Name: Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (en Inglés)
SKU: 56687026
Price: 5280.42 MXN
Availability: InStock
Author: Panda, Mrutyunjaya ; Misra, Harekrishna
ISBN: 9781799866596

portada Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (en Inglés)

Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (en Inglés)

Formato

Libro Físico

Editorial

Engineering Science Reference

Autor

Panda, Mrutyunjaya ; Misra, Harekrishna

Idioma

Inglés

N° páginas

335

Encuadernación

Tapa Dura

Dimensiones

27.9x21.6x2.4 cm

Peso

1.27 kg.

ISBN13

9781799866596

Categorías

Minería de datos («data mining»)
Aprendizaje automático («machine learning»)

Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (en Inglés)

Panda, Mrutyunjaya ; Misra, Harekrishna (Autor) · Engineering Science Reference · Tapa Dura

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Reseña del libro "Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (en Inglés)"

In today's digital world, the huge amount of data being generated is unstructured, messy, and chaotic in nature. Dealing with such data, and attempting to unfold the meaningful information, can be a challenging task. Feature engineering is a process to transform such data into a suitable form that better assists with interpretation and visualization. Through this method, the transformed data is more transparent to the machine learning models, which in turn causes better prediction and analysis of results. Data science is crucial for the data scientist to assess the trade-offs of their decisions regarding the effectiveness of the machine learning model implemented. Investigating the demand in this area today and in the future is a necessity. The Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science provides an in-depth analysis on both the theoretical and the latest empirical research findings on how features can be extracted and transformed from raw data. The chapters will introduce feature engineering and the recent concepts, methods, and applications with the use of various data types, as well as examine the latest machine learning applications on the data. While highlighting topics such as detection, tracking, selection techniques, and prediction models using data science, this book is ideally intended for research scholars, big data scientists, project developers, data analysts, and computer scientists along with practitioners, researchers, academicians, and students interested in feature engineering and its impact on data.