Yüksek Lisans Tezleri

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

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Department: search.filters.department.Lisansüstü Eğitim Enstitüsü, Büyük Veri Analitiği Yüksek Lisans ProgramıSubject: search.filters.subject.Tahminleme

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Now showing 1 - 8 of 8
  • Master Term Project
    Flight Delay Prediction
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Kurt, Mustafa; Taş Küten, Duygu
    This study aims to create a model to predict flight departure delays. Various factors might affect a flight delay, and thus different features might be selected as input to create a model concerning priorities and the power of control over the features for the party who makes the analysis. In this study, domestic commercial flights in the U.S. operated in August 2018 are studied. Besides, airplane, passenger boarding, and cargo data are combined with flight data to benefit from possible insights related to these factors. For predicting the flight delays, machine learning methods such as decision trees, random forest, bagging classifier, extra trees classifier, gradient boosting and xgboost classifier are used and results are analyzed. Further studies could be adding extra features such as data related to flight planning, personnel data, loading data, data about technical processes to prepare a plane to a flight to improve prediction capacity.
  • Master Term Project
    Predicting Customer Perfection on Brands Functional Near-Infrared Spectroscopy Measurements
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Kemerci, Emre; Koç, Utku
    Customer perception on the brands have importance to give strategic decisions by marketing professionals. In classical ways, customer perception on brands are researched through conducting field surveys. Similarly, neuromarketing discipline have studies on customer behaviors, their perceptions, communication techniques etc. under the frame of decision-making process of human. In neuromarketing, functional near-infrared spectroscopy (fNIRS) is a technology used to measure oxy and deoxy hemoglobin concentration in the tissues in order to enable to analyze hemodynamic responses of the brain activities. In this study, a group of participants’ activations of prefrontal cortex so the hemodynamic responses that were collected against a set of stimuli, which is a brand logo and adjective associated with the brand is used as dataset. Measured hemodynamic response metrics are oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), total hemoglobin (HbT) and Oxygenation (Oxy) and the dataset includes 168 participants’ measurements for 30 stimuli. In addition, the information regarding the responses of the participants and common perception of stimuli (field study results for same stimuli) are also exists in dataset. The aim of the project is to predict through machine learning algorithms whether relation between brand and the relevant adjective is Positive, Negative or Neutral using these feature set. As methodology of this study, fNIRS measurements in the data is cleaned and Null values are handled, measurements are consolidated per participant and stimuli with two different method as feature creation and classification algorithms are used as supervised learning to predict brand perception. In conclusion, performance of support vector classifier and XGBoosting algorithms are become very low, slightly over 50% accuracy despite the optimization with different classifier parameters. Further studies are addressed as performing feature engineering studies with different options.
  • Master Term Project
    Analyzing the Drivers of Customer Satisfaction Via Social Media
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yücel, Kadir Kutlu; Koç, Utku
    Social 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 Term Project
    Tractor Sales Forecast Using Machine Learning
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunay, Yiğitcan; Özlük, Özgür
    This study presents a machine learning model to forecast tractor sales using four years of number of tractor sales based on year, month, city, town, brand and model provided by Turkey Statistical Institute. Tractor sales can vary depending on many different factors. Therefore, it is a challenging task for any company to estimate number of tractor sales that will be sold next year. Having the ability to predict that accurately will contribute companies in many distinct ways. Foreseeing market trends, keeping pace with the competition, delivering the right product to the right customer at the right time, reducing inventory costs, better production planning and cash flow management are major advantages of accurate forecasting. Within the scope of this study, models were developed to predict tractor sales using different statistical and machine learning methods. In further steps of the study, meaningful variables can be added to the dataset in order to reach a better result. Also, market share can be estimated by using different simulation methods which take into consideration those variables.
  • Master Term Project
    Predicting the Reasonable Departments for the Human Resources Related Questions by Using the Text Classification Algorithms
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Sancı, Yavuz; Özlük, Özgür
    The employees of Yapı Kredi Bank use a help desk system to ask their Human Resources related questions to the employees of the Human Resources departments. The questions are assigned automatically to the relevant departments by the system according to the subjects of the questions. In some cases, the mismatches between the contents and the subjects of the questions may cause the wrong Human Resources department assignments of the questions. Even though the application allows Human Resources employees to redirect the questions to the appropriate Human Resources departments, which are responsible for answering, the response time of these questions lasts longer. This project aims to analyze the content of the Human Resources related questions by using the text classification algorithms to predict the responsible Human Resources departments. Thus, it is aimed to respond to the questions in a much shorter time.
  • Master Term Project
    Predicting Birth Defects
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Korkut Özer, Selen; Koç, Utku
    Many couples are eager to have a healthy baby. For this reason, the pregnant woman is trying to take their baby through the steps of adjusting their lives during the pregnancy, such as healthy nutrition, organic life, avoiding cosmetics. Even though the woman can do it, health problems can be observed in the baby at the time of birth or after birth. The causes of these health problems may be factors such as genetic, the physiological characteristics of the mother, environmental. In this paper, we tried to answer the question whether the health problems that occur in babies after childbirth can be estimated before birth. This includes the birth records of the American Centers for Disease Control and Prevention (CDC). Approximately 3M data was analyzed and the prediction model worked on the baby dataset. Boosting, Random Forest, Neural Network, Logistic Regression and SVM models were used to estimate the babies who could have any disease at birth. Sick babies were estimated with an accuracy of 69.5%.
  • Master Term Project
    Predicting Transaction Numbers İn Atm
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Karasu, Ahsen Ceren; Özlük, Özgür
    ATMs continue to be one of the most important channels for banks to touch their customers. They play an active role in life in terms of cash access and banking experience. The ability of a bank to predict the number of transactions that will occur from ATMs is crucial for the proper control of the budgetary source. When cash is loaded into ATMs, the average transaction made from that ATM is taken into consideration and alarm mechanisms can be activated when a decreasing trend is observed on transaction basis.Before a new ATM is set up, the banks investigate how often customers in that area use other bank ATMs and calculate the commission costs incurred from those uses. As a result, the number of transactions made from ATMs is one of the most monitored KPIs of a bank and has important place in the cash management of the bank.The aim of this study is to estimate the number of future transactions with Auto Regressive Moving Average (ARIMA) method based on the number of transactions that occurred from ATMs.
  • Master Term Project
    Understandng Emotion Fluctuations Using Social Media
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Ceran, Serkan; Akpınar, Ezgit
    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.