Yüksek Lisans Tezleri

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

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2017 - 20187

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Now showing 1 - 7 of 7
  • Master Term Project
    Predicting Facebook Ad Impressions & Cpm Values
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tekten, Semih; Özlük, Özgür
    It is estimated that there are more than two billion active users on Facebook as of the first quarter of 2018 and social media has tremendous opportunities for advertisers in terms of performance and measurability. However, for marketing managers, it is very difficult to manage all the campaigns on different marketing channels and optimize for better results.For that reason, Facebook Marketing Partners or other optimization solutions emerged in the ad­tech market. In order to improve existing optimization solutions in the market, ad impression costs will be predicted in this study by using different machine learning techniques and different algorithms. The main goal of this study is to generate a robust model for predicting CPM values on Facebook, and to use that model as an in put for the existing optimization solution Adphorus offers for its clients. Adphorus is one of the Facebook Marketing Partners in the market.
  • Master Term Project
    Steel Product Clustering and Feature-Based Product Price Estimation for Flat Secondary Materials
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kemerci, Meryem; Özlük, Özgür
    Machine Learning replaces manual and repeatable processes every day, none of the industries can resist these developments. Older systems were rule-based which would bring some level of automation, but all had their limits. One of the goals of Machine Learning is prediction, and it can be used to obtain higher accuracy and better forecasts. Price predictions are made by hand according to market expectations and countries’ conjuncture in the past, but it is changing fast with the developments of Artificial Intelligence tools. In steel Industry, price levels are determining based on human intuition and simpler statistics. Profits are directly connected to the right pricing for the right time, machine learning algorithms may do the quotation of the steel properly to increase the company profits. This study aims to classify items as per quality and estimate the price level for the products. Feature selection preprocessing steps are used to enhance the performance and scalability of the classification method. The second part is feature-based product price estimation for the secondary products and selects the predictors of each quality under the product family.
  • 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
    Second-Hand Car Price Estimation Using Machine Learning
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kütükde, Şule; Özlük, Özgür
    The ones who think to sell their cars always think about their cars’ second-hand market worth, at first. Both for the sellers and the buyers, it is crucially important to estimate the car’s realistic worth, in order not to suffer a loss of money or time. In this research, arabam.com’s advertisement data is obtained with the help of web scraping technique, and later machine learning algorithms like Linear Regression, Decision Tree, Random Forest and Gradient Boosting are applied for collected advertisement data in order to estimate cars’ prices. In addition, some hyperparameter tuning is applied for robust estimation. The models’ performances are discussed, and some remarks offered for further researches.
  • Master Term Project
    Text Classification Using Apache Spark
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Azizoğlu, Umut Rezan; Özlük, Özgür
    One of the biggest problems of enterprises which are marketplace e-commerce business model with social platform; The improper communication of their social platform is the negative impact of the customer experience and the damage of the brand's value both materially and morally. As the number of daily commentaries is in numbers that cannot be read manually with optimal human resources in terms of company profitability, the interpretation modules in social market places are left unconscious. With this Project; established a model that prevents sentences that spoil the customer experience in their social platforms. Both data preparation and machine learning model were developed on Databricks notebook, using the apache spark platform with SparkML libraries and Pyspark language. The “Text Classification” approach is adopted when determining the model.
  • Master Term Project
    Online Check-In Likelihood of Hotel Guests
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Tiryaki, Yusuf; Özlük, Özgür
    Hotel operators benefit from current technological developments in order to provide the best experience for their guests to stay. In the case of an enterprise which providing guest hospitality service, the flow is composed of 4 steps: booking, check-in, accommodating and check-out. Online reservation systems have been in use for a long time and are services that offer room reservations for date ranges that guests will stay with. Online check-in applications are a new type of service that has just begun to be implemented in the hospitality sector. The advanced online hotel check-in systems enable users to save time by creating an entry log on the internet, specifying floor and room selection, assigning additional services, notifying the check-in time during the process, and reducing waiting times for hotel help desk during check-in. In the online check-in forecasting process, a data analytics application was implemented that computes the score of the user's proximity to online check-in after the booking step and the booking information was obtained. The score calculation process uses statistical learning algorithms. Within the scope of the study, the guests were classified according to closeness to service reception with Random Forest and DNN(Deep Neural Networks) methods using a dataset in which the guests had hotel booking and provided online information. The trained model for classification was presented as a web service to return the likelihood score of new booking guests.
  • Master Term Project
    Sentiment Analysis of Hürriyet Emlak
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Korkmaz, Alev; Özlük, Özgür
    Sentiment analysis refer to the task of natural language processing to determine whether a piece of text contains some subjective information and what subjective information it expresses, whether the attitude behind a text is positive, negative or neutral.