Search Results

Now showing 1 - 2 of 2

Machine Learning Applications To Increase Customer Satisfaction In Finance Sector
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Yiğit, Leyla; Çakar, Tuna
In 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.
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, Tuna
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.

Yüksek Lisans Tezleri

Browse

Filters

Settings

Sort By

Results per page

Search Results