Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785
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Master Term Project Search Engine Optimization Tool With Web Crawler, Page Density Checker, Search Density Checker, and Similar Page Checker(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Körpe, Yiğitalp; Berk GökberkFor this project, I built an SEO tool. I am working in digital marketing, and we are using various tools, and services frequently. But due to budget constraints or a variety of tools, we cannot reach each tool all the time. Even these tools are not easy to reach, some of their features are fundamental for jobs we are handling every day. Therefore, we needed to find their free / less expensive versions or use them within their free limits. But since I learned some coding and saw programming possibilities, I see that some must-have features are not that hard to code or complicated. Therefore, I created a small program to help my career and my budget. This script helps generally SEO reporting.This script has 4 main features. Web crawler feature can crawl the website and provide website’s page details. Page density checker feature can report the word density of the page. Search density checker searches the input query on Google, reports top 10 results and their word density. Finally similar page checker crawls the website and runs cosine similarity test for each page of the website.Master Term Project Credit Risk Estimation With Machine Learning and Artifical Neural Networks Algorithms(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Yıldız, İlker; Berk GökberkCredit risk assessment is very important for financial institutions today. The probability that a financial institution customer will not be able to repay the credits used is called credit risk. Financial institutions accept or reject credit applications. Institutions evaluate credit applications according to the personal information of the customers, life situation, loyalty, etc. If these data are below various values, financial institutions reject the application. The organization rejected the application because the client anticipated financial difficulties in the future. In the project, "German Credit" data on the Kaggle platform was used. In this data set, customers information and credit status are found as "good" and "bad". By using these data, it is aimed to evaluate new credit application requests. The data set used was passed through various pre-data processing steps and models such as Logistic Regression, Artificial Neural Networks, K-NN, Support Vector, Naïve Bayes, Decision Trees, Random Forest, LGBM and XGB were trained. The highest accuracy is achieved using the XGB model. (0.74)