Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785
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Master Term Project Location and Cluster Based Sales Channel Potential Analysis in Retail(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Bilgin, Birtuğ; Adem KarahocaThis 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.Master Term Project Turkish Private Pension Fund Size Forecasting as an Application of Data Analytics(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Kara, Serdar Ufuk; Tuna ÇakarIn 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.Master Term Project Customer Clustring With Machine Learning(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Kara, Ömer Faruk; Tuna ÇakarWhen 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.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)Master Term Project A Study on Churn Prediction in Telecommunication and Pay Tv Area(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2020) Şayık, Murat; …...Master Term Project Retail Data Predictive Analysis Using Machine Learning Models(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2020) Güner, Müjde; Tuna ÇakarMachine 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.