Hardcover. This reprint comprises a compilation of 15 insightful research papers published in the Special Issue "Applied Statistical Modeling and Data Mining" within the Mathematics journal. Spanning multiple domains at the intersection of statistics, machine learning, and real-world applications, the collection presents diverse perspectives and innovative approaches in the field of applied statistical modeling and data mining. These selected papers explore diverse and important topics, including predictive modeling in healthcare, cybersecurity in transportation systems, data quality assessment, emergency call modeling, and innovative crime prevention strategies, among others. By covering a wide spectrum of subjects, this reprint serves as a valuable resource for researchers, academics, and practitioners interested in addressing contemporary challenges across diverse domains through statistical modeling, data mining, and machine learning applications. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

N� de ref. del art�culo 9783725811052

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T�tulo

Applied Statistical Modeling and Data Mining (Hardcover)

Editor

Mdpi AG

Fecha de publicaci�n

2024

Idioma

Ingl�s

ISBN 10

3725811059

ISBN 13

9783725811052

Encuadernaci�n

Hardcover

Estado

new

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Sinopsis

This reprint comprises a compilation of 15 insightful research papers published in the Special Issue "Applied Statistical Modeling and Data Mining" within the Mathematics journal. Spanning multiple domains at the intersection of statistics, machine learning, and real-world applications, the collection presents diverse perspectives and innovative approaches in the field of applied statistical modeling and data mining. These selected papers explore diverse and important topics, including predictive modeling in healthcare, cybersecurity in transportation systems, data quality assessment, emergency call modeling, and innovative crime prevention strategies, among others. By covering a wide spectrum of subjects, this reprint serves as a valuable resource for researchers, academics, and practitioners interested in addressing contemporary challenges across diverse domains through statistical modeling, data mining, and machine learning applications.

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