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Predictive models for charitable giving using machine learning techniques.

Leily Farrokhvar1, Azadeh Ansari1, Behrooz Kamali1

  • 1Department of Industrial and Management Systems Engineering, West Virginia University, Morgantown, West Virginia, United States of America.

Plos One
|October 4, 2018
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Summary
This summary is machine-generated.

Predicting charitable giving is crucial for the nonprofit sector. This study identifies population, education, and prior donations as key factors, with Artificial Neural Networks (ANN) offering the most accurate donation predictions.

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Area of Science:

  • Economics
  • Data Science
  • Sociology

Background:

  • Private giving constitutes over 75% of US charitable donations, significantly funding the nonprofit sector (over 10% of GDP).
  • Despite available data, factors influencing private donations and predictive models remain underdeveloped.
  • Understanding donation drivers is essential for nonprofit sustainability and economic planning.

Purpose of the Study:

  • To develop accurate predictive models for future charitable giving.
  • To identify key socioeconomic and demographic factors influencing private donation behavior.
  • To compare the efficacy of different statistical and machine learning models in forecasting charitable contributions.

Main Methods:

  • Utilized Stepwise Regression to identify significant variables from a dataset including unemployment rate, income, poverty, population, demographics, education, and vehicle ownership.
  • Developed predictive models using Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Support Vector Regression (SVR).
  • Validated models using 10-fold cross-validation and evaluated performance with nine distinct metrics.

Main Results:

  • Population, education level, and previous year's charitable giving emerged as the most significant independent predictors of future donations.
  • All developed models (MLR, ANN, SVR) demonstrated good accuracy in predicting regional charitable giving.
  • Artificial Neural Networks (ANN) exhibited superior predictive performance compared to Support Vector Regression (SVR) and Multiple Linear Regression (MLR) on test data.

Conclusions:

  • Population, education, and past donation history are critical factors for forecasting charitable giving.
  • Machine learning models, particularly ANN, offer a robust approach to predicting future donation amounts.
  • Accurate prediction of private giving can aid nonprofit organizations in financial planning and resource allocation.