Veri Bilimi Anabilim Dalı Tez Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/220

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An analysis of impacts of economic uncertainty and covid 19 outbreak on the labor force of Turkey by education level: Svar approach
(Abdullah Gül Üniversitesi, Sosyal Bilimleri Enstitüsü, 2022) Kılıç, Edanur; AGÜ, Sosyal Bilimler Enstitüsü, Veri Bilimi Anabilim Dalı
The economic downturns affect the fluctuations in the labor force participation rate. The pandemic brings about many changes in different areas and the labor force participation rate is one of them. This study analyses the impact of economic uncertainty innovations on the labor force participation rate in Turkey for the period January 2011 to November 2019. We also consider different educational attainment, which consists of five categories, because finding a job gets hard in recent years and there is no academic study on whether education level matters or not in Turkey. We obtain an economic uncertainty index to analyze the effect of Covid-19 and generate multivariate models (SVAR) to determine the relationship between uncertainty and labor force participation rate. We examine labor force statistics at different educational attainment levels to understand whether there are any changes or not. Thus, the main research question is that “How is the impact of uncertainty shocks on labor force participation rate in different levels of education in Turkey?”. The results show that the labor force participation rate decreases, while the unemployment rate increases in such an economic downturn.
Customer segmentation using a developed RFM model: An application in a rug&carpet manufacturing company
(Abdullah Gül Üniversitesi, Sosyal Bilimleri Enstitüsü, 2022) İmdad, Yağmur Gizem; AGÜ, Sosyal Bilimler Enstitüsü, Veri Bilimi Anabilim Dalı
Data science has gained enormous importance by contributing to the in-depth understanding and interpretation of information. Especially companies consult on data analysis to make strategic decisions in the competitive market. Much more important than the decisions taken is a determination of the customer or customer groups to which these decisions will be adapted. For that reason, customer segmentation by identifying similarities and differences between customers becomes crucial. In recent times, the RFM model is preferred mostly for customer segmentation. The RFM model is based on the customer's last purchase date, how often they purchase, and how much money contributes to the company. It is an easy model to understand and interpret results in a clear way. Many researchers prefer to apply the RFM method by adding extra variables to the analysis. Thus, customers are evaluated from a broader perspective. This study aims to present a developed RFM model by adding extra variables which are Loyalty, Dependence, and Expectation which are determined by a broad literature review and as a result of a survey relating to 106 dealers. There are some studies that create a segmentation model by using loyalty and the RFM model. However, this study developed a new model by including the dependence and expectation variables, which are not been used previously with the RFM model, besides loyalty. In the study, dealers are analyzed by the K-means clustering method and the optimum number of clusters is indicated as six. Each cluster has its specific customer behavior and this study guides the company to constitute marketing strategies regarding customers' specifications.
Inequality in mobility during COVİD-19: Global to local analysis
(Abdullah Gül Üniversitesi, Sosyal Bilimleri Enstitüsü, 2022) Gençaslan, Elif; AGÜ, Sosyal Bilimler Enstitüsü, Veri Bilimi Anabilim Dalı
This thesis analyzes mobility patterns during the Covid-19 pandemic from a global and local perspective. The global framework includes 37 European countries and the local framework comprises 81 Turkish cities. The study follows the daily mobility trajectories of people from February 2020 to January 2022. The analyzes are conducted to understand the economic opportunities available in countries -at a macro scale- that facilitate or hinder the “proper” mobility behavior of individuals while focusing on the captive commuters, i.e., the share of the population who need to commute to the work despite the risk of infection and governmental policies. The results indicate that the workforce in regions with higher GDP per capita, education level, and life expectancy at birth was able to reduce their workplace mobility higher than commuters in areas with low income, education level, and life expectancy at birth. Therefore, unprivileged populations were exposed to higher health risks against rapid Covid-19 transmission in Europe and Turkish cities.

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