Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785
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Master Term Project Forecasting With Ensemble Methods: an Application Using Fashion Retail Sales Data(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yüzbaşıoğlu, Orkun Berk; Küçükaydın, HandeIn this project, ensemble methods of machine learning are used to predict short term store sales of a fashion retailer. Sales forecasts of various products at different stores are generated for a span of three months with bagging tree regressor, random forest regressor, and gradient boosting regressor algorithm. Algorithms are trained and evaluated with real past sales data of a Turkish fashion retailer. The predictive performance of the models is compared with linear regression. The results of the study show that random forest regressor shows the best performanceMaster Term Project Suicide Tendency Classification and Suicide Number Prediction Forpopulation Subgroups(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Ak, Mehmet; Küçükaydın, HandeSuicide is becoming a bigger problem for the world day by day and detecting population subgroups who are more prone to suicide is seen as one of the most important steps for taking precautions to decrease the suicide rates. This study consists of five machine learning models for suicide tendency classification and three machine learning models for prediction of suicide numbers by population subgroups. The dataset provided by World Health Organization is used in the project. Obtained models classify population subgroups as suicide-prone or less suicide prone with 86% accuracy and explain 90 % of the variance in the suicide number per 100,000 population of specific countries.Master Term Project Scoring Neighborhoods for Locating Atm Using Machine Learning(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Yıldırım, Oğuzhan; Küçükaydın, HandeFacility location is a general problem that is important for many different sectors and it is even more important when building the facility costs too much. In this project we analyzed the neighborhoods of Turkey and built two different models to estimate the good and bad neighborhoods for locating an ATM, which has significant costs for banks to build one. We used demographic and socio-economic data of 4,504 neighborhoods in Turkey and built models using Linear Regression and Decision Tree techniques of Machine Learning to find the best neighborhoods for locating a new ATM for a new bank entering the market. We compared the results of two machine learning methods and the results showed that we can make successful predictions of the neighborhoods by using machine learning methods which are good to locate an ATM without classical optimization techniques that requires complex calculations and machine learning methods.Master Term Project Predicting Outcomes and Improving Game Models for Football Matches(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Göçer, Murat; Küçükaydın, HandeThis study is conducted to predict the results of the 2017/2018 English Premier League football matches and show the teams what they should pay attention to in order to win. In this study, classification algorithms are used and the algorithm that gives the best results is applied to real matches. After evaluating the results, some suggestions are made for similar future studies and for the teams to develop their game models.Master Term Project Predicting Customer Satisfaction Via Structed and Unstructured Data Using Classification and Regression(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Danışman, Efehan; Küçükaydın, HandeAccording to different studies, retaining existing customers is five or more times more costly than acquiring new ones. This study aim to understand what customers expect from an airline using machine techniques. Dataset is scraped from Skytrax’s Airline Quality website and consists of 65947 observations with 17 columns consisting of one free format column that includes customer review. In order to do predict whether a customer recommends an airline or not, we try to utilize classification and regression algorithms. In addition to insights, this study also aims to compare the performance of the models and viability of using only free text in order to predict customer satisfaction.Master Term Project Fastseller&worstseller Project (boston Matrix Text Classification Analysis)(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunçel, Ahmet; Küçükaydın, Hande…Master Term Project Benchmarking of Recommendation Models for an On-Line Fast Fashion Retailer(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tilkat, Mustafa; Küçükaydın, HandeThis project studies the usage of the recommendation engines to improve the sales in an online fashion retailer. Fashion retailers sale variety of products throughout their online channels. Since the number of products can be huge compared to an in-line shop, customers may miss some of them while shopping online. Hence, it is crucial to display products that are more likely to be purchased by a customer when the customer is surfing on the website. Our problem is motivated by practice at an online fashion retailer in Turkey. Four collaborative filtering-based algorithms and a random recommender are utilized to design a recommendation engine. 80% of the data is used for training while the other 20% is to used test the designed method. Based on our experiments, User Based Collaborative Filtering (UBCF) using Pearson correlation outperform the other algorithms based on Receiver Operating Characteristic (ROC) curve.Master Term Project Association Rule Mining on Ticket Sales Data(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Genç, Özge; Küçükaydın, HandeThis study aims to analyze the ticket sales data of a cultural institution and define association rules between the festivals/event group and festival/event group venues by market basket analysis. Market basket analysis is a well-known data mining method that is used to discover similarities between products or product groups. For market basket analysis, the apriori algorithm is applied which is considered as a popular data mining algorithm and helps to discover frequent item sets and forms association rules within the dataset. In this project, the apriori algorithm is applied using Python to discover the association rule: between the venues (implementation 1), between the venues excluding the venues used for a specific festival type (implementation 2), between festivals and event groups if any (implementation 3). According to the results of implementation 1, the associations are mostly between the venues of a specific festival type. According to the implementation 2, when we exclude this specific festival type from the dataset, it is seen the rules are mostly between the venues of another festival type. In implementation 3, when the festival venues variable is excluded and only the event names are considered, it is seen that the support, lift and confidence values are lower than the previous implementations.Master Term Project Hotel Recommendation for Online Travel Agencis(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Kılıçlı, Cem; Küçükaydın, HandeSince the early 2000s, online travel agencies (OTAs) have become a central online market source, used by millions of users in all over the world. Recommendation systems became one of the essential tools for them to increase their profit.Master Term Project Churn Prediction of a Deal E-Commerce Website Customers(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Çevik, Müge; Küçükaydın, HandeToday, there is a lot of deal e-commerce sites which are essentially marketplaces. They provide deals which are offered by merchandisers. Because of the nature of these sites there is no subscription model; customers continue because of price or interest or quality not because of subscription. It is normal to have some customers who stop buying, which is defined by "churn". Data mining is now a new technique to define "churned" customers and to have prediction who will churn and what should be against. In this project customers are clustered via unsupervised clustering technique for clusters as "newly purchased", "frequently purchased" and "mostly payed" and "churned". Random Forest Classifier is used to prove that the "churned" customer clusters have homogeneous character and also it has been proved that the "churned" labelled customers have actually no deal order after the observed time period. To recommend what should be done to regain the churned customers to the site the deal order history of these customers have been explored and the deal categories from which they have bought have been found.