Koç, Utku
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Koç U
Utku Koç
Koc U
Utku Koç
Koc U
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kocu@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
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Scholarly Output
15
Articles
3
Views / Downloads
3700/37581
Supervised MSc Theses
12
Supervised PhD Theses
0
WoS Citation Count
18
Scopus Citation Count
23
WoS h-index
3
Scopus h-index
3
Patents
0
Projects
6
WoS Citations per Publication
1.20
Scopus Citations per Publication
1.53
Open Access Source
13
Supervised Theses
12
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| Journal | Count |
|---|---|
| Computers & Industrial Engineering | 1 |
| Operations Research Letters | 1 |
| Turkish Journal of Electrical Engineering & Computer Sciences | 1 |
Current Page: 1 / 1
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Scholarly Output Search Results
Now showing 1 - 10 of 15
Master Thesis Prediction of Credit Card Default(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Akalın, Selçuk; Utku KoçAs profitable customer acquisition becomes more and more critical for the banking sector in terms of competition, the requirement to predict customer defaults with different machine learning algorithms is increasing. Thanks to similar practices, possible damages can be prevented. Due to the rapid change of machine learning with the changing technology, the fields of application and development in different sectors are also changing and developing rapidly. In this study, the aim is to make a comparison over model outcomes and making observations on outcomes to determine the areas that can be developed or researched with running different supervised and unsupervised machine learning algorithms on the final dataset gathered by doing following methods such as key points discovered in exploratory data analysis on an imbalanced credit card dataset, generating different features according to learned key points, eliminating imbalance with different oversampling and undersampling methods.Master Thesis Sms Spam Detection in Turkish Language(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Gürkan, Cem Kaya; Koç, UtkuShort message (SMS) is one of the most common communication methods. The growth of mobile phone users has led to a dramatic increase in using short messages. With the increasing number of mobile phone users, mobile phone users have started receiving unsolicited text messages. The use of SMS as a spam tool after the e-mail is due to a direct access to customer and high reversion to the users. These unsolicited short messages are disturbing the users even content intended for deceiving or defrauding (phishing). Up to date, all of the research carried out on SMS Spam detection was focused on the English language. In this study, Turkish datasets tagged with spam information is introduced and existing methods for English are applied to these datasets. The SMS dataset used in this study is gathered from different people and all messages are tagged according to whether they are spam or not. Naïve Bayes, Logistic Regression, SGD, SVM and Random Forest classification algorithms are tested with three feature extraction methods and a number of performance measures are evaluated. The evaluation resulted in a f-measure of 96.4% for SVM classification algorithm with TF-IDF (Term Frequency-Inverse Document Frequency) extraction method.Master Thesis Consumer Loans' First Payment Default (fpd) Detection and Predictive Model(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Sevgili, Türkan; Koç, UtkuThe project is based on the opinion that whether the loan applications which are profitable could be granted instead of prone the default (FPD) ones by using predictive models in machine learning by the credit decision authorities in banking sector. Default Loan (also called non-performing loan) occurs when there is a failure to meet bank conditions and cannot be repaid in accordance with the terms of the loan which has reached its maturity. This report is a research effort in the analysis of default loan applicants, especially FPD, from a real dataset obtained from a bank. Expectation from the study is that increase the efficiency of consumer loan allocation by providing predictive analysis of the consumer behavior concerning loan’s first payment default. FPD detection analysis is a crucial role for the determination of consumer loans at the application level. The study also provides an understanding on the reasons of non-performing loans and helps to manage credit risks more consciously. The methods proposed in this study can be extended to other individual consumer loans such as car credits and mortgage.Master Thesis Analyzing the Drivers of Customer Satisfaction Via Social Media(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yücel, Kadir Kutlu; Koç, UtkuSocial media became a great influence force during the last decade. Active social media user population increased with the new generations. Thus, data started to accumulate in tremendous amounts. Data accumulated through social media offers an opportunity to reach valuable insights and support business decisions. The aim of this project is to understand the drivers of customer satisfaction by public sentiments on Twitter towards a financial institution. Data was extracted from the most popular microblogging platform Twitter and sentiment analysis was performed. The unstructured data was classified by their sentiments with a lexicon-based model and a machine learning based model. The outcome of this study showed machine learning based model successfully overcame the language specific problems and was able to make better predictions where lexicon-based model struggled. Further analysis was performed on the extreme daily average sentiment scores to match these days with prominent events. The results showed that the public sentiment on Twitter is driven by three main themes; complaints related to services, advertisement campaigns, and influencers’ impact.Master Thesis Football Player Profiling Using Opta Match Event Data: Hierarchical Clustering(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Kalenderoğlu, Uğurcan; Koç, UtkuIncreasing popularity of data analytics has impacted the sport industry. Dimension of available data and best practices on the usage of data analytics increased as a result of this trend. Player profiling is one of emerging hot topics among those, especially in football. On the other hand, income and expense balance of transfers has been biggest burden on clubs’ financials while it should be reverse. Scouting processes are currently dominated by bilateral relations and intuitive comments of scouting staff. It is an important step to transform into data driven decision framework to overcome this situation. It is crucial to replace a player who leave the team with someone who has potential and very close playing style. Player profiling is the first step to do this. The data set used in this project is obtained from Opta – a sport focused data company – and contains all actions performed on-ball at player level from Turkish Super League, English Premier League and German Bundesliga in three seasons between 2015 and 2018. Principal component analysis is applied to the dataset in order to reduce dimensionality to the 15 features which consists of 2469 players and 271 features at the beginning. As a result of this study, it is observed that there are twelve different player clusters within the traditional main positions; three for defenders, four for midfielders and five for forwards. Clubs can enrich and benefit from these clusters in three ways: 1) evaluation of a player style over a period of time and detecting the best role fit 2) analyzing the effect of cluster combination to decide which line-up yields better team results 3) finding the closest match to a player who is subject to replacement.Master Thesis Mortality Prediction of Countries(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Üşenmez, Elif Efser; Koç, UtkuIn this study mortality reasons of countries detailed by sex and age-group is analyzed and different forecasting models are developed by using different machine learning algorithms. The dataset is obtained from the World Health Organization(WHO) Mortality Database. In WHO database there are different datasets for countries mortality reason number. The study used the dataset that used ICD-10 for classifying mortality reasons.ICD-10 is the 10 revision of International Statistical Classification of Diseases and Related Health Problems published by the World Health Organization. In addition to main mortality reason datasets, we add different independent variables and try to find the best features to fit models without biasing and overfitting and reaching high R2 and Mean Square Errors. To find the best model for forecasting mortality reasons by age-groups and sex different machine learning algorithms are fitted and results of these algorithms are analyzed.Master Thesis Prediction of Up and Down Signalsın Selected Blues Chip Stocks(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yıldız, Mustafa; Koç, UtkuEfforts have been made to predict the direction in which equity stocks will move in the capital markets. In most of these studies, Technical Analysis and Fundamental Analysis based models have been used. For daily price estimations, macroeconomic variables or financial ratios of financial instruments are used. On the other hand trade book data are taken into consideration in intraday price estimates. In this study, equity market data analytics, which are created by Borsa İstanbul as a benchmark for intraday price signals, are used. These analytics are derived from trade and order book data. For 5 minute periods, intraday price and equity market data analytics data sets are created, and different algorithms are tried over these data sets. The study is carried out using one-week data of 4 selected blue chip stocks. The signals for increase is 1, for decreases is -1 and 0 for non-change signals. As a result of the study, the decision jungle algorithm is the most successful algorithm. In addition this, the lack of volatility and liquidity in the market have caused overfitting problems in ensemble algorithms. According to the multiclass decision jungle confusion matrix, the positive true results for 1 (or increase of the price) are promising. If an investors can just use the algorithm for the price increase, it will be meaningful. The true positive ratio of 1, 54.5%, is too high when it is compared with its false trues value for decrease (or -1), which is just 13.6%. The difference between true positive and false negative (54.5% - 13.6%) will be the earning ratio for the investor, if he/she decides to invest the price increase of Yapi Kredi stock with the decision jungle algorithm. Although it is stated that big data algorithms (machine learning techniques) can give the best results for the data, domain knowledge related to the data is still very important. As it is seen in the study, in order to overcome the problems of overfitting or bias that occur in other studies, it is necessary to obtain sufficient domain knowledge in consultation with the experts and practitioners of the subject. In addition, the increase in the studies on intraday trading, which is a shallow area in the literature, will provide better results in the studies conducted on price forecasts in the future. In the results of this study, parallel with the literature, it is revealed that there is difficulty in estimating the stock price movements.Master Thesis Predicting the Price of Bitcoin: Using Machine Learning Time Series Methods(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Ulutaş, Sezer; Utku KoçCryptocurrencies have greatly increased their Bitcoin-led popularity in recent years due to increased trading volumes and massive capitalization in the market. These cryptographic forms of money are not just utilized for exchanging nowadays, they are additionally acknowledged for fiscal exchanges. It appears to be evident that financial specialists, dealers and people, in general, are progressively intrigued by bitcoin and altcoins as costs rise and the arrival on ventures made increments. This examination centres around applying estimate models that will make precise value forecasts forcryptographic forms of money. The data were taken from two different exchanges and evaluated as combined dataset. As a result of the evaluation, it was determined that the prices were close to each other in terms of value and the data were combined. We obtained the daily time series data by determining the Bitcoin weighted price as a dependent variable and Open, Close, High, Low and Volume as independent variable. We predicted the next 6 months with ARIMA, LSTM and XGBoost methods. We compared these estimates using MSE, MAE, MAPE and R squared performance metrics. LSTM is the model with the best R squared value of 29.7%. In the process performed by taking the average of LSTM, XGBoost and ARIMA performed with the name of Average ML method, the R square value was found to be 41.6% as a much better result than LSTM.Master Thesis Product Recommendation for C2c Marketplace With Collaborative Filtering Als Algorithm(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kıran Çelebi, Bilgehan; Utku KoçIn this project, a machine learning recommendation model is created for an e-commerce company which runs a customer to customer business. The raw data consisted order reviews, order details, product like event information and product details. The explicit and implicit feedbacks are used together and a rating generation logic per user-product couple is applied to create the source data of the model by using Google Cloud BigQuery tool. The ALS algorithm which uses matrix factorization is applied for predicting the top items which have highest ratings for each user. PySpark which is Apache Spark’s python API is used for implementing the ALS model. The best hyperparameters are determined comparing the root mean square error results by using grid search and cross validation and 0.78 of RMSE is reached. The predictions for the empty ratings are sorted then top rated 10 products are taken as recommendations. The evaluation of the model is done by comparing those recommendations with the user preferences. The user preferences are specified by using averagely top rated product categories and most interacted product categories in count. The recommendations are observed to be consistent with the user preferences.Article Citation - WoS: 5Citation - Scopus: 6Generation of Feasible Integer Solutions on a Massively Parallel Computer Using the Feasibility Pump(2017) Mehrotra, Sanjay; Koç, UtkuWe present an approach to parallelize generation of feasible mixed integer solutions of mixed integer linear programs in distributed memory high performance computing environments. This approach combines a parallel framework with feasibility pump (FP) as the rounding heuristic. It runs multiple FP instances with different starting solutions concurrently, while allowing them to share information. Our computational results suggest that the improvement resulting from parallelization using our approach is statistically significant. (C) 2017 Elsevier B.V. All rights reserved.
