Yüksek Lisans Tezleri

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

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Author: search.filters.author.Evren GüneyCOAR Access Rights: search.filters.coarAccessRights.open access

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Now showing 1 - 4 of 4
  • Master Term Project
    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.
  • Master Term Project
    Music Generation Using Deep Learning Techniques
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Akalın, Kutay; Evren Güney
    This project aims to generate songs using the Jukebox model and its architecture. Jukebox’s Vector Quantized Variational AutoEncoder (VQ-VAE) architecture is state-of-the-art deep generative model used for music generation and gives an outstanding result. For this purpose, different Elvis Presley songs were analyzed in audio domain using various Music Information Retrieval (MIR) methods. The top level of the Jukebox model was retrained with these songs in order to increase the quality of the songs that will be produced in the style of Elvis Presley. After that, 3 new samples were generated using the first six seconds of Elvis Presley - Jailhouse Rock as the input signal. At the end, these new songs were analyzed and compared.
  • Master Term Project
    Customer Segmentation and Customer Churn Prediction for Babil.com
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Çakar, Berk; Evren Güney
    In the past decade, a lot of players have joined into e-commerce market and competition in the market has been increasing lately. The e-commerce companies want to use their resources more efficiently to stay ahead in the competition. Personal communication with customers, increasing customer loyalty, acquiring new customers and preventing customer churn are some of the ways to achieve this goal.Babil.com is an e-retailer that sells books online and it is one of the companies which wants to stay ahead in the competition. It is founded in 2013 and now it is a 8 years old mature company. So, instead of spending much resources on acquiring new customers, trying to keep existing customers and increasing retention rate is a more ideal goal for the company. Also, personal communication with customers and reaching them with the right product in the right time is crucial.In this project, a customer segmentation with two levels is implemented to help Babil.com. For the first level of segmentation, customers’ value to company is identified by RFM segmentation and in the second level of segmentation customers’ behaviors are identified by K-Means clustering. To prevent customer churn, a machine learning algorithm which predicts customers who will churn in the next 6 months. With this algorithm, it will be easy to take an action for the right customers in the right time.
  • Master Term Project
    Yelp Review Dataset Sentiment Analysis Using Machine Learning Techniques
    (MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Atik, Anılcan; Evren Güney
    Today, internet review sites are becoming a significant criterion for users’ consumption habits on products and services, while being vital source of feedback for businesses. This project aims to present quick feedback on whether consumers are satisfied with businesses’ product and services, lessen the allocation of resources on information extraction towards these reviews, and provide a more agile environment for businesses, by automatizing the extraction of the information “whether the sentiment towards the business service or product is positive or negative” from textual data. The problem, binary classification out of textual data, is addressed through Yelp Company reviews dataset. Yelp is an internet review website, it enables users to review products, services, and businesses. Alongside with the text formatted restaurant reviews, star-rating is converted to 1 (positive) and 0 (negative). These values are obtained to provide the target column to predict the sentiment of the review text. 100,000 restaurant review records are used in 4 different machine learning algorithms to predict the binary classification problem of predicting whether the review sentiment is positive or negative. 2 neural networks one with pre-trained GloVe, SVM, and Logistic Regression models are used, and the success of these models is compared using F1-Score as a performance metric. These results are presented in the paper.