Yüksek Lisans Tezleri
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1785
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Master Term Project Machine Learning Applications To Increase Customer Satisfaction In Finance Sector(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yiğit, Leyla; Çakar, TunaIn this project, consumers’ complaints about financial data are analyzed. After the analysis, we aim to provide a tool for financial companies such as banks, Lenders that will help them in managing communication with the consumers. Our main aim is to answer the question “How do consumers feel?” This analysis will give a complete picture of consumers’ feedback. We start the project by clustering the customers into different groups. In order to classify customers, we use classification algorithms XGBOOST and Random Forest. XGBOOST is used to predict the probability of getting a complaint. XGBOOST is also tested as an ensemble learning technique. By Using Random Forest the comparison of Bagging and Boosting is performed. This kind of model is very useful for a customer service department that wants to classify the complaints they receive from their customers. These kinds of models can also be expanded into a system that can recommend automatic solutions to future complaints as they come. The topic is motivated by the researcher’s experience in finance where she intends to increase credit sell numbers by anticipating customer feelings. The data set that we use has many measures and dimensions that facilitate to use more than 3 machine learning algorithms. The complaints database is published by the Consumer Financial Protection Bureau (https://www.consumerfinance.gov/). It provides consumers’ feedback in a string format. We also aim to analyze consumers’ complaints dataset from the perceptive of a consumer dispute.Master Term Project Boarding Pass Detection In Social Media To Prevent Flight Information Thft(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Ekici, Hasan Oktay; Çakar, TunaDuring the past few years, along with social media gaining bigger share on people’s lives, everyone has started to share their moments with detailed information on multiple platforms instantly. Sharing these kinds of information on posts may cause security bugs in people’s lives, such as undesired flight changes/cancellations because of flight information theft, frequent flyers miles theft, and even car theft and burglary. This project’s aim is to develop an artificial intelligence algorithm that can help to prevent these security bugs. In this project, we use data that is collected from instagram posts that contain boarding passes. Our main purpose is to build an artificial intelligence that makes decisions and processes following procedures: a machine learning algorithm that decides if the shared instagram post contains a boarding pass shared with #boardingpass; an optical character recognition algorithm that gathers text information from the post and scripts that send the information instantly to the relevant air carrier about the shared post. With this information, air carrier will be able to inform the passenger about their concern on the flight safety only in a couple of minutes after the post is shared.Master Term Project Trangling Weratedogs Twitter Data To Create Interesting and Trustworthy Explosatory/Predictive Anaylses and Visulation Using Different Machine Learning Algorithms(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Arı, Esra; Çakar, TunaSocial media usage has rapidly grown in recent years and knowledge in these environments increased due to this expansion. Therefore, doing exploratory and predictive analysis from intensive data of social media became so popular. However, almost all of the large datasets obtained are uncleaned / raw data. Therefore, the assessing and cleaning of the data is at least as important as the exploratory and predictive analysis. The open source WeRateDogs twitter account tweets have been gathered, assessed, cleaned, analyzed and predicted for this thesis. As a result of the study, it was understood that the most important and most time-consuming part of the predictive data analysis is the data gathering and cleaning. As a result of this project, probability of dog’s breed whether retriever or not is predicted from the tweet’s text body. 24 points increase (%34 change) in accuracy values has been achieved by doing oversampling in the data sets which contain low event observation. At the same time, the decision tree, logistic regression and random forest algorithms are compared and it is shown that the random forest's model performance is better than the others. The algorithm works 13 points better than logistic regression, 21 points better than decision tree.Master Term Project Market Basket Analysis Using Apriori Algorithm(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Şimşek, Yıldırım Murat; Çakar, TunaPredictive analysis is a branch of data engineering that predicts some occurrence or probabilities depend on the data. To make predictions about future events, predictive analytics uses data mining techniques. The process of these techniques involves an analysis of historic data and predicts the future events based on that analysis. Also using predictive analytics modelling techniques, a model can be created to predict. Depending on the data that they are using these predictive models can be varied. Predictive analytics is made of various statistical and analytical techniques used to develop models that will predict future occurrence, events or probabilities. Market basket analysis is one of the data mining techniques that focusing on discovering purchasing pattern by extracting associations from a store’s transactional data. The electronic commerce point-of-sale expanded the utilization and application of transactional data in Market Basket Analysis. The needs of the customers have to be known and adapted to them from the retailers. The retailers collect information about their customers and what they purchase with the help of the advanced technology. Analysing this information is extremely valuable for understanding purchasing behaviour in retail commerce. Market basket analysis is one possible way to discover which items can be sold together. This analysis gives retailer valuable information about related sales on a group of goods basis customers who buy bread often also buy several products related to bread like milk or butter. It makes sense that these groups are placed side by side in a store so that customers can reach them quickly. Market basket analysis is very useful technique for the related group of products that are bought together, and to reorganize the supermarket layout, and also to design promotional campaigns such that products’ purchase can be improved. The main aim of this capstone project is to find the co-occurring items in consumer shopping baskets in the data set that provided by GittiGidiyor E-Commerce Company with the help of the association rule mining algorithm; apriori. Mining association rules from transactional data will provide us with valuable information about co-occurrences and copurchases of products. Such information can be used as a basis for decisions about marketing activity such as promotional support, inventory control and cross-sale campaigns.Master Term Project Market Analysis - Aydınlı Group(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Öney, Çağlayan Özgür; Çakar, TunaIn this paper, we have analyzed the purchase transaction data of Aydınlı Group. Aydınlı offers their customers diverse set of products by providing Polo, Cacharel and Pierre Cardin brands on both retail and online store. The million dollar question that we seek an answer in our research is "can we determine the purchase pattern of customers?".Master Term Project Underlying the Bias for Human Music Evaluation(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Yıldırım, Burak; Çakar, TunaPredictive analysis is the process of using data analytics to predict the future over historical data. Data analytics is the use of statistical modelling and / or machine learning methods to measure the future. In short, it is one of the data mining techniques for predictive analysis that focuses on creating a predictive model for the future by extracting relationships from the data.Master Term Project Chuen Analysis of Gittigidiyor Customers(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Kantarcı, Özlem Hazal; Çakar, TunaIn this project, it is aimed to estimate the loyalty of the customers of the e-commerce company named GittiGidiyor by analzying the customer movements and examined which movements affected the customer loyalty positively / negtively. In the dataset studied, it was seen that the number of active customers is much higher than that of passive customers. Several methods have been tried to solve this "Class imbalance" problem and it has been decided to replicate some lines of passive customers. Rows of smaller classes are duplicated to compensate classes with generated code. The data set was divided into training, validation and test and different algorithms were used. One of the innovative approaches was training and validating models in an earlier time window and testing the model with samples from a later time window. As a result of the studies, it was decided to use "Linear Discriminant Analysis" considering its short training time and especially the success of predicting passive customers.Master Term Project Credit Risk Models Using Machine Learning Models(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Akman, Özkan; Çakar, TunaCredit scoring is an important subject in financial institutions, mainly in banks. I want to examine some machine learning techniques to find out a model that performs good in predicting or classifying the loaner person a good credit or a bad one by evaluating his/her demographic features as marital status, wealth, job seniority, monthly income and expenses.