Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785

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Now showing 1 - 5 of 5
  • Master 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, Semra
    Crude 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
    Software Projects Clustering and Selection by Machine Learning Methods
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Torun, Elif; Ağralı, Semra
    In today’s hyper volatile business world, software development projects play key roles in maintain the current situation of the company and they are vital in taking the company one step further. Selecting the right project to invest is a critical decision point regarding the hard competition, diminishing profitability and high cost of the projects. The main aim of this study is clustering the projects and deciding which project to invest by using machine learning methods. We use IT project demands data of one of the biggest banks due to the capital, number of transactions and number of customer portfolio in Turkey. The data includes 2048 Information Technology related project demands occurred in 2017 and 2018. For the clustering part of the project both unsupervised and supervised learning methods are used and success rates are compared. We observe that supervised learning methods are more successful than the unsupervised ones. For the project selection part all process of the bank and output of the all steps are reviewed. According to our results, second workshop, which is the last step of the project assessment and selection process, has almost 50% of the total process effort and gives the precise effort estimation as an outcome, can be eliminated, and the project selection decision can be made with around 90% success ratio with machine learning methods. The result of this study provides an efficient way to select projects and a platform to see the complexity of the project portfolio.
  • Master Term Project
    Carbon Price Forecasting
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Karakaya, Nurhak; Ağralı, Semra
    In last twenty years great improvements occurred both in technological advances and in the world economic capacity. The total production capacity of countries has been increasing rapidly. These increases need great usage of energy. For that reason, prices of energy related products are very important as they dramatically affect company budgets. Energy budgets get a great deal in total budget of companies and countries. A unit increase in an energy related product can severely affect the budget. The carbon price is one of those products. Besides carbon prices, carbon usage also affects global environment so its price also has an impact on global temperature. To forecast future carbon price different machine learning methods are used. In literature, support vector machines (SVM) [1, 2, 3], random forest (RF) [4, 5], artificial neural networks (ANN) [6, 7, 8] and Auto Regressive Moving Average (ARMA) [9] are commonly used methods. All these methods have pros and cons over the others. In this project, we also apply different machine learning methods, ANN, SVM, RF, Lasso Regression (LG)[11] and Ridge Regression (RR) [10] to forecast the carbon price over time, and give an explanation for future price movements. Then, we compare those five models by analyzing model validation methods. Finally, we choose the best model for further experiments. We have four data types: daily carbon price (CP), electricity price (EP), natural gas price (NG) and coal price (COP) that cover the period of 2009 and 2017. Prices are provided in different currencies. First of all, we work on the data to have all prices in the same currency. We completely eliminate null data. Then, graphically we investigate overall trend by smoothing the data. For analyzing data, we look for daily, monthly, yearly and seasonally time scales. For every weekday or weekends in train data set we keep a day in test data set so that we can keep the time effect in our model. After the data management process, we apply different forecasting methods to explain future carbon price tendencies.
  • Master Term Project
    Credit Card Churn Prediction With Machine Learning Algorithms
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Konuksal, Serap; Ağralı, Semra
    Credit card is one of the main products in banking sector and there is a big competition in credit card business. This competition makes retention of customers critical. To retain the customers, it is very important to interpret the customers that may churn. Targeting right customers with right offer is the main aim of Customer Relationship Management (CRM) in marketing. When the churn probability of customers is predicted, it is easier to retain the customers by proposing the retention offers directly to the ones with high churn probability. This will allow banks to manage their marketing budgets efficiently. In this project, a private bank’s credit card customer data is used. Data includes many different types of features of customers, such as number and type of transactions, credit card limits, feature usage, credit bureau information and demographic information. We develop a set of churn prediction models by implementing different machine learning algorithms. We compare these algorithms to find the best model with highest accuracy to be offered to the bank. We also share the main indicators that affect churn so that the bank can use them in retention activities.
  • Master Term Project
    Churn Prediction in Vodafone Turkey
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Genel, Gökhan; Ağralı, Semra
    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.