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