Titlebook: Machine Learning and Artificial Intelligence for Agricultural Economics; Prognostic Data Anal Chandrasekar Vuppalapati Book 2021 The Editor

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Data Engineering and Exploratory Data Analysis Techniquesn Databases (KDD) framework, the reference architecture for data mining applications, feature engineering techniques, and exploratory data analysis (EDA) process. Next, the chapter introduces feature reduction techniques such as principal component analysis (PCA). In agriculture data, most of the ti

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Agricultural Economy and ML Modelsomics and influence of macrolevel economic indicators on the local agriculture. Next, study of econometrics-related agricultural models is introduced. Finally, it concludes with the challenges that agriculture faces, especially for small-scale farmers and the role of machine learning in addressing s

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Commodity Markets: Machine Learning Techniquestors that would provide a comprehensive market view to small farmers. As part of the chapter, demand and supply framework is introduced. Next, commodity stocks to use ratio indictor are introduced and the impact of stocks to use ratio on the pricing model developed. Time series techniques are introd