Yüksek Lisans Tezleri

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

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Author: search.filters.author.Özgür ÖzlükCOAR Access Rights: search.filters.coarAccessRights.open access

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Now showing 1 - 8 of 8
  • Master Term Project
    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.
  • Master Term Project
    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.
  • Master Term Project
    Segmentation With Unsupervised Learning: an Application Using the Walker's Data
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Polat, Taylan; Özgür Özlük
    In 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
    The Effect of Bert-Based Grammatical Analysis on Google Search Results
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çolak, Oğuz; Özgür Özlük
    This study aims to study the BERT, namely Bidirectional Encoder Representations from Transformers model, which is introduced by Google and is of great importance in content analysis, and to examine the role of grammatical accuracy in the process of content quality measurement and Search Engine Results Pages (SERP). BERT has an important role among the algorithms used by Google in order to maintain the quality of search results and to provide more relevant content to users by understanding the content more effectively.In this study, CoLA data, which is accepted as the most reliable data in this field and therefore used frequently in similar BERT studies, is used. The main purpose here is to make a BERT-based grammatical evaluation of sentences in a content and then examine these results on pages with optimal ranking values, to examine the connection between search results and grammatical accuracy and the importance of this parameter.In this context, the project consists of two phases. In the first phase, the content of the pages that are visible in the first 20 in 50 different queries are scored with the pre-trained BERT model. In the second phase, a dataset that includes different SEO-focused metrics of the same pages is created manually, and the importance of the BERT score among these features is investigated.
  • Master Term Project
    Forecasting Organic Traffic With Different Source of Data
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çolak, Mehtap; Özgür Özlük
    In this project, the results are compared using different data sets for the organic traffic forecasting of a website. Two different models were developed based on the data obtained from Google Search Console (GSC), Google Analytics (GA), Ahrefs and Google Trends and trained with XGBoost and Random Forest machine learning algorithms. Although the .. value and accuracy rate of the first model developed on the GSC, GA and Ahrefs data obtained between 2019-2020 was high; it is not suitable for predictive analysis because the data sets consist of dependent variables. The second model was developed with Google Trends data for brand and non-brand queries with the highest Impression value. The future trends of the relevant queries were predicted using the Prophet algorithm. Through this model, Impression values of the relevant website were estimated for the remainder of 2021.
  • Master Term Project
    Airbnb Host Recommendation Engine
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, Batuhan; Özgür Özlük
    In this project, a fifth rule is proposed to reveal guests ' comments about hosts using the recommendation system and sentiment analysis for the super hosts' selection for Airbnb. This project is aimed to contribute to Airbnb's selection of Super hosts. In this study, sentiment analysis and comment data are examined, and polarity scores are created for use in suggestion systems. A collaborative filtering method is used for the recommendation system. The FunkSVD algorithm received the best RMSE score. Polarity scores are estimated for each latent user by looking at the host and listing id. The recommendation system developed ranked the polarity scores of hosts for each user.
  • Master Term Project
    Big Data Analysis on Hotel Reviews
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Demir, Burcu; Özgür Özlük
    This analysis aims to get a regression model of the reviews and the score by the guests to observe the effects of the content of the reviews on scores. The content of the reviews is also suitable for a sentiment analysis. These analyses are useful indicators of the hotel sector to catch the market direction positively. In this analysis, clustering hotel-based reviews and customer segmentation based on the reviews will be the key point. Nationality of the guests will be helpful information of the guests to get them into the segmentation pool. The guest who wants to stay in the best hotel in Europe while their trip could choose the best hotel. They can conclude that selection by meeting their needs.
  • Master Term Project
    Duplicate Record Detection: a Rule-Based Approach
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Malkaralı, Gülce; Özgür Özlük
    The study presents a rule based algorithm to detect dublicate and near-dublicate rocords within a dataset that is extracted from a leading online reality platform.