Description

Book Synopsis

This book highlights artificial intelligence algorithms used in implementation of automated pricing. It presents the process for building automated pricing models from crawl data, preprocessed data to implement models, and their applications. The book also focuses on machine learning and deep learning methods for pricing, including from regression methods to hybrid and ensemble methods. The computational experiments are presented to illustrate the pricing processes and models.



Table of Contents

1. Pricing based on product descriptions: problem, data, and methods.- 2. Extract product data from descriptions by NLP techniques.- 3. Segmentation and Quantity the qualify features.- 4. Pricing prediction using machine learning and ensemble methods.- 5. Applications & Discussions.

Artificial Intelligence for Automated Pricing

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Order before 4pm tomorrow for delivery by Tue 30 Dec 2025.

A Paperback / softback by Nguyen Thi Ngoc Anh, Tran Ngoc Thang, Vijender Kumar Solanki

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    View other formats and editions of Artificial Intelligence for Automated Pricing by Nguyen Thi Ngoc Anh

    Publisher: Springer Verlag, Singapore
    Publication Date: 29/08/2021
    ISBN13: 9789811647017, 978-9811647017
    ISBN10: 9811647011
    Also in:
    Artificial intelligence (AI) Machine learning Mathematical modelling

    Description

    Book Synopsis

    This book highlights artificial intelligence algorithms used in implementation of automated pricing. It presents the process for building automated pricing models from crawl data, preprocessed data to implement models, and their applications. The book also focuses on machine learning and deep learning methods for pricing, including from regression methods to hybrid and ensemble methods. The computational experiments are presented to illustrate the pricing processes and models.



    Table of Contents

    1. Pricing based on product descriptions: problem, data, and methods.- 2. Extract product data from descriptions by NLP techniques.- 3. Segmentation and Quantity the qualify features.- 4. Pricing prediction using machine learning and ensemble methods.- 5. Applications & Discussions.

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