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dc.contributor.advisorKuyven, Patrícia Sorgatto
dc.contributor.authorBoiani , Fabiano Alberto
dc.date.accessioned2021-09-22T21:55:33Z
dc.date.accessioned2022-09-22T19:44:23Z
dc.date.available2021-09-22T21:55:33Z
dc.date.available2022-09-22T19:44:23Z
dc.date.issued2019-01-01
dc.identifier.urihttps://hdl.handle.net/20.500.12032/64411
dc.description.abstractResearch and implementation of a web service capable of classifying jurisprudential summaries as to its result, provided or unprovided. Using the python programming language, a supervised machine learning model was developed, which was trained through the use of a predefined jurisprudence base with their respective results. For this, some machine learning algorithms were selected in order to define which one has the best performance: Naive Bayes, Random Forest and K-Nearest Neighbors. After an evaluation of the performance of these algorithms, it was chosen the model based on the Random Forest algorithm, because it has a better performance regarding assertiveness.en
dc.publisherUniversidade do Vale do Rio dos Sinospt_BR
dc.subjectClassificação de jurisprudênciaspt_BR
dc.subjectPythonen
dc.titleAplicação de aprendizado de máquina para classificação de jurisprudênciaspt_BR
dc.typeTCCpt_BR


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