Business Analytics with R and Python [electronic resource] / by David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi.

Por: Olson, David L [author.]Colaborador(es): Wu, Desheng Dash [author.] | Luo, Cuicui [author.] | Nabavi, Majid [author.] | SpringerLink (Online service)Tipo de material: TextoTextoSeries AI for RisksEditor: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edición: 1st ed. 2024Descripción: X, 196 p. 85 illus., 78 illus. in color. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9789819747726Tema(s): Business information services | Business Information SystemsFormatos físicos adicionales: Printed edition:: Sin título; Printed edition:: Sin título; Printed edition:: Sin títuloClasificación CDD: 658.4038 Clasificación LoC:HF54.5-.56Recursos en línea: Libro electrónicoTexto
Contenidos:
Data Mining in Business -- Data Mining Processes -- Data Mining Software -- Association Rules -- Cluster Analysis.-Regression Algorithms in Data Mining -- Classification Tools -- Variable Selection -- Dataset Balancing.
En: Springer Nature eBookResumen: This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.
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Data Mining in Business -- Data Mining Processes -- Data Mining Software -- Association Rules -- Cluster Analysis.-Regression Algorithms in Data Mining -- Classification Tools -- Variable Selection -- Dataset Balancing.

This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or artificial intelligence to provide forecasting capability. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of artificial intelligence.

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