Deep learning models have been used widely in various areas and applications of
our everyday lives. They could also change the way non-profit organizations work
and help optimize fundraising results. In this thesis, sequential models are applied
in fundraising to compare their performance against the traditional machine learning
model. Sequential model is a type of neural network that is specialized for processing
sequential data. Although some research utilizing machine learning algorithms in
fundraising context exists, it is based on the data extracted from the specific time
window, which does not take time-dependency of features into account; therefore,
time-series features are independent at each data point relative to others. This approach results in loss of time notion. In this thesis, we experiment with the application
of time-dependent sequential models including Long Short Term Memory (LSTM),
Gated Recurrent Unit (GRU) and their variants in the fundraising domain to predict
the alumni monetary contribution to the university. We also expand our study by
including the architecture that treats time-invariant demographic data as a condition
to the sequential layers. In this model, the time-dependent data is concatenated after
running the sequential model. Sequential deep learning is empirically evaluated and
compared against the traditional machine learning models. The results demonstrate
the potential use of both traditional machine learning and sequential deep learning
in the prediction of fundraising outcomes and offer non-profit organizations solutions
to achieve their mission. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/13950 |
| Date | 09 May 2022 |
| Creators | Umeki, Atsuko |
| Contributors | Branzan Albu, Alexandra |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
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