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Keywords

nonprofit, machine learning, supervised, unsupervised, reinforcement

Abstract

Nonprofit organizations are often looking for ways to increase efficiency while operating with limited resources. With data more readily available than in the recent past, machine learning provides powerful tools for unlocking valuable insights and enabling nonprofits to do more with limited resources. This paper explores various machine learning techniques to enhance nonprofit operations with an overview of machine learning approaches, including supervised, unsupervised, semi-supervised, and reinforcement learning, and their potential benefits for nonprofit organizations. A practical use-case of donor prediction for fundraising is presented to demonstrate how supervised learning can be employed to identify potential repeat donors. The neural network model developed in this study achieved an accuracy of 86% in predicting whether a donor with donate again. This machine learning example provides one example of how a nonprofit can more efficiently work towards meeting their mission objectives.

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