Modelos de analítica de dados para predição da intenção de compra em ambientes digitais. Um estudo metodológico comparativo
Palavras-chave:
analítica de dados; aprendizado de maquina; inteligencia artificial; intencao de compra; comercio eletronico.Sinopse
Este livro examina de forma sistemática e comparativa os principais modelos de analítica de dados aplicados a predicao da intencao de compra em ambientes de comercio eletronico. Com base em uma revisao critica da literatura indexada na Scopus, Web of Science e SciELO para o periodo de 2020 a 2026, o estudo identifica e classifica oito modelos preditivos representativos organizados em tres categorías: modelos estatisticos classicos, modelos de aprendizado de maquina avancado e modelos de aprendizado profundo. E apresentado um estudo comparativo sistemático usando o benchmark UCI Online Shoppers Purchasing Intention Dataset, avaliando cinco métricas de desempenho. Os resultados confirmam a superioridade do modelo LSTM (AUC-ROC=0,962), com XGBoost e LightGBM como alternativas de alto desempenho e menor custo computacional.
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