Modelos Predictivos para Identificar Probabilidades de Recomendació
This book, authored by William Rafael Raymondi Lomas and Sergio Israel Peña Guano, focuses on the development and application of predictive models to enhance customer recommendation systems. It explores the theoretical and practical aspects of predictive modeling, emphasizing the use of technologies...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Texas :
Editorial Tecnocientifica Americana (ETECAM)
2023.
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| Edición: | 1st ed |
| Colección: | Tecnología Series
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009757869406719 |
| Sumario: | This book, authored by William Rafael Raymondi Lomas and Sergio Israel Peña Guano, focuses on the development and application of predictive models to enhance customer recommendation systems. It explores the theoretical and practical aspects of predictive modeling, emphasizing the use of technologies such as big data, machine learning, and data mining. The primary aim is to enable businesses, like the example company Distecom, to analyze customer satisfaction and improve customer experiences by predicting recommendation likelihoods. The book discusses methodologies like CRISP-DM and Kimball, and tools like Python, to develop these models. It is intended for researchers, data analysts, and business professionals interested in leveraging predictive analytics to drive business growth and customer loyalty. |
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| Notas: | Incluye índice. |
| Descripción Física: | 1 online resource (109 pages) |
| Bibliografía: | Incluye bibliografía. |
