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Machine-learning Techniques in Economics: New Tools for Predicting Economic Grow

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Last updated on 01 May, 2024 16:38:00 BSTView all revisionsView all revisions

Item specifics

Condition
Very Good: A book that has been read and does not look new, but is in excellent condition. No ...
Book Title
Machine-learning Techniques in Economics: New Tools for Predictin
Genre
Economic growth
ISBN
9783319690131
Publication Year
2018
Type
Textbook
Format
Paperback
Language
English
Publication Name
Machine-Learning Techniques in Economics: New Tools for Predicting Economic Growth
Item Height
235mm
Author
Tinni Sen, Atin Basuchoudhary, James T. Bang
Publisher
Springer International Publishing A&G
Item Width
155mm
Subject
Economics, Computer Science, Mathematics
Item Weight
226g
Number of Pages
94 Pages

About this product

Product Information

This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.

Product Identifiers

Publisher
Springer International Publishing A&G
ISBN-13
9783319690131
eBay Product ID (ePID)
11046571611

Product Key Features

Author
Tinni Sen, Atin Basuchoudhary, James T. Bang
Publication Name
Machine-Learning Techniques in Economics: New Tools for Predicting Economic Growth
Format
Paperback
Language
English
Subject
Economics, Computer Science, Mathematics
Publication Year
2018
Type
Textbook
Number of Pages
94 Pages

Dimensions

Item Height
235mm
Item Width
155mm
Item Weight
226g

Additional Product Features

Title_Author
Atin Basuchoudhary, Tinni Sen, James T. Bang
Series Title
Springerbriefs in Economics
Country/Region of Manufacture
Switzerland

Item description from the seller

Business seller information

Revival Books Ltd
Revival Books
Hall Carr Mill
Fallbarn Road
Rawtenstall
Lancashire
BB4 7NX
United Kingdom
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:enohP70272260710
:liamEku.oc.skooblaviver@skoob
Value added tax number:
  • GB 901578627
Trade registration number:
  • 07693718
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