A Review of Supervised Machine Learning Algorithms to Classify Donors for Charity

Pooja Mittal


Abstract--Machine Learning has several supervised algorithms which have the capability for potential prediction based on the data collected from external or internal sources. Different supervised algorithms are employed to find the best-chosen algorithms based on the preliminary results and then optimized further to find the best outcomes. Non-profit organization survives on donations and predicting the individual’s income helps to identify how big a donation can be made by the individuals. Therefore, it helps whether to approach to the individuals or not based on their income. With the

 help of this paper, different algorithms are constructed and discussed based on their accuracy, complexity, speed, and overfitting to choose the best candidate model. Best optimized model helps to predict the individual’s income efficiently and help making decision whether to reach out to them or not which helps in the non-profit organization survival.


Supervised Machine Learning, Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor, XGBoost

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DOI: https://doi.org/10.26483/ijarcs.v12i1.6685


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