{"product_id":"machine-learning-approaches-for-evaluating-statistical-information-in-the-agricultural-sector-9783031546075","title":"Machine Learning Approaches for Evaluating Statistical Information in the Agricultural Sector","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e The book presents machine learning approaches to identify important predictors of crucial variables in the agricultural sector, focusing on the European Union and the Farm Accountancy Data Network (FADN). It uses IBM SPSS Modeler procedures and models to support policy and management planning. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 135 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 22 February 2024\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer International Publishing AG\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eMachine learning approaches are presented in this book to identify the most important predictors of crucial variables for managing production units and designing agriculture policies. The focus is on the agricultural sector in the European Union, utilizing statistical information from the Farm Accountancy Data Network (FADN). Statistical databases provide a wealth of information for various indicators, and the primary task is to identify the most important predictors. The book offers methods for identifying the most relevant variables that support the development of adjusted farming policies and management plans. These subjects are of significant interest to students, public institutions, and farmers. To achieve these objectives, the book employs IBM SPSS Modeler procedures and the corresponding models suggested by the software. The book is read by students in production engineering, economics, and agricultural studies, as well as public bodies and managers in the farming sector.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9783031546075\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2024\u003c\/p\u003e","brand":"Vitor Joao Pereira Domingues Martinho","offers":[{"title":"Paperback \/ softback","offer_id":45866684154106,"sku":"9783031546075","price":37.47,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/files\/1714168106022_book.jpg?v=1715111941","url":"https:\/\/shulphink.com\/products\/machine-learning-approaches-for-evaluating-statistical-information-in-the-agricultural-sector-9783031546075","provider":"Shulph Ink","version":"1.0","type":"link"}