Yüksek Lisans Tezleri

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

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Publisher: search.filters.publisher.MEF Üniversitesi, Fen Bilimleri EnstitüsüSubject: search.filters.subject.Decision Tree

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Now showing 1 - 5 of 5
  • Master Term Project
    Predicttion of Brent Oil Spot Prices Using Country Based Inventory and Trading Data
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Ağralı, Semra; Ağralı, Semra; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
    Crude oil price forecasting has been the focus of numerous authorities, yet the task still persists on being a challenging one. The extremely volatile nature of oil market and high number of active players in it makes establishing a solid forecasting model that is constantly relevant to time very difficult. Recent advancements on data technologies, mainly ever-increasing computing power and trending big data technologies allowed new approaches to be born. From online learners to natural language processing, advanced data analytics models were employed with the help of easily accessible and diverse data. This project is an attempt on making use of such available data in order to forecast Brent oil spot price. By using monthly country by country inventory, trading and economic data, strong drivers of crude price was explored. The data used in this project comes from various sources and in multiple formats, with the final merged data frame has over 17000 observations and contains information on 86 countries. To enhance prediction power, a specialized learner is fit on each country individually and then the predictions are accumulated and filtered before outputting a single prediction. Compared to a single predictor, this approach enhanced the predictive power of the algorithm by adapting to dynamics of each country.
  • Master Term Project
    Second-Hand Car Price Estimation Using Machine Learning
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Özlük, Özgür; Özlük, Özgür; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
    The 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
    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) Çakar, Tuna; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    Social 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
    Chuen Analysis of Gittigidiyor Customers
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Çakar, Tuna; Çakar, Tuna; 02.02. Department of Computer Engineering; 02. Faculty of Engineering; 01. MEF University
    In 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
    Churn Prediction in Vodafone Turkey
    (MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2017) Ağralı, Semra; Ağralı, Semra; 02.01. Department of Industrial Engineering; 02. Faculty of Engineering; 01. MEF University
    This Capstone Project focuses on finding a churn pattern in Vodafone postpaid consumer subscribers. The churn value refers to disconnection from subscription via port-out / Mobile number portability (MNP). It is one of the most important items that demonstrate revenue-loss. The subscriber who churned with MNP switches to a rival GSM operator. The cost of keeping an existing customer is generally cheaper than the cost of acquisition of a new customer. Focusing on customer retention is one of the most profitable strategy for growth. Statistical analysis and machine learning can help analyze churn activities and they can even alert companies when their existing customers are likely to churn. By using machine-learning algorithms, this project aims to detect Vodafone postpaid consumer subscribers who are likely to churn. This project will help the company to decrease its revenue loss.