Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785
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Master Term Project Segmentation With Unsupervised Learning: an Application Using the Walker's Data(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Polat, Taylan; Özgür ÖzlükIn this project, the Walkers suitable for the service were filtered by using the dataset shared by the DogGo company. Then, unsupervised machine learning methods such as K-Means, Gaussian, Principal Component Analysis were used to score and cluster the most suitable walkers according to performance, willingness, and experience.DogGo is the first mobile application in Turkey that provides pet walking and grooming services to its customers in a safe and professional manner. DogGo provides a professional service where dogs are taken care of in dog families' own homes or at the caretaker's home for any need of dog families. DogGo Company wants to provide the best matching of walkers and animals, using Machine Learning algorithms, through a 5-step acquisition process for their walkers.While the results of the K-means models created on the unique sliders were compared with the help of the Elbow method and the Silhouette score, the results of the Gaussian models were compared with the AIC and BIC method. In addition, an RFM scoring in a classical structure has also been created. When the results of the study were examined considering the Elbow and Silhouette scores, it was shown that the model created with K-Means gave the best results, and the number of clusters was decided as 2.Master Term Project Game Recommendation System for Steam Platform(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Bayram, Serhan; Semra AğralıIncreasing number of choices and competition in the markets, force companies to differ in services they provide to their customers. Offering better services have a positive impact on customer loyalty, and to do so, companies should understand their customers’ interests and act accordingly. One popular method for this purpose is building recommendation engines to make personalized suggestions. In this project, collaborative filtering methods with implicit feedback are used to make recommendations to users of theSteam platform. The recommendation systems are built using two different matrix factorization techniques, Alternating Least Squares and Bayesian Personalized Ranking. Different models are created with implicit playtime data of the users and the results are evaluated by using Precision at k metric. Additionally, similar items that are offered by the models are analyzed. Results show that the models are considerably successful at finding personal choices and similar items. The best model finds the item in the libraries of 33% ofthe users.Master Term Project Location and Cluster Based Sales Channel Potential Analysis in Retail(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Bilgin, Birtuğ; Adem KarahocaThis analysis project was conducted on the need to obtain new analysis and inferences for the existing traditional sales channels of company, which wants to progress in line with its omni-channel goals. In order to reach the customer with the same level of service in all channels it is necessary to analyze the dynamics of the channel well. In this project, I aimed to make sense of demographic data with the linear model and future selection model and to transform it into meaningful information that will guide sales strategies. Especially for diffusion strategies, in addition to traditional methods, data-based location analysis and analysis of sales weights of existing points are required. With the information to be provided, new dealer opening processes will also be based on data.Master Term Project 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 Term Project Online Shopping Purchasing Prediction(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kazezyılmaz, İdil; Evren GüneyThis 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.Master Term Project Rfm Based Customer Segmentation for a Mobile Application(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Baykan, Ozan Barış; Özgür ÖzlükIn 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.Master Term Project Employee Performance Prediction(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Sivas, Barış; Özgür ÖzlükDogGo 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.Master Term Project Süt ve Süt Ürünleri Sektöründe Faaliyet Gösteren Şirketlerin 3pl (3. Parti Lojistik) Kullanım Tercihinin Satış ve Lojistik Departmanlarının Çalışma Alanları Açısından Swot Analizi(MEF Üniversitesi Sosyal Bilimler Enstitüsü, 2021) Ayaz, Demet; Hande KaradağŞirketler, ürettikleri/sattıkları ürün ve/veya hizmetin, gerek üretim aşaması gerekse de nihai kullanıcıya ulaştırılması sürecinde lojistik şirketlerini kullanmayı tercih edebilmektedirler. Bu tercihlerinin farklı sebepleri olduğu gibi, bu sebeplerin şirketlerin farklı departmanları tarafından, önemlerinin boyutları da değişebilmektedir. 2020 Pandemi sürecinin başlamasıyla birlikte şirketlerin tedarik zinciri çözümlerinin doğru seçiminin yanı sıra çeşitlilik tercihlerinin ne kadar önemli olduğu ortaya çıkmıştır.Bu çalışmada, 3PL şirketiyle çalışma tercihi, bu kararı veren lojistik departmanlarının yanı sıra ürün ve/veya hizmetin satışından sorumlu olan satış departmanlarının sorumluluklarını da dikkate alınarak, detaylı bir şekilde SWOT analizi ile incelenmiştir. Bunun sonucunda kuşkusuz bir maliyet avantajı sağlayacak 3PL çalışma tercihinin, iyi bir planlama ve ciddi bir kontrolle desteklenmesi gerektiği, aksi takdirde orta ve uzun vadede sorunlar yaşanabileceği ortaya konulmuştur. Projenin, 3PL tercihinin Lojistik ve Satış departmanlarında yaşanabilecek çatışma konularına ve bu çatışmaların önüne geçilmesi için yapılması gerekenler anlamında literatüre katkı yapması beklenmektedir.Master Term Project QPICAR Deep Learning(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Beğde, Özge; Tuna ÇakarThe aim of the project is to train a smart tool kit named "Sunfounder Raspberry Pi Robot Car" to move without hitting the walls in a closed area. The goal is to maximize the driving time without crashing by reducing the number of hits. Ultrasonic sensor data collected from the vehicle are processed with reinforcement learning and deep reinforcement learning algorithms and the results are compared. In this study, Python programminglanguage is used. In this study, firstly, the Q-Learning method, which is a reinforcement learning algorithm based on Markov decision processes, is used. The method basically relies on a memory table, Q-Table, in which the Q-values of the agent moving from one state to another are kept. This table is updated according to the results of the Bellman equation in every action of the agent, and as a result of this iterative process, it is optimized to provide that the agent moves to maximize its rewards. Deep Q-Learning (DQN) is used as a deep reinforcement learning algorithm. This algorithm was developed by the DeepMind Technologies team in 2013. Basically, it is based on the use of the Bellman equation, which is an element of the Q-Learning method, incombination with neural networks. This method is often used for training agents in complex and multidimensional environments such as video games. Due to the different type of the data used on the algorithm, minor changes were made to adapt it to the study. RElu and Softplus are used as activation functions. The results of the training process show that the DQN algorithm has an important advantage in terms of training the agent in a short time. At this point, the results are in accordance with other academic studies demonstrating the success of the DQN algorithm for complex environments.For future work, by differentiating the equipment that collects data on the vehicle, different data types such as image, temperature value, oxygen value can be collected and processed. At the same time, with changes to the reward setup in the algorithm, the agent can be trained to move to a specific target or to take actions to avoid a specific target.Master Term Project Türk İş Hukuku Hakkında Kısa Çalışma(MEF Üniversitesi Sosyal Bilimler Enstitüsü, 2021) Suner, Uğur; Ercan AkyiğitDünyamızda yaşanan teknolojik ve ekonomik gelişmeler akabinde çalışma yaşamında değişikler yaşanmaktadır. Teknoloji çağında sözgelimi dünyamızda artık ticari olarak sınırlar kalkarak rekabetçi ortam daha da büyümüştür. Bu gelişmeler ışığında, ilk olarak etkilenen çalışma yaşamı olmaktadır. 4857 sayılı Kanun’un 65. Maddesi ile sadece İş Kanunu’na bağlı olarak çalışan1 işçilere uygulanan kısa çalışma kavramı literatürümüze girerek çalışma hayatına olumlu yönde etki edilmek istense de, uygulamada bu pek mümkün olmamıştır. Uygulamada yaşanan sıkıntılar nedeni ile kısa çalışma düzenlemesi 4857 sayılı İş Kanunundan çıkartılarak, İşsizlik Sigortası Kanunun ek 2. Maddesinde uygulanma alanı bulmuştur. Kısa çalışma; genel ekonomik kriz, sektörel ve bölgesel kriz ile zorlayıcı sebeplerin varlığı halinde çalışma süresinin azaltılması ya da işin tamamen durdurulması olarak tanımlanabilir. Çalışmamızda, kısa çalışmanın koşul ve şartlarından, başvuru şekli ile sona ermesine ilişkin bir değerlendirme yapılmıştır.