Forest. XGBOOST is used to predict the probability of getting a complaint. XGBOOST is also tested as an ensemble learning technique. By Using Random Forest the comparison of Bagging and Boosting is performed. This kind of model is very useful for a customer service department that wants to classify the complaints they receive from their customers. These kinds of models can also be expanded into a system that can recommend automatic solutions to future complaints as they come. The topic is motivated by the researcher’s experience in finance where she intends to increase credit sell numbers by anticipating customer feelings. The data set that we use has many measures and dimensions that facilitate to use more than 3 machine learning algorithms. The complaints database is published by the Consumer Financial Protection Bureau (https://www.consumerfinance.gov/). It provides consumers’ feedback in a string format. We also aim to analyze consumers’ complaints dataset from the perceptive of a consumer dispute.Master Term Project Predicting Outcomes and Improving Game Models for Football Matches(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Göçer, Murat; Küçükaydın, HandeThis study is conducted to predict the results of the 2017/2018 English Premier League football matches and show the teams what they should pay attention to in order to win. In this study, classification algorithms are used and the algorithm that gives the best results is applied to real matches. After evaluating the results, some suggestions are made for similar future studies and for the teams to develop their game models.Master Term Project Scoring Neighborhoods for Locating Atm Using Machine Learning(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Yıldırım, Oğuzhan; Küçükaydın, HandeFacility 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.Master Term Project Vote Transtition Analysis and Comparison of Turkish Local Elections in 2014 and 2019(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Baydoğan, Ufuk; Güney, EvrenDebates around how voters switched their votes relative to previous elections are always the topic after the Election Day. Turkish local election of 2019 was important because of three reasons: first, because it was the first local election after Turkey adapted the new presidential system and the President also participated in the election campaign for his party; second, because İstanbul election, originally run on March 31, was ruled for rerun by Supreme Election Council and the third, because the electoral alliances had significant impact on the results where the votes for The People's Alliance significantly collapsed. This study presents a comparative analysis of 2014 and 2019 official Turkish Local Election Results as well as 2019 Re-Run Election Results of Istanbul to understand the vote transitions. As the outcomes are considered, there are significant changes in the distribution of voting rates between these elections, especially in critical metropolitans. Using the aggregate level vote counts, the vote transition probabilities between the elections are inferred using ecological inference. Proposed clustering approach on vote transition probabilities show that CHP and IYI Party have benefited from forming Nation’s Alliance for most of the cities mainly due to the vote switches from HDP and MHP. For the re-run election case, the slight number of vote difference between the alliances in March has increased significantly. This is mainly because of the contribution of absentees to Nation’s Alliance and around %5 of the People’s Alliance supporters in March who estimated to vote for Nation’s Alliance.