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.
Analysis of online marketplace sales prediction based on machine learning algorithms: A case of Turkish e-commerce site
(Abdullah Gül Üniversitesi / Sosyal Bilimler Enstitüsü, 2023) Kaya, Ecem; AGÜ, Sosyal Bilimler Enstitüsü, İşletme ve Ekonomi İçin Veri Bilimi Ana Bilim Dalı
Internet shopping has grown in popularity as more of our daily requirements have begun to be addressed online. Learning about the preferences and motivations of customers in the Turkish market and guiding e-commerce platforms to adapt their marketing strategies and increase customer satisfaction is important for both resource allocation and cost minimization. The purpose of this paper is to estimate future sales for popular e-commerce sites based on behavioral factors such as discounts, price or free shipping. Therefore, real-time and experiment-independent data are collected from the sales made by one of Turkey's most popular e-commerce sites. In order to produce predictions, we employ Artificial Neural Networks, Support Vector Regression, K-Nearest Neighbors Regressor, OLS regression, and Nu-Support Vector Regressor. The models developed using machine learning algorithms attempt to estimate the number of sales based on independent factors such as price, discount rate, and user ratings. As the result of this research, we calculate and compare the accuracy of the models with root mean squared errors and R².
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.
EFFECT OF IMPLEMENTED POLICIES IN TÜRKİYE AND SWEDEN ON EQUAL OPPORTUNITY AND HIGHER EDUCATION OUTCOMES DURING COVID-19 PERIOD
(Abdullah Gül Üniversitesi, Sosyal Bilimler Enstitüsü, 2024) Dalan, Ayşenur; AGÜ, Sosyal Bilimler Enstitüsü, İşletme ve Ekonomi İçin Veri Bilimi Ana Bilim Dalı
This thesis delves into the impact of COVID-19 policies implemented in Türkiye and Sweden on equal opportunity within higher education and explores the consequential higher education outcomes. The research employs a mixed-methods approach, incorporating both qualitative and quantitative methodologies. Through an examination of socio economic structure, education system, the study elucidates the distinctive approaches taken by Türkiye and Sweden. The findings contribute significantly to a comprehensive understanding of global education policy responses during crises, emphasizing the pivotal role of ensuring equal opportunity. By scrutinizing the specific measures undertaken by both countries, this study not only informs the on education policy during extraordinary times but also provides valuable insights for policymakers, educators, and stakeholders seeking to enhance equal opportunity and foster positive outcomes in higher education.
Enhancing grouping-scoring-modeling (G-S-M) approach through a statistical pre-scoring component: A case study for high-dimensional transcriptomic data analysis
(Abdullah Gül Üniversitesi / Sosyal Bilimler Enstitüsü, 2024) KHOKHAR, MAHAM; AGÜ, Sosyal Bilimler Enstitüsü, İşletme ve Ekonomi İçin Veri Bilimi Ana Bilim Dalı
Rapid advancements in transcriptomic technologies have significantly increased the volume of data available for analysis, which presents challenges in terms of efficiency and computational demand. This thesis introduces a Pre-Scoring component to the Grouping-Scoring-Modeling (G-S-M) framework to address inefficiencies caused by the excessive number of gene groups generated by traditional GSM. By selectively prioritizing gene groups based on their statistical significance, this innovation aims to reduce the computational demands associated with scoring these groups using machine learning models, thereby streamlining the analysis process. Assessed across nine diverse Gene Expression datasets, the Pre-Scoring G-S-M framework not only maintained accuracy comparable to the traditional approach but did so with significantly fewer genes. This refinement conserves resources while maintaining the robustness and reliability of the data analysis, crucial for advancing research in personalized medicine and therapeutic strategies. The findings suggest that the modified G-S-M framework serves as a valuable tool in bioinformatics, offering a more efficient approach to handling large-scale genomic datasets. Future work will focus on adapting this enhanced framework to incorporate diverse types of omics knowledge, such as proteomics and metabolomics, further optimizing its performance to broaden its applicability in both clinical and research settings
How does quality of life (QoL) affect city attractiveness and internal migration in Turkey?
(Abdullah Gül Üniversitesi / Sosyal Bilimler Enstitüsü, 2023) Özer, İsmet Selçuk; AGÜ, Sosyal Bilimler Enstitüsü, İşletme ve Ekonomi İçin Veri Bilimi Ana Bilim Dalı
The current study reexamines the link between quality of life (QoL) factors, city attractiveness, and internal migration in Turkey. The management of internal flows can bring significant benefits to a country in balancing the opportunities between regions or cities. To tackle unequal access to opportunities, the factors that induce internal migration should be understood. This study examines a set of pull and push factors for internal migration by spatial econometric analysis and GIS applications. This thesis finds that when the accessibility of amenities increases, the city becomes more attractive and preferable for migrants. In addition, socioeconomic factors also play a significant role in the decision-making process of migrants. In this study, this thesis used a panel dataset that includes socioeconomic and contextual data such as distances to the amenities for each Turkish city in the years between 2012 and 2021. The results show that, in Turkey, internal migration flows from the East to the West, where opportunities are better. Finally, the human capital level of migrants can cause a variety of thoughts about factors, and it can change the order of significance of the variables for people who have a different level of human capital such as education level. Based on the findings, the paper offers several policy suggestions for ensuring a balanced migration in Turkey.
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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