Abstract

This thesis presents a comprehensive application of machine learning techniques, namely Fine Gaussian SVM and RUS Boosted Trees, to enhance fundraising strategies in higher education institutions. Analyzing a rich dataset from Blackbaud Raiser's Edge NXT, spanning 2012 to 2022, the study focuses on donor profiles, including demographics, donation history, and engagement patterns. Key demographic insights include the increasing engagement of younger donors (20-29 age group) and significant contributions from older donors (70-99 age group). Geographical trends are also examined, revealing distinct patterns based on donors' city, state, and ZIP code. The Fine Gaussian SVM model demonstrates moderate discriminatory power, with an AUC-ROC of 0.9105, indicating a strong ability to differentiate potential donors from non-donors. It particularly excels in identifying true positives but shows some limitations in accurately predicting negatives. The RUS Boosted Trees model, tailored for the dataset's imbalance, achieves a higher AUC-ROC of 0.9416, indicating superior performance in distinguishing repeat donors. These models are evaluated using accuracy, precision, recall, F1 score, and AUC-ROC to ensure robustness and applicability.

Date of publication

Summer 8-6-2024

Document Type

Thesis

Language

english

Persistent identifier

http://hdl.handle.net/10950/4752

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