Yüksek Lisans, Proje Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/215

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A Study on Churn Prediction in Telecommunication and Pay Tv Area
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2020) Şayık, Murat; …
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Calculation of the Capacity of a Retail Clothing Store
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Türkoğlu, Murat; Tuna Çakar
The purpose of this study is to define and improve the capacity calculation for clothing store companies. It is important to know how many products need to be sent to the relevant store in order to sell more products, whether more or less products will be sent to the stores, how much the product can sell and how much capacity the stores will have for the relevant products during the season. Planning and producing more products than necessary may also cause insufficient capacity to consume the stocks of that product in the relevant season. For these reasons, a detailed capacity management system is needed. The capacity of certain product groups in the stores in certain seasons can be determined by calculating the capacities of the relevant units in the stores and the relations of these units with the product groups. The relevant system will produce output for both planning and allocation units. At the same time, converting the capacity of products and store display units into a common unit (LCM) will be one of the factors that facilitate our work in capacity calculation. A short version of the LC Waikiki capacity system platform is used to obtain the data. ASP.Net Core Web API, ADO.NET, T-SQL, C # programming were used as program tools. Azure Microsoft SQL Server was used as a database server. Azure App Services has been used to keep the business codes.
Convolutional Neural Network for Facial Emotion Recognition With Geometrical Features of Face
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Arslan, İlker; Arslan, İlker; Tuna Çakar
One of the recent challenging machine learning problems is to make predictions on image datasets. The aim of the project is to construct a convolutional neural network to guess emotions for a face of a human given in an image file considering the face. After the geometrical features are extracted using pretrained models, we construct five models which are convolutional networks fed with handcrafted geometrical features extracted. The last model uses the outputs of other four models to predict more accurately.
Credit Risk Estimation With Machine Learning and Artifical Neural Networks Algorithms
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Yıldız, İlker; Berk Gökberk
Credit 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)
Customer Clustring With Machine Learning
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kara, Ömer Faruk; Tuna Çakar
When analyzing a company that sells in very different product ranges, you are likely to encounter different types of customers. Grouping customers correctly can set standard actions while serving them. Standardization of marketing processes leads to speed and they are easy to improve. While making this classification, the KMeans algorithm was used in Machine Learning. Inertia and Silhouette Points values were used to find the most suitable cluster number. Principal Components Analysis (PCA) was used to show customers with multidimensional features in 2 dimensions.
Location and Cluster Based Sales Channel Potential Analysis in Retail
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Bilgin, Birtuğ; Adem Karahoca
This analysis project was conducted on the need to obtain new analysis and inferences for the existing traditional sales channels of company, which wants to progress in line with its omni-channel goals. In order to reach the customer with the same level of service in all channels it is necessary to analyze the dynamics of the channel well. In this project, I aimed to make sense of demographic data with the linear model and future selection model and to transform it into meaningful information that will guide sales strategies. Especially for diffusion strategies, in addition to traditional methods, data-based location analysis and analysis of sales weights of existing points are required. With the information to be provided, new dealer opening processes will also be based on data.
Retail Data Predictive Analysis Using Machine Learning Models
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Güner, Müjde; Tuna Çakar
Machine Learning (ML) is a popular field which deals with training the system with data (experience), performing some task (regression or classification) and evaluating the system with the desired performance metrics. ML automatically extracts useful and meaningful insights from the data. ML models for sales prediction applies computational intelligence in many real world applications such as stock market, production, economics, weather, retail, census analysis and so on. Sales prediction can be viewed as a regression problem and various algorithms can be applied. In this project, real life data analysis has been done to predict the sales for four categories of products like Cold Cereal, Bag Snacks, Oral Hygiene Products, and Frozen Pizza. Exploratory Data Analysis (EDA) has been applied to the dataset to make exact predictions even during an unpredictable environment. The different phases of EDA used in this project are Data Preprocessing and Analysis, Feature Selection and Feature Extraction, Model Building and Regression Analysis, Clustering, Time Series Analysis and Model Evaluation using the Performance Metrics. For outlier detection, InterQuartile Range (IQR) method is used. For Filter Based Feature Selection, Univariate Feature Analysis using SelectK-Best and SelectPercentile, Decision Tree Regressor method has been used. For Wrapper Based Feature Selection, Sequential Feature Selector method has been deployed. For Regression Analysis, various algorithms such as Linear Regression, XGBoost Regression and Support Vector Regression (SVR) are analyzed. K-Means Clustering Algorithm has been used on the dataset to generate 4 different clusters. In Time Series Analysis, the week end date and average weekly basket attributes are analyzed, and the sequential data has been rendered for a given time period of occurrence. In model evaluation phase, the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R2 and Adjusted R2 accuracy has been calculated and validated. The project has been implemented in an open source software called Anaconda which includes Jupyter Notebook platform for scientific computations. Python programming language with different packages such as Numpy, Pandas, Scikit learn has been used.
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 Gökberk
For 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.
The Automatic Identification of Butterfly Species Using Deep Learning Methodologies
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Tek Kara, Seda Emel; Tuna Çakar
Automatic identification of butterflies, especially at an expert level, is needed for important topics such as species conservation studies, minimizing the insect damage on plants in agriculture, and biodiversity conservation. An efficient and performing model which can define species even in small datasets may reduce the need for experts on the subject or reduce the time spent for identification. By the model proposed in this study, automatic taxonomic classification of butterflies was studied. Convolutional Neural Network (CNN) applications were applied on 7148 photographs of six butterfly species used in the study. 80 percent of the data set was reserved for training and 20 percent for testing, and the model was run with the relevant parameters. At the end of the study, an accuracy degree of 92.73% was obtained.
Turkish Private Pension Fund Size Forecasting as an Application of Data Analytics
(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Kara, Serdar Ufuk; Tuna Çakar
In this study univariate and multivariate models are used to forecast the net changes in total pension fund size of a private pension company in Turkey, using the daily data between November 2003 and November 2020. Univariate models include the naïve, autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) models. Multivariate models include vector autoregression (VAR) and multiple linear regression models. Our findings suggest that multivariate model predictions outperform univariate model predictions. Univariate model predictions can be improved with walk forward approach. Increased lag size can help improve AR, MA, ARMA and VAR model predictions. Naïve model produces the weakest predictions.

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Browsing Yüksek Lisans, Proje Koleksiyonu by Department "Lisansüstü Eğitim Enstitüsü, Bilişim Teknolojileri Yüksek Lisans Programı"