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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.
Carbon Price Forecasting
(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2018) Karakaya, Nurhak; Ağralı, Semra
In last twenty years great improvements occurred both in technological advances and in the world economic capacity. The total production capacity of countries has been increasing rapidly. These increases need great usage of energy. For that reason, prices of energy related products are very important as they dramatically affect company budgets. Energy budgets get a great deal in total budget of companies and countries. A unit increase in an energy related product can severely affect the budget. The carbon price is one of those products. Besides carbon prices, carbon usage also affects global environment so its price also has an impact on global temperature. To forecast future carbon price different machine learning methods are used. In literature, support vector machines (SVM) [1, 2, 3], random forest (RF) [4, 5], artificial neural networks (ANN) [6, 7, 8] and Auto Regressive Moving Average (ARMA) [9] are commonly used methods. All these methods have pros and cons over the others. In this project, we also apply different machine learning methods, ANN, SVM, RF, Lasso Regression (LG)[11] and Ridge Regression (RR) [10] to forecast the carbon price over time, and give an explanation for future price movements. Then, we compare those five models by analyzing model validation methods. Finally, we choose the best model for further experiments. We have four data types: daily carbon price (CP), electricity price (EP), natural gas price (NG) and coal price (COP) that cover the period of 2009 and 2017. Prices are provided in different currencies. First of all, we work on the data to have all prices in the same currency. We completely eliminate null data. Then, graphically we investigate overall trend by smoothing the data. For analyzing data, we look for daily, monthly, yearly and seasonally time scales. For every weekday or weekends in train data set we keep a day in test data set so that we can keep the time effect in our model. After the data management process, we apply different forecasting methods to explain future carbon price tendencies.

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