Yüksek Lisans Tezleri

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785

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2018 - 20195

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Subject: search.filters.subject.Regresyon

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Now showing 1 - 5 of 5
  • Master Term Project
    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.
  • Master Term Project
    Predicting Customer Satisfaction Via Structed and Unstructured Data Using Classification and Regression
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Danışman, Efehan; Küçükaydın, Hande
    According to different studies, retaining existing customers is five or more times more costly than acquiring new ones. This study aim to understand what customers expect from an airline using machine techniques. Dataset is scraped from Skytrax’s Airline Quality website and consists of 65947 observations with 17 columns consisting of one free format column that includes customer review. In order to do predict whether a customer recommends an airline or not, we try to utilize classification and regression algorithms. In addition to insights, this study also aims to compare the performance of the models and viability of using only free text in order to predict customer satisfaction.
  • Master Term Project
    Suicide Tendency Classification and Suicide Number Prediction Forpopulation Subgroups
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Ak, Mehmet; Küçükaydın, Hande
    Suicide is becoming a bigger problem for the world day by day and detecting population subgroups who are more prone to suicide is seen as one of the most important steps for taking precautions to decrease the suicide rates. This study consists of five machine learning models for suicide tendency classification and three machine learning models for prediction of suicide numbers by population subgroups. The dataset provided by World Health Organization is used in the project. Obtained models classify population subgroups as suicide-prone or less suicide prone with 86% accuracy and explain 90 % of the variance in the suicide number per 100,000 population of specific countries.
  • Master Term Project
    The Effect of Exchange Rate Volatility on Export and Import of Turkey on Sectoral Basis
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Ulutürk Tekten, Yağmur; Karamollaoğlu, Nazlı
    In this study, the effects of Exchange rate volatility on export and import of Turkey is analysed by employing monthly trade data for the period from January 2004 to November 2015. The study is extended to cover both sectoral and country specific export and import volumes. The major aim of this study is to show how fluctuations in foreign exchange rate change the volume of exports and imports among various sectors in Turkey. In this paper export and import volume equation is formulated using sectoral data in which explanatory variables are derived from the volatility of each country’s nominal exchange rate against the TRY, bilateral real effective exchange rates for each country that Turkey has foreign trade relationship. The dependent or target variable is the percentage change in the trade size in USD amount both for export and import. In this analysis, 6 different regression algorithms are utilized to explain the effect of exchange rate volatility on industrial activities for exportand import in Turkey. The impact of features on the target feature is analyzed using linear, ridge, lasso, random forest, decision ree and gradient boosting regression algorithms. According to results of these 6 algorithms, for Turkey, the volatility of exchangerate has significant impact on some sectors and on broad product group categories in both export and import up to 26%. The sectors that most exposed to Exchange rate volatilities are seen in ‘Giyim Eşyası’ in export and ‘Binek otomobilleri’ in import. For export, ‘Rusya Federasyonu’, and for import ‘İtalya’ is the most sensitive countries against exchange rate volatility in Turkey.
  • Master Term Project
    Predicting Transaction Numbers İn Atm
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Karasu, Ahsen Ceren; Özlük, Özgür
    ATMs continue to be one of the most important channels for banks to touch their customers. They play an active role in life in terms of cash access and banking experience. The ability of a bank to predict the number of transactions that will occur from ATMs is crucial for the proper control of the budgetary source. When cash is loaded into ATMs, the average transaction made from that ATM is taken into consideration and alarm mechanisms can be activated when a decreasing trend is observed on transaction basis.Before a new ATM is set up, the banks investigate how often customers in that area use other bank ATMs and calculate the commission costs incurred from those uses. As a result, the number of transactions made from ATMs is one of the most monitored KPIs of a bank and has important place in the cash management of the bank.The aim of this study is to estimate the number of future transactions with Auto Regressive Moving Average (ARIMA) method based on the number of transactions that occurred from ATMs.