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A Comprasion of Ensemble Learning Methods in Retail Sales Forecasting
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Süer, Serhan; Güney, Evren
Forecasting has always been an essential skill which companies try to have and implement in various areas. Sales forecasting is one of the major usage areas of forecasting which is used in almost all sectors. This study refers to forecasting sales of Walmart Stores based on several features such as store id, department id, date, and store size. Walmart sales data which was used in this study contains information of stores between 2010 and 2012. At the beginning of the study, the introduction of the dataset and exploratory data analysis were made to identify dependent/independent variables and their characteristics. To apply machine learning algorithms, data preprocessing methods such as missing value treatment, outlier treatment, and feature selection was applied. Ensemble learning methods in machine learning algorithms were applied in the modeling stage. These methods were addressed in three parts such as Bootstrap Aggregation, Boosting, and Stacked Generalization and these parts consist of six different algorithms in total. The models were compared based on four regression metrics as Root Mean Square Error, Mean Absolute Error, R-Squared, and runtime. After selecting the main metric which models were evaluated, cross-validation was applied to achieve unbiased estimates. Finally, parameters of the model which have the highest score in cross-validation were tuned in the hyperparameter optimization stage and a machine learning model which can be used in forecasting sales of Walmart stores and its success score were obtained.
Vote Transtition Analysis and Comparison of Turkish Local Elections in 2014 and 2019
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Baydoğan, Ufuk; Güney, Evren
Debates around how voters switched their votes relative to previous elections are always the topic after the Election Day. Turkish local election of 2019 was important because of three reasons: first, because it was the first local election after Turkey adapted the new presidential system and the President also participated in the election campaign for his party; second, because İstanbul election, originally run on March 31, was ruled for rerun by Supreme Election Council and the third, because the electoral alliances had significant impact on the results where the votes for The People's Alliance significantly collapsed. This study presents a comparative analysis of 2014 and 2019 official Turkish Local Election Results as well as 2019 Re-Run Election Results of Istanbul to understand the vote transitions. As the outcomes are considered, there are significant changes in the distribution of voting rates between these elections, especially in critical metropolitans. Using the aggregate level vote counts, the vote transition probabilities between the elections are inferred using ecological inference. Proposed clustering approach on vote transition probabilities show that CHP and IYI Party have benefited from forming Nation’s Alliance for most of the cities mainly due to the vote switches from HDP and MHP. For the re-run election case, the slight number of vote difference between the alliances in March has increased significantly. This is mainly because of the contribution of absentees to Nation’s Alliance and around %5 of the People’s Alliance supporters in March who estimated to vote for Nation’s Alliance.
Retention Period Prediction for Pension Policies
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Bayır, Ömer; Güney, Evren
Customer Retention in Pension market refers to the activities and actions companies and organizations take to reduce the number of customer defections. How long the customer will be with our company or will stay in the system is retention. There are already workings in my company and other companies in the market about customer retention. Existing works generally contains how to measure customer retention and how to define distribution channels are successful in customer retention. Also existing predictive models are working on the feature set customer fund, total collection, un-paid premium frequency in general. In pension market companies have small margin of profit from pension policies. To make a profit from pension policies the companies have to retain their customer for long years. It ‘s approximately nine year to make profit from a pension policy because of high sales costs. Therefore to gain a new customer is less profitable than retaining present customers in Pension Market. In my project, I want to look retention in the pension application phase of customer. My main purpose is when the customer applied for pension product predict its retention period. If I produce an applicable model, It will be used in my company’s sales channels.

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