Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785
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Master Term Project Tractor Sales Forecast Using Machine Learning(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tunay, Yiğitcan; Özlük, ÖzgürThis study presents a machine learning model to forecast tractor sales using four years of number of tractor sales based on year, month, city, town, brand and model provided by Turkey Statistical Institute. Tractor sales can vary depending on many different factors. Therefore, it is a challenging task for any company to estimate number of tractor sales that will be sold next year. Having the ability to predict that accurately will contribute companies in many distinct ways. Foreseeing market trends, keeping pace with the competition, delivering the right product to the right customer at the right time, reducing inventory costs, better production planning and cash flow management are major advantages of accurate forecasting. Within the scope of this study, models were developed to predict tractor sales using different statistical and machine learning methods. In further steps of the study, meaningful variables can be added to the dataset in order to reach a better result. Also, market share can be estimated by using different simulation methods which take into consideration those variables.Master Term Project Predicting the Reasonable Departments for the Human Resources Related Questions by Using the Text Classification Algorithms(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Sancı, Yavuz; Özlük, ÖzgürThe employees of Yapı Kredi Bank use a help desk system to ask their Human Resources related questions to the employees of the Human Resources departments. The questions are assigned automatically to the relevant departments by the system according to the subjects of the questions. In some cases, the mismatches between the contents and the subjects of the questions may cause the wrong Human Resources department assignments of the questions. Even though the application allows Human Resources employees to redirect the questions to the appropriate Human Resources departments, which are responsible for answering, the response time of these questions lasts longer. This project aims to analyze the content of the Human Resources related questions by using the text classification algorithms to predict the responsible Human Resources departments. Thus, it is aimed to respond to the questions in a much shorter time.Master Term Project Credit Card Fraud Detection Analysis and Machine Learning Application(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Meker, Tuğrul; Özlük, ÖzgürCredit card fraud transaction is a common term for theft and fraud action involving a payment card such as payment or credit card or debit card as a source of funds in transactions. With increased usage of POS channel or internet in recent years, the risks of credit card fraud have increased. Mostly, these illegal activities start with a compromise of data associated with the account number or important information that required to start the financial transaction. After, literature review and exploratory data analysis, machine learning algorithms are going to use to decide whether the transaction is fraud or not. Logistic regression, decision tree, Naive-Bayes, decision forest and linear SVC’s classifier algorithms are used in this study. With re-sampling choices (random-under, random-over sampling & SMOTE), these algorithms’ performances are compared. Logistic regression, decision tree, and random forest provide best results in terms of accuracy metrics. Grid-Search is applied to those three algorithms. Decision tree algorithm is chosen as the best algorithm for credit card fraud detection. Python 3.7 is used in this study.Master Term Project Predicting Facebook Ad Impressions & Cpm Values(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Tekten, Semih; Özlük, ÖzgürIt is estimated that there are more than two billion active users on Facebook as of the first quarter of 2018 and social media has tremendous opportunities for advertisers in terms of performance and measurability. However, for marketing managers, it is very difficult to manage all the campaigns on different marketing channels and optimize for better results.For that reason, Facebook Marketing Partners or other optimization solutions emerged in the adtech market. In order to improve existing optimization solutions in the market, ad impression costs will be predicted in this study by using different machine learning techniques and different algorithms. The main goal of this study is to generate a robust model for predicting CPM values on Facebook, and to use that model as an in put for the existing optimization solution Adphorus offers for its clients. Adphorus is one of the Facebook Marketing Partners in the market.Master Term Project Steel Product Clustering and Feature-Based Product Price Estimation for Flat Secondary Materials(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kemerci, Meryem; Özlük, ÖzgürMachine Learning replaces manual and repeatable processes every day, none of the industries can resist these developments. Older systems were rule-based which would bring some level of automation, but all had their limits. One of the goals of Machine Learning is prediction, and it can be used to obtain higher accuracy and better forecasts. Price predictions are made by hand according to market expectations and countries’ conjuncture in the past, but it is changing fast with the developments of Artificial Intelligence tools. In steel Industry, price levels are determining based on human intuition and simpler statistics. Profits are directly connected to the right pricing for the right time, machine learning algorithms may do the quotation of the steel properly to increase the company profits. This study aims to classify items as per quality and estimate the price level for the products. Feature selection preprocessing steps are used to enhance the performance and scalability of the classification method. The second part is feature-based product price estimation for the secondary products and selects the predictors of each quality under the product family.Master Term Project Predicting Transaction Numbers İn Atm(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Karasu, Ahsen Ceren; Özlük, ÖzgürATMs 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.Master Term Project Second-Hand Car Price Estimation Using Machine Learning(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Kütükde, Şule; Özlük, ÖzgürThe ones who think to sell their cars always think about their cars’ second-hand market worth, at first. Both for the sellers and the buyers, it is crucially important to estimate the car’s realistic worth, in order not to suffer a loss of money or time. In this research, arabam.com’s advertisement data is obtained with the help of web scraping technique, and later machine learning algorithms like Linear Regression, Decision Tree, Random Forest and Gradient Boosting are applied for collected advertisement data in order to estimate cars’ prices. In addition, some hyperparameter tuning is applied for robust estimation. The models’ performances are discussed, and some remarks offered for further researches.Master Term Project Text Classification Using Apache Spark(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Azizoğlu, Umut Rezan; Özlük, ÖzgürOne of the biggest problems of enterprises which are marketplace e-commerce business model with social platform; The improper communication of their social platform is the negative impact of the customer experience and the damage of the brand's value both materially and morally. As the number of daily commentaries is in numbers that cannot be read manually with optimal human resources in terms of company profitability, the interpretation modules in social market places are left unconscious. With this Project; established a model that prevents sentences that spoil the customer experience in their social platforms. Both data preparation and machine learning model were developed on Databricks notebook, using the apache spark platform with SparkML libraries and Pyspark language. The “Text Classification” approach is adopted when determining the model.Master Term Project Smart Precision Agriculture With Autonomous Irrigation System Using Rnn-Based Techniques(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Anuşlu, Timuçin; Özlük, ÖzgürThe study presents a solution to improve freshwater usage for irrigation in the agriculture by building a neural network model to predict soil moisture at 20 cm level with time series data over longer periods of time.Master Term Project Online Check-In Likelihood of Hotel Guests(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Tiryaki, Yusuf; Özlük, ÖzgürHotel operators benefit from current technological developments in order to provide the best experience for their guests to stay. In the case of an enterprise which providing guest hospitality service, the flow is composed of 4 steps: booking, check-in, accommodating and check-out. Online reservation systems have been in use for a long time and are services that offer room reservations for date ranges that guests will stay with. Online check-in applications are a new type of service that has just begun to be implemented in the hospitality sector. The advanced online hotel check-in systems enable users to save time by creating an entry log on the internet, specifying floor and room selection, assigning additional services, notifying the check-in time during the process, and reducing waiting times for hotel help desk during check-in. In the online check-in forecasting process, a data analytics application was implemented that computes the score of the user's proximity to online check-in after the booking step and the booking information was obtained. The score calculation process uses statistical learning algorithms. Within the scope of the study, the guests were classified according to closeness to service reception with Random Forest and DNN(Deep Neural Networks) methods using a dataset in which the guests had hotel booking and provided online information. The trained model for classification was presented as a web service to return the likelihood score of new booking guests.