ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Predicting demographic and financial attributes in a bank marketing dataset

Description
Bank institutions employ several marketing strategies to maximize new customer acquisition as well as current customer retention. Telemarketing is one such approach taken where individual customers are contacted by bank representatives with offers. These telemarketing strategies can be improved in combination with data mining techniques that allow predictability…

Bank institutions employ several marketing strategies to maximize new customer acquisition as well as current customer retention. Telemarketing is one such approach taken where individual customers are contacted by bank representatives with offers. These telemarketing strategies can be improved in combination with data mining techniques that allow predictability of customer information and interests. In this thesis, bank telemarketing data from a Portuguese banking institution were analyzed to determine predictability of several client demographic and financial attributes and find most contributing factors in each. Data were preprocessed to ensure quality, and then data mining models were generated for the attributes with logistic regression, support vector machine (SVM) and random forest using Orange as the data mining tool. Results were analyzed using precision, recall and F1 score.
ContributorsEjaz, Samira (Author) / Davulcu, Hasan (Thesis advisor) / Balasooriya, Janaka (Committee member) / Candan, Kasim (Committee member) / Arizona State University (Publisher)
Created2016
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A Comparative Analysis of Bitcoin Price Prediction Models

Description
Bitcoin (BTC) shares many characteristics with traditional stocks, but it is much more volatile since the cryptocurrency market is unregulated. The high volatility makes BTC a very high risk-high reward investment and predictive analysis can be very useful to obtain good returns and minimize risk. Taking Cocco et al. [1]…

Bitcoin (BTC) shares many characteristics with traditional stocks, but it is much more volatile since the cryptocurrency market is unregulated. The high volatility makes BTC a very high risk-high reward investment and predictive analysis can be very useful to obtain good returns and minimize risk. Taking Cocco et al. [1] as the primary reference, this thesis tries to reproduce their findings by building two BTC price forecasting models, Long Short-Term Memory (LSTM) and Bayesian Neural Network (BNN), and finding that the Mean Absolute Percentage Error (MAPE) is lower for the initial BNN model in comparison to the initial LSTM model. In addition to forecasting the value of BTC, a metric called trend% is developed to gauge the models’ ability to capture the trend of how the price varies from one timestep to the next and used to compare the trend prediction performance. It is found that both initial models make random predictions for the trend. Improvements like removing the stochastic component from the data and forecasting returns as opposed to price values show that both models show comparable performance in terms of both MAPE and trend%. The thesis concludes by discussing the future work that can be done to potentially improve the above models. One of the possibilities mentioned is to use on-chain data from the BTC blockchain coupled with the real-world knowledge of BTC exchanges and feed this as input features to the models.
ContributorsMittal, Shivansh (Author) / Boscovic, Dragan (Thesis advisor) / Davulcu, Hasan (Committee member) / Candan, Kasim (Committee member) / Arizona State University (Publisher)
Created2022