04. Enstitüler / Lisansüstü Eğitim Enstitüsü

Permanent URI for this communityhttps://hdl.handle.net/20.500.11779/204

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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.
Employee Performance Prediction
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Sivas, Barış; Özgür Özlük
DogGo is a company that aims to provide safe and professional dog walking and grooming services to dog owners through the mobile application. Thanks to the DogGo application, dog owners and people who is employee of company and wants to walk their dogs (to be called Walkers) can meet on the same platform on the mobile application interface. The problem was determined by company that they needed to be able to accurately predict the performance of the walkers in the upcoming dog-walker matches, thus ensuring the correct dog walker match. This study will be planned to serve to this company for calculating their current walkers’ performance in an accurate way. The relevant machine-learning model will first be based on the manual scoring system made by the company for the performance of existing employees, and then the model will be developed in the light of the gains obtained from this. For the performance of the model, the employees and their characteristics are important for the first time.
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.
Convolutional Neural Network for Facial Emotion Recognition With Geometrical Features of Face
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, İlker; Tuna Çakar
One of the recent challenging machine learning problems is to make predictions on image datasets. The aim of the project is to construct a convolutional neural network to guess emotions for a face of a human given in an image file considering the face. After the geometrical features are extracted using pretrained models, we construct five models which are convolutional networks fed with handcrafted geometrical features extracted. The last model uses the outputs of other four models to predict more accurately.
Big Data Analytics on Used Car Information
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Demir, Efe; Utku Koç
In this research, a decision support system is implemented on a used car dataset. The main purpose is to predict the price information and reveal the related features. The price prediction problem is classified as a regression problem. The key point is to find the best-fitting model and obtain the best accurate prediction outcomes. Should we buy this car, or at what price may I sell my car? This work is about to answer these questions. Various regression models are compared, and detailed results are explained correspondingly.The constructed models will help customers to know about their car price and salability. And they can identify the buying opportunities. The percentage error approach which is detailed in the results section will be a guideline for customers/firms to make a market analysis or detect fraudulent listing information.
Smart Precision Agriculture With Autonomous Irrigation System Using Rnn-Based Techniques
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Anuşlu, Timuçin; Özlük, Özgür
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.
Fastseller&worstseller Project (boston Matrix Text Classification Analysis)
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunçel, Ahmet; Küçükaydın, Hande
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Credit Card Fraud Detection Analysis and Machine Learning Application
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Meker, Tuğrul; Özlük, Özgür
Credit card fraud transaction is a common term for theft and fraud action involving a payment card such as payment or credit card or debit card as a source of funds in transactions. With increased usage of POS channel or internet in recent years, the risks of credit card fraud have increased. Mostly, these illegal activities start with a compromise of data associated with the account number or important information that required to start the financial transaction. After, literature review and exploratory data analysis, machine learning algorithms are going to use to decide whether the transaction is fraud or not. Logistic regression, decision tree, Naive-Bayes, decision forest and linear SVC’s classifier algorithms are used in this study. With re-sampling choices (random-under, random-over sampling & SMOTE), these algorithms’ performances are compared. Logistic regression, decision tree, and random forest provide best results in terms of accuracy metrics. Grid-Search is applied to those three algorithms. Decision tree algorithm is chosen as the best algorithm for credit card fraud detection. Python 3.7 is used in this study.
E-Commerce Customer Shurn Prediction Based Machine Learning Algortihms
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Eser, Ahmet Yetkin; Arısoy Saraçlar, Ebru
With the development and popularization of a digital world, human behavior has changed so remarkably. A lot of sectors affected because of this change. One of the most affected areas is the retail sector. People have left their regular shopping habits and started shopping on e-commerce sites. Thanks to increasing of variety and volume of collected data and velocity of new machines, companies can use sophisticated algorithms efficiently on their data. In this paper, we discuss about how companies can predict potential churned customers with machine learning methods.
A critical study on sustainability within environmental assessment methodologies: Evaluating Volu-te in search of an alternative vision in architecture
(MEF Üniversitesi, 2022) Kaleli, Damla; Yücel, Şebnem
Bu araştırma, mimaride sürdürülebilirliğin değerlendirilmesine ve bunun araçları olarak çevresel değerlendirme metodolojilerine odaklanmaktadır. Bu metodolojiler, mimari ürünün sürdürülebilirliğinin ölçüm mekanizması olarak işlev görür; ancak etiketleme ve sertifikasyon gereklilikleri ile mevcut kapitalist sistemin çarkı olarak da çalışırlar. Bu nedenle, bu çalışma proje üzerindeki ekolojik etkiyi ve finansal stresi değerlendirmeye odaklanmayı amaçlamaktadır. BREEAM sertifikasının proje bütçesine ekstra üç kat, Pasif Ev sertifikası ise projenin toplam maliyetine ekstra iki kat daha masraf eklendiği görülmektedir. Bu değerlendirmede Volu-te, bu sertifikasyon sistemlerinin mikro ölçekli yaşam projelerinde maliyet etkisini tartışmak ve maliyet etkisini ortaya koymak için mimari bir ürün olarak kullanılmıştır.
Predictive Cahce Management
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Baltaoğlu, Olcay Gürsel; Akbari, Vahid
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.
Prediction of Up and Down Signalsın Selected Blues Chip Stocks
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yıldız, Mustafa; Koç, Utku
Efforts 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.
Online Shopping Purchasing Prediction
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kazezyılmaz, İdil; Evren Güney
This project aims to understand the purchasing behavior of the consumers and make predictions about purchasing according to website metrics such as page values, bounce rates.An existing dataset is used in this project. This dataset is available in the collection of data from an e-commerce website by Google Analytics, which consists of 10 numerical and 8 categorical attributes coming from 12,330 sessions. The 'Revenue' attribute is used as the class label. The attributes that have high impact on the prediction are; "Administrative", "Administrative Duration", "Informational", "Informational Duration", "Product Related" and "Product-Related Duration". They represent the number of different types of pages visited by the visitor in that session and the total time spent in each of these page categories.The "Bounce Rate", "Exit Rate" and "Page Value" features represent the metrics measured by Google Analytics for each page in the e-commerce site. The "Special Day '' feature indicates the closeness of the site visiting time to a specific special day (e.g. Mother’s Day, Valentine's Day) in which the sessions are more likely to be finalized with a transaction.Since the purpose of this project is to predict potential purchasing using existing data, in the prediction part several machine learning algorithms such as decision trees, random forests will be applied to compare the models. The most suitable model will be chosen among these algorithms.
Prototyping as a tool and a process in architectural education
(MEF Üniversitesi, 2022) Şahinkaya Bucak, Ebru; Özdemir, Kürşad; Sezgin, Ahmet
Bir kavram ve uygulama olarak mimarlık için prototipleme, diğer alanlarda olduğu gibi somut bir çalışma modeli oluşturmanın ötesindedir. İnşa ederken tasarımın devam ettiği ve aynı anda deneyimlemenin sağlanabildiği bütünsel, öğretici bir süreci kapsar. Ayrıca prototipleme, teori ve pratik arasında gidip gelen mimarlığın alanını anlamaya yardımcı olan bir eyleme dönüşebilir. Bu tez çalışmasının amacı, prototipleme kavramının mimarlık eğitimi başta olmak üzere mimari çalışmalara dahil edilmesinin faydalarını belirlemektir. Zaman içinde, ustadan çırağa aktarılan mimarlık bilgisi bugün genellikle sanat ve mühendisliğin iç içe olduğu okullarda öğretilen formal bir sisteme dönüşmüştür. Bu duruma karşılık, eğitim sürecinde mimarlık ürünü praksisini, sadece kağıt üzerinde bir tasarım olarak var etmemek, inşa süreçlerini ve sonrasını da tahayyül etmek, tasarımcı ve öğrencisinin farklı alan potansiyelleri ile yüzleşmesini ve çözüm üretebilmesini sağlar. İnşa etmeyi mimarlık eğitim sürecine dahil etmiş programlar bu anlamda kritik örneklerdir. Bu tezde inşa pratiği prototip üretimi üzerinden okunacak ve çeşitli Tasarla-İnşa et Programları hakkında bilgiler kullanılarak değerlendirme yapılacaktır. Ek olarak, 21. yüzyılda değişen mimar ve mimarlık pratiğine bir cevap olarak, tasarım, inşa ve sonrası bir süreç olarak değerlendirilecek, inşa ederek öğrenmeyi önemseyen; AA Hooke Park, ITKE University of Stuttgart, Rural Studio, Ciudad Abierta/Open City, MEF FADA DBS/AAP olmak üzere farklı beş coğrafyadan beş programın prototiplemeye yaklaşımları, öğrenme süreçleri, katılımcıları ve kullandıkları araçlar incelenecektir.
Fraud Detection In the Bitcoin Exchange Market
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Namlı, Hüseyin; Güntay, Levent
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.
Rfm Based Customer Segmentation for a Mobile Application
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Baykan, Ozan Barış; Özgür Özlük
In this project, customer segmentation was made for Doggo, a mobile application that brings together trained dog walkers for people who are not able to provide daily needs of their dogs. The data was organized by obtaining the columns of recency, frequency, monetary and tenure, and RFM-based customer segmentation was made using machine learning algorithms such as K-means and Gaussian Mixture Model (GMM). Then, the model was built with the part of the dataset that includes recency, monetary and tenure columns using K-means. In addition, with a function developed, the RFM and tenure will be repeated at intervals determined by the Doggo operation team, and this tool is used to monitor the customer condition changing. Various marketing campaigns have been proposed according to the current situation and the transitions they have made.
Interviewster: A chatbot evaluating competency based interviews using transformer models
(MEF Üniversitesi, 2022) Atıcı, Onur Emre; Demir, Şeniz
İşe alım, insan kaynaklarının en sözel ve iletişimsel alanlarından biridir. Bu departmanın insan faktörünün baskın olması nedeniyle yeniliğe açık olduğu kadar önyargıya da açık olan birçok yönü bulunmaktadır. Bu da yapay zeka teknolojilerindeki ilerlemeyle birlikte birçok inovasyon ihtiyacını (ve şansını) beraberinde getirmektedir. Bu çalışmada adayları karşılayan, bilgi toplayan (ad-soyad, iş durumu, bilgisayar bilgisi, eğitimi, hobileri gibi) ve geçmiş deneyimleri hakkında yetkinlik bazlı sorular sunan ve bu soruları doğru cevaplayabilmesi için onlara yardımcı olan "Interviewster" adlı bir sohbet robotru oluşturmaya odaklanılmaktadır. Bu sohbet robotu adayı karşılar ve konuşmayı başlatır, adaydan toplanan verileri kaydeder, yetkinlik bazlı görüşme yapar ve sinir ağları mimarileri ve transformer tabanlı teknolojileri kullanan doğal dil işleme teknikleri ile adayın gerekli yetkinliğe sahip olup olmadığına karar verir. Web üzerinde çalışmakta olan bu sohbet robotu Python ile kodlanmış ve Flask ile web'de yayınlanmış olup Mysql veritabanını kullanan bir Python çekirdeği üzerinde çalışmaktadır. Bu tezde ilk olarak mülakat uygulamaları tanıtılmakta ve yetkinlik bazlı mülakatların yöntem ve uygulamaları anlatılmaktadır. Sonrasında Interviewster olarak adlandırılan sohbet robotunun mimarisi, kullanılan teknolojiler, kütüphaneler ve makine öğrenmesi teknikleri, detayları verilerek açıklanmıştır. Son olarak da transformer tabanlı modeller olan BERT, DeBERTa ve ELECTRA modellerinin gerçek adayların yetkinlik bazlı mülakat sonuçlarına uygulandığı bir değerlendirme çalışmasının sonuçları detaylı olarak tartışılmıştır.
Calculation of the Capacity of a Retail Clothing Store
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Türkoğlu, Murat; Tuna Çakar
The purpose of this study is to define and improve the capacity calculation for clothing store companies. It is important to know how many products need to be sent to the relevant store in order to sell more products, whether more or less products will be sent to the stores, how much the product can sell and how much capacity the stores will have for the relevant products during the season. Planning and producing more products than necessary may also cause insufficient capacity to consume the stocks of that product in the relevant season. For these reasons, a detailed capacity management system is needed. The capacity of certain product groups in the stores in certain seasons can be determined by calculating the capacities of the relevant units in the stores and the relations of these units with the product groups. The relevant system will produce output for both planning and allocation units. At the same time, converting the capacity of products and store display units into a common unit (LCM) will be one of the factors that facilitate our work in capacity calculation. A short version of the LC Waikiki capacity system platform is used to obtain the data. ASP.Net Core Web API, ADO.NET, T-SQL, C # programming were used as program tools. Azure Microsoft SQL Server was used as a database server. Azure App Services has been used to keep the business codes.
Predicting the Price of Bitcoin: Using Machine Learning Time Series Methods
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Ulutaş, Sezer; Utku Koç
Cryptocurrencies 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.
Product Recommendation for C2c Marketplace With Collaborative Filtering Als Algorithm
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kıran Çelebi, Bilgehan; Utku Koç
In 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.

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