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dc.rights.licenseAll rights reserveden_US
dc.contributor.advisorRodríguez Jiménez, Othoniel
dc.contributor.authorVázquez Rodríguez, Rubén A.
dc.date.accessioned2020-06-23T12:48:56Z
dc.date.available2020-06-23T12:48:56Z
dc.date.issued2019
dc.identifier.citationVázquez Rodríguez, R. A. (2019). Predicting the downfall of non-profit organizations using machine learning [Unpublished manuscript]. Graduate School, Polytechnic University of Puerto Rico.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12475/167
dc.descriptionDesign Project Article for the Graduate Programs at Polytechnic University of Puerto Ricoen_US
dc.description.abstractMachine learning can be applied to finances of non-profit organizations taken from IRS Tax Forms 990ez to determine if an organization will be dissolved. This is useful to determine if a cause is viable. Data stored on an online database is extracted, formatted, parsed and segregated using Python. The code selects the attributes used to predict the organization’s downfall. Finances were compared and attributes that were critical were identified. Three supervised predictive algorithms, Decision Tree, K Nearest Neighbors and Naïve Bayes, were used. Results from the algorithm's predictions for organizations that were dissolved and non-dissolved are presented in this paper and discussed. This study also determined the average duration of non-profit organizations based on the current financials. Key Terms ⎯ Algorithms, Analytics, Big Data, Prediction, Machine Learning.en_US
dc.language.isoen_USen_US
dc.publisherPolytechnic University of Puerto Ricoen_US
dc.relation.ispartofComputer Engineering
dc.relation.ispartofseriesFall-2019
dc.relation.haspartSan Juan Campusen_US
dc.subject.lcshPython (Computer program language)en_US
dc.subject.lcshNonprofit organizations--Taxation
dc.subject.lcshMachine learning
dc.subject.lcshPolytechnic University of Puerto Rico--Graduate students--Research
dc.subject.lcshPolytechnic University of Puerto Rico--Graduate students--Posters
dc.titlePredicting the downfall of non-profit organizations using machine learningen_US
dc.typeArticleen_US
dc.rights.holderPolytechnic University of Puerto Rico, Graduate Schoolen_US


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